{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. Find weights to apply to account for sample selection\n", "\n", "The counts we found in [./3_servs_lf.ipynb](./3_servs_lf.ipynb) are much too low. Here we will try to understand the objects thrown away at each stage." ] }, { "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) [with local modifications]\n", "This notebook was executed on: \n", "2018-11-05 12:08:13.902162\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": 50, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from astropy.table import Table\n", "\n", "%matplotlib inline\n", "#%config InlineBackend.figure_format = 'svg'\n", "\n", "import matplotlib.pyplot as plt\n", "plt.rc('figure', figsize=(10, 6))\n", "\n", "from astropy import units as u\n", "from astropy.coordinates import SkyCoord\n", "\n", "from herschelhelp_internal.masterlist import merge_catalogues, nb_merge_dist_plot, specz_merge" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In Gruppioni et al. (2017) they apply weights due to missing photometric redshifts in the application of the Vmax method\n", "\n", "$$\n", "\\Phi (L,z) = \\frac{1}{\\Delta L} \\sum_i \\frac{1}{w_i V_{\\textrm{max}, i}}\n", "$$\n", "\n", "\n", "There are various stages to the selection:\n", "\n", "1. Survey selection. The object must be bright enough to be detected by at least one of the observing surveys.\n", "\n", "2. Our detection criteria. We insist objects must have at least two optical and at least 2 near infrared detections.\n", "\n", "3. Photometric redshift. Objects must have a redshift. What determines whether an object gets a redshift is not entirely understood.\n", "\n", "4. XID+ fluxes. To pass to CIGALE the object must have at least two FIR fluxes with s/n greater than 5.\n", "\n", "5. CIGALE fit.\n", "\n", "First, lets look at the numbers.\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#dmu = '/Users/rs548/GitHub/dmu_products/'\n", "dmu = '/mnt/hedam/dmu_products/'\n", "cat = Table.read(dmu + 'dmu32/dmu32_ELAIS-N1/data/ELAIS-N1_20171016.fits')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['help_id',\n", " 'field',\n", " 'ra',\n", " 'dec',\n", " 'hp_idx',\n", " 'ebv',\n", " 'redshift',\n", " 'zspec',\n", " 'f_wfc_u',\n", " 'ferr_wfc_u',\n", " 'f_ap_wfc_u',\n", " 'ferr_ap_wfc_u',\n", " 'm_wfc_u',\n", " 'merr_wfc_u',\n", " 'm_ap_wfc_u',\n", " 'merr_ap_wfc_u',\n", " 'flag_wfc_u',\n", " 'f_megacam_u',\n", " 'ferr_megacam_u',\n", " 'f_ap_megacam_u',\n", " 'ferr_ap_megacam_u',\n", " 'm_megacam_u',\n", " 'merr_megacam_u',\n", " 'm_ap_megacam_u',\n", " 'merr_ap_megacam_u',\n", " 'flag_megacam_u',\n", " 'f_suprime_g',\n", " 'ferr_suprime_g',\n", " 'f_ap_suprime_g',\n", " 'ferr_ap_suprime_g',\n", " 'm_suprime_g',\n", " 'merr_suprime_g',\n", " 'm_ap_suprime_g',\n", " 'merr_ap_suprime_g',\n", " 'flag_suprime_g',\n", " 'f_megacam_g',\n", " 'ferr_megacam_g',\n", " 'f_ap_megacam_g',\n", " 'ferr_ap_megacam_g',\n", " 'm_megacam_g',\n", " 'merr_megacam_g',\n", " 'm_ap_megacam_g',\n", " 'merr_ap_megacam_g',\n", " 'flag_megacam_g',\n", " 'f_gpc1_g',\n", " 'ferr_gpc1_g',\n", " 'f_ap_gpc1_g',\n", " 'ferr_ap_gpc1_g',\n", " 'm_gpc1_g',\n", " 'merr_gpc1_g',\n", " 'm_ap_gpc1_g',\n", " 'merr_ap_gpc1_g',\n", " 'flag_gpc1_g',\n", " 'f_wfc_g',\n", " 'ferr_wfc_g',\n", " 'f_ap_wfc_g',\n", " 'ferr_ap_wfc_g',\n", " 'm_wfc_g',\n", " 'merr_wfc_g',\n", " 'm_ap_wfc_g',\n", " 'merr_ap_wfc_g',\n", " 'flag_wfc_g',\n", " 'f_suprime_r',\n", " 'ferr_suprime_r',\n", " 'f_ap_suprime_r',\n", " 'ferr_ap_suprime_r',\n", " 'm_suprime_r',\n", " 'merr_suprime_r',\n", " 'm_ap_suprime_r',\n", " 'merr_ap_suprime_r',\n", " 'flag_suprime_r',\n", " 'f_gpc1_r',\n", " 'ferr_gpc1_r',\n", " 'f_ap_gpc1_r',\n", " 'ferr_ap_gpc1_r',\n", " 'm_gpc1_r',\n", " 'merr_gpc1_r',\n", " 'm_ap_gpc1_r',\n", " 'merr_ap_gpc1_r',\n", " 'flag_gpc1_r',\n", " 'f_wfc_r',\n", " 'ferr_wfc_r',\n", " 'f_ap_wfc_r',\n", " 'ferr_ap_wfc_r',\n", " 'm_wfc_r',\n", " 'merr_wfc_r',\n", " 'm_ap_wfc_r',\n", " 'merr_ap_wfc_r',\n", " 'flag_wfc_r',\n", " 