BrokenPowerLaw1D¶
-
class
astropy.modeling.powerlaws.
BrokenPowerLaw1D
(amplitude=1, x_break=1, alpha_1=1, alpha_2=1, **kwargs)[source] [edit on github]¶ Bases:
astropy.modeling.Fittable1DModel
One dimensional power law model with a break.
Parameters: - amplitude : float
Model amplitude at the break point.
- x_break : float
Break point.
- alpha_1 : float
Power law index for x < x_break.
- alpha_2 : float
Power law index for x > x_break.
See also
Notes
Model formula (with \(A\) for
amplitude
and \(\alpha_1\) foralpha_1
and \(\alpha_2\) foralpha_2
):\[\begin{split}f(x) = \left \{ \begin{array}{ll} A (x / x_{break}) ^ {-\alpha_1} & : x < x_{break} \\ A (x / x_{break}) ^ {-\alpha_2} & : x > x_{break} \\ \end{array} \right.\end{split}\]Attributes Summary
alpha_1
alpha_2
amplitude
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_break
Methods Summary
evaluate
(x, amplitude, x_break, alpha_1, alpha_2)One dimensional broken power law model function fit_deriv
(x, amplitude, x_break, alpha_1, …)One dimensional broken power law derivative with respect to parameters Attributes Documentation
-
alpha_1
¶
-
alpha_2
¶
-
amplitude
¶
-
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_break', 'alpha_1', 'alpha_2')¶
-
x_break
¶
Methods Documentation
-
static
evaluate
(x, amplitude, x_break, alpha_1, alpha_2)[source] [edit on github]¶ One dimensional broken power law model function
-
static
fit_deriv
(x, amplitude, x_break, alpha_1, alpha_2)[source] [edit on github]¶ One dimensional broken power law derivative with respect to parameters