def test_spectrum_to_spectra(make_datasets):
""" Import a spectrum into a spectrum """
spectrum, spectra, hsi, make_dataset_shape = make_datasets
new_dataset = Spectra()
new_dataset.data = spectrum
assert new_dataset.size == spectrum.size
np.testing.assert_array_equal(np.squeeze(new_dataset.data), spectrum)
def test_hsi_to_spectra(make_datasets):
""" Import an hsi into spectra """
spectrum, spectra, hsi, make_dataset_shape = make_datasets
new_dataset = Spectra()
new_dataset.data = hsi
assert new_dataset.size == hsi.size
np.testing.assert_array_equal(hsi.reshape((-1, hsi.shape[-1])),
new_dataset.data)
def test_spectra():
N = 101
data = np.dot(np.ones((10, 1)), np.linspace(0, 100, N)[None, :])
f = np.arange(N)
spectra = Spectra(data=data, freq=f)
np_testing.assert_allclose(spectra._data_idx_freq[0], spectra._data[...,
0])
# Note: _data_idx_freq is INCLUSIVE-- normal indexing is NOT
# This the :11 and not 10
np_testing.assert_allclose(spectra._data_idx_freq[0:10],
spectra._data[..., 0:11])
np_testing.assert_allclose(spectra._data_idx_freq[0.0:10.1:.1],
spectra._data[..., 0:11])
np_testing.assert_allclose(spectra._data_idx_freq[0.0:10.1],
spectra._data[..., 0:11])
np_testing.assert_allclose(spectra._data_idx_freq[10], spectra._data[...,
10])
np_testing.assert_allclose(spectra._data_idx_freq[[10, 11]],
spectra._data[..., [10, 11]])
# Slice the spatial dimension as well:
np_testing.assert_allclose(spectra._data_idx_freq[:, 0:10],
spectra._data[..., 0:11])
np_testing.assert_allclose(spectra._data_idx_freq[:, 0.0:10.1:.1],
spectra._data[..., 0:11])
np_testing.assert_allclose(spectra._data_idx_freq[:, 0.0:10.1],
spectra._data[..., 0:11])
np_testing.assert_allclose(spectra._data_idx_freq[10, 11],
spectra._data[..., [10, 11]])
np_testing.assert_allclose(spectra._data_idx_freq[:, 10],
spectra._data[..., 10])
def test_spectrum_to_spectra_rng(make_datasets):
""" Import spectrum into spectra -- defined range"""
spectrum, spectra, hsi, make_dataset_shape = make_datasets
rng = [10, 11]
new_dataset = Spectra()
new_dataset.freq.data = np.arange(spectra.shape[-1])
new_dataset.data = spectrum
new_dataset.freq.op_list_pix = rng
new_dataset.data = spectrum
assert new_dataset.size == spectrum.size
assert (new_dataset.data != 0).sum() == len(rng)
np.testing.assert_almost_equal(np.squeeze(new_dataset.data[..., rng]),
spectrum[rng])
def test_hsi_to_spectra_rng(make_datasets):
""" Import an hsi into spectra -- defined range"""
spectrum, spectra, hsi, make_dataset_shape = make_datasets
rng = [10, 11]
new_dataset = Spectra()
new_dataset.freq.data = np.arange(spectra.shape[-1])
new_dataset.data = hsi
new_dataset.freq.op_list_pix = rng
new_dataset.data = hsi
assert new_dataset.size == hsi.size
assert (new_dataset.data != 0).sum() == len(rng) * np.prod(hsi.shape[:-1])
np.testing.assert_almost_equal(new_dataset.data[..., rng],
hsi[..., rng].reshape((-1, len(rng))))
def test_hdf_import_spectra_to_spectra(hdf_dataset):
""" Import a spectra into a spectra """
filename, fid, hdf_data_shape = hdf_dataset
hdf_data_shape = np.array(hdf_data_shape)
dname = 'spectra'
dataset = Spectra()
out = hdf_import_data('.', filename, dname, output_cls_instance=dataset)
assert out is True
assert dataset.size == hdf_data_shape[1:].prod()
np.testing.assert_array_equal(dataset.data, fid[dname].value)
def test_reshape_axes_static(make_datasets):
""" Test reshape axes static method """
spectrum, spectra, hsi, make_dataset_shape = make_datasets
Spectra._reshape_axes(shape=hsi.shape,
spectral_axis=-1) == (-1, hsi.shape[-1])
Spectra._reshape_axes(shape=spectra.shape,
spectral_axis=-1) == (-1, spectra.shape[-1])
Spectra._reshape_axes(shape=spectrum.shape,
spectral_axis=-1) == (-1, spectrum.shape[-1])
def test_mean_axes_static():
""" Ensure mean_axes_static raises an error"""
with pytest.raises(NotImplementedError):
Spectra._mean_axes()