obj_images = list(list_images["filename"][np.where( (list_images["objname"] == obj))])
output_db = os.path.join(directory, obj+".db")
utilities.if_exists_remove(output_db)
coord_file = utilities.replace_extension(obj_images[0], "radec")
photometry.main(arguments=["--maximum", str(max_counts), "--uik", "", "--margin", "20", "--gaink", gaink,
"--aperture", "4.", "--annulus", "6", "--dannulus", "2", "--individual-fwhm", "--objectk", objectk,
"--filterk", filterk, "--datek", datek, "--expk", exptimek, "--fwhmk", seeingk, "--airmk", airmassk,
"--coordinates", coord_file, obj_images[0]] + obj_images + [output_db])
print "Find extinction coefficient from photometry of standards"
standards_set = set(x for x,t in zip(list_images["objname"], list_images["type"]) if t == "standards")
coefficient_list = []
for obj in standards_set:
input_db = os.path.join(directory, obj+".db")
airmasses, magnitudes, filters, SNR = extract.main(input_db)
magnitude_errors_minus, magnitude_errors_plus = snr.snr_to_error(SNR)
mag_errors = (-magnitude_errors_minus + magnitude_errors_plus) / 2.
for filt in set(filters.flatten()):
# find columns with the current filter
columns = [ii for ii in range(len(filters[0, :])) if filters[0,ii] == filt]
# if there are more than two images
if len(airmasses[0,columns]) > 0 and len(airmasses[0,columns]) > 2:
ext_coeff, serror_ext_coeff = calculate_extinction(airmasses[:, columns],
magnitudes[:, columns],
err_magnitudes=mag_errors[:,columns])
coefficient_list.append(ext_coeff)
#plt.plot(airmasses[:, columns], magnitudes[:, columns], 'o')
#plt.show()
print "Extinction coefficient for ", obj, " for filter ", filt, ":", ext_coeff, "+/-", serror_ext_coeff
else:
print "Not used ", obj, " with filter " + filt + ". Too few elements."
def calculate_extinctions(args):
""" Iterate through the databases and find the extinction coefficient for every filter
"""
results = {} # Dictionary for the outputs
for DB in args.inputdb:
print "\nStudying database {0}:".format(DB)
airmass, magnitudes, filters, SNR, im_paths = extract.main(DB)
# SNR is the signal-to-noise-ratio, we want to use the errors in the magnitudes
magnitude_errors_minus, magnitude_errors_plus = snr.snr_to_error(SNR)
mag_errors = (-magnitude_errors_minus + magnitude_errors_plus) / 2.
# Which filters are present?
filter_set = set(filters.flatten().tolist())
# Prepare the figure for the plots.
if args.plot:
figure = plt.figure()
figure.set_label("BD+25")
ax0 = plt.subplot2grid((2,2),(0, 0))
ax1 = plt.subplot2grid((2,2),(0, 1))
ax2 = plt.subplot2grid((2,2),(1, 0))
ax = [ax0, ax1, ax2]
plt.subplots_adjust(hspace=0.3)
plt.subplots_adjust(wspace=0.12)
plt.subplots_adjust(top=0.95)
plt.subplots_adjust(bottom=0.05)
plt.subplots_adjust(left=0.05)
plt.subplots_adjust(right=0.95)
for ii, filt in enumerate(filter_set):
# Select only those data for the particular filter
whr_filt = numpy.where(filters[0, :] == filt)[0]
# If the airmass difference is not, at least, 0.2, skip this test because the fit will be meaningless
airm_diff = numpy.max(airmass[:, whr_filt]) - numpy.min(airmass[:, whr_filt])
if airm_diff < 0.2:
print "Airmasses are too close for filter {0} ".format(filt)
plt.close()
continue
# Select the brightest objects and do the fit
mags_filt, airm_filt, errors_filt = select_brightest(magnitudes[:, whr_filt],
airmass[:, whr_filt],
mag_errors[:, whr_filt],
nstars=args.nstars)
ext, ext_err = extinction_fit(airm_filt, mags_filt, errors_filt)
print "Extinction coefficient for filter {0}: {1} +- {2}".format(filt, ext, ext_err)
results[(DB, filt)] = (ext, ext_err)
# Do the plot of the selected stars
if args.plot:
for star_airmass, star_mag in zip(airm_filt, mags_filt):
ax[ii].plot(star_airmass, star_mag, 'o')
ax[ii].set_title(filt)
ax[ii].set_xlabel("Airmass")
if args.plot:
plt.show()
return results