def test_read_cutoff(self, save_path_nordif):
scan_size = (10, 3)
scan_size_reloaded = (10, 20)
pattern_size = (5, 5)
data_shape = scan_size + pattern_size
s = EBSD((255 * np.random.rand(*data_shape)).astype(np.uint8))
s.save(save_path_nordif, overwrite=True)
# Reload data but with a scan_size bigger than available file bytes,
# so that the data has to be padded
with pytest.warns(UserWarning): # No background or setting files
s_reload = load(
save_path_nordif,
scan_size=scan_size_reloaded[::-1],
pattern_size=pattern_size,
)
# To check if the data padding works as expected, the original data is
# padded and compared to the reloaded data
cut_data = s.data.flatten()
pw = [
(
0,
(scan_size_reloaded[1] - scan_size[1])
* scan_size[0]
* np.prod(pattern_size),
)
]
cut_data = np.pad(cut_data, pw, mode="constant")
cut_data = cut_data.reshape(scan_size_reloaded + pattern_size)
assert np.allclose(cut_data, s_reload.data)
def test_init(self):
# Signal shape
array0 = np.zeros(shape=(10, 10, 10, 10))
s0 = EBSD(array0)
assert array0.shape == s0.axes_manager.shape
# Cannot initialise signal with one signal dimension
with pytest.raises(ValueError):
_ = EBSD(np.zeros(10))
# Shape of one-image signal
array1 = np.zeros(shape=(10, 10))
s1 = EBSD(array1)
assert array1.shape == s1.axes_manager.shape
# SEM metadata
kp_md = ebsd_metadata()
sem_node = metadata_nodes("sem")
assert_dictionary(
kp_md.get_item(sem_node), s1.metadata.get_item(sem_node)
)
# Phases metadata
assert s1.metadata.has_item("Sample.Phases")
def test_read_patterns(self, save_path_hdf5):
s = EBSD((255 * np.random.rand(10, 3, 5, 5)).astype(np.uint8))
s.save(save_path_hdf5)
with File(save_path_hdf5, mode="r+") as f:
del f["Scan 1/EBSD/Data/patterns"]
with pytest.raises(KeyError, match="Could not find patterns"):
_ = load(save_path_hdf5)
def test_write_data_single(self, save_path_nordif):
pattern_size = (5, 5)
s = EBSD((255 * np.random.rand(*pattern_size)).astype(np.uint8))
s.save(save_path_nordif, overwrite=True)
with pytest.warns(UserWarning): # No background or setting files
s_reload = load(
save_path_nordif, scan_size=1, pattern_size=pattern_size
)
assert np.allclose(s.data, s_reload.data)
def test_get_dask_array(self):
s = EBSD((255 * np.random.rand(10, 10, 120, 120)).astype(np.uint8))
dask_array = _get_dask_array(s)
assert dask_array.chunksize == (8, 8, 120, 120)
# Make data lazy
s.data = dask_array.rechunk((5, 5, 120, 120))
dask_array = _get_dask_array(s)
assert dask_array.chunksize == (5, 5, 120, 120)
def test_save_fresh(self, save_path_nordif):
scan_size = (10, 3)
pattern_size = (5, 5)
data_shape = scan_size + pattern_size
s = EBSD((255 * np.random.rand(*data_shape)).astype(np.uint8))
s.save(save_path_nordif, overwrite=True)
with pytest.warns(UserWarning): # No background or setting files
s_reload = load(save_path_nordif,
scan_size=scan_size[::-1],
pattern_size=pattern_size)
assert np.allclose(s.data, s_reload.data)
def test_load_manufacturer(self, save_path_hdf5):
s = EBSD((255 * np.random.rand(10, 3, 5, 5)).astype(np.uint8))
s.save(save_path_hdf5)
# Change manufacturer
with File(save_path_hdf5, mode="r+") as f:
manufacturer = f["manufacturer"]
manufacturer[()] = "Nope".encode()
with pytest.raises(
OSError,
match="Manufacturer Nope not among recognised manufacturers"):
_ = load(save_path_hdf5)
def test_check_h5ebsd(self, save_path_hdf5, delete, error):
s = EBSD((255 * np.random.rand(10, 3, 5, 5)).astype(np.uint8))
s.save(save_path_hdf5)
with File(save_path_hdf5, mode="r+") as f:
if delete == "man_ver":
del f["manufacturer"]
del f["version"]
with pytest.raises(OSError, match=error):
check_h5ebsd(f)
else:
del f["Scan 1"]
with pytest.raises(OSError, match=error):
check_h5ebsd(f)
def test_chunk_bytes_indirectly(self):
s = EBSD(np.zeros((10, 10, 8, 8)))
array_out0 = get_dask_array(s)
array_out1 = get_dask_array(s, chunk_bytes="25KiB")
array_out2 = get_dask_array(s, chunk_bytes=25e3)
assert array_out0.chunksize != array_out1.chunksize
assert array_out1.chunksize == array_out2.chunksize
def test_save_fresh(self, save_path_hdf5, tmp_path):
scan_size = (10, 3)
pattern_size = (5, 5)
data_shape = scan_size + pattern_size
s = EBSD((255 * np.random.rand(*data_shape)).astype(np.uint8))
s.save(save_path_hdf5, overwrite=True)
s_reload = load(save_path_hdf5)
np.testing.assert_equal(s.data, s_reload.data)
# Test writing of signal to file when no file name is passed to save()
del s.tmp_parameters.filename
with pytest.raises(ValueError, match="Filename not defined"):
s.save(overwrite=True)
s.metadata.General.original_filename = "an_original_filename"
os.chdir(tmp_path)
s.save(overwrite=True)
