def test_axes_configuration_binning(tmp_path, file):
fname = tmp_path / file
s = BaseSignal(np.zeros((2, 2, 2)))
s.axes_manager.signal_axes[-1].is_binned = True
s.save(fname)
s = load(fname)
assert s.axes_manager.signal_axes[-1].is_binned
def test_lazy_metadata_arrays(tmpfilepath):
s = BaseSignal([1, 2, 3])
s.metadata.array = np.arange(10.)
s.save(tmpfilepath)
l = load(tmpfilepath + ".hspy", lazy=True)
# Can't deepcopy open hdf5 file handles
with pytest.raises(TypeError):
l.deepcopy()
del l
def test_lazy_metadata_arrays(tmpfilepath):
s = BaseSignal([1, 2, 3])
s.metadata.array = np.arange(10.)
s.save(tmpfilepath)
l = load(tmpfilepath + ".hspy", lazy=True)
# Can't deepcopy open hdf5 file handles
with pytest.raises(TypeError):
l.deepcopy()
del l
def test_hdf5_extension(tmpfilepath):
try:
hspy_extension = preferences.General.hspy_extension
preferences.General.hspy_extension = False
s = BaseSignal([0])
s.save(tmpfilepath)
assert os.path.exists(tmpfilepath + ".hdf5")
finally:
preferences.General.hspy_extension = hspy_extension
def test_save_ragged_array(tmpfilepath):
a = np.array([0, 1])
b = np.array([0, 1, 2])
s = BaseSignal(np.array([a, b])).T
filename = os.path.join(tmpfilepath, "test_save_ragged_array.hspy")
s.save(filename)
s1 = load(filename)
for i in range(len(s.data)):
np.testing.assert_allclose(s.data[i], s1.data[i])
assert s.__class__ == s1.__class__
def test_save_ragged_array(tmp_path):
a = np.array([0, 1])
b = np.array([0, 1, 2])
s = BaseSignal(np.array([a, b], dtype=object)).T
fname = tmp_path / 'test_save_ragged_array.hspy'
s.save(fname)
s1 = load(fname)
for i in range(len(s.data)):
np.testing.assert_allclose(s.data[i], s1.data[i])
assert s.__class__ == s1.__class__
def test_axes_configuration(tmp_path, file):
fname = tmp_path / file
s = BaseSignal(np.zeros((2, 2, 2, 2, 2)))
s.axes_manager.signal_axes[0].navigate = True
s.axes_manager.signal_axes[0].navigate = True
s.save(fname, overwrite=True)
s = load(fname)
assert s.axes_manager.navigation_axes[0].index_in_array == 4
assert s.axes_manager.navigation_axes[1].index_in_array == 3
assert s.axes_manager.signal_dimension == 3
def setup_method(self, method):
s = BaseSignal(np.empty((5, 5, 5)))
s.save('tmp.hdf5', overwrite=True)
self.shape = (10000, 10000, 100)
del s
f = h5py.File('tmp.hdf5', mode='r+')
s = f['Experiments/__unnamed__']
del s['data']
s.create_dataset('data',
shape=self.shape,
dtype='float64',
chunks=True)
f.close()
def test_save_ragged_dim2(tmp_path, file):
x = np.empty(5, dtype=object)
for i in range(1, 6):
x[i - 1] = np.array([list(range(i)), list(range(i))])
s = BaseSignal(x, ragged=True)
filename = tmp_path / file
s.save(filename)
s2 = load(filename)
for i, j in zip(s.data,s2.data):
np.testing.assert_array_equal(i,j)
def setUp(self):
s = BaseSignal(np.empty((5, 5, 5)))
s.save('tmp.hdf5', overwrite=True)
self.shape = (10000, 10000, 100)
del s
f = h5py.File('tmp.hdf5', model='r+')
s = f['Experiments/__unnamed__']
del s['data']
s.create_dataset(
'data',
shape=self.shape,
dtype='float64',
chunks=True)
f.close()
def test_lazy_loading(tmp_path):
s = BaseSignal(np.empty((5, 5, 5)))
fname = tmp_path / 'tmp.hdf5'
s.save(fname, overwrite=True)
shape = (10000, 10000, 100)
del s
f = h5py.File(fname, mode='r+')
s = f['Experiments/__unnamed__']
del s['data']
s.create_dataset('data', shape=shape, dtype='float64', chunks=True)
f.close()
s = load(fname, lazy=True)
assert shape == s.data.shape
assert isinstance(s.data, da.Array)
assert s._lazy
s.close_file()
def setup_method(self, method):
self.filename = 'testfile.hdf5'
s = BaseSignal(np.zeros((2, 2, 2, 2, 2)))
s.axes_manager.signal_axes[0].navigate = True
s.axes_manager.signal_axes[0].navigate = True
s.save(self.filename)
def setUp(self):
self.filename = 'testfile.hdf5'
s = BaseSignal(np.zeros((2, 2, 2, 2, 2)))
s.axes_manager.signal_axes[0].navigate = True
s.axes_manager.signal_axes[0].navigate = True
s.save(self.filename)
