def main(path, cat_path):
print("\nExecuting Python script pipeline_fors2/9-esorex_zeropoint.py, with:")
print(f"\tcalibration data path {path}")
print(f"\tESO calibration path {cat_path}")
print()
print('ESO CALIB DIR:', cat_path)
raw_path = path + '/0-data_with_raw_calibs/'
table_path = raw_path + 'fits_table.csv'
fits_table(input_path=raw_path, output_path=table_path, science_only=False)
# Write bias sof files.
ff.write_sof(table_path=table_path,
output_path=raw_path + 'bias_up.sof',
sof_type='fors_bias', chip=1)
ff.write_sof(table_path=table_path,
output_path=raw_path + 'bias_down.sof',
sof_type='fors_bias', chip=2)
# Write sky flats sof files
ff.write_sof(table_path=table_path,
output_path=raw_path + 'flats_up.sof',
sof_type='fors_img_sky_flat', chip=1)
ff.write_sof(table_path=table_path,
output_path=raw_path + 'flats_down.sof',
sof_type='fors_img_sky_flat', chip=2)
# Write zeropoint sof file
ff.write_sof(table_path=table_path,
output_path=raw_path + 'zp_up.sof',
sof_type='fors_zeropoint', chip=1, cat_path=cat_path)
ff.write_sof(table_path=table_path,
output_path=raw_path + 'zp_down.sof',
sof_type='fors_zeropoint', chip=2, cat_path=cat_path)
def main(data_dir, data_title, destination, fil, object):
table = fits_table(destination + '/' + fil, science_only=False)
fil = fil.replace('/', '')
header_file = destination + '/' + fil[0] + '_template.hdr'
header_stream = open(header_file, 'r')
header = header_stream.readlines()
header_stream.close()
# Transfer and/or modify header information
params = p.object_output_params(data_title, instrument='XSHOOTER')
airmass = table['airmass'].mean()
saturate = table['saturate'].mean()
old_gain = table['gain'].mean()
n_frames = params[fil[0] + '_n_exposures']
gain = gain_median_combine(old_gain=old_gain, n_frames=n_frames)
header.insert(-1, f'AIRMASS = {airmass}\n')
header.insert(-1, f'FILTER = {fil}\n')
header.insert(-1, f'OBJECT = {object}\n')
header.insert(-1, f'EXPTIME = 1.\n')
header.insert(-1, f'GAIN = {gain}\n')
header.insert(-1, f'SATURATE= {saturate}\n')
header.insert(-1, f'MJD-OBS = {float(np.nanmean(table["mjd_obs"]))}\n')
os.remove(header_file)
with open(header_file, 'w') as file:
file.writelines(header)
def main(data_dir, data_title, origin, destination, fil):
print("\nExecuting Python script pipeline_fors2/6-montage.py, with:")
print(f"\tepoch {data_title}")
print(f"\tdata directory {data_dir}")
print(f"\torigin directory {destination}")
print(f"\tdestination directory {destination}")
print(f"\tfilter {fil}")
print()
table = fits_table(origin + '/' + fil, science_only=False)
table.sort('identifier')
fil = fil.replace('/', '')
header_file = destination + '/' + fil[0] + '_template.hdr'
header_stream = open(header_file, 'r')
header = header_stream.readlines()
header_stream.close()
# Transfer and/or modify header information
params = p.load_params(data_dir + '/output_values')
airmass = table['airmass'].mean()
saturate = table['saturate'].mean()
obj = table['object'][0]
old_gain = table['gain'].mean()
n_frames = params[fil[0] + '_n_exposures']
gain = gain_median_combine(old_gain=old_gain, n_frames=n_frames)
header.insert(-1, f'AIRMASS = {airmass}\n')
header.insert(-1, f'FILTER = {fil}\n')
header.insert(-1, f'OBJECT = {obj}\n')
header.insert(-1, f'EXPTIME = 1.\n')
header.insert(-1, f'GAIN = {gain}\n')
header.insert(-1, f'SATURATE= {saturate}\n')
header.insert(-1, f'MJD-OBS = {float(np.nanmin(table["mjd_obs"]))}\n')
dates = table["mjd_obs"]
dates.sort()
header.insert(-1, f'DATE-OBS = {table["date_obs"][0]}\n')
os.remove(header_file)
with open(header_file, 'w') as file:
file.writelines(header)
def main(data_title: 'str'):
print("\nExecuting Python script pipeline_fors2/1-initial.py, with:")
print(f"\tepoch {data_title}")
print()
epoch_params = p.object_params_fors2(obj=data_title)
data_dir = epoch_params['data_dir']
output_dir = data_dir + "/0-data_with_raw_calibs/"
# Write tables of fits files to main directory; firstly, science images only:
table = fits_table(input_path=output_dir,
output_path=data_dir + data_title + "_fits_table_science.csv",
science_only=True)
# Then including all calibration files
table_full = fits_table(input_path=output_dir,
output_path=data_dir + data_title + "_fits_table_all.csv",
science_only=False)
fits_table_all(input_path=output_dir,
output_path=data_dir + data_title + "_fits_table_detailled.csv",
science_only=False)