'f_megacam_r',\n", " 'ferr_megacam_r',\n", " 'f_ap_megacam_r',\n", " 'ferr_ap_megacam_r',\n", " 'm_megacam_r',\n", " 'merr_megacam_r',\n", " 'm_ap_megacam_r',\n", " 'merr_ap_megacam_r',\n", " 'flag_megacam_r',\n", " 'f_gpc1_i',\n", " 'ferr_gpc1_i',\n", " 'f_ap_gpc1_i',\n", " 'ferr_ap_gpc1_i',\n", " 'm_gpc1_i',\n", " 'merr_gpc1_i',\n", " 'm_ap_gpc1_i',\n", " 'merr_ap_gpc1_i',\n", " 'flag_gpc1_i',\n", " 'f_suprime_i',\n", " 'ferr_suprime_i',\n", " 'f_ap_suprime_i',\n", " 'ferr_ap_suprime_i',\n", " 'm_suprime_i',\n", " 'merr_suprime_i',\n", " 'm_ap_suprime_i',\n", " 'merr_ap_suprime_i',\n", " 'flag_suprime_i',\n", " 'f_wfc_i',\n", " 'ferr_wfc_i',\n", " 'f_ap_wfc_i',\n", " 'ferr_ap_wfc_i',\n", " 'm_wfc_i',\n", " 'merr_wfc_i',\n", " 'm_ap_wfc_i',\n", " 'merr_ap_wfc_i',\n", " 'flag_wfc_i',\n", " 'f_gpc1_z',\n", " 'ferr_gpc1_z',\n", " 'f_ap_gpc1_z',\n", " 'ferr_ap_gpc1_z',\n", " 'm_gpc1_z',\n", " 'merr_gpc1_z',\n", " 'm_ap_gpc1_z',\n", " 'merr_ap_gpc1_z',\n", " 'flag_gpc1_z',\n", " 'f_wfc_z',\n", " 'ferr_wfc_z',\n", " 'f_ap_wfc_z',\n", " 'ferr_ap_wfc_z',\n", " 'm_wfc_z',\n", " 'merr_wfc_z',\n", " 'm_ap_wfc_z',\n", " 'merr_ap_wfc_z',\n", " 'flag_wfc_z',\n", " 'f_megacam_z',\n", " 'ferr_megacam_z',\n", " 'f_ap_megacam_z',\n", " 'ferr_ap_megacam_z',\n", " 'm_megacam_z',\n", " 'merr_megacam_z',\n", " 'm_ap_megacam_z',\n", " 'merr_ap_megacam_z',\n", " 'flag_megacam_z',\n", " 'f_suprime_z',\n", " 'ferr_suprime_z',\n", " 'f_ap_suprime_z',\n", " 'ferr_ap_suprime_z',\n", " 'm_suprime_z',\n", " 'merr_suprime_z',\n", " 'm_ap_suprime_z',\n", " 'merr_ap_suprime_z',\n", " 'flag_suprime_z',\n", " 'f_suprime_n921',\n", " 'ferr_suprime_n921',\n", " 'f_ap_suprime_n921',\n", " 'ferr_ap_suprime_n921',\n", " 'm_suprime_n921',\n", " 'merr_suprime_n921',\n", " 'm_ap_suprime_n921',\n", " 'merr_ap_suprime_n921',\n", " 'flag_suprime_n921',\n", " 'f_gpc1_y',\n", " 'ferr_gpc1_y',\n", " 'f_ap_gpc1_y',\n", " 'ferr_ap_gpc1_y',\n", " 'm_gpc1_y',\n", " 'merr_gpc1_y',\n", " 'm_ap_gpc1_y',\n", " 'merr_ap_gpc1_y',\n", " 'flag_gpc1_y',\n", " 'f_suprime_y',\n", " 'ferr_suprime_y',\n", " 'f_ap_suprime_y',\n", " 'ferr_ap_suprime_y',\n", " 'm_suprime_y',\n", " 'merr_suprime_y',\n", " 'm_ap_suprime_y',\n", " 'merr_ap_suprime_y',\n", " 'flag_suprime_y',\n", " 'f_ukidss_j',\n", " 'ferr_ukidss_j',\n", " 'f_ap_ukidss_j',\n", " 'ferr_ap_ukidss_j',\n", " 'm_ukidss_j',\n", " 'merr_ukidss_j',\n", " 'm_ap_ukidss_j',\n", " 'merr_ap_ukidss_j',\n", " 'flag_ukidss_j',\n", " 'f_ukidss_k',\n", " 'ferr_ukidss_k',\n", " 'f_ap_ukidss_k',\n", " 'ferr_ap_ukidss_k',\n", " 'm_ukidss_k',\n", " 'merr_ukidss_k',\n", " 'm_ap_ukidss_k',\n", " 'merr_ap_ukidss_k',\n", " 'flag_ukidss_k',\n", " 'f_irac_i1',\n", " 'ferr_irac_i1',\n", " 'f_ap_irac_i1',\n", " 'ferr_ap_irac_i1',\n", " 'm_irac_i1',\n", " 'merr_irac_i1',\n", " 'm_ap_irac_i1',\n", " 'merr_ap_irac_i1',\n", " 'flag_irac_i1',\n", " 'f_irac_i2',\n", " 'ferr_irac_i2',\n", " 'f_ap_irac_i2',\n", " 'ferr_ap_irac_i2',\n", " 'm_irac_i2',\n", " 'merr_irac_i2',\n", " 'm_ap_irac_i2',\n", " 'merr_ap_irac_i2',\n", " 'flag_irac_i2',\n", " 'f_irac_i3',\n", " 'ferr_irac_i3',\n", " 'f_ap_irac_i3',\n", " 'ferr_ap_irac_i3',\n", " 'm_irac_i3',\n", " 'merr_irac_i3',\n", " 'm_ap_irac_i3',\n", " 'merr_ap_irac_i3',\n", " 'flag_irac_i3',\n", " 'f_irac_i4',\n", " 'ferr_irac_i4',\n", " 'f_ap_irac_i4',\n", " 'ferr_ap_irac_i4',\n", " 'm_irac_i4',\n", " 'merr_irac_i4',\n", " 'm_ap_irac_i4',\n", " 'merr_ap_irac_i4',\n", " 'flag_irac_i4',\n", " 'f_mips_24',\n", " 'ferr_mips_24',\n", " 'flag_mips_24',\n", " 'f_pacs_green',\n", " 'ferr_pacs_green',\n", " 'flag_pacs_green',\n", " 'f_pacs_red',\n", " 'ferr_pacs_red',\n", " 'flag_pacs_red',\n", " 'f_spire_250',\n", " 'ferr_spire_250',\n", " 'flag_spire_250',\n", " 'f_spire_350',\n", " 'ferr_spire_350',\n", " 'flag_spire_350',\n", " 'f_spire_500',\n", " 'ferr_spire_500',\n", " 'flag_spire_500',\n", " 'cigale_mstar',\n", " 'cigale_mstar_err',\n", " 'cigale_sfr',\n", " 'cigale_sfr_err',\n", " 'cigale_dustlumin',\n", " 'cigale_dustlumin_err',\n", " 'cigale_dustlumin_ironly',\n", " 