def test_deepcopy():
s = BaseSignal([0])
s.metadata.test = [0]
s.original_metadata.test = [0]
s_deepcopy = s.deepcopy()
s.metadata.test.append(1)
s.original_metadata.test.append(1)
assert s.metadata.test == [0, 1]
assert s.original_metadata.test == [0, 1]
assert s_deepcopy.metadata.test == [0]
assert s_deepcopy.original_metadata.test == [0]
def reconstruct_phase(self,
reference=None,
sb_size=None,
sb_smoothness=None,
sb_unit=None,
sb='lower',
sb_position=None,
output_shape=None,
plotting=False,
show_progressbar=False,
store_parameters=True,
parallel=None):
"""Reconstruct electron holograms. Operates on multidimensional
hyperspy signals. There are several usage schemes:
1. Reconstruct 1d or Nd hologram without reference
2. Reconstruct 1d or Nd hologram using single reference hologram
3. 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, :class:`~hyperspy.signals.Signal2D, None
Vacuum reference hologram.
sb_size : float, ndarray, :class:`~hyperspy.signals.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, :class:`~hyperspy.signals.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, :class:`~hyperspy.signals.Signal1D, None
The sideband position (y, x), referred to the non-shifted FFT. If
None, sideband is determined automatically from FFT.
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 : boolean
Shows details of the reconstruction (i.e. SB selection).
show_progressbar : boolean
Shows progressbar while iterating over different slices of the
signal (passes the parameter to map method).
parallel : bool
Run the reconstruction in parallel
store_parameters : boolean
Store reconstruction parameters in metadata
Returns
-------
wave : :class:`~hyperspy.signals.WaveImage
Reconstructed electron wave. By default object wave is devided by
reference wave
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
>>> wave = s.reconstruct_phase(sb_position=sb_position, sb_size=sb_size)
"""
# TODO: Use defaults for choosing sideband, smoothness, relative filter
# size and output shape if not provided
# TODO: Plot FFT with marked SB and SB filter if plotting is enabled
# Parsing reference:
if not isinstance(reference, HologramImage):
if isinstance(reference, Signal2D):
if (not reference.axes_manager.navigation_shape
== self.axes_manager.navigation_shape
and reference.axes_manager.navigation_size):
raise ValueError('The navigation dimensions of object and'
'reference holograms do not match')
_logger.warning('The reference image signal type is not '
'HologramImage. It will be converted to '
'HologramImage automatically.')
reference.set_signal_type('hologram')
elif reference is not None:
reference = HologramImage(reference)
if isinstance(reference.data, daArray):
reference = reference.as_lazy()
# Testing match of navigation axes of reference and self
# (exception: reference nav_dim=1):
if (reference and not reference.axes_manager.navigation_shape
== self.axes_manager.navigation_shape
and reference.axes_manager.navigation_size):
raise ValueError('The navigation dimensions of object and '
'reference holograms do not match')
if reference and not reference.axes_manager.signal_shape == self.axes_manager.signal_shape:
raise ValueError('The signal dimensions of object and reference'
' holograms do not match')
# Parsing sideband position:
if sb_position is None:
_logger.warning('Sideband position is not specified. The sideband '
'will be found automatically which may cause '
'wrong results.')
if reference is None:
sb_position = self.estimate_sideband_position(
sb=sb, parallel=parallel)
else:
sb_position = reference.estimate_sideband_position(
sb=sb, parallel=parallel)
else:
if isinstance(sb_position, BaseSignal) and \
not sb_position._signal_dimension == 1:
raise ValueError('sb_position dimension has to be 1')
if not isinstance(sb_position, Signal1D):
sb_position = Signal1D(sb_position)
if isinstance(sb_position.data, daArray):
sb_position = sb_position.as_lazy()
if not sb_position.axes_manager.signal_size == 2:
raise ValueError('sb_position should to have signal size of 2')
if sb_position.axes_manager.navigation_size != self.axes_manager.navigation_size:
if sb_position.axes_manager.navigation_size:
raise ValueError('Sideband position dimensions do not match'
' neither reference nor hologram dimensions.')
