def standard_calibration(field, CCD, epochs, FILTER):
radec_file = np.sort(glob.glob("%s/SHARED/%s/%s/CALIBRATIONS/matchRADEC_%s_%s_*-02.npy" %
(astropath, field, CCD, field, CCD)), kind='mergesort')
mRADEC_all = []
for npy in radec_file: mRADEC_all.append(np.load(npy))
mRADEC_all = np.asarray(mRADEC_all)
mRADEC = mRADEC_all[np.argmin(mRADEC_all[:,2])]
wheather_all = []
print epochs
for epoch in epochs[:,0]:
print 'Loading epoch %s' % (epoch)
#################################### loading catalogs, fits and match files ###################################
imag_file = "%s/DATA/%s/%s/%s_%s_%s_image_crblaster.fits" % (astropath, field, CCD, field, CCD, epoch)
if not os.path.exists(imag_file):
print 'No image file: %s' % (imag_file)
continue
cata_file = "%s/catalogues/%s/%s/%s_%s_%s_image_crblaster_thresh%s_minarea%s_backsize64_zp.dat" % \
(jorgepath, field, CCD, field, CCD, epoch, str(thresh), str(minarea))
if not os.path.exists(cata_file):
print 'No catalog file: %s' % (cata_file)
continue
cata = Table.read(cata_file, format = 'ascii')
#sys.exit()
##################################### Reading image and header ################################################
print 'Applying transformation from ADU to MAG'
hdufits = fits.open(imag_file)
GAIN = float(hdufits[0].header['GAINA'])
AIRMASS = float(hdufits[0].header['AIRMASS'])
SCALE = float(hdufits[0].header['PIXSCAL1'])
FWHM_p = float(hdufits[0].header['FWHM'])
SEEING = FWHM_p*SCALE
EXP_TIME = float(hdufits[0].header['EXPTIME'])
MJD = float(hdufits[0].header['MJD-OBS'])
FILTER = hdufits[0].header['FILTER'][0]
CCDID = int(hdufits[0].header['CCDNUM'])
CTE_file = np.loadtxt('%s/sextractor/zeropoint/psmFitDES-mean-%s.csv' % (jorgepath, FILTER),
delimiter = ',', skiprows = 1, usecols = [4,5,6,7,8,9,10,11,19], dtype = str)
Ag = float(CTE_file[CCDID-1][2])
err_Ag = float(CTE_file[CCDID-1][3])
Kg = float(CTE_file[CCDID-1][6])
err_Kg = float(CTE_file[CCDID-1][7])
wheather_all.append([epoch, AIRMASS, SEEING])
sigma1, alpha, beta = empty_aperture(field, CCD, epoch, n_aper = 100, verbose = False)
if sigma1 != None:
num_pix = np.pi * cata['A_IMAGE'] * cata['B_IMAGE'] * cata['KRON_RADIUS']**2
new_flux_err = np.sqrt(sigma1**2 * alpha**2 * num_pix**beta + cata['FLUX_AUTO']/GAIN)
########################## Transformation from ADU to magnitudes and errors ###################################
mag_auto = -2.5 * np.log10(cata['FLUX_AUTO']) + 2.5 * np.log10(EXP_TIME) - Ag
mag_auto_err = np.sqrt(((2.5*cata['FLUXERR_AUTO'])/(cata['FLUX_AUTO'][:]*np.log(10)))**2 + err_Ag**2)
new_mag_auto_zac = -2.5 * np.log10(cata['FLUX_AUTO']) + 2.5 * np.log10(EXP_TIME) - Ag - Kg * AIRMASS
new_mag_auto_zac_err = np.sqrt(((2.5 * cata['FLUXERR_AUTO']) / (cata['FLUX_AUTO'] * np.log(10))) **2 +
err_Ag **2 + (AIRMASS * err_Kg) **2)
if sigma1 != None:
new_mag_auto_zac_err_cor = np.sqrt(((2.5 * new_flux_err) / (cata['FLUX_AUTO'] * np.log(10))) **2 +
err_Ag **2 + (AIRMASS * err_Kg) **2)
print '\tTransformation Done!'
