def __init__(self, lineshape=None, n=1, model=[], background=[], op=operator.add, preftext='lp', **kws):
""" Initialize class.
"""
self.op = op
self.lineshape = self._model_convert(lineshape)
self.background = self._model_convert(background)
self.model = model
self.preftext = preftext
# Initialize the number of components
self.nbg = 0 # number of background components
self.nlp = 0 # number of line profiles
if 'independent_vars' not in kws:
try:
kws['independent_vars'] = self.components[0].independent_vars
except:
pass
if 'missing' not in kws:
try:
kws['missing'] = self.components[0].missing
except:
pass
def _tmp(self, *args, **kws):
pass
Model.__init__(self, _tmp, **kws)
for side in self.components:
prefix = side.prefix
for basename, hint in side.param_hints.items():
self.param_hints["%s%s" % (prefix, basename)] = hint
def __init__(self, model=[], n=0, lineshape=[], background=[], op=operator.add, preftext='lp', **kws):
""" Initialize class.
"""
self.components = []
self.op = op
self.preftext = preftext
# Initialize the number of components
self.nbg = 0 # number of background components
self.nlp = 0 # number of line profiles
# Introduce a background function
if background:
try:
bg_comp = background.components
if type(bg_comp) == list:
self.components += bg_comp
self.nbg = len(bg_comp)
except:
bg_comp = list(background)
self.components += bg_comp
self.nbg = len(bg_comp)
# Construct the lineshape components
if lineshape:
self.components += [lineshape(prefix=preftext+str(i+1)+'_', **kws) for i in range(n)]
self.nlp = n
if model:
self.components += model.components
self.nlp += len(model.components)
if 'independent_vars' not in kws:
try:
kws['independent_vars'] = self.components[0].independent_vars
except:
pass
if 'missing' not in kws:
try:
kws['missing'] = self.components[0].missing
except:
pass
# def _tmp(self, *args, **kws):
# pass
# Model.__init__(self, _tmp, **kws)
Model.__init__(self, self._tmp, **kws)
for side in self.components:
prefix = side.prefix
for basename, hint in side.param_hints.items():
self.param_hints["%s%s" % (prefix, basename)] = hint
def __init__(
self, left, right, on_undefined_conv="numeric", convMap=None, **kws
):
if not isinstance(left, Model):
raise ValueError(self._bad_arg.format(arg=left))
if not isinstance(right, Model):
raise ValueError(self._bad_arg.format(arg=right))
if not np.isin(on_undefined_conv, ["numeric", "raise"]):
raise ValueError(
"Parameter 'on_undefined_conv' should be either "
"'numeric' or 'raise'."
)
self.left = left
self.right = right
self._on_undefined_conv = on_undefined_conv
name_collisions = set(left.param_names) & set(right.param_names)
if len(name_collisions) > 0:
msg = ""
for collision in name_collisions:
msg += self._names_collide.format(clash=collision)
raise NameError(msg)
# we assume that all the sub-models have the same independent vars
if "independent_vars" not in kws:
kws["independent_vars"] = self.left.independent_vars
if "nan_policy" not in kws:
kws["nan_policy"] = self.left.nan_policy
def _tmp(self, *args, **kws):
pass
Model.__init__(self, _tmp, **kws)
for side in (left, right):
prefix = side.prefix
for basename, hint in side.param_hints.items():
self.param_hints["%s%s" % (prefix, basename)] = hint
# set a default convolution map
self.convMap = {
"lorentzian": {
"lorentzian": conv_lorentzian_lorentzian,
"gaussian": conv_gaussian_lorentzian,
"pvoigt": conv_lorentzian_pvoigt,
"delta": conv_delta,
"linear": conv_linear,
},
"gaussian": {
"lorentzian": conv_gaussian_lorentzian,
"gaussian": conv_gaussian_gaussian,
"delta": conv_delta,
"linear": conv_linear,
"pvoigt": conv_gaussian_pvoigt,
},
"pvoigt": {
"lorentzian": conv_lorentzian_pvoigt,
"gaussian": conv_gaussian_pvoigt,
"jump_diff": conv_jumpdiff_pvoigt,
"linear": conv_linear,
"delta": conv_delta,
"rotations": conv_rotations_pvoigt,
},
"jump_diff": {
"gaussian": conv_gaussian_jumpdiff,
"delta": conv_delta,
"linear": conv_linear,
"pvoigt": conv_jumpdiff_pvoigt,
},
"rotations": {
"gaussian": conv_gaussian_rotations,
"delta": conv_delta,
"linear": conv_linear,
"pvoigt": conv_rotations_pvoigt,
},
"delta": {
"gaussian": conv_delta,
"lorentzian": conv_delta,
"voigt": conv_delta,
"pvoigt": conv_delta,
"jump_diff": conv_delta,
"rotations": conv_delta,
"linear": conv_linear,
"two_diff_state": conv_delta,
},
"linear": {
"gaussian": conv_linear,
"lorentzian": conv_linear,
"voigt": conv_linear,
"pvoigt": conv_linear,
"jump_diff": conv_linear,
"rotations": conv_linear,
"two_diff_state": conv_linear,
},
}
# override default convolutions with provided ones
if convMap is not None:
self.convMap.update(convMap)
