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 """A class for generating 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, however, be overwritten by the :attr:`abundances` argument. 

15 

16 The scattering lengths have been extracted from the following NIST 

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

18 which in turn have been extracted from 

19 Neutron News **3**, 29 (1992). 

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 ).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()**2).real 

85 ).to_dict() 

86 

87 supports_currents = False 

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

89 

90 @property 

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

92 abundance_dict = {} 

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

94 abundance_dict[species] = {} 

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

96 abundance_dict[species][isotope] = abundance 

97 return abundance_dict 

98 

99 

100def _read_scattering_lengths() -> DataFrame: 

101 """ 

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

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

104 

105 The scattering lengths have been extracted from the following NIST 

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

107 which in turn have been extracted from 

108 Neutron News **3**, No. 3***, 29 (1992). 

109 """ 

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

111 with open(data_file) as fp: 

112 scattering_lengths = json.load(fp) 

113 

114 data = [] 

115 for