# hyperspy._components.gaussianhf module

class hyperspy._components.gaussianhf.GaussianHF(height=1.0, fwhm=1.0, centre=0.0, module='numexpr', **kwargs)

Bases: Expression

Normalized gaussian function component, with a fwhm parameter instead of the sigma parameter, and a height parameter instead of the area parameter A (scaling difference of $$\sigma \sqrt{\left(2\pi\right)}$$). This makes the parameter vs. peak maximum independent of $$\sigma$$, and thereby makes locking of the parameter more viable. As long as there is no binning, the height parameter corresponds directly to the peak maximum, if not, the value is scaled by a linear constant (signal_axis.scale).

$f(x) = h\cdot\exp{\left[-\frac{4 \log{2} \left(x-c\right)^{2}}{W^{2}}\right]}$

Variable

Parameter

$$h$$

height

$$W$$

fwhm

$$c$$

centre

Parameters:
• height (float) – The height of the peak. If there is no binning, this corresponds directly to the maximum, otherwise the maximum divided by signal_axis.scale

• fwhm (float) – The full width half maximum value, i.e. the width of the gaussian at half the value of gaussian peak (at centre).

• centre (float) – Location of the gaussian maximum, also the mean position.

• **kwargs – Extra keyword arguments are passed to the Expression component.

A

Convenience attribute to get, set the area and defined for compatibility with Gaussian component.

Type:

float

sigma

Convenience attribute to get, set the width and defined for compatibility with Gaussian component.

Type:

float

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.

Return type:

bool

Notes

Examples

>>> g = hs.model.components1D.GaussianHF()
>>> 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[-1].offset = -10
>>> s.axes_manager[-1].scale = 0.01
>>> g.estimate_parameters(s, -10, 10, False)

integral_as_signal()

Utility function to get gaussian integral as Signal1D