{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Herschel Stripe 82 VICS82 - VHS merging\n", "\n", "VICS 82 and VHS contain VISTA J and Ks fluxes which need to be merged" ] }, { "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", "2021-05-13 17:42:59.765542\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/pyenv/versions/3.7.2/lib/python3.7/site-packages/matplotlib/__init__.py:855: MatplotlibDeprecationWarning: \n", "examples.directory is deprecated; in the future, examples will be found relative to the 'datapath' directory.\n", " \"found relative to the 'datapath' directory.\".format(key))\n", "/opt/pyenv/versions/3.7.2/lib/python3.7/site-packages/matplotlib/__init__.py:846: MatplotlibDeprecationWarning: \n", "The text.latex.unicode rcparam was deprecated in Matplotlib 2.2 and will be removed in 3.1.\n", " \"2.2\", name=key, obj_type=\"rcparam\", addendum=addendum)\n", "/opt/pyenv/versions/3.7.2/lib/python3.7/site-packages/seaborn/apionly.py:9: 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": [ "%matplotlib inline\n", "#%config InlineBackend.figure_format = 'svg'\n", "\n", "import matplotlib.pyplot as plt\n", "plt.rc('figure', figsize=(10, 6))\n", "\n", "import os\n", "import time\n", "from collections import OrderedDict\n", "\n", "from astropy import units as u\n", "from astropy.coordinates import SkyCoord\n", "from astropy.table import Column, Table\n", "import numpy as np\n", "from pymoc import MOC\n", "\n", "from herschelhelp_internal.masterlist import merge_catalogues, nb_merge_dist_plot\n", "from herschelhelp_internal.utils import coords_to_hpidx, ebv, gen_help_id, inMoc" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "TMP_DIR = os.environ.get('TMP_DIR', \"./data_tmp\")\n", "OUT_DIR = os.environ.get('OUT_DIR', \"./data\")\n", "SUFFIX = os.environ.get('SUFFIX', time.strftime(\"_%Y%m%d\"))\n", "\n", "try:\n", " os.makedirs(OUT_DIR)\n", "except FileExistsError:\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## I - Reading the prepared pristine catalogues" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "vics82 = Table.read(\"{}/VICS82.fits\".format(TMP_DIR))\n", "vhs = Table.read(\"{}/VISTA-VHS.fits\".format(TMP_DIR))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## II - Merging tables\n", "\n", "We first merge the optical catalogues and then add the infrared ones: VHS, VICS82, \n", "\n", "At every step, we look at the distribution of the distances separating the sources from one catalogue to the other (within a maximum radius) to determine the best cross-matching radius." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### VHS" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue = vhs\n", "master_catalogue['vhs_ra'].name = 'ra'\n", "master_catalogue['vhs_dec'].name = 'dec'\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Add VICS82" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "nb_merge_dist_plot(\n", " SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n", " SkyCoord(vics82['vics82_ra'], vics82['vics82_dec'])\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Given the graph above, we use 0.8 arc-second radius\n", "master_catalogue = merge_catalogues(master_catalogue, vics82, \"vics82_ra\", \"vics82_dec\", radius=0.8*u.arcsec)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cleaning\n", "\n", "When we merge the catalogues, astropy masks the non-existent values (e.g. when a row comes only from a catalogue and has no counterparts in the other, the columns from the latest are masked for that row). We indicate to use NaN for masked values for floats columns, False for flag columns and -1 for ID columns." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for col in master_catalogue.colnames:\n", " if \"m_\" in col or \"merr_\" in col or \"f_\" in col or \"ferr_\" in col or \"stellarity\" in col:\n", " master_catalogue[col].fill_value = np.nan\n", " elif \"flag\" in col:\n", " master_catalogue[col].fill_value = 0\n", " elif \"id\" in col:\n", " master_catalogue[col].fill_value = -1\n", " \n", "master_catalogue = master_catalogue.filled()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=10\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
