def read_data_rdb(file):
"""
Read spectrum from rdb file with the following headers:
'obj','obs_date','bjd','wave','flux','error_pixel' (optional)
"""
print("Reading file:\t{}".format(file))
try:
data, hdr = ac_tools.read_rdb(file)
except:
print("*** ERROR: Cannot read {}".format(file))
sys.exit()
print("Object:\t\t{}".format(data['obj'][0]))
data['wave'] = np.asarray(data['wave'])
data['flux'] = np.asarray(data['flux'])
data['tel'] = "unknown"
data['instr'] = "unknown"
data['obj'] = data['obj'][0]
data['obs_date'] = data['obs_date'][0]
data['bjd'] = data['bjd'][0]
data['blaze'] = np.ones(len(data['flux']))
data['snr'] = None
data['median_snr'] = None
data['noise'] = 0.0
return data
def check_duplicate(obj, date, instr, file_type, out_dir):
"""
Check if measurement is a duplicate in the output file.
Parameters:
-----------
obj : str
Object identification.
date : str
Date of observation in the fits file format.
file_type : str
Type of file used: 'S2D', 'S1D', 'e2ds', 's1d', 'ADP', or 'rdb'.
output_dir : str
Directory of output file.
Returns:
--------
bool
True if duplicate, False otherwise.
"""
print()
print("Executing check_duplicate:")
if obj is None or date is None or out_dir is None: return
file_name = "{}_{}_{}_{}".format(obj, instr, file_type,
ac_set.fnames['data'])
pfile_name = os.path.join(out_dir, obj, file_name)
if os.path.isfile(pfile_name):
try:
rdb_data = ac_tools.read_rdb(pfile_name)[0]
except:
print("*** ERROR: Cannot read rdb file:")
print(pfile_name)
sys.exit()
if date in rdb_data['obs_date']:
print("Date {} already saved in:".format(date))
print(pfile_name)
print("*** ACTION: Ignoring measurement")
return True
else:
print("Date {} not present in {}".format(date, pfile_name))
else:
print("No data saved for {}, {}.".format(obj, date))
return False
def plt_time(info, out_dir, save_plt=False, rmv_flgs=False):
"""
Saves timeseries plots of the indices identified in the rdb file by
starting with 'I_'.
"""
if out_dir is False: return
print()
print("Executing plt_time:")
obj = info['obj']
file_type = info['file_type']
instr = info['instr']
rdb_name = "{}_{}_{}_{}".format(obj, instr, file_type,
ac_set.fnames['data'])
rdb_file = os.path.join(out_dir, obj, rdb_name)
width, height = ac_tools.plot_params()
data = ac_tools.read_rdb(rdb_file)[0]
bjd_raw = np.asarray(data['bjd'])
bjd = bjd_raw - int(min(bjd_raw))
if len(bjd) == 1:
print("Only one data point, no need to plot.")
return
data_keys = list(data)
ind_ids = []
for k in range(len(data_keys)):
if any(map(data_keys[k].startswith, 'I_')):
ind_ids.append(('_').join(data_keys[k].split('_')[:2]))
if not ind_ids:
print("*** ERROR: No indices detected in file:")
print("***", rdb_file)
return
# remove duplicates of ind_id and gives a list of selected indices
ind_ids = list(set(ind_ids))
# make new dictionary just for plots
ind = {}
for k in range(len(ind_ids)):
ind[ind_ids[k]] = data[ind_ids[k]]
ind_err_key = '{}_err'.format(ind_ids[k])
ind_flg_key = '{}_flg'.format(ind_ids[k])
ind[ind_err_key] = data[ind_err_key]
ind[ind_flg_key] = data[ind_flg_key]
for k in range(len(ind_ids)):
plt.figure(figsize=(width, height))
N = 0
for i in range(len(ind[ind_ids[k]])):
ind_flg = ind['{}_flg'.format(ind_ids[k])][i]
ind_err = ind['{}_err'.format(ind_ids[k])][i]
ind_val = ind[ind_ids[k]][i]
if ind_flg == 'None':
N += 1
plt.errorbar(bjd[i],
ind_val,
ind_err,
c='k',
marker='.',
ls='')
if ind_flg == 'negFlux':
if rmv_flgs is False:
N += 1
plt.errorbar(bjd[i],
ind_val,
ind_err,
c='r',
marker='.',
ls='')
else:
pass
plt.annotate(obj,
xy=(0.05, 0.9),
xycoords='axes fraction',
textcoords='axes fraction')
plt.annotate("N = {}".format(N),
xy=(0.05, 0.85),
xycoords='axes fraction',
textcoords='axes fraction')
plt.xlabel('BJD - {} [days]'.format(int(min(bjd_raw))))
plt.ylabel(ind_ids[k])
if save_plt is True:
save_name = '{}_{}_{}_{}_{}'.format(obj, instr, file_type,
ind_ids[k],
ac_set.fnames['time_plt'])
save_file = os.path.join(out_dir, obj, save_name)
plt.savefig(save_file)
print("{} timeseries saved to:\n{}".format(ind_ids[k], save_file))
plt.close()
return
def plt_time_mlty(info,
out_dir,
save_plt=False,
rmv_flgs=False,
hdrs=['I_CaII', 'I_Ha', 'I_NaI', 'I_HeI']):
"""
Saves timeseries plots of the indices identified in the rdb file by
starting with 'I_' in a 'multi-plot' format.
