def read_old_fitstbl(spectrograph, f):
fitstbl = PypeItMetaData(spectrograph, data=Table.read(f))
type_bits = np.zeros(len(fitstbl), dtype=fitstbl.bitmask.minimum_dtype())
for bit in fitstbl.bitmask.keys():
type_bits[fitstbl[bit]] = fitstbl.bitmask.turn_on(
type_bits[fitstbl[bit]], flag=bit)
fitstbl.set_frame_types(type_bits)
return fitstbl
def dummy_fitstbl(nfile=10,
spectro_name='shane_kast_blue',
directory='',
notype=False):
"""
Generate a dummy fitstbl for testing
Parameters
----------
nfile : int, optional
Number of files to mimic
spectro_name : str, optional
Name of spectrograph to mimic
notype : bool (optional)
If True, do not add image type info to the fitstbl
Returns
-------
fitstbl : PypeItMetaData
"""
fitsdict = {}
fitsdict['index'] = np.arange(nfile)
fitsdict['directory'] = [directory] * nfile
fitsdict['filename'] = ['b{:03d}.fits.gz'.format(i) for i in range(nfile)]
# TODO: The below will fail at 60
dates = ['2015-01-23T00:{:02d}:11.04'.format(i) for i in range(nfile)]
ttime = time.Time(dates, format='isot')
fitsdict['mjd'] = ttime.mjd
fitsdict['target'] = ['Dummy'] * nfile
fitsdict['ra'] = ['00:00:00'] * nfile
fitsdict['dec'] = ['+00:00:00'] * nfile
fitsdict['exptime'] = [300.] * nfile
fitsdict['dispname'] = ['600/4310'] * nfile
fitsdict['dichroic'] = ['560'] * nfile
fitsdict["binning"] = ['1,1'] * nfile
fitsdict["airmass"] = [1.0] * nfile
if spectro_name == 'shane_kast_blue':
fitsdict['numamplifiers'] = [1] * nfile
# Lamps
for i in range(1, 17):
fitsdict['lampstat{:02d}'.format(i)] = ['off'] * nfile
fitsdict['exptime'][0] = 0 # Bias
fitsdict['lampstat06'][1] = 'on' # Arc
fitsdict['exptime'][1] = 30 # Arc
fitsdict['lampstat01'][2] = 'on' # Trace, pixel, slit flat
fitsdict['lampstat01'][3] = 'on' # Trace, pixel, slit flat
fitsdict['exptime'][2] = 30 # flat
fitsdict['exptime'][3] = 30 # flat
fitsdict['ra'][4] = '05:06:36.6' # Standard
fitsdict['dec'][4] = '52:52:01.0'
fitsdict['airmass'][4] = 1.2
fitsdict['ra'][5] = '07:06:23.45' # Random object
fitsdict['dec'][5] = '+30:20:50.5'
fitsdict['decker'] = ['0.5 arcsec'] * nfile
# arrays
for k in fitsdict.keys():
fitsdict[k] = np.array(fitsdict[k])
spectrograph = load_spectrograph(spectro_name)
fitstbl = PypeItMetaData(spectrograph,
spectrograph.default_pypeit_par(),
data=fitsdict)
fitstbl['instrume'] = spectro_name
type_bits = np.zeros(len(fitstbl),
dtype=fitstbl.type_bitmask.minimum_dtype())
# Image typing
if not notype:
if spectro_name == 'shane_kast_blue':
#fitstbl['sci_ID'] = 1 # This links all the files to the science object
type_bits[0] = fitstbl.type_bitmask.turn_on(type_bits[0],
flag='bias')
type_bits[1] = fitstbl.type_bitmask.turn_on(type_bits[1],
flag='arc')
type_bits[1] = fitstbl.type_bitmask.turn_on(type_bits[1],
flag='tilt')
type_bits[2:4] = fitstbl.type_bitmask.turn_on(
type_bits[2:4], flag=['pixelflat', 'trace', 'illumflat'])
type_bits[4] = fitstbl.type_bitmask.turn_on(type_bits[4],
flag='standard')
type_bits[5:] = fitstbl.type_bitmask.turn_on(type_bits[5:],
flag='science')
fitstbl.set_frame_types(type_bits)
# Calibration groups
cfgs = fitstbl.unique_configurations(
ignore_frames=['bias', 'dark'])
fitstbl.set_configurations(cfgs)
fitstbl.set_calibration_groups(global_frames=['bias', 'dark'])
return fitstbl