fjaviersanchez/gcr_coadd_test.ipynb

## gcr_coadd_test.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import GCRCatalogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'instance_2443890': {'subclass_name': 'instance_catalog.InstanceCatalog', 'header_file': '/global/cscratch1/sd/danielsf/example_instance_catalogs/phosim_cat_2443890.txt', 'description': 'Instance Catalogs for DC2'}, 'buzzard_test': {'alias': 'buzzard_v1.6_test', 'included_by_default': True}, 'buzzard_high-res_v1.1': {'subclass_name': 'buzzard.BuzzardGalaxyCatalog', 'description': 'Buzzard \"high-res\" galaxy catalog v1.1.\\nThis is a 400-square-degree mock galaxy catalog with 238 million galaxies\\ndown to r=29 for 0 < z <= 8.7, constructed with SHAM and ADDSEDs.\\nRead more at https://confluence.slac.stanford.edu/x/ko6bDQ\\n', 'high_res': True, 'sky_area': 400.0, 'lightcone': True, 'cosmology': {'H0': 70.0, 'Om0': 0.286, 'Ob0': 0.047}, 'halo_mass_def': 'vir', 'catalog_path_template': {'hsc': 'hsc/Buzzard-highres_hsc.{}.fit', 'irac': 'irac/Buzzard-highres_irac.{}.fit', 'vista': 'vista/Buzzard-highres_vista.{}.fit', 'deep': 'deep/Buzzard-highres_deep.{}.fit', 'twomass': 'twomass/Buzzard-highres_twomass.{}.fit', 'euclid': 'euclid/Buzzard-highres_euclid.{}.fit', 'sdss25': 'sdss25/Buzzard-highres_sdss25.{}.fit', 'cfhtls': 'cfhtls/Buzzard-highres_cfhtls.{}.fit', 'wise': 'wise/Buzzard-highres_wise.{}.fit', 'johnson': 'johnson/Buzzard-highres_johnson.{}.fit', 'des': 'des/Buzzard-highres_decam.{}.fit', 'flamex': 'flamex/Buzzard-highres_flamex.{}.fit', 'candels': 'candels/Buzzard-highres_candels.{}.fit', 'lsst': 'lsst/Buzzard-highres_lsst.{}.fit', 'desy5': 'desy5/Buzzard-highres_DES_Mock_mag.{}.fit', 'truth': 'truth/Buzzard-highres_galaxies_shmatch.{}.fit', 'halos': 'halos/Buzzard-highres_halos.{}.fit', 'stripe82': 'stripe82/Buzzard-highres_Stripe82_Mock_mag.{}.fit'}, 'creators': ['Joe DeRose', 'Risa Wechsler', 'Eli Rykoff', 'Matt Becker'], 'catalog_root_dir': '/global/projecta/projectdirs/lsst/groups/CS/Buzzard/Buzzard-highres'}, 'protoDC2_addon_knots': {'subclass_name': 'alphaq_addon.AlphaQAddonCatalog', 'version': '2.1.2', 'addon_filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/protoDC2_knots_v2.1.2.hdf5', 'addon_group': 'knots', 'lightcone': True, 'filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/ANL_AlphaQ_v2.1.2.hdf5'}, 'proto-dc2_v2.0_test': {'subclass_name': 'alphaq.AlphaQGalaxyCatalog', 'version': '2.0', 'description': 'This test version of ProtoDC2 is a small subset of protoDC2 that can be used for code testing and development.\\nFor a description of the catalog and the methods, please see https://goo.gl/fXDQwP\\n', 'creators': ['Eve Kovacs', 'Danila Korytov', 'Katrin Heitmann'], 'lightcone': True, 'filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/ANL_AlphaQ_v2_small.hdf5'}, 'proto-dc2_v4.5_test': {'subclass_name': 'alphaq.AlphaQGalaxyCatalog', 'version': '4.5.0', 'description': 'ProtoDC2 is a down-scaled version of the catalog to be generated for LSST-DESC DC2.\\nFor a description of the catalog and the methods, please see https://goo.gl/fXDQwP\\n', 'creators': ['Andrew Benson', 'Andrew Hearin', 'Katrin Heitmann', 'Danila Korytov', 'Eve Kovacs'], 'lightcone': True, 'filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/v4.5.full.all.hdf5'}, 'proto-dc2_v4.4_test': {'subclass_name': 'alphaq.AlphaQGalaxyCatalog', 'version': '4.4.0', 'description': 'ProtoDC2 is a down-scaled version of the catalog to be generated for LSST-DESC DC2.\\nFor a description of the catalog and the methods, please see https://goo.gl/fXDQwP\\n', 'creators': ['Andrew Benson', 'Andrew Hearin', 'Katrin Heitmann', 'Danila Korytov', 'Eve Kovacs'], 'lightcone': True, 'filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/v4.4.full.all.hdf5'}, 'proto-dc2_v4.3_redmapper': {'subclass_name': 'redmapper.RedMapperCatalog', 'description': 'cluster catalog and member catalog from the redMaPPer cluster finder run on proto-dc2. Red \\nsequence was fit in the standard way, but centers are on halo centers, i.e. a real halo finding\\nrun was not done.', 'sky_area': 25.0, 'lightcone': True, 'cosmology': {'H0': 71.0, 'Om0': 0.265, 'Ob0': 0.0448}, 'catalog_path_template': {'clusters': 'proto-dc2_v4.3_test_halos_lambda_chisq.fit', 'members': 'proto-dc2_v4.3_test_halos_lambda_chisq_members_00.fit'}, 'creators': ['Andrew Benson', 'Andrew Hearin', 'Katrin Heitmann', 'Danila Korytov', 'Eve Kovacs', 'Eli Rykoff'], 'catalog_root_dir': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/'}, 'proto-dc2_v2.1.2_addon_knots': {'subclass_name': 'alphaq_addon.AlphaQAddonCatalog', 'version': '2.1.2', 'addon_filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/protoDC2_knots_v2.1.2.hdf5', 'addon_group': 'knots', 'lightcone': True, 'filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/ANL_AlphaQ_v2.1.2.hdf5'}, 'buzzard_high-res': {'alias': 'buzzard_high-res_v1.1', 'included_by_default': True}, 'buzzard_v1.6_21': {'subclass_name': 'buzzard.BuzzardGalaxyCatalog', 'description': 'Buzzard galaxy catalog v1.6 (realization 21).