def test__reshape_dict_by_value(tmp_path):
_create_minimal_dataset(tmp_path)
db = DatasetBuilder(tmp_path)
# create dict
tissues = ['tissue1', 'tissue2', 'tissue3']
platforms = ['platform1', 'platform2', 'platform3']
data_dict = _create_test_dict(tissues=tissues, platforms=platforms)
# same size as input data
output_shape = (40, 40)
reshaped_dict = db._reshape_dict(data_dict=data_dict, resize=3,
output_shape=output_shape)
X_reshaped, tissue_list_reshaped = reshaped_dict['X'], reshaped_dict['tissue_list']
assert X_reshaped.shape[1:3] == output_shape
# make sure that for each tissue, the arrays with correct value have correct tissue label
for constant_val, tissue in enumerate(tissues):
# each image was tagged with a different, compute that here
image_val = np.max(X_reshaped, axis=(1, 2, 3))
tissue_idx = image_val == constant_val
tissue_labels = np.array(tissue_list_reshaped)[tissue_idx]
assert np.all(tissue_labels == tissue)
# There were originally 5 images of each tissue type. Each dimension was resized 3x,
# so there should be 9x more images
assert len(tissue_labels) == 5 * 9
# now with a resize to make images smaller
reshaped_dict = db._reshape_dict(data_dict=data_dict, resize=0.5,
output_shape=output_shape)
X_reshaped, tissue_list_reshaped = reshaped_dict['X'], reshaped_dict['tissue_list']
assert X_reshaped.shape[1:3] == output_shape
# make sure that for each tissue, the arrays with correct value have correct tissue label
for constant_val, tissue in enumerate(tissues):
# each image was tagged with a different, compute that here
image_val = np.max(X_reshaped, axis=(1, 2, 3))
tissue_idx = image_val == constant_val
tissue_labels = np.array(tissue_list_reshaped)[tissue_idx]
assert np.all(tissue_labels == tissue)
# There were originally 5 images of each tissue type. Each dimension was resized 0.5,
# and because the images are padded there should be the same total number of images
assert len(tissue_labels) == 5
def test__reshape_dict_by_image(tmp_path, mocker):
mocker.patch('caliban_toolbox.dataset_builder.compute_cell_size', mocked_compute_cell_size)
_create_minimal_dataset(tmp_path)
db = DatasetBuilder(tmp_path)
# create dict
tissues = ['tissue1', 'tissue2', 'tissue3']
platforms = ['platform1', 'platform2', 'platform3']
data_dict = _create_test_dict(tissues=tissues, platforms=platforms)
# same size as input data
output_shape = (40, 40)
reshaped_dict = db._reshape_dict(data_dict=data_dict, resize='by_image',
output_shape=output_shape)
X_reshaped, tissue_list_reshaped = reshaped_dict['X'], reshaped_dict['tissue_list']
assert X_reshaped.shape[1:3] == output_shape
# make sure that for each tissue, the arrays with correct value have correct tissue label
for constant_val, tissue in enumerate(tissues):
# each image was tagged with a different, compute that here
image_val = np.max(X_reshaped, axis=(1, 2, 3))
tissue_idx = image_val == constant_val
tissue_labels = np.array(tissue_list_reshaped)[tissue_idx]
assert np.all(tissue_labels == tissue)
# There were originally 5 images of each tissue type. Tissue types with even values
# are resized to be 2x larger on each dimension, and should have 4x more images
if constant_val % 2 == 0:
assert len(tissue_labels) == 5 * 4
# tissue types with odd values are resized to be smaller, which leads to same number
# of unique images due to padding
else:
assert len(tissue_labels) == 5
def test__reshape_dict_no_resize(tmp_path):
_create_minimal_dataset(tmp_path)
db = DatasetBuilder(tmp_path)
# create dict
tissues = ['tissue1', 'tissue2', 'tissue3']
platforms = ['platform1', 'platform2', 'platform3']
data_dict = _create_test_dict(tissues=tissues, platforms=platforms)
# this is 1/2 the size on each dimension as original, so we expect 4x more crops
output_shape = (20, 20)
reshaped_dict = db._reshape_dict(data_dict=data_dict, resize=False, output_shape=output_shape)
X_reshaped, tissue_list_reshaped = reshaped_dict['X'], reshaped_dict['tissue_list']
assert X_reshaped.shape[1:3] == output_shape
assert X_reshaped.shape[0] == 4 * data_dict['X'].shape[0]
# make sure that for each tissue, the arrays with correct value have correct tissue label
for constant_val, tissue in enumerate(tissues):
tissue_idx = X_reshaped[:, 0, 0, 0] == constant_val
tissue_labels = np.array(tissue_list_reshaped)[tissue_idx]
assert np.all(tissue_labels == tissue)