{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![HELP-Logo](https://avatars1.githubusercontent.com/u/7880370?s=200&v=4)\n",
    "\n",
    "\n",
    "# DMU 24 - CDFS-SWIRE: Photo-z Selection Function\n",
    "\n",
    "The goal is to create a selection function for the photometric redshifts that varies spatially across the field. We will use the depth maps for the optical masterlist to find regions of the field that have similar photometric coverage and then calculate the fraction of sources meeting a given photo-z selection within those pixels.\n",
    "\n",
    "1. For optical depth maps: do clustering analysis to find HEALpix with similar photometric properties.\n",
    "2. Calculate selection function within those groups of similar regions as a function of magnitude in a given band.\n",
    "3. Paramatrise the selection function in such a way that it can be easily applied for a given sample of sources or region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was run with herschelhelp_internal version: \n",
      "017bb1e (Mon Jun 18 14:58:59 2018 +0100)\n",
      "This notebook was executed on: \n",
      "2018-06-27 15:04:19.046058\n"
     ]
    }
   ],
   "source": [
    "from herschelhelp_internal import git_version\n",
    "print(\"This notebook was run with herschelhelp_internal version: \\n{}\".format(git_version()))\n",
    "import datetime\n",
    "print(\"This notebook was executed on: \\n{}\".format(datetime.datetime.now()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "#%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "\n",
    "import os\n",
    "import time\n",
    "\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy.table import Column, Table, join\n",
    "import numpy as np\n",
    "from pymoc import MOC\n",
    "import healpy as hp\n",
    "#import pandas as pd #Astropy has group_by function so apandas isn't required.\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "#We ignore warnings - this is a little dangerous but a huge number of warnings are generated by empty cells later\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from herschelhelp_internal.utils import coords_to_hpidx\n",
    "#from herschelhelp_internal.masterlist import find_last_ml_suffix, nb_ccplots\n",
    "\n",
    "from astropy.io.votable import parse_single_table\n",
    "from astropy.io import fits\n",
    "from astropy.stats import binom_conf_interval\n",
    "from astropy.utils.console import ProgressBar\n",
    "from astropy.modeling.fitting import LevMarLSQFitter\n",
    "\n",
    "from sklearn.cluster import MiniBatchKMeans, MeanShift\n",
    "from collections import Counter\n",
    "\n",
    "from astropy.modeling import Fittable1DModel, Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def coords_to_hpidx(ra, dec, order):\n",
    "    \"\"\"Convert coordinates to HEALPix indexes\n",
    "    Given to list of right ascension and declination, this function computes\n",
    "    the HEALPix index (in nested scheme) at each position, at the given order.\n",
    "    Parameters\n",
    "    ----------\n",
    "    ra: array or list of floats\n",
    "        The right ascensions of the sources.\n",
    "    dec: array or list of floats\n",
    "        The declinations of the sources.\n",
    "    order: int\n",
    "        HEALPix order.\n",
    "    Returns\n",
    "    -------\n",
    "    array of int\n",
    "        The HEALPix index at each position.\n",
    "    \"\"\"\n",
    "    ra, dec = np.array(ra), np.array(dec)\n",
    "\n",
    "    theta = 0.5 * np.pi - np.radians(dec)\n",
    "    phi = np.radians(ra)\n",
    "    healpix_idx = hp.ang2pix(2**order, theta, phi, nest=True)\n",
    "\n",
    "    return healpix_idx\n",
    "\n",
    "class GLF1D(Fittable1DModel):\n",
    "    \"\"\"\n",
    "    Generalised Logistic Function \n",
    "    \"\"\"\n",
    "    inputs = ('x',)\n",
    "    outputs = ('y',)\n",
    "\n",
    "    A = Parameter()\n",
    "    B = Parameter()\n",
    "    K = Parameter()\n",
    "    Q = Parameter()\n",
    "    nu = Parameter()\n",
    "    M = Parameter()\n",
    "    \n",
    "    @staticmethod\n",
    "    def evaluate(x, A, B, K, Q, nu, M):\n",
    "        top = K - A\n",
    "        bottom = (1 + Q*np.exp(-B*(x-M)))**(1/nu)\n",
    "        return A + (top/bottom)\n",
    "\n",
    "    @staticmethod\n",
    "    def fit_deriv(x, A, B, K, Q, nu, M):\n",
    "        d_A = 1 - (1 + (Q*np.exp(-B*(x-M)))**(-1/nu))\n",
    "        \n",
    "        d_B = ((K - A) * (x-M) * (Q*np.exp(-B*(x-M)))) / (nu * ((1 + Q*np.exp(-B*(x-M)))**((1/nu) + 1)))\n",
    "\n",
    "        d_K = 1 + (Q*np.exp(-B*(x-M)))**(-1/nu)\n",
    "        \n",
    "        d_Q = -((K - A) * (Q*np.exp(-B*(x-M)))) / (nu * ((1 + Q*np.exp(-B*(x-M)))**((1/nu) + 1)))\n",
    "        \n",
    "        d_nu = ((K-A) * np.log(1 + (Q*np.exp(-B*(x-M))))) / ((nu**2) * ((1 + Q*np.exp(-B*(x-M)))**((1/nu))))\n",
    "        \n",
    "        d_M = -((K - A) * (Q*B*np.exp(-B*(x-M)))) / (nu * ((1 + Q*np.exp(-B*(x-M)))**((1/nu) + 1)))\n",
    "\n",
    "        return [d_A, d_B, d_K, d_Q, d_nu, d_M]\n",
    "    \n",
    "class InverseGLF1D(Fittable1DModel):\n",
    "    \"\"\"\n",
    "    Generalised Logistic Function \n",
    "    \"\"\"\n",
    "    inputs = ('x',)\n",
    "    outputs = ('y',)\n",
    "\n",
    "    A = Parameter()\n",
    "    B = Parameter()\n",
    "    K = Parameter()\n",
    "    Q = Parameter()\n",
    "    nu = Parameter()\n",
    "    M = Parameter()\n",
    "    \n",
    "    @staticmethod\n",
    "    def evaluate(x, A, B, K, Q, nu, M):\n",
    "        return M - (1/B)*(np.log((((K - A)/(x -A))**nu - 1)/Q))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0 - Set relevant initial parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "FIELD = 'CDFS-SWIRE'\n",
    "ORDER = 10\n",
    "\n",
    "OUT_DIR = 'data'\n",
    "SUFFIX = 'depths_20171016_photoz_20170725'\n",
    "\n",
    "DEPTH_MAP = '../../dmu1/dmu1_ml_CDFS-SWIRE/data/depths_cdfs-swire_20180613.fits'\n",
    "MASTERLIST = '../../dmu1/dmu1_ml_CDFS-SWIRE/data/master_catalogue_cdfs-swire_20171103.fits'\n",
