{ "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)\n", "This notebook was executed on: \n", "2018-11-05 16:15:07.194247\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": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/seaborn/apionly.py:6: UserWarning: As seaborn no longer sets a default style on import, the seaborn.apionly module is deprecated. It will be removed in a future version.\n", " warnings.warn(msg, UserWarning)\n" ] } ], "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": 5, "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": 6, "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": 7, "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": 8, "metadata": {}, "outputs": [], "source": [ "any_xid = np.sum(np.sum(list(good.values()), axis=0) >= 1)" ] }, { "cell_type": "code", "execution_count": 9, "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": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Total number: 4026292\n", "Photometric redshifts: 2714686\n", "Spectrographic redshifts: 5109\n", "observed in optical and nir:3431269\n", "Detected in optical and nir: 463203\n", "Any XID+: 101548\n", "Two or more XID+ fluxes with a S/N above two: 51153\n", "CIGALE dust luminosities: 49986\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": 11, "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
HELP_BLIND_J160632.875+532632.977241.6369783384941753.44249360641131076.32841076.80021075.2565427.05466427.24368426.666159.72685163.28047156.23094-1.5558592-2.4243228-3.06282163.35838063.84398483.6612494nannan0.99897522000.02000.02000.01.01.00.023TrueTrueFalse1140.50629391992524.775354027588683468.56555738590094.087799332594563168.506495476553744.4195157025252720.79107565304168751.0241.6369783384941753.4424936064113998.88132862265964.6659917200200790.0ELAIS-N1
HELP_BLIND_J162125.844+560032.529245.3576835904884856.00903569669183664.98486665.04596664.8602290.6198290.95413289.9357113.4069116.6214109.76138-1.7619379-2.8314667-3.68506383.1206253.55521563.4286942nan0.998888970.99905412000.02000.02000.01.00.9770.0TrueTrueFalse739.70553871730034.344738560519794311.952465567703264.315147464027572120.880137992207894.1585840475061720.7915400350712941.0245.359703123825956.00955128135834702.78658389673274.2082691129512560.0ELAIS-N1
HELP_BLIND_J162142.463+550507.737245.4269280068318255.08548243993107587.186587.2036587.14795348.35855348.39044348.28958181.8383181.95042181.59132-2.0296905-3.0494676-4.1221444.8860883.99158223.165648nannan1.00034842000.02000.02000.01.01.01.0TrueTrueTrue647.62612582307624.0595903706872685392.91200530554154.1260286274262015212.292063308805844.72892359291057750.91891049690109791.0245.4283297608949455.08461993177872531.37859234955584.03539488033531150.0ELAIS-N1
HELP_BLIND_J160440.548+553408.377241.1689516684953455.56899355821793351.0219351.2573350.5223154.45348155.92227152.204455.50042358.4821352.47555-2.2414632-3.0505497-3.9988383.62814743.5041782.88796970.999996660.998482941.00048922000.02000.02000.01.00.4140.0TrueFalseFalse382.604669324850534.1673608927044565164.50944573213694.22380545629681957.106118603690974.2198432498740010.80960073717892131.0241.1703878397555.5698473331343390.08964601002794.07405174704187440.0ELAIS-N1
HELP_BLIND_J161457.172+555221.809243.7382149465410655.8727246474237480.51782480.54697480.45612280.6323280.74744280.36774129.12306129.68927127.9111-2.0879714-3.1434977-4.4604853.71346193.61119083.334691nannan0.99902812000.02000.02000.01.01.00.907TrueTrueTrue516.44428111201594.320671429225326284.311314575026874.09436185982543135.82547950981134.062308883172760.84420168525152211.0243.7382149465410655.8727246474237388.29548067000444.22411263460893950.0ELAIS-N1
HELP_BLIND_J162211.111+550253.624245.5462937685718155.04822891325986380.147383.476376.7589138.50343139.2859136.9170248.7031851.9373645.277966-2.0296905-3.0494676-4.1221444.8860883.99158223.1656481.00059311.00116920.999318662000.02000.02000.01.00.2230.0TrueFalseFalse389.07987301364724.241361258221392151.283621243637924.14443547451271148.450933222507174.6313230651843540.78069677070530031.0245.5453618739792655.0488047962468382.511852457344.1533703466888430.0ELAIS-N1
HELP_BLIND_J161819.200+541858.711244.579999281754654.31630860240197346.7966346.9452346.48157150.57436151.8159148.6761349.50332652.4268946.5055-1.8306484-2.818987-3.5414733.6924633.94389563.5155766nan0.99904840.999646072000.02000.02000.01.00.6220.013TrueTrueFalse378.010902186526152.9981505518668965158.142850860031793.098393065029221353.915950431792913.30892308803822480.81693949340883021.0244.580928367198954.315740237207976366.413881019210352.93496704345470.0ELAIS-N1
HELP_BLIND_J162147.815+543923.579245.4492311875971254.656549748161375365.20602365.28586365.03278169.03613169.29257168.5060179.5718581.71818577.11866-0.49873042-2.2856119-0.62873570.0075851612.79762770.016324265nan0.99874821.00148762000.02000.02000.01.01.00.0TrueTrueFalse392.181170515147364.303898374807947177.020813139660484.11187631612225982.313589385101034.28506306346155250.82625884863054631.0245.4477403280631354.65574733384051353.95171319128074.1527691764087310.0ELAIS-N1
HELP_BLIND_J160958.915+563743.981242.4954801204964756.62888364117058367.26205367.31006367.1583199.15033199.26122198.9128397.4716698.2492895.96605-1.877185-2.9383488-4.03810553.25176383.50547052.9632168nan0.998594940.9990752000.02000.02000.01.01.00.068TrueTrueFalse408.881757480798344.184866131275384215.903292057673954.059505426768796101.845758706964594.2276505311053270.79034856840224311.0242.494464520328856.629435372264396346.33808298254874.0896103252797510.0ELAIS-N1
....................................................................................................................................