\\nThis is a 10,000-square-degree catalog created with the ADDGALS algorithm.\\nIt has galaxies down to 2 magnitudes fainter than projected DES Y5 magnitude limits.\\nRead more at https://confluence.slac.stanford.edu/x/4o2bDQ\\n', 'sky_area': 10313.24, 'lightcone': True, 'cosmology': {'H0': 70.0, 'Om0': 0.286, 'Ob0': 0.047}, 'halo_mass_def': 'vir', 'catalog_path_template': {'truth': 'truth_v1.6/Chinchilla-21_lensed.{}.fits'}, 'creators': ['Joe DeRose', 'Risa Wechsler', 'Eli Rykoff', 'Matt Becker'], 'catalog_root_dir': '/global/projecta/projectdirs/lsst/groups/CS/Buzzard/Buzzard-21/addgalspostprocess'}, 'dc1': {'subclass_name': 'dc1.DC1GalaxyCatalog', 'db_info_fname': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/dc1_db_info.txt', 'included_by_default': True, 'description': 'Galaxy catalog used for DC1 (also known as \"the catalog on fatboy\")', 'sky_area': 20.25}, 'dc2_coadd_run1.1p': {'subclass_name': 'dc2_coadd.DC2CoaddCatalog', 'creators': ['Michael Wood-Vasey'], 'base_dir': '/global/projecta/projectdirs/lsst/global/in2p3/Run1.1/coadd_catalog', 'included_by_default': True, 'description': 'DC2 Run 1.1p Coadd Catalog'}, 'buzzard_v1.6_5': {'subclass_name': 'buzzard.BuzzardGalaxyCatalog', 'description': 'Buzzard galaxy catalog v1.6 (realization 5).\\nThis is a 10,000-square-degree catalog created with the ADDGALS algorithm.\\nIt has galaxies down to 2 magnitudes fainter than projected DES Y5 magnitude limits.\\nRead more at https://confluence.slac.stanford.edu/x/4o2bDQ\\n', 'sky_area': 10313.24, 'lightcone': True, 'cosmology': {'H0': 70.0, 'Om0': 0.286, 'Ob0': 0.047}, 'halo_mass_def': 'vir', 'catalog_path_template': {'truth': 'truth_v1.6/Chinchilla-5_lensed.{}.fits'}, 'creators': ['Joe DeRose', 'Risa Wechsler', 'Eli Rykoff', 'Matt Becker'], 'catalog_root_dir': '/global/projecta/projectdirs/lsst/groups/CS/Buzzard/Buzzard-5/addgalspostprocess'}, 'focal_plane_0_test': {'subclass_name': 'focal_plane.FocalPlaneCatalog', 'description': 'PhoSim e-images rebinned', 'catalog_root_dir': '/global/cscratch1/sd/descpho/Pipeline-tasks/DC2-R1-2p-WFD-r/000000/work', 'rebinning': 0, 'included_by_default': True}, 'buzzard_v1.6_1': {'subclass_name': 'buzzard.BuzzardGalaxyCatalog', 'description': 'Buzzard galaxy catalog v1.6 (realization 1).\\nThis is a 10,000-square-degree catalog created with the ADDGALS algorithm.\\nIt has galaxies down to 2 magnitudes fainter than projected DES Y5 magnitude limits.\\nRead more at https://confluence.slac.stanford.edu/x/4o2bDQ\\n', 'sky_area': 10313.24, 'lightcone': True, 'cosmology': {'H0': 70.0, 'Om0': 0.286, 'Ob0': 0.047}, 'halo_mass_def': 'vir', 'catalog_path_template': {'truth': 'truth_v1.6/Chinchilla-1_lensed.{}.fits'}, 'creators': ['Joe DeRose', 'Risa Wechsler', 'Eli Rykoff', 'Matt Becker'], 'catalog_root_dir': '/global/projecta/projectdirs/lsst/groups/CS/Buzzard/Buzzard-1/addgalspostprocess'}, 'buzzard': {'alias': 'buzzard_v1.6', 'included_by_default': True}, 'buzzard_v1.6_3': {'subclass_name': 'buzzard.BuzzardGalaxyCatalog', 'description': 'Buzzard galaxy catalog v1.6 (realization 3).\\nThis is a 10,000-square-degree catalog created with the ADDGALS algorithm.\\nIt has galaxies down to 2 magnitudes fainter than projected DES Y5 magnitude limits.\\nRead more at https://confluence.slac.stanford.edu/x/4o2bDQ\\n', 'sky_area': 10313.24, 'lightcone': True, 'cosmology': {'H0': 70.0, 'Om0': 0.286, 'Ob0': 0.047}, 'halo_mass_def': 'vir', 'catalog_path_template': {'truth': 'truth_v1.6/Chinchilla-3_lensed.{}.fits'}, 'creators': ['Joe DeRose', 'Risa Wechsler', 'Eli Rykoff', 'Matt Becker'], 'catalog_root_dir': '/global/projecta/projectdirs/lsst/groups/CS/Buzzard/Buzzard-3/addgalspostprocess'}, 'buzzard_v1.6_2': {'subclass_name': 'buzzard.BuzzardGalaxyCatalog', 'description': 'Buzzard galaxy catalog v1.6 (realization 2).\\nThis is a 10,000-square-degree catalog created with the ADDGALS algorithm.\\nIt has galaxies down to 2 magnitudes fainter than projected DES Y5 magnitude limits.\\nRead more at https://confluence.slac.stanford.edu/x/4o2bDQ\\n', 'sky_area': 10313.24, 'lightcone': True, 'cosmology': {'H0': 70.0, 'Om0': 0.286, 'Ob0': 0.047}, 'halo_mass_def': 'vir', 'catalog_path_template': {'truth': 'truth_v1.6/Chinchilla-2_lensed.