'cigale_dustlumin_ironly_err',\n", " 'flag_cigale',\n", " 'flag_cigale_opt',\n", " 'flag_cigale_ir',\n", " 'flag_cigale_ironly',\n", " 'stellarity',\n", " 'stellarity_origin',\n", " 'flag_cleaned',\n", " 'flag_merged',\n", " 'flag_gaia',\n", " 'flag_optnir_obs',\n", " 'flag_optnir_det',\n", " 'zspec_qual',\n", " 'zspec_association_flag']" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cat.colnames" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "total = len(cat)\n", "photoz = np.sum(~np.isnan(cat['redshift']))\n", "specz = np.sum(~np.isnan(cat['zspec']))\n", "optnir_obs = np.sum(((cat['flag_optnir_obs'] == 3) \n", " | (cat['flag_optnir_obs'] == 7)))\n", "optnir_det = np.sum(((cat['flag_optnir_det'] == 3) \n", " | (cat['flag_optnir_det'] == 7)))\n", "cigale_dustlumin = np.sum(~np.isnan(cat['cigale_dustlumin']))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "good = {}\n", "for band in ['pacs_green', 'pacs_red', 'spire_250', 'spire_350', 'spire_500']:\n", " good[band] = (~np.isnan(cat['f_{}'.format(band)]) & \n", " ~cat['flag_{}'.format(band)])\n", " good[band][good[band]] &= (cat[good[band]]['f_{}'.format(band)] /\n", " cat[good[band]]['ferr_{}'.format(band)] >= 2)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "combined_good = np.sum(list(good.values()), axis=0) >= 2\n", "num_good = np.sum(combined_good)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "any_xid = np.sum(np.sum(list(good.values()), axis=0) >= 1)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "blind = Table.read(dmu + 'dmu22/dmu22_ELAIS-N1/data/dmu22_XID+SPIRE_ELAIS-N1_BLIND_Matched_MF.fits')" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Total number: 40263\n", "Photometric redshifts: 27327\n", "Spectrographic redshifts: 46\n", "observed in optical and nir:34273\n", "Detected in optical and nir: 4653\n", "Any XID+: 1028\n", "Two or more XID+ fluxes with a S/N above two: 518\n", "CIGALE dust luminosities: 507\n", "\n", "Blind fluxes: 34501\n", "\n" ] } ], "source": [ "print(\n", "\"\"\"\n", "Total number: {}\n", "Photometric redshifts: {}\n", "Spectrographic redshifts: {}\n", "observed in optical and nir:{}\n", "Detected in optical and nir: {}\n", "Any XID+: {}\n", "Two or more XID+ fluxes with a S/N above two: {}\n", "CIGALE dust luminosities: {}\n", "\n", "Blind fluxes: {}\n", "\"\"\".format(total, photoz, specz, optnir_obs, optnir_det, any_xid, num_good, cigale_dustlumin, len(blind))\n", ")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=34501\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
HELP_IDRADecF_SPIRE_250FErr_SPIRE_250_uFErr_SPIRE_250_lF_SPIRE_350FErr_SPIRE_350_uFErr_SPIRE_350_lF_SPIRE_500FErr_SPIRE_500_uFErr_SPIRE_500_lBkg_SPIRE_250Bkg_SPIRE_350Bkg_SPIRE_500Sig_conf_SPIRE_250Sig_conf_SPIRE_350Sig_conf_SPIRE_500Rhat_SPIRE_250Rhat_SPIRE_350Rhat_SPIRE_500n_eff_SPIRE_250n_eff_SPIRE_500n_eff_SPIRE_350Pval_res_250Pval_res_350Pval_res_500flag_spire_250flag_spire_350flag_spire_500F_BLIND_MF_SPIRE_250FErr_BLIND_MF_SPIRE_250F_BLIND_MF_SPIRE_350FErr_BLIND_MF_SPIRE_350F_BLIND_MF_SPIRE_500FErr_BLIND_MF_SPIRE_500rPRA_pixDec_pixF_BLIND_pix_SPIREFErr_BLIND_pix_SPIREflagfield
degdegmJymJymJymJymJymJymJymJymJymJy/BeammJy/BeammJy/BeammJy/BeammJy/BeammJy/BeammJymJymJymJymJymJydegdegmJymJy
bytes33float64float64float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32boolboolboolfloat64float64float64float64float64float64float64float64float64float64float64float64float64bytes8
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HELP_BLIND_J160958.915+563743.981242.4954801256.6288836412367.262367.31367.158199.15199.261198.91397.471798.249395.966-1.87718-2.93835-4.038113.251763.505472.96322nan0.9985950.9990752000.02000.02000.01.01.00.068TrueTrueFalse408.8817574814.18486613128215.9032920584.05950542677101.8457587074.227650531110.7903485684021.0242.4944645256.6294353723346.3380829834.089610325280.0ELAIS-N1
....................................................................................................................................
