MexicanHat2D¶
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class
astropy.modeling.functional_models.
MexicanHat2D
[source] [edit on github]¶ Bases:
astropy.modeling.Fittable2DModel
Two dimensional symmetric Mexican Hat model.
Parameters: amplitude : float
Amplitude
x_0 : float
x position of the peak
y_0 : float
y position of the peak
sigma : float
Width of the Mexican hat
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:
\[f(x, y) = A \left(1 - \frac{\left(x - x_{0}\right)^{2} + \left(y - y_{0}\right)^{2}}{\sigma^{2}}\right) e^{\frac{- \left(x - x_{0}\right)^{2} - \left(y - y_{0}\right)^{2}}{2 \sigma^{2}}}\]Attributes Summary
amplitude
param_names
sigma
x_0
y_0
Methods Summary
evaluate
(x, y, amplitude, x_0, y_0, sigma)Two dimensional Mexican Hat model function Attributes Documentation
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amplitude
¶
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param_names
= ('amplitude', 'x_0', 'y_0', 'sigma')¶
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sigma
¶
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x_0
¶
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y_0
¶
Methods Documentation
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static
evaluate
(x, y, amplitude, x_0, y_0, sigma)[source] [edit on github]¶ Two dimensional Mexican Hat model function
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