{}.fits'}, 'creators': ['Joe DeRose', 'Risa Wechsler', 'Eli Rykoff', 'Matt Becker'], 'catalog_root_dir': '/global/projecta/projectdirs/lsst/groups/CS/Buzzard/Buzzard-2/addgalspostprocess'}, 'proto-dc2_v2.0_redmapper': {'subclass_name': 'redmapper.RedMapperCatalog', 'description': 'cluster catalog and member catalog from the redMaPPer cluster finder run on proto-dc2. Red \\nsequence was fit in the standard way, but centers are on halo centers, i.e. a real halo finding\\nrun was not done.', 'sky_area': 25.0, 'lightcone': True, 'cosmology': {'H0': 71.0, 'Om0': 0.265, 'Ob0': 0.0448}, 'catalog_path_template': {'clusters': 'proto-dc2_v2.0_halos_lambda_chisq.fit', 'members': 'proto-dc2_v2.0_halos_lambda_chisq_members_00.fit'}, 'creators': ['Andrew Benson', 'Andrew Hearin', 'Katrin Heitmann', 'Danila Korytov', 'Eve Kovacs', 'Eli Rykoff'], 'catalog_root_dir': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/'}, 'dc2_reference_run1.1p': {'subclass_name': 'reference_catalog.ReferenceCatalogReader', 'description': 'DC2 Reference Catalog Run 1.1p', 'filename': '/global/projecta/projectdirs/lsst/groups/SSim/DC2/reference_catalogs/dc2_reference_catalog_dc2v2.1.2.txt'}, 'buzzard_v1.6': {'subclass_name': 'buzzard.BuzzardGalaxyCatalog', 'description': 'Buzzard galaxy catalog v1.6 (realization 0).\\nThis is a 10,000-square-degree catalog created with the ADDGALS algorithm.\\nIt has galaxies down to 2 magnitudes fainter than projected DES Y5 magnitude limits.\\nRead more at https://confluence.slac.stanford.edu/x/4o2bDQ\\n', 'sky_area': 10313.24, 'lightcone': True, 'cosmology': {'H0': 70.0, 'Om0': 0.286, 'Ob0': 0.047}, 'halo_mass_def': 'vir', 'catalog_path_template': {'lsst': 'mags_v1.6/Chinchilla-0_LSST.{}.fits', 'truth': 'truth_v1.6/Chinchilla-0_lensed.{}.fits'}, 'creators': ['Joe DeRose', 'Risa Wechsler', 'Eli Rykoff', 'Matt Becker'], 'catalog_root_dir': '/global/projecta/projectdirs/lsst/groups/CS/Buzzard/Buzzard-0/addgalspostprocess'}, 'protoDC2_test': {'alias': 'proto-dc2_v4.5_test'}, 'proto-dc2_v3.0_redmapper': {'subclass_name': 'redmapper.RedMapperCatalog', 'description': 'cluster catalog and member catalog from the redMaPPer cluster finder run on proto-dc2. Red \\nsequence was fit in the standard way, but centers are on halo centers, i.e. a real halo finding\\nrun was not done.', 'sky_area': 25.0, 'lightcone': True, 'cosmology': {'H0': 71.0, 'Om0': 0.265, 'Ob0': 0.0448}, 'catalog_path_template': {'clusters': 'proto-dc2_v3.0_halos_lambda_chisq.fit', 'members': 'proto-dc2_v3.0_halos_lambda_chisq_members_00.fit'}, 'creators': ['Andrew Benson', 'Andrew Hearin', 'Katrin Heitmann', 'Danila Korytov', 'Eve Kovacs', 'Eli Rykoff'], 'catalog_root_dir': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/'}, 'proto-dc2_v4.6.1': {'subclass_name': 'alphaq.AlphaQGalaxyCatalog', 'version': '4.6.1', 'description': 'ProtoDC2 is a down-scaled version of the catalog to be generated for LSST-DESC DC2.\\nFor a description of the catalog and the methods, please see https://goo.gl/fXDQwP\\n', 'creators': ['Andrew Benson', 'Andrew Hearin', 'Katrin Heitmann', 'Danila Korytov', 'Eve Kovacs'], 'lightcone': True, 'md5': '6f3b90401923fa5edc365035e6425d87', 'filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/v4.6.1.all.hdf5'}, 'proto-dc2_v3.0': {'subclass_name': 'alphaq.AlphaQGalaxyCatalog', 'version': '3.0.0', 'description': 'ProtoDC2 is a down-scaled version of the catalog to be generated for LSST-DESC DC2.\\nFor a description of the catalog and the methods, please see https://goo.gl/fXDQwP\\n', 'creators': ['Andrew Benson', 'Andrew Hearin', 'Katrin Heitmann', 'Danila Korytov', 'Eve Kovacs'], 'lightcone': True, 'md5': '1efca46756b59b75d23e5382ee97cd10', 'filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/ANL_AlphaQ_v3.0.hdf5'}, 'protoDC2': {'alias': 'proto-dc2_v4.5', 'included_by_default': True}, 'proto-dc2_v2.1': {'subclass_name': 'alphaq.AlphaQGalaxyCatalog', 'version': '2.1', 'description': 'ProtoDC2 is a down-scaled version of the catalog to be generated for LSST-DESC DC2.\\nFor a description of the catalog and the methods, please see https://goo.gl/fXDQwP\\n', 'creators': ['Eve Kovacs', 'Danila Korytov', 'Katrin Heitmann', 'Andrew Benson'], 'lightcone': True, 'filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/ANL_AlphaQ_v2.1.hdf5'}, 'proto-dc2_v2.0': {'subclass_name': 'alphaq.AlphaQGalaxyCatalog', 'version': '2.0', 'description': 'ProtoDC2 is a down-scaled version of the catalog to be generated for LSST-DESC DC2.\\nFor a description of the catalog and the methods, please see https://goo.gl/fXDQwP\\n', 'creators': ['Eve Kovacs', 'Danila Korytov', 'Katrin Heitmann', 'Andrew Benson'], 'lightcone': True, 'filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/ANL_AlphaQ_v2.hdf5'}, 'proto-dc2_v4.6.1_test': {'subclass_name': 'alphaq.AlphaQGalaxyCatalog', 'version': '4.6.1', 'description': 'ProtoDC2 is a down-scaled version of the catalog to be generated for LSST-DESC DC2.