Source code for dynasor.post_processing.weights

from typing import Dict


[docs]class Weights: """ Class holding weights and support functions for weighting of samples Parameters ---------- weights_coh A dict with keys and values representing the atom types and their corresponding coherent scattering length, ``{'A': b_A }``. weights_incoh A dict with keys and values representing the atom types and their corresponding incoherent scattering length, ``{'A': b_A }``. supports_currents whether or not the coherent weights should be applied to current-correlation functions """ def __init__( self, weights_coh: Dict[str, float], weights_incoh: Dict[str, float] = None, supports_currents: bool = True ): self._weights_coh = weights_coh self._weights_incoh = weights_incoh self._supports_currents = supports_currents
[docs] def get_weight_coh(self, atom_type, q_norm=None): """ Get the coherent weight for a given atom type and q-vector norm. """ return self._weights_coh[atom_type]
[docs] def get_weight_incoh(self, atom_type, q_norm=None): """ Get the incoherent weight for a given atom type and q-vector norm. """ return self._weights_incoh[atom_type]
@property def supports_currents(self): """ Wether or not this :class:`Weights` object supports weighting of current correlations. """ return self._supports_currents @property def supports_incoherent(self): """ Whether or not this :class:`Weights` object supports weighting of incoherent correlation functions. """ return self._weights_incoh is not None def __str__(self): s = ['weights coherent:'] for key, val in self._weights_coh.items(): s.append(f' {key}: {val}') s = ['weights incoherent:'] for key, val in self._weights_incoh.items(): s.append(f' {key}: {val}')