    "PHOTOZS = 'data/master_catalogue_cdfs-swire_20171103_photoz_20180210.fits'\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I - Find clustering of healpix in the depth maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "depth_map = Table.read(DEPTH_MAP)\n",
    "\n",
    "# Get Healpix IDs\n",
    "hp_idx = depth_map['hp_idx_O_{0}'.format(ORDER)]\n",
    "\n",
    "# Calculate RA, Dec of depth map Healpix pixels for later plotting etc.\n",
    "dm_hp_ra, dm_hp_dec = hp.pix2ang(2**ORDER, hp_idx, nest=True, lonlat=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The depth map provides two measures of depth:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_values = Table(depth_map.columns[2::2]) # Mean 1-sigma error within a cell\n",
    "p90_values = Table(depth_map.columns[3::2]) # 90th percentile of observed fluxes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the photo-z selection functions we will make use of the mean 1-sigma error as this can be used to accurately predict the completeness as a function of magnitude.\n",
    "\n",
    "We convert the mean 1-sigma uncertainty to a 3-sigma magnitude upper limit and convert to a useable array.\n",
    "When a given flux has no measurement in a healpix (and *ferr_mean* is therefore a *NaN*) we set the depth to some semi-arbitrary bright limit separate from the observed depths:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dm_clustering = 23.9 - 2.5*np.log10(3*mean_values.to_pandas().as_matrix())\n",
    "dm_clustering[np.isnan(dm_clustering)] = 14\n",
    "dm_clustering[np.isinf(dm_clustering)] = 14\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To encourage the clustering to group nearby Healpix together, we also add the RA and Dec of the healpix to the inpux dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dm_clustering = np.hstack([dm_clustering, np.array(dm_hp_ra.data, ndmin=2).T, np.array(dm_hp_dec.data, ndmin=2).T])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we find clusters within the depth maps using a simple k-means clustering. For the number of clusters we assume an initial guess on the order of the number of different input magnitdues (/depths) in the dataset. This produces good initial results but may need further tuning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "NCLUSTERS = dm_clustering.shape[1]*2\n",
    "km = MiniBatchKMeans(n_clusters=NCLUSTERS)\n",
    "\n",
    "km.fit(dm_clustering)\n",
    "\n",
    "counts = Counter(km.labels_) # Quickly calculate sizes of the clusters for reference\n",
    "\n",
    "clusters = dict(zip(hp_idx.data, km.labels_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we illustrate the different clusters with each colour corresponding to a different group of similar sources (although the colours may not be unique to a single cluster of sources). You can see structures and patterns within the field, e.g. the outline of the HSC coverage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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au5t9PK/Dep/OfhmAPfp4LmfHOJcil33xhPeQi3xMzr4UcyY877JnINOXg317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\nYOOPN6K+v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z02TngcvRbZym3cTNqS5Hu5PpjSf1LXchBpTdRUowBAk0cSlbPVZaWlX8fp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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ffd70d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Fig, Ax = plt.subplots(1,1,figsize=(8,8))\n",
    "Ax.scatter(dm_hp_ra, dm_hp_dec, c=km.labels_, cmap=plt.cm.tab20, s=6)\n",
    "\n",
    "Ax.set_xlabel('Right Ascension [deg]')\n",
    "Ax.set_ylabel('Declination [deg]')\n",
    "Ax.set_title('{0}'.format(FIELD))\n",
    "Fig.savefig('plots/dmu24_{0}_sf_hpclusters_illustration.png'.format(FIELD), \n",
    "            format='png', bbox_inches='tight', dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II - Map photo-z and masterlist objects to their corresponding depth cluster\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now load the photometric redshift catalog and keep only the key columns for this selection function.\n",
    "Note: if using a different photo-$z$ measure than the HELP standard `z1_median`, the relevant columns should be retained instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "photoz_catalogue = Table.read(PHOTOZS)\n",
    "\n",
    "photoz_catalogue.keep_columns(['help_id', 'RA', 'DEC', 'id', 'z1_median', 'z1_min', 'z1_max', 'z1_area'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we load the relevant sections of the masterlist catalog (including the magnitude columns) and map the Healpix values to their corresponding cluster. For each of the masterlist/photo-$z$ sources and their corresponding healpix we find the respective cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "masterlist_hdu = fits.open(MASTERLIST, memmap=True)\n",
    "masterlist = masterlist_hdu[1]\n",
    "\n",
    "masterlist_catalogue = Table()\n",
    "masterlist_catalogue['help_id'] = masterlist.data['help_id']\n",
    "masterlist_catalogue['RA'] = masterlist.data['ra']\n",
    "masterlist_catalogue['DEC'] = masterlist.data['dec']\n",
    "\n",
    "for column in masterlist.columns.names:\n",
    "    if (column.startswith('m_') or column.startswith('merr_')):\n",
    "        masterlist_catalogue[column] = masterlist.data[column]\n",
    "\n",
    "masterlist_hpx = coords_to_hpidx(masterlist_catalogue['RA'], masterlist_catalogue['DEC'], ORDER)\n",
    "masterlist_catalogue[\"hp_idx_O_{:d}\".format(ORDER)] = masterlist_hpx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "masterlist_cl_no = np.array([clusters[hpx] for hpx in masterlist_hpx])\n",
    "masterlist_catalogue['hp_depth_cluster'] = masterlist_cl_no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = join(masterlist_catalogue, photoz_catalogue, join_type='left', keys=['help_id', 'RA', 'DEC'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constructing the output selection function table:\n",
    "\n",
    "The photo-$z$ selection function will be saved in a table that mirrors the format of the input optical depth maps, with matching length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pz_depth_map = Table()\n",
    "pz_depth_map.add_column(depth_map['hp_idx_O_13'])\n",