\\nFor a description of the catalog and the methods, please see https://goo.gl/fXDQwP\\n', 'creators': ['Andrew Benson', 'Andrew Hearin', 'Katrin Heitmann', 'Danila Korytov', 'Eve Kovacs'], 'lightcone': True, 'filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/v4.6.1.all.hdf5'}, 'protoDC2_addon_tidal': {'subclass_name': 'alphaq_addon.AlphaQTidalCatalog', 'filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/tidal_247_protoDC2_v2.1.hdf5', 'is_addon': True}, 'proto-dc2_v2.1.2_test': {'subclass_name': 'alphaq.AlphaQGalaxyCatalog', 'version': '2.1.2', 'description': 'ProtoDC2 is a down-scaled version of the catalog to be generated for LSST-DESC DC2.\\nFor a description of the catalog and the methods, please see https://goo.gl/fXDQwP\\nNo check of MD5 sum in this version.\\n', 'creators': ['Eve Kovacs', 'Danila Korytov', 'Katrin Heitmann', 'Andrew Benson'], 'lightcone': True, 'filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/ANL_AlphaQ_v2.1.2.hdf5'}, 'proto-dc2_v2.1.1': {'subclass_name': 'alphaq.AlphaQGalaxyCatalog', 'version': '2.1.1', 'description': 'ProtoDC2 is a down-scaled version of the catalog to be generated for LSST-DESC DC2.\\nFor a description of the catalog and the methods, please see https://goo.gl/fXDQwP\\n', 'creators': ['Eve Kovacs', 'Danila Korytov', 'Katrin Heitmann', 'Andrew Benson'], 'lightcone': True, 'filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/ANL_AlphaQ_v2.1.1.hdf5'}, 'proto-dc2_v2.1.2': {'subclass_name': 'alphaq.AlphaQGalaxyCatalog', 'version': '2.1.2', 'description': 'ProtoDC2 is a down-scaled version of the catalog to be generated for LSST-DESC DC2.\\nFor a description of the catalog and the methods, please see https://goo.gl/fXDQwP\\n', 'creators': ['Eve Kovacs', 'Danila Korytov', 'Katrin Heitmann', 'Andrew Benson'], 'lightcone': True, 'md5': '1724d2a746caabaca9337063bdf096c1', 'filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/ANL_AlphaQ_v2.1.2.hdf5'}, 'proto-dc2_v4.5': {'subclass_name': 'alphaq.AlphaQGalaxyCatalog', 'version': '4.5.0', 'description': 'ProtoDC2 is a down-scaled version of the catalog to be generated for LSST-DESC DC2.\\nFor a description of the catalog and the methods, please see https://goo.gl/fXDQwP\\n', 'creators': ['Andrew Benson', 'Andrew Hearin', 'Katrin Heitmann', 'Danila Korytov', 'Eve Kovacs'], 'lightcone': True, 'md5': '64458a94527b2f34453ed87edba5180f', 'filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/v4.5.full.all.hdf5'}, 'proto-dc2_v4.4': {'subclass_name': 'alphaq.AlphaQGalaxyCatalog', 'version': '4.4.0', 'description': 'ProtoDC2 is a down-scaled version of the catalog to be generated for LSST-DESC DC2.\\nFor a description of the catalog and the methods, please see https://goo.gl/fXDQwP\\n', 'creators': ['Andrew Benson', 'Andrew Hearin', 'Katrin Heitmann', 'Danila Korytov', 'Eve Kovacs'], 'lightcone': True, 'md5': 'ff56bace5247f97cd9470d44900977fe', 'filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/v4.4.full.all.hdf5'}, 'proto-dc2_v3.0_addon_knots': {'subclass_name': 'alphaq_addon.AlphaQAddonCatalog', 'version': '3.0.0', 'addon_filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/protoDC2_knots_v3.0.hdf5', 'addon_group': 'knots', 'md5': '1efca46756b59b75d23e5382ee97cd10', 'lightcone': True, 'filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/ANL_AlphaQ_v3.0.hdf5'}, 'proto-dc2_v4.7_test': {'subclass_name': 'alphaq.AlphaQGalaxyCatalog', 'version': '4.7.0', 'description': 'ProtoDC2 is a down-scaled version of the catalog to be generated for LSST-DESC DC2.\\nFor a description of the catalog and the methods, please see https://goo.gl/fXDQwP\\n', 'creators': ['Andrew Benson', 'Andrew Hearin', 'Katrin Heitmann', 'Danila Korytov', 'Eve Kovacs'], 'lightcone': True, 'filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/v4.7.2substep.all.hdf5'}, 'instance_2145656': {'subclass_name': 'instance_catalog.InstanceCatalog', 'header_file': '/global/cscratch1/sd/danielsf/example_instance_catalogs/phosim_cat_2145656.txt', 'description': 'Instance Catalogs for DC2'}, 'focal_plane_16_test': {'subclass_name': 'focal_plane.FocalPlaneCatalog', 'description': 'PhoSim e-images rebinned', 'catalog_root_dir': '/global/cscratch1/sd/descpho/Pipeline-tasks/DC2-R1-2p-WFD-r/000004/work', 'rebinning': 16, 'included_by_default': True}, 'buzzard_v1.6_test': {'subclass_name': 'buzzard.BuzzardGalaxyCatalog', 'description': 'This is a smaller version of the Buzzard galaxy catalog v1.6 (realization 0),\\nwith only one healpixal, for testing purpose.\\nRead more at https://confluence.slac.stanford.edu/x/4o2bDQ\\n', 'sky_area': 53.7148, 'lightcone': True, 'healpix_pixels': [42], 'cosmology': {'H0': 70.0, 'Om0': 0.286, 'Ob0': 0.047}, 'halo_mass_def': 'vir', 'catalog_path_template': {'lsst': 'mags_v1.6/Chinchilla-0_LSST.{}.fits', 'truth': 'truth_v1.6/Chinchilla-0_lensed.