# sb_position navdim=0, therefore map function should not iterate:
else:
sb_position_temp = sb_position.data
else:
sb_position_temp = sb_position.deepcopy()
## Parsing sideband size
# Default value is 1/2 distance between sideband and central band
if sb_size is None:
if reference is None:
sb_size = self.estimate_sideband_size(sb_position,
parallel=parallel)
else:
sb_size = reference.estimate_sideband_size(sb_position,
parallel=parallel)
else:
if not isinstance(sb_size, BaseSignal):
if isinstance(sb_size,
(np.ndarray, daArray)) and sb_size.size > 1:
# transpose if np.array of multiple instances
sb_size = BaseSignal(sb_size).T
else:
sb_size = BaseSignal(sb_size)
if isinstance(sb_size.data, daArray):
sb_size = sb_size.as_lazy()
if sb_size.axes_manager.navigation_size != self.axes_manager.navigation_size:
if sb_size.axes_manager.navigation_size:
raise ValueError('Sideband size dimensions do not match '
'neither reference nor hologram dimensions.')
# sb_position navdim=0, therefore map function should not iterate:
else:
sb_size_temp = np.float64(sb_size.data)
else:
sb_size_temp = sb_size.deepcopy()
# Standard edge smoothness of sideband aperture 5% of sb_size
if sb_smoothness is None:
sb_smoothness = sb_size * 0.05
else:
if not isinstance(sb_smoothness, BaseSignal):
if isinstance(
sb_smoothness,
(np.ndarray, daArray)) and sb_smoothness.size > 1:
sb_smoothness = BaseSignal(sb_smoothness).T
else:
sb_smoothness = BaseSignal(sb_smoothness)
if isinstance(sb_smoothness.data, daArray):
sb_smoothness = sb_smoothness.as_lazy()
if sb_smoothness.axes_manager.navigation_size != self.axes_manager.navigation_size:
if sb_smoothness.axes_manager.navigation_size:
raise ValueError('Sideband smoothness dimensions do not match'
' neither reference nor hologram '
'dimensions.')
# sb_position navdim=0, therefore map function should not iterate it:
else:
sb_smoothness_temp = np.float64(sb_smoothness.data)
else:
sb_smoothness_temp = sb_smoothness.deepcopy()
# Convert sideband size from 1/nm or mrad to pixels
if sb_unit == 'nm':
f_sampling = np.divide(
1,
[a * b for a, b in \
zip(self.axes_manager.signal_shape,
(self.axes_manager.signal_axes[0].scale,
self.axes_manager.signal_axes[1].scale))]
)
sb_size_temp = sb_size_temp / np.mean(f_sampling)
sb_smoothness_temp = sb_smoothness_temp / np.mean(f_sampling)
elif sb_unit == 'mrad':
f_sampling = np.divide(
1,
[a * b for a, b in \
zip(self.axes_manager.signal_shape,
(self.axes_manager.signal_axes[0].scale,
self.axes_manager.signal_axes[1].scale))]
)
try:
ht = self.metadata.Acquisition_instrument.TEM.beam_energy
except:
raise AttributeError("Please define the beam energy."