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HELP_BLIND_J155910.565+540339.058239.79402089554.060849365624.63729.611219.500419.660725.545913.82741.521862.854970.587725-0.0347439-0.0475197-0.03517460.002132350.002934150.004340240.9991980.9984060.9992112000.02000.02000.00.00.00.0FalseFalseTrue25.66024585458.5606309274914.501216768610.177481759719.147573779310.10008742340.5505557755440.852459016393239.79475463754.063646685918.60899980277.907346814842.0ELAIS-N1
HELP_BLIND_J160649.569+561132.277241.70653690556.19229906055.438568.037772.8672513.975616.715811.300910.836914.22457.38754-1.79608-2.80142-3.884062.996073.456963.23851.001830.9989911.001612000.02000.02000.00.0440.0710.002TrueFalseFalse-0.752815498663.9136708450711.83732160664.0331482608315.00006818614.442078875820.4126224012410.852459016393241.70257956156.191149737318.60313271864.348403059762.0ELAIS-N1
" ], "text/plain": [ "\n", " HELP_ID RA ... flag field \n", " deg ... \n", " bytes33 float64 ... float64 bytes8 \n", "--------------------------------- ------------- ... ------- --------\n", "HELP_BLIND_J160632.875+532632.977 241.636978338 ... 0.0 ELAIS-N1\n", "HELP_BLIND_J162125.844+560032.529 245.35768359 ... 0.0 ELAIS-N1\n", "HELP_BLIND_J162142.463+550507.737 245.426928007 ... 0.0 ELAIS-N1\n", "HELP_BLIND_J160440.548+553408.377 241.168951668 ... 0.0 ELAIS-N1\n", "HELP_BLIND_J161457.172+555221.809 243.738214947 ... 0.0 ELAIS-N1\n", "HELP_BLIND_J162211.111+550253.624 245.546293769 ... 0.0 ELAIS-N1\n", "HELP_BLIND_J161819.200+541858.711 244.579999282 ... 0.0 ELAIS-N1\n", "HELP_BLIND_J162147.815+543923.579 245.449231188 ... 0.0 ELAIS-N1\n", "HELP_BLIND_J160958.915+563743.981 242.49548012 ... 0.0 ELAIS-N1\n", " ... ... ... ... ...\n", "HELP_BLIND_J161804.940+550319.828 244.520582162 ... 2.0 ELAIS-N1\n", "HELP_BLIND_J161630.733+564918.601 244.128052883 ... 2.0 ELAIS-N1\n", "HELP_BLIND_J162237.071+560704.132 245.65446323 ... 2.0 ELAIS-N1\n", "HELP_BLIND_J160949.222+560206.563 242.455090645 ... 2.0 ELAIS-N1\n", "HELP_BLIND_J160951.426+534541.718 242.464274239 ... 2.0 ELAIS-N1\n", "HELP_BLIND_J160159.046+551318.017 240.496023647 ... 2.0 ELAIS-N1\n", "HELP_BLIND_J160727.422+535809.991 241.864260061 ... 2.0 ELAIS-N1\n", "HELP_BLIND_J160340.484+543006.982 240.918683488 ... 2.0 ELAIS-N1\n", "HELP_BLIND_J155910.565+540339.058 239.794020895 ... 2.0 ELAIS-N1\n", "HELP_BLIND_J160649.569+561132.277 241.706536905 ... 