def load_dmodel_common(cls, info, ndim, dataset, dataset_cls):
desc = parseutils.make_typed_desc(cls, 'dmodel')
info = copy.deepcopy(info)
if dataset:
dataset_desc = parseutils.make_typed_desc(dataset_cls, 'dataset')
if not isinstance(dataset, dataset_cls):
raise RuntimeError(
f"{desc} "
f"is not compatible with "
f"{dataset_desc}")
info.update(dict(
size=dataset.size(),
step=info.get('step', dataset.step()),
rpix=info.get('rpix', dataset.rpix()),
rval=info.get('rval', dataset.rval()),
rota=info.get('rota', dataset.rota()),
dtype=info.get('dtype', str(dataset.dtype))))
opts = parseutils.parse_options_for_callable(info, desc, cls.__init__)
for attr in ['size', 'step', 'cval', 'scale']:
if attr in opts:
opts[attr] = opts[attr][:ndim]
if 'dtype' in opts:
opts['dtype'] = np.dtype(opts['dtype']).type
if 'psf' in opts:
opts['psf'] = gbkfit.psflsf.psf_parser.load(opts['psf'])
if 'lsf' in opts:
opts['lsf'] = gbkfit.psflsf.lsf_parser.load(opts['lsf'])
return opts
def load(cls, info):
desc = parseutils.make_typed_desc(cls, 'gmodel component')
opts = parseutils.parse_options_for_callable(info, desc, cls.__init__)
opts.update(
dict(rptraits=traits.rpt_parser.load(opts.get('rptraits')),
sptraits=traits.spt_parser.load(opts.get('sptraits')),
wptraits=traits.wpt_parser.load(opts.get('wptraits'))))
return cls(**opts)
def load(cls, info, **kwargs):
info = _parse_min_and_max(info, **kwargs)
desc = parseutils.make_typed_desc(cls, 'prior')
opts = parseutils.parse_options_for_callable(
info, desc, cls.__init__, fun_rename_args=dict(
minimum='min',
maximum='max'))
return cls(**opts)
def load(cls, info):
desc = parseutils.make_typed_desc(cls, 'fitter')
opts = parseutils.parse_options_for_callable(info, desc, cls.__init__)
if 'crossover' in opts:
opts['crossover'] = pymooutils.crossover_parser.load(
opts['crossover'])
if 'mutation' in opts:
opts['mutation'] = pymooutils.mutation_parser.load(
opts['mutation'])
if 'selection' in opts:
opts['selection'] = pymooutils.selection_parser.load(
opts['selection'])
if 'termination' in opts:
opts['termination'] = pymooutils.termination_parser.load(
opts['termination'])
return cls(**opts)
def __init__(self, data):
desc = parseutils.make_typed_desc(self.__class__, 'dataset')
# At least one data item must be defined
if not data:
raise RuntimeError(f"{desc} contains no data items")
if all([v is None for v in data.values()]):
raise RuntimeError()
# All data items must have the same properties
_ensure_same_attrib_value(data, 'size', desc)
_ensure_same_attrib_value(data, 'step', desc)
_ensure_same_attrib_value(data, 'rpix', desc)
_ensure_same_attrib_value(data, 'rval', desc)
_ensure_same_attrib_value(data, 'rota', desc)
_ensure_same_attrib_value(data, 'dtype', desc)
# We need to copy the data to ensure they are kept intact
self._data = copy.deepcopy(data)
def load_dataset_common(cls, info, names, **kwargs):
prefix = kwargs.get('prefix', '')
desc = parseutils.make_typed_desc(cls, 'dataset')
opts = parseutils.parse_options_for_callable(
info,
desc,
cls.__init__,
add_optional=['step', 'rpix', 'rval', 'rota'])
step = opts.pop('step', None)
rpix = opts.pop('rpix', None)
rval = opts.pop('rval', None)
rota = opts.pop('rota', None)
for name in names:
if name in opts:
opts[name] = data_parser.load(opts[name],
step,
rpix,
rval,
rota,
prefix=prefix)
return opts
def load(cls, info):
desc = parseutils.make_typed_desc(cls, 'fitter')
opts = parseutils.parse_options_for_callable(info, desc, cls.__init__)
opts.update(moves=load_moves_with_weights(opts.get('moves')))
return cls(**opts)
def load(cls, info):
desc = parseutils.make_typed_desc(cls, 'fitter')
opts = parseutils.parse_options_for_callable(info, desc, cls.__init__)
return cls(**opts)
def load(cls, info):
desc = parseutils.make_typed_desc(cls, 'gmodel')
opts = parseutils.parse_options_for_callable(info, desc, cls.__init__)
opts.update(components=_dcmp_parser.load(info['components']))
return cls(**opts)
def _load_psf_common(cls, info):
desc = parseutils.make_typed_desc(cls, 'PSF')
return parseutils.parse_options_for_callable(info, desc, cls.__init__)
