def _parse_yanny(self):
"""
Parse the yanny file (provided by :attr:`file`) for the
bandhead database.
Returns:
:obj:`list`: The list of
:class:`mangadap.par.parset.ParSet` instances for each
line of the database.
"""
# Read the yanny file
par = yanny(filename=self.file, raw=True)
if len(par['DAPABI']['index']) == 0:
raise ValueError('Could not find DAPABI entries in {0}!'.format(
self.file))
# Check if any of the bands are dummy bands and warn the user
self.dummy = numpy.any(numpy.array(par['DAPABI']['blueside']) < 0,
axis=1)
self.dummy |= numpy.any(numpy.array(par['DAPABI']['redside']) < 0,
axis=1)
self.dummy |= numpy.any(numpy.array(par['DAPABI']['primary']) < 0,
axis=1)
if numpy.sum(self.dummy) > 0:
warnings.warn(
'Bands with negative wavelengths are used to insert dummy values.'
' Ignoring input bands with indices: {0}'.format(
numpy.array(par['DAPABI']['index'])[self.dummy]))
# Setup the array of absorption-line index database parameters
self.size = len(par['DAPABI']['index'])
parlist = []
for i in range(self.size):
invac = par['DAPABI']['waveref'][i] == 'vac'
comp = par['DAPABI']['component'][i] != 0
parlist += [ BandPassFilterPar(index=par['DAPABI']['index'][i],
name=par['DAPABI']['name'][i],
blueside=par['DAPABI']['blueside'][i] if invac \
else airtovac(numpy.array(par['DAPABI']['blueside'][i])),
redside=par['DAPABI']['redside'][i] if invac \
else airtovac(numpy.array(par['DAPABI']['redside'][i])),
primary=par['DAPABI']['primary'][i] if invac \
else airtovac(numpy.array(par['DAPABI']['primary'][i])),
units=par['DAPABI']['units'][i],
integrand='flambda',
component=comp) ]
return parlist
def _parse_yanny(self):
"""
Parse the yanny file (provided by :attr:`file`) for the emission-line
database.
Returns:
:obj:`list`: The list of :class:`~mangadap.par.parset.ParSet`
instances for each line of the database.
"""
# Read the yanny file
par = yanny(filename=self.file, raw=True)
if len(par['DAPEML']['index']) == 0:
raise ValueError('Could not find DAPEML entries in {0}!'.format(
self.file))
# Setup the array of emission line database parameters
self.size = len(par['DAPEML']['index'])
parlist = []
for i in range(self.size):
invac = par['DAPEML']['waveref'][i] == 'vac'
tie_index = -1 if par['DAPEML']['tie'][i][0] == 'None' \
else int(par['DAPEML']['tie'][i][0])
tie_par = [
None if t == 'None' else t for t in par['DAPEML']['tie'][i][1:]
]
parlist += [EmissionLinePar(index=par['DAPEML']['index'][i],
name=par['DAPEML']['name'][i],
restwave=par['DAPEML']['restwave'][i] if invac
else airtovac(par['DAPEML']['restwave'][i]),
action=par['DAPEML']['action'][i], tie_index=tie_index,
tie_par=tie_par,
blueside=par['DAPEML']['blueside'][i] if invac else \
airtovac(numpy.array(par['DAPEML']['blueside'][i])),
redside=par['DAPEML']['redside'][i] if invac else \
airtovac(numpy.array(par['DAPEML']['redside'][i])))]
return parlist
def _parse_yanny(self):
"""
Parse the yanny file (provided by :attr:`file`) for the artifact
database.
Returns:
:obj:`list`: The list of
:class:`mangadap.par.parset.ParSet` instances for each
line of the database.
