ivo://\getConfig{ivoa}{authority}/~?\rdId/ 2024-07-23T08:00:00Z 20 BGDS DR2 time series BGDS light curves DR1 10.21938/dChYyzuCGA00rsfzfQ8v:Q From the Bochum Galactic Disk Survey, time series have been obtained in the r and i bands on an (up to) nightly basis. Depending on the field, the time series contain up to more than 300 nights over 9 years. Each measurement in r and i represents the averaged flux over 10 minutes of observation (from 9 averaged 10s images). Additionally, intermittent measurements in Johnson UVB, Sloan z and the narrowbands OIII, NB, Halpha and SII have been recorded as well. The Bochum Galactic Disk Survey is a project to monitor the stellar content of the Galactic disk in a 6 degree wide stripe centered on the Galactic plane. The data has been recorded from September 2010 to September 2019 in Sloan r and i simultaneously with the Robotic Bochum Twin Telescope (RoBoTT) at the Universitaetssternwarte Bochum near Cerro Armazones in the Chilean Atacama desert. It contains measurements of about 2x10^7 stars over nine years. The source images are available from ivo://org.gavo.dc/bgds/q/sia. The time series from BGDS DR2 by default come as IVOA Spectral Data Model compliant VOTables. If you what plain text instead, add ``&format=txt`` to the dlget URIs. The columns you get back then are epoch in MJD, magnitude and magnitude error in mag, and a link to a cutout showing where the measurement came from. Accessing and Plotting Light Curves from pyVO --------------------------------------------- Let us briefly illustrate how to access and plot the GDS data with pyVO_. First, some basic definitions that we will need in the code below; there is no need to understand this code in detail:: import pyvo as vo import matplotlib.pyplot as plt import numpy as np service = vo.dal.TAPService("http://dc.g-vo.org/tap") def get_lc(ra, dec, rad, filter): """Returns a light curve given by mjds and mags around the coordinates ra and dec. """ # Search for the object coordinates in the metadata table: id_set = service.search("SELECT obs_id" " FROM bgds2.ssa_time_series" f" WHERE DISTANCE({ra}, {dec}, ra, dec)<{rad}/3600." " AND ssa_bandpass like '% " + filter + "%'") if len(id_set) >= 1: # Use the ID from the first result in the time series metadata table # (there is only one result for the examples here, but sources in # overlapping observation fields can have up to four light curves # per filter) to receive the corresponding light curve from the # light curve table: lc_set = service.search( "SELECT mjds, mags FROM bgds2.lc_all" " WHERE obs_id = '" + id_set.getcolumn("obs_id")[0] + "'") mjds = lc_set.getcolumn("mjds")[0] mags = lc_set.getcolumn("mags")[0] return mjds, mags else: print(f"There is no GDS light curve for the filter {filter}" f" in a radius of {rad}'' around the coordinates {ra} {dec}.") def plot_ts(r_mjds, r_mags, i_mjds, i_mags): """Plot r' and i' light curves given by r_mjds, i_mjds and r_mags, i_mags. """ plt.plot(r_mjds, r_mags, 'ro', markersize=3.0) plt.plot(i_mjds, i_mags, 'ko', markersize=3.0) plt.gca().invert_yaxis() plt.xlabel('MJD (d)') plt.ylabel('Magnitude') plt.show() return def fold_lc(mjds, mags, p): """Fold a light curve given by mjds and mags with a period p. """ # Fold the dates mjds by the period p: mjds_fold = mjds % p / p # Sort mjds and mags by the phase: idx_fold = np.argsort(mjds_fold) mjds_fold = mjds_fold[idx_fold] mags_fold = mags[idx_fold] return