LogParabola1D¶
- class astropy.modeling.powerlaws.LogParabola1D(amplitude=1, x_0=1, alpha=1, beta=0, **kwargs)[source]¶
Bases:
Fittable1DModel
One dimensional log parabola model (sometimes called curved power law).
- Parameters:
- amplitude
python:float
Model amplitude
- x_0
python:float
Reference point
- alpha
python:float
Power law index
- beta
python:float
Power law curvature
- amplitude
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