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)

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.

minfloat, optional

Minimum (sometimes used in fitting).

maxfloat, optional

Maximum (sometimes used in fitting).

frozenbool, optional

Frozen (used in fitting).

errorfloat

Parameter error.

scan_minfloat

Minimum value for the parameter scan. Overwrites scan_n_sigma.

scan_maxfloat

Minimum value for the parameter scan. Overwrites scan_n_sigma.

scan_n_values: int

Number of values to be used for the parameter scan.

scan_n_sigmaint

Number of sigmas to scan.

scan_values: `numpy.array`

Scan values. Overwrites all the scan keywords before.

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

Method used to set factor and scale.

interp{“lin”, “sqrt”, “log”}

Parameter scaling to use for the scan.

priorPrior

Prior set on the 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.
factor_min	Factor minimum as a float.
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.
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()	Autoscale the parameters.
check_limits()	Emit a warning or error if value is outside the minimum/maximum range.
copy()	Deep copy.
prior_stat_sum()
to_dict()	Convert to dictionary.
update_from_dict(data)	Update parameters from a dictionary.

Attributes Documentation

conf_max

Confidence maximum value as a float.

Return parameter maximum if defined, otherwise return the scan_max.

conf_min

Confidence minimum value as a float.

Return parameter minimum if defined, otherwise return the scan_min.

error

factor

Factor as a float.

factor_max

Factor maximum as a float.

This factor_max = max / scale is for the optimizer interface.

factor_min

Factor minimum as a float.

This factor_min = min / scale is for the optimizer interface.

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.

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()

Autoscale the parameters.

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.

check_limits()

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

copy()

Deep copy.

prior_stat_sum()

to_dict()

Convert to dictionary.

update_from_dict(data)

Update parameters from a dictionary.