def test_select_fake_method_from_method_dict(method_dict):
with pytest.raises(NotImplementedError):
_ = select_method_from_method_dict("fake_choice",
method_dict,
print_help=True)
def test_select_method_from_method_dict_print_help(method_dict):
method_dict = {"dummy_choice": select_method_from_method_dict}
_ = select_method_from_method_dict("dummy_choice",
method_dict,
print_help=True)
def correlate(
self,
n_largest=5,
method="fast_correlation",
mask=None,
print_help=False,
**kwargs,
):
"""Correlates the library of simulated diffraction patterns with the
electron diffraction signal.
Parameters
----------
n_largest : int
The n orientations with the highest correlation values for each phase are returned.
method : str
Name of method used to compute correlation between templates and diffraction patterns. Can be
'fast_correlation' or 'zero_mean_normalized_correlation'.
mask : hs.BaseSignal or None
Only apply the method a unmasked (value=1) pixel, default is None (index all pixels)
print_help : bool
Display information about the method used.
**kwargs : arguments
Keyword arguments passed map().
Returns
-------
matching_results : TemplateMatchingResults
"""
signal = self.signal
library = self.library
method_dict = {
"fast_correlation": fast_correlation,
"zero_mean_normalized_correlation":
zero_mean_normalized_correlation,
}
if mask is None:
# Index at all real space pixels
mask = 1
# tests if selected method is valid and can print help for selected method.
_ = select_method_from_method_dict(method, method_dict, print_help)
# adds a normalisation to library #TODO: Port to diffsims
for phase in library.keys():
# initialise a container for the norms
norm_array = np.ones(library[phase]["intensities"].shape[0])
for i, intensity_array in enumerate(library[phase]["intensities"]):
norm_array[i] = np.linalg.norm(intensity_array)
library[phase]["pattern_norms"] = norm_array
matches = signal.map(
_correlate_templates,
library=library,
n_largest=n_largest,
method=method,
mask=mask,
inplace=False,
**kwargs,
)
matching_results = TemplateMatchingResults(matches)
return matching_results