mjds_fold, mags_fold def find_p(mjds, mags, min_p, max_p, p_step): """Find the period p in a given range from min_p to max_p in steps of p_step with minimal theta. """ theta_list = [] p_candidates = np.arange(min_p, max_p, p_step) for j in range(len(p_candidates)): mjds_fold, mags_fold = fold_lc(mjds, mags, p_candidates[j]) # Shift the magnitudes of the folded light curve mags_fold by one step to # compute differences: mags_shift = [mags_fold[1:len(mags_fold)]] mags_shift = np.append(mags_shift,mags_fold[0]) theta = np.sum((mags_fold - mags_shift)**2) theta_list.append(theta) theta_list = np.asarray(theta_list) min_ind = np.argmin(theta_list) p = p_candidates[min_ind] return p, p_candidates, theta_list def plot_theta(p_candidates, theta_list): """Plot theta (given by a list/array theta_list) in dependence of the period given by a an array p_candidates. """ plt.plot(p_candidates, theta_list, 'ko', markersize=2.5) plt.xlim([np.min(p_candidates), np.max(p_candidates)]) plt.xlabel('$P$ (d)') plt.ylabel('$\\\\theta$') plt.show() return def plot_phase(r_mjds, r_mags, i_mjds, i_mags, p): """Fold r' and i' light curves given by r_mjds, i_mjds and r_mags, i_mags with a period p and plot two phases. """ r_mjds_fold, r_mags_fold = fold_lc(r_mjds, r_mags, p) i_mjds_fold, i_mags_fold = fold_lc(i_mjds, i_mags, p) plt.plot([r_mjds_fold, [x+1 for x in r_mjds_fold]], [r_mags_fold, r_mags_fold], 'ro', markersize=3.0) plt.plot([i_mjds_fold, [x+1 for x in i_mjds_fold]], [i_mags_fold, i_mags_fold], 'ko', markersize=3.0) plt.axvline(1, color = 'gray', linestyle='-', markersize=0.3) plt.xlim([0.,2.]) plt.gca().invert_yaxis() plt.xlabel('Phase') plt.ylabel('Magnitude') plt.title('$P = %.4f\\,\\mathrm{d}$' % p) plt.show() return def get_multi_wl_phot(ra, dec, rad): """Returns an entry from the multi-wavelength photometry catalogue (matched Gaia DR3 ID gdr3_id, arrays for magnitudes multi_wl_mags and spectral flux density in mJy multi_wl_fluxes in UBVr'i'z', and an estimated spectral type spt from UBV photometry) around the coordinates ra and dec. """ wl_cols = ["u", "b", "v", "r", "i", "z"] multi_wl_set = service.search( "SELECT gdr3_id, u_med_mag, b_med_mag, v_med_mag," " r_med_mag, i_med_mag, z_med_mag, u_flux, b_flux, v_flux," " r_flux, i_flux, z_flux, spt FROM bgds2.matched" f" WHERE DISTANCE({ra}, {dec}, ra, dec)<{rad}/3600.") if len(multi_wl_set) >= 1.: multi_wl_mags = np.zeros(6) multi_wl_fluxes = np.zeros(6) for n in range(len(wl_cols)): multi_wl_mags[n] = multi_wl_set.getcolumn(wl_cols[n] + "_med_mag")[0] multi_wl_fluxes[n] = multi_wl_set.getcolumn(wl_cols[n] + "_flux")[0] gdr3_id = str(multi_wl_set.getcolumn("gdr3_id")[0]) spt = multi_wl_set.getcolumn("spt")[0] return gdr3_id, multi_wl_mags, multi_wl_fluxes, spt else: print(f"There is no GDS photometry in a radius of {rad}" f" around the coordinates {ra} {dec}.") def plot_sed(multi_wl_fluxes): """Plot the spectral flux density in UBVr'i'z' in dependence of the filter wavelength. """ wls = np.array([365, 433, 550, 623, 764, 906]) filters = ["U", "B", "V", "r'", "i'", "z'"] plt.plot(wls, multi_wl_fluxes, 'ko', markersize=5.0) for n in range(len(multi_wl_fluxes)): plt.text(wls[n]+7, multi_wl_fluxes[n]-8, filters[n]) plt.xlabel('Wavelength (nm)') plt.ylabel('Flux (mJy)') plt.show() return def calc_abs_mag(mag, plx): """Calculate the absolute magnitude abs_mag from the relative magnitude mag and the parallax plx in milli-second of arc. """ # Approximation of the distance dist in parsec: dist = 1000./plx abs_mag = mag - 5.