hyperspy._components.gaussian module¶

class hyperspy._components.gaussian.Gaussian(A=1.0, sigma=1.0, centre=0.0, module='numexpr', **kwargs)¶

Bases: hyperspy._components.expression.Expression

Normalized Gaussian function component.

$f(x) = \frac{A}{\sigma \sqrt{2\pi}}\exp\left[ -\frac{\left(x-x_0\right)^{2}}{2\sigma^{2}}\right]$

Variable	Parameter
$A$	A
$\sigma$	sigma
$x_0$	centre

Parameters

A (float) – Height scaled by $\sigma\sqrt{(2\pi)}$ . GaussianHF implements the Gaussian function with a height parameter corresponding to the peak height.
sigma (float) – Scale parameter of the Gaussian distribution.
centre (float) – Location of the Gaussian maximum (peak position).
**kwargs – Extra keyword arguments are passed to the Expression component.

For convenience the fwhm attribute can be used to get and set the full-with-half-maximum.

estimate_parameters(signal, x1, x2, only_current=False)¶

Estimate the Gaussian by calculating the momenta.

Parameters

signal (Signal1D instance) –
x1 (float) – Defines the left limit of the spectral range to use for the estimation.
x2 (float) – Defines the right limit of the spectral range to use for the estimation.
only_current (bool) – If False estimates the parameters for the full dataset.

Returns

Return type

bool

Notes

Adapted from http://www.scipy.org/Cookbook/FittingData

Examples

>>> g = hs.model.components1D.Gaussian()
>>> x = np.arange(-10, 10, 0.01)
>>> data = np.zeros((32, 32, 2000))
>>> data[:] = g.function(x).reshape((1, 1, 2000))
>>> s = hs.signals.Signal1D(data)
>>> s.axes_manager._axes[-1].offset = -10
>>> s.axes_manager._axes[-1].scale = 0.01
>>> g.estimate_parameters(s, -10, 10, False)

property fwhm¶