print cata.colnames
if sigma1 != None:
print 'Mean absolute deviation of FLUX_ERR_COR \t%0.6f' % \
np.mean(np.abs(cata['FLUXERR_AUTO'] - new_flux_err))
print 'Mean absolute deviation of MAG_AUTO \t\t%0.6f' % np.mean(np.abs(mag_auto - new_mag_auto_zac))
print 'Mean absolute deviation of ERR_MAG_AUTO \t%0.6f' % np.mean(np.abs(mag_auto_err - new_mag_auto_zac_err))
if sigma1 != None:
print 'Mean absolute deviation of ERR_MAG_AUTO_COR \t%0.6f' % \
np.mean(np.abs(new_mag_auto_zac_err - new_mag_auto_zac_err_cor))
# cata.remove_columns(['MAG_AUTO', 'MAGERR_AUTO'])
# cata.remove_columns(['FLUXERR_AUTO_COR','MAG_AUTO_AIRMASS','MAGERR_AUTO_AIRMASS','MAGERR_AUTO_AIRMASS_COR'])
if sigma1 != None:
cata.add_column(Column(new_flux_err, name = 'FLUXERR_AUTO_COR', unit = u.count), index = 7)
# cata.add_column(Column(mag_auto, name = 'MAG_AUTO', unit = u.mag), index = 8)
# cata.add_column(Column(mag_auto, name = 'MAGERR_AUTO', unit = u.mag), index = 9)
cata['MAG_AUTO'] = mag_auto
cata['MAGERR_AUTO'] = mag_auto_err
cata.add_column(Column(new_mag_auto_zac, name = 'MAG_AUTO_ZAC', unit = u.mag), index = 9)
cata.add_column(Column(new_mag_auto_zac_err, name = 'MAGERR_AUTO_ZAC', unit = u.mag), index = 11)
if sigma1 != None:
cata.add_column(Column(new_mag_auto_zac_err_cor, name = 'MAGERR_AUTO_ZAC_COR', unit = u.mag), index = 12)
print cata.colnames
new_cata_file = cata_file
cata.write(new_cata_file, format = 'ascii.commented_header', delimiter = '\t')
print '_______________________________________________________________________________________'
return wheather_all
def standard_calibration_color(field, CCD, epochs, FILTER, color_table):
radec_file = np.sort(glob.glob("%s/SHARED/%s/%s/CALIBRATIONS/matchRADEC_%s_%s_*-02.npy" %
(astropath, field, CCD, field, CCD)), kind='mergesort')
mRADEC_all = []
for npy in radec_file: mRADEC_all.append(np.load(npy))
mRADEC_all = np.asarray(mRADEC_all)
mRADEC = mRADEC_all[np.argmin(mRADEC_all[:,2])]
wheather_all = []
print epochs
for epoch in epochs[:,0]:
print 'Loading epoch %s' % (epoch)
################################### loading catalogs, fits and match files ####################################
imag_file = "%s/DATA/%s/%s/%s_%s_%s_image_crblaster.fits" % (astropath, field, CCD, field, CCD, epoch)
if not os.path.exists(imag_file):
print 'No image file: %s' % (imag_file)
continue
cata_file = "%s/catalogues/%s/%s/%s_%s_%s_image_crblaster_thresh%s_minarea%s_backsize64_zp.dat" % \
(jorgepath, field, CCD, field, CCD, epoch, str(thresh), str(minarea))
if not os.path.exists(cata_file):
print 'No catalog file: %s' % (cata_file)
continue
cata = Table.read(cata_file, format = 'ascii')
print 'Length of catalogue: %i' % (len(cata))
#sys.exit()
######################################## Reading image and header #############################################
print 'Applying transformation from ADU to MAG'
hdufits = fits.open(imag_file)
GAIN = float(hdufits[0].header['GAINA'])
AIRMASS = float(hdufits[0].header['AIRMASS'])