idxvhs_idradecvhs_stellaritym_vhs_ymerr_vhs_ym_ap_vhs_ymerr_ap_vhs_ym_vhs_jmerr_vhs_jm_ap_vhs_jmerr_ap_vhs_jm_vhs_hmerr_vhs_hm_ap_vhs_hmerr_ap_vhs_hm_vhs_kmerr_vhs_km_ap_vhs_kmerr_ap_vhs_kf_vhs_yferr_vhs_yflag_vhs_yf_ap_vhs_yferr_ap_vhs_yf_vhs_jferr_vhs_jflag_vhs_jf_ap_vhs_jferr_ap_vhs_jf_vhs_hferr_vhs_hflag_vhs_hf_ap_vhs_hferr_ap_vhs_hf_vhs_kferr_vhs_kflag_vhs_kf_ap_vhs_kferr_ap_vhs_kvhs_flag_cleanedvhs_flag_gaiaflag_mergedvics82_idvics82_stellarityvics82_flag_cleanedf_wircam_jf_vista_jferr_wircam_jferr_vista_jm_wircam_jm_vista_jmerr_wircam_jmerr_vista_jflag_wircam_jflag_vista_jf_ap_wircam_jf_ap_vista_jferr_ap_wircam_jferr_ap_vista_jm_ap_wircam_jm_ap_vista_jmerr_ap_wircam_jmerr_ap_vista_jf_wircam_ksf_vista_ksferr_wircam_ksferr_vista_ksm_wircam_ksm_vista_ksmerr_wircam_ksmerr_vista_ksflag_wircam_ksflag_vista_ksf_ap_wircam_ksf_ap_vista_ksferr_ap_wircam_ksferr_ap_vista_ksm_ap_wircam_ksm_ap_vista_ksmerr_ap_wircam_ksmerr_ap_vista_ksvics82_flag_gaia
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24725520846170.43818836458293725-7.0259496611538080.999980910.1779580.0002281300710.7625560.0003527182610.4167940.0002274869911.8231270.0004265082810.2922610.0002839702612.4206940.000643061511.2854050.0004471017511.8294570.0005164086308188.7864.75526False179877.7358.436073247333.1251.822044False67725.0226.60433277392.9472.551094False39059.0923.133976111131.6645.763565False67331.2832.024773False3False-1nanFalsenannannannannannannannanFalseFalsenannannannannannannannannannannannannannannannanFalseFalsenannannannannannannannan0
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44725520834120.5080929454812719-6.96580681684383050.999980910.4840380.000259921310.8516760.0003660537811.1096760.000330766911.5884010.0003802018811.1516380.0004384139311.7072150.0004470087311.386490.000529828111.628490.00046659572232479.4255.65473False165702.6655.866318130655.9839.804024False84069.69529.439428125702.69550.75801False75355.2831.024542101252.0649.409966False81022.12534.8193False3False-1nanFalsenannannannannannannannanFalseFalsenannannannannannannannannannannannannannannannanFalseFalsenannannannannannannannan0
54725520831360.3606172701062671-6.9529969422260710.999980910.5721280.000280615510.9243690.0003735097710.9581860.0003100811211.8008190.000410480911.14907550.0004476474412.1905690.000554907111.5166950.0006026926611.7189310.00048460744214362.3655.403324False154971.6653.312607150219.1742.901882False69130.8826.136097125999.73451.949505False48280.56224.67563289809.4149.853237False74546.51633.27309False3False-1nanFalsenannannannannannannannanFalseFalsenannannannannannannannannannannannannannannannanFalseFalsenannannannannannannannan0
64725347148230.14560287650863246-5.5517686641996460.999980910.3001370.000240276510.7997640.0003742416411.2544630.0003148670711.9314180.000455072111.862780.000783345712.559650.0006353833611.7488870.000753476912.3752960.00071129453275388.1260.944176False173817.8459.91315114344.30533.160225False61296.04325.69143365296.2147.110435False34366.8520.11181472517.8750.325794False40726.92626.68129False3False-1nanFalsenannannannannannannannanFalseFalsenannannannannannannannannannannannannannannannanFalseFalsenannannannannannannannan0
7472548405385357.72488275644616-5.8663111849628540.999980910.5273170.0002849610510.7988960.0003758493410.8195040.0003235499311.4229090.0003997754211.8271060.000596844312.736230.000727982211.4100930.0006439616611.8272560.0005556511223394.858.63193False173956.8460.218647170686.1950.864567False97912.136.051967477.2337.09317False29208.40619.58413199074.6458.762215False67467.9334.528305False3False-1nanFalsenannannannannannannannanFalseFalsenannannannannannannannannannannannannannannannanFalseFalsenannannannannannannannan0
