ExponentialCutoffPowerLaw1D

class astropy.modeling.powerlaws.ExponentialCutoffPowerLaw1D(amplitude=1, x_0=1, alpha=1, x_cutoff=1, **kwargs)

Bases: astropy.modeling.Fittable1DModel

One dimensional power law model with an exponential cutoff.

Parameters:

amplitude : float

Model amplitude

x_0 : float

Reference point

alpha : float

Power law index

x_cutoff : float

Cutoff point

See also

PowerLaw1D, BrokenPowerLaw1D, LogParabola1D

Notes

Model formula (with \(A\) for amplitude and \(\alpha\) for alpha):

\[f(x) = A (x / x_0) ^ {-\alpha} \exp (-x / x_{cutoff})\]

Attributes Summary

alpha
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_0
x_cutoff

Methods Summary

evaluate(x, amplitude, x_0, alpha, x_cutoff)	One dimensional exponential cutoff power law model function
fit_deriv(x, amplitude, x_0, alpha, x_cutoff)	One dimensional exponential cutoff power law derivative with respect to parameters

Attributes Documentation

alpha = Parameter('alpha', value=1.0)

amplitude = Parameter('amplitude', value=1.0)

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', 'x_cutoff')

x_0 = Parameter('x_0', value=1.0)

x_cutoff = Parameter('x_cutoff', value=1.0)

Methods Documentation

static evaluate(x, amplitude, x_0, alpha, x_cutoff)

One dimensional exponential cutoff power law model function

static fit_deriv(x, amplitude, x_0, alpha, x_cutoff)

One dimensional exponential cutoff power law derivative with respect to parameters