"You can do this e.g. by using the "
"set_microscope_parameters method")
momentum = 2 * constants.m_e * constants.elementary_charge * ht * \
1000 * (1 + constants.elementary_charge * ht * \
1000 / (2 * constants.m_e * constants.c ** 2))
wavelength = constants.h / np.sqrt(momentum) * 1e9 # in nm
sb_size_temp = sb_size_temp / (1000 * wavelength *
np.mean(f_sampling))
sb_smoothness_temp = sb_smoothness_temp / (1000 * wavelength *
np.mean(f_sampling))
# Find output shape:
if output_shape is None:
## Future improvement will give a possibility to choose
# if sb_size.axes_manager.navigation_size > 0:
# output_shape = (np.int(sb_size.inav[0].data*2), np.int(sb_size.inav[0].data*2))
# else:
# output_shape = (np.int(sb_size.data*2), np.int(sb_size.data*2))
output_shape = self.axes_manager.signal_shape
output_shape = output_shape[::-1]
# Logging the reconstruction parameters if appropriate:
_logger.info('Sideband position in pixels: {}'.format(sb_position))
_logger.info('Sideband aperture radius in pixels: {}'.format(sb_size))
_logger.info(
'Sideband aperture smoothness in pixels: {}'.format(sb_smoothness))
# Reconstructing object electron wave:
# Checking if reference is a single image, which requires sideband
# parameters as a nparray to avoid iteration trough those:
wave_object = self.map(
reconstruct,
holo_sampling=(self.axes_manager.signal_axes[0].scale,
self.axes_manager.signal_axes[1].scale),
sb_size=sb_size_temp,
sb_position=sb_position_temp,
sb_smoothness=sb_smoothness_temp,
output_shape=output_shape,
plotting=plotting,
show_progressbar=show_progressbar,
inplace=False,
parallel=parallel,
ragged=False)
# Reconstructing reference wave and applying it (division):
if reference is None:
wave_reference = 1
# case when reference is 1d
elif reference.axes_manager.navigation_size != self.axes_manager.navigation_size:
# Prepare parameters for reconstruction of the reference wave:
if reference.axes_manager.navigation_size == 0 and \
sb_position.axes_manager.navigation_size > 0:
# 1d reference, but parameters are multidimensional
sb_position_ref = _first_nav_pixel_data(sb_position_temp)
else:
sb_position_ref = sb_position_temp
if reference.axes_manager.navigation_size == 0 and \
sb_size.axes_manager.navigation_size > 0:
# 1d reference, but parameters are multidimensional
sb_size_ref = _first_nav_pixel_data(sb_size_temp)
else:
sb_size_ref = sb_size_temp
if reference.axes_manager.navigation_size == 0 and \
sb_smoothness.axes_manager.navigation_size > 0:
# 1d reference, but parameters are multidimensional
sb_smoothness_ref = np.float64(
_first_nav_pixel_data(sb_smoothness_temp))
else:
sb_smoothness_ref = sb_smoothness_temp
#
wave_reference = reference.map(
reconstruct,
holo_sampling=(self.axes_manager.signal_axes[0].scale,
self.axes_manager.signal_axes[1].scale),
sb_size=sb_size_ref,
sb_position=sb_position_ref,
sb_smoothness=sb_smoothness_ref,
output_shape=output_shape,
plotting=plotting,
show_progressbar=show_progressbar,
inplace=False,
parallel=parallel,
ragged=False)
else:
wave_reference = reference.map(
reconstruct,
holo_sampling=(self.axes_manager.signal_axes[0].scale,
self.axes_manager.signal_axes[1].scale),
sb_size=sb_size_temp,
sb_position=sb_position_temp,
sb_smoothness=sb_smoothness_temp,
output_shape=output_shape,
plotting=plotting,
show_progressbar=show_progressbar,
inplace=False,
parallel=parallel,
ragged=False)
wave_image = wave_object / wave_reference
# New signal is a complex
wave_image.set_signal_type('complex_signal2d')
wave_image.axes_manager.signal_axes[0].scale = \
self.axes_manager.signal_axes[0].scale * \
self.axes_manager.signal_shape[0] / output_shape[1]
wave_image.axes_manager.signal_axes[1].scale = \
self.axes_manager.signal_axes[1].scale * \
self.axes_manager.signal_shape[1] / output_shape[0]
# Reconstruction parameters are stored in holo_reconstruction_parameters:
if store_parameters:
rec_param_dict = OrderedDict([('sb_position', sb_position_temp),