    "pz_depth_map.add_column(depth_map['hp_idx_O_10'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III - Creating the binary photo-z selection function\n",
    "\n",
    "With the sources now easily grouped into regions of similar photometric properties, we can calculate the photo-$z$ selection function within each cluster of pixels. To begin with we want to create the most basic set of photo-$z$ selection functions - a map of the fraction of sources in the masterlist in a given region that have a photo-$z$ estimate. We will then create more informative selection function maps that make use of the added information from clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "108"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NCLUSTERS # Fixed during the clustering stage above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 In cluster: 36639  With photo-z: 27890     Fraction: 0.761 \n",
      "1 In cluster: 73308  With photo-z: 62396     Fraction: 0.851 \n",
      "2 In cluster: 11712  With photo-z: 0         Fraction: 0.000 \n",
      "3 In cluster: 3516   With photo-z: 617       Fraction: 0.175 \n",
      "4 In cluster: 24349  With photo-z: 0         Fraction: 0.000 \n",
      "5 In cluster: 788    With photo-z: 0         Fraction: 0.000 \n",
      "6 In cluster: 7483   With photo-z: 0         Fraction: 0.000 \n",
      "7 In cluster: 10412  With photo-z: 8132      Fraction: 0.781 \n",
      "8 In cluster: 6259   With photo-z: 1329      Fraction: 0.212 \n",
      "9 In cluster: 144425 With photo-z: 120226    Fraction: 0.832 \n",
      "10 In cluster: 8906   With photo-z: 0         Fraction: 0.000 \n",
      "11 In cluster: 75776  With photo-z: 64177     Fraction: 0.847 \n",
      "12 In cluster: 3938   With photo-z: 536       Fraction: 0.136 \n",
      "13 In cluster: 19211  With photo-z: 2403      Fraction: 0.125 \n",
      "14 In cluster: 9100   With photo-z: 0         Fraction: 0.000 \n",
      "15 In cluster: 14031  With photo-z: 1970      Fraction: 0.140 \n",
      "16 In cluster: 197362 With photo-z: 163733    Fraction: 0.830 \n",
      "17 In cluster: 22550  With photo-z: 3660      Fraction: 0.162 \n",
      "18 In cluster: 5192   With photo-z: 1199      Fraction: 0.231 \n",
      "19 In cluster: 9154   With photo-z: 4221      Fraction: 0.461 \n",
      "20 In cluster: 3055   With photo-z: 0         Fraction: 0.000 \n",
      "21 In cluster: 10956  With photo-z: 9695      Fraction: 0.885 \n",
      "22 In cluster: 20695  With photo-z: 0         Fraction: 0.000 \n",
      "23 In cluster: 24369  With photo-z: 0         Fraction: 0.000 \n",
      "24 In cluster: 17799  With photo-z: 11404     Fraction: 0.641 \n",
      "25 In cluster: 143484 With photo-z: 123379    Fraction: 0.860 \n",
      "26 In cluster: 5722   With photo-z: 508       Fraction: 0.089 \n",
      "27 In cluster: 62512  With photo-z: 16555     Fraction: 0.265 \n",
      "28 In cluster: 22707  With photo-z: 0         Fraction: 0.000 \n",
      "29 In cluster: 26305  With photo-z: 17900     Fraction: 0.680 \n",
      "30 In cluster: 76185  With photo-z: 62704     Fraction: 0.823 \n",
      "31 In cluster: 13072  With photo-z: 0         Fraction: 0.000 \n",
      "32 In cluster: 24885  With photo-z: 18954     Fraction: 0.762 \n",
      "33 In cluster: 3268   With photo-z: 357       Fraction: 0.109 \n",
      "34 In cluster: 58901  With photo-z: 47766     Fraction: 0.811 \n",
      "35 In cluster: 22941  With photo-z: 3797      Fraction: 0.166 \n",
      "36 In cluster: 13487  With photo-z: 4992      Fraction: 0.370 \n",
      "37 In cluster: 26499  With photo-z: 19910     Fraction: 0.751 \n",
      "38 In cluster: 106429 With photo-z: 93259     Fraction: 0.876 \n",
      "39 In cluster: 22288  With photo-z: 18058     Fraction: 0.810 \n",
      "40 In cluster: 14442  With photo-z: 11900     Fraction: 0.824 \n",
      "41 In cluster: 83     With photo-z: 0         Fraction: 0.000 \n",
      "42 In cluster: 10647  With photo-z: 3274      Fraction: 0.308 \n",
      "43 In cluster: 9587   With photo-z: 7208      Fraction: 0.752 \n",
      "44 In cluster: 2302   With photo-z: 313       Fraction: 0.136 \n",
      "45 In cluster: 179522 With photo-z: 151649    Fraction: 0.845 \n",
      "46 In cluster: 2457   With photo-z: 0         Fraction: 0.000 \n",
      "47 In cluster: 48713  With photo-z: 38785     Fraction: 0.796 \n",
      "48 In cluster: 64147  With photo-z: 37763     Fraction: 0.589 \n",
      "49 In cluster: 2529   With photo-z: 0         Fraction: 0.000 \n",
      "50 In cluster: 97581  With photo-z: 83223     Fraction: 0.853 \n",
      "51 In cluster: 27206  With photo-z: 18568     Fraction: 0.682 \n",
      "52 In cluster: 14434  With photo-z: 0         Fraction: 0.000 \n",
      "53 In cluster: 17387  With photo-z: 11757     Fraction: 0.676 \n",
      "54 In cluster: 4679   With photo-z: 0         Fraction: 0.000 \n",
      "55 In cluster: 4      With photo-z: 0         Fraction: 0.000 \n",
      "56 In cluster: 7165   With photo-z: 594       Fraction: 0.083 \n",
      "57 In cluster: 91511  With photo-z: 76077     Fraction: 0.831 \n",
      "58 In cluster: 62117  With photo-z: 51790     Fraction: 0.834 \n",
      "59 In cluster: 3681   With photo-z: 1         Fraction: 0.000 \n",
      "60 In cluster: 16768  With photo-z: 0         Fraction: 0.000 \n",
      "61 In cluster: 3643   With photo-z: 1086      Fraction: 0.298 \n",
      "62 In cluster: 13100  With photo-z: 9950      Fraction: 0.760 \n",
      "63 In cluster: 41989  With photo-z: 33546     Fraction: 0.799 \n",
      "64 In cluster: 778    With photo-z: 46        Fraction: 0.059 \n",
      "65 In cluster: 8703   With photo-z: 5725      Fraction: 0.658 \n",
      "66 In cluster: 24603  With photo-z: 5479      Fraction: 0.223 \n",
      "67 In cluster: 47402  With photo-z: 37418     Fraction: 0.789 \n",
      "68 In cluster: 7135   With photo-z: 3945      Fraction: 0.553 \n",
      "69 In cluster: 110226 With photo-z: 92204     Fraction: 0.836 \n",
      "70 In cluster: 111429 With photo-z: 91098     Fraction: 0.818 \n",
      "71 In cluster: 4193   With photo-z: 586       Fraction: 0.140 \n",
      "72 In cluster: 8734   With photo-z: 6874      Fraction: 0.787 \n",
      "73 In cluster: 90079  With photo-z: 77610     Fraction: 0.862 \n",