SCALE = float(hdufits[0].header['PIXSCAL1'])
FWHM_p = float(hdufits[0].header['FWHM'])
SEEING = FWHM_p*SCALE
EXP_TIME = float(hdufits[0].header['EXPTIME'])
MJD = float(hdufits[0].header['MJD-OBS'])
FILTER = hdufits[0].header['FILTER'][0]
CCDID = int(hdufits[0].header['CCDNUM'])
CTE_file = np.loadtxt('%s/sextractor/zeropoint/psmFitDES-mean-%s.csv' %
(jorgepath, FILTER), delimiter = ',', skiprows = 1, usecols = [4,5,6,7,8,9,10,11,19], dtype = str)
Ag = float(CTE_file[CCDID-1][2])
err_Ag = float(CTE_file[CCDID-1][3])
Kg = float(CTE_file[CCDID-1][6])
err_Kg = float(CTE_file[CCDID-1][7])
bg = float(CTE_file[CCDID-1][4])
err_bg = float(CTE_file[CCDID-1][4])
g_r0 = bg = float(CTE_file[CCDID-1][8])
wheather_all.append([epoch, AIRMASS, SEEING])
########################################### open color table ##################################################
tree_XY_color = cKDTree(np.transpose([color_table['X'],color_table['Y']]))
XY_cata = np.transpose([cata['X_IMAGE'], cata['Y_IMAGE']])
superpos_ind = tree_XY_color.query(XY_cata, k = 1, distance_upper_bound=4)[1]
index_filter = (superpos_ind < len(color_table)) # dice los obj de single epoch encontrados en color
index = superpos_ind[index_filter] # indices de los correspondientes colores
print len(color_table['Median_g'][index])
print len(cata['FLUX_AUTO'])
if FILTER == 'g':
if len(color_table['Median_g'][index]) != len(cata['FLUX_AUTO']):
color_mask_aux = []
g_r = []
for l, bol in enumerate(index_filter):
if bol:
position = superpos_ind[l]
if color_table['Median_r'][position] != 0.:
color_mask_aux.append(True)
g_r.append(color_table['Median_g'][position] - color_table['Median_r'][position])
else:
color_mask_aux.append(False)
g_r.append(None)
else:
color_mask_aux.append(False)
g_r.append(None)
color_mask = np.array(color_mask_aux)
g_r = np.array(g_r)
else:
color_mask = (color_table['Median_r'][index] != 0.)
g_r = np.array(color_table['Median_g'][index] - color_table['Median_r'][index])
if FILTER == 'r':
color_mask_aux = []
g_r = []
for l, bol in enumerate(index_filter):
if bol:
position = superpos_ind[l]
if color_table['Median_r'][position] != 0.:
color_mask_aux.append(True)
g_r.append(color_table['Median_g'][position] - color_table['Median_r'][position])
else:
color_mask_aux.append(False)
g_r.append(None)
else:
color_mask_aux.append(False)
g_r.append(None)
color_mask = np.array(color_mask_aux)
g_r = np.array(g_r)
print len(g_r)
print len(cata['FLUX_AUTO'])
########################################### new errors estimation #############################################
sigma1, alpha, beta = empty_aperture(field, CCD, epoch, n_aper = 100, verbose = False)
if sigma1 != None:
num_pix = np.pi * cata['A_IMAGE'] * cata['B_IMAGE'] * cata['KRON_RADIUS']**2
new_flux_err = np.sqrt(sigma1**2 * alpha**2 * num_pix**beta + cata['FLUX_AUTO']/GAIN)
############################ Transformation from ADU to magnitudes and errors #################################
print 'Empty aperture done!'