*(np.log10(dist) - 1.) return abs_mag .. _pyVO: https://github.com/astropy/pyvo Light curves ------------ The GDS contains light curves in up to 10 filters per observation field, among which the r’ and i’ light curves contain the largest numbers of data points. We can take a look at the r’ and i’ light curves of a star, e.g., the highly variable *V352 Nor*:: r_mjds, r_mags = get_lc(241.71551, -52.07636, 1., "r") i_mjds, i_mags = get_lc(241.71551, -52.07636, 1., "i") plot_ts(r_mjds, r_mags, i_mjds, i_mags) The result is: .. figure:: /\rdId/tsdl/static/V352_Nor_lc.png :alt: A scatterplot of two fairly parallel sets of red and black dots over more than 2000 days, showing steep maxima up to 7 mag, reaching down to 16 mag. Prominent gaps in the coverage are visible. Period determination -------------------- The periods of many periodic variables in the GDS are still unknown or uncertain. Short periods are not obvious when looking at the (unfolded) light curve, as is the case for the eclipsing binary EH Mon:: r_mjds, r_mags = get_lc(103.03654, -7.06476, 1., "r") i_mjds, i_mags = get_lc(103.03654, -7.06476, 1., "i") plot_ts(r_mjds, r_mags, i_mjds, i_mags) .. figure:: /\rdId/tsdl/static/EH_Mon_lc.png :alt: A scatterplot, again in two bands; most dots are about 13, but there are five steep drops down to 15. Among the numerous options for an automated period classification, here we will use the `Lafler-Kinman algorithm`_. .. _Lafler-Kinman algorithm: https://ui.adsabs.harvard.edu/abs/1965ApJS...11..216L/abstract The light curve is folded by trial periods and the sum of squares θ of the differences between two magnitude measurements of adjacent phase is calculated. Basically, θ measures how smooth a folded light is when folded by a trial period. Usually, θ is minimized when a light curve is folded by a multiple of the correct period, therefore, test values with local minima in θ are the best candidates for the period, in order from low to high. :: r_p, r_p_candidates, r_theta_list = find_p(r_mjds, r_mags, 1., 15., 1e-4) i_p, i_p_candidates, i_theta_list = find_p(i_mjds, i_mags, 1., 15., 1e-4) # Exemplarily plot theta for the filter i': plot_theta(i_p_candidates, i_theta_list) .. figure:: /\rdId/tsdl/static/EH_Mon_theta.png :alt: A plot of θ versus the period, ranging from 1 to 15 days. there is a broad, dense black field across the plot, with sharp dips at about 3.6 days and its harmonics. :: # The final period p is the average between period results from the # r' and i' light curves: p = (r_p + i_p) / 2. plot_phase(r_mjds, r_mags, i_mjds, i_mags, p) .. figure:: /\rdId/tsdl/static/EH_Mon_phase.png :alt: a scatter plot over the phase in two bands, showing fairly parallel primary and secondary dips. Multi-wavelength catalogue -------------------------- Median magnitudes and light curves are not only available separately for filters and fields, but there is also a multi-wavelength photometry catalogue that is matched with Gaia DR3 sources. The filter magnitudes can be noticeably effected by reddening; in this case, the roughly estimated spectral types from the GDS UBV photometry might be more useful. For *Gaia DR3 5334136458661121280*, median magnitudes in UBVr’i’z’ are available:: gdr3_id, multi_wl_mags, multi_wl_fluxes, spt = get_multi_wl_phot( 175.11591, -62.02608, 1.) print("Gaia DR3 ID: " + gdr3_id + ", Spectral type from UBV photometry: " + spt) print(f"U = {np.round(multi_wl_mags[0], 3)}, " f"B = {np.round(multi_wl_mags[1], 3)}, " f"V = {np.round(multi_wl_mags[2], 3)}, " f"r' = {np.round(multi_wl_mags[3], 3)}, " f"i' = {np.round(multi_wl_mags[4], 3)}, " f"z' = {np.round(multi_wl_mags[5], 3)}") plot_sed(multi_wl_fluxes) The result is:: Gaia DR3 ID: 5334136458661121280, Spectral type from UBV photometry: K5 U = 14.15, B = 12.996, V = 11.781, r' = 11.366, i' = 10.853, z' = 10.551 .. figure:: /\rdId/tsdl/static/Gaia_DR3_5334136458661121280_sed.png :alt: A sparse scatterplot of calibrated flux over wavelength, showing an almost linear growth of dots labelled with the filter designations between 380 and 900 nm. The match with Gaia DR3 allows to directly get information from the Gaia DR 3 catalogue like the Gaia DR parallaxes to compute absolute magnitudes:: gdr3_set = service.search( "SELECT parallax FROM gaia.dr3lite WHERE source_id=" + gdr3_id) plx = gdr3_set.getcolumn("parallax")[0] multi_wl_abs_mags = np.zeros(6) for n in range(len(multi_wl_abs_mags)): multi_wl_abs_mags[n] = calc_abs_mag(multi_wl_mags[n], plx) print(f"M_U = {np.round(multi_wl_abs_mags[0], 3)}, " f"M_B = {np.round(multi_wl_abs_mags[1], 3)}, " f"M_V = {np.round(multi_wl_abs_mags[2], 3)}, " f"M_r' = {np.round(multi_wl_abs_mags[3], 3)}, " f"M_i' = {np.round(multi_wl_abs_mags[4], 3)}, " f"M_z' = {np.round(multi_wl_abs_mags[5], 3)}") This gives:: M_U = 8.957, M_B = 7.803, M_V = 6.588, M_r' = 6.173, M_i' = 5.66, M_z' = 5.358 ivo://org.gavo.dc/bgds/l/ssa"BGDS DR1 Lightcurves surveys galaxy-planes milky-way-galaxy variable-stars broad-band-photometry time-domain-astronomy Blex, J.; Hackstein, M.; Westhues, C.; Ramolla, M.; Demleitner, M.; Fein, C.; Chini, R. Robotic Bochum Twin Telescope (RoBoTT) Universitätssternwarte Bochum near Cerro Armazones 2026AN....34770087B Research Survey timeseries If you use GDS data, please cite :bibcode:`2026AN....34770087B`. Optical 1.777e-19 6.621e-19 55439 58751.4 3/336,338,450-451,461,472,591-592,613,615,621,651-652,659,662-663 4/1301,1303,1358,1376-1377,1379-1380,1382,1794,1816,1818-1819,1822-1823,1829,1840-1841,1892,1894,1897,2327,2333,2335,2357-2359,2376,2378,2400,2402,2408,2492-2493,2495,2528,2530,2536,2538,2591,2599,2601-2603,2612-2614,2616,2634-2635,2643,2646-2647,2656-2657,2688-2690 b, Johnson B, B_j, em.opt.b u, Johnson U, U_j, em.opt.U v, Johnson V, V_j, em.opt.V z, SDSS z', z_s, em.opt.I i, SDSS i', i_s, em.opt.I r, SDSS r', r_s, em.opt.r ha, Astrodon Halpha, Ha, em.line.halpha nb, Astrodon NB, NB, em.line oiii, Astrodon OIII, OIII, em.line.oiii sii, Astrodon SII, SII, em.line \photfilters obs_id, ra, dec, field, amp, flux, err_flux, nobs, gdr3_id obs_id band, filter, pathspec, ucd \photfilters //scs#pgs-pos-index100 BGDS median photometry in the \filter band. This is available for all objects found in the fields observed. Median photometry in the i' and r' bands is given in the lc_(band) tables together with the lightcurves. Position ICRS "ra" "dec"
data/dr2phot/*_\pathspec\+_photcat*.fits.gz ra:RA, dec:DEC, err_mag_\band: MAGERR, amp: AMP, flux: FLUX, err_flux: FLUXERR, nobs: NOBS, gdr3_id: Gaia_DR3_ID band, filter, pathspec, ucd \lcfilters //scs#pgs-pos-index//scs#q3cindex90 BGDS DR2 lightcurves in the \filter band. Lightcurves are available in i' and r' for all fields and partially in U, B, V, z, OIII, NB, Halpha and SII if they have enough observations. ('\\pubDIDBase' || obs_id)
