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frequencypowdergenerator.py
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frequencypowdergenerator.py
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# Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2018 ISIS Rutherford Appleton Laboratory UKRI,
# NScD Oak Ridge National Laboratory, European Spallation Source,
# Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
# SPDX - License - Identifier: GPL - 3.0 +
import numpy as np
import abins
from abins.constants import (FLOAT_ID, INT_ID, INT_TYPE,
FUNDAMENTALS, HIGHER_ORDER_QUANTUM_EVENTS)
class FrequencyPowderGenerator(object):
"""
Class which generates frequencies for quantum order events.
"""
def __init__(self):
super(FrequencyPowderGenerator, self).__init__()
@staticmethod
def construct_freq_combinations(previous_array=None, previous_coefficients=None,
fundamentals_array=None, fundamentals_coefficients=None, quantum_order=None):
"""
Generates frequencies for the given order of quantum event.
:param previous_array: array with frequencies for the previous quantum event
:param previous_coefficients: coefficients which correspond to the previous order quantum event
:param fundamentals_array: array with frequencies for fundamentals
:param fundamentals_coefficients: coefficients for fundamentals
:param quantum_order: number of quantum order event for which new array should be constructed
:returns: array with frequencies for the required quantum number event, array which stores coefficients for all
frequencies
"""
if not (isinstance(fundamentals_array, np.ndarray)
and len(fundamentals_array.shape) == 1
and fundamentals_array.dtype.num == FLOAT_ID):
raise ValueError("Fundamentals in the form of one dimensional array are expected.")
if not (isinstance(fundamentals_coefficients, np.ndarray)
and len(fundamentals_coefficients.shape) == 1
and fundamentals_coefficients.dtype.num == INT_ID):
raise ValueError("Coefficients of fundamentals in the form of one dimensional array are expected.")
if fundamentals_coefficients.size != fundamentals_array.size:
raise ValueError("Inconsistent size of fundamentals and corresponding coefficients. "
"(%s != %s)" % (fundamentals_coefficients.size, fundamentals_array.size))
if not (isinstance(quantum_order, int)
and FUNDAMENTALS <= quantum_order
<= HIGHER_ORDER_QUANTUM_EVENTS + FUNDAMENTALS):
raise ValueError("Improper value of quantum order event (quantum_order = %s)" % quantum_order)
# frequencies for fundamentals
if quantum_order == FUNDAMENTALS:
return fundamentals_array, np.arange(start=0,
step=1,
stop=fundamentals_array.size,
dtype=INT_TYPE)
# higher order quantum events.
else:
if not (isinstance(previous_array, np.ndarray)
and len(previous_array.shape) == 1
and previous_array.dtype.num == FLOAT_ID):
raise ValueError("One dimensional previous_array is expected.")
if not (isinstance(previous_coefficients, np.ndarray)
and len(previous_coefficients.shape) == min(2, quantum_order - 1)
and previous_coefficients.dtype.num == INT_ID):
raise ValueError("Numpy array of previous_coefficients is expected. (%s)" % previous_coefficients,
type(previous_coefficients), previous_coefficients.dtype)
# generate indices
fundamentals_size = fundamentals_array.size
previous_size = previous_array.size
prev_indices = np.arange(start=0, step=1, stop=previous_size, dtype=INT_TYPE)
# indices in fundamentals array. Not necessarily the same as fundamentals_coefficients!!!
# This will be the same in case full array with transitions is processed
# but in case array of transitions is huge and we proceed chunk by chunk then
# fundamentals_ind differ from fundamentals_coefficients
fundamentals_ind = np.arange(start=0, step=1, stop=fundamentals_size,
dtype=INT_TYPE)
n = fundamentals_size * previous_size
num_of_arrays = 2
ind = np.zeros(shape=(n, num_of_arrays), dtype=INT_TYPE)
ind[:, 0] = np.repeat(prev_indices, fundamentals_size)
ind[:, 1] = np.tile(fundamentals_ind, previous_size)
# calculate energies for quantum order event
energies = np.take(a=previous_array, indices=ind[:, 0]) + np.take(a=fundamentals_array, indices=ind[:, 1])
# calculate coefficients which allow to express those energies in terms of fundamentals
coeff = np.zeros(shape=(quantum_order, energies.size), dtype=INT_TYPE)
if previous_coefficients.ndim == 1:
previous_coefficients_dim = 1
else:
previous_coefficients_dim = previous_coefficients.shape[-1]
coeff[:previous_coefficients_dim] = np.take(a=previous_coefficients, indices=ind[:, 0])
coeff[previous_coefficients_dim] = np.take(a=fundamentals_coefficients, indices=ind[:, 1])
coeff = coeff.T
# extract energies within valid energy window
valid_indices = energies < abins.parameters.sampling['max_wavenumber']
return energies[valid_indices], coeff[valid_indices]