      "74 In cluster: 3837   With photo-z: 877       Fraction: 0.229 \n",
      "75 In cluster: 40430  With photo-z: 9773      Fraction: 0.242 \n",
      "76 In cluster: 124829 With photo-z: 107456    Fraction: 0.861 \n",
      "77 In cluster: 2389   With photo-z: 465       Fraction: 0.195 \n",
      "78 In cluster: 3033   With photo-z: 0         Fraction: 0.000 \n",
      "79 In cluster: 2762   With photo-z: 0         Fraction: 0.000 \n",
      "80 In cluster: 34715  With photo-z: 21187     Fraction: 0.610 \n",
      "81 In cluster: 8138   With photo-z: 0         Fraction: 0.000 \n",
      "82 In cluster: 41536  With photo-z: 29055     Fraction: 0.700 \n",
      "83 In cluster: 39740  With photo-z: 8386      Fraction: 0.211 \n",
      "84 In cluster: 46551  With photo-z: 15154     Fraction: 0.326 \n",
      "85 In cluster: 12851  With photo-z: 1797      Fraction: 0.140 \n",
      "86 In cluster: 20080  With photo-z: 0         Fraction: 0.000 \n",
      "87 In cluster: 8064   With photo-z: 0         Fraction: 0.000 \n",
      "88 In cluster: 51082  With photo-z: 34000     Fraction: 0.666 \n",
      "89 In cluster: 120382 With photo-z: 95355     Fraction: 0.792 \n",
      "90 In cluster: 17146  With photo-z: 2820      Fraction: 0.164 \n",
      "91 In cluster: 54944  With photo-z: 28513     Fraction: 0.519 \n",
      "92 In cluster: 25452  With photo-z: 5486      Fraction: 0.216 \n",
      "93 In cluster: 16068  With photo-z: 9         Fraction: 0.001 \n",
      "94 In cluster: 19124  With photo-z: 3556      Fraction: 0.186 \n",
      "95 In cluster: 28935  With photo-z: 6203      Fraction: 0.214 \n",
      "96 In cluster: 37997  With photo-z: 29598     Fraction: 0.779 \n",
      "97 In cluster: 79267  With photo-z: 63755     Fraction: 0.804 \n",
      "98 In cluster: 10168  With photo-z: 2103      Fraction: 0.207 \n",
      "99 In cluster: 40401  With photo-z: 24022     Fraction: 0.595 \n",
      "100 In cluster: 11889  With photo-z: 0         Fraction: 0.000 \n",
      "101 In cluster: 116009 With photo-z: 98692     Fraction: 0.851 \n",
      "102 In cluster: 25608  With photo-z: 0         Fraction: 0.000 \n",
      "103 In cluster: 157284 With photo-z: 131496    Fraction: 0.836 \n",
      "104 In cluster: 136476 With photo-z: 111219    Fraction: 0.815 \n",
      "105 In cluster: 47724  With photo-z: 37344     Fraction: 0.782 \n",
      "106 In cluster: 9628   With photo-z: 7463      Fraction: 0.775 \n",
      "107 In cluster: 93107  With photo-z: 68706     Fraction: 0.738 \n"
     ]
    }
   ],
   "source": [
    "cluster_photoz_fraction = np.ones(NCLUSTERS)\n",
    "\n",
    "pz_frac_cat = np.zeros(len(merged))\n",
    "pz_frac_map = np.zeros(len(dm_hp_ra))\n",
    "\n",
    "for ic, cluster in enumerate(np.arange(NCLUSTERS)):\n",
    "    ml_sources = (merged['hp_depth_cluster'] == cluster)\n",
    "    has_photoz = (merged['z1_median'] > -90.)\n",
    "    \n",
    "    in_ml = np.float(ml_sources.sum())\n",
    "    withz = np.float((ml_sources*has_photoz).sum())\n",
    "    \n",
    "    if in_ml > 0:\n",
    "        frac = withz / in_ml \n",
    "    else:\n",
    "        frac = 0.\n",
    "    \n",
    "    cluster_photoz_fraction[ic] = frac\n",
    "    print(\"\"\"{0} In cluster: {1:<6.0f} With photo-z: {2:<6.0f}\\\n",
    "    Fraction: {3:<6.3f}\"\"\".format(cluster, in_ml, withz, frac))\n",
    "    \n",
    "    # Map fraction to catalog positions for reference\n",
    "    where_cat = (merged['hp_depth_cluster'] == cluster)\n",
    "    pz_frac_cat[where_cat] = frac\n",
    "    \n",
    "    # Map fraction back to depth map healpix \n",
    "    where_map = (km.labels_ == cluster)\n",
    "    pz_frac_map[where_map] = frac"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The binary photo-$z$ selection function of the field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rcKDC5Er8A9RL08/PvT+m0PBQdZDH7/qX8yvk9nNKFCnihZVkD5dKlzMRKaAS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Sy5AJQvWsCgaXn8wWC3+EmtqrvFE91T+fHi+jg+7k2UKV1QHxM69xeePFyzQnRLrchnMCR+oD5X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yd5ClUkAq/QPuz4ge0KZVe7ZcT5KTnH6V/snK94jMIqNSPw2bWSvlPMHWT6zGdl9EVlQEn+an\n/zvAgW2HAOhbZbDl8c0fbrVoP/500qKdOe1OLSWmZGz0ZJMKUnRUkALuFux6Ge3G0yMBc9mx+gLX\ne4SoYxWk6Md6rxR1bKfp/hN1nNbn6t4QNdaPTvY0NOWfcZx0aJq1bPo8eyyauTT6Ysqf7pSYClJ0\n7Eqn9VJkR8JyACNIAejxUwTgDjz1Y92noo51r4k6TrNWqgfJSU7L44KQaUgLfUHwXU7+kWjRduzY\nkQkjsXKNfZk9hNvgtEW5irUvTWZwwsbnc4kbe5g8Sga+lCAIN0cCFcGnaRj2uHFcu1kIAPXq1cvw\ncdjtFdO08McWzZ36Kaad50r9lKWqoanUT1nc1UQqVaN7X9Sxnu4pSSfT+eDayfl6TfVo0dNRrs0E\nzOcpri+dziw6B0+1aDH3TQDgjjvcmkr9lCiVx9BU6kffx0elb/S+KOrYTtPTPRPWjb61NyEIHsTf\nzbTiUfETsmP++ejRowwe5drFd+aUlpQtWxaHw8Gc913VQP16VjcCDOVf6dCmOmFtXZoqYX6wbBGm\nd3WlSVQJcxFy8UHIcJOWE1ie4l+xa8mf1nbyegpIBSx2z7Vr3Z/WUmU9BaSCEjtt3an2wAUAWhT5\nLGXMzwHHTGN2nJ7OJTZepy3mEtHXaW4Pj2qLr7+XunQzNkoM/8nVKbc+99H3PpcfZm58FwAKUJFn\nS7quo/6dSuYNJGagy3xrpHsCYMmaQWYNz5Qq+xvZ8R7hbTLKoxL6tHc8Ko7PxKMiCF5FBSn6sQpS\n9GO9z8ry/7o0vc/KrqOnAHPwcYoki6acF9f3YwGXQdQXsCtLXnKyn0VzBy0XDG3dqadTjo4Zmgq0\nVJBi1qJtNLeHRx3rAde3uFrVqyAFYDs/Ae4gBeAcvwHmf6f4i67ac9OePim/ZvXsZN7nB2BF5GqL\nJghZEqfTOz9ZBAlUBCGdlCK/RQtMSb1kNnZlySW1lJMb+zLwLEGAVapcpaRFK173BlsyCIKQZZBA\nRciyLI9+yXKcVk3tqqwfqxSOfqxrg3D5ZeaGDLJo0Rg4iwAAIABJREFUeoBg13MkkActWj6s/Vog\nr3Fk7k/iIhd3WbSSPGHRdO+L8p7o3pdni8cCUAB3fxKV+tHLqdXx7WgqbaUfK8+Jfty35BJDU8f6\nv9PeGa6qL5XqAej9isu3NHpCZ4um+4p0z4pC39+ndFWXn+eRtnUt5wlCZiMeFfGo+AXZOf/cses7\nRqGGCkRM6Z4UTS9tVUZMPbWgvhTtND3dM4jHCQ0JtWgdGregwkJ3Lrl3gWq8EBpqKi3OR02aF5tA\nXGJn3OW8RehY7ENWJb7CFX5J0QLoWGw1Hx1ZzG6+N57/RqUoHMdX8y3utNew0gtxHHcYaRWlATT9\nzm1MXV/HVU6d1vb7afXI6G316xfclqq2IMEdwKleKXZeH7v5t/u385QnZcSGgdSsWRN/JzvfI7xF\nRnlUnnjKO52Zt2xIvZFicnIyERERHDx4kFy5cjFx4kQqVKhgPP7xxx+zaNEiAgMDqVKlChEREeTI\n4Z21D1lREbI8N6sm3b/f2mp/4IB5Fu36/YFS06L4Mk2a3odFccHoRXJRU10eGXeQAsp8oQcpCj1I\ncWuLLZoepCiu328HzEGKQm0y6IssX+awaF1aWT0qdj1Q7LRpT71l0QTB39m0aRNJSUnExcUxZMgQ\npk51308uXbpEVFQUixcvJjY2lvPnz7Nli7U/kqeQQEXI9lSvbm2r36ZdFYvW+N6yFq1MviCLZmOR\n8Eny2Wj5sa4clMTaSE/f8djX6BAWatF6Dw6xaHpqR5GnaG6LdkfhPBZNEHyJAKfTKz83YteuXdSv\nXx+AkJAQ9u1z943KlSsXsbGx3JHSL+Dq1avkzm39v+XB9y+pH38guy/rHjh4nGr3ljZpb835nIH9\nmpi0iRPiGDvO7CEZHr2SZ8oVM3ka7LTxuxfxBBUIDbHX1Bz3+3w5HXIHm567JTGKAEoSWqzzDTVH\n4lKcxPNEMbcP4/Mjn5FAAs9Wcvf8