def test_s_data(self):
abins.parameters.sampling['min_wavenumber'] = 100
abins.parameters.sampling['max_wavenumber'] = 150
s_data = SData(temperature=10,
sample_form='Powder',
data=self.sample_data,
frequencies=self.frequencies)
self.assertTrue(
np.allclose(s_data.extract()['frequencies'], self.frequencies))
self.assertTrue(
np.allclose(s_data.extract()['atom_0']['s']['order_1'],
self.sample_data['atom_0']['s']['order_1']))
self.assertTrue(
np.allclose(s_data.extract()['atom_1']['s']['order_1'],
self.sample_data['atom_1']['s']['order_1']))
with self.assertRaises(AssertionError):
with self.assertLogs(logger=self.logger, level='WARNING'):
s_data.check_thresholds(logger=self.logger)
abins.parameters.sampling['s_absolute_threshold'] = 0.5
with self.assertLogs(logger=self.logger, level='WARNING'):
s_data.check_thresholds(logger=self.logger)
def test_s_data_apply_dw(self):
dw = np.random.RandomState(42).rand(2, 5)
for min_order, max_order, expected in [
(1, 1, {
'atom_0': {
'order_1': np.linspace(0, 2, 5) * dw[0, :],
'order_2': np.linspace(2, 4, 5)
},
'atom_1': {
'order_1': np.linspace(3, 1, 5) * dw[1, :],
'order_2': np.linspace(2, 1, 5)
}
}),
(2, 2, {
'atom_0': {
'order_1': np.linspace(0, 2, 5),
'order_2': np.linspace(2, 4, 5) * dw[0, :]
},
'atom_1': {
'order_1': np.linspace(3, 1, 5),
'order_2': np.linspace(2, 1, 5) * dw[1, :]
}
}),
(1, 2, {
'atom_0': {
'order_1': np.linspace(0, 2, 5) * dw[0, :],
'order_2': np.linspace(2, 4, 5) * dw[0, :]
},
'atom_1': {
'order_1': np.linspace(3, 1, 5) * dw[1, :],
'order_2': np.linspace(2, 1, 5) * dw[1, :]
}
})
]:
sdata = SData(data=deepcopy(self.sample_data_two_orders),
frequencies=self.frequencies)
sdata.apply_dw(dw, min_order=min_order, max_order=max_order)
for atom_key, atom_data in sdata.extract().items():
if atom_key == 'frequencies':
continue
for order_key in atom_data['s']:
assert_almost_equal(atom_data['s'][order_key],
expected[atom_key][order_key])
def _broaden_sdata(self, sdata: SData,
broadening_scheme: str = 'auto') -> SData:
"""
Apply instrumental broadening to scattering data
"""
sdata_dict = sdata.extract()
frequencies = sdata_dict['frequencies']
del sdata_dict['frequencies']
for atom_key in sdata_dict:
for order_key in sdata_dict[atom_key]['s']:
_, sdata_dict[atom_key]['s'][order_key] = (
self._instrument.convolve_with_resolution_function(
frequencies=frequencies, bins=self._bins,
s_dft=sdata_dict[atom_key]['s'][order_key],
scheme=broadening_scheme))
return SData(data=sdata_dict, frequencies=self._bin_centres,
temperature = sdata.get_temperature(),
sample_form = sdata.get_sample_form())
def _broaden_sdata(self,
sdata: SData,
broadening_scheme: str = 'auto') -> SData:
"""
Apply instrumental broadening to scattering data
If the data is 2D, process line-by-line.
(There is room for improvement, by reworking all the broadening
implementations to accept 2-D input.)
"""
sdata_dict = sdata.extract()
frequencies = sdata_dict['frequencies']
del sdata_dict['frequencies']
if 'q_bins' in sdata_dict:
del sdata_dict['q_bins']
for atom_key in sdata_dict:
for order_key, s_dft in sdata_dict[atom_key]['s'].items():
if len(s_dft.shape) == 1:
_, sdata_dict[atom_key]['s'][order_key] = (
self._instrument.convolve_with_resolution_function(
frequencies=frequencies,
bins=self._bins,
s_dft=s_dft,
scheme=broadening_scheme))
else: # 2-D data, broaden one line at a time
for q_i, s_dft_row in enumerate(
sdata_dict[atom_key]['s'][order_key]):
_, sdata_dict[atom_key]['s'][order_key][q_i] = (
self._instrument.convolve_with_resolution_function(
frequencies=frequencies,
bins=self._bins,
s_dft=s_dft_row,
scheme=broadening_scheme))
return SData(data=sdata_dict,
frequencies=self._bin_centres,
temperature=sdata.get_temperature(),
sample_form=sdata.get_sample_form(),
q_bins=sdata.get_q_bins())