('sb_size', sb_size_temp),
('sb_units', sb_unit),
('sb_smoothness', sb_smoothness_temp)
])
wave_image.metadata.Signal.add_node('Holography')
wave_image.metadata.Signal.Holography.add_node(
'Reconstruction_parameters')
wave_image.metadata.Signal.Holography.Reconstruction_parameters.add_dictionary(
rec_param_dict)
_logger.info('Reconstruction parameters stored in metadata')
return wave_image
def test_apodization(lazy, window_type, inplace):
SIZE_NAV0 = 2
SIZE_NAV1 = 3
SIZE_NAV2 = 4
SIZE_SIG0 = 50
SIZE_SIG1 = 60
SIZE_SIG2 = 70
ax_dict0 = {'size': SIZE_NAV0, 'navigate': True}
ax_dict1 = {'size': SIZE_SIG0, 'navigate': False}
ax_dict2 = {'size': SIZE_SIG1, 'navigate': False}
ax_dict3 = {'size': SIZE_SIG2, 'navigate': False}
# 1. Test apodization for signal 1D, 2D, 3D:
data = np.random.rand(SIZE_NAV0 * SIZE_NAV1 * SIZE_SIG0 *
SIZE_NAV2).reshape(
(SIZE_NAV0, SIZE_NAV1, SIZE_NAV2, SIZE_SIG0))
data2 = np.random.rand(SIZE_NAV0 * SIZE_NAV1 * SIZE_SIG0 *
SIZE_SIG1).reshape(
(SIZE_NAV0, SIZE_NAV1, SIZE_SIG0, SIZE_SIG1))
data3 = np.random.rand(SIZE_NAV0 * SIZE_SIG2 * SIZE_SIG0 *
SIZE_SIG1).reshape(
(SIZE_NAV0, SIZE_SIG0, SIZE_SIG1, SIZE_SIG2))
signal1d = Signal1D(data)
signal2d = Signal2D(data2)
signal3d = BaseSignal(data3, axes=[ax_dict0, ax_dict1, ax_dict2, ax_dict3])
if lazy:
signal1d = signal1d.as_lazy()
signal2d = signal2d.as_lazy()
signal3d = signal3d.as_lazy()
if window_type == 'hann':
window = np.hanning(SIZE_SIG0)
window1 = np.hanning(SIZE_SIG1)
window2 = np.hanning(SIZE_SIG2)
window2d = np.outer(window, window1)
window3d = outer_nd(window, window1, window2)
if inplace:
signal1d_a = signal1d.deepcopy()
signal1d_a.apply_apodization(window=window_type, inplace=inplace)
else:
signal1d_a = signal1d.apply_apodization(window=window_type)
data_a = data * window[np.newaxis, np.newaxis, np.newaxis, :]
if inplace:
signal2d_a = signal2d.deepcopy()
signal2d_a.apply_apodization(window=window_type, inplace=inplace)
else:
signal2d_a = signal2d.apply_apodization(window=window_type,
inplace=inplace)
data2_a = data2 * window2d[np.newaxis, np.newaxis, :, :]
if inplace:
signal3d_a = signal3d.deepcopy()
signal3d_a.apply_apodization(window=window_type, inplace=inplace)
else:
signal3d_a = signal3d.apply_apodization(window=window_type,
inplace=inplace)
data3_a = data3 * window3d[np.newaxis, :, :, :]
assert np.allclose(signal1d_a.data, data_a)
assert np.allclose(signal2d_a.data, data2_a)
assert np.allclose(signal3d_a.data, data3_a)
for hann_order in 9 * (np.random.rand(5)) + 1:
window = hann_window_nth_order(SIZE_SIG0, order=int(hann_order))
signal1d_a = signal1d.apply_apodization(window=window_type,
hann_order=int(hann_order))
data_a = data * window[np.newaxis, np.newaxis, np.newaxis, :]
assert np.allclose(signal1d_a.data, data_a)
elif window_type == 'hamming':
window = np.hamming(SIZE_SIG0)
signal1d_a = signal1d.apply_apodization(window=window_type)
data_a = data * window[np.newaxis, np.newaxis, np.newaxis, :]
assert np.allclose(signal1d_a.data, data_a)
elif window_type == 'tukey':
for tukey_alpha in np.random.rand(5):
window = tukey(SIZE_SIG0, alpha=tukey_alpha)
signal1d_a = signal1d.apply_apodization(window=window_type,
tukey_alpha=tukey_alpha)
data_a = data * window[np.newaxis, np.newaxis, np.newaxis, :]
assert np.allclose(signal1d_a.data, data_a)
# 2. Test raises:
with pytest.raises(ValueError):
signal1d.apply_apodization(window='hamm')
def reconstruct_phase(self,
reference=None,
sb_size=None,
sb_smoothness=None,
sb_unit=None,
sb='lower',
sb_position=None,
output_shape=None,
plotting=False,
show_progressbar=False,
store_parameters=True,
parallel=None):
"""Reconstruct electron holograms. Operates on multidimensional
hyperspy signals. There are several usage schemes:
1. Reconstruct 1d or Nd hologram without reference
2. Reconstruct 1d or Nd hologram using single reference hologram
3. 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, :class:`~hyperspy.signals.Signal2D, None
Vacuum reference hologram.