# Clear output files for fresh start.
u.rm_check(data_dir + '/output_values.yaml')
u.rm_check(data_dir + '/output_values.json')
# Collect list of filters used:
filters = []
columns = []
for j in [1, 2, 3, 4, 5]:
column = 'filter' + str(j)
for name in table[column]:
if name != 'free':
if name not in filters:
filters.append(name)
columns.append(column)
# Collect pointings of standard-star observations.
std_ras = []
std_decs = []
std_pointings = []
# TODO: This is a horrible way to do this. Take the time to find a better one.
for ra in table_full[table_full['object'] == 'STD']['ref_ra']:
if ra not in std_ras:
std_ras.append(ra)
for dec in table_full[table_full['object'] == 'STD']['ref_dec']:
if dec not in std_decs:
std_decs.append(dec)
for i, ra in enumerate(std_ras):
std_pointings.append(f'RA{ra}_DEC{std_decs[i]}')
print(std_ras)
print(std_decs)
print(std_pointings)
# Collect and save some stats on those filters:
param_dict = {}
exp_times = []
ns_exposures = []
param_dict['filters'] = filters
param_dict['object'] = table['object'][0]
param_dict['obs_name'] = table['obs_name'][0]
mjd = param_dict['mjd_obs'] = float(table['mjd_obs'][0])
for i, f in enumerate(filters):
f_0 = f[0]
exp_time = table['exp_time'][table[columns[i]] == f]
exp_times.append(exp_time)
airmass_col = table['airmass'][table[columns[i]] == f]
n_frames = sum(table[columns[i]] == f)
n_exposures = n_frames / 2
ns_exposures.append(n_exposures)
airmass = float(np.nanmean(airmass_col))
param_dict[f_0 + '_exp_time_mean'] = float(np.nanmean(exp_time))
param_dict[f_0 + '_exp_time_err'] = float(2 * np.nanstd(exp_time))
param_dict[f_0 + '_airmass_mean'] = airmass
param_dict[f_0 + '_airmass_err'] = float(
max(np.nanmax(airmass_col) - airmass, airmass - np.nanmin(airmass_col)))
param_dict[f_0 + '_n_frames'] = float(n_frames)
param_dict[f_0 + '_n_exposures'] = float(n_exposures)
param_dict[f_0 + '_mjd_obs'] = float(np.nanmean(table['mjd_obs'][table[columns[i]] == f]))
std_filter_dir = f'{data_dir}calibration/std_star/{f}/'
u.mkdir_check(std_filter_dir)
print(f'Copying {f} calibration data to std_star folder...')
# Sort the STD files by filter, and within that by pointing.
for j, ra in enumerate(std_ras):
at_pointing = False
pointing = std_pointings[j]
pointing_dir = std_filter_dir + pointing + '/'
for file in \
table_full[
(table_full['object'] == 'STD') &
(table_full['ref_ra'] == ra) &
(table_full[columns[i]] == f)]['identifier']:
at_pointing = True
u.mkdir_check(pointing_dir)
shutil.copyfile(output_dir + file, pointing_dir + file)
if at_pointing:
for file in table_full[table_full['object'] == 'BIAS']['identifier']:
shutil.copyfile(output_dir + file, pointing_dir + file)
for file in table_full[(table_full['object'] == 'FLAT,SKY') & (table_full[columns[i]] == f)][
'identifier']:
shutil.copyfile(output_dir + file, pointing_dir + file)
p.add_output_values(obj=data_title, params=param_dict)
if "new_epoch" in data_dir:
mjd = f"MJD{int(float(mjd))}"
new_data_dir = data_dir.replace("new_epoch", mjd)
p.add_epoch_param(obj=data_title, params={"data_dir": new_data_dir})
def test_fits_table():
tbl = img.fits_table(good_input)
print(tbl.colnames)
assert isinstance(tbl, table.Table)
assert len(tbl) == 2
assert isinstance(tbl["exp_time"][0], float)