2.0 ELAIS-N1" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "blind" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Number per bin')" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(np.log10(1.e3*blind['F_SPIRE_250']), \n", " bins=np.linspace(2.5, 6.0, num=30), color='b', alpha=0.5, \n", " label = 'blind SPIRE 250')\n", "plt.hist(np.log10(cat['f_spire_250'][~np.isnan(cat['f_spire_250'])]), \n", " bins=np.linspace(2.5, 6.0, num=30), color='r', alpha=0.5, \n", " label = 'main SPIRE 250')\n", "plt.legend()\n", "plt.xlabel('log10(flux [uJy])')\n", "plt.ylabel('Number per bin')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Number per bin')" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ULwe6p2xeCKyvpbz6saa4e7G7FxcUFDT0jyIiIkHGEoeZHWhmbSs+A2cAK4HZQEmoVgI8FT7PBi60yInAttCU9Qxwhpl1CJ3iZ4QyERFpAplsquoCPBEmEGkBPOTu88zsVeBRMxsNvA2cG+rPJboVdx3R7bijANx9i5ndCrwa6t3i7lsyGLeIiNQiY4nD3d8E+qQp3wwMTlPuQNoZTtz9AeCBho5RRESS05AjIiKSiBKHiIgkosQhIiKJaJBDkYSyYRY+kaakKw4REUlEiUNERBJR4hARkUSUOEREJBElDhERSUSJQ0REElHiEBGRRJQ4REQkESUOERFJRIlDREQSUeIQEZFElDhERCQRJQ4REUlEiUNERBJR4hARkUSUOEREJBElDhERSUSJQ0REElHiEBGRRJQ4REQkkRZxKpnZSUCP1PruPiNDMYmISBarM3GY2YPA4cAKYFcodkCJQ0SkGYpzxVEMHOXuXp8DmFk+sAR4192HmVkRMAvoCCwDfuLun5tZK6JkdDywGTjP3cvCPq4DRhMlrp+7+zP1iUVERPZenD6OlcAhe3GMK4DVKcv/Dkx0957AVqKEQHjf6u5HABNDPczsKOB84GjgLOAPIRmJiEgTiJM4OgOvmdkzZja74hVn52ZWCHwHuC8sG3Aa8FioMh04J3weHpYJ6weH+sOBWe7+mbu/BawD+sc5voiINLw4TVXj9mL//wlcA7QNy52AD9x9Z1guB7qFz92AdwDcfaeZbQv1uwGLUvaZuo2IiDSyOhOHu79Ynx2b2TBgo7svNbOBFcXpDlHHutq2ST3eGGAMwKGHHpo4XhERiafGpiozeyW8f2hm21NeH5rZ9hj7Phk428zKiDrDTyO6AmlvZhUJqxBYHz6XA93DMVsA7YAtqeVptqnk7lPcvdjdiwsKCmKEJyIi9VFj4nD3b4b3tu5+UMqrrbsfVNeO3f06dy909x5EndvPu/sFwAvAiFCtBHgqfJ4dlgnrnw93cs0GzjezVuGOrJ7A4sQ/qYiINIi4DwAeB3yTqInoFXdfvhfH/DUwy8zGA8uB+0P5/cCDZraO6ErjfAB3X2VmjwKvATuBse6+a8/diuz7xo3LTF2RJOI8AHgjcC7w51A0zcz+5O7j4x7E3RcCC8PnN0lzV5S7fxqOk27724Db4h5PREQyJ84Vxw+BfuGLHTO7g+jBvdiJQ0RE9h1xnuMoA1qnLLcC3shINCIikvVqvOIws/8i6tP4DFhlZvPD8unAK40TnoiIZJvamqqWhPelwBMp5QszFo2IiGS9GhOHu0+vaZ2IiDRfmshJREQSUeIQEZFEak0cZpZvZnc2VjAiIpL9ak0c4Qnt48Pw5iIiIrEeAFwOPGVmfwI+rih09z/XvImIiOyr4iSOjkRTuZ6WUuZ8OQSJiIg0I3Hm4xjVGIGIiEhuqPOuKjP7mpktMLOVYflYM7sh86GJiEg2inM77h+B64AvANy9lDDkuYiIND9xEscB7l594qSdaWuKiMg+L07ieN/MDifM821mI4ANGY1KRESyVpy7qsYCU4Ajzexd4C3ggoxGJSIiWSvOXVVvAt82swOBPHf/MPNhiYhItopzV1UnM5sEvAwsNLO7zKxT5kMTEZFsFKePYxawCfg+MCJ8fiSTQYmISPaK9eS4u9+asjzezM7JVEAiIpLd4lxxvGBm55tZXnj9APhLpgMTEZHsFCdxXAo8RDT3+GdETVdXmtmHZrY9k8GJiEj2iXNXVdvGCERERHJDnD4OkX3euHFNHYFI7tDUsSIikkjGEoeZtTazxWb2dzNbZWY3h/IiM/ubma01s0fMbL9Q3iosrwvre6Ts67pQ/rqZnZmpmEVEpG51zTmeVzGcej18Bpzm7n2AvsBZZnYi8O/ARHfvCWwFRof6o4Gt7n4EMDHUw8yOIhqN92jgLOAPZpZfz5hERGQv1TXn+G7g72Z2aNIde+SjsNgyvJxoJsHHQvl0oOKZkOFhmbB+cJjrfDgwy90/c/e3gHVA/6TxiIhIw4jTOd4VWGVmi6k65/jZdW0YrgyWAkcAk4E3gA/cvWJY9nKgW/jcDXgn7HunmW0DOoXyRSm7Td1GREQaWZzEcXN9d+7uu4C+ZtYeeALola5aeLca1tVUXoWZjQHGABx6aOILJBERianOznF3fxEoA1qGz68Cy5IcxN0/ABYCJwLtzawiYRUC68PncqA7QFjfDtiSWp5mm9RjTHH3YncvLigoSBKeiIgkEGd03EuI+hz+OxR1A56MsV1BuNLAzPYHvg2sBl4gGiwRoAR4KnyeHZYJ6593dw/l54e7roqAnkD1GQlFRKSRxJ3IqT/wNwB3X2tmB8fYriswPfRz5AGPuvvTZvYaMMvMxgPLgftD/fuBB81sHdGVxvnheKvM7FHgNaIpa8eGJjAREWkCcRLHZ+7+eXSDU2Uz0h59DNW5eynQL035m6S5K8rdPwXOrWFftwG3xYhVREQyLM4DgC+a2fXA/mZ2OvAnYE5mwxIRkWwVJ3FcSzR50z+IRsqdC9yQyaBERCR7xRkdd7eZTSfq43Dg9dBpLSIizVCdicPMvgPcS/TwngFFZnapu/9PpoMTEZHsE6dz/D+AQe6+DsDMDieaAVCJQ0SkGYrTx7GxImkEbwIbMxSPiIhkuRqvOMzse+HjKjObCzxK1MdxLtHT4yIi0gzV1lT13ZTP7wGnhs+bgA4Zi0hERLJajYnD3Uc1ZiAiIpIb4txVVQT8DOiRWj/OsOoiIrLviXNX1ZNE40jNAXZnNhwREcl2cRLHp+4+KeORiIhIToiTOO4ys5uAZ4nmEQfA3RPNySEiIvuGOImjN/ATornCK5qqKuYOFxGRZiZO4vg34DB3/zzTwYiISPaL8+T434H2mQ5ERERyQ5wrji7AGjN7lap9HLodV0SkGYqTOG7KeBQiIpIz4szH8WJjBCIiIrkhzpPjH/LlHOP7AS2Bj939oEwGJiIi2SnOFUfb1GUzOwfon7GIRBrQuHFNHYHIvifOXVVVuPuT6BkOEZFmK05T1fdSFvOAYr5suhIRkWYmzl1VqfNy7ATKgOEZiUZERLJenD4OzcshIiKVaps69sZatnN3vzUD8YiISJarrXP84zQvgNHAr+vasZl1N7MXzGy1ma0ysytCeUczm29ma8N7h1BuZjbJzNaZWamZHZeyr5JQf62ZldTzZxURkQZQ29Sx/1Hx2czaAlcAo4BZwH/UtF2KncBV7r4sbL/UzOYDI4EF7n6HmV0LXEuUiIYAPcPrBOAe4AQz60j09HpFp/xSM5vt7luT/rAiIrL3ar0dN1wdjAdKiZLMce7+a3ffWNeO3X1DxZwd7v4hsBroRtSxPj1Umw6cEz4PB2Z4ZBHQ3sy6AmcC8919S0gW84Gzkv6gIiLSMGrr47gT+B4wBejt7h/V9yBm1gPoB/wN6OLuGyBKLmZ2cKjWDXgnZbPyUFZTuYiINIHarjiuAr4C3ACsN7Pt4fWhmW2PewAzawM8DvzC3WvbztKUeS3l1Y8zxsyWmNmSTZs2xQ1PREQSqjFxuHueu+/v7m3d/aCUV9u441SZWUuipDHT3f8cit8LTVCE94pmr3Kge8rmhcD6WsqrxzvF3YvdvbigoCBOeCIiUg9xHgCsFzMz4H5gtbv/PmXVbKAEuCO8P5VSfrmZzSLqHN8WmrKeAW6vuPsKOAO4LlNxi+wr4o7TpfG8JKmMJQ7gZKK5yv9hZitC2fVECeNRMxsNvA2cG9bNBYYC64BPiO7gwt23mNmtwKuh3i3uviWDcYuISC0yljjc/RXS908ADE5T34GxNezrAeCBhotORETqK/HouCIi0rwpcYiISCJKHCIikogSh4iIJKLEISIiiShxiIhIIkocIiKSiBKHiIgkosQhIiKJKHGIiEgiShwiIpKIEoeIiCSixCEiIokocYiISCKZnI9DJCM08ZBI09IVh4iIJKLEISIiiShxiIhIIkocIiKSiBKHiIgkosQhIiKJKHGIiEgiShwiIpKIEoeIiCSixCEiIokocYiISCIZSxxm9oCZbTSzlSllHc1svpmtDe8dQrmZ2SQzW2dmpWZ2XMo2JaH+WjMryVS8IiISTyavOKYBZ1UruxZY4O49gQVhGWAI0DO8xgD3QJRogJuAE4D+wE0VyUZERJpGxhKHu78EbKlWPByYHj5PB85JKZ/hkUVAezPrCpwJzHf3Le6+FZjPnslIREQaUWMPq97F3TcAuPsGMzs4lHcD3kmpVx7KaiqXfZCGSxfJDdnSOW5pyryW8j13YDbGzJaY2ZJNmzY1aHAiIvKlxk4c74UmKML7xlBeDnRPqVcIrK+lfA/uPsXdi929uKCgoMEDFxGRSGMnjtlAxZ1RJcBTKeUXhrurTgS2hSatZ4AzzKxD6BQ/I5SJiEgTyVgfh5k9DAwEOptZOdHdUXcAj5rZaOBt4NxQfS4wFFgHfAKMAnD3LWZ2K/BqqHeLu1fvcBcRkUaUscTh7j+sYdXgNHUdGFvDfh4AHmjA0EREZC9kS+e4iIjkCCUOERFJpLGf45BmSM9nZLck/z76txTQFYeIiCSkxCEiIokocYiISCJKHCIikog6x6Ve1Ekq0nzpikNERBJR4hARkUSUOEREJBH1cUgV6rsQkbroikNERBJR4hARkUSUOEREJBH1cTQD6rcQkYakKw4REUlEVxw5TFcSItIUlDikXgYuHBe77sKB8euKSPZT4pAqkiSEht6nEoxIblDiaAYykQwyQQlGJDcoceSwXEkIsu+I26+m/rd9mxJHlknyH25gpoLIcupfEWlaShyyT1Pzl0jDU+IQQVcxIkkocTQStfmKyL4iZxKHmZ0F3AXkA/e5+x1NHFJGqMM7+6n5S5q7nBhyxMzygcnAEOAo4IdmdlTTRiUi0jzlyhVHf2Cdu78JYGazgOHAa00ZVKI7oHQl0ew0536TJP831Iybe3IlcXQD3klZLgdOyNTB4v4iKxlIQ8nE71KuJKNMJA4lo8zKlcRhacq8SgWzMcCYsPiRmb2+F8frDLxfV6Wb9+IADShWrFkkl+LN7VhfzJLf0PQyem5vbtgfPZd+D2Dv4v1qnEq5kjjKge4py4XA+tQK7j4FmNIQBzOzJe5e3BD7yrRcihVyK17Fmjm5FG8uxQqNE29OdI4DrwI9zazIzPYDzgdmN3FMIiLNUk5ccbj7TjO