HELP_BLIND_J161804.940+550319.828244.5205821618721755.055507682635184.61208347.4018492.058397315.82488418.81808712.66836514.2759417.21983311.36828-2.1534927-3.342994-4.08663.25136023.42144513.03644180.999151050.998108741.00019182000.02000.02000.00.00.00.001TrueFalseFalse5.0190157788599084.35399781425056411.5041025386904544.68066304578550412.6440211979572764.04392173216338250.48381853696902640.8524590163934427244.5245045360434555.0565671790658218.67573025330594.2273901171117822.0ELAIS-N1
HELP_BLIND_J161630.733+564918.601244.128052883041656.8218336310104914.3537118.21965810.47789910.19208414.1426.18767557.225275511.8982112.8455591-0.015896691-0.022662254-0.0219314270.00193295190.00277014570.0040033350.99950680.99930380.99952042000.02000.02000.00.00.00.0FalseFalseFalse13.7003495644560996.3417972347116847.8613381215250976.18029571869345914.6988705229080796.8012722101206280.61224448459996160.8524590163934427244.130046331078456.8207041169094518.6708843793183236.685540648964212.0ELAIS-N1
HELP_BLIND_J162237.071+560704.132245.654463230005656.1178143636592145.44323979.6171332.3229353.18857126.10319231.03724652.47702675.50796650.68480104-0.022483164-0.034014806-0.027972190.00206776170.00292398250.00388222980.99955041.00262050.998199462000.02000.02000.00.00.00.0TrueTrueTrue9.5632802044508097.06445506023010510.8251573303485146.73430791289232112.7273075863007858.3081169688778420.60920317858937920.8524590163934427245.6625786418019956.11985656017852618.6620382041548327.0077368404962292.0ELAIS-N1
HELP_BLIND_J160949.222+560206.563242.4550906446425856.0351563739217811.39924514.1536418.64799712.02498714.7527249.31544314.41804117.57613811.525324-2.227684-3.236-4.0931333.1203013.46710063.06466670.998708670.99928690.99922442000.02000.02000.00.00.0010.0FalseFalseFalse7.9292096686587894.0386886714781658.9080215833071454.03153023189992713.0383338660051784.2031462083753570.68637394284570190.8524590163934427242.453089167856156.0362597389907118.656901004173324.128828466378132.0ELAIS-N1
HELP_BLIND_J160951.426+534541.718242.464274238873953.76158845321788.30105210.4359726.13336714.4687516.65501612.30004722.83860624.82202320.579857-1.6941366-2.6907272-3.81169273.82946834.08041673.72662040.99943060.99927220.998450762000.02000.02000.00.9881.01.0TrueTrueTrue6.1954626502025012.24327632011564239.8051146252167442.213932928503284313.3676344578946882.2853159132081990.71508592050549570.8524590163934427242.4614738510558753.7599121156607218.6477530938771142.23075394704680762.0ELAIS-N1
HELP_BLIND_J160159.046+551318.017240.4960236474824755.221671279466119.989912.7841547.211852614.41680917.20877811.64958415.20822718.65629811.879891-1.7379303-2.67626-3.69801073.03074983.35987763.02479771.00022630.9993830.99872762000.02000.02000.00.00.00.0FalseFalseFalse3.37869008963230754.240281316974926.8562546126458964.09136444846961711.6405207900815564.6351205781682650.46861007662664990.8524590163934427240.4988412936341155.2233941153601418.6390324352246034.4140438087409732.0ELAIS-N1
HELP_BLIND_J160727.422+535809.991241.864260060616553.9694419959982316.08117318.13780613.94630617.94649120.00843815.72328113.37748115.66381510.9707-1.8089198-2.9364748-3.8027493.7095654.1892943.83929940.99855281.00103780.999536752000.02000.02000.00.9710.990.76TrueTrueTrue13.5959057272314112.21524579809121119.390326670980242.258628754906394618.122593812113462.31060759414911930.6719924629005820.8524590163934427241.8585954700624353.9693925613815218.636842814519022.22553909748817922.0ELAIS-N1
HELP_BLIND_J160340.484+543006.982240.9186834879508654.5019395524690911.85936914.1909589.47351219.30612621.54866417.00573317.5427120.70094314.585322-1.7918924-2.6597214-3.5856463.47208213.78420853.44389870.99908360.999138830.99988392000.02000.02000.00.0210.7820.135FalseTrueFalse9.4841063788511132.933384567191646316.576175120152562.765759284098441317.267499960058842.94721605642841130.70205427865338950.8524590163934427240.9149144318090854.5007662245758918.6174256549939872.8110988605185982.0ELAIS-N1