{}.fits'}, 'creators': ['Joe DeRose', 'Risa Wechsler', 'Eli Rykoff', 'Matt Becker'], 'catalog_root_dir': '/global/projecta/projectdirs/lsst/groups/CS/Buzzard/Buzzard-0/addgalspostprocess'}, 'dc2_reference_run1.2p': {'subclass_name': 'reference_catalog.ReferenceCatalogReader', 'description': 'DC2 Reference Catalog Run 1.2p', 'filename': '/global/projecta/projectdirs/lsst/groups/SSim/DC2/reference_catalogs/dc2_reference_catalog_dc2v3_fov4.txt'}, 'dc2_instance_example1': {'subclass_name': 'instance_catalog.InstanceCatalog', 'header_file': '/global/projecta/projectdirs/lsst/groups/SSim/DC2/example_instance_catalogs/phosim_cat_2145656.txt', 'description': 'Example Instance Catalogs for DC2'}, 'dc2_instance_example2': {'subclass_name': 'instance_catalog.InstanceCatalog', 'header_file': '/global/projecta/projectdirs/lsst/groups/SSim/DC2/example_instance_catalogs/phosim_cat_2443890.txt', 'description': 'Example Instance Catalogs for DC2'}, 'dc2_coadd_run1.1p_tract4850': {'subclass_name': 'dc2_coadd.DC2CoaddCatalog', 'filename_pattern': 'merged_tract_4850.hdf5', 'description': 'DC2 Run 1.1p Coadd Catalog', 'creators': ['Michael Wood-Vasey'], 'included_by_default': True, 'base_dir': '/global/projecta/projectdirs/lsst/global/in2p3/Run1.1/coadd_catalog'}, 'proto-dc2_v3.0_test': {'subclass_name': 'alphaq.AlphaQGalaxyCatalog', 'version': '3.0.0', 'description': 'ProtoDC2 is a down-scaled version of the catalog to be generated for LSST-DESC DC2.\\nFor a description of the catalog and the methods, please see https://goo.gl/fXDQwP\\n', 'creators': ['Andrew Benson', 'Andrew Hearin', 'Katrin Heitmann', 'Danila Korytov', 'Eve Kovacs'], 'lightcone': True, 'filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/ANL_AlphaQ_v3.0.hdf5'}, 'instancecatalogs': {'subclass_name': 'instance_catalogs.InstanceCatalog', 'filename': '/global/cscratch1/sd/descpho/Pipeline-tasks/DC2-phoSim-3_WFD-r/000000/instCat/phosim_cat_162700.txt', 'included_by_default': True, 'description': 'Instance Catalogs for DC2'}}\n"
     ]
    }
   ],
   "source": [
    "print(GCRCatalogs.available_catalogs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the DM coadd catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/homes/j/jsanch87/.local/cori/2.7-anaconda-4.4/lib/python2.7/site-packages/GCR-0.6.3-py2.7.egg/GCR.py:404: UserWarning: Native quantity `y_slot_PsfFlux_fluxSigma` does not exist (required by `y_psFlux_err`)\n"
     ]
    }
   ],
   "source": [
    "gc = GCRCatalogs.load_catalog('dc2_coadd_run1.1p_tract4850')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the reference catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "gc_ref = GCRCatalogs.load_catalog('dc2_reference_run1.1p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['r_IyyPSF',\n",
       " 'g_psFlux_err',\n",
       " 'u_psFlux_err',\n",
       " 'z_SN_CModel',\n",
       " 'g_mag_CModel',\n",
       " 'u_IyyPSF',\n",
       " 'Ixy',\n",
       " 'r_Ixy',\n",
       " 'u_IxyPSF',\n",
       " 'u_Iyy',\n",
       " 'z_psFlux_err',\n",
       " 'r_I_flag',\n",
       " 'psNdata',\n",
       " 'i_psFlux_err',\n",
       " 'objectId',\n",
       " 'z_I_flag',\n",
       " 'Iyy',\n",
       " 'mag_g_lsst',\n",
       " 'r_IxxPSF',\n",
       " 'y_psFlux',\n",
       " 'y_psFlux_flag',\n",
       " 'magerr_g_lsst',\n",
       " 'u_IxxPSF',\n",
       " 'g_IxxPSF',\n",
       " 'good',\n",
       " 'u_Ixx',\n",
       " 'u_mag_CModel',\n",
       " 'z_psf_size',\n",
       " 'mag_z_lsst',\n",
       " 'g_I_flag',\n",
       " 'IxyPSF',\n",
       " 'IyyPSF',\n",
       " 'i_IyyPSF',\n",
       " 'mag_i_lsst',\n",
       " 'centroidY',\n",
       " 'centroidX',\n",
       " 'u_SN_CModel',\n",
       " 'g_Ixx',\n",
       " 'g_Ixy',\n",
       " 'Ixx',\n",
       " 'i_psFlux_flag',\n",
       " 'i_IxyPSF',\n",
       " 'extendedness',\n",
       " 'r_IxyPSF',\n",
       " 'z_IyyPSF',\n",
       " 'dec',\n",
       " 'g_psFlux',\n",
       " 'u_I_flag',\n",
       " 'z_IxyPSF',\n",
       " 'z_mag_CModel',\n",
       " 'parentObjectId',\n",
       " 'y_IxyPSF',\n",
       " 'y_psf_size',\n",
       " 'z_psFlux_flag',\n",
       " 'z_psFlux',\n",
       " 'u_psFlux_flag',\n",
       " 'i_I_flag',\n",
       " 'i_mag_CModel',\n",
       " 'y_Iyy',\n",
       " 'z_Iyy',\n",
       " 'magerr_r_lsst',\n",
       " 'mag_u_lsst',\n",
       " 'g_SN_CModel',\n",
       " 'magerr_i_lsst',\n",
       " 'i_psf_size',\n",
       " 'I_flag',\n",
       " 'g_Iyy',\n",
       " 'y_Ixy',\n",
       " 'y_SN_CModel',\n",
       " 'IxxPSF',\n",
       " 'ra',\n",
       " 'i_Ixx',\n",
       " 'i_Ixy',\n",
       " 'i_SN_CModel',\n",
       " 'g_psFlux_flag',\n",
       " 'centroidX_err',\n",
       " 'r_Ixx',\n",
       " 'r_psFlux_flag',\n",
       " 'y_mag_CModel',\n",
       " 'magerr_z_lsst',\n",
       " 'r_psf_size',\n",
       " 'g_IyyPSF',\n",
       " 'magerr_u_lsst',\n",
       " 'y_IxxPSF',\n",
       " 'mag_r_lsst',\n",
       " 'i_psFlux',\n",
       " 'mag_y_lsst',\n",
       " 'r_Iyy',\n",
       " 'y_psFlux_err',\n",
