{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yoachim/gitRepos/sims_featureScheduler/python/lsst/sims/featureScheduler/utils.py:29: UserWarning: Could not import ts.scheduler. This is required to load the FieldsDatabase. In this case\n",
      "it will fallback to loading fields from the local \"fieldID.lis\" file.\n",
      "  it will fallback to loading fields from the local \"fieldID.lis\" file.''')\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import lsst.sims.featureScheduler as fs\n",
    "from lsst.sims.speedObservatory import Speed_observatory\n",
    "import matplotlib.pylab as plt\n",
    "import healpy as hp\n",
    "import time\n",
    "from gen_new_map import generate_goal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = time.time()\n",
    "\n",
    "survey_length = 20. #365.25*10  # days\n",
    "nside = fs.set_default_nside(nside=32)\n",
    "# Define what we want the final visit ratio map to look like\n",
    "years = np.round(survey_length/365.25)\n",
    "\n",
    "# The new target map\n",
    "target_map = generate_goal(nside)\n",
    "\n",
    "norm_factor = fs.calc_norm_factor(target_map)\n",
    "# List to hold all the surveys (for easy plotting later)\n",
    "surveys = []\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: ErfaWarning: ERFA function \"d2dtf\" yielded 1 of \"dubious year (Note 5)\" [astropy._erfa.core]\n",
      "/Users/yoachim/gitRepos/sims_seeingModel/python/lsst/sims/seeingModel/seeingModel.py:133: RuntimeWarning: invalid value encountered in power\n",
      "  airmass_correction = np.power(airmass, 0.6)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "progress = 86.8%"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yoachim/gitRepos/sims_skybrightness_pre/python/lsst/sims/skybrightness_pre/SkyModelPre.py:363: UserWarning: Requested MJD between sunrise and sunset, returning closest maps\n",
      "  warnings.warn('Requested MJD between sunrise and sunset, returning closest maps')\n",
      "/Users/yoachim/gitRepos/sims_skybrightness_pre/python/lsst/sims/skybrightness_pre/SkyModelPre.py:279: UserWarning: Requested MJD between sunrise and sunset, returning closest maps\n",
      "  warnings.warn('Requested MJD between sunrise and sunset, returning closest maps')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "progress = 100.0%Skipped 0 observations\n",
      "Completed 11778 observations\n",
      "ran in 14.3 min = 0.2 hours\n"
     ]
    }
   ],
   "source": [
    "# Set up observations to be taken in blocks\n",
    "filter1s = ['u', 'g', 'r', 'i', 'z', 'y']\n",
    "filter2s = [None, 'g', 'r', 'i', None, None]\n",
    "\n",
    "pair_surveys = []\n",
