The sherpa.models.parameter module

Support for model parameter values.

Parameter creation, evaluation, and combination are normally done as part of the model interface provided by sherpa.models.model.ArithmeticModel.

In the following the p variable corresponds to

>>> from sherpa.models.parameter import Parameter
>>> p = Parameter('model', 'eta', 2)
>>> print(p)
val         = 2.0
min         = -3.4028234663852886e+38
max         = 3.4028234663852886e+38
units       =
frozen      = False
link        = None
default_val = 2.0
default_min = -3.4028234663852886e+38
default_max = 3.4028234663852886e+38

Naming

The first two arguments to a parameter are the model name and then the parameter name, so "model" and "eta" here. They are used to display the name and fullname attributes:

>>> p.name
'eta'
>>> p.fullname
'model.eta'

A units field can be attached to the parameter, but this is used purely for screen output by the sherpa.models.model.ArithmeticModel class and is not used when changing or evaluating the parameter.

Changing parameter values

The val attribute is used to retrieve or change the parameter value:

>>> p.val
2.0
>>> p.val = 3

Parameter limits

The parameter is forced to lie within the min and max attributes of the parameter (known as the “soft” limits). The default values for these are the 32-bit floating point maximum value, and it’s negative:

>>> p.max
3.4028234663852886e+38
>>> p.min
-3.4028234663852886e+38

Setting a value outside this range will raise a sherpa.utils.err.ParameterErr exception:

>>> p.val = 1e40
Traceback (most recent call last):
  ...
sherpa.utils.err.ParameterErr: parameter model.eta has a maximum of 3.40282e+38

These limits can be changed, as shown below, but they must lie within the hard_min to hard_max range of the parameter (the “hard” limits), which can not be changed:

>>> p.min = 0
>>> p.max = 10

Freezing and thawing

When fitting a model expression it is useful to be able to restrict the fit to a subset of parameters. This is done by only selecting those parameters which are not “frozen”. This can be indicated by calling the freeze and thaw methods, or changing the frozen attribute directly:

>>> p.frozen
False
>>> p.freeze()
>>> p.frozen
True
>>> p.thaw()
>>> p.frozen
False

Note that the frozen flag is used to indicate what parameters to vary in a fit, but it is still possible to directly change a parameter value when it is frozen:

>>> p.freeze()
>>> p.val = 6
>>> print(p)
val         = 6.0
min         = 0.0
max         = 10.0
units       =
frozen      = True
link        = None
default_val = 6.0
default_min = -3.4028234663852886e+38
default_max = 3.4028234663852886e+38

Changing multiple settings at once

The set method should be used when multiple settings need to be changed at once, as it allows for changes to both the value and limits, such as changing the value to be 20 and the limits to 8 to 30:

>>> p.val = 20
Traceback (most recent call last):
  ...
sherpa.utils.err.ParameterErr: parameter model.eta has a maximum of 10
>>> p.set(val=20, min=8, max=30)
>>> print(p)
val         = 20.0
min         = 8.0
max         = 30.0
units       =
frozen      = True
link        = None
default_val = 20.0
default_min = -3.4028234663852886e+38
default_max = 3.4028234663852886e+38

Linking parameters

A parameter can be “linked” to another parameter, in which case the value of the parameter is calculated based on the link expression, such as being twice the other parameter:

>>> q = Parameter('other', 'beta', 4)
>>> p.val = 2 * q
>>> print(p)
val         = 8.0
min         = 8.0
max         = 30.0
units       =
frozen      = True
link        = 2 * other.beta
default_val = 8.0
default_min = -3.4028234663852886e+38
default_max = 3.4028234663852886e+38

The link attribute stores the expression:

>>> p.link
<BinaryOpParameter '2 * other.beta'>

A ParameterErr exception will be raised whenever the linked expression is evaluated and the result lies outside the parameters soft limits (the min to max range). For this example, p must lie between 8 and 30 and so changing parameter q to a value of 3 will cause an error, but only when parameter p is checked, not when the related parameter (here q) is changed:

>>> q.val = 3
>>> print(p)
Traceback (most recent call last):
  ...
sherpa.utils.err.ParameterErr: parameter model.eta has a minimum of 8
>>> p.val
Traceback (most recent call last):
  ...
sherpa.utils.err.ParameterErr: parameter model.eta has a minimum of 8

Resetting a parameter

The reset method is used to restore the parameter value and soft limits to a known state. The idea is that if the values were changed in a fit then reset will change them back to the values before a fit.

Classes

`Parameter`(modelname, name, val[, min, max, ...])	Represent a model parameter.
`CompositeParameter`(name, parts)	Represent a parameter with composite parts.
`BinaryOpParameter`(lhs, rhs, op, opstr[, ...])	Combine two parameter expressions.
`ConstantParameter`(value)	Represent an expression containing 1 or more parameters.
`UnaryOpParameter`(arg, op, opstr[, strformat])	Apply an operator to a parameter expression.