sb_size : float, ndarray, :class:`~hyperspy.signals.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, :class:`~hyperspy.signals.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, :class:`~hyperspy.signals.Signal1D, None
The sideband position (y, x), referred to the non-shifted FFT. If
None, sideband is determined automatically from FFT.
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 : boolean
Shows details of the reconstruction (i.e. SB selection).
show_progressbar : boolean
Shows progressbar while iterating over different slices of the
signal (passes the parameter to map method).
parallel : bool
Run the reconstruction in parallel
store_parameters : boolean
Store reconstruction parameters in metadata
Returns
-------
wave : :class:`~hyperspy.signals.WaveImage
Reconstructed electron wave. By default object wave is devided by
reference wave
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
>>> wave = s.reconstruct_phase(sb_position=sb_position, sb_size=sb_size)
"""
# TODO: Use defaults for choosing sideband, smoothness, relative filter
# size and output shape if not provided
# TODO: Plot FFT with marked SB and SB filter if plotting is enabled
# Parsing reference:
if not isinstance(reference, HologramImage):
if isinstance(reference, Signal2D):
if (not reference.axes_manager.navigation_shape ==
self.axes_manager.navigation_shape and
reference.axes_manager.navigation_size):
raise ValueError('The navigation dimensions of object and'
'reference holograms do not match')
_logger.warning('The reference image signal type is not '
'HologramImage. It will be converted to '
'HologramImage automatically.')
reference.set_signal_type('hologram')
elif reference is not None:
reference = HologramImage(reference)
if isinstance(reference.data, daArray):
reference = reference.as_lazy()
# Testing match of navigation axes of reference and self
# (exception: reference nav_dim=1):
if (reference and not reference.axes_manager.navigation_shape ==
self.axes_manager.navigation_shape and
reference.axes_manager.navigation_size):
raise ValueError('The navigation dimensions of object and '
'reference holograms do not match')
if reference and not reference.axes_manager.signal_shape == self.axes_manager.signal_shape:
raise ValueError('The signal dimensions of object and reference'
' holograms do not match')
# Parsing sideband position:
if sb_position is None:
_logger.warning('Sideband position is not specified. The sideband '
'will be found automatically which may cause '
'wrong results.')
if reference is None:
sb_position = self.estimate_sideband_position(
sb=sb, parallel=parallel)
else:
sb_position = reference.estimate_sideband_position(
sb=sb, parallel=parallel)
else:
if isinstance(sb_position, BaseSignal) and \
not sb_position._signal_dimension == 1:
raise ValueError('sb_position dimension has to be 1')
if not isinstance(sb_position, Signal1D):
sb_position = Signal1D(sb_position)
if isinstance(sb_position.data, daArray):
sb_position = sb_position.as_lazy()
if not sb_position.axes_manager.signal_size == 2:
raise ValueError('sb_position should to have signal size of 2')
if sb_position.axes_manager.navigation_size != self.axes_manager.navigation_size:
if sb_position.axes_manager.navigation_size:
raise ValueError('Sideband position dimensions do not match'
' neither reference nor hologram dimensions.')
# sb_position navdim=0, therefore map function should not iterate:
else:
sb_position_temp = sb_position.data
else:
sb_position_temp = sb_position.deepcopy()
## Parsing sideband size
# Default value is 1/2 distance between sideband and central band
if sb_size is None:
if reference is None:
sb_size = self.estimate_sideband_size(
sb_position, parallel=parallel)
else:
sb_size = reference.estimate_sideband_size(
sb_position, parallel=parallel)
else:
if not isinstance(sb_size, BaseSignal):
if isinstance(sb_size,
(np.ndarray, daArray)) and sb_size.size > 1:
# transpose if np.array of multiple instances
sb_size = BaseSignal(sb_size).T
else:
sb_size = BaseSignal(sb_size)
if isinstance(sb_size.data, daArray):
sb_size = sb_size.as_lazy()
if sb_size.axes_manager.navigation_size != self.axes_manager.navigation_size:
if sb_size.axes_manager.navigation_size:
raise ValueError('Sideband size dimensions do not match '
'neither reference nor hologram dimensions.')
# sb_position navdim=0, therefore map function should not iterate:
else:
sb_size_temp = np.float64(sb_size.data)
else:
sb_size_temp = sb_size.deepcopy()
# Standard edge smoothness of sideband aperture 5% of sb_size
if sb_smoothness is None:
sb_smoothness = sb_size * 0.05
else:
if not isinstance(sb_smoothness, BaseSignal):
if isinstance(
sb_smoothness,
(np.ndarray, daArray)) and sb_smoothness.size > 1:
sb_smoothness = BaseSignal(sb_smoothness).T
else:
sb_smoothness = BaseSignal(sb_smoothness)
if isinstance(sb_smoothness.data, daArray):
sb_smoothness = sb_smoothness.as_lazy()
if sb_smoothness.axes_manager.navigation_size != self.axes_manager.navigation_size:
if sb_smoothness.axes_manager.navigation_size:
raise ValueError('Sideband smoothness dimensions do not match'
' neither reference nor hologram '
'dimensions.')
# sb_position navdim=0, therefore map function should not iterate it:
else:
sb_smoothness_temp = np.float64(sb_smoothness.data)
else:
sb_smoothness_temp = sb_smoothness.deepcopy()
# Convert sideband size from 1/nm or mrad to pixels
if sb_unit == 'nm':
f_sampling = np.divide(
1,
[a * b for a, b in \
zip(self.axes_manager.signal_shape,
(self.axes_manager.signal_axes[0].scale,
self.axes_manager.signal_axes[1].scale))]
)
sb_size_temp = sb_size_temp / np.mean(f_sampling)
sb_smoothness_temp = sb_smoothness_temp / np.mean(f_sampling)
elif sb_unit == 'mrad':
f_sampling = np.divide(
1,
[a * b for a, b in \
zip(self.axes_manager.signal_shape,
(self.axes_manager.signal_axes[0].scale,
self.axes_manager.signal_axes[1].scale))]
)
try:
ht = self.metadata.Acquisition_instrument.TEM.beam_energy
except:
raise AttributeError("Please define the beam energy."
