def fit_select(function: str = 'gaussian',
fit_type: str = 'spectrum',
**kwargs):
if fit_type == 'spectrum':
file_name = pkg_resources.resource_filename(
'ifscube', 'examples/manga_onedspec.fits')
spec = onedspec.Spectrum(file_name,
primary='PRIMARY',
scidata='F_OBS',
variance='F_VAR',
flags='F_FLAG',
stellar='F_SYN')
fit = modeling.LineFit(spec,
function=function,
fitting_window=(6400.0, 6800.0),
**kwargs)
elif fit_type == 'cube':
file_name = pkg_resources.resource_filename(
'ifscube', 'examples/ngc3081_cube.fits')
cube = datacube.Cube(file_name, variance='ERR')
fit = modeling.LineFit3D(cube,
function=function,
fitting_window=(6400.0, 6800.0),
**kwargs)
else:
raise RuntimeError(f'fit_type "{fit_type}" not understood.')
return fit
def test_missing_k_group():
file_name = pkg_resources.resource_filename(
'ifscube', 'examples/manga_onedspec.fits')
spec = onedspec.Spectrum(file_name,
primary='PRIMARY',
scidata='F_OBS',
variance='F_VAR',
flags='F_FLAG',
stellar='F_SYN')
fit = modeling.LineFit(spec,
function='gaussian',
fitting_window=(6400.0, 6800.0))
fit.add_feature(name='line_a',
rest_wavelength=6548,
amplitude=1,
velocity=0.0,
sigma=100.0)
fit.add_feature(name='line_b',
rest_wavelength=6563,
amplitude=1,
velocity=0.0,
sigma=100.0,
kinematic_group=1)
fit.pack_groups()
assert True
def setup_fit(data: Union[onedspec.Spectrum, datacube.Cube], **line_fit_args):
if isinstance(data, datacube.Cube):
general_fit_args = {
_: line_fit_args[_]
for _ in [
'function', 'fitting_window', 'instrument_dispersion',
'individual_spec', 'spiral_loop', 'spiral_center', 'refit',
'refit_radius', 'bounds_change'
] if _ in line_fit_args.keys()
}
fit = modeling.LineFit3D(data, **general_fit_args)
elif isinstance(data, onedspec.Spectrum):
general_fit_args = {
_: line_fit_args[_]
for _ in ['function', 'fitting_window', 'instrument_dispersion']
if _ in line_fit_args.keys()
}
fit = modeling.LineFit(data, **general_fit_args)
else:
raise RuntimeError(f'Data instance "{str(data)}" not recognized.')
for feature in line_fit_args['features']:
fit.add_feature(**feature)
for bounds in line_fit_args['bounds']:
fit.set_bounds(*bounds)
for constraint in line_fit_args['constraints']:
fit.add_constraint(*constraint)
if line_fit_args['optimize_fit']:
fit.optimize_fit(width=line_fit_args['optimization_window'])
return fit
def test_skip_feature():
file_name = pkg_resources.resource_filename('ifscube',
'examples/ngc6300_nuc.fits')
spec = onedspec.Spectrum(file_name)
fit = modeling.LineFit(spec, fitting_window=(6400.0, 6700.0))
with pytest.warns(UserWarning):
fit.add_feature(name='hb',
rest_wavelength=4861.0,
amplitude=0,
velocity=0,
sigma=10)
fit.add_feature(name='ha',
rest_wavelength=6562.8,
amplitude=1.0e-14,
velocity=0.0,
sigma=100.0)
fit.fit()
assert 1
def test_fixed_features():
file_name = pkg_resources.resource_filename('ifscube',
'examples/ngc6300_nuc.fits')
spec = onedspec.Spectrum(file_name)
fit = modeling.LineFit(spec,
function='gaussian',
fitting_window=(6400.0, 6800.0))
names = ['n2_6548', 'ha', 'n2_6583', 's2_6716', 's2_6731']
r_wl = [6548.04, 6562.8, 6583.46, 6716.44, 6730.86]
for name, wl, k in zip(names, r_wl, [0, 1, 0, 2, 2]):
amp = 1e-14 if name == 'n2_6548' else 3.e-14
fit.add_feature(name=name,
rest_wavelength=wl,
amplitude=amp,
velocity=0.0,
sigma=100.0,
fixed='n2' in name,
kinematic_group=k)
fit.fit()
assert 1
def load_fit(file_name):
"""
Loads the result of a previous fit, and put it in the
appropriate variables for the plot function.
Parameters
----------
file_name : string
Name of the FITS file containing the fit results.
Returns
-------
Nothing.
"""
with fits.open(file_name, mode='readonly') as h:
if len(h['FITSPEC'].data.shape) == 3:
data = datacube.Cube(file_name,
scidata='FITSPEC',
variance='VAR',
flags='FLAGS',
stellar='STELLAR',
primary='PRIMARY',
spatial_mask='MASK2D',
wavelength='RESTWAVE',
redshift=0.0)
# Redshift is set to zero because LineFit already puts everything in the rest frame.
elif len(h['FITSPEC'].data.shape) == 1:
data = onedspec.Spectrum(file_name,
scidata='FITSPEC',
variance='VAR',
flags='FLAGS',
stellar='STELLAR',
primary='PRIMARY',
wavelength='RESTWAVE',
redshift=0.0)
else:
raise RuntimeError(
f'Data dimensions are expected to be either 1 or 3, got "{len(h["FITSPEC"].data.shape)}".'
)
if 'FITCONFIG' in h:
config = table_to_config(table.Table(h['FITCONFIG'].data))
line_fit_config = parser.LineFitParser(config).get_vars()
line_fit_config['refit'] = False
fit = setup_fit(data, **line_fit_config)
fit.pack_groups()
else:
if len(h['FITSPEC'].data.shape) == 3:
fit = modeling.LineFit3D(data)
elif len(h['FITSPEC'].data.shape) == 1:
fit = modeling.LineFit(data)
fit = features_from_table(fit,
parameter_names=h['parnames'].data,
solution=h['solution'].data,
rest_wavelength=h['featwl'].data)
translate_extensions = {
'pseudo_continuum': 'fitcont',
'model': 'model',
'eqw_model': 'eqw_m',
'eqw_direct': 'eqw_d',
'flux_model': 'flux_m',
'flux_direct': 'flux_d',
'status': 'status',
'reduced_chi_squared': 'red_chi'
}
# Backwards compatibility
key = 'solution'
if len(fit.parameter_names) == (len(h[key].data) - 1):
warnings.warn(
'It seems you are trying to read a file from IFSCube v1.0. '
'Removing last plane from solution extension.',
stacklevel=2)
setattr(fit, key, h[key].data[:-1])
else:
setattr(fit, key, h[key].data)
for key in translate_extensions:
value = translate_extensions[key]
if value in h:
setattr(fit, key, h[translate_extensions[key]].data)
else:
warnings.warn(
f'Extension {value} not found in file {h.filename()}. Adding place holder data to this extension.'
)
if key == 'reduced_chi_squared':
setattr(fit, key, np.zeros(data.data.shape[1:]))
else:
warnings.warn(
f'No behaviour set for extension {key}. Leaving empty.'
)
return fit