def load(cls, info, step=None, rpix=None, rval=None, rota=None, prefix=''):
desc = parseutils.make_basic_desc(cls, 'data')
opts = parseutils.parse_options_for_callable(info, desc, cls.__init__)
data_d, header_d, wcs_d = _load_fits(prefix + opts['data'])
data_m = None
data_e = None
if 'mask' in opts:
data_m = _load_fits(prefix + opts['mask'])[0]
if 'error' in opts:
data_e = _load_fits(prefix + opts['error'])[0]
# Local information has higher priority than global
step = opts.get('step', step)
rpix = opts.get('rpix', rpix)
rval = opts.get('rval', rval)
# If no information is provided, use fits header
if step is None:
step = wcs_d.wcs.cdelt # noqa
if rpix is None:
rpix = wcs_d.wcs.crpix # noqa
if rval is None:
rval = wcs_d.wcs.crval # noqa
# todo: deal with rotation (PC Matrix and CROTA (deprecated))
# Build class arguments dict
opts.update(dict(
data=data_d,
mask=data_m,
error=data_e,
step=step,
rpix=rpix,
rval=rval,
rota=0))
return cls(**opts)
def load(cls, info, pdescs):
desc = parseutils.make_basic_desc(cls, 'fit params')
opts = parseutils.parse_options_for_callable(
info, desc, cls.__init__, fun_ignore_args=['pdescs'])
opts = paramutils.load_params_parameters_conversions(
opts, pdescs, collections.abc.Mapping, cls.load_param)
return cls(pdescs, **opts)
def load(cls, info, pdescs):
desc = parseutils.make_basic_desc(cls, 'params')
opts = parseutils.parse_options_for_callable(
info, desc, cls.__init__, fun_ignore_args=['pdescs'])
opts = paramutils.load_params_parameters_conversions(
opts, pdescs, Real, lambda x: x)
return cls(pdescs, **opts)
def load(cls, info):
desc = parseutils.make_basic_desc(cls, 'fit parameter')
opts = parseutils.parse_options_for_callable(
info, desc, cls.__init__, fun_rename_args=dict(
initial_value='value',
initial_scale='scale',
minimum='min',
maximum='max'))
return cls(**opts)
def load(cls, info):
info['prior'] = fitutils.prepare_param_prior_info(info)
info['prior'] = prior_parser.load(info['prior'], param_info=info)
desc = parseutils.make_basic_desc(cls, 'fit parameter')
opts = parseutils.parse_options_for_callable(
info,
desc,
cls.__init__,
fun_rename_args=dict(initial_value='value', initial_width='width'))
return cls(**opts)
def load(cls, info, descs):
desc = parseutils.make_basic_desc(cls, 'fit params')
opts = parseutils.parse_options_for_callable(info,
desc,
cls.__init__,
fun_ignore_args=['descs'])
parameters = load_parameters(opts.get('parameters'), descs,
cls.load_param)
value_conversions = paramparsers.load_params_value_conversions(
opts.get('value_conversions'))
return cls(descs, parameters, value_conversions)
def load(cls, info, descs):
desc = parseutils.make_basic_desc(cls, 'fit parameters')
opts = parseutils.parse_options_for_callable(
info, desc, cls.__init__, fun_ignore_args=['descs'])
infos, exprs = paramutils.parse_param_info(
opts['parameters'], descs)[4:]
for key, val in infos.items():
try:
infos[key] = FitParamPygmo.load(val)
except RuntimeError as e:
raise RuntimeError(
f"could not parse information for parameter '{key}'; "
f"reason: {str(e)}") from e
parameters = infos | exprs
expressions = paramutils.load_param_value_conversions(
opts.get('value_conversions'))
return cls(descs, parameters, expressions)
def load(cls, info, datasets, drivers, dmodels, gmodels):
desc = parseutils.make_basic_desc(cls, ' goodness objective')
opts = parseutils.parse_options_for_callable(
info, desc, cls.__init__, fun_ignore_args=[
'datasets', 'drivers', 'dmodels', 'gmodels'])
return cls(datasets, drivers, dmodels, gmodels, **opts)
def load(cls, info):
info['prior'] = fitutils.prepare_param_prior_info(info)
info['prior'] = prior_parser.load(info['prior'], param_info=info)
desc = parseutils.make_basic_desc(cls, 'fit parameter')
opts = parseutils.parse_options_for_callable(info, desc, cls.__init__)
return cls(**opts)
