Box1D¶
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class
astropy.modeling.functional_models.
Box1D
[source] [edit on github]¶ Bases:
astropy.modeling.Fittable1DModel
One dimensional Box model.
Parameters: amplitude : float
Amplitude A
x_0 : float
Position of the center of the box function
width : float
Width of the box
Other Parameters: fixed : a dict
A dictionary
{parameter_name: boolean}
of parameters to not be varied during fitting. True means the parameter is held fixed. Alternatively thefixed
property of a parameter may be used.tied : dict
A dictionary
{parameter_name: callable}
of parameters which are linked to some other parameter. The dictionary values are callables providing the linking relationship. Alternatively thetied
property of a parameter may be used.bounds : dict
eqcons : list
A list of functions of length
n
such thateqcons[j](x0,*args) == 0.0
in a successfully optimized problem.ineqcons : list
A list of functions of length
n
such thatieqcons[j](x0,*args) >= 0.0
is a successfully optimized problem.See also
Notes
Model formula:
\[\begin{split}f(x) = \left \{ \begin{array}{ll} A & : x_0 - w/2 \geq x \geq x_0 + w/2 \\ 0 & : \textnormal{else} \end{array} \right.\end{split}\]Examples
import numpy as np import matplotlib.pyplot as plt from astropy.modeling.models import Box1D plt.figure() s1 = Box1D() r = np.arange(-5, 5, .01) for factor in range(1, 4): s1.amplitude = factor s1.width = factor plt.plot(r, s1(r), color=str(0.25 * factor), lw=2) plt.axis([-5, 5, -1, 4]) plt.show()
(Source code, png, hires.png, pdf)
Attributes Summary
amplitude
param_names
width
x_0
Methods Summary
evaluate
(x, amplitude, x_0, width)One dimensional Box model function fit_deriv
(x, amplitude, x_0, width)One dimensional Box model derivative with respect to parameters Attributes Documentation
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amplitude
¶
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param_names
= ('amplitude', 'x_0', 'width')¶
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width
¶
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x_0
¶
Methods Documentation
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static
evaluate
(x, amplitude, x_0, width)[source] [edit on github]¶ One dimensional Box model function
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classmethod
fit_deriv
(x, amplitude, x_0, width)[source] [edit on github]¶ One dimensional Box model derivative with respect to parameters
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