"""
if not out_dir: return
print()
print("Executing plt_time_mlty:")
if not info:
print("*** ERROR: There is no 'info' dictionary.")
return
if not hdrs:
print(
"*** ERROR: No input indices. Use '-i' and insert index names that are listed in the config file. To know location of config file call ACTIN without any arguments. To read a config file from a different location use '-cf' and add the path/file."
)
return
if len(hdrs) == 1:
print("Only one index selected, no need for multiplot")
return
obj = info['obj']
file_type = info['file_type']
instr = info['instr']
rdb_name = "{}_{}_{}_{}".format(obj, instr, file_type,
ac_set.fnames['data'])
rdb_file = os.path.join(out_dir, obj, rdb_name)
data = ac_tools.read_rdb(rdb_file)[0]
bjd_raw = np.asarray(data['bjd'])
bjd = bjd_raw - int(min(bjd_raw))
if len(bjd) == 1:
print("Only one data point, no need to plot.")
return
ind = {} # make new dictionary just for plots
ind_ids = [] # only the indices in data
for k in range(len(hdrs)):
if hdrs[k] in list(data):
ind[hdrs[k]] = data[hdrs[k]]
ind_err_key = '{}_err'.format(hdrs[k])
ind_flg_key = '{}_flg'.format(hdrs[k])
ind[ind_err_key] = data[ind_err_key]
ind[ind_flg_key] = data[ind_flg_key]
ind_ids.append(hdrs[k])
if len(ind_ids) == 1:
print("Only one index in rdb file, no need for multiplot.")
return
if len(ind_ids) > 5:
print("Too many indices for multiplot.")
return
width, height = ac_tools.plot_params(7, 4.5)
height = 2 * len(ind_ids)
plot_n = 0
n_subplots = len(ind_ids)
plt.figure(figsize=(width, height))
for k in range(len(ind_ids)):
plot_n += 1
ax1 = plt.subplot(n_subplots, 1, plot_n)
N = 0
for i in range(len(ind[ind_ids[k]])):
ind_flg = ind['{}_flg'.format(ind_ids[k])][i]
ind_val = ind[ind_ids[k]][i]
ind_err = ind['{}_err'.format(ind_ids[k])][i]
if ind_flg == 'None':
N += 1
plt.errorbar(bjd[i],
ind_val,
ind_err,
c='k',
marker='.',
ls='')
if ind_flg == 'negFlux':
if rmv_flgs == False:
N += 1
plt.errorbar(bjd[i],
ind_val,
ind_err,
c='r',
marker='.',
ls='')
else:
pass
plt.xlabel('BJD - {} [days]'.format(int(min(bjd_raw))))
plt.ylabel(ind_ids[k])
from matplotlib.ticker import MaxNLocator
ax1.yaxis.set_major_locator(MaxNLocator(prune='both'))
if plot_n != len(ind_ids):
frame1 = plt.gca()
frame1.axes.get_xaxis().set_ticklabels([])
n = len(ind_ids)
plt.annotate(obj,
xy=(0.05, 1. * n + 0.05),
xycoords='axes fraction',
textcoords='axes fraction')
plt.annotate("N = {}".format(N),
xy=(0.85, 1. * n + 0.05),
xycoords='axes fraction',
textcoords='axes fraction')
plt.subplots_adjust(hspace=0.000)
if save_plt == True:
save_name = '{}_{}_{}_{}_{}'.format(obj, instr, file_type,
('_').join(ind_ids),
ac_set.fnames['time_mlty_plt'])
save_file = os.path.join(out_dir, obj, save_name)
plt.savefig(save_file)
print("{} multiplot saved to:\n{}".format((', ').join(ind_ids),
save_file))
plt.close()
return