LogParabola1D¶
-
class
astropy.modeling.powerlaws.
LogParabola1D
(amplitude=1, x_0=1, alpha=1, beta=0, **kwargs)[source]¶ Bases:
astropy.modeling.Fittable1DModel
One dimensional log parabola model (sometimes called curved power law).
- Parameters
Notes
Model formula (with \(A\) for
amplitude
and \(\alpha\) foralpha
and \(\beta\) forbeta
):\[f(x) = A \left( \frac{x}{x_{0}}\right)^{- \alpha - \beta \log{\left (\frac{x}{x_{0}} \right )}}\]Attributes Summary
This property is used to indicate what units or sets of units the evaluate method expects, and returns a dictionary mapping inputs to units (or
None
if any units are accepted).Names of the parameters that describe models of this type.
Methods Summary
evaluate
(x, amplitude, x_0, alpha, beta)One dimensional log parabola model function
fit_deriv
(x, amplitude, x_0, alpha, beta)One dimensional log parabola derivative with respect to parameters
Attributes Documentation
-
alpha
= Parameter('alpha', value=1.0)¶
-
amplitude
= Parameter('amplitude', value=1.0)¶
-
beta
= Parameter('beta', value=0.0)¶
-
input_units
¶
-
param_names
= ('amplitude', 'x_0', 'alpha', 'beta')¶ Names of the parameters that describe models of this type.
The parameters in this tuple are in the same order they should be passed in when initializing a model of a specific type. Some types of models, such as polynomial models, have a different number of parameters depending on some other property of the model, such as the degree.
When defining a custom model class the value of this attribute is automatically set by the
Parameter
attributes defined in the class body.
-
x_0
= Parameter('x_0', value=1.0)¶
Methods Documentation