    "for filtername, filtername2 in zip(filter1s, filter2s):\n",
    "    bfs = []\n",
    "    bfs.append(fs.M5_diff_basis_function(filtername=filtername, nside=nside))\n",
    "    if filtername2 is not None:\n",
    "        bfs.append(fs.M5_diff_basis_function(filtername=filtername2, nside=nside))\n",
    "    bfs.append(fs.Target_map_basis_function(filtername=filtername,\n",
    "                                            target_map=target_map[filtername],\n",
    "                                            out_of_bounds_val=hp.UNSEEN, nside=nside,\n",
    "                                            norm_factor=norm_factor))\n",
    "    if filtername2 is not None:\n",
    "        bfs.append(fs.Target_map_basis_function(filtername=filtername2,\n",
    "                                                target_map=target_map[filtername2],\n",
    "                                                out_of_bounds_val=hp.UNSEEN, nside=nside,\n",
    "                                                norm_factor=norm_factor))\n",
    "    bfs.append(fs.Slewtime_basis_function(filtername=filtername, nside=nside))\n",
    "    bfs.append(fs.Strict_filter_basis_function(filtername=filtername))\n",
    "    bfs.append(fs.Zenith_shadow_mask_basis_function(nside=nside, shadow_minutes=60., max_alt=76.))\n",
    "    weights = np.array([3.0, 3.0, .3, .3, 3., 3., 0.])\n",
    "    if filtername2 is None:\n",
    "        # Need to scale weights up so filter balancing still works properly.\n",
    "        weights = np.array([6.0, 0.6, 3., 3., 0.])\n",
    "    # XXX-\n",
    "    # This is where we could add a look-ahead basis function to include m5_diff in the future.\n",
    "    # Actually, having a near-future m5 would also help prevent switching to u or g right at twilight?\n",
    "    # Maybe just need a \"filter future\" basis function?\n",
    "    if filtername2 is None:\n",
    "        survey_name = 'blob, %s' % filtername\n",
    "    else:\n",
    "        survey_name = 'blob, %s%s' % (filtername, filtername2)\n",
    "    surveys.append(fs.Blob_survey(bfs, weights, filtername=filtername, filter2=filtername2,\n",
    "                                  survey_note=survey_name, ignore_obs='DD'))\n",
    "    pair_surveys.append(surveys[-1])\n",
    "\n",
    "\n",
    "# Let's set up some standard surveys as well to fill in the gaps. This is my old silly masked version.\n",
    "# It would be good to put in Tiago's verion and lift nearly all the masking. That way this can also\n",
    "# chase sucker holes.\n",
    "#filters = ['u', 'g', 'r', 'i', 'z', 'y']\n",
    "filters = ['i', 'z', 'y']\n",
    "greedy_surveys = []\n",
    "for filtername in filters:\n",