band, filter, pathspec, ucd \lcfilters nobs: NOBS, ra: RA, dec: DEC, err_mag: MAGERR, flux: FLUX, err_flux: FLUXERR, amp: AMP, field: Field, re.search("GDS_([^_]*)", \\inputRelativePath).group(1) @LCMJD = @LCMJD[@LCMAG!=99.0] @LCMAGERR = @LCMAGERR[@LCMAG!=99.0] @LCMAG = @LCMAG[@LCMAG!=99.0] base.NotGiven "Filter" { "i": "i_s", "r": "r_s", "b": "B_j", "u": "U_j", "v": "V_j", "z": "z_s", "nb": "NB", "ha": "Ha", "oiii": "OIII", "sii": "SII", } BANDMAP = { "i_s": ("SDSS i'", 6.65e-7, 8.39e-7, 7.44e-7, 1.5e-3, "i"), "r_s": ("SDSS r'", 5.42e-7, 6.99e-7, 6.12e-7, 1.3e-3, "r"), "B_j": ('Johnson B', 3.70e-7, 5.50e-7, 4.41e-7, 1.4e-3, "b"), "U_j": ('Johnson U', 3.00e-7, 3.90e-7, 3.46e-7, 1.7e-3, "u"), "V_j": ('Johnson V', 4.80e-7, 7.30e-7, 5.50e-7, 1.5e-3, "v"), "z_s": ("SDSS z'", 7.78e-7, 11.2e-7, 8.96e-7, 1.6e-3, "z"), "NB": ('Astrodon NB', 6.42e-7, 6.48e-7, 6.45e-7, 1.3e-3, "nb"), "Ha": ('Astrodon Halpha', 6.53e-7, 6.56e-7, 6.60e-7, 1.6e-3, "ha"), "OIII": ('Astrodon OIII', 4.97e-7, 5.04e-7, 5.00e-7, 1.2e-3, "oiii"), "SII": ('Astrodon SII', 6.69e-7, 6.75e-7, 6.71e-7, 1.4e-3, "sii"),} @ssa_bandpass, @ssa_specstart, @ssa_specend, \ @ssa_specmid, @ssa_aperture, @shortband = BANDMAP[@Filter] "BGDS-%s-%s-%.7f%+.7f"%(@fieldid, @shortband, @RA, @DEC) @LCMJD[0] @LCMJD[-1] sum(@LCMJD)/len(@LCMJD) int(vars.get("VARIABLE") or 0) Extracted sources from the Bochum Galactic Disk Survey. We provide median photometry in U, B, V, z, r, and i broadbands as well as the narrow bands OIII, NB, Halpha and SII for particular fields. Note that sources in different bands are not matched. Also, sources sitting in the regions imaged in multiple fields have not been matched even within one band. BGDS light curves are available in i and r for all fields and partially in U, B, V, z, OIII, NB, Halpha and SII in a separate SSAP service and via TAP. For median photometry matched to Gaia DR3 objects, see the bgds2.matched table and the matchedphot service. \photall_description 15True CREATE VIEW \curtable AS (SELECT \colNames FROM ( SELECT 'Johnson B' as band_name, med_mag_b as med_mag, err_mag_b as err_mag, \phot_all_cols FROM \schema.phot_b UNION ALL SELECT 'Johnson B' as band_name, med_mag, err_mag, \phot_all_cols FROM \schema.lc_b UNION ALL SELECT 'Johnson U' as band_name, med_mag_u as med_mag, err_mag_u as err_mag, \phot_all_cols FROM \schema.phot_u UNION ALL SELECT 'Johnson U' as band_name, med_mag, err_mag, \phot_all_cols FROM \schema.lc_u UNION ALL SELECT 'Johnson V' as band_name, med_mag_v as med_mag, err_mag_v as err_mag, \phot_all_cols FROM \schema.phot_v UNION ALL SELECT 'Johnson V' as band_name, med_mag, err_mag, \phot_all_cols FROM \schema.lc_v UNION ALL SELECT 'SDSS z''' as band_name, med_mag_z as med_mag, err_mag_z as err_mag, \phot_all_cols FROM \schema.phot_z UNION ALL SELECT 'SDSS z''' as band_name, med_mag, err_mag, \phot_all_cols FROM \schema.lc_z UNION ALL SELECT 'SDSS i''' as band_name, med_mag, err_mag, \phot_all_cols FROM \schema.lc_i UNION ALL SELECT 'SDSS r''' as band_name, med_mag, err_mag, \phot_all_cols FROM \schema.lc_r UNION ALL SELECT 'Astrodon Halpha' as band_name, med_mag, err_mag, \phot_all_cols FROM \schema.lc_ha UNION ALL SELECT 'Astrodon NB' as band_name, med_mag, err_mag, \phot_all_cols FROM \schema.lc_nb UNION ALL SELECT 'Astrodon OIII' as band_name, med_mag, err_mag, \phot_all_cols FROM \schema.lc_oiii UNION ALL SELECT 'Astrodon SII' as band_name, med_mag, err_mag, \phot_all_cols FROM \schema.lc_sii ) as allbands)
