def calc_coords(self,
indices=None,
drop_zero=False,
frame_shape=None,
r=0):
'''
Shorthand to calculate peak coordinates.
Parameters
----------
indices : numpy.ndarray
Indices to calculate coordinates for. Both an array of (y, x) pairs
and the output of np.mgrid are supported.
drop_zero : bool
Drop the zero order peak. This is important for virtual darkfield imaging.
frame_shape : Tuple[int, int]
If set, the peaks are filtered with :meth:`~libertem.analysis.gridmatching.within_frame`
r : float
Radius for :meth:`~libertem.analysis.gridmatching.within_frame`
Returns
-------
numpy.ndarray
A list of (y, x) coordinate pairs for peaks
Raises
------
ValueError
If the shape of :code:`indices` is not as expected.
'''
if indices is None:
indices = self.indices
s = indices.shape
# Output of mgrid
if (len(s) == 3) and (s[0] == 2):
indices = np.concatenate(indices.T)
# List of (i, j) pairs
elif (len(s) == 2) and (s[1] == 2):
pass
else:
raise ValueError(
"Shape of indices is %s, expected (n, 2) or (2, n, m)" %
str(indices.shape))
selector = np.ones(len(indices), dtype=np.bool)
if drop_zero:
nz = np.any(indices != 0, axis=1)
selector *= nz
peaks = calc_coords(self.zero, self.a, self.b, indices)
if frame_shape is not None:
fy, fx = frame_shape
selector *= within_frame(peaks, r, fy, fx)
return peaks[selector]
def calculated_refineds(self):
'''
numpy.ndarray : Calculated peak positions based on lattice parameters and indices.
'''
return calc_coords(self.zero, self.a, self.b, self.indices)