def replace_photo_uncert(catalog, columns): """ """ data = C.loaddata(catalog) # Loading the whole catalog content. head = C.loadheader(catalog) # Loading the original header. mm = A.get_magnitudes(catalog, columns) em = A.get_errmagnitudes(catalog, columns) filters = B.get_filter_list(columns) nl = len(mm[:, 0]) # nl is the number of detections inside every single band. nf = len(mm[0, :]) # nf is the number of bands inside the catalog. errmag = U.zeros((nl, nf), float) # Where the new photo errors will be saved. for jj in range(nf): maglim = B.get_limitingmagnitude(mm[:, jj], em[:, jj], 1., 0.25) print 'Limiting Magnitude for filter %s: %.3f' % (filters[jj], maglim) for ii in range(nl): if mm[ii, jj] == -99.: errmag[ii, jj] = 0.00 elif mm[ii, jj] == 99.: errmag[ii, jj] = maglim else: errmag[ii, jj] = em[ii, jj] # New values of mags error overwrites now the original data. vars, evars, posref, zpe, zpo = get_usefulcolumns(columns) data[:, evars] = errmag[:, U.arange(nf)] finalcatalog = catalog[:-3] + 'upp.cat' C.savedata(data, finalcatalog, dir="", header=head) # Saving & creating a new catalog.
def minimum_photouncert(catalog, columns): """ """ data = C.loaddata(catalog) # Loading the whole catalog content. head = C.loadheader(catalog) # Loading the original header. mm = A.get_magnitudes(catalog, columns) em = A.get_errmagnitudes(catalog, columns) nl = len(mm[:, 0]) # nl is the number of detections inside every single band. nf = len(mm[0, :]) # nf is the number of bands inside the catalog. errmag = N.zeros((nl, nf), float) # Where the new photo errors will be saved. for jj in range(nf): for ii in range(nl): if em[ii, jj] < 0.01: errmag[ii, jj] = 0.03 elif em[ii, jj] > 1.0: errmag[ii, jj] = 1.0 else: errmag[ii, jj] = em[ii, jj] # New values of mags error overwrites now the original data. vars, evars, posref, zpe, zpo = A.get_usefulcolumns(columns) data[:, evars] = errmag[:, N.arange(nf)] finalcatalog = catalog[:-3] + 'ecor.cat' C.savedata(data, finalcatalog, dir="", header=head) # Saving & creating a new catalog.
def replace_kerttu_errmags(catalog, columns, finalcatalog): """ import alhambra_kerttu_fixerrmags as AFM catalog = '/Users/albertomolino/doctorado/articulos/ALHAMBRA/kerttu/test_photoz/kerttu.cat' columns = '/Users/albertomolino/doctorado/articulos/ALHAMBRA/kerttu/test_photoz/kerttu.columns' finalcatalog = '/Users/albertomolino/doctorado/articulos/ALHAMBRA/kerttu/test_photoz/kerttu3.cat' AFM.replace_kerttu_errmag(catalog,columns,finalcatalog) ------ """ data = C.loaddata(catalog) # Loading the whole catalog content. head = C.loadheader(catalog) # Loading the original header. mm = A.get_magnitudes(catalog, columns) em = A.get_errmagnitudes(catalog, columns) filters = B.get_filter_list(columns) nl = len(mm[:, 0]) # nl is the number of detections inside every single band. nf = len(mm[0, :]) # nf is the number of bands inside the catalog. errmag = U.zeros((nl, nf), float) # Where the new photo errors will be saved. for jj in range(nf): for ii in range(nl): if mm[ii, jj] == -99.: errmag[ii, jj] = 0.00 else: errmag[ii, jj] = em[ii, jj] # New values of mags error overwrites now the original data. vars, evars, posref, zpe, zpo = A.get_usefulcolumns(columns) data[:, evars] = errmag[:, U.arange(nf)] C.savedata(data, finalcatalog, dir="", header=head) # Saving & creating a new catalog.