84725520669690.05488055441913312-5.9566913332979050.999980911.9876130.00065894410.9306510.0003762528411.2881980.0003851937711.8362170.000412143711.5098350.0004814972612.3163520.000579876411.2894180.000509449311.8660870.000516109558204.2635.32474False154077.6253.394318110846.1239.325603False66913.4125.4002290378.6340.080696False42999.07422.9652110721.6551.952827False65097.6130.944437False3False-1nanFalsenannannannannannannannanFalseFalsenannannannannannannannannannannannannannannannanFalseFalsenannannannannannannannan0
9472533572298355.4202510231727-5.5070531797872640.99998099.6350820.0001835704210.7981570.0003767234710.3105160.00025101611.9848920.000463844989.9998780.000274352513.64040850.001191935910.1112890.0003045248211.8513780.0005479353508120.7885.91032False174075.360.3998272767.963.062378False58350.29324.928238363118.7891.755745False12700.95613.943281327705.8891.9142False65985.4933.300705False3False-1nanFalsenannannannannannannannanFalseFalsenannannannannannannannannannannannannannannannanFalseFalsenannannannannannannannan0
\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "master_catalogue[:10].show_in_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## III - Merging flags and stellarity\n", "\n", "Each pristine catalogue contains a flag indicating if the source was associated to a another nearby source that was removed during the cleaning process. We merge these flags in a single one." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "flag_cleaned_columns = [column for column in master_catalogue.colnames\n", " if 'flag_cleaned' in column]\n", "\n", "flag_column = np.zeros(len(master_catalogue), dtype=bool)\n", "for column in flag_cleaned_columns:\n", " flag_column |= master_catalogue[column]\n", " \n", "master_catalogue.add_column(Column(data=flag_column, name=\"vista_flag_cleaned\"))\n", "master_catalogue.remove_columns(flag_cleaned_columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each pristine catalogue contains a flag indicating the probability of a source being a Gaia object (0: not a Gaia object, 1: possibly, 2: probably, 3: definitely). We merge these flags taking the highest value." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "flag_gaia_columns = [column for column in master_catalogue.colnames\n", " if 'flag_gaia' in column]\n", "\n", "master_catalogue.add_column(Column(\n", " data=np.max([master_catalogue[column] for column in flag_gaia_columns], axis=0),\n", " name=\"vista_flag_gaia\"\n", "))\n", "master_catalogue.remove_columns(flag_gaia_columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each prisitine catalogue may contain one or several stellarity columns indicating the probability (0 to 1) of each source being a star. We merge these columns taking the highest value." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "stellarity_columns = [column for column in master_catalogue.colnames\n", " if 'stellarity' in column]\n", "\n", "master_catalogue.add_column(Column(\n", " data=np.nanmax([master_catalogue[column] for column in stellarity_columns], axis=0),\n", " name=\"vista_stellarity\"\n", "))\n", "master_catalogue.remove_columns(stellarity_columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## VIII - Cross-identification table\n", "\n", "We are producing a table associating to each HELP identifier, the identifiers of the sources in the pristine catalogue. This can be used to easily get additional information from them." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue.add_column(Column(data=np.arange(len(master_catalogue)), \n", " name=\"vista_intid\"))\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['vhs_id', 'vics82_id', 'vista_intid']\n" ] } ], "source": [ "\n", "id_names = []\n", "for col in master_catalogue.colnames:\n", " if '_id' in col:\n", " id_names += [col]\n", " if '_intid' in col:\n", " id_names += [col]\n", " \n", "print(id_names)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## VI - Choosing between multiple values for the same filter\n", "\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "vista_origin = Table()\n", "vista_origin.add_column(master_catalogue['vista_intid'])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "vista_stats = Table()\n", "vista_stats.add_column(Column(data=['j','ks'], name=\"Band\"))\n", "for col in [\"VHS\", \"VICS82\"]:\n", " vista_stats.add_column(Column(data=np.full(2, 0), name=\"{}\".format(col)))\n", " vista_stats.add_column(Column(data=np.full(2, 0), name=\"use {}\".format(col)))\n", " vista_stats.add_column(Column(data=np.full(2, 0), name=\"{} ap\".format(col)))\n", " vista_stats.add_column(Column(data=np.full(2, 0), name=\"use {} ap\".format(col)))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "vista_bands = ['j','ks'] # Lowercase naming convention (k is Ks)\n", "for band in vista_bands:\n", "\n", "\n", " # VISTA total flux \n", " has_vhs = ~np.isnan(master_catalogue['f_vhs_' + band.strip('s')])\n", " has_vics82 = ~np.isnan(master_catalogue['f_vista_' + band])\n", "\n", " #take VICS82 if available\n", " use_vhs = has_vhs & ~has_vics82\n", " use_vics82 = has_vics82 \n", "\n", "\n", " master_catalogue['f_vista_' + band][use_vhs] = master_catalogue['f_vhs_' + band.strip('s')][use_vhs]\n", " master_catalogue['ferr_vista_' + band][use_vhs] = master_catalogue['ferr_vhs_' + band.strip('s')][use_vhs]\n", " master_catalogue['m_vista_' + band][use_vhs] = master_catalogue['m_vhs_' + band.strip('s')][use_vhs]\n", " master_catalogue['merr_vista_' + band][use_vhs] = master_catalogue['merr_vhs_' + band.strip('s')][use_vhs]\n", " master_catalogue['flag_vista_' + band][use_vhs] = master_catalogue['flag_vhs_' + band.strip('s')][use_vhs]\n", "\n", " master_catalogue.remove_columns(['f_vhs_' + band.strip('s'),\n", " 'ferr_vhs_' + band.strip('s'),\n", " 'm_vhs_' + band.strip('s'), \n", " 'merr_vhs_' + band.strip('s'),\n", " 'flag_vhs_' + band.strip('s')])\n", "\n", " origin = np.full(len(master_catalogue), ' ', dtype='Table length=2\n", "\n", "\n", "\n", "\n", "
idxBandVHSuse VHSVHS apuse VHS apVICS82use VICS82VICS82 apuse VICS82 ap
0j32459572753317324554027529391817615181761517952501795250
1ks24089101991555240628519889785214989521498949193404919340
\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vista_stats.show_in_notebook()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "vista_origin.write(\"{}/herschel-stripe-82_vista_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rename old vhs cols" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "renaming = OrderedDict({\n", " '_vhs_y': '_vista_y',\n", " '_vhs_h': '_vista_h',\n", "\n", "})\n", "\n", "\n", "for col in master_catalogue.colnames:\n", " for rename_col in list(renaming):\n", " if rename_col in col:\n", " master_catalogue.rename_column(col, col.replace(rename_col, renaming[rename_col]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## IX - Saving the catalogue" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue[\"ra\"].name = \"vista_ra\"\n", "master_catalogue[\"dec\"].name = \"vista_dec\"\n", "master_catalogue[\"flag_merged\"].name = \"vista_flag_merged\"" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "columns = [\"vhs_id\", \"vics82_id\", \"vista_intid\",\n", " 'vista_ra', 'vista_dec', 'vista_flag_merged', \n", " 'vista_flag_cleaned', 'vista_flag_gaia', 'vista_stellarity']\n", "\n", "bands = [column[5:] for column in master_catalogue.colnames if 'f_ap' in column]\n", "for band in bands:\n", " columns += [\"f_ap_{}\".format(band), \"ferr_ap_{}\".format(band),\n", " \"m_ap_{}\".format(band), \"merr_ap_{}\".format(band),\n", " \"f_{}\".format(band), \"ferr_{}\".format(band),\n", " \"m_{}\".format(band), \"merr_{}\".format(band),\n", " \"flag_{}\".format(band)] \n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Missing columns: set()\n" ] } ], "source": [ "# We check for columns in the master catalogue that we will not save to disk.\n", "print(\"Missing columns: {}\".format(set(master_catalogue.colnames) - set(columns)))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=10\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
