hyperspy._signals.hologram_image module

class hyperspy._signals.hologram_image.HologramImage(*args, **kw)

Bases: hyperspy._signals.signal2d.Signal2D

Image subclass for holograms acquired via off-axis electron holography.

Create a Signal from a numpy array.

Parameters

  • data (numpy.ndarray) – The signal data. It can be an array of any dimensions.

  • axes (dict, optional) – Dictionary to define the axes (see the documentation of the AxesManager class for more details).

  • attributes (dict, optional) – A dictionary whose items are stored as attributes.

  • metadata (dict, optional) – A dictionary containing a set of parameters that will to stores in the metadata attribute. Some parameters might be mandatory in some cases.

  • original_metadata (dict, optional) – A dictionary containing a set of parameters that will to stores in the original_metadata attribute. It typically contains all the parameters that has been imported from the original data file.

estimate_sideband_position(ap_cb_radius=None, sb='lower', high_cf=True, show_progressbar=False, parallel=None, max_workers=None)

Estimates the position of the sideband and returns its position.

Parameters

  • ap_cb_radius (float, None) – The aperture radius used to mask out the centerband.

  • sb (str, optional) – Chooses which sideband is taken. ‘lower’ or ‘upper’

  • high_cf (bool, optional) – If False, the highest carrier frequency allowed for the sideband location is equal to half of the Nyquist frequency (Default: True).

  • show_progressbar (None or bool) – If True, display a progress bar. If None, the default from the preferences settings is used.

  • parallel (None or bool) – If True, perform computation in parallel using multithreading. If None, the default from the preferences settings is used. The number of threads is controlled by the max_workers argument.

  • max_workers (None or int) – Maximum number of threads used when parallel=True. If None, defaults to min(32, os.cpu_count()).

Returns

Return type

Signal1D instance of sideband positions (y, x), referred to the unshifted FFT.

Examples

>>> import hyperspy.api as hs
>>> s = hs.datasets.example_signals.object_hologram()
>>> sb_position = s.estimate_sideband_position()
>>> sb_position.data

array([124, 452])

estimate_sideband_size(sb_position, show_progressbar=False, parallel=None, max_workers=None)

Estimates the size of the sideband and returns its size.

Parameters

  • sb_position (BaseSignal) – The sideband position (y, x), referred to the non-shifted FFT.

  • show_progressbar (None or bool) – If True, display a progress bar. If None, the default from the preferences settings is used.

  • parallel (None or bool) – If True, perform computation in parallel using multithreading. If None, the default from the preferences settings is used. The number of threads is controlled by the max_workers argument.

  • max_workers (None or int) – Maximum number of threads used when parallel=True. If None, defaults to min(32, os.cpu_count()).

Returns

sb_size – Sideband size referred to the unshifted FFT.

Return type

Signal1D

Examples

>>> import hyperspy.api as hs
>>> s = hs.datasets.example_signals.object_hologram()
>>> sb_position = s.estimate_sideband_position()
>>> sb_size = s.estimate_sideband_size(sb_position)
>>> sb_size.data
array([ 68.87670143])
reconstruct_phase(reference=None, sb_size=None, sb_smoothness=None, sb_unit=None, sb='lower', sb_position=None, high_cf=True, output_shape=None, plotting=False, store_parameters=True, show_progressbar=False, parallel=None, max_workers=None)

Reconstruct electron holograms. Operates on multidimensional hyperspy signals. There are several usage schemes:

  • Reconstruct 1d or Nd hologram without reference

  • Reconstruct 1d or Nd hologram using single reference hologram

  • Reconstruct Nd hologram using Nd reference hologram (applies each reference to each hologram in Nd stack)

The reconstruction parameters (sb_position, sb_size, sb_smoothness) have to be 1d or to have same dimensionality as the hologram.

Parameters

  • reference (ndarray, Signal2D, None) – Vacuum reference hologram.

  • sb_size (float, ndarray, BaseSignal, None) – Sideband radius of the aperture in corresponding unit (see ‘sb_unit’). If None, the radius of the aperture is set to 1/3 of the distance between sideband and center band.

  • sb_smoothness (float, ndarray, BaseSignal, None) – Smoothness of the aperture in the same unit as sb_size.

  • sb_unit (str, None) – Unit of the two sideband parameters ‘sb_size’ and ‘sb_smoothness’. Default: None - Sideband size given in pixels ‘nm’: Size and smoothness of the aperture are given in 1/nm. ‘mrad’: Size and smoothness of the aperture are given in mrad.

  • sb (str, None) – Select which sideband is selected. ‘upper’ or ‘lower’.

  • sb_position (tuple, Signal1D, None) – The sideband position (y, x), referred to the non-shifted FFT. If None, sideband is determined automatically from FFT.

  • high_cf (bool, optional) – If False, the highest carrier frequency allowed for the sideband location is equal to half of the Nyquist frequency (Default: True).

  • output_shape (tuple, None) – Choose a new output shape. Default is the shape of the input hologram. The output shape should not be larger than the input shape.

  • plotting (bool) – Shows details of the reconstruction (i.e. SB selection).

  • store_parameters (bool) – Store reconstruction parameters in metadata

  • show_progressbar (None or bool) – If True, display a progress bar. If None, the default from the preferences settings is used.

  • parallel (None or bool) – If True, perform computation in parallel using multithreading. If None, the default from the preferences settings is used. The number of threads is controlled by the max_workers argument.

