def make_airmass_model(self):
print "### start to calibrate the inst mag with apparent mag in catalog I/329"
filter_ = self.filter_
if filter_ != 'V' and filter_ != 'B' and filter_ != 'R':
print "This photometry doesn't this filter."
return 1
# Choose the 10 brightest stars
self.index_INST_MAG = TAT_env.obs_data_titles.index('INST_MAG')
self.index_E_INST_MAG = TAT_env.obs_data_titles.index('E_INST_MAG')
inst_mag_array = np.array(self.stars[:,self.index_INST_MAG], dtype = float)
mag_order_stars = self.stars[inst_mag_array.argsort()]
print ('The number of sources: {0}'.format(len(mag_order_stars)))
picking = np.arange(10)
mag_order_stars = mag_order_stars[picking]
mag_delta_list = []
for star in mag_order_stars:
#-------------------------------------------------
# Find the info of the source from catalog I/329
index_RA = TAT_env.obs_data_titles.index('RA')
index_DEC = TAT_env.obs_data_titles.index('`DEC`')
RA = float(star[index_RA])
DEC = float(star[index_DEC])
failure, match_stars = get_catalog(RA, DEC, TAT_env.URAT_1, TAT_env.index_URAT_1)
if failure:
continue
# Find the apparent magnitude to the found source
failure, app_mag = get_app_mag(match_stars, filter_)
if failure:
continue
inst_mag = float(star[self.index_INST_MAG])
mag_delta = app_mag - inst_mag
print "INST_MAG = {0}, CATA_MAG = {1}, delta = {2}".format(inst_mag, app_mag, mag_delta)
mag_delta_list.append(mag_delta)
# Check if the number of source is enough or not.
if len(mag_delta_list) == 0:
print "No enough source found in catalogue for comparison"
return 1
mag_delta_list = get_rid_of_exotic(mag_delta_list)
if len(mag_delta_list) < 3:
print "No enough source found in catalogue for comparison"
return 1
# remove np.nan
mag_delta_array = np.array(mag_delta_list)
mag_delta_array = mag_delta_array[~np.isnan(mag_delta_array)]
# Find the median of the delta of the magnitude, and apply the result on all sources.
self.median_mag_delta = np.median(mag_delta_array)
return 0
def show_cata_mag(mag, ra, dec, VERBOSE=1):
# Load the index of columes
reduce_data = np.transpose(np.array([mag, ra, dec], dtype=float))
reduce_data = reduce_data[mag.argsort()]
reduce_data = reduce_data[~np.isnan(reduce_data[:, 0])]
# Pick 50 brightest stars from the data
reduce_data = reduce_data[:20]
world = reduce_data[:, 1:]
#--------------------------------------------------
# Find the catalog magnitude.
# Query data from vizier
mag_delta_list = []
filter_ = 'V'
for i in xrange(len(reduce_data)):
inst_mag = reduce_data[i, 0]
RA = float(world[i, 0])
DEC = float(world[i, 1])
failure, match_star = get_catalog(RA, DEC, TAT_env.URAT_1,
TAT_env.index_URAT_1)
if failure:
continue
failure, app_mag = get_app_mag(match_star, filter_)
if failure:
continue
if np.isnan(inst_mag):
continue
mag_delta = app_mag - inst_mag
if VERBOSE == 1:
print "INST_MAG = {0}, CATA_MAG = {1}, delta = {2}".format(
inst_mag, app_mag, mag_delta)
mag_delta_list.append(mag_delta)
# Find the average of delta_mag
# Check if the number of source is enough or not.
if len(mag_delta_list) == 0:
print "No enough source found in catalogue for comparison"
return 1
mag_delta_list = get_rid_of_exotic(mag_delta_list)
if len(mag_delta_list) < 3:
print "No enough source found in catalogue for comparison"
return 1
# remove np.nan
mag_delta_array = np.array(mag_delta_list)
mag_delta_array = mag_delta_array[~np.isnan(mag_delta_array)]
# Find the median of the delta of the magnitude, and apply the result on all sources.
median_mag_delta = np.median(mag_delta_array)
app_mag = mag + median_mag_delta
return 0, app_mag
def show_mini_mag(se_table, VERBOSE=0):
# Load the index of columes
index_mag = TAT_env.SE_table_titles.index('MAG_AUTO')
index_x = TAT_env.SE_table_titles.index('X_IMAGE')
index_y = TAT_env.SE_table_titles.index('Y_IMAGE')
# Take the minimum magnitude in INST_MAG
mag = se_table[:, index_mag]
mini_mag = np.amax(mag)
if VERBOSE > 0: print 'instrumental minimum mag: {0}'.format(mini_mag)
xcenter = se_table[:, index_x]
ycenter = se_table[:, index_y]
reduce_data = np.transpose(np.array([mag, xcenter, ycenter]))
reduce_data = reduce_data[mag.argsort()]
# Pick 10 brightest stars from the data
reduce_data = reduce_data[:50]
pixcrd = reduce_data[:, 1:]
try:
header_wcs = pyfits.getheader("stacked_image.wcs")
except:
print "WCS not found"
return 1, None
w = wcs.WCS(header_wcs)
world = w.wcs_pix2world(pixcrd, 1)
#--------------------------------------------------
# Find the catalog magnitude.
# Query data from vizier
mag_delta_list = []
filter_ = 'V'
for i in xrange(len(reduce_data)):
inst_mag = reduce_data[i, 0]
RA = float(world[i, 0])
DEC = float(world[i, 1])
failure, match_star = get_catalog(RA, DEC, TAT_env.URAT_1,
TAT_env.index_URAT_1)
if failure:
continue
failure, app_mag = get_app_mag(match_star, filter_)
if failure:
continue
mag_delta = app_mag - inst_mag
if VERBOSE == 1:
print "INST_MAG = {0}, CATA_MAG = {1}, delta = {2}".format(
inst_mag, app_mag, mag_delta)
mag_delta_list.append(mag_delta)
# Find the average of delta_mag
# Check if the number of source is enough or not.
if len(mag_delta_list) == 0:
print "No enough source found in catalogue for comparison"
return 1
mag_delta_list = get_rid_of_exotic(mag_delta_list)
if len(mag_delta_list) < 3:
print "No enough source found in catalogue for comparison"
return 1
# remove np.nan
mag_delta_array = np.array(mag_delta_list)
mag_delta_array = mag_delta_array[~np.isnan(mag_delta_array)]
# Find the median of the delta of the magnitude, and apply the result on all sources.
median_mag_delta = np.median(mag_delta_array)
app_mag = mag + median_mag_delta
app_mini_mag = mini_mag + median_mag_delta
return 0, app_mag, app_mini_mag