BGDS2 median pho BGDS DR2 Median Photometry Cone Search \photall_description 10.21938/vjJw6sq0gmS5psH9uM5_NA \internallink{\rdId/matchedphot/info} testQuery.ra: 145.2949318 testQuery.dec: -56.932663 testQuery.sr: 0.0001 band, filter, pathspec, ucd \lcfilters A union of all BGDS2 light curves. Note that the magnitudes here are in different bands.10 CREATE VIEW \curtable AS ( SELECT 'SDSS i''' as \colNames FROM \schema.lc_i UNION ALL SELECT 'SDSS r''' as \colNames FROM \schema.lc_r UNION ALL SELECT 'SDSS z''' as \colNames FROM \schema.lc_z UNION ALL SELECT 'Astrodon Halpha' as \colNames FROM \schema.lc_ha UNION ALL SELECT 'Astrodon NB' as \colNames FROM \schema.lc_nb UNION ALL SELECT 'Astrodon OIII' as \colNames FROM \schema.lc_oiii UNION ALL SELECT 'Astrodon SII' as \colNames FROM \schema.lc_sii UNION ALL SELECT 'Johnson B' as \colNames FROM \schema.lc_b UNION ALL SELECT 'Johnson U' as \colNames FROM \schema.lc_u UNION ALL SELECT 'Johnson V' as \colNames FROM \schema.lc_v )
Median photometry in U, B, V, z, r, and i broadbands as well as the narrow bands OIII, NB, Halpha and SII for particular objects; all measurements have been matched to the nearest Gaia DR3 object. For the raw measurements, see the bgds2.phot_all table and the medphot service. 10BGDS2 Median Photometry Matched to Gaia DR3 \matched_description band, filter, pathspec, ucd \lcfilters
@spt.strip() or None BGDS2 median pho BGDS DR2 Matched Photometry Cone Search \matched_description \internallink{\rdId/medphot/info} 10.21938/whiHSDPrL:m8cI6AWZ:CFg testQuery.ra: 145.2949318 testQuery.dec: -56.932663 testQuery.sr: 0.0001 This table contains metadata about the photometric time series from BGDS in IVOA SSA format. The actual data is available through a datalink service or in the lc_all table. Truetsdlssa_pubDID//ssap#mixc//obscore#publishSSAPMIXC TimeInterval UTC TOPOCENTER "time_min" "time_max" CREATE VIEW \curtable AS (SELECT \colNames FROM (SELECT obs_id, amp, med_mag, field, '\getConfig{web}{serverURL}/bgds/l2/tsdl/dlget?ID='||obs_id as accref, NULL::text as owner, NULL::date as embargo, 'application/x-votable+xml'::TEXT as mime, 10000 as accsize, 'BGDS DR2 ' || ssa_bandpass || ' time series for ' || obs_id as ssa_dstitle, NULL::text as ssa_creatorDID, '\pubDIDBase' || obs_id as ssa_pubDID, NULL::timestamp as ssa_cdate, NULL::timestamp as ssa_pdate, band_name as ssa_bandpass, NULL::text as ssa_cversion, field as ssa_targname, 'survey field' as ssa_targclass, NULL::real as ssa_redshift, NULL::spoint as ssa_targetpos, NULL::real as ssa_snr, ssa_aperture, spoint(radians(ra), radians(dec)) as ssa_location, time_avg as ssa_dateObs, time_max-time_min as ssa_timeExt, ssa_specmid, ssa_specend-ssa_specstart as ssa_specext, ssa_specstart, ssa_specend, nobs as ssa_length, 'timeseries'::TEXT as ssa_dstype, 'ivo://org.gavo.dc/org'::TEXT as ssa_publisher, 'Blex, J. et al'::TEXT as ssa_creator, 'BGDS DR2 time series'::TEXT as ssa_collection, '\metaString{instrument}'::TEXT as ssa_instrument, 'survey'::TEXT as ssa_datasource, 'archival'::TEXT as ssa_creationtype, '\metaString{source}'::TEXT as ssa_reference, NULL::real as ssa_fluxStatError, NULL::real as ssa_fluxSysError, 'ABSOLUTE'::TEXT as ssa_fluxcalib, NULL::real as ssa_binSize, NULL::real as ssa_spectStatError, NULL::real as ssa_spectSysError, 'ABSOLUTE'::TEXT as ssa_speccalib, NULL::real as ssa_specres, time_min as time_min, time_max as time_max, ra, dec, gdr3_id, var, mjds as mjds, mags as mags, mag_errs as mag_errs FROM \schema.lc_all ) as src)
Bochum Galactic Disk Survey (BGDS) DR2 light curves browser service ssa_bandpass, med_mag, amp BGDS TS SSAP survey archival MAXREC=1 query Bochum Galactic Disk Survey (BGDS) DR2 light curves This service exposes the light curves of stars produced by the Bochum Galactic Disk Survey DR2; several million light curves are provided in the SDSS i and r bands. The lightcurves are published per-band and are also available through obscore. parsed = pql.PQLFloatPar.fromLiteral( inPars.get("TIME", None), "TIME") yield parsed.getSQLForInterval( "time_min", "time_max", outPars) //timeseries#phot-0 (ivoa:Measurement) { value: @phot statError: @mag_error } (ds:Dataset) { dataProductType: TIMESERIES title: @title curation: (ds:Curation) { calibLevel: 1 publisher: (ds:Publisher) { name: "GAVO Data Center" publisherId: "ivo://org.gavo.dc" } } }
band_short, band_human, band_ucd, effective_wavelength i, SDSS/i, em.opt.I, 7.44e-7 r, SDSS/r, em.opt.R, 6.12e-7 b, Johnson/B, em.opt.B, 4.45e-7 u, Johnson/U, em.opt.U, 3.55e-7 v, Johnson/V, em.opt.V, 5.50e-7 z, SDSS/z, em.opt.I, 9.10e-7 ha, Astrodon/Halpha, spect.line, 6.563e-7 nb, Astrodon/NB, spect.line, 6.45e-7 sii, Astrodon/SII, spect.line, 6.716e-7 oiii, Astrodon/OIII, spect.line, 5.007e-7 epochs, mags, errors = ( self.sourceToken.row["mjds"], self.sourceToken.row["mags"], self.sourceToken.row["mag_errs"]) labels = ["obs_time", "phot", "mag_error"] for tup in zip(epochs, mags, errors): res = dict(zip(labels, tup)) res["obs_id"] = self.sourceToken.row["obs_id"] yield res with base.getTableConn() as conn: res = list(conn.queryToDicts( "SELECT * FROM \schema.ssa_time_series WHERE obs_id=%(obs_id)s" " LIMIT 2", {"obs_id": self.sourceToken.obsId})) return res[0] ("BGDS DR2 %(ssa_bandpass)s time series for" " the observation %(obs_id)s")%vars @ssa_location.asDALI()[0] @ssa_location.asDALI()[1] "-".join(@obs_id.split("-")[1:3]) @ssa_bandpass pars = targetTable.getParamDict() imgId = "MAGIC/%s/%s/%f"%( pars["field"], pars["filter"], @obs_time) @stamp_url = ("http://dc.g-vo.org/bgds/q/dl/dlget?ID=" +urllib.parse.quote(imgId) +"&CIRCLE=%s+%s+%s"%(pars["ra"], pars["dec"], 0.01)) BGDS DR2 time series datalink This service produces time series datasets for Bochum Galactic Disk Survey lightcurves. static from gavo import svcs class TSDescriptor(ProductDescriptor): def __init__(self, pubDID): # We accept "local" pubDIDs (without ivo://...?), too if pubDID.startswith("ivo://"): self.pubDID = pubDID self.obsId = pubDID.split("?")[-1].split("/")[-1] else: self.obsId = pubDID self.pubDID = '\pubDIDBase'+pubDID self.band = self.obsId.split("-")[3] self.suppressAutoLinks = True with base.getTableConn() as conn: res = list(conn.queryToDicts( "SELECT * FROM \schema.lc_all" " WHERE obs_id=%(obsId)s", {"obsId": self.obsId})) if not res: raise svcs.UnknownURI( "BGDS has no lightcurve for %s"%self.obsId) self.row = res[0] return TSDescriptor(pubDID) yield descriptor.makeLink( makeAbsoluteURL("\rdId/tsdl/dlget?ID=%s"%(descriptor.pubDID)), description="BGDS time series for %s."