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).sum(), 

78 include_groups=False 

79 ).to_dict() 

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

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

82 # dynasor only applies a single weighting factor w_incoh. 

83 weights_inc = by_species.apply( 

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

85 include_groups=False 

86 ).to_dict() 

87 

88 supports_currents = False 

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

90 

91 @property 

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

93 abundance_dict = {} 

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

95 abundance_dict[species] = {} 

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

97 abundance_dict[species][isotope] = abundance 

98 return abundance_dict 

99 

100 

101def _read_scattering_lengths() -> DataFrame: 

102 """ 

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

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

105 

106 The scattering lengths have been extracted from the following NIST 

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

108 which in turn have been extracted from 

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

110 """ 

111 data_file = files(__package__) / 'form-factors/neutron_scattering_lengths.json' 

112 with open(data_file) as fp: 

113 scattering_lengths = json.load(fp) 

114 

115 data = [] 

116 for species in scattering_lengths: 

117 for isotope in scattering_lengths[species]: 

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

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

120 if 'None' in val: 

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

122 elif 'j' in val: 

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

124 else: 

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

126 data.append( 

127 dict( 

128 species=species, 

129 isotope=int(isotope), 

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

131 / 100, # % -> fraction 

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

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

134 ) 

135 ) 

136 scattering_lengths = DataFrame.from_dict(data) 

137 return scattering_lengths