HELP_BLIND_J155910.565+540339.058239.7940208954766254.0608493656413624.63697429.61120219.50038319.6606925.54588313.8273821.5218642.8549710.5877245-0.034743875-0.0475197-0.0351746230.00213234870.00293415480.0043402450.999197840.998406350.99921152000.02000.02000.00.00.00.0FalseFalseTrue25.6602458544975048.56063092749231714.50121676860772310.17748175967212319.14757377928331310.1000874234382230.5505557755443880.8524590163934427239.794754636782154.0636466859381918.6089998026820427.90734681484492.0ELAIS-N1
HELP_BLIND_J160649.569+561132.277241.7065369051002856.192299060536775.4385618.03776552.867249713.97556716.71577611.30094310.8368514.2244657.3875446-1.796083-2.8014154-3.88406422.9960673.45695953.23849581.0018270.99899131.00161162000.02000.02000.00.0440.0710.002TrueFalseFalse-0.75281549866021423.91367084507006711.8373216065699054.03314826083348215.0000681861498224.4420788758245430.41262240124145490.8524590163934427241.7025795605737256.1911497372544718.6031327186405244.348403059760182.0ELAIS-N1
" ], "text/plain": [ "\n", " HELP_ID RA ... flag field \n", " deg ... \n", " bytes33 float64 ... float64 bytes8 \n", "--------------------------------- ------------------ ... ------- --------\n", "HELP_BLIND_J160632.875+532632.977 241.63697833849417 ... 0.0 ELAIS-N1\n", "HELP_BLIND_J162125.844+560032.529 245.35768359048848 ... 0.0 ELAIS-N1\n", "HELP_BLIND_J162142.463+550507.737 245.42692800683182 ... 0.0 ELAIS-N1\n", "HELP_BLIND_J160440.548+553408.377 241.16895166849534 ... 0.0 ELAIS-N1\n", "HELP_BLIND_J161457.172+555221.809 243.73821494654106 ... 0.0 ELAIS-N1\n", "HELP_BLIND_J162211.111+550253.624 245.54629376857181 ... 0.0 ELAIS-N1\n", "HELP_BLIND_J161819.200+541858.711 244.5799992817546 ... 0.0 ELAIS-N1\n", "HELP_BLIND_J162147.815+543923.579 245.44923118759712 ... 0.0 ELAIS-N1\n", "HELP_BLIND_J160958.915+563743.981 242.49548012049647 ... 0.0 ELAIS-N1\n", " ... ... ... ... ...\n", "HELP_BLIND_J161804.940+550319.828 244.52058216187217 ... 2.0 ELAIS-N1\n", "HELP_BLIND_J161630.733+564918.601 244.1280528830416 ... 2.0 ELAIS-N1\n", "HELP_BLIND_J162237.071+560704.132 245.6544632300056 ... 2.0 ELAIS-N1\n", "HELP_BLIND_J160949.222+560206.563 242.45509064464258 ... 2.0 ELAIS-N1\n", "HELP_BLIND_J160951.426+534541.718 242.4642742388739 ... 2.0 ELAIS-N1\n", "HELP_BLIND_J160159.046+551318.017 240.49602364748247 ... 2.0 ELAIS-N1\n", "HELP_BLIND_J160727.422+535809.991 241.8642600606165 ... 2.0 ELAIS-N1\n", "HELP_BLIND_J160340.484+543006.982 240.91868348795086 ... 2.0 ELAIS-N1\n", "HELP_BLIND_J155910.565+540339.058 239.79402089547662 ... 2.0 ELAIS-N1\n", "HELP_BLIND_J160649.569+561132.277 241.70653690510028 ... 2.0 ELAIS-N1" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "blind" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Number per bin')" ] }, "execution_count": 12, "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": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Number per bin')" ] }, "execution_count": 13, "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_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": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Number per bin')" ] }, "execution_count": 14, "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": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] }, { "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": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] }, { "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": 17, "metadata": {}, "outputs": [], "source": [ "good_cat = cat[combined_good]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "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": 19, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "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": 20, "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 }