def correct_zero(data, header, prefix, zero_file):
"""
Subtract zero from data.
Args:
data (numpy.ndarray) : A 2D numpy array that contains the data.
header (astropy.io.fits.Header) : A header that will be updated.
prefix (str) : File prefix that is added after each reduce.
zero_file (str | None) : Master Bias filename. If None is given,
nothing is done.
"""
from os.path import abspath
if zero_file is not None:
zero = _pyfits.open(abspath(zero_file))[0]
data = data - zero.data
header['BIASFILE'] = zero_file
prefix = 'z' + prefix
return data, header, prefix
def correct_flat(data, header, prefix, flat_file):
"""
Divide the image by the master flat file and add HISTORY to header.
Args:
data (numpy.ndarray) : A 2D numpy array that contains the data.
header (astropy.io.fits.Header) : A header that will be updated.
prefix (str) : File prefix that is added after each reduce.
flat_file (str or None) : Master flat filename. If None is given,
nothing is done.
"""
if not isinstance(prefix, str):
raise (TypeError,
'Expected string but found %s instead.' % prefix.__class__)
if flat_file is not None:
flat = _pyfits.open(flat_file)[0]
data /= flat.data
header['FLATFILE'] = flat_file
prefix = 'f' + prefix
return data, header, prefix
def correct_dark(data, header, prefix, dark_file=None):
"""
Subtract the dark file from data and add HISTORY to header.
Args:
data (numpy.ndarray) : A 2D numpy array that contains the data.
header (astropy.io.fits.Header) : A header that will be updated.
prefix : str
File prefix that is added after each reduce.
dark_file: str | None
Master Dark filename. If None is given, nothing is done.
"""
if not isinstance(prefix, str):
raise (TypeError,
'Expected string but found %s instead.' % prefix.__class__)
if dark_file is not None:
dark = _pyfits.open(dark_file)[0]
dark.data = dark.data / float(dark.header['EXPTIME'])
data = data - dark.data * header['EXPTIME']
header['DARKFILE'] = dark_file
prefix = 'd' + prefix
return data, header, prefix
def build_table(list_of_files):
"""
Return a pandas.DataFrame used to organize the pipeline.
Args:
list_of_files (list) : list of files to be included in the dataframe.
Returns:
table (pandas.Dataframe) : a dataframe with information needed for the
pipeline.
"""
log.info('Reading raw files')
table = pd.DataFrame(
columns=[
'filename',
'instrume',
'obstype',
'filters',
'filter1',
'filter2',
'binning',
'dark_file',
'flat_file',
'zero_file',
]
)
list_of_files.sort()
for _file in list_of_files:
try:
hdu = pyfits.open(_file)
except OSError:
log.warning("Could not read file: {}".format(_file))
continue
if numpy.std(hdu[1].data.ravel()) == 0:
log.warning("Bad data found on file: {}".format(_file))
continue
row = pd.Series(data={
'filename': _file,
'obstype': hdu[0].header['obstype'],
'instrume': hdu[0].header['instrume'].strip().upper(),
'filters': hdu[0].header['filters'],
'filter1': hdu[0].header['filter1'],
'filter2': hdu[0].header['filter2'],
'binning': hdu[1].header['ccdsum'].strip(),
'dark_file': None,
'flat_file': None,
'zero_file': None,
})
table = table.append(row, ignore_index=True, sort=True)
return table
def get_header(self, hdu_source):
"""
Return the header of the primary HDU extension of a FITS file.
Args:
hdu_source (str or astropy.io.fits.HDUList) : HDUList or name of the
file which contains a HDUList.
"""
from os.path import exists
if isinstance(hdu_source, str):
if not exists(hdu_source):
raise (IOError, '%s file not found.' % hdu_source)
hdu_source = _pyfits.open(hdu_source)
h0 = hdu_source[0].header
h1 = hdu_source[1].header
h0.append('UNITS')
h0.set('UNITS', value='ADU', comment='Pixel intensity units.')