"You can do this e.g. by using the "
"set_microscope_parameters method")
momentum = 2 * constants.m_e * constants.elementary_charge * ht * \
1000 * (1 + constants.elementary_charge * ht * \
1000 / (2 * constants.m_e * constants.c ** 2))
wavelength = constants.h / np.sqrt(momentum) * 1e9 # in nm
sb_size_temp = sb_size_temp / (1000 * wavelength *
np.mean(f_sampling))
sb_smoothness_temp = sb_smoothness_temp / (1000 * wavelength *
np.mean(f_sampling))
# Find output shape:
if output_shape is None:
## Future improvement will give a possibility to choose
# if sb_size.axes_manager.navigation_size > 0:
# output_shape = (np.int(sb_size.inav[0].data*2), np.int(sb_size.inav[0].data*2))
# else:
# output_shape = (np.int(sb_size.data*2), np.int(sb_size.data*2))
output_shape = self.axes_manager.signal_shape
output_shape = output_shape[::-1]
# Logging the reconstruction parameters if appropriate:
_logger.info('Sideband position in pixels: {}'.format(sb_position))
_logger.info('Sideband aperture radius in pixels: {}'.format(sb_size))
_logger.info('Sideband aperture smoothness in pixels: {}'.format(
sb_smoothness))
# Reconstructing object electron wave:
# Checking if reference is a single image, which requires sideband
# parameters as a nparray to avoid iteration trough those:
wave_object = self.map(
reconstruct,
holo_sampling=(self.axes_manager.signal_axes[0].scale,
self.axes_manager.signal_axes[1].scale),
sb_size=sb_size_temp,
sb_position=sb_position_temp,
sb_smoothness=sb_smoothness_temp,
output_shape=output_shape,
plotting=plotting,
show_progressbar=show_progressbar,
inplace=False,
parallel=parallel,
ragged=False)
# Reconstructing reference wave and applying it (division):
if reference is None:
wave_reference = 1
# case when reference is 1d
elif reference.axes_manager.navigation_size != self.axes_manager.navigation_size:
# Prepare parameters for reconstruction of the reference wave:
if reference.axes_manager.navigation_size == 0 and \
sb_position.axes_manager.navigation_size > 0:
# 1d reference, but parameters are multidimensional
sb_position_ref = _first_nav_pixel_data(sb_position_temp)
else:
sb_position_ref = sb_position_temp
if reference.axes_manager.navigation_size == 0 and \
sb_size.axes_manager.navigation_size > 0:
# 1d reference, but parameters are multidimensional
sb_size_ref = _first_nav_pixel_data(sb_size_temp)
else:
sb_size_ref = sb_size_temp
if reference.axes_manager.navigation_size == 0 and \
sb_smoothness.axes_manager.navigation_size > 0:
# 1d reference, but parameters are multidimensional
sb_smoothness_ref = np.float64(
_first_nav_pixel_data(sb_smoothness_temp))
else:
sb_smoothness_ref = sb_smoothness_temp
#
wave_reference = reference.map(
reconstruct,
holo_sampling=(self.axes_manager.signal_axes[0].scale,
self.axes_manager.signal_axes[1].scale),
sb_size=sb_size_ref,
sb_position=sb_position_ref,
sb_smoothness=sb_smoothness_ref,
output_shape=output_shape,
plotting=plotting,
show_progressbar=show_progressbar,
inplace=False,
parallel=parallel,
ragged=False)
else:
wave_reference = reference.map(
reconstruct,
holo_sampling=(self.axes_manager.signal_axes[0].scale,
self.axes_manager.signal_axes[1].scale),
sb_size=sb_size_temp,
sb_position=sb_position_temp,
sb_smoothness=sb_smoothness_temp,
output_shape=output_shape,
plotting=plotting,
show_progressbar=show_progressbar,
inplace=False,
parallel=parallel,
ragged=False)
wave_image = wave_object / wave_reference
# New signal is a complex
wave_image.set_signal_type('complex_signal2d')
wave_image.axes_manager.signal_axes[0].scale = \
self.axes_manager.signal_axes[0].scale * \
self.axes_manager.signal_shape[0] / output_shape[1]
wave_image.axes_manager.signal_axes[1].scale = \
self.axes_manager.signal_axes[1].scale * \
self.axes_manager.signal_shape[1] / output_shape[0]
# Reconstruction parameters are stored in holo_reconstruction_parameters:
if store_parameters:
rec_param_dict = OrderedDict(
[('sb_position', sb_position_temp), ('sb_size', sb_size_temp),
('sb_units', sb_unit), ('sb_smoothness', sb_smoothness_temp)])
wave_image.metadata.Signal.add_node('Holography')
wave_image.metadata.Signal.Holography.add_node(
'Reconstruction_parameters')
wave_image.metadata.Signal.Holography.Reconstruction_parameters.add_dictionary(
rec_param_dict)
_logger.info('Reconstruction parameters stored in metadata')
return wave_image
