`hyperspy.api.data`#

The hyperspy.api.data module includes synthetic data signal.

hyperspy.api.data.atomic_resolution_image(size=512, spacing=0.2, column_radius=0.05, rotation_angle=0, pixel_size=0.015, add_noise=False, random_state=None)#

Make an artificial atomic resolution image. The atomic columns are modelled with Gaussian functions.

Parameters:

sizeint or tuple of int, default=512: The number of pixels of the image in horizontal and vertical directions. If int, the size is the same in both directions.
spacingfloat or tuple of float, default=14: The spacing between the atomic columns in horizontal and vertical directions in nanometer.
column_radiusfloat, default=0.05: The radius of the atomic column, i. e. the width of the Gaussian in nanometer.
rotation_angleint or float, default=0: The rotation of the lattice in degree.
pixel_sizefloat, default=0.015: The pixel size in nanometer.
add_noisebool: If True, add noise to the signal. Use random_state to seed the noise to generate reproducible noise.
random_stateNone, int or numpy.random.Generator, default None: Random seed used to generate the data.

Returns:

Signal2D

Examples

>>> import hyperspy.api as hs
>>> s = hs.data.atomic_resolution_image()
>>> s.plot()

hyperspy.api.data.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_dimensionint: The navigation dimension(s) of the signal. 0 = single spectrum, 1 = linescan, 2 = spectral map etc…
uniformbool: return uniform (wavelength) or non-uniform (energy) spectrum
add_baselinebool: If true, adds a constant baseline to the spectrum. Conversion to energy representation will turn the constant baseline into inverse powerlaw.
add_noisebool: If True, add noise to the signal. Use random_state to seed the noise to generate reproducible noise.
random_stateNone, int or numpy.random.Generator, default None: Random seed used to generate the data.

Returns:

Signal1D

See also

atomic_resolution_image

Examples

>>> import hyperspy.api as hs
>>> s = hs.data.luminescence_signal()
>>> s.plot()

With constant baseline

>>> s = hs.data.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 = hs.data.luminescence_signal(random_state=10)
>>> s2 = hs.data.luminescence_signal(random_state=10)
>>> print((s1.data == s2.data).all())
True

2D map

>>> s = hs.data.luminescence_signal(navigation_dimension=2)
>>> s.plot()

hyperspy.api.data.two_gaussians(add_noise=True, return_model=False)#

Create synthetic data consisting of two Gaussian functions with random centers and area

Parameters:

add_noisebool: If True, add noise to the signal.
return_modelbool: If True, returns the model in addition to the signal.

Returns:

BaseSignal or tuple: Returns tuple when return_model=True.

hyperspy.api.data.wave_image(angle=45, wavelength=10, shape=(256, 256), add_noise=True, random_state=None)#

Returns a wave image generated using the sinus function.

Parameters:

anglefloat, optional: The angle in degree.
wavelengthfloat, optional: The wavelength the wave in pixel. The default is 10
shapetuple of float, optional: The shape of the data. The default is (256, 256).
add_noisebool: If True, add noise to the signal. Use random_state to seed the noise to generate reproducible noise.
random_stateNone, int or numpy.random.Generator, default None: Random seed used to generate the data.

Returns:

Signal2D

hyperspy.api.data#

`hyperspy.api.data`#