hyperspy.datasets.artificial_data module

Functions for generating artificial data.

For use in things like docstrings or to test HyperSpy functionalities.

hyperspy.datasets.artificial_data.get_atomic_resolution_tem_signal2d()

Get an artificial atomic resolution TEM Signal2D.

Return type:: Signal2D

Example

>>> s = hs.datasets.artificial_data.get_atomic_resolution_tem_signal2d()
>>> s.plot()

hyperspy.datasets.artificial_data.get_core_loss_eels_line_scan_signal(add_powerlaw=False, add_noise=True, random_state=None)

Get an artificial core loss electron energy loss line scan spectrum.

Similar to a Mn-L32 and Fe-L32 edge from a perovskite oxide.

Parameters:

add_powerlaw (bool) – If True, adds a powerlaw background to the spectrum. Default is False.
add_noise (bool) – If True, add noise to the signal. See note to seed the noise to generate reproducible noise.
random_state (None or int or RandomState instance, default None) – Random seed used to generate the data.

Return type:

EELSSpectrum

Example

>>> s = hs.datasets.artificial_data.get_core_loss_eels_line_scan_signal()
>>> s.plot()

hyperspy.datasets.artificial_data.get_core_loss_eels_model(add_powerlaw=False, add_noise=True, random_state=None)

Get an artificial core loss electron energy loss model.

Similar to a Mn-L32 edge from a perovskite oxide.

Parameters:

add_powerlaw (bool) – If True, adds a powerlaw background to the spectrum. Default is False.
add_noise (bool) – If True, add noise to the signal. See note to seed the noise to generate reproducible noise.
random_state (None or int or RandomState instance, default None) – Random seed used to generate the data.

Return type:

EELSModel

Example

>>> import hs.datasets.artifical_data as ad
>>> s = ad.get_core_loss_eels_model()
>>> s.plot()

With the powerlaw background

>>> s = ad.get_core_loss_eels_model(add_powerlaw=True)
>>> s.plot()

See also

get_core_loss_eels_signal

hyperspy.datasets.artificial_data.get_core_loss_eels_signal(add_powerlaw=False, add_noise=True, random_state=None)

Get an artificial core loss electron energy loss spectrum.

Similar to a Mn-L32 edge from a perovskite oxide.

Some random noise is also added to the spectrum, to simulate experimental noise.

Parameters:

add_powerlaw (bool) – If True, adds a powerlaw background to the spectrum. Default is False.
add_noise (bool) – If True, add noise to the signal. See note to seed the noise to generate reproducible noise.
random_state (None or int or RandomState instance, default None) – Random seed used to generate the data.

Return type:

EELSSpectrum

Example

>>> import hs.datasets.artifical_data as ad
>>> s = ad.get_core_loss_eels_signal()
>>> s.plot()

With the powerlaw background

>>> s = ad.get_core_loss_eels_signal(add_powerlaw=True)
>>> s.plot()

To make the noise the same for multiple spectra, which can be useful for testing fitting routines

>>> s1 = ad.get_core_loss_eels_signal(random_state=10)
>>> s2 = ad.get_core_loss_eels_signal(random_state=10)
>>> (s1.data == s2.data).all()
True

hyperspy.datasets.artificial_data.get_low_loss_eels_line_scan_signal(add_noise=True, random_state=None)

Get an artificial low loss electron energy loss line scan spectrum.

The zero loss peak is offset by 4.1 eV.

Parameters:

add_noise (bool) – If True, add noise to the signal. See note to seed the noise to generate reproducible noise.
random_state (None or int or RandomState instance, default None) – Random seed used to generate the data.

Return type:

EELSSpectrum

Example

>>> s = hs.datasets.artificial_data.get_low_loss_eels_signal()
>>> s.plot()

See also

artificial_low_loss_line_scan_signal: EELSSpectrum

hyperspy.datasets.artificial_data.get_low_loss_eels_signal(add_noise=True, random_state=None)

Get an artificial low loss electron energy loss spectrum.

The zero loss peak is offset by 4.1 eV.

Parameters:

add_noise (bool) – If True, add noise to the signal. See note to seed the noise to generate reproducible noise.
random_state (None or int or RandomState instance, default None) – Random seed used to generate the data.

Return type:

EELSSpectrum

Example

>>> s = hs.datasets.artificial_data.get_low_loss_eels_signal()
>>> s.plot()

hyperspy.datasets.artificial_data.get_luminescence_signal(navigation_dimension=0, uniform=False, add_baseline=False, add_noise=True, random_state=None)

Get an artificial luminescence signal in wavelength scale (nm, uniform) or energy scale (eV, non-uniform), simulating luminescence data recorded with a diffracting spectrometer. Some random noise is also added to the spectrum, to simulate experimental noise.

Parameters:

navigation_dimension (positive int.) – The navigation dimension(s) of the signal. 0 = single spectrum, 1 = linescan, 2 = spectral map etc…
uniform (bool.) – return uniform (wavelength) or non-uniform (energy) spectrum
add_baseline (bool) – If true, adds a constant baseline to the spectrum. Conversion to energy representation will turn the constant baseline into inverse powerlaw.
add_noise (bool) – If True, add noise to the signal. See note to seed the noise to generate reproducible noise.
random_state (None or int or RandomState instance, default None) – Random seed used to generate the data.

Example

>>> import hyperspy.datasets.artificial_data as ad
>>> s = ad.get_luminescence_signal()
>>> s.plot()

With constant baseline

>>> s = ad.get_luminescence_signal(uniform=True, add_baseline=True)
>>> s.plot()

To make the noise the same for multiple spectra, which can be useful for testing fitting routines

>>> s1 = ad.get_luminescence_signal(random_state=10)
>>> s2 = ad.get_luminescence_signal(random_state=10)
>>> (s1.data == s2.data).all()
True

2D map

>>> s = ad.get_luminescence_signal(navigation_dimension=2)
>>> s.plot()