def plot_point_mags(output_data, visits, outfile, dataId):
# get a butler
butler = dp.Butler(output_data)
# The following value for refcatId is "mandatory, but meaningless",
# so we won't try to generalize it.
refcatId = {'tract':0, 'patch':'0,0'}
ref = butler.get('deepCoadd_ref', dataId=refcatId)
# visits to use for lightcurves
visit_list = [int(x) for x in visits.split('^')]
# get the sources and calib objects for each single epoch visit
forced_srcs = {}
calibs = {}
for visit in visit_list:
dataId['visit'] = visit
forced_srcs[visit] = butler.get('forced_src', dataId=dataId)
calexp = butler.get('calexp', dataId=dataId)
calibs[visit] = calexp.getCalib()
del calexp
# initialize dictionaries to hold lightcurve arrays. Get
# extendedness from the coadd catalog.
lightcurve_fluxes = {}
extendedness = {}
for idx, ext in zip(ref.get('id'),
ref.get('base_ClassificationExtendedness_value')):
lightcurve_fluxes[idx] = []
extendedness[idx] = ext
# pivot the source tables to assemble lightcurves
for visit, forced_src in forced_srcs.iteritems():
calib = calibs[visit]
for idx, flux in zip(forced_src.get('objectId'),
forced_src.get('base_PsfFlux_flux')):
if extendedness[idx] > 0.5:
continue
if flux <= 0.:
continue
lightcurve_fluxes[idx].append(afw_image.fluxFromABMag(calib.getMagnitude(flux)))
# compute aggregate quantities for each object and plot
for lightcurve in lightcurve_fluxes.values():
if len(lightcurve) == len(visit_list):
plt.scatter(afw_image.abMagFromFlux(numpy.median(lightcurve)),
numpy.std(lightcurve)/numpy.median(lightcurve),
alpha=0.5)
mags, invsnrs = make_invsnr_arr()
plt.plot(mags, invsnrs, color='red', linewidth=2, alpha=0.75)
plt.xlabel("Calibrated magnitude of median flux")
plt.ylabel("stdev(flux)/median(flux)")
plt.xlim(15.5, 25)
plt.ylim(0., 0.5)
plt.savefig(outfile)
# initialize dictionaries to hold lightcurve arrays. Get extendedness from the coadd catalog.
lightcurve_fluxes = {}
extendedness = {}
for idx, ext in zip(ref.get('id'), ref.get('base_ClassificationExtendedness_value')):
lightcurve_fluxes[idx] = []
extendedness[idx] = ext
# pivot the source tables to assemble lightcurves
for visit, forced_src in forced_srcs.iteritems():
calib = calibs[visit]
for idx, flux in zip(forced_src.get('objectId'), forced_src.get('base_PsfFlux_flux')):
if extendedness[idx] > 0.5:
continue
if flux <= 0.:
continue
lightcurve_fluxes[idx].append(afw_image.fluxFromABMag(calib.getMagnitude(flux)))
# compute aggregate quantities for each object and plot
for lightcurve in lightcurve_fluxes.values():
if len(lightcurve) == 9:
plt.scatter(afw_image.abMagFromFlux(numpy.median(lightcurve)),
numpy.std(lightcurve)/numpy.median(lightcurve), alpha=0.5)
mags, invsnrs = make_invsnr_arr()
plt.plot(mags, invsnrs, color='red', linewidth=2, alpha=0.75)
plt.xlabel("Calibrated magnitude of median flux")
plt.ylabel("stdev(flux)/median(flux)")
plt.xlim(15.5, 25)
plt.ylim(0., 0.5)
plt.show()
# compute aggregate quantities for each object and plot
med_mags = []
med_err = []
for lightcurve in lightcurve_fluxes.values():
if len(lightcurve) == 10:
med_mags.append(afw_image.abMagFromFlux(numpy.median(lightcurve)))
med_err.append(numpy.std(lightcurve)/numpy.median(lightcurve))
fit_func = partial(fit_invsnr, bandpass_name=bandpass_name)
minMag=17.
mid_cut=20.
maxMag=26.
med_mags = numpy.array(med_mags)
med_err = numpy.array(med_err)
idxs = numpy.where((med_mags<mid_cut)&(med_mags>minMag))
sys_floor = numpy.median(med_err[idxs])
idxs = numpy.where((med_mags<maxMag)&(med_mags>mid_cut))
popt, pcov = curve_fit(fit_invsnr, med_mags[idxs], med_err[idxs], p0=[0.01, 24.5])
scatter = plt.scatter(med_mags, med_err, alpha=0.3, color=bandpass_color_map[bandpass_name],
marker=bandpass_symbol_map[bandpass_name],
label="filter=%s, Floor=%.1f%%, m_5=%0.2f"%(bandpass_name,sys_floor*100.,popt[1]))
scatters.append(scatter)
mags, invsnrs = make_invsnr_arr(floor=sys_floor, m5=popt[1])
plt.plot(mags, invsnrs, color='black', linewidth=3, alpha=0.75)
print bandpass_name
plt.legend(handles=scatters, scatterpoints=1, loc=2)
plt.xlabel("Calibrated magnitude of median flux")
plt.ylabel("stdev(flux)/median(flux)")
plt.xlim(15.5, 25)
plt.ylim(0., 0.5)
plt.show()
