LogParabola1D¶
-
class
astropy.modeling.powerlaws.
LogParabola1D
(amplitude=1, x_0=1, alpha=1, beta=0, **kwargs)[source] [edit on github]¶ Bases:
astropy.modeling.Fittable1DModel
One dimensional log parabola model (sometimes called curved power law).
Parameters: - amplitude : float
Model amplitude
- x_0 : float
Reference point
- alpha : float
Power law index
- beta : float
Power law curvature
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
alpha
amplitude
beta
input_units
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).param_names
x_0
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
¶
-
amplitude
¶
-
beta
¶
-
input_units
¶ 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).Model sub-classes can also use function annotations in evaluate to indicate valid input units, in which case this property should not be overridden since it will return the input units based on the annotations.
-
param_names
= ('amplitude', 'x_0', 'alpha', 'beta')¶
-
x_0
¶
Methods Documentation
-
static
evaluate
(x, amplitude, x_0, alpha, beta)[source] [edit on github]¶ One dimensional log parabola model function
-
static
fit_deriv
(x, amplitude, x_0, alpha, beta)[source] [edit on github]¶ One dimensional log parabola derivative with respect to parameters