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

1import json 

2from importlib.resources import files 

3from typing import Optional 

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: Optional[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 for species in atom_types: 

48 if not np.any(scat_lengths.species == species): 

49 raise ValueError('Missing tabulated values ' 

50 f'for requested species {species}.') 

51 

52 # Update the abundances if another weighting is desired 

53 if abundances is not None: 

54 for species in abundances: 

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

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

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

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

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

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

61 

62 self._scattering_lengths = scat_lengths 

63 

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

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

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

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

68 if not nan_rows.empty: 

69 # Grab first offending entry 

70 row = nan_rows.iloc[0] 

71 raise ValueError(f'{row.isotope}{row.species} is missing tabulated values for either' 

72 ' the coherent or incoherent scattering length.' 

73 ' Adjust the abundance parameter to set the fraction of' 

74 f' {row.isotope}{row.species} to zero.') 

75 

76 # Make sure abundances add up to 100% 

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

78 for species, species_df in by_species: 

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

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

81 

82 # Compute scattering lengths weighted by abundance 

83 weights_coh = by_species.apply( 

84 lambda s: (s.b_coh * s.abundance).sum(), 

85 include_groups=False 

86 ).to_dict() 

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

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

89 # dynasor only applies a single weighting factor w_incoh. 

90 weights_inc = by_species.apply( 

91 lambda s: (s.b_inc * s.abundance).sum(), 

92 include_groups=False 

93 ).to_dict() 

94 

95 supports_currents = False 

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

97 

98 @property 

99 def abundances(self) -> dict[str, dict[int, float]]: 

100 """Abundances used for calculating scattering lengths.""" 

101 abundance_dict = {} 

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

103 abundance_dict[species] = {} 

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

105 abundance_dict[species][isotope] = abundance 

106 return abundance_dict 

107 

108 @property 

109 def parameters(self) -> DataFrame: 

110 """Scattering lengths used to compute the coherent and 

111 incoherent weights for the selected isotopes. 

112 """ 

113 return self._scattering_lengths 

114 

115 

116def _read_scattering_lengths() -> DataFrame: 

117 """ 

118 Extracts the scattering lengths from the file `neutron-scattering-lengths-nist-1992.json` 

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

120 

121 The scattering lengths have been extracted from the following NIST 

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

123 which in turn have been extracted from 

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

125 """ 

126 data_file = files(__package__) / 'form-factors/neutron-scattering-lengths-nist-1992.json' 

127 with open(data_file) as fp: 

128 scattering_lengths = json.load(fp) 

129 

130 data = [] 

131 for species in scattering_lengths: 

132 for isotope in scattering_lengths[species]: 

133 for fld in 'b_coh b_inc'.split(): 

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

135 if 'None' in val: 

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

137 elif 'j' in val: 

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

139 else: 

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

141 data.append( 

142 dict( 

143 species=species, 

144 isotope=int(isotope), 

145 abundance=float(scattering_lengths[species][isotope]['abundance']) 

146 / 100, # % -> fraction 

147 b_coh=complex(scattering_lengths[species][isotope]['b_coh']), 

148 b_inc=complex(scattering_lengths[species][isotope]['b_inc']), 

149 ) 

150 ) 

151 scattering_lengths = DataFrame.from_dict(data) 

152 return scattering_lengths