def get_available_detectors():
detector_classes = set()
detector_names = []
for key, item in ALL_DETECTORS.items():
detector_classes.add(item)
for detector in detector_classes:
if len(detector.aliases) > 0:
detector_names.append(detector.aliases[0])
else:
detector_names.append(detector().__class__.__name__)
sorted_indices = sorted(range(len(detector_names)),
key=detector_names.__getitem__)
detector_names_sorted = [detector_names[i] for i in sorted_indices]
detector_classes_sorted = [
list(detector_classes)[i] for i in sorted_indices
]
base_class_index = detector_names_sorted.index('Detector')
del detector_names_sorted[base_class_index]
del detector_classes_sorted[base_class_index]
return detector_names_sorted, detector_classes_sorted
def test_detector_instanciate(self):
"""
this method try to instantiate all the detectors
"""
for k, v in ALL_DETECTORS.items():
logger.debug(k)
v()
def test_detector_instanciate(self):
"""
this method try to instantiate all the detectors
"""
for k, v in ALL_DETECTORS.items():
logger.debug(k)
v()
def guess_detector_by_shape(shape):
# for every detector known to pyFAI
for name, detector in sorted(ALL_DETECTORS.items()):
# if a shape limit is set
if hasattr(detector, 'MAX_SHAPE'):
if detector.MAX_SHAPE == shape:
return detector()
if hasattr(detector, 'BINNED_PIXEL_SIZE'):
for binning in detector.BINNED_PIXEL_SIZE.keys():
if shape == tuple(np.array(detector.MAX_SHAPE) / binning): # possibly needs to be reversed [::-1]
detector = detector()
detector.set_binning(binning)
return detector
return None