# Save the CCD binning in the main header
h0['CCDSUM'] = h1['CCDSUM']
h0['DETSEC'] = h1['DETSEC']
# Save the area that corresponds to each amplifier
bin_size = _np.array(h0['CCDSUM'].split(' '), dtype=int)
dx, dy = slices.iraf2python(h0['DETSEC'])
dx, dy = dx // bin_size[0], dy // bin_size[1]
h0['AMP_SEC1'] = slices.python2iraf(dx[0], dx[1], dy[0], dy[1])
h0['AMP_SEC2'] = slices.python2iraf(dx[0] + dx[1], dx[1] + dx[1],
dy[0], dy[1])
h0['AMP_SEC3'] = slices.python2iraf(dx[0], dx[1], dy[0] + dy[1],
dy[1] + dy[1])
h0['AMP_SEC4'] = slices.python2iraf(dx[0] + dx[1], dx[1] + dx[1],
dy[0] + dy[1], dy[1] + dy[1])
return h0
def process_object_files(df, red_path):
"""
Args:
df (pandas.DataFrame) : a data-frame containing the all the data being
processed.
red_path (str) : the path where the reduced data is stored.
Returns:
updated_table (pandas.DataFrame) : an updated data-frame where each
file now is attached to the corresponding master Zero file.
"""
sami_pipeline = reduce.SamiReducer()
sami_pipeline.cosmic_rays = True
log.info('Processing OBJECT files.')
object_df = df.loc[df.obstype.values == 'OBJECT']
for index, row in object_df.iterrows():
sami_pipeline.zero_file = row.zero_file
sami_pipeline.dark_file = row.dark_file
sami_pipeline.flat_file = row.flat_file
obj_file = row.filename
path, fname = os.path.split(obj_file)
prefix = sami_pipeline.get_prefix()
output_obj_file = os.path.join(red_path, prefix + fname)
if os.path.exists(output_obj_file):
log.warning(
'Skipping existing OBJECT file: {}'.format(output_obj_file))
continue
log.info('Processing OBJECT file: {}'.format(obj_file))
hdul = pyfits.open(obj_file)
data, header, prefix = sami_pipeline.reduce(hdul)
pyfits.writeto(output_obj_file, data, header)
return df
def process_zero_files(df, red_path):
"""
Args:
df (pandas.DataFrame) : a data-frame containing the all the data being
processed.
red_path (str) : the path where the reduced data is stored.
Returns:
updated_table (pandas.DataFrame) : an updated data-frame where each
file now is attached to the corresponding master Zero file.
"""
sami_pipeline = reduce.SamiReducer()
sami_pipeline.cosmic_rays = True
binning = df.binning.unique()
log.debug('Binning formats found in data: ')
for b in binning:
log.debug(' {:s}'.format(b))
for b in binning:
bx, by = b.split(' ')
log.info('Processing ZERO files with binning: {} x {}'.format(bx, by))
mask1 = df.obstype.values == 'ZERO'
mask2 = df.binning.values == b
zero_table = df.loc[mask1 & mask2]
zero_table = zero_table.sort_values('filename')
zero_list = []
for index, row in zero_table.iterrows():
sami_pipeline.zero_file = None
sami_pipeline.flat_file = None
zero_file = row.filename
path, fname = os.path.split(zero_file)
prefix = sami_pipeline.get_prefix()
output_zero_file = os.path.join(red_path, prefix + fname)
zero_list.append(prefix + fname)
if os.path.exists(output_zero_file):
log.warning('Skipping existing file: {}'.format(
output_zero_file))
continue
log.info('Processing ZERO file: {}'.format(zero_file))
hdul = pyfits.open(zero_file)
data, header, prefix = sami_pipeline.reduce(hdul)
pyfits.writeto(output_zero_file, data, header)
if len(zero_list) == 0:
continue
zero_list_name = os.path.join(red_path, "0Zero{}x{}".format(bx, by))
with open(zero_list_name, 'w') as zero_list_buffer:
for zero_file in zero_list:
zero_list_buffer.write('{:s}\n'.format(zero_file))
log.info('Combining ZERO files.')