       " 'r_psFlux_err',\n",
       " 'centroid_flag',\n",
       " 'u_Ixy',\n",
       " 'u_psf_size',\n",
       " 'r_SN_CModel',\n",
       " 'g_IxyPSF',\n",
       " 'z_Ixy',\n",
       " 'z_Ixx',\n",
       " 'centroidY_err',\n",
       " 'y_IyyPSF',\n",
       " 'y_Ixx',\n",
       " 'u_psFlux',\n",
       " 'r_psFlux',\n",
       " 'y_I_flag',\n",
       " 'magerr_y_lsst',\n",
       " 'r_mag_CModel',\n",
       " 'g_psf_size',\n",
       " 'i_IxxPSF',\n",
       " 'i_Iyy',\n",
       " 'z_IxxPSF']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.list_all_quantities()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = gc.get_quantities(['u_mag_CModel','g_mag_CModel','r_mag_CModel','i_mag_CModel','z_mag_CModel','y_mag_CModel','mag_u_lsst','mag_g_lsst','mag_r_lsst','mag_i_lsst','mag_z_lsst','mag_y_lsst','r_SN_CModel','ra','dec','extendedness'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sigma_ra', 'dec_smeared', 'ra_smeared', 'dec', 'sigma_dec', 'ra']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The reference catalog has more quantities?!\n",
    "gc_ref.list_all_quantities()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll need the magnitude and the isresolved flag to do some tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/homes/j/jsanch87/.local/cori/2.7-anaconda-4.4/lib/python2.7/site-packages/GCRCatalogs-0.7.8-py2.7.egg/GCRCatalogs/reference_catalog.py:59: UserWarning: genfromtxt: Empty input file: \"<open file '/global/projecta/projectdirs/lsst/groups/SSim/DC2/reference_catalogs/dc2_reference_catalog_dc2v2.1.2.txt', mode 'rb' at 0x2b3610033b70>\"\n",
      "  data = np.genfromtxt(f, self._data_dtype, delimiter=',', max_rows=self._nlines)\n"
     ]
    }
   ],
   "source": [
    "data_ref = gc_ref.get_quantities(['ra_smeared','dec_smeared','lsst_r_smeared','lsst_i_smeared','lsst_g_smeared','uniqueId','isresolved'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2b86fb9acfd0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEKCAYAAAA1qaOTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b86fb9d2c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(data['ra'],data['dec'],bins=50);\n",
    "plt.xlabel('RA [deg]')\n",
    "plt.ylabel('DEC [deg]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(data_ref['ra_smeared'],label='Reference',bins=100,histtype='step',range=(54.6,56.4))\n",
    "plt.hist(data['ra'],label='Coadd data',bins=100,histtype='step',range=(54.6,56.4))\n",
    "plt.xlabel('RA [deg]')\n",
    "plt.ylabel('Number of objects')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "h1, _ = np.histogram(data_ref['ra_smeared'],bins=30,range=(54.6,56.4))\n",
    "h2, _ = np.histogram(data['ra'],bins=30,range=(54.6,56.4))\n",
    "plt.plot(h1*1.0/h2)\n",
    "plt.xlabel('RA [deg]')\n",
    "plt.ylabel('Number of objects')\n",
    "plt.ylim(0,8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the g-r r-i colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/common/cori/software/python/2.7-anaconda-4.4/lib/python2.7/site-packages/ipykernel/__main__.py:1: RuntimeWarning: invalid value encountered in subtract\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2b3619b36cd0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAENCAYAAAAorJMrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEomFIH9QEolEIrEQ5A9KIpFIJBaC/x9Jr99gH6o4AAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3619a7f890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(data['g_mag_CModel']-data['r_mag_CModel'],data['r_mag_CModel']-data['i_mag_CModel'],bins=100,range=[(-1,2),(-1,2)]);\n",
    "plt.xlabel('$g-r$')\n",
    "plt.ylabel('$r-i$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we select what we think are stars? (i.e, not extended objects -- `psf_mag-CModel_mag < epsilon -> extendedness==0`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/common/cori/software/python/2.7-anaconda-4.4/lib/python2.7/site-packages/ipykernel/__main__.py:3: RuntimeWarning: invalid value encountered in subtract\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2b361f07fc50>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAENCAYAAAAorJMrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKGiK2EZ8+YgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3619b0cd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stars_data = data['extendedness']==0\n",
    "galaxy_data = data['extendedness']==1\n",
    "plt.hist2d(data['g_mag_CModel'][stars_data]-data['r_mag_CModel'][stars_data],data['r_mag_CModel'][stars_data]-data['i_mag_CModel'][stars_data],bins=100,range=[(-1,2),(-1,2)]);\n",
    "plt.xlabel('$g-r$')\n",