%( descriptor.obsId), contentType="application/x-votable+xml", contentLength=15000, contentQualifier="#timeseries") yield descriptor.makeLink( makeAbsoluteURL("\rdId/preview/qp/%s"%(descriptor.obsId)), description="Preview for %s"%descriptor.obsId, contentType="image/png", contentLength="2000") yield MS(InputKey, name="format", type="text", description="Requested output format", multiplicity="single", values=MS(Values, options=[ MS(Option, content_="vodml"), MS(Option, content_="txt")])) from gavo import rsc dd = rd.getById("make_instance") descriptor.data = rsc.Data.createWithTable(dd, rd.getById("instance_"+descriptor.band)) descriptor.data = rsc.makeData( dd, data=descriptor.data, forceSource=descriptor) from gavo import formats outputFormat = args.get("format") or "vodml" return (base.votableType, formats.getFormatted(outputFormat, descriptor.data)) obs_id BGDS DR2 previews A service returning PNG thumbnails for time series. It takes the obs id for which to generate a preview from the remaining fragments. from gavo.svcs import UnknownURI from gavo.helpers.processing import SpectralPreviewMaker obsId = inputTable.getParam("obs_id") # always go through a mapping here or you'll create a SQL injection. try: srcTable = { "r": "bgds2.lc_r", "i": "bgds2.lc_i", }[obsId.split("-")[3]] except KeyError: raise UnknownURI("No time series in the %s band here"% obsId.split("-")[3]) with base.getUntrustedConn() as conn: res = list(conn.query( "SELECT mjds, mags FROM %s WHERE obs_id=%%(obs_id)s"% srcTable, {"obs_id": obsId})) if not res: raise UnknownURI("No time series for obs id %s."%obsId) return "image/png", SpectralPreviewMaker.get2DPlot( list(zip(*res[0]))) medphot/scs.xml rows = self.getVOTableRows() rows.sort(key=lambda r: r["band_name"]) self.assertTrue(set(r["band_name"] for r in rows).issuperset( set(["Johnson V", "SDSS i'", "SDSS z'"]))) self.assertAlmostEqual(rows[0]["med_mag"], 14.346710205078125) self.assertEqual(rows[-1]["obs_id"], "BGDS-0941-5551-z-145.2949100-56.9326400") ssa/ssap.xml row = self.getFirstVOTableRow() self.assertEqual(row["ssa_pubDID"], "ivo://org.gavo.dc/~?bgds/l2/BGDS-0941-5551-i-145.2028193-56.9319994") self.assertAlmostEqual(row["time_max"], 58253.98748) self.assertTrue(row["accref"].endswith( "bgds/l2/tsdl/dlget?ID=BGDS-0941-5551-i-145.2028193-56.9319994")) tsform/form SDSS i'", "16.196") ]]> tsdl/dlget row = self.getFirstVOTableRow(rejectExtras=False) self.assertAlmostEqual(row["obs_time"], 55934.25169) self.assertAlmostEqual(row["phot"], 16.158958) self.assertTrue(row["stamp_url"].endswith( "ID=MAGIC/0941-5551/SDSS%20i%27/55934.251690&CIRCLE=145.2028193+-56.9319994+0.01")) self.assertXpath("//v:GROUP[@name='photcal']" "/v:PARAM[@name='filterIdentifier']", dict( ucd="meta.id;instr.filter", value="SDSS/i")) tsdl/dlmeta bySemantics = self.datalinkBySemantics() self.assertTrue(bySemantics["#this"][0]["access_url"].endswith( "l2/tsdl/dlget?ID=ivo://org.gavo.dc/~?bgds/l2/BGDS-0941-5551-i-145.2028193-56.9319994")) self.assertEqual(bySemantics["#this"][0]["content_qualifier"], "#timeseries") self.assertTrue(bySemantics["#preview"][0]["access_url"].endswith( "/bgds/l2/preview/qp/BGDS-0941-5551-i-145.2028193-56.9319994")) preview/qp/BGDS-0941-5551-b-145.2028193-56.9319994 self.assertHasStrings("No time series in the b band here") self.assertHTTPStatus(404) preview/qp/BGDS-0941-5551-i-145.2028193-56.9319994 self.assertHasStrings( "PNG", "27UHs") # well, the latter is just binary nonsense and is subject to change /tap/sync row = self.getFirstVOTableRow() self.assertAlmostEqual(row["v_med_mag"], 17.03499984741211) self.assertAlmostEqual(row['err_v_med_mag'], 0.1889999955892563) self.assertEqual(row['var'], 0)