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', is_norm=False)

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 of 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.

is_normbool

Whether the parameter represents the flux norm of the model.

Attributes Summary

conf_max	Confidence max value (float)
conf_min	Confidence min value (float)
error
factor	Factor (float).
factor_max	Factor max (float).
factor_min	Factor min (float).
frozen	Frozen? (used in fitting) (bool).
is_norm	Whether the parameter represents the norm of the model
max	Maximum (float).
min	Minimum (float).
name	Name (str).
quantity	Value times unit (Quantity).
scale	Scale (float).
scale_method	Method used to set factor and scale
scan_max	Stat scan max
scan_min	Stat scan min
scan_n_sigma	Stat scan n sigma
scan_values	Stat scan values (ndarray)
type
unit	Unit (Unit).
value	Value = factor x scale (float).

Methods Summary

autoscale()	Autoscale the parameters.
check_limits()	Emit a warning or error if value is outside the min/max range
copy()	A deep copy
to_dict()	Convert to dict.
update_from_dict(data)	Update parameters from a dict.

Attributes Documentation

conf_max

Confidence max value (float)

Returns parameter maximum if defined else the scan_max

conf_min

Confidence min value (float)

Returns parameter minimum if defined else the scan_min

error

factor

Factor (float).

factor_max

Factor max (float).

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

factor_min

Factor min (float).

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

frozen

Frozen? (used in fitting) (bool).

is_norm

Whether the parameter represents the norm of the model

max

Maximum (float).

min

Minimum (float).

name

Name (str).

quantity

Value times unit (Quantity).

scale

Scale (float).

scale_method

Method used to set factor and scale

scan_max

Stat scan max

scan_min

Stat scan min

scan_n_sigma

Stat scan n sigma

scan_values

Stat scan values (ndarray)

type

unit

Unit (Unit).

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 min/max range

copy()

A deep copy

to_dict()

Convert to dict.

update_from_dict(data)

Update parameters from a dict. Protection against changing parameter model, type, name.