"""
# Read the yanny file
par = yanny(filename=self.file, raw=True)
if len(par['DAPART']['index']) == 0:
raise ValueError('Could not find DAPART entries in {0}!'.format(self.file))
# Setup the array of emission line database parameters
self.size = len(par['DAPART']['index'])
parlist = []
for i in range(self.size):
invac = par['DAPART']['waveref'][i] == 'vac'
parlist += [ ArtifactPar(index=par['DAPART']['index'][i], name=par['DAPART']['name'][i],
waverange=numpy.asarray(par['DAPART']['waverange'][i]) \
if invac else airtovac(par['DAPEML']['waverange'][i]) )]
return parlist
def available_reduction_assessments():
r"""
Return the list of available reduction assessment methods. To get a
list of default methods provided by the DAP do::
from mangadap.proc.reductionassessments import available_reduction_assessments
rdx_methods = available_reduction_assessments()
print(rdx_methods)
Each element in the `rdx_methods` list is an instance of
:class:`ReductionAssessmentDef`, which is printed using the
:class:`ParSet` base class representation function.
New methods can be included by adding ini config files to
`$MANGADAP_DIR/mangadap/config/reduction_assessments`. See an
example file at
`$MANGADAP_DIR/example_ini/example_reduction_assessment_config.ini`.
Returns:
:obj:`list`: A list of :class:`ReductionAssessmentDef`
objects, each defining a separate assessment method.
Raises:
IOError:
Raised if no reduction assessment configuration files
could be found.
KeyError:
Raised if the assessment method keywords are not all
unique.
ValueError:
Raised if a wavelength range or a response function are
not defined by any of the methods.
"""
# Check the configuration files exist
search_dir = os.path.join(defaults.dap_config_root(),
'reduction_assessments')
ini_files = glob.glob(os.path.join(search_dir, '*.ini'))
if len(ini_files) == 0:
raise IOError('Could not find any configuration files in {0} !'.format(
search_dir))
# Build the list of library definitions
assessment_methods = []
for f in ini_files:
# Read the config file
cnfg = DefaultConfig(f=f, interpolate=True)
# Ensure it has the necessary elements to define the template
# library
def_range, def_response = validate_reduction_assessment_config(cnfg)
in_vacuum = cnfg.getbool('in_vacuum', default=False)
if def_range:
waverange = cnfg.getlist('wave_limits', evaluate=True)
if not in_vacuum:
waverange = airtovac(waverange)
assessment_methods += [
ReductionAssessmentDef(key=cnfg['key'],
waverange=waverange,
covariance=cnfg.getbool('covariance',
default=False),
minimum_frac=cnfg.getfloat(
'minimum_frac', default=0.8))
]
elif def_response:
response = numpy.genfromtxt(cnfg['response_function_file'])[:, :2]
if not in_vacuum:
response[:, 0] = airtovac(response[:, 0])
assessment_methods += [
ReductionAssessmentDef(key=cnfg['key'],
response_func=response,
covariance=cnfg.getbool('covariance',
default=False),
minimum_frac=cnfg.getfloat(
'minimum_frac', default=0.8))
]
else:
raise ValueError(
'Must define a wavelength range or a response function.')
# Check the keywords of the libraries are all unique
if len(numpy.unique( numpy.array([ method['key'] for method in assessment_methods ]) )) \
!= len(assessment_methods):
raise KeyError(
'Reduction assessment method keywords are not all unique!')
# Return the default list of assessment methods
return assessment_methods
for filename_redshifts in filelist_redshifts:
print(filename_redshifts)
filename_spec1D = filename_redshifts.replace(
'_redshift.fits', '.fits')
print(filename_spec1D)
print(filename_redshifts)
redshifts = Table.read(filename_redshifts)
spec = Table.read(filename_spec1D)
print(redshifts)
print(spec)
redshifts['z'] = redshifts['z'] + dv_vacair / c_kms * (
1 + redshifts['z'])
redshifts.write(filename_redshifts.replace('_spec1D',
'_spec1D_vac'),
overwrite=False)
spec['wave'] = airtovac(spec['wave'] * u.Angstrom)
spec.write(filename_spec1D.replace('_spec1D', '_spec1D_vac'),
overwrite=False)
print('')
os.rename('{}_spec1D'.format(args.m), '{}_spec1D_air'.format(args.m))
os.rename('{}_spec1D_vac'.format(args.m), '{}_spec1D'.format(args.m))
else:
print('Vacuum directory already exists, will not overwrite')