master_zero = zero_list_name + '.fits'
if os.path.exists(master_zero):
log.warning(
'Skipping existing MASTER ZERO: {:s}'.format(master_zero))
else:
log.info("Writing master zero to: {}".format(master_zero))
zero_combine_files = [os.path.join(red_path, f) for f in zero_list]
zero_combine = combine.ZeroCombine(input_list=zero_combine_files,
output_file=master_zero)
zero_combine.run()
log.info('Done.')
mask1 = df['obstype'].values != 'ZERO'
mask2 = df['binning'].values == b
df.loc[mask1 & mask2, 'zero_file'] = master_zero
return df
def process_flat_files(df, red_path):
"""
Args:
df (pandas.DataFrame) : a data-frame containing the all the data being
processed.
red_path (str) : the path where the reduced data is stored.
Returns:
updated_table (pandas.DataFrame) : an updated data-frame where each
file now is attached to the corresponding master Zero file.
"""
log.info('Processing FLAT files (SFLAT + DFLAT)')
sami_pipeline = reduce.SamiReducer()
binning = df.binning.unique()
for b in binning:
bx, by = b.split(' ')
log.info('Processing FLAT files with binning: {} x {}'.format(bx, by))
mask1 = (df.obstype.values == 'SFLAT') | (df.obstype.values == 'DFLAT')
mask2 = df.binning.values == b
flat_df = df.loc[mask1 & mask2]
filters_used = flat_df.filters.unique()
for _filter in filters_used:
log.info('Processing FLATs for filter: {}'.format(_filter))
filter_flat_df = flat_df.loc[flat_df.filters.values == _filter]
filter_flat_df.sort_values('filename')
log.info(
'Filter Wheel 1: {}'.format(filter_flat_df.filter1.unique()[0]))
log.info(
'Filter Wheel 2: {}'.format(filter_flat_df.filter2.unique()[0]))
flat_list = []
for index, row in filter_flat_df.iterrows():
sami_pipeline.zero_file = row.zero_file
sami_pipeline.dark_file = row.dark_file
sami_pipeline.flat_file = None
flat_file = row.filename
prefix = sami_pipeline.get_prefix()
path, fname = os.path.split(flat_file)
output_flat = os.path.join(red_path, prefix + fname)
flat_list.append(prefix + fname)
if os.path.exists(output_flat):
log.warning(
'Skipping existing FLAT file: {}'.format(output_flat))
continue
log.info('Processing FLAT file: {}'.format(flat_file))
hdul = pyfits.open(flat_file)
data, header, prefix = sami_pipeline.reduce(hdul)
pyfits.writeto(output_flat, data, header)
if len(flat_list) == 0:
continue
flat_list_name = os.path.join(
red_path, "1FLAT_{}x{}_{}".format(bx, by, _filter))
with open(flat_list_name, 'w') as flat_list_buffer:
for flat_file in flat_list:
flat_list_buffer.write('{:s}\n'.format(flat_file))
master_flat = flat_list_name + '.fits'
if os.path.exists(master_flat):
log.warning(
'Skipping existing MASTER FLAT: {:s}'.format(master_flat))
else:
log.info('Writing master FLAT to file: {}'.format(master_flat))
flat_combine_files = [os.path.join(red_path, f) for f in flat_list]
flat_combine = combine.FlatCombine(
input_list=flat_combine_files, output_file=master_flat)
flat_combine.run()
mask1 = df['obstype'].values == 'OBJECT'
mask2 = df['binning'].values == b
mask3 = df['filters'].values == _filter
df.loc[mask1 & mask2 & mask3, 'flat_file'] = master_flat
return df