7HHiG6HbcB9x9VROHJSLSLOVE4gBw97nA3EY6XIM0eTWSXIoVcitexZo5uRRvLsUKjRCvuXvdtURERIJc6eMQEZEs0SwTh5l1N7MXzGy1ma0ysyvS1BloZtvMbEV43dgUsYZYWpvZYjP7e4h3j5sNzayVmT1iZuvM7G9m1qPxI40d60gz25Rybi9uilirxZRvZsvN7Ok067Li3KbEU1usWXVuzazMzP4RYlmSZr2Z2aRwbkvN7LimiDPEUlesWfOdEOJpb2aPmdma8F02oNr6jJ3bnOnjaGA7gavcfZmZtQWWmtl8d6/+JPrL7j6sCeKr7jPgNHf/yMxaAq+Y2f+4+6KUOqOBre5+hJmdD/w7cF6WxgrwiLtf3gTx1eQKYDVwUJp12XJuK9QWK2TfuR3k7jU9VzAE6BleJwD3kMGHe2OoLVbInu8EiMbum+fuI8LdpgdUW5+xc9ssrzjcfYO7LwufPyT6T9itaaOqmUc+Costw6t659RwYHr4/Bgw2MzSPTiZUTFjzSpmVgh8B7ivhipZcW4hVqy5ZjgwI/zeLALam1nXpg4q25nZQcC3gPsB3P1zd/+gWrWMndtmmThShWaHfsDf0qweEJpc/sfMjm7UwKoJzRMrgI3AfHevHm/lsCzuvhPYBnRq3CgjMWIF+H64fH7MzLqnWd+Y/hO4Bthdw/qsObfUHStk17l14FkzWxpGd6gu3XBCTfVHXF2xQvZ8JxwGbAKmhmbL+8zswGp1MnZum3XiMLM2wOPAL9x9e7XVy4Cvunsf4L+AJxs7vlTuvsvd+xI9Nd/fzI6pVqXOYVkaS4xY5wA93P1Y4Dm+/Gu+0ZnZMGCjuy+trVqaskY/tzFjzZpzG5zs7scRNZuMNbNvVVufFec2qCvWbPpOaAEcB9zj7v2Aj4Frq9XJ2LlttokjtL9CzkZ9AAAFIklEQVQ/Dsx09z9XX+/u2yuaXMIzJC3NrHMjh7mHcDm6EDir2qrKYVnMrAXQDtjSqMFVU1Os7r7Z3T8Li38Ejm/k0FKdDJxtZmXALOA0M/u/1epky7mtM9YsO7e4+/rwvhF4gmik61R1DifUWOqKNcu+E8qB8pSr+ceIEkn1Ohk5t80ycYT26fuB1e7++xrqHFLRjm1m/YnO1ebGi7JKLAVm1j583h/4NrCmWrXZQEn4PAJ43pvgIZ04sVZrZz2bqI+pSbj7de5e6O49iIayed7df1ytWlac2zixZtO5NbMDw80nhGaUM4CV1arNBi4MdwCdCGxz9w2NHGqsWLPpO8Hd/xd4x8y+HooGs+c0Exk7t831rqqTgZ8A/wht8QDXA4cCuPu9RF8Ql5nZTmAHcH5TfFkEXYHpFk1olQc86u5Pm9ktwBJ3n02UCB80s3VEfw2fn8Wx/tzMzia6u20LMLKJYq1Rlp7btLL43HYBngjftS2Ah9x9npn9FCr/n80FhgLrgE+AUVkcazZ9JwD8DJgZ7qh6ExjVWOdWT46LiEgizbKpSkRE6k+JQ0REElHiEBGRRJQ4REQkESUOERFJRIlD9jlm9lHdtWrc9vIwmqinPtxV20ijZtbVUkaqNbOHQ51fmtk0MxtR/5+mcp/jzOzdcOttXXVr/fnN7BQze83MVoblYZZmFGORmihxiFT1/4geWvxXtfLUkUbHEI00WuFKoqe0MbNDgJPc/Vh3n9jAsU10970eytvdXya6v7/CX4ieSK8+uqpIWkocss8KVwl3mtlKi+ZZOC+U55nZHyyaL+RpM5tbcVXg7svdvSzN7mobafT7wLzw+VngYIvmazilWjxlFVcxZlZsZgvD50kW5nYwszPN7CUzq/X/ZrgC+VXK8kqrNk+ImT1oZsNTlmeGhwOrCA+xLQSyZbhwyXJKHLIv+x7QF+hDdBVxZ/iy/x7QA+gNXAwMqGkHKdKONGpmRURzdVSMD3U28Ia79w1/2cdxLXCemQ0CJgGj3L220W/juo/wtLCZtQNOInqaOJ0lwCk1rBOpQolD9mXfBB4Oo/W+B7wIfCOU/8ndd4cxf16Isa+aRhrtSjS8db25+yfAJcB84G53f2Nv9pey3xeBI8zsYOCHwONhWPh0NgJfaYjjyr5PiUP2ZTVNtlSfSZhqGml0B9A65j528uX/uerb9CYaMC/ul3fqvtLtr8KDwAVEVx5Ta9lfa6KfRaROShyyL3uJqAko38wKiGZMWwy8QjTZUZ6ZdQEGxthXTSON/pOo2SuOMr4c5vz7FYVm9lXgKqIJxYaYWZzpPcsIw2iHO7yKaqg3DfgFgLuvqmV/X2PPkWtF0lLikH3ZE0Ap8HfgeeCa0DT1ONEVxErgv4lmf9wGYGY/N7NyoiuKUjOrmKJ1LtEIpOuI7qD6PwDu/jHwhpkdESOem4G7zOxlYFc4XsUQ/78K80GMBu4zs7quYh4HOobRnS8jSmAV84VU9LcQmuhWU/vVBsAgorurROqk0XGlWTKzNu7+kZl1IroKOTkklfrs69+A4939hgYNsuoxxgEfufuEOur1Af7o7v3D8gHAP4Dj3H1bSr0ewNPufky46nrI3QdnKHzZx+iKQ5qrp8Nf6y8Dt9Y3aQC4+xNETUeZ9BEwprYHAMNcDA8DN4Tlikm0/qta0jiFaIrZ90PRoURNZSKx6IpDREQS0RWHiIgkosQhIiKJKHGIiEgiShwiIpKIEoeIiCSixCEiIon8fwx1yrBnkBiDAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(np.log10(1.e3*blind['F_SPIRE_350']), \n", " bins=np.linspace(2.5, 