  • max_workers (None or int) – Maximum number of threads used when parallel=True. If None, defaults to min(32, os.cpu_count()).

Returns

wave – Reconstructed electron wave. By default object wave is divided by reference wave.

Return type

ComplexSignal2D

Examples

>>> import hyperspy.api as hs
>>> s = hs.datasets.example_signals.object_hologram()
>>> sb_position = s.estimate_sideband_position()
>>> sb_size = s.estimate_sideband_size(sb_position)
>>> wave = s.reconstruct_phase(sb_position=sb_position, sb_size=sb_size)
set_microscope_parameters(beam_energy=None, biprism_voltage=None, tilt_stage=None)

Set the microscope parameters.

If no arguments are given, raises an interactive mode to fill the values.

Parameters

  • beam_energy (float) – The energy of the electron beam in keV

  • biprism_voltage (float) – In volts

  • tilt_stage (float) – In degrees

Examples

>>> s.set_microscope_parameters(beam_energy=300.)
>>> print('Now set to %s keV' %
>>>       s.metadata.Acquisition_instrument.
>>>       TEM.beam_energy)

Now set to 300.0 keV

statistics(sb_position=None, sb='lower', high_cf=False, fringe_contrast_algorithm='statistical', apodization='hanning', single_values=True, show_progressbar=False, parallel=None, max_workers=None)

Calculates following statistics for off-axis electron holograms:

1. Fringe contrast using either statistical definition or Fourier space approach (see description of fringe_contrast_algorithm parameter) 2. Fringe sampling (in pixels) 3. Fringe spacing (in calibrated units) 4. Carrier frequency (in calibrated units, radians and 1/px)

Parameters

  • sb_position (tuple, Signal1D, None) – The sideband position (y, x), referred to the non-shifted FFT. It has to be tuple or to have the same dimensionality as the hologram. If None, sideband is determined automatically from FFT.

  • sb (str, None) – Select which sideband is selected. ‘upper’, ‘lower’, ‘left’ or ‘right’.

  • high_cf (bool, optional) – If False, the highest carrier frequency allowed for the sideband location is equal to half of the Nyquist frequency (Default: False).

  • fringe_contrast_algorithm (str) –

    Select fringe contrast algorithm between:

    • ’fourier’: fringe contrast is estimated as 2 * <I(k_0)> / <I(0)>, where I(k_0) is intensity of sideband and I(0) is the intensity of central band (FFT origin). This method delivers also reasonable estimation if the interference pattern do not cover full field of view.

    • ’statistical’: fringe contrast is estimated by dividing the standard deviation by the mean of the hologram intensity in real space. This algorithm relies on regularly spaced fringes and covering the entire field of view.

    (Default: ‘statistical’)

  • apodization (str or None, optional) – Used with fringe_contrast_algorithm=’fourier’. If ‘hanning’ or ‘hamming’ apodization window will be applied in real space before FFT for estimation of fringe contrast. Apodization is typically needed to suppress striking due to sharp edges of the image, which often results in underestimation of the fringe contrast. (Default: ‘hanning’)

  • single_values (bool, optional) – If True calculates statistics only for the first navigation pixels and returns the values as single floats (Default: True)

  • show_progressbar (None or bool) – If True, display a progress bar. If None, the default from the preferences settings is used.

  • parallel (None or bool) – If True, perform computation in parallel using multithreading. If None, the default from the preferences settings is used. The number of threads is controlled by the max_workers argument.

  • max_workers (None or int) – Maximum number of threads used when parallel=True. If None, defaults to min(32, os.cpu_count()).

Returns

Dictionary with the statistics

Return type

statistics_dict

Examples

>>> import hyperspy.api as hs
>>> s = hs.datasets.example_signals.reference_hologram()
>>> sb_position = s.estimate_sideband_position(high_cf=True)
>>> s.statistics(sb_position=sb_position)
{'Fringe spacing (nm)': 3.4860442674236256,
'Carrier frequency (1/px)': 0.26383819985575441,
'Carrier frequency (mrad)': 0.56475154609203482,
'Fringe contrast': 0.071298357213623778,
'Fringe sampling (px)': 3.7902017241882331,
'Carrier frequency (1 / nm)': 0.28685808994016415}
class hyperspy._signals.hologram_image.LazyHologramImage(*args, **kw)

Bases: hyperspy._signals.lazy.LazySignal, hyperspy._signals.hologram_image.HologramImage

Create a Signal from a numpy array.

Parameters

  • data (numpy.ndarray) – The signal data. It can be an array of any dimensions.

  • axes (dict, optional) – Dictionary to define the axes (see the documentation of the AxesManager class for more details).

  • attributes (dict, optional) – A dictionary whose items are stored as attributes.

  • metadata (dict, optional) – A dictionary containing a set of parameters that will to stores in the metadata attribute. Some parameters might be mandatory in some cases.

  • original_metadata (dict, optional) – A dictionary containing a set of parameters that will to stores in the original_metadata attribute. It typically contains all the parameters that has been imported from the original data file.

hyperspy._signals.hologram_image._estimate_fringe_contrast_statistical(holo)

Estimates average fringe contrast of a hologram using statistical definition: V = STD / MEAN.

Parameters

holo_data (ndarray) – The data of the hologram.

Returns

Return type

Fringe contrast as a float