cBx3cIxv6VJ62I217xxs4E8m1+mqvYaDsziMdvhKO88XNC/m\n9o4ALDgeQa/SESZtReIQ2hd706RtP9ub+gXn31RbmjDS0idlyO65vBnS16T1fW8pc1/obNLGjY1h\nwsRwkzZ53FLqPVnKNNeTO0RSr1/dm2pzRyygXnhdSfukkN3vEd4go1I/Tza2ro56gi82jkz1sTFj\nxvDUU0/RsGFDwOX52rRpE0FB5l/eoqOj2bp1K/PnzycgwDu/xkmg4if4401o5zcHmfHGBuPvscsH\nsG3bNt6e7/7NYHn0SzgcDt6d86PpPLD3udh5KtKqpbXPSlp7qthpo353G2ab0I7QCqGm66m9fvr/\n7A5m6hDCc3d3MaWAclKZtsVmXJcqykt4sSUMPTIInTcqRRF1ortJG1Rqka12fWn36pCxlvTa5lG9\nzCXIuDwodlrPTlFcumjWBtcbQfwvCYYWE7+Q9wa+z9bYr0yaYMYf7xG3S4YFKo28FKhsTj1QmTJl\nCjVr1uSZZ54BoEGDBmzbts14PDk5mcjISI4cOcLMmTON1RVvIKkfIdvy1swNFk0PUhSLFlpbxy9b\n5fDGkLyGnafkc1ZZNH1fIsV37LZoZr+M4qKNlrlcshmSHqQo9CBFELIamZH6qV27thGY7N69mypV\nzOnycePGcfnyZd555x2vBikggYqQjZn+ZluLNqB3DYv2wWJr63jVvTarYOcpmVLB2oSuNAMtWnua\nWTTVpl/Hzt9SmCI2o7l9z0eQTZuXMuWs/WvuC7H2T7ErRX5x+XO3PSZB8CcaN25Mrly56NSpE1Om\nTGHUqFGsW7eOuLg4fvrpJ1asWMGhQ4fo3r07Xbt2ZePGjTe/6C0iqR8/QZZ1rXTq8DZFitzB3Hnu\nXh0q3aP3XrHTVLpC7/NR732XtqOnW1OpDr0fi52mOsXqe/C8ecLVLn5IqQ8MTaV79N4kds+NS2wP\nXDHtERR5vBeQfN1zewKnTc+106b+Pp4z/MOUCu4y4Q9OjOMMvxPC84SWCgVg6YlpxLPfpPX7fDnf\nJJ3kjQLVDI/I1FXr2XjwKK8+erehrYxxsCp2N207hdAu3KzVf/IuXp3UhYSEczgcDubPcGkvDmoJ\nuHa3ntdxMTUaVGP08mEmrWLN8kzeEIFwY+QekX4yKvXT6IkpXrnu5i2jvHJdTyMrKoJfovpynDr1\nr3Gse1KMvYFsNN1ToY5VkKIf634MdWyn6XvwqGMVpOjHuidFHds91+U1uaIdw8zjA1GF3Oq5a06+\nhtpFWT3XcTLOqv3u4Az/AGYPzBl+B2A37kAqnv0W7ZukkwAM1cq1Nx48CsDrX/9saKtid5v+1I+3\nf/Groc2fYdXmdXSVZ+/bdsCi/bbnDwRByLpYay8FQch2XMX6m/I59lq0o1h3hD6REpDoOE44PDIu\nQRDSgO8mPjIEWVER/JLgYLffQVX5BAW6H+/X09V7pXDBAItWpai7Q0m3R+8G4KEibq9G07Kufiwd\nU9rrA9SkHOBuuQ/uPYP01vaqhf6DmCtmwJzuUejpGYWe7knvc58tHm3ROlewjkWldQRB8D7SQl88\nKn6B5J/t6VNjAOcTLnBvw7sZv2w0kPY27Hq6Z0fPXgQHF+CeqTNNmt15qWkNls3j3xTt27A+ADRa\nNo+z12ntln3AHympHaV1WxXDgavnTVr/j1fwzcV/TNr4LZ/xacKfN9XmORzM//uQSXM4HAy7DU2l\nYvTSYDWvGa0JVuQekX4yyqPyn4aTvXLdTVtHe+W6nkZWVAS/5nzCBQAObv35Jmd6n3+1Y4fDAWAE\nKbqmghRwBRmAEaSAK5ABjCAF4NFlri0DVEACUPcGmgpSdG3YbWgqSAF34KAHfxmpCUKWw+n0zk8W\nQQIVQcjCJF64YNEuaYGMIAhCVkcCFcGvuaOIq1FRhZBymTwSs7NdlezeYaMV1LTZzdsDUB5345FP\nUtIt1YLcPpyvU7SH8xY1NJWWeSa4XJq03iWqWLTINGp94tzt/1UKxk7T0zPe0gQhqxGQ7J2frIJ4\nVPwEyT+nTnJyMv+dsY4rl6/QYUQbAoMCOXToEBH1XW2rn3yuAb2mPWfyWZSsHMzMHdNwOByM/sWV\nNrozZyCruj1v0goC66/zpQQC29PpX1EpFHB/+fu6llavT0Zows2Re0T6ySiPSuPHJ3nluhu/HOOV\n63oaWVER/J6vVv4fKyPXsHbWp2xa5NqqXAUpAF986GojrfssVJt2FZAA/HXlmkVTHhM9+Lhmo3ED\nTQ8CMlJrYKMp/4uO8snoKD+NIAgeQDwqguDflK9WlqKli1AouCDlqpbN7OHcMnZNkQraaHqaSKGn\nhBRPBFvTYQNKVLKeF2Dd50PfpVgQhNvE6aWfLIIEKoLfU6FGeaZ+8RrTHBOo/lhVwJUyyJUvF+S4\nsd9Bb5d/eORgi6aOb0dTqRT92E772kbbHNbHCGCUtjLseYs2u3l7ww+jtNeeeNoIdJQWGhpqq5W4\nTgMoVNq1LK57UUpWDk6TVrFmeYt2T91KFq1Gg2oAtB3a0tA6jnXt+Dx4qXUPJ0EQsh7iUfETJP98\na7xUczCn/zpDQGAAS46/D9yeVyKtnpRb8alElqhCaGhohmhhXdxbC7RvW52wdqGm99t2aEvaD2tt\n0uq3f5S+c3rfVFP79VyvzXRMoHGODoam9vDRz6tctxKvf/wqQvqRe0T6ySiPylOPvu6V6274Omv8\nX5EVFUG4Aaf/OgOA85p9PL9r1y6LtiJytUWb3CHSonVc9KFFe/I2fCt6/5KM1Fas2m/RVr2x1qJt\nX/F1mjR9v54baXZ7+Pzy7RGLJghC1kb2+hGEG5AzT06uXEq9L8mDDz5o0doPa23R1I6+OnHdn7No\n4yrfbdEm22h6CbCidxq1Z2y8J3aanW9FL3lWVKpo/a1SpXN0VCpIJ0/R3BYtKL/N70/W01zlU4Lg\nD/hu4iNDkNSPnyDLut5h3+7/USPE5WtRc7z2rU9pOfAZ03krIldbAhhPaysXO2jXLdRj2qrFDtqm\n4byMeG8rIlfTd3pX02f4vzPW0eaVFgieQe4R6SfDUj8PTfDKdTfsHOeV63oaCVT8BLkJeZaFY6LZ\ntGCL8feY+IUMqTeSE7/8bdL61hrImePnTNrgeiOM8uYbaaOfijClN1LTpoxcwk/fnzC0jzYMTbP2\n7vTVfLnpZ5P23vTVfLnxsKFFbxzGysUO/vvRd6bzVkSuNqV4YuIX2mp6/xlPaNu3b2du+w9MmnD7\nyD0i/UigkjFI6kcQbgE9SFHoQYpCD1IUekByI83Og2Gn6cFHejU9SDE0LUhR6EGKws6HYqfpgYah\ndbNqC3t+ZNHiBv/Xoq0Zbe3bIgjZmQDfXU/IEMRMKwi3wIw91t1MX/tysEXTS2kVdisAt6N9tGGo\nR7XojVY/jd15t/U+/rBqi3+xmobn/vCWRXtju3d2khUEwTeR1I+fIMu63iEpKYmIFlNo0O4Ruo5p\nT0LCOfbt28fkRjMA95e0nsLwprZgsmvlQwUWqWnvT/oWcAcl6XluRryPtGhXrlxhepcoHm/7MA07\nNbD/BxLSjNwj0k9GpX6a1InwynU//8471/U0Eqj4CXIT8g7PlulF8lXX7l5vOiIoVa28qa9HoZIF\nmbs3yqQRCDHHF3pce/apN0xj+2jDUFuta2NzqXT0xmFpfm5GvI+0as9VeJGkS0kAvL7+VSrXsnbN\nFdKO3CPST4YFKg+O98p1P9/1mleu62m86lFp06YN+fO7yhnLli1Lz549efXVV3E6nVSsWJGJEycS\nFCQ2GSHrooIUgOO/xlOqWnnT42f+Pnv9U9yb/XhTywgy4n3cQLuS5C4bP3n0pAQqgpBN8ZpH5fLl\nyzidTqKjo4mOjmbKlCnMmDGDV155hdjYWAC2bLEaEgUhKzFq5RACAgIoGFyQps89CUD+YvmMx2P+\n8qwfxdO+lbT6UTzuUfGANv7jkQQGBVKiYjAPt6hrOU8Qsg3JXvrJIngt9bNnzx6GDx9OmTJluHr1\nKq+88gr3338/gYGBJCUl0bdvX3r16sWjjz6a6jVkGdJzyLKu97l+jlW64onnH6f31B4mDdLfft/Q\nUlIfN9Vyu02rRhonB3y0fuhNtcDcsGjdUNP1gvLnMAyvSstTNDcLD8z1mFaodAHDQHu9FhxcwGih\n32lcO1r2a4bgOeQekX4yLPVTy0upnx/8PPWTJ08eevbsSYcOHfjtt9/o3bs369ev59ixYzz//PPk\nz5+fqlWr3vAaRYrkJShI2k96ioz6T+XPqDn+ct1OQ9vywZeMfn8gz1buazm3TcluFq15kc4WrUk+\n9x43XHNpjXPbaNpeOFxOea5+k0tOm3btsvV6V88nW7RL/1z2qHbm+LlUtU7l3Rsexk5YSc+ITgie\nRe4Rvom/lyd7LVCpVKkSFSpUICAggEqVKlG4cGESEhIoU6YMGzZsYPny5UydOpVp06aleo1Tpy56\na3h+h/y25H30OU4ONGdVExLOUeqeUsQfOWnSipcO5nzCHyatcPEixJ9JMGnBpYNNvVYSEs5RKLiA\nqU9LQsI58hTNzaV/Lpu0wNyuwONm2vUkJJxztam/lobzMkArWSGYxKOnbniecOvIPSL9ZFhg5+eB\nitc8KitWrGDq1KkAxMfHc/78ecaNG8dvv/0GQL58+ciRQ9q4CNmTanXvpcL9LmPt9F2urpLDo919\nVsJGtgVg8oYIQ2s7tCUAM3dMS5Om9xhR/VpUKkXXVAoHoNfoOqlqug9FaSqdpF9P941kpPbW9kkE\n5AwAYNZP0xEEwT/wmkclKSmJUaNGcfz4cQICAhg61HUTnD59Ojlz5uSOO+5g4sSJlChRItVrSHTv\nOeS3Je9jN8cHvznItPC3uKPgHcz6bjqBgYEm/8nkb8ZRsWJFk9YnrhuhoaGe8bNoml5urIKSbveP\nMLTFP0677dfwprYxebl8hr2I3CPST0atqDR9YKxXrrt+70SvXNfTeG1JI1euXLz55pssXbqUmJgY\nateuTe3atYmNjSU6Opp58+bdMEgRhOxAVK+5XDp/iVPHT7Fi6mrL46Mftu7hYddy3tRHxAPa9T1S\nwBy0eOR1S1u1HtX6WrTB9ayvO7lDpEXbuHa7RRMEIfsjuRdB8CLVHrnXdRAAD7exltCWrByctgvZ\necpzW6U8Ra1iodLW3/pKlLnDolW4706LVqNBNYtWv721Uk+lo0zaYKvWbW5Hi9Zq8tMWbfRya9l0\n45b1LZog+AVOp3d+sggSqAiCF3l5fl/m7J3Jh0fepWJ1l2clJn4htZ8Joc/sbob3JCZ+IXmK5qZ+\n+0eNtIdJO27W2g5taZQex8QvJCh/DtoObWl4VHRNeVk+2jCUwNzQ5tk6zPigH+BK9wTmhdZ9/8Pr\nsYMtz1UBg671ndPbpPWJ60b7Ya3TpIWGhhoauUlVU8TEL+TOqiUZv8266iIIgn8gLfT9BMk/e5/0\nzrEn/Rz6Xjjp1VZErjbtehwTv5C5/eazfcXXJm1yh0j2bTtg0kY/FWHa0TkmfiGD640wVSjFxC+k\nb62BpgqlmPiF9KjW11ShFBO/kOfueYGks1dMmkI+w95F5jf9ZJhH5b4xXrnu+p8meeW6nkb61wuC\nj/D9999btLn95lu0vrUGWrR54VZfi53XxU7TgxSFHqQo9CBFoQcpCj1IUehBikIPUhR6kCIIggt/\n76MiqR9B8BFq165t0VSaRUcvS1b0ielm1eJuXbPznNh5U+w0O19LxZrlLZqdP6dQqYIWTRAE/0ZS\nP36CLOt6H0/O8dAGY7irdiVeiuplaOEle5hazN9IK1k52NR7JTWtYs3ypl4uqWk1GlQzGVxT0+q3\nf9QUXKVF27dvH5MbzaDPWz0I7fR4qnMin2HvIvObfjIq9fN0tVFeue5nB6Z45bqeRlZUBMHHCC/Z\ng+MHT/Dl0h10KW/eI+jM8XPG8Y20+F8SjPJgXetWuZdJ+23PH0Z5sK6p8mCl7dt2gBWRqy2aw+Ew\naXrKKK3a5EYzAJg30LoBoSAIggQqguDDOK02jrRzzSpdPW/dMtXOU2LnR1k10+plWTP6M4umAhod\nFdAIgnALJDu985NFkEBFEHyMEtWKG8eR379uedzOU6JXx3hNO27V9FSSQpUl66gSZEEQbgE/76Mi\nHhU/QfLP3sfTc7x00jI+m7uJV9eO4J7alQF36qROixBeWfCySdO9JUrTfSRK0/0hSms7tKURYChN\n73GSXk0PdNKq3Qz5DHsXmd/0k2EelSre6SP02aHUNwX2JWRFRRB8lHWz1nP1ylXGP+3qdfC///3P\neOy7dbsBc5pFlQrr7edVCkdvXa/8IXovFlWirGuqlPlWtOs9MzfTBEG4AX6+oiKBiiBkMxKOnbRo\nly7cjtlFEAQh85BARRB8lOr1q0IO6Da5MwBVq1Z1Pxjg+sPOD2LyjaRs/aPa7YN7PyA97aJ6muia\n6n2ia6pHiq6pXiq6pvqw6Jry1thpgiDcAD9fURGPip8g+Wfvk1Fz/OG4JWx4bzPg/tLvc19/zp+8\naNI82aI/o7QbIZ9h7yLzm34yzKNy11CvXPezX627qPsisqIiCFkMFaSA+8teBSm6pnM7ml3LfrsS\nZEEQBG8ge/0Igr+S2yrZtbVvFP6ERStet7A3RiQIgh1Oa/8jf0JWVAQhi6HSJIF5cxjHeuokzdof\nVk35W3TviPLBGFqguy+K0oLyu28lyp+ivDDg9rHogVC3qeGmPwVBEOwQj4qfIPln75PRc/zmc29z\naOfPNOnViLavuIKDW/WHOBwO087KqWlz+803tb+PiV/I6KciTLsox8QvpEe1vqbdkWPiFzKw3nAS\nfnFVJJWufidvbJmcrvcrn2HvIvObfjLMo1JxsFeu+9lvM71yXU8jKyqCkEX5YeMeziWe4/MFmwBY\n/vZ/Leeo/X5Mmo0PRQ9IbqTpQYpCD1IUepCiUEEKwPH9f1keFwRBsEMCFUHIopQoX5wcQTm4p46r\na22HAW0s5/SJsZb/qjSMjp03RU/nCIKQifj5Xj+S+vETZFnX+8gcexeZX+8i85t+Miz1U85aeecJ\nPvvzLa9c19NI1Y8gZDP01E6XCWE0e6GpSavW4F5eXT7CpKn9f3RN7eGTkb1SBEEQrkfWdgUhG7Pk\ntWUW7cC2gxbNznuysOdHFs3hcHhkXIIgpAM/70wrgYogZDPyFrnDOJ76fxGWxweufcGi2a12LP5l\ngUVTZcmCIAgZhXhU/ATJP3sfX5vj86fOc/j7X6n5RA1y5HD9TjL8ybEc/em4KTBR6Rlvap7A1+Y3\nuyHzm34yzKNSZoBXrvvZsbe9cl1PIysqgpANSU5OZmCd4USGRzGh1VQAPnotlqM/HQfcwUR4ObeH\nxNBKelYTBOE2SU72zk8WQQIVQciGXE26yuV/kwBIPP4PAF+v2Wk9MSkjRyUIgpB+JFARhGxIrjy5\naNa3KeWrl2XA3D4AzPl+huU8u/SMSQu0aqWr3AnAqE3ubpkNez0OQNjItoamHwuCcBv4uZlWPCp+\nguSfvU9WmeNf9/7G2MYTyFvoDhYcmgNA/weH8M/RU4A7KOlSsQfOfzFpdrza7HV++e4I5aqXYdqW\n1wFYP38j/436mBdn96TWEw94ZNxZZX6zKjK/6SfDPCp3vuSV63721zteua6nkRUVQfAzxjaeAMDF\nM/+ycsZqACNIARjT9DUAI0gB2LFjR6rX++W7IwD8uf+YoS0eu5RzJ88R2TnKY+MWBL/Fz1dUpOGb\nIPgxufPmsWh3FLrD5kxBEDKNLNTu3hvIioog+Bmzvp0OQNEyRWj+YlMAqodWNR4fGzccgIJ3upe1\n69Wrl+r16jQLASDkKXeKZ8D8FylSqggTPhvtuYELguCXiEfFT5D8s/fJDnOsSorrhT1C/7f7ZPJo\nzGSH+fVlZH7TT0Z5VJoW987/xfUn53nlup5GVlQEQQDg+bv7Gsc7lv1fJo5EEATBjQQqgiAAUK/d\nI5k9BEEQ7Eh2eucniyBmWkEQAOg9rTulKpTkx237GRX7SmYPRxAEAZBARRAEjeYvNaX5S00zexiC\nIOj4rpU0Q5BARRAEQRB8mSy0L483EI+KIAiCIAg+i6yoCIIgCIIv4+epH1lREQRBEATBZ5EVFUEQ\nBEHwYZx+7lGRQEUQBEEQfBlJ/QiCIAiCIPgmsqIiCIIgCL5MFuoi6w1kRUUQBEEQBJ9FVlQEQRAE\nwZdxiplWEARBEAQfxSmpH0EQBEEQBN9EVlQEQRAEwZfx89SPrKgIgiAIguCzyIqKIAiCIPgwmeFR\nSU5OJiIigoMHD5IrVy4mTpxIhQoVjMe/+OIL5syZQ1BQEO3atSMsLMxrY5EVFUEQBEEQTGzatImk\npCTi4uIYMmQIU6dONR67cuUKU6ZMYeHChURHRxMXF8fJkye9NhYJVARBEATBl3Eme+fnBuzatYv6\n9esDEBISwr59+4zHfvnlF8qXL0+hQoXIlSsXDz74IN9++63X3r5Pp36Cgwtk9hCyFTKf3kfm2LvI\n/HoXmV/fZGPy8gx/zfPnz5M/f37j74GBgVy9epWgoCDOnz9PgQLuz0q+fPk4f/6818YiKyqCIAiC\nIJjInz8/Fy5cMP6enJxMUFCQ7WMXLlwwBS6eRgIVQRAEQRBM1K5dm23btgGwe/duqlSpYjxWuXJl\nfv/9d06fPk1SUhLfffcdtWrV8tpYApxOP98/WhAEQRAEE6rq59ChQzidTiZPnsz+/fu5ePEiHTt2\nNKp+nE4n7dq1o0uXLl4biwQqgiAIgiD4LJL6EQRBEATBZ5FARRAEQRAEn0UClWzClStXGDZsGOHh\n4bRv357Nmzcbj61bt46OHTsaf1+2bBlt27YlLCyMLVu2ZMZwsxzpmd8PP/yQDh060KFDB2bPnp0Z\nw82SpGeOwZVD79WrF0uXLs3ooWZJ0jO/W7duJSwsjA4dOhAREYE4BITMxKf7qAhpZ+3atRQuXJjI\nyEhOnz5N69atadSoEfv372fFihXGjSYhIYHo6GhWrlzJ5cuXCQ8P57HHHiNXrlyZ/A58m7TO759/\n/snatWtZvnw5OXLkoHPnzvznP/+hatWqmfwOfJ+0zrEiKiqKs2fPZtJosx5pnd/z588TGRnJ4sWL\nKVq0KPPnz+fUqVMULVo0k9+B4K/Iiko2oWnTpgwcOBAAp9NJYGAgp06dYsaMGYwePdo4b+/evdSq\nVYtcuXJRoEABypcvz//+97/MGnaWIa3ze+edd7JgwQICAwMJCAjg6tWr5M6dO7OGnaVI6xwDrF+/\nnoCAAKNzpnBz0jq/P/zwA1WqVGHatGmEh4dTvHhxCVKETEVWVLIJ+fLlA1y/Db388ssMHDiQMWPG\nMGrUKNMXZUZ3FMwupHV+c+bMSdGiRXE6nUyfPp3q1atTqVKlzBp2liKtc3zo0CE+/vhjZs2axZw5\nczJruFmOtM7vqVOn+Oabb1i9ejV58+alS5cuhISEyOdYyDQkUMlGnDhxgn79+hEeHk7FihX5/fff\niYiI4PLly/z8889MmjSJRx55JEM7CmYn0jK/Y8aM4fLly4wePZp8+fIxfvz4zB52liItc5wzZ07i\n4+Pp3r07x44dI2fOnJQpU4YGDRpk9vB9nrTMb/369bn//vsJDg4GoE6dOhw4cEACFSHzcArZgoSE\nBGfTpk2dO3bssDz2559/Ojt06OB0Op3Ov//+29m8eXPnpUuXnGfPnnU2adLEeenSpYwebpYjrfOb\nnJzs7NGjh/O9997L6CFmedI6xzqzZs1yxsTEZMTwsjxpnd+TJ086n3jiCWdiYqLzypUrzvbt2zsP\nHjyY0cMVBANZUckmvPvuu5w9e5Z33nmHd955B4D58+eTJ08e03nBwcF07dqV8PBwnE4ngwcPFg9F\nGkjr/G7atImdO3eSlJTE9u3bAXjllVe82l46u5DWORZujbTOb7FixRgyZAi9evUCXN4WvX26IGQ0\n0plWEARBEASfRap+BEEQBEHwWSRQEQRBEARq/GawAAAH+ElEQVTBZ5FARRAEQRAEn0UCFUEQBEEQ\nfBYJVARBEARB8FkkUBEEG44ePUqNGjVo1aoVrVq1okWLFjz55JPMmjULgB9//JExY8bc8BojR45k\n1apVFn3v3r1ERkam+rwtW7Zw7733sm/fvtt7E7fJ0qVLPbLhX9euXWncuDHr16+3PHbvvffe0jWH\nDBnCQw89ZDu/giBkL6SPiiCkQokSJVizZo3x9/j4eJo0aUKzZs24//77uf/++2/puj///DOJiYmp\nPr5q1SqaNGlCbGwsEydOvKXX8ASdO3f22LUmTpzIww8/7LHrvfnmm4wcOdJj1xMEwXeRQEUQ0khC\nQgJOp5N8+fLxzTffMHv2bKKjozl06BAjR47k2rVr1KlTh23btrFx40YAHA4HMTExJCYm8uKLL/L0\n008za9YsLl68yNy5c+nbt6/pNf755x++/vprVq9eTevWrRk5ciT58+fnypUrjB49msOHDwMQHh5O\nWFgYx44dY9SoUfzzzz/kyZOHiRMnUrVqVVavXs2iRYtITk7mvvvuY/z48eTOnZvHH3+cJk2asGvX\nLgIDA4mKiqJcuXJMmzaNr776isDAQBo1akT//v15++23ARgwYABbtmwhKiqK5ORkypUrx4QJEyhe\nvDhPPvkkLVu25Msvv+Tff/9l2rRp1KhRI9U5PHr0KMOGDePixYvUrFnT0C9cuMCECRM4fPgw165d\no3fv3jRv3pwrV64wfvx4du3aRcmSJQkICOCll17yaNAjCIJvI6kfQUiFv//+m1atWtG0aVMefvhh\noqKimD17NnfeeafpvJEjRzJw4EDWrFlDuXLluHbtmvFYUlISy5cv57333mPmzJkULFiQl19+mSef\nfNISpACsW7eOxx57jLJly1KjRg1jReeHH37gzJkzrF69mg8++IDvv/8egNdee40mTZrw8ccfM2DA\nAObOncvhw4dZtmwZsbGxrFmzhmLFivH+++8DrmDr0UcfZfXq1dStW5clS5Zw7Ngxtm3bxtq1a4mN\njeW3337j8uXLxpgSExMZN24cc+bMYd26ddSuXZsJEyYYjxcuXJgVK1bQqVMn3nvvvRvO6euvv07b\ntm1Zs2YNtWvXNvS5c+dy3333sWrVKpYsWcK7777Ln3/+SWxsLP/++y/r169nypQp/Pjjj2n95xME\nIZsggYogpIJK/Xz66ae0atWKK1eu8Mgjj5jOOX36NMeOHaNhw4YAtGvXzvR4o0aNCAgI4J577uHU\nqVM3fc1Vq1bRvHlzAJ555hni4uIAuOeeezhy5Ag9e/Zk7dq1DB06FIBvv/2WVq1aAdCwYUPeeust\nvvnmG37//XfCwsJo1aoVmzdv5tdffzVeo379+sY1z5w5Q8mSJcmdOzedOnXiww8/ZNCgQaZtFfbu\n3csDDzxA2bJlAejYsSP/93//Z3u906dP3/D97dy5k6effhqAli1bkjNnTgB27NhBbGwsrVq1okuX\nLly8eJHDhw/z1Vdf0aJFCwICAihTpgyPPvroTedQEITshaR+BOEm5MiRg+HDh9O6dWsWLlzICy+8\nYDwWGBjIjXahCAwMBCAgIOCmr7N//34OHTrEpEmTmDJlCteuXePvv//mhx9+oFatWnzyySd89dVX\nbN26lTZt2vDJJ58QFOT+L+x0Ovnll1+4du0aTz/9NGPHjgVcaRV9lUcFIQEBATidToKCgli+fDk7\nd+5k27ZtdOrUiejoaOP85ORk0zidTidXr161vV5aUPMVEBBgPCc5OZnIyEjuu+8+AE6ePEmhQoVY\nuXKl5fUFQfAvZEVFENJAUFAQw4cP59133yUhIcHQCxQoQPny5dm6dSvgSt3cjMDAQNMXvWLVqlWE\nhYXhcDj44osv2Lp1K61atSIuLo7NmzczdOhQQkNDGTt2LHnz5uXEiRPUqVOHTz75BHCtSrz66qs8\n/PDDbNy4kcTERJxOJxERESxatCjV8ezfv59nn32WunXrMmLECCpXrsyRI0eMx2vWrMmePXs4evQo\nAHFxcbfsEalXrx5r164FYMOGDSQlJQHwyCOPGBVGf//9Ny1btuTEiRPUq1ePTz/9FKfTSXx8PDt3\n7kxzQCQIQvZAAhVBSCMNGjQgJCSEqKgokz5t2jTeeecd2rRpw969e2+62+8DDzzAnj17eOONNwwt\nKSmJdevWER4ebjr3ueee47PPPqNWrVrkyZOHZs2a0aFDB5566inuvfdexo0bx4YNG2jVqhVvv/02\nr7/+OlWrVqV///50796dZs2akZycTJ8+fVIdT/Xq1QkJCaF58+a0adOGMmXK0KBBA+Px4sWLM2HC\nBPr370+zZs3YuXMnr732WnqmzmDcuHF8/vnntGjRgq1bt5IvXz4A+vfvz6VLl2jevDndu3dn2LBh\nlC9fnrCwMPLly0eLFi0YOXIkpUuXlt2UBcHPkN2TBeE2mT17NmFhYZQoUYINGzawbt06o2JGcPVR\n6d+//y2twjgcDpxOJ0888QTnzp2jdevWrFy5ksKFCzNy5Egeeugh2rZt64VRC4LgK4hHRRBuk9Kl\nS9OjRw+CgoIoWLAgkyZNyuwh+Rxjx45lyJAhNG3aNF3Pq1y5MsOHDzdWsV5++WUKFy7MkCFD2L59\nOw899JA3hisIgg8hKyqCIAiCIPgs4lERBEEQBMFnkUBFEARBEASfRQIVQRAEQRB8FglUBEEQBEHw\nWSRQEQRBEATBZ5FARRAEQRAEn+X/AesKDaqPwjsbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a45afd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Fig, Ax = plt.subplots(1,1,figsize=(9.5,8))\n",
