Coverage for local_installation/dynasor/post_processing/neutron_scattering_lengths.py: 100%

1import json 

2from importlib.resources import files 

3from typing import Dict, List 

4 

5import numpy as np 

6from pandas import DataFrame 

7from .weights import Weights 

8 

9 

10class NeutronScatteringLengths(Weights): 

11 """This class provides sample weights corresponding to neutron scattering lengths. 

12 By default, the coherent and incoherent scattering lengths are weighted by the natural 

13 abundance of each isotope of the considered atomic species. 

14 This weighting can be overwritten using the :attr:`abundances` argument. 

15 

16 The scattering lengths have been extracted from `this NIST 

17 database <https://www.ncnr.nist.gov/resources/n-lengths/list.html>`__, 

18 which in turn have been taken from Table 1 of Neutron News **3**, 26 (1992); 

19 `doi: 10.1080/10448639208218770 <https://doi.org/10.1080/10448639208218770>`_. 

20 

21 Parameters 

22 ---------- 

23 atom_types 

24 List of atomic species for which to retrieve scattering lengths. 

25 abundances 

26 Dict of the desired fractional abundance of each isotope for 

27 each species in the sample. For example, to use an equal 

28 weighting of all isotopes of oxygen, one can write 

29 ``abundances['O'] = dict(16=1/3, 17=1/3, 18=1/3)``. Note that 

30 the abundance for any isotopes that are *not* included in this 

31 dict is automatically set to zero. In other words, you need to 

32 ensure that the abundances provided sum up to 1. By default 

33 the neutron scattering lengths are weighted proportionally to 

34 the natural abundance of each isotope. 

35 """ 

36 

37 def __init__( 

38 self, 

39 atom_types: List[str], 

40 abundances: Dict[str, Dict[int, float]] = None, 

41 ): 

42 scat_lengths = _read_scattering_lengths() 

43 

44 # Sub select only the relevant species 

45 scat_lengths = scat_lengths[scat_lengths.species.isin(atom_types)].reset_index() 

46 

47 # Update the abundances if another weighting is desired 

48 if abundances is not None: 

49 for species in abundances: 

50 scat_lengths.loc[scat_lengths.species == species, 'abundance'] = 0 

51 for Z, frac in abundances[species].items(): 

52 match = (scat_lengths.species == species) & (scat_lengths.isotope == Z) 

53 if not np.any(match): # Check if any row+column matches 

54 raise ValueError(f'No match in database for {species} and isotope {Z}') 

55 scat_lengths.loc[match, 'abundance'] = frac 

56 

57 self._scattering_lengths = scat_lengths 

58 

59 # Check if any of the fetched scattering lengths is None, 

60 # indicating that it is missing in the experimental database. 

61 # Only raise an error if the desired abundance is greater than 0. 

62 nan_rows = scat_lengths[scat_lengths.isnull().any(axis=1) & (scat_lengths.abundance > 0.0)] 

63 if not nan_rows.empty: 

64 # Grab first offending entry 

65 row = nan_rows.iloc[0] 

66 raise ValueError(f'Non-zero abundance of {row.isotope}{row.species}' 

67 ' with missing tabulated scattering length.') 

68 

69 # Make sure abundances add up to 100% 

70 by_species = self._scattering_lengths.groupby('species') 

71 for species, species_df in by_species: 

72 if not np.isclose(species_df.abundance.sum(), 1): 

73 raise ValueError(f'Abundance values for {species} do not sum up to 1.0') 

74 

75 # Compute scattering lengths weighted by abundance 

76 weights_coh = by_species.apply( 

77 lambda s: (s.b_coh * s.abundance) 

78 .sum().real, # weights in dynasor can only be real atm 

79 include_groups=False 

80 ).to_dict() 

81 # First compute the average scattering length, then take the square 

82 # since the incoherent scattering lengths enter as b_incoh**2, but 

83 # dynasor only applies a single weighting factor w_incoh. 

84 weights_inc = by_species.apply( 

85 lambda s: ((s.b_inc * s.abundance).sum()**2).real, 

86 include_groups=False 

87 ).to_dict() 

88 

89 supports_currents = False 

90 super().__init__(weights_coh, weights_inc, supports_currents) 

91 

92 @property 

93 def abundances(self) -> Dict[str, Dict[int, float]]: 

94 abundance_dict = {} 

95 for (species), species_df in self._scattering_lengths.groupby('species'): 

96 abundance_dict[species] = {} 

97 for (isotope, abundance), _ in species_df.groupby(['isotope', 'abundance']): 

98 abundance_dict[species][isotope] = abundance 

99 return abundance_dict 

100 

101 

102def _read_scattering_lengths() -> DataFrame: 

103 """ 

104 Extracts the scattering lengths from the file `neutron_scattering_lengths.json` 

105 for each of the supplied species. Scattering lengths are in units of fm. 

106 

107 The scattering lengths have been extracted from the following NIST 

108 database: https://www.ncnr.nist.gov/resources/n-lengths/list.html, 

109 which in turn have been extracted from 

110 Neutron News **3**, No. 3***, 26 (1992). 

111 """ 

112 data_file = files(__package__) / 'neutron_scattering_lengths.json' 

113 with open(data_file) as fp: 

114 scattering_lengths = json.load(fp) 

115 

116 data = [] 

117 for species in scattering_lengths: 

118 for isotope in scattering_lengths[species]: 

119 for fld in 'b_c b_i'.split(): 

120 val = scattering_lengths[species][isotope][fld] 

121 if 'None' in val: 

122 scattering_lengths[species][isotope][fld] = np.nan 

123 elif 'j' in val: 

124 scattering_lengths[species][isotope][fld] = complex(val) 

125 else: 

126 scattering_lengths[species][isotope][fld] = float(val) 

127 data.append( 

128 dict( 

129 species=species, 

130 isotope=int(isotope), 

131 abundance=scattering_lengths[species][isotope]['abundance'] 

132 / 100, # % -> fraction 

133 b_coh=scattering_lengths[species][isotope]['b_c'], 

134 b_inc=scattering_lengths[species][isotope]['b_i'], 

135 ) 

136 ) 

137 scattering_lengths = DataFrame.from_dict(data) 

138 return scattering_lengths