    "plt.ylabel('$r-i$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare to the reference catalog `isresolved==True -> galaxy`, `isresolved==False -> star (or AGN?? -- check with Scott)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/common/cori/software/python/2.7-anaconda-4.4/lib/python2.7/site-packages/ipykernel/__main__.py:3: RuntimeWarning: invalid value encountered in subtract\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2b36497ec1d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAENCAYAAAAorJMrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nf44fGXkK4doAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b369edfbb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "star = data_ref['isresolved']==False\n",
    "galaxy = data_ref['isresolved']==True\n",
    "plt.scatter(data_ref['lsst_g_smeared'][galaxy][::100]-data_ref['lsst_r_smeared'][galaxy][::100],data_ref['lsst_r_smeared'][galaxy][::100]-data_ref['lsst_i_smeared'][galaxy][::100],s=0.2,alpha=0.5,label='galaxies')\n",
    "plt.scatter(data_ref['lsst_g_smeared'][star][::15]-data_ref['lsst_r_smeared'][star][::15],data_ref['lsst_r_smeared'][star][::15]-data_ref['lsst_i_smeared'][star][::15],s=0.2,alpha=0.5,color='r',label='stars')\n",
    "plt.xlim(-1,2)\n",
    "plt.ylim(-1,2)\n",
    "plt.xlabel('$g-r$')\n",
    "plt.ylabel('$r-i$')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2b3619cc20d0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAENCAYAAAAorJMrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAE8dJREFUeJzt3X2MXNV5x/Hfb9dru8ZezMaNMcbIIDkUDHmhlk2TqKUi\nL9ildYLSyJGaUFRplahIpGoj0aQKVd9E+0ekpnmhqwQFtwSSNAlxigEBSkQiBQdwjbFNQhzHKTYG\nE5vamBeT3X36x1zmzTu7s+szc++d/X6k0Zwz9+zMc7l4n33OvXOuI0IAAJyuvrwDAAD0BhIKACAJ\nEgoAIAkSCgAgCRIKACAJEgoAIIncE4rtFba/Z3uP7d22b5hgjG1/1vZe2zttX5ZHrACA1ubkHYCk\nUUl/GRHbbS+S9Jjt+yNiT92Y9ZJWZY91kr6YPQMACiL3CiUiDkXE9qz9oqQnJS1vGrZR0uaoeFjS\nYtvLuhwqAGASRahQqmyvlPQ2SduaNi2X9HRd/0D22qEJ3mNY0rAk9av/txdosBOhAigLu9ZmZZAp\nvaqX9Fqc9NQjT1WYhGJ7oaRvSvp4RByf6ftExIikEUka9FCs85WJIgRQRp43r9qOkydzjKQctsWD\nM/7ZQiQU2wOqJJPbI+JbEww5KGlFXf/c7DUAmBRJpHtyP4di25K+LOnJiPhMi2FbJH0ku9rrcknH\nIuKU6S4AQH6KUKG8Q9KHJT1he0f22iclnSdJEXGLpK2SNkjaK+llSdflECcAYBK5J5SI+KGkSU8A\nRWWN/T/vTkQAgJnIfcoLANAbSCgAgCRIKACAJEgoAIAkSCgAgCRIKACAJEgoAIAkSCgAgCRIKACA\nJEgoAIAkSCgAgCRyX8sLALrlvmd2NPTfe85bc4qkN1GhAACSIKEAAJJgygvArMEUV2dRoQAAkiCh\nAACSIKEAAJIgoQAAkiChAACS4CovALmac+7yhv7ogYM5RYLTRYUCAEiCCgVArqhIegcVCgAgCRIK\nACAJEgoAIAkSCgAgiUIkFNu32j5se1eL7VfYPmZ7R/b4dLdjBABMrihXeX1F0uckbZ5kzA8i4uru\nhAMAmK5CJJSIeMj2yrzjANA5cy5YWW2P7tufWxzonEJMebXp7bZ32r7H9uq8gwEANCpEhdKG7ZLO\ni4gTtjdIukvSqokG2h6WNCxJ87WgexECmBRVSe8rRYUSEccj4kTW3ippwPaSFmNHImJNRKwZ0Lyu\nxgkAs1kpEorts207a69VJe4j+UYFAKhXiCkv23dIukLSEtsHJN0kaUCSIuIWSR+Q9DHbo5JekbQp\nIiKncAEAEyhEQomID02x/XOqXFYMoMeduPeCanvh+l80buTvyEIrxZQXAKD4SCgAgCQKMeUFAK9b\neNW+vEPADFGhAACSIKEAAJIgoQAAkiChAACSIKEAAJIgoQAAkiChAACSIKEAAJIgoQAAkiChAACS\nYOkVAIX1R3sab3v03TcvrbZjdLTb4WAKVCgAgCSoUAAU1uabr27oLx79UU6RoB1UKACAJKhQABTW\n4s1UJGVChQIASIKEAgBIgoQCAEiChAIASIKEAgBIgqu8ABTWS/de0NB/7Y7aN+XPuo0rwIqGCgUA\nkAQJBQCQBFNeAArrjA2/bOyP78spErSDCgUAkEQhEortW20ftr2rxXbb/qztvbZ32r6s2zECyMH4\nWOMDhVaIhCLpK5KummT7ekmrssewpC92ISYAwDQUIqFExEOSjk4yZKOkzVHxsKTFtpd1JzoAQDsK\nkVDasFzS03X9A9lrp7A9bPtR24/+Wie7EhwAoDwJpW0RMRIRayJizYDm5R0OAMwaZUkoByWtqOuf\nm70GACiIsnwPZYuk623fKWmdpGMRcSjnmAB0mt3Q/fntb6m2l391brU9/+5HGn8uoqNhYWKFSCi2\n75B0haQltg9IuknSgCRFxC2StkraIGmvpJclXZdPpACAVhw9nMkHPRTrfGXeYQBAaWyLB3U8jnrq\nkacqyzkUAEDBkVAAAEmQUAAASZBQAABJFOIqLwCYrkN3XVRtL7vmqcaNLCSZCyoUAEASVCgAiquv\nv6HrgdqvrOWbflFtj1ORFAIVCgAgCSoUAMXVVHnEyVq/d7+SXV5UKACAJKhQAJRS/xuGqu2j69/U\nsO3M/3y42+FAVCgAgERIKACAJJjyAlBY/YODDf1X166qtv39x6ttpriKgQoFAJAEFQqAwho7fryh\nP/DAY9U2lw0XDxUKACAJKhQApdG/+sJa55nD1ebYCy/kEA2aUaEAAJKgQgFQLPULQjYtvTK+YG61\nHVQlhUOFAgBIggoFQLFMshR9PPJEFwPBdE1Zodj+2+z5HbYXdTwiAEAptVOh3Jc93yDpEtsDkvZI\n2ilpZ0R8o1PBAUC9/otq35TX0WPV5thzhycYjW6bMqFExI+y5w9Kku15klZLulTSWkkkFADA9M+h\nRMRJSduzBwAAkjgpD6DA+i75rcYXXj1ZbTLNVTzTvmzY9h+mDsL2VbZ/anuv7Rsn2H6F7WO2d2SP\nT6eOAQBwemZSofyjpO+mCsB2v6TPS3q3pAOSHrG9JSL2NA39QURcnepzARSD5zT+GvIltbsvev/B\nhm1j/3dMKK6ZfLHRiWNYK2lvROyLiNck3SlpY+LPAAB02EwqlNSrRi+X9HRd/4CkdROMe7vtnZIO\nSvqriNg90ZvZHpY0LEnztSBxqABSmHP20lrnN+Y3bBvb9VS1PT462q2QkEBZTspvl3ReRJywvUHS\nXZJWTTQwIkYkjUjSoIe4ZQIAdEkREspBSSvq+udmr1VFxPG69lbbX7C9JCJ+1aUYAUxT3/zGyqNv\nWa0qGf/V0Vr72ee6FhM6aybnUFIf/UckrbJ9vu25kjZJ2lI/wPbZtp2116oS95HEcQAATsNMvtj4\n7pQBRMSo7etVWeKlX9KtEbHb9kez7bdI+oCkj9kelfSKpE0RwXQWUASuXafTv2RJy2FjBw5V2/Hr\n1zoaEvLhXv69POihWOcr8w4D6G1tJpTxukt+SSjFtS0e1PE4OqOredua8rLdZ/uTM/kAAMDs0NaU\nV0SM275a0j91OB4ABdd3xhmN/bMWV9tjz9euk4mTJ4XZZTon5Xfavsk2d3kEAJxiOiflhyT9nion\nx7eJ+6EAvcWN0+b9i+rupzdvXrUZL77YMG70QOPyKJi92k4o3A8FADAZ7ocCzDZ1lUjfwoW1l+cO\nNAyLuqXix59/vvNxofQ4HwIASKIIS68ASKx+Sfi+BU2LpNZVIvHKq9X22AtNS8OPj3UkNvQuKhQA\nQBJUKECZ1J//aKo8vPCM5tGSGr+hLjVdpdXDK2Wg+6hQAABJUKEARVBXeXju3IZNfYvqrsSqq0Li\nxEsN4xrWyuJb6sgBFQoAIAkSCgAgCaa8gC7xQG0qq6/pPup64xtq7b6mv/Oer93dcPSXT9de54Q6\nCoYKBQCQBBUKkFD/WWc19H1mbYHF8cG6y3xfabzBVDxTu7P2+EuNJ9uBsqBCAQAkQYUCzED/4GC1\nPbr6/Gp7rOm8Rv+RE9X2+K6f1TawrAl6EBUKACAJKhSghTkrz6u2X7j8nIZtAyfGq+2Fj/1vtT36\n7HMN45orFqCXUaEAAJKgQsGs1v+GoYb+kT+4sNoeeLlWhZx59+6GceN1CyyOdig2oGyoUAAASZBQ\nAABJMOWFWad/dW1a68ULFzdsO+tr26vt+hV7xwVgKlQoAIAkClGh2L5K0r9K6pf0pYi4uWm7s+0b\nJL0s6U8jYvspbwS0Une/kbE9T1XbC3Y3XtbLRb7AzOVeodjul/R5SeslXSzpQ7Yvbhq2XtKq7DEs\n6YtdDRIAMKXcE4qktZL2RsS+iHhN0p2SNjaN2Shpc1Q8LGmx7WXdDhQFZ7d+REz8AJBMERLKckl1\nN3nQgey16Y6RJNketv2o7Ud/LW6DCgDdUohzKClFxIikEUka9BB/gva6vv5amwUXgVwVoUI5KGlF\nXf/c7LXpjgEA5KgICeURSatsn297rqRNkrY0jdki6SOuuFzSsYg41O1AkZO+/tqj2fhY7QEgV7lP\neUXEqO3rJd2nymXDt0bEbtsfzbbfImmrKpcM71XlsuHr8ooXADAxRw9f6TLooVjnK/MOA+2o+55I\npV9XPFN9AF2zLR7U8TjqqUeeqghTXgCAHkBCAQAkkfs5FMxizdNc9ZjmAkqHCgUAkAQVCrpnsoqk\nhy8OAWYLKhQAQBJUKOis+qqEKgToaVQoAIAkqFBw+prPjdRXIlQlwKxBhQIASIIKBTPDuREATahQ\nAABJkFAAAEkw5YXWONkOYBqoUAAASVChoPWSKFQhAKaBCgUAkAQVCqhEACRBhQIASIKEAgBIgoQC\nAEiChAIASIKEAgBIgoQCAEiChAIASIKEAgBIgoQCAEgi12/K2x6S9DVJKyXtl/TBiHhhgnH7Jb0o\naUzSaESs6V6UAIB25F2h3CjpwYhYJenBrN/K70fEW0kmAFBMeSeUjZJuy9q3SXpfjrEAAE5D3gll\naUQcytrPSlraYlxIesD2Y7aHuxMaAGA6On4OxfYDks6eYNOn6jsREbZbLXv7zog4aPuNku63/ZOI\neKjF5w1LGpak+VpwGpEDAKaj4wklIt7Vapvt52wvi4hDtpdJOtziPQ5mz4dtf1vSWkkTJpSIGJE0\nIkmDHmJddgDokrynvLZIujZrXyvpO80DbJ9he9HrbUnvkbSraxECANqSd0K5WdK7bf9M0ruyvmyf\nY3trNmappB/aflzSjyXdHRH35hItAKClXL+HEhFHJF05wevPSNqQtfdJekuXQwMATFPeFQoAoEeQ\nUAAASZBQAABJkFAAAEmQUAAASZBQAABJkFAAAEmQUAAASZBQAABJkFAAAEmQUAAASZBQAABJkFAA\nAEmQUAAASZBQAABJkFAAAEmQUAAASZBQAABJkFAAAEmQUAAASZBQAABJkFAAAEmQUAAASZBQAABJ\nkFAAAEmQUAAASZBQAABJkFAAAEnkmlBs/7Ht3bbHba+ZZNxVtn9qe6/tG7sZIwCgPXlXKLskXSPp\noVYDbPdL+ryk9ZIulvQh2xd3JzwAQLvm5PnhEfGkJNmebNhaSXsjYl829k5JGyXt6XiAAIC25ZpQ\n2rRc0tN1/QOS1rUabHtY0nDWPflA/NeuDsaWpyWSfpV3EB3E/pUb+1deF870BzueUGw/IOnsCTZ9\nKiK+k/rzImJE0kj22Y9GRMtzM2XWy/smsX9lx/6Vl+1HZ/qzHU8oEfGu03yLg5JW1PXPzV4DABRI\n3ifl2/GIpFW2z7c9V9ImSVtyjgkA0CTvy4bfb/uApN+RdLft+7LXz7G9VZIiYlTS9ZLuk/SkpK9H\nxO42P2KkA2EXRS/vm8T+lR37V14z3jdHRMpAAACzVBmmvAAAJUBCAQAk0TMJpdeXcbE9ZPt+2z/L\nns9qMW6/7Sds7zidy/+6Zarj4YrPZtt32r4sjzhnqo39u8L2sex47bD96TzinAnbt9o+bHvC73r1\nwLGbav/KfOxW2P6e7T3Z780bJhgz/eMXET3xkHSRKl/I+b6kNS3G9Ev6uaQLJM2V9Liki/OOvc39\n+xdJN2btGyX9c4tx+yUtyTveNvdpyuMhaYOkeyRZ0uWStuUdd+L9u0LSf+cd6wz373clXSZpV4vt\npT12be5fmY/dMkmXZe1Fkp5K8W+vZyqUiHgyIn46xbDqMi4R8Zqk15dxKYONkm7L2rdJel+OsaTS\nzvHYKGlzVDwsabHtZd0OdIbK/P/blCLiIUlHJxlS5mPXzv6VVkQciojtWftFVa6gXd40bNrHr2cS\nSpsmWsal+T9iUS2NiENZ+1lJS1uMC0kP2H4sW4amyNo5HmU+Zu3G/vZsSuEe26u7E1pXlPnYtav0\nx872Sklvk7StadO0j18Z1vKq6vYyLt022f7VdyIibLe63vudEXHQ9hsl3W/7J9lfWiim7ZLOi4gT\ntjdIukvSqpxjQntKf+xsL5T0TUkfj4jjp/t+pUoo0ePLuEy2f7afs70sIg5lZefhFu9xMHs+bPvb\nqky7FDWhtHM8Cn3MpjBl7PX/iCNiq+0v2F4SEb2w8GCZj92Uyn7sbA+okkxuj4hvTTBk2sdvtk15\nlXkZly2Srs3a10o6pSKzfYbtRa+3Jb1HlXvOFFU7x2OLpI9kV5xcLulY3dRf0U25f7bPtiv3b7C9\nVpV/k0e6HmlnlPnYTanMxy6L+8uSnoyIz7QYNu3jV6oKZTK23y/p3yT9pirLuOyIiPfaPkfSlyJi\nQ0SM2n59GZd+SbdG+8u45O1mSV+3/WeSfinpg1JlmRpl+6fKeZVvZ/+Pz5H01Yi4N6d4p9TqeNj+\naLb9FklbVbnaZK+klyVdl1e809Xm/n1A0sdsj0p6RdKmyC6xKTrbd6hypdMSV5ZQuknSgFT+Yye1\ntX+lPXaS3iHpw5KesL0je+2Tks6TZn78WHoFAJDEbJvyAgB0CAkFAJAECQUAkAQJBQCQBAkFAJAE\nCQUAkAQJBQCQBAkFKDDb/XnHALSLhAIkYvsi2w9lq89+wvbeGb7PN2z/u+2HJf114jCBjiGhAAnY\nniPpdkk3RMSbVbmp1kzXUbtU0nMRcXlE/EOqGIFO65m1vICcXSPp8Yj4n6y/R00rQrdz+wXb8yUN\nSfq7DsYKdAQJBUjjzZJ21PUvkdSwMGebt19YrcqtVkcTxgZ0BVNeQBpHJL1Jkmy/VdKfqHIP+em6\nVNLOhHEBXUOFAqTxH6rcNuEJSd+XtD8i9s3gfS6V9OOUgQHdwvL1QAK2F0bEiaz9CUlnRsTf5BwW\n0FVMeQFp/IXt3dnNilZK+vuc4wG6jgoFAJAEFQoAIAkSCgAgCRIKACAJEgoAIAkSCgAgCRIKACAJ\nEgoAIIn/B0OMqC83530cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3619c158d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(data_ref['lsst_g_smeared'][star][::15]-data_ref['lsst_r_smeared'][star][::15],data_ref['lsst_r_smeared'][star][::15]-data_ref['lsst_i_smeared'][star][::15],bins=100,range=[(-1,2),(-1,2)]);\n",
    "plt.xlabel('$g-r$')\n",
    "plt.ylabel('$r-i$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about the galaxies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/common/cori/software/python/2.7-anaconda-4.4/lib/python2.7/site-packages/ipykernel/__main__.py:1: RuntimeWarning: invalid value encountered in subtract\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2b361f1cca90>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAENCAYAAAAorJMrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wNHVzI+XPTsmQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3619cc2310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(data_ref['lsst_g_smeared'][galaxy][::100]-data_ref['lsst_r_smeared'][galaxy][::100],data_ref['lsst_r_smeared'][galaxy][::100]-data_ref['lsst_i_smeared'][galaxy][::100],bins=100,range=[(-1,2),(-1,2)]);\n",
    "plt.xlabel('$g-r$')\n",
    "plt.ylabel('$r-i$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "nmt",
   "language": "python",
   "name": "nmt"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}