idxvhs_idvista_ravista_decm_vista_ymerr_vista_ym_ap_vista_ymerr_ap_vista_ym_vista_hmerr_vista_hm_ap_vista_hmerr_ap_vista_hf_vista_yferr_vista_yflag_vista_yf_ap_vista_yferr_ap_vista_yf_vista_hferr_vista_hflag_vista_hf_ap_vista_hferr_ap_vista_hvista_flag_mergedvics82_idf_wircam_jf_vista_jferr_wircam_jferr_vista_jm_wircam_jm_vista_jmerr_wircam_jmerr_vista_jflag_wircam_jflag_vista_jf_ap_wircam_jf_ap_vista_jferr_ap_wircam_jferr_ap_vista_jm_ap_wircam_jm_ap_vista_jmerr_ap_wircam_jmerr_ap_vista_jf_wircam_ksf_vista_ksferr_wircam_ksferr_vista_ksm_wircam_ksm_vista_ksmerr_wircam_ksmerr_vista_ksflag_wircam_ksflag_vista_ksf_ap_wircam_ksf_ap_vista_ksferr_ap_wircam_ksferr_ap_vista_ksm_ap_wircam_ksm_ap_vista_ksmerr_ap_wircam_ksmerr_ap_vista_ksvista_flag_cleanedvista_flag_gaiavista_stellarityvista_intid
degdeg
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24725520846170.43818836458293725-7.02594966115380810.1779580.0002281300710.7625560.0003527182610.2922610.0002839702612.4206940.0006430615308188.7864.75526False179877.7358.436073277392.9472.551094False39059.0923.133976False-1nan247333.125nan51.822044372558594nan10.416793823242188nan0.00022748699120711535FalseFalsenan67725.02nan26.60433006286621nan11.823127nan0.00042650828254409134nan111131.65625nan45.76356506347656nan11.285405158996582nan0.0004471017455216497FalseFalsenan67331.28nan32.02477264404297nan11.829457nan0.0005164085887372494False30.99998092651367192
3472533513039350.41845602259264-5.66823053354209310.9136720.0003229807810.9420020.000364705711.5337270.0004925123611.7254940.0004095544156505.9446.55681False152475.1151.21735488411.5740.105312False74097.2527.950485False-1nan128101.6171875nan37.76606750488281nan11.131113052368164nan0.0003200895444024354FalseFalsenan109579.69nan33.972259521484375nan11.300674nan0.00033660358167253435nan80783.078125nan45.76707077026367nan11.631698608398438nan0.00061511603416875FalseFalsenan78049.11nan33.270545959472656nan11.66908nan0.0004628244787454605False30.99998092651367193
44725520834120.5080929454812719-6.965806816843830510.4840380.000259921310.8516760.0003660537811.1516380.0004384139311.7072150.00044700873232479.4255.65473False165702.6655.866318125702.69550.75801False75355.2831.024542False-1nan130655.9765625nan39.80402374267578nan11.109676361083984nan0.00033076689578592777FalseFalsenan84069.695nan29.439428329467773nan11.588401nan0.0003802018763963133nan101252.0625nan49.40996551513672nan11.386489868164062nan0.0005298280739225447FalseFalsenan81022.125nan34.81930160522461nan11.62849nan0.0004665957239922136False30.99998092651367194
54725520831360.3606172701062671-6.95299694222607110.5721280.000280615510.9243690.0003735097711.14907550.0004476474412.1905690.0005549071214362.3655.403324False154971.6653.312607125999.73451.949505False48280.56224.675632False-1nan150219.171875nan42.90188217163086nan10.958186149597168nan0.00031008111545816064FalseFalsenan69130.88nan26.136096954345703nan11.800819nan0.00041048089042305946nan89809.40625nan49.85323715209961nan11.516695022583008nan0.0006026926566846669FalseFalsenan74546.516nan33.27309036254883nan11.718931nan0.00048460744437761605False30.99998092651367195
64725347148230.14560287650863246-5.55176866419964610.3001370.000240276510.7997640.0003742416411.862780.000783345712.559650.00063538336275388.1260.944176False173817.8459.9131565296.2147.110435False34366.8520.111814False-1nan114344.3046875nan33.16022491455078nan11.254463195800781nan0.00031486706575378776FalseFalsenan61296.043nan25.69143295288086nan11.931418nan0.00045507209142670035nan72517.8671875nan50.3257942199707nan11.748887062072754nan0.0007534769247286022FalseFalsenan40726.926nan26.681289672851562nan12.375296nan0.0007112945313565433False30.99998092651367196
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\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "master_catalogue[:10].show_in_notebook()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "master_catalogue[columns].write(\"{}/vista_merged_catalogue_herschel-stripe-82.fits\".format(TMP_DIR), overwrite=True)" ] } ], "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": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 2 }