def check_variable_candidate(alhambraid): """ It replaces failed magnitudes in the ALHAMBRA catalogues (artificial absorptions, non-observed sources assigned as non-detected with upper limits) by m=-99,em=99 It might decrease the amount of low Odds at bright magnitudes. ---- import alhambra_photools as A A.replacing_fakeabsorptions(2,1,2) A.check_sample(image,catalog,posID,posX,posY,posMAG) A.alhambra_id_finder(ra1,dec1) idd = int(id[pos]) A.alhambra_colorstamp_byID(id) f,p,c,ids = A.alhambra_id_finder(37.4992,1.2482) """ field = int(str(alhambraid)[3]) pointing = int(str(alhambraid)[4]) ccd = int(str(alhambraid)[5]) numero = str(alhambraid)[-5:] print numero for ii in range(2): if numero[0] == '0': numero = numero[1:] print numero root2cats = '/Volumes/amb22/catalogos/reduction_v4f/f0%i/' % (field) catalog = root2cats + 'originals/f0%ip0%i_colorproext_%i_ISO.cat' % ( field, pointing, ccd) cols1 = root2cats + 'f0%ip0%i_%i_tot_ISO_eB10.columns' % (field, pointing, ccd) cols2 = root2cats + 'f0%ip0%i_colorproext_%i_ISO_phz_eB10.columns' % ( field, pointing, ccd) if os.path.exists(cols1): columns = cols1 else: columns = cols2 filters = B.get_filter_list(columns) print filters data = C.loaddata(catalog) # Loading the whole catalog content. head = C.loadheader(catalog) # Loading the original header. m = A.get_magnitudes(catalog, columns) # em = get_errmagnitudes(catalog,columns) root2 = '/Volumes/amb22/catalogos/reduction_v4e/' fluxc1 = root2 + 'f0%i/f0%ip0%i_colorproext_%i_ISO_phz_eB11.flux_comparison' % ( field, field, pointing, ccd) fluxc2 = root2 + 'f0%i/f0%ip0%i_colorproext_%i_ISO.flux_comparison' % ( field, field, pointing, ccd) if os.path.exists(fluxc1): fluxc = fluxc1 else: fluxc = fluxc2 ido, ftt, foo, efoo, zb, tb, mm = P.get_usefulfluxcomparison( columns, fluxc) pos = A.get_position(ido, int(numero)) print ido[pos], mm[pos] plt.figure(1, figsize=(10, 7), dpi=80, facecolor='w', edgecolor='k') plt.clf() # P.plot1sedfitting(foo[:,jj],efoo[:,jj],ftt[:,jj],zb[jj],tb[jj],root_bpz_sed+'eB11.list',filters) plt.plot(U.arange(20) + 1, foo[0:20, pos], 'k-', alpha=0.4, lw=6) plt.plot(U.arange(20) + 1, foo[0:20, pos], 'ko', alpha=0.4, ms=12) plt.errorbar(U.arange(20) + 1, foo[0:20, pos], (efoo[0:20, pos] / 1.), fmt="ko", alpha=0.4, ms=10) minf = (foo[0:20, pos].min()) * 1.1 maxf = (foo[0:20, pos].max()) * 1.1 maxef = (efoo[0:20, pos].max()) * 1.1 # plt.ylim(minf-maxef,maxf+maxef) plt.xlim(0, 21) plt.xlabel('Filter', size=25) plt.ylabel('Flux', size=25) plt.legend(['Magnitude: %.2f' % (m[pos][-1])], loc='upper right', numpoints=1, fontsize=20) plt.title(alhambraid, size=25) plt.grid() plt.show() namefig = '/Users/albertomolino/doctorado/photo/variability/analysis/vocheck.ID%s.png' % ( alhambraid) plt.savefig(namefig, dpi=125) outcat = '/Users/albertomolino/doctorado/photo/variability/analysis/vocheck.ID%s.cat' % ( alhambraid) A.select_rows_bylist_pro(catalog, ido[pos], outcat) print ' '
import alhambra_photools as A import matplotlib.pyplot as plt """ mainroot = '/Users/albertomolino/Postdoc/T80S_Pipeline/Commisioning/S82/Sept2017/' root2cats = mainroot + 'data_quality/' master_catalogue = root2cats + 'splus_master.cat' master_columns = root2cats + 'splus_auto.columns' output_filename = root2cats + 'magvsnoise.txt' """ master_catalogue = '/Users/albertomolino/jplus_data_download/SV02_March07/SV02_March07.clean.cat' master_columns = '/Users/albertomolino/jplus_data_download/SV02_March07/jplus11.columns' output_filename = '/Users/albertomolino/Postdoc/T80S_Pipeline/Commisioning/S82/Sept2017/data_quality/jplus_depth.txt' # Reading data from master catalogue mags = A.get_magnitudes(master_catalogue, master_columns) emags = A.get_errmagnitudes(master_catalogue, master_columns) #defining variables basem = N.arange(14, 22.5, 0.5) values = N.zeros((len(basem), 13), float) values[:, 0] = basem[:] #starting the party for ii in range(11): mm = mags[:, ii] em = emags[:, ii] good = N.greater_equal(mm, 14) * N.less_equal(mm, 22) mr = mm[good] emr = em[good] values[:, ii + 1] = U.bin_stats(mr, emr, basem, 'mean_robust')