    "Sc = Ax.scatter(dm_hp_ra, dm_hp_dec, c=pz_frac_map, cmap=plt.cm.viridis, s=6, vmin=0, vmax=1)\n",
    "\n",
    "Ax.set_xlabel('Right Ascension [deg]')\n",
    "Ax.set_ylabel('Declination [deg]')\n",
    "Ax.set_title('{0}'.format(FIELD))\n",
    "CB = Fig.colorbar(Sc)\n",
    "CB.set_label('Fraction with photo-z estimate')\n",
    "Fig.savefig('plots/dmu24_{0}_sf_binary_map.png'.format(FIELD), \n",
    "            format='png', bbox_inches='tight', dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add the binary photo-$z$ selection function to output catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "pz_depth_map.add_column(Column(name='pz_fraction', data=pz_frac_map))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## IV - Magnitude dependent photo-z selection functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The binary selection function gives a broad illustration of where photo-$z$s are available in the field (given the availability of optical datasets etc.). However, the fraction of sources that have an estimate available will depend on the brightness of a given source in the bands used for photo-$z$s.\n",
    "Furthermore, the quality of those photo-$z$ is also highly dependent on the depth, wavelength coverage and sampling of the optical data in that region.\n",
    "\n",
    "To calculate the likelihood of a given source having a photo-$z$ that passes the defined quality selection or be able to select samples of homogeneous photo-$z$ quality, we therefore need to estimate the magnitude (and spatially) dependent selection function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the photo-$z$ quality criteria\n",
    "\n",
    "A key stage in the photo-$z$ estimation methodology is the explicit calibration of the redshift posteriors as a function of magnitude. The benefit of this approach is that by making a cut based on the width of redshift posterior, $P(z)$, we can select sources with a desired estimated redshift precision.\n",
    "Making this cut based on the full $P(z)$ is impractical. However the main photo-$z$ catalog contains information about the width of the primary and secondary peaks above the 80% highest probability density (HPD) credible interval, we can use this information to determine our redshift quality criteria."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parse columns to select the available magnitudes within the masterlist:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52 magnitude columns present in the masterlist.\n"
     ]
    }
   ],
   "source": [
    "filters = [col for col in merged.colnames if col.startswith('m_')]\n",
    "print('{0} magnitude columns present in the masterlist.'.format(len(filters)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_photoz_error = (0.5*(merged['z1_max']- merged['z1_min'])) / (1 + merged['z1_median'])\n",
    "\n",
    "photoz_quality_cut = (scaled_photoz_error < 0.2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To calculate the magnitude dependent selection function in a given masterlist filter, for each of the Healpix clusters we do the following:\n",
    "1. Find the number of masterlist sources within that cluster that have a measurement in the corresponding filter. (If this is zero - see stage 3B)\n",
    "2. Calculate the fraction of sources at that magnitude that haThis relation typically follows a form of a sigmoid function the declines towards fainter magnitudes - however depending on the selection being applied it may not start at 1. Similarly, the rate of decline and the turnover point depends on the depth of the optical selection properties of that cluster.\n",
    "3. \n",
    "    1. Fit the magnitude dependence using the generalised logistic function (GLF, or Richards' function). Provided with conservative boundary conditions and plausible starting conditions based on easily estimated properties (i.e. the typical magnitude in the cluster and the maximum point), this function is able to describe well almost the full range of measured selection functions.\n",
    "    2. If no masterlist sources in the cluster have an observation in the filter - all parameters set to zero (with the GLF then returning zero for all magnitudes).\n",
    "4. Map the parameters estimated for a given healpix cluster back to the healpix belonging to that cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m_ap_wfc_u\n"
     ]
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     "text": [
      "\n",
      "m_wfc_u\n"
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     "text": [
      "\n",
      "m_ap_wfc_g\n"
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     "text": [
      "\n",
      "m_wfc_g\n"
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     "text": [
      "\n",
      "m_ap_wfc_r\n"
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     "text": [
      "\n",
      "m_wfc_r\n"
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     "text": [
      "\n",
      "m_ap_wfc_i\n"
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     "text": [
      "\n",
      "m_wfc_i\n"
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     "text": [
      "\n",
      "m_ap_wfc_z\n"
     ]
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      "\n",
      "m_wfc_z\n"
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     "text": [
      "\n",
      "m_ap_cfht_megacam_u\n"
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      "\n",
      "m_cfht_megacam_u\n"
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      "\n",