mag_auto = -2.5*np.log10(cata['FLUX_AUTO']) + 2.5*np.log10(EXP_TIME) - Ag
mag_auto_err = np.sqrt(((2.5*cata['FLUXERR_AUTO'])/(cata['FLUX_AUTO'][:]*np.log(10)))**2 + err_Ag**2)
new_mag_auto_zac = np.zeros (len(cata['FLUX_AUTO']))
new_mag_auto_zac_err = np.zeros (len(cata['FLUX_AUTO']))
if sigma1 != None: new_mag_auto_zac_err_cor = np.zeros (len(cata['FLUX_AUTO']))
for kk in range(len(cata['FLUX_AUTO'])):
if color_mask[kk]:
new_mag_auto_zac[kk] = -2.5 * np.log10(cata['FLUX_AUTO'][kk]) + 2.5 * np.log10(EXP_TIME) - \
Ag - Kg * AIRMASS - bg * (g_r[kk] - g_r0)
new_mag_auto_zac_err[kk] = np.sqrt(((2.5 * cata['FLUXERR_AUTO'][kk]) / (cata['FLUX_AUTO'][kk] *
np.log(10))) **2 + err_Ag **2 + (AIRMASS * err_Kg ) **2)# + (g_r[kk]*err_bg)**2)
if sigma1 != None:
new_mag_auto_zac_err_cor[kk] = np.sqrt(((2.5 * new_flux_err[kk]) / (cata['FLUX_AUTO'][kk] *
np.log(10))) **2 + err_Ag **2 + (AIRMASS * err_Kg)**2)# + (g_r[kk]*err_bg)**2)
else:
new_mag_auto_zac[kk] = -2.5 * np.log10(cata['FLUX_AUTO'][kk]) + 2.5 * np.log10(EXP_TIME) -\
Ag - Kg * AIRMASS
new_mag_auto_zac_err[kk] = np.sqrt(((2.5 * cata['FLUXERR_AUTO'][kk]) / (cata['FLUX_AUTO'][kk] *
np.log(10))) **2 + err_Ag **2 + (AIRMASS * err_Kg) **2)
if sigma1 != None:
new_mag_auto_zac_err_cor[kk] = np.sqrt(((2.5 * new_flux_err[kk]) / (cata['FLUX_AUTO'][kk] *
np.log(10))) **2 + err_Ag **2 + (AIRMASS * err_Kg) **2)
print '\tTransformation Done!'
print cata.colnames
if sigma1 != None:
print 'Mean absolute deviation of FLUX_ERR_COR \t%0.6f' % \
np.mean(np.abs(cata['FLUXERR_AUTO'] - new_flux_err))
print 'Mean absolute deviation of MAG_AUTO \t\t%0.6f' % np.mean(np.abs(mag_auto - new_mag_auto_zac))
print 'Mean absolute deviation of ERR_MAG_AUTO \t%0.6f' % np.mean(np.abs(mag_auto_err - new_mag_auto_zac_err))
if sigma1 != None:
print 'Mean absolute deviation of ERR_MAG_AUTO_COR \t%0.6f' % \
np.mean(np.abs(new_mag_auto_zac_err - new_mag_auto_zac_err_cor))
# cata.remove_columns(['MAG_AUTO', 'MAGERR_AUTO'])
# cata.remove_columns(['FLUXERR_AUTO_COR','MAG_AUTO_AIRMASS','MAGERR_AUTO_AIRMASS','MAGERR_AUTO_AIRMASS_COR'])
if sigma1 != None:
cata.add_column(Column(new_flux_err, name = 'FLUXERR_AUTO_COR', unit = u.count), index = 7)
# cata.add_column(Column(mag_auto, name = 'MAG_AUTO', unit = u.mag), index = 8)
# cata.add_column(Column(mag_auto, name = 'MAGERR_AUTO', unit = u.mag), index = 9)
cata['MAG_AUTO'] = mag_auto
cata['MAGERR_AUTO'] = mag_auto_err
cata.add_column(Column(new_mag_auto_zac, name = 'MAG_AUTO_ZAC', unit = u.mag), index = 9)
cata.add_column(Column(new_mag_auto_zac_err, name = 'MAGERR_AUTO_ZAC', unit = u.mag), index = 11)
if sigma1 != None:
cata.add_column(Column(new_mag_auto_zac_err_cor, name = 'MAGERR_AUTO_ZAC_COR', unit = u.mag), index = 12)
print cata.colnames
new_cata_file = cata_file
cata.write(new_cata_file, format = 'ascii.commented_header', delimiter = '\t')
print '_______________________________________________________________________________________'
return wheather_all