def get_concentric_vdf_images(self,
k_min,
k_max,
k_steps,
normalize=False):
"""Obtain the intensity scattered at each navigation position in an
ElectronDiffraction Signal by summation over a series of concentric
in annuli between a specified inner and outer radius in a number of
steps.
Parameters
----------
k_min : float
Minimum radius of the annular integration window in reciprocal
angstroms.
k_max : float
Maximum radius of the annular integration window in reciprocal
angstroms.
k_steps : int
Number of steps within the annular integration window
Returns
-------
vdfs : VDFImage
VDFImage object containing virtual dark field images for all steps
within the annulus.
"""
k_step = (k_max - k_min) / k_steps
k0s = np.linspace(k_min, k_max - k_step, k_steps)
k1s = np.linspace(k_min + k_step, k_max, k_steps)
ks = np.array((k0s, k1s)).T
vdfs = []
for k in ks:
annulus = roi.CircleROI(cx=0, cy=0, r=k[1], r_inner=k[0])
vdf = annulus(self.signal,
axes=self.signal.axes_manager.signal_axes)
vdfs.append(vdf.sum((2, 3)).as_signal2D((0, 1)).data)
vdfim = VDFImage(np.asarray(vdfs))
if normalize == True:
vdfim.map(normalize_vdf)
# Set calibration to same as signal
x = vdfim.axes_manager.signal_axes[0]
y = vdfim.axes_manager.signal_axes[1]
x.name = 'x'
x.scale = self.signal.axes_manager.navigation_axes[0].scale
x.units = 'nm'
y.name = 'y'
y.scale = self.signal.axes_manager.navigation_axes[0].scale
y.units = 'nm'
return vdfim
def get_vector_vdf_images(self,
radius,
normalize=False):
"""Obtain the intensity scattered to each diffraction vector at each
navigation position in an ElectronDiffraction Signal by summation in a
circular window of specified radius.
Parameters
----------
radius : float
Radius of the integration window in reciprocal angstroms.
normalize : boolean
If True each VDF image is normalized so that the maximum intensity
in each VDF is 1.
Returns
-------
vdfs : VDFImage
VDFImage object containing virtual dark field images for all unique
vectors.
"""
if self.vectors:
vdfs = []
for v in self.vectors.data:
disk = roi.CircleROI(cx=v[0], cy=v[1], r=radius, r_inner=0)
vdf = disk(self.signal,
axes=self.signal.axes_manager.signal_axes)
vdfs.append(vdf.sum((2, 3)).as_signal2D((0, 1)).data)
vdfim = VDFImage(np.asarray(vdfs))
if normalize == True:
vdfim.map(normalize_vdf)
else:
raise ValueError("DiffractionVectors non-specified by user. Please "
"initialize VDFGenerator with some vectors. ")
# Set calibration to same as signal
x = vdfim.axes_manager.signal_axes[0]
y = vdfim.axes_manager.signal_axes[1]
x.name = 'x'
x.scale = self.signal.axes_manager.navigation_axes[0].scale
x.units = 'nm'
y.name = 'y'
y.scale = self.signal.axes_manager.navigation_axes[0].scale
y.units = 'nm'
# Assign vectors used to generate images to vdfim attribute.
vdfim.vectors = self.vectors.data
return vdfim
def get_vdf_images(self,
electron_diffraction,
radius,
unique_vectors=None):
"""Obtain the intensity scattered to each diffraction vector at each
navigation position in an ElectronDiffraction Signal by summation in a
circular window of specified radius.
Parameters
----------
unique_vectors : list (optional)
Unique list of diffracting vectors if pre-calculated. If None the
unique vectors in self are determined and used.
electron_diffraction : ElectronDiffraction
ElectronDiffraction signal from which to extract the reflection
intensities.
radius : float
Radius of the integration window summed over in reciprocal angstroms.
Returns
-------
vdfs : Signal2D
Signal containing virtual dark field images for all unique g-vectors.
"""
if unique_vectors==None:
unique_vectors = self.get_unique_vectors()
else:
unique_vectors = unique_vectors
vdfs = []
for v in unique_vectors:
disk = roi.CircleROI(cx=v[1], cy=v[0], r=radius, r_inner=0)
vdf = disk(electron_diffraction,
axes=electron_diffraction.axes_manager.signal_axes)
vdfs.append(vdf.sum((2,3)).as_signal2D((0,1)).data)
return Signal2D(np.asarray(vdfs))