    "    bfs = []\n",
    "    bfs.append(fs.M5_diff_basis_function(filtername=filtername, nside=nside))\n",
    "    bfs.append(fs.Target_map_basis_function(filtername=filtername,\n",
    "                                            target_map=target_map[filtername],\n",
    "                                            out_of_bounds_val=hp.UNSEEN, nside=nside,\n",
    "                                            norm_factor=norm_factor))\n",
    "\n",
    "    bfs.append(fs.North_south_patch_basis_function(zenith_min_alt=50., nside=nside))\n",
    "    bfs.append(fs.Slewtime_basis_function(filtername=filtername, nside=nside))\n",
    "    bfs.append(fs.Strict_filter_basis_function(filtername=filtername))\n",
    "    bfs.append(fs.Zenith_shadow_mask_basis_function(nside=nside, shadow_minutes=60., max_alt=76.))\n",
    "    weights = np.array([3.0, 0.3, 1., 3., 3., 0.])\n",
    "    # Might want to try ignoring DD observations here, so the DD area gets covered normally--DONE\n",
    "    surveys.append(fs.Greedy_survey_fields(bfs, weights, block_size=1, filtername=filtername,\n",
    "                                           dither=True, nside=nside, ignore_obs='DD'))\n",
    "    greedy_surveys.append(surveys[-1])\n",
    "\n",
    "# Set up the DD surveys\n",
    "dd_surveys = fs.generate_dd_surveys()\n",
    "surveys.extend(dd_surveys)\n",
    "\n",
    "survey_list_o_lists = [dd_surveys, pair_surveys, greedy_surveys]\n",
    "\n",
    "# Debug to stop at a spot if needed\n",
    "n_visit_limit = None\n",
    "\n",
    "# put in as list-of-lists so pairs get evaluated first.\n",
    "scheduler = fs.Core_scheduler(survey_list_o_lists, nside=nside)\n",
    "observatory = Speed_observatory(nside=nside, quickTest=True)\n",
    "observatory, scheduler, observations = fs.sim_runner(observatory, scheduler,\n",
    "                                                     survey_length=survey_length,\n",
    "                                                     filename='new_wfd%iyrs.db' % years,\n",
    "                                                     delete_past=True, n_visit_limit=n_visit_limit)\n",
    "t1 = time.time()\n",
    "delta_t = t1-t0\n",
    "print('ran in %.1f min = %.1f hours' % (delta_t/60., delta_t/3600.))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lfobcxxDAdAiFAJCHqIMh1UIAyEfKVbWUq6GYj4/u5Ki7kvu8AYRDYDwqhwhFqkEq5fCLeRTdyXQlAwAQu1QDL+KRTTCk0gEAiAHhEEvKpiu5im5loB++WCEkuQQnupUxBF3JPREKAQAAmmVTMSQQAuNROUQoUq8aUi3EUFQMAQDZITgB08gmGFLpAMZhG0JoUg6HqVdEEa5supJLdCkD/RAIEbqUQ1TK4RfToCu5Jw5yQD98mULoUg5PKYdehCm7YAigP8IhQkc4BPzIMhhSNQT6IxwCQPqyDIYc4IBh2HaA+aVcDUV4sgyGAIYjHCJEqXa3Egoxt6yuSuaABozHqRgIFeEQ4KrkzgiFwHiEQmB+qQZehCmLYEgoBID0pVxZIxxiLskHQ0Ih4A/bE0JHOATGST4YAvCLcIjQEQ6B4ZIPhpwTBfjFNoUYEA6BYZIPhhIHMsAXtiXEhHAI9JdFMAQAICUph14sK6v7GEqcHwX4QOUQoUu5okYoxCbcx7AnDmgAAADNsqsYVlE9BPrhixViQtUQOaJiCABAg5TDU8qhF8siGALojCo7YpNyOASmkGVXMgc3YBy6lBGbVCtsBF/UZd2VXLz43jioAUA+Ug2FUtqvDf0NzUV75uGhHVEiHALDsO0AYSEcwqdsg6HEAQ7oi20GsSE0Af1EHwx9lE0BdMP5uYhNDufg5fAa0c5XHoo+GI7BQQ7oj+0GCAehEL5lHQzpFgOA9KUcnugqh29JBEO6k4H58IUKMUo5HAI+c1ASwXCM46dPcaADgAykGg6pGsKn7INhiXAIdMM5hogZ4RDYjGBYQTgEgPSlGg4BH6L+Sbz6gKE/kVdFNQTorvwyVd1u+IKFkOVQWSP45mXN+YV5/iSeb4RCoJ+Lzjm8b7thOwKAeCUZDLscmDiYAdNhewKQkpz2aUl1JUur7uS2bq36B3z89KmsPnRgLnQrIzQ5dCWX6FL2Y8ipMnOdXrPhNjV0JZeGVAIJhcA02LYQGsISumo6Vabr86Yw1/40uWB49fZV+4ZxcALC0OWcxKE7Y6CrHMJhDq9xSl2LSnOdltY036l+3CO5rmRJunDrsXO2A8BE6IrGVFLuUiYUjtMW7uqnn607Hc3H/mvTqW9NhbAKupIBpIVQCGBuIV282jTfOXpTDk6+hAVcvX0VVUMAwD4pVwoxXEinr3RpS0u1cJRkK4ZTvmmpoTKTFj5PIG+E334IhXslGwyxq0tQIEzE6fjpU3s+Oz5HYLNczr8jHHYzZSgMKXD2keTFJ1W5dynXg0KXE2RjXZlz0yUEpvBZEnYxhRyCUy4heIg5940+99U9qoVcfDKHenVm3TRLqS+7qS1t06QQJFLgaz1LIVSxTgKIUZfMECKCoUflChBSOBw6DZYTwnoEpC6HaloOVVH4l3xXsuSvO7nt5/W6/PzeVHyFCKozy2ur4g75rGP/XAnJmEoO4SmHEDzEVPvFqfbRPS86oSt5Ex9X8Aw9MM1xQPO1jIdecqnOHD3iZV4YblMQzDEUAlPKITTlEH6HmOr43HefG8KVyFVZBMMpdT3vMKaKB+EwPDGtPwCAdqH+/GeSN7j2LYfz9urfKMtweOjYyT3Dqo8xr3VdzOXwptMaQtzpACGhmpavkLqSQ5JNxZAbXg9TBsT6vwgH4Q8YJqdQmNNr7SKmUDh3fsni4pOqvhei+PyQp6rojG3j0B0G1cP9uv64epf5bJJrGIz9mzjCkVtQyuFcyq7G7j/rx+8x+6W2towIhVx80tWSlcNQTnT1herhXus+3/rFIyHfCxPIBUEJQ8y5f14qr2QXDJc21U+YEQ7D0RT+un7uufyayVA5v3b4l0s4zOV1duXrLiOxXVjaVXZdyaWyS7lr11+f0vGQ8rLPA16f5frsTqFredztZNbdB3PJ+2OGLMUdMpaRercywXDXFKf2jLEuf3ioFtKV3Ff1Ta//0kTTt4D6ytRl5Vrq4E31cDk+dzrlvJpuaUAoAoB+QguF1flXl7P0xbLZBkOp/cbBc4XDKYIc4TAuQ3Y+uYdDqqYAuuq7v5izm3iK08vGyLYrubT9pfM6TddlpWrqhqY7OW9DfkaxPs26eeYejELYgSJ+qXcjl3LvTu6zv1xy37J19vW+ZkVX8lBdP4S5ftpuiRXymiuvyH6nsZQh4S7Uu+UDscklFKK7RELhKNkHQ5+aDtYhntMwJbqS9wr5XNSYxbyNIAyEQtSxX1nJviu51KVL2ddNMfsYuswhy/K9o6Rbea+2m11zq5pu2HnDl9zCYY49Q11PA1vaBNVCupLHmqOES/Uwb22ffyw7sCXl/vrhV45BKRddTrkJ5T6EoXQhl6gY1kxdOdx0pfMUP4PW9f6LU3xzpmI43JCLUnISws4c6cipcph6GI7lIpPShKGQiqEvXT6kqb5ldPl2M3aeBIs4+DpfNVW8FwDqYtsvhFYpLBEMe/Bxk+G2eRAOAQDAUuhK3qDerdzU7TtVt/K6acYuc107puxKoUu5P34Gr5sQuoKQBrqT4xfLedozVQrpSp6bj3vJ5fIzZ2eOHuFClJEIhc14XwB0leox1jeC4QZL9P93WXF9r9w5fVOORS5fGnwgHGIs9oGYS6jnFVYRDFtUP8SQDs5jL4Apq3hzVfLoTu6vKRyGtA6GhHCIoQiFmEsMoVAiGHay5Ic5xcUoiEfX804B9EcoXOF9mF4soVCSDi7dgFhsnX19p3sc9tXlPoZt9yD0cSEMwrTpM+fz3kVYxhDXXHlF1qEoldceegElplAoUTHsZeoPt8svY7T9pNrSGwDmw2cNYKgUQmEMx8TYQqFEMOzt6u2rJp1/qlUgrkqextI7PSB2qd66ZZNUQmHoYgyFEl3Jg1y9fZUu3Hrs0s1YK9RuxjIcciFKd22nEVTHhfiZz6XL+wQgbjHs48o2Tl1EmhIVw4GW/NBj2Dg2oXrYX+yfOQDMaYkviSmEQolgOErsH/6SCIf9EQ7b8R4B8K3pXMZ1+5oUcgFdySMt1a3c5WrmTarduUuFtOpy6V7uhi7Tdhedc5j3B0jMEl/66sfZTW246JzDSYRCiYqhF0uvDGM3mCVCGUHQLyplAOBP1wphOW7pHOATwdCTpVeKsb+ScejYScJaRAiCAHLQdkuauiG3qKnOv+vyqstY+vjvmznnlm7DUME2PIQrlusbRt8gMXX3MiEUU6EbGV2lcNsWX0K7bU/fY9aYMNj3Th7l9IEHQhv6RCqGiWqqIPZRD25tQa5v0OPiEwBLIhSut/R7MzYUtnX79ukmXjePlFExnFAIlcPSmHsb1u8/2PR4zMUsVA/hC9VC9LF0AApJ9ecBl6oeDj1G1Y9vbY/HCrxSWBpcMSQYTiyVcNimHg7LYV0RDjEWoRB9EQybzR0MfR6Xpv6Bh0hCoURXMrrwvbFwuxkAwBi+j0upd/POgfsYTqz67SKk6qEvvn7mrqniCHRFtRDAVCKqEnpBxRBenDl6hAtKAACIHMFwRql962i79yH3RsQS6EpCV6HdoiV1Y68GXkJqx+0uuPhkISl2K7fpUlEkSGKM6onndC+jCy5A2WxseJ7y6uApJRAIufgkNgmsdL0R+jC1+i8YAFhOjBVCKc/jcxXBcEE5rnxNN84mMGIKVAzRhmrhdGIJgXU5Hpfr6EoORI5dyxJXI2M6BENsQijsr61bOdYwKCUZCOlKjl2CK2UnhEIAiE9KwTrX4+863McwIKnf8xCYU/2kdwDj1X82L9YqIWFwPSqGAIDscKuafqq/pVyKNRRiM84xDByVQ8APqoaoS6k7dE59TwHqe5uaKW9rk1GlcPA5hgTDSBAQgeEIhagjFPY3JBBKzZXF6jZZv/foReccbhw/RkaBsDQ4GHKOYSSu3r6KcAgAWESfUNj3i9jUX9wyDIWjcI5hRFi5AWA8qoX9VENh/Res2h7Xq31njh7ZGAQfesmlG6uFXX5Bq4rjZn9UDCNTruRUD4H1fHdDIR2Ewn4OHTu5J4w1PZZ2A1v9IpVD2j9e2n9187rH9ed3RSAcjnMMI0Y4BJpxTiGaEArj16VLm1AoiYtP8kZABPYiGGIdwmE81lUn1yEQ7sHFJzmjexkAkIpqAKRCOD8uPkkIGwdAtRCI1aFjJ3vfEofjnn9UDBPDz+ohN1xoAuSFMDgtKoYJY+NBbqgWYhPOL4wfx7XpEQwTx0YEAIhN0+1pOJ7Ng6uSM0P3MlJDlRB9UDWMx6FjJwmDw3FVMrrhCmYAQOhOvOW5SzchW3QlZ4pvYUgFF5wAcatfjUwoXBZdyaB6iGTQrYx16EIOS/22NGeOHiEQ+kVXMoajexlAygiFYaMHKyx0JWMHGycAYE4cd8JDVzLWooKI2NCVjKp6pfCaK6+gehgAuoxnMbgrmWCIVgRExIRwCGlvKLzmyiv2DcO0ynMIq/cjJBDOimCIeRASEQsCIkoEwnk0/c4xXcWL4eITzIMLVRCq46dPcesawLOmyt+m6UoEwnhx8QkGYaNHSMrqIFVC1FEtHK6pAtg0DaEwLXQlwxuqiFgKgRB1BML+1gXBerWQIBiFwV3JVAzhDTsHACEgFPZHdRAlKoaYBNVDzImKIUqEwn7aAuGZo0e4qCROXJWMcBESMTWCIaTdUMjtaVaqga7sDu5SGVyHMBgVgiHiQEiEb4RCSKsQWAbC8nGumn6HeGggJAxGi3MMEQd2MgCmUA2FOWsKgIRC9EHFEIujioihqBaijkrhcATBpNCVjPgRENEXwRBVhMJhCIRJIhgiPQRFbEIoRCnHQEgQRAuCIdJFQMQ6hENIeQVDAiE6IhgifQRErENAzBOBsBsCYZYIhsgPQRFNCIl5qIfCa668IpmgyEUk8IBgiLwRElFFOExXNfyldiNrqoLwiGAISARE7CIcpqd+E+vq8JgRCDEBgiHQhKCYN8JhumIIg2XgK3+OrjpsKIIgOhocDA/6bAUQmupOlJCYF0IhluDz5+hKhEHMiYohskZYTBfBMD2hVgnHBr8qQiA8oWIIDFHuhAmIQNhCDYW+EAgRCiqGQA0hMR1UDdMQ6q1p6CJGwLj4BJgSYTENBMX4LREIfXQVEwIxM4IhMBdCYpwIhfGLLRQSBrEggiGwBEJiPAiGcYslFBIGEQiCIRASAmP4jp8+1Rg2mn5No3pT5XXD28Z3/cWOdefPDW1X1/Pz1rWpbbmbluNj+Fz6hkACIAJHMARCRUgMW/Xmw8gH1UAkjmAIxIKgGCYCYvqoCiIjBEMgdgTG8BAW4zL0QhECIBJEMARSQ1AMBwExXHQJA40IhkBuCI7zIyBOoyncle81wQ8YhGAI5I6gOA/C4Xg+f1tYIggCDQiGAPYiKE6PkNiPr0BIEARaEQwBDEOAHI+AuF6fMEjgA7whGALwh7DYT1swPHTs5NppNo3rMj5UbYGQEAhMimAIYHoExmbV4LYuELVdTNE2j1DDIQEQCBLBEEB4CJLDTBEChwbWOoIeEAWCIYD4EByb9Q2GPq/yJfgBSSAYAkgXAXI8Ah+QFYIhAAAAJI0Ihgd9tmJmg180AAAA9ttaugEAAAAIA8EQAAAAkgiGAAAAKBAMAQAAIIlgCAAAgALBEAAAAJIIhgAAACgQDAEAACCJYAgAAIACwRAAAACSCIYAAAAoEAwBAAAgiWAIAACAAsEQAAAAkgiGAAAAKBAMAQAAIIlgCAAAgALBEAAAAJKkg0s3AFjSWXYXd0Y3rx6YycoRtvO/XVZ53Di+NsxqIzc+bpvvilv3vKbl7Xvu/lk3P1c7r9VtmqZp/j2W