PriorParameter#

class gammapy.modeling.PriorParameter(name, value, unit='', scale=1, min=nan, max=nan, error=0, scale_method='scale10', scale_transform='lin')[source]#

Bases: Parameter

Attributes Summary

conf_max	Confidence maximum value as a float.
conf_min	Confidence minimum value as a float.
error
factor	Factor as a float.
factor_max	Factor maximum as a float (used by the optimizer).
factor_min	Factor minimum as a float (used by the optimizer).
frozen	Frozen (used in fitting) (bool).
max	Maximum as a float.
min	Minimum as a float.
name	Name as a string.
prior	Prior applied to the parameter as a Prior.
quantity	Value times unit as a Quantity.
scale	Scale as a float.
scale_method	Method used to set factor and scale.
scale_transform	scale interp : {"lin", "sqrt", "log"}
scan_max	Stat scan maximum.
scan_min	Stat scan minimum.
scan_n_sigma	Stat scan n sigma.
scan_values	Stat scan values as a ndarray.
type
unit	Unit as a Unit object.
value	Value = factor x scale (float).

Methods Summary

autoscale()	Apply `interpolation_scale' and `scale_method' to the parameter.
check_limits()	Emit a warning or error if value is outside the minimum/maximum range.
copy()	Deep copy.
inverse_transform(factor)	Inverse tranform from factor (used by the optimizer) to value.
prior_stat_sum()
reset_autoscale()	Reset scaling such as factor=value, scale=1.
set_lim([min, max])	Set the min and/or max value for the parameter.
to_dict()	Convert to dictionary.
transform(value[, update_scale])	Tranform from value to factor (used by the optimizer).
update_from_dict(data)	Update parameters from a dictionary.
update_scale(value)	Update the parameter scale.

Attributes Documentation

conf_max#

Confidence maximum value as a float. Return parameter maximum if defined, otherwise a default is estimated from value and error.

conf_min#

Confidence minimum value as a float. Return parameter minimum if defined, otherwise a default is estimated from value and error.

error#

factor#

Factor as a float.

factor_max#

Factor maximum as a float (used by the optimizer).

By default when no transform is applied, factor_max = max / scale, otherwise factor_max = transform(max).

factor_min#

Factor minimum as a float (used by the optimizer).

By default when no transform is applied, factor_min = min / scale, otherwise factor_min = transform(min).

frozen#

Frozen (used in fitting) (bool).

max#

Maximum as a float.

min#

Minimum as a float.

name#

Name as a string.

prior#

Prior applied to the parameter as a Prior.

quantity#

Value times unit as a Quantity.

scale#

Scale as a float.

scale_method#

Method used to set factor and scale.

scale_transform#

scale interp : {“lin”, “sqrt”, “log”}

scan_max#

Stat scan maximum.

scan_min#

Stat scan minimum.

scan_n_sigma#

Stat scan n sigma.

scan_values#

Stat scan values as a ndarray.

type#

unit#

Unit as a Unit object.

value#

Value = factor x scale (float).

Methods Documentation

autoscale()#

Apply `interpolation_scale’ and `scale_method’ to the parameter.

check_limits()#

Emit a warning or error if value is outside the minimum/maximum range.

copy()#

Deep copy.

inverse_transform(factor)#

Inverse tranform from factor (used by the optimizer) to value.

Parameters:

valuefloat

Parameter factor

prior_stat_sum()#

reset_autoscale()#

Reset scaling such as factor=value, scale=1.

set_lim(min=None, max=None)#

Set the min and/or max value for the parameter.

Parameters:

min, max: float, `~astropy.units.Quantity`, str

Minimum and Maximum value to assign to the parameter min and max. Default is None, which set min and max to np.nan.

to_dict()[source]#

Convert to dictionary.

transform(value, update_scale=False)#

Tranform from value to factor (used by the optimizer).

Parameters:

valuefloat

Parameter value

update_scalebool, optional

Update the scaling (used by the autoscale). Default is False.

update_from_dict(data)#

Update parameters from a dictionary.

update_scale(value)#

Update the parameter scale.

Set factor and scale according to scale_method attribute.

Available scale_method.

• scale10 sets scale to power of 10, so that abs(factor) is in the range 1 to 10

• factor1 sets factor, scale = 1, value

In both cases the sign of value is stored in factor, i.e. the scale is always positive. If scale_method is None the scaling is ignored.