      "m_ap_cfht_megacam_g\n"
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   "source": [
    "for photometry_band in filters:\n",
    "    print(photometry_band)\n",
    "    \n",
    "    pz_frac_cat = np.zeros(len(merged))\n",
    "    pz_M_map = np.zeros((len(dm_hp_ra),6))\n",
    "\n",
    "    m001, m999 = np.nanpercentile(merged[photometry_band], [0.1, 99.9])\n",
    "\n",
    "    counts, binedges = np.histogram(merged[photometry_band], \n",
    "                                    range=(np.minimum(m001, 17.), np.minimum(m999, 29.)),\n",
    "                                   bins=10)\n",
    "\n",
    "    binmids = 0.5*(binedges[:-1] + binedges[1:])\n",
    "\n",
    "\n",
    "    with ProgressBar(NCLUSTERS, ipython_widget=True) as bar:\n",
    "        for ic, cluster in enumerate(np.arange(NCLUSTERS)[:]):\n",
    "            ml_sources = (merged['hp_depth_cluster'] == cluster)\n",
    "            has_photoz = (merged['z1_median'] > -90.) * photoz_quality_cut\n",
    "            has_mag = (merged[photometry_band] > -90.)\n",
    "\n",
    "            in_ml = np.float(ml_sources.sum())\n",
    "            withz = (has_photoz)\n",
    "\n",
    "            frac = []\n",
    "            frac_upper = []\n",
    "            frac_lower = []\n",
    "\n",
    "            iqr25_mag = (np.nanpercentile(merged[photometry_band][ml_sources*has_photoz], 25))\n",
    "\n",
    "            if (ml_sources*has_photoz*has_mag).sum() > 1:\n",
    "                for i in np.arange(len(binedges[:-1])):\n",
    "                    mag_cut = np.logical_and(merged[photometry_band] >= binedges[i],\n",
    "                                             merged[photometry_band] < binedges[i+1])\n",
    "\n",
    "                    if (ml_sources * mag_cut).sum() > 0:\n",
    "                        pass_cut = np.sum(ml_sources * withz * mag_cut)\n",
    "                        total_cut = np.sum(ml_sources * mag_cut)\n",
    "                        frac.append(np.float(pass_cut) / total_cut)\n",
    "\n",
    "                        lower, upper = binom_conf_interval(pass_cut, total_cut)\n",
    "                        frac_lower.append(lower)\n",
    "                        frac_upper.append(upper)\n",
    "\n",
    "                    else:\n",
    "                        frac.append(0.)\n",
    "                        frac_lower.append(0.)\n",
    "                        frac_upper.append(1.)\n",
    "\n",
    "                frac = np.array(frac)\n",
    "                frac_upper = np.array(frac_upper)\n",
    "                frac_lower = np.array(frac_lower)\n",
    "\n",
    "\n",
    "                model = GLF1D(A=np.median(frac[:5]), K=0., B=0.9, Q=1., nu=0.4, M=iqr25_mag, \n",
    "                              bounds={'A': (0,1), 'K': (0,1), 'B': (0., 5.),\n",
    "                                      'M': (np.minimum(m001, 17.), np.minimum(m999, 29.)),\n",
    "                                      'Q': (0., 10.),\n",
    "                                      'nu': (0, None)})\n",
    "\n",
    "                fit = LevMarLSQFitter()\n",
    "                m = fit(model, x=binmids, y=frac, maxiter=1000,\n",
    "                        weights=1/(0.5*((frac_upper-frac) + (frac-frac_lower))), \n",
    "                        estimate_jacobian=False)\n",
    "                parameters = np.copy(m.parameters)\n",
    "\n",
    "            else:\n",
    "                frac = np.zeros(len(binmids))\n",
    "                frac_upper = np.zeros(len(binmids))\n",
    "                frac_lower = np.zeros(len(binmids))\n",
    "\n",
    "                parameters = np.zeros(6)\n",
    "                \n",
    "            # Map parameters to cluster\n",
    "\n",
    "            # Map parameters back to depth map healpix \n",
    "            where_map = (km.labels_ == cluster)\n",
    "            pz_M_map[where_map] = parameters\n",
    "\n",
    "            bar.update()\n",
    "            \n",
    "        c = Column(data=pz_M_map, name='pz_glf_{0}'.format(photometry_band), shape=(1,6))\n",
    "        try:\n",
    "            pz_depth_map.add_column(c)\n",
    "        except:\n",
    "            pz_depth_map.replace_column('pz_glf_{0}'.format(photometry_band), c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The selection function catalog consists of a set of parameters for the generalised logistic function (GLF, or Richards' function) that can be used to calculate the fraction of masterlist sources that have a photo-$z$ estimate satisfying the quality cut as a function of a given magnitude. e.g.\n",
    "\n",
    "$S = \\rm{GLF}(M_{f}, \\textbf{P}_{\\rm{Healpix}})$,\n",
    "\n",
    "where $S$ is the success fraction for a given magnitude $M_{f}$ in a given filter, $f$, and $\\textbf{P}_{\\rm{Healpix}}$ corresponds to the set of 6 parameters fit for that healpix.\n",
    "\n",
    "\n",
    "In practical terms, using the GLF function defined in this notebook this would be `S = GLF1D(*P)(M)`. Similarly, to estimate the magnitude corresponding to a desired photo-$z$ completeness one can use the same parameters and the corresponding inverse function: `M = InverseGLF1D(*P)(S)`.\n",
    "\n",
    "### Save the photo-$z$ selection function catalog:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "pz_depth_map.write('{0}/photo-z_selection_{1}_{2}.fits'.format(OUT_DIR, FIELD, SUFFIX).lower(), format='fits', overwrite=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ![HELP LOGO](https://avatars1.githubusercontent.com/u/7880370?s=75&v=4)\n",
    " \n",
    "**Author**: [Kenneth Duncan](http://dunkenj.github.io)\n",
    "\n",
    "The Herschel Extragalactic Legacy Project, ([HELP](http://herschel.sussex.ac.uk/)), is a [European Commission Research Executive Agency](https://ec.europa.eu/info/departments/research-executive-agency_en)\n",
    "funded project under the SP1-Cooperation, Collaborative project, Small or medium-scale focused research project, FP7-SPACE-2013-1 scheme, Grant Agreement\n",
    "Number 607254.\n",
    "\n",
    "[Acknowledgements](http://herschel.sussex.ac.uk/acknowledgements)\n",
    "\n"
   ]
  }
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