0Xf6nef0mL463K0Z3nnZfZe7dpxrfS+bh7t9wxufsvPR73vFe1bhYm2qPWfvsqy2gHKetuc5rjK+8q+5PcP2PL/ymjaN3z/v3elt3/wr46rtbJhnfbpN828ct6/dDdOZVNmj1KZpGrc75AMfvvm4c+6ogAUQDJG1M7pZD9y6ULZlkm3t/Kst2zmS2NbW6u+df01WTiOthlXH7zyujq/Ms2G8M1vV7yvjXXkkqwzfM520emzFPLbKee2Ol2nnOeUrw0FYAAAEjElEQVS01fGrvyVt2c7fTePLYTvTS8Xy9o/bCVBWf36lvdVlN0y3drx2xzc+r+O8Ny1r/+uWZG7986vjVR/vKtOU49y+8Vb+req05bjKv8X4cnozV6wau2HTzBWrxO74rTJE1YaZOW3J7awuO8Mq/67Gu53xe/7T7t97xu8M394dVxl2YGea3fEHKo9X44u/tb0z7wO2rS0V/9r2znOqzz9QeV45vaTi79WyV9Ns78y7XN6B8vnFMg6obPv2zuOdecvtafvqcdEOSQdMOlB8KKvHpi2ZDqj418pxW8Xj1V+SdOAu158lYCF0JQMAAEASwRAAAAAFgiEAAAAkEQwBAABQIBgCAABAEsEQAAAABYIhAAAAJBEMAQAAUCAYAgAAQBLBEAAAAAVzbv/vaQK5MLNrJX1v6XYAQMWtnXPnL90I5InfSkbuvuec+4WlGwEAJTN7/9JtQL7oSgYAAIAkgiEAAAAKBEPk7jVLNwAAatgvYTFcfAIAAABJVAwBAABQIBgCAABAEsEQmTKzo2b2cTP7pJn93tLtARCvtv2Jmf2Imb3JzD5sZu8zs/Mr455pZtea2XVm9qzK8MNm9h4zO2Vm7zezB1TGPb9Y1sfN7KLK8ENm9hoz+4SZfczMHjPl60aaCIbIjpkdkPSXkh4p6b6SHm9m9122VQBi1HF/8gJJp5xzPyPpiZJeVTz3fEmXSnqApJ+V9Ktmdq/iOS+TdLlz7rCkFxWPVcz7cZLuJ+mopL8q2iBJvy/pK865exdtOeH/FSN1BEPk6AGSPumc+7Rz7oykN0h61MJtAhCnLvuT+0p6myQ55z4m6SfM7M6S7iPpvc657zjnbtEqyD26eI6TdPvi7ztIOl38/ShJb3DO3eyc+4ykTxZtkKRLJL2kWM62c+5Gvy8VOSAYIkd3lXRD5fHni2EA0FeX/cmHVAS+okv4HpJ+XNK1kn7RzH7UzG4r6WJJdyue8yxJLzezGyT9qaTnb1qemd2xePyHZvbfZnZVET6BXgiGAABM608k3dHMTkl6hqQPSvqBc+6jkl4q6a2Sjkk6JekHxXN+W9KznXN3k/RsSa9tWcZBrcLmfznn7i/p3VoFSqAXgiFy9AXtfiuXVjvTLyzUFgBxa92fOOducs49qThf8ImS7iTp08W41zrnft4590uSvi7pE8XTflPSG4u/r9Jud/G65X1N0ndqz7n/6FeH7BAMkaOTks4zs3PN7JBWJ3L/68JtAhCn1v2Jmd2xGCdJT5H0DufcTcW4Hyv+vbtW3c1/X0x3WtIFxd8Pk3R98fe/Snqcmf2QmZ0r6TxJ73OrX6v4N0kPKaZ7uKSP+HyhyMPBpRsAzM05d4uZPV3ScUkHJF3pnLtu4WYBiNC6/YmZPa0Y/2qtLjL5GzNzkq6T9OTKLP7FzH5U0vcl/Y5z7hvF8EslvcrMDkr6nqSnFvO7zsz+SavQd0vxnLL7+XmSXm9mr5T0VUlPmuyFI1n8JB4AAAAk0ZUMAACAAsEQAAAAkgiGAAAAKBAMAQAAIIlgCAAAgALBEAAAAJIIhgAAACj8PzY71Y0jBHkJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120e8d1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hp.mollview(pair_surveys[4].basis_functions[1](), min=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = 0.\n",
    "for bf, weight in zip(pair_surveys[2].basis_functions, pair_surveys[1].basis_weights):\n",
    "    bval = bf()\n",
    "    mask = np.where((bval == hp.UNSEEN) | (result ==hp.UNSEEN))\n",
    "    result += bval*weight\n",
    "    result[mask] = hp.UNSEEN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b052390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hp.mollview(result, min=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6. ,  0.6,  3. ,  3. ,  0. ])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair_surveys[0].basis_weights"
   ]
  },
  {
   "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
}