6.0, num=30), color='b', alpha=0.5, \n", " label = 'blind SPIRE 350')\n", "plt.hist(np.log10(cat['f_spire_350'][~np.isnan(cat['f_spire_350'])]), \n", " bins=np.linspace(2.5, 6.0, num=30), color='r', alpha=0.5, \n", " label = 'main SPIRE 350')\n", "plt.legend()\n", "plt.xlabel('log10(flux [uJy])')\n", "plt.ylabel('Number per bin')" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Number per bin')" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(np.log10(1.e3*blind['F_SPIRE_500']), \n", " bins=np.linspace(2.5, 6.0, num=30), color='b', alpha=0.5, \n", " label = 'blind SPIRE 500')\n", "plt.hist(np.log10(cat['f_spire_500'][~np.isnan(cat['f_spire_500'])]), \n", " bins=np.linspace(2.5, 6.0, num=30), color='r', alpha=0.5, \n", " label = 'main SPIRE 500')\n", "plt.legend()\n", "plt.xlabel('log10(flux [uJy])')\n", "plt.ylabel('Number per bin')" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nb_merge_dist_plot(\n", " SkyCoord(cat['ra'], cat['dec']),\n", " SkyCoord(blind['RA'], blind['Dec'])\n", ")" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nb_merge_dist_plot(\n", " SkyCoord(cat['ra'][combined_good], cat['dec'][combined_good]),\n", " SkyCoord(blind['RA'], blind['Dec'])\n", ")" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "good_cat = cat[combined_good]" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: MergeConflictWarning: Cannot merge meta key 'EXTNAME' types and , choosing EXTNAME='Tile_39933_7_SPIRE_cat.fits' [astropy.utils.metadata]\n", "WARNING: MergeConflictWarning: Cannot merge meta key 'DATE-HDU' types and , choosing DATE-HDU='2018-06-29T09:22:20' [astropy.utils.metadata]\n", "WARNING: MergeConflictWarning: Cannot merge meta key 'STILVERS' types and , choosing STILVERS='3.0-3+' [astropy.utils.metadata]\n", "WARNING: MergeConflictWarning: Cannot merge meta key 'EXTNAME' types and , choosing EXTNAME='Tile_39933_7_SPIRE_cat.fits' [astropy.utils.metadata]\n", "WARNING: MergeConflictWarning: Cannot merge meta key 'DATE-HDU' types and , choosing DATE-HDU='2018-06-29T09:22:20' [astropy.utils.metadata]\n", "WARNING: MergeConflictWarning: Cannot merge meta key 'STILVERS' types and , choosing STILVERS='3.0-3+' [astropy.utils.metadata]\n" ] } ], "source": [ "# Given the graph above, we use 0.8 arc-second radius\n", "merge = merge_catalogues(good_cat, blind, \"RA\", \"Dec\", radius=5.0*u.arcsec)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(merge['f_spire_250'], 1.e3*merge['F_SPIRE_250'])\n", "plt.plot([1.e4,1.e5],[1.e4,1.e5])\n", "plt.yscale('log')\n", "plt.xscale('log')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python (herschelhelp_internal)", "language": "python", "name": "helpint" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }