def test_main_dset_1D(self):
translator = ArrayTranslator()
with self.assertRaises(ValueError):
delete_existing_file(file_path)
_ = translator.translate(file_path, 'Blah', np.arange(4), 'quant', 'unit',
Dimension('Position_Dim', 'au', 5),
Dimension('Spec_Dim', 'au', 3))
with self.assertRaises(ValueError):
delete_existing_file(file_path)
_ = translator.translate(file_path, 'Blah', da.from_array(np.arange(4), chunks=(4)), 'quant', 'unit',
Dimension('Position_Dim', 'au', 5),
Dimension('Spec_Dim', 'au', 3))
def test_not_numpy_or_dask_array_main(self):
translator = ArrayTranslator()
with self.assertRaises(TypeError):
delete_existing_file(file_path)
_ = translator.translate(file_path, 'Blah', {'This is not a dataset': True}, 'quant', 'unit',
Dimension('Position_Dim', 'au', 5),
Dimension('Spec_Dim', 'au', 3))
def test_objects(self):
translator = ArrayTranslator()
with self.assertRaises(TypeError):
delete_existing_file(file_path)
_ = translator.translate(file_path, 'Blah', np.random.rand(5, 13), 'quant', 'unit',
Dimension('Dim_1', 'au', 5),
['blah',
Dimension('Dim_2', 'au', 4)])
def test_object_single(self):
translator = ArrayTranslator()
with self.assertRaises(TypeError):
delete_existing_file(file_path)
_ = translator.translate(file_path, 'Blah', np.random.rand(5, 13), 'quant', 'unit',
'my_string_Dimension',
[Dimension('Spec_Dim', 'au', 3),
Dimension('Dim_2', 'au', 4)])
def test_spec(self):
translator = ArrayTranslator()
with self.assertRaises(ValueError):
delete_existing_file(file_path)
_ = translator.translate(file_path, 'Blah', np.random.rand(5, 13), 'quant', 'unit',
Dimension('Dim_1', 'au', 5),
[Dimension('Spec_Dim', 'au', 3),
Dimension('Dim_2', 'au', 4)])
def test_empty_name(self):
translator = ArrayTranslator()
with self.assertRaises(ValueError):
delete_existing_file(file_path)
_ = translator.translate(file_path, 'Blah', np.random.rand(5, 3), 'quant', 'unit',
Dimension('Position_Dim', 'au', 5),
Dimension('Spec_Dim', 'au', 3),
extra_dsets={' ': [1, 2, 3]})
def test_not_arrays(self):
translator = ArrayTranslator()
with self.assertRaises(TypeError):
delete_existing_file(file_path)
_ = translator.translate(file_path, 'Blah', np.random.rand(5, 3), 'quant', 'unit',
Dimension('Position_Dim', 'au', 5),
Dimension('Spec_Dim', 'au', 3),
extra_dsets={'Blah_other': 'I am not an array'})
def test_position(self):
translator = ArrayTranslator()
with self.assertRaises(ValueError):
delete_existing_file(file_path)
_ = translator.translate(file_path, 'Blah', np.random.rand(15, 3), 'quant', 'unit',
[write_utils.Dimension('Dim_1', 'au', 5),
write_utils.Dimension('Dim_2', 'au', 4)],
write_utils.Dimension('Spec_Dim', 'au', 3))
def test_not_dicts(self):
translator = ArrayTranslator()
with self.assertRaises(TypeError):
delete_existing_file(file_path)
_ = translator.translate(file_path, 'Blah', np.random.rand(5, 3), 'quant', 'unit',
write_utils.Dimension('Position_Dim', 'au', 5),
write_utils.Dimension('Spec_Dim', 'au', 3),
extra_dsets=np.arange(4))
def test_reserved_names(self):
translator = ArrayTranslator()
with self.assertRaises(KeyError):
delete_existing_file(file_path)
_ = translator.translate(file_path, 'Blah', np.random.rand(5, 3), 'quant', 'unit',
Dimension('Position_Dim', 'au', 5),
Dimension('Spec_Dim', 'au', 3),
extra_dsets={'Spectroscopic_Indices': np.arange(4),
'Blah_other': np.arange(15)})
def test_not_strings(self):
translator = ArrayTranslator()
with self.assertRaises(TypeError):
delete_existing_file(file_path)
_ = translator.translate(file_path, 1.2345, np.random.rand(5, 3), 'quant', 'unit',
Dimension('Position_Dim', 'au', 5),
Dimension('Spec_Dim', 'au', 3))
with self.assertRaises(TypeError):
delete_existing_file(file_path)
_ = translator.translate(file_path, 'Blah', np.random.rand(5, 3), {'quant': 1}, 'unit',
Dimension('Position_Dim', 'au', 5),
Dimension('Spec_Dim', 'au', 3))
with self.assertRaises(TypeError):
delete_existing_file(file_path)
_ = translator.translate(file_path, 'Blah', np.random.rand(5, 3), 'quant', ['unit'],
Dimension('Position_Dim', 'au', 5),
Dimension('Spec_Dim', 'au', 3))
def base_translation_tester(self, main_dset_as_dask=False, extra_dsets_type='numpy', use_parm_dict=True):
data_name = 'My_Awesome_Measurement'
if use_parm_dict:
attrs = {'att_1': 'string_val',
'att_2': 1.2345,
'att_3': [1, 2, 3, 4],
'att_4': ['str_1', 'str_2', 'str_3']}
else:
attrs = None
extra_dsets = {}
if extra_dsets_type is not None:
ref_dsets = {'dset_1': np.random.rand(5), 'dset_2': np.arange(25)}
if extra_dsets_type == 'numpy':
extra_dsets = ref_dsets
elif extra_dsets_type == 'dask':
for key, val in ref_dsets.items():
extra_dsets.update({key: da.from_array(val, chunks=val.shape)})
else:
extra_dsets_type = None
delete_existing_file(file_path)
main_data = np.random.rand(15, 14)
if main_dset_as_dask:
main_data = da.from_array(main_data, chunks=main_data.shape)
quantity = 'Current'
units = 'nA'
pos_sizes = [5, 3]
pos_names = ['X', 'Y']
pos_units = ['nm', 'um']
pos_dims = []
for name, unit, length in zip(pos_names, pos_units, pos_sizes):
pos_dims.append(Dimension(name, unit, np.arange(length)))
pos_data = np.vstack((np.tile(np.arange(5), 3),
np.repeat(np.arange(3), 5))).T
spec_sizes = [7, 2]
spec_names = ['Bias', 'Cycle']
spec_units = ['V', '']
spec_dims = []
for name, unit, length in zip(spec_names, spec_units, spec_sizes):
spec_dims.append(Dimension(name, unit, np.arange(length)))
spec_data = np.vstack((np.tile(np.arange(7), 2),
np.repeat(np.arange(2), 7)))
translator = ArrayTranslator()
_ = translator.translate(file_path, data_name, main_data, quantity, units, pos_dims, spec_dims, parm_dict=attrs,
extra_dsets=extra_dsets)
with h5py.File(file_path, mode='r') as h5_f:
# we are not interested in most of the attributes under root besides two:
self.assertEqual(data_name, hdf_utils.get_attr(h5_f, 'data_type'))
# self.assertEqual('NumpyTranslator', hdf_utils.get_attr(h5_f, 'translator'))
# First level should have absolutely nothing besides one group
self.assertEqual(len(h5_f.items()), 1)
self.assertTrue('Measurement_000' in h5_f.keys())
h5_meas_grp = h5_f['Measurement_000']
self.assertIsInstance(h5_meas_grp, h5py.Group)
# check the attributes under this group
# self.assertEqual(len(h5_meas_grp.attrs), len(attrs))
if use_parm_dict:
for key, expected_val in attrs.items():
self.assertTrue(np.all(hdf_utils.get_attr(h5_meas_grp, key) == expected_val))
# Again, this group should only have one group - Channel_000
self.assertEqual(len(h5_meas_grp.items()), 1)
self.assertTrue('Channel_000' in h5_meas_grp.keys())
h5_chan_grp = h5_meas_grp['Channel_000']
self.assertIsInstance(h5_chan_grp, h5py.Group)
# This channel group is not expected to have any (custom) attributes but it will contain the main dataset
self.assertEqual(len(h5_chan_grp.items()), 5 + len(extra_dsets))
for dset_name in ['Raw_Data', 'Position_Indices', 'Position_Values', 'Spectroscopic_Indices',
'Spectroscopic_Values']:
self.assertTrue(dset_name in h5_chan_grp.keys())
h5_dset = h5_chan_grp[dset_name]
self.assertIsInstance(h5_dset, h5py.Dataset)
usid_main = USIDataset(h5_chan_grp['Raw_Data'])
self.assertIsInstance(usid_main, USIDataset)
self.assertEqual(usid_main.name.split('/')[-1], 'Raw_Data')
self.assertEqual(usid_main.parent, h5_chan_grp)
self.assertTrue(np.allclose(main_data, usid_main[()]))
validate_aux_dset_pair(self, h5_chan_grp, usid_main.h5_pos_inds, usid_main.h5_pos_vals, pos_names, pos_units,
pos_data, h5_main=usid_main, is_spectral=False)
validate_aux_dset_pair(self, h5_chan_grp, usid_main.h5_spec_inds, usid_main.h5_spec_vals, spec_names,
spec_units,
spec_data, h5_main=usid_main, is_spectral=True)
# Now validate each of the extra datasets:
if extra_dsets_type is not None:
for key, val in extra_dsets.items():
self.assertTrue(key in h5_chan_grp.keys())
h5_dset = h5_chan_grp[key]
self.assertIsInstance(h5_dset, h5py.Dataset)
if extra_dsets_type == 'dask':
val = val.compute()
self.assertTrue(np.allclose(val, h5_dset[()]))
os.remove(file_path)