def test_s_data_add_dict(self):
from copy import deepcopy
s_data = SData(data=deepcopy(self.sample_data),
frequencies=self.frequencies)
s_data.add_dict({'atom_1': {'s': {'order_1': np.ones(5)}}})
assert_almost_equal(s_data[1]['order_1'],
self.sample_data['atom_1']['s']['order_1'] + 1)
def _calculate_s_powder_one_atom(self, *, atom_index: int, k_index: int, q2: np.ndarray,
sdata: SData, bins: np.ndarray,
min_order: int = 1) -> None:
"""
:param atom_index: number of atom
:param k_index: Index of k-point in phonon data
:param q2: Array of squared absolute q-point values in angstrom^-2. (Columns correspond to energies.)
:sdata: Data container to which results will be summed in-place
:bins: Frequency bins consistent with sdata
:min_order: Lowest quantum order to evaluate. (The max is determined by self._quantum_order_num.)
"""
kpoint_weight = self._abins_data.get_kpoints_data()[k_index].weight
fundamentals = self._powder_data.get_frequencies()[k_index]
fund_coeff = np.arange(fundamentals.size, dtype=INT_TYPE)
# Initialise with fundamentals regardless of whether starting with order 1 or 2
frequencies = np.copy(fundamentals)
coefficients = np.copy(fund_coeff)
a_tensor = self._powder_data.get_a_tensors()[k_index][atom_index]
a_trace = self._powder_data.get_a_traces(k_index)[atom_index]
b_tensor = self._powder_data.get_b_tensors()[k_index][atom_index]
b_trace = self._powder_data.get_b_traces(k_index)[atom_index]
calculate_order = {1: self._calculate_order_one,
2: self._calculate_order_two}
# Chunking to save memory has been removed pending closer examination
for order in range(min_order, self._quantum_order_num + 1):
frequencies, coefficients = FrequencyPowderGenerator.construct_freq_combinations(
previous_array=frequencies,
previous_coefficients=coefficients,
fundamentals_array=fundamentals,
fundamentals_coefficients=fund_coeff,
quantum_order=order)
scattering_intensities = calculate_order[order](
q2=q2, frequencies=frequencies, indices=coefficients,
a_tensor=a_tensor, a_trace=a_trace, b_tensor=b_tensor, b_trace=b_trace)
rebinned_spectrum, _ = np.histogram(frequencies, bins=bins,
weights=(scattering_intensities * kpoint_weight),
density=False)
sdata.add_dict({f'atom_{atom_index}':
{'s': {f'order_{order}': rebinned_spectrum}}})
# Prune modes with low intensity; these are assumed not to contribute to higher orders
frequencies, coefficients = self._calculate_s_over_threshold(scattering_intensities,
freq=frequencies,
coeff=coefficients)
