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_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)