def make_parameter_dict(pdict, fixed_par=False, rescale=True,
update_bounds=False):
"""
Update a parameter dictionary. This function will automatically
set the parameter scale and bounds if they are not defined.
Bounds are also adjusted to ensure that they encompass the
parameter value.
"""
o = copy.deepcopy(pdict)
o.setdefault('scale', 1.0)
if rescale:
value, scale = utils.scale_parameter(o['value'] * o['scale'])
o['value'] = np.abs(value) * np.sign(o['value'])
o['scale'] = np.abs(scale) * np.sign(o['scale'])
if 'error' in o:
o['error'] /= np.abs(scale)
if update_bounds:
o['min'] = o['value'] * 1E-3
o['max'] = o['value'] * 1E3
if fixed_par:
o['min'] = o['value']
o['max'] = o['value']
if float(o['min']) > float(o['value']):
o['min'] = o['value']
if float(o['max']) < float(o['value']):
o['max'] = o['value']
return o
def make_parameter_dict(pdict,
fixed_par=False,
rescale=True,
update_bounds=False):
"""
Update a parameter dictionary. This function will automatically
set the parameter scale and bounds if they are not defined.
Bounds are also adjusted to ensure that they encompass the
parameter value.
"""
o = copy.deepcopy(pdict)
o.setdefault('scale', 1.0)
if rescale:
value, scale = utils.scale_parameter(o['value'] * o['scale'])
o['value'] = np.abs(value) * np.sign(o['value'])
o['scale'] = np.abs(scale) * np.sign(o['scale'])
if 'error' in o:
o['error'] /= np.abs(scale)
if update_bounds:
o['min'] = o['value'] * 1E-3
o['max'] = o['value'] * 1E3
if fixed_par:
o['min'] = o['value']
o['max'] = o['value']
if float(o['min']) > float(o['value']):
o['min'] = o['value']
if float(o['max']) < float(o['value']):
o['max'] = o['value']
return o
def make_parameter_dict(pdict, fixed_par=False, rescale=True):
"""
Update a parameter dictionary. This function will automatically
set the parameter scale and bounds if they are not defined.
Bounds are also adjusted to ensure that they encompass the
parameter value.
"""
o = copy.deepcopy(pdict)
if 'scale' not in o or o['scale'] is None:
if rescale:
value, scale = utils.scale_parameter(o['value'])
else:
value, scale = o['value'], 1.0
o['value'] = value
o['scale'] = scale
if 'error' in o:
o['error'] /= np.abs(scale)
if 'min' not in o:
o['min'] = o['value'] * 1E-3
if 'max' not in o:
o['max'] = o['value'] * 1E3
if fixed_par:
o['min'] = o['value']
o['max'] = o['value']
if float(o['min']) > float(o['value']):
o['min'] = o['value']
if float(o['max']) < float(o['value']):
o['max'] = o['value']
# for k, v in o.items():
# o[k] = str(v)
return o