def split_data_by_image(self, test_fraction=0.5):
"""
Method for splitting data given a boolean mask.
This method will designate full images as belonging to the
train or test sets.
Parameters
----------
test_fraction : float (optional)
The fraction of images which will be reserved for testing.
Returns
-------
train : tuple
test : tuple
"""
image_id = BaseModel.get_image_id(self.inputs)
test_idx = np.random.random(image_id.max() + 1) <= test_fraction
# Low image count edge case (mostly just for testing purposes)
if True not in test_idx:
test_idx[0] = True
elif False not in test_idx:
test_idx[0] = False
test_idx = test_idx[image_id]
if BaseModel.is_laue(self.inputs):
train, test = self.split_laue_data_by_mask(test_idx)
else:
train, test = self.split_mono_data_by_mask(test_idx)
#return self.get_tf_dataset(train), self.get_tf_dataset(test)
return train, test
def split_data_by_refl(self, test_fraction=0.5):
"""
Method for splitting data given a boolean mask.
Parameters
----------
test_fraction : float (optional)
The fraction of reflections which will be reserved for testing.
Returns
-------
train : tuple
test : tuple
"""
if BaseModel.is_laue(self.inputs):
harmonic_id = BaseModel.get_harmonic_id(self.inputs)
test_idx = (np.random.random(harmonic_id.max() + 1) <=
test_fraction)[harmonic_id]
train, test = self.split_laue_data_by_mask(test_idx)
#return self.get_tf_dataset(train), self.get_tf_dataset(test)
return train, test
test_idx = np.random.random(len(self.inputs[0])) <= test_fraction
train, test = self.split_mono_data_by_mask(test_idx)
#return self.get_tf_dataset(train), self.get_tf_dataset(test)
return train, test
def test_getters(laue_inputs, mono_inputs):
for inputs in laue_inputs, mono_inputs:
if BaseModel.is_laue(inputs):
BaseModel.get_harmonic_id(inputs)
BaseModel.get_wavelength(inputs)
BaseModel.get_image_id(inputs)
BaseModel.get_intensities(inputs)
BaseModel.get_metadata(inputs)
BaseModel.get_refl_id(inputs)
BaseModel.get_uncertainties(inputs)
def test_is_laue(laue_inputs, mono_inputs):
assert BaseModel.is_laue(laue_inputs)
assert not BaseModel.is_laue(mono_inputs)