Parameter

class gammapy.modeling.Parameter(name, value, unit='', scale=1, min=nan, max=nan, frozen=False, error=0, scan_min=None, scan_max=None, scan_n_values=11, scan_n_sigma=2, scan_values=None, scale_method='scale10', interp='lin', prior=None, scale_transform='lin')

Bases: object

A model parameter.

Note that the parameter value has been split into a factor and scale like this:

value = factor x scale

Users should interact with the value, quantity or min and max properties and consider the fact that there is a factor and scale an implementation detail.

That was introduced for numerical stability in parameter and error estimation methods, only in the Gammapy optimiser interface do we interact with the factor, factor_min and factor_max properties, i.e. the optimiser “sees” the well-scaled problem.

Parameters:

namestr

Name.

valuefloat or Quantity

Value.

scalefloat, optional

Scale (sometimes used in fitting).

unitUnit or str, optional

Unit. Default is “”.

minfloat, str or quantity, optional

Minimum (sometimes used in fitting). If None, set to numpy.nan. Default is None.

maxfloat, str or quantity, optional

Maximum (sometimes used in fitting). Default is numpy.nan.

frozenbool, optional

Frozen (used in fitting). Default is False.

errorfloat, optional

Parameter error. Default is 0.

scan_minfloat, optional

Minimum value for the parameter scan. Overwrites scan_n_sigma. Default is None.

scan_maxfloat, optional

Maximum value for the parameter scan. Overwrites scan_n_sigma. Default is None.

scan_n_values: int, optional

Number of values to be used for the parameter scan. Default is 11.

scan_n_sigmaint, optional

Number of sigmas to scan. Default is 2.

scan_values: `numpy.array`, optional

Scan values. Overwrites all the scan keywords before. Default is None.

scale_method{‘scale10’, ‘factor1’, None}, optional

Method used to set factor and scale. Default is “scale10”.

interp{“lin”, “sqrt”, “log”}, optional

Parameter scaling to use for the scan. Default is “lin”.

priorPrior, optional

Prior set on the parameter. Default is None.

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 numpy.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 transform 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])	Transform 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 numpy.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 transform from factor (used by the optimizer) to value.

Parameters:

factorfloat

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` or str, optional

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

to_dict()

Convert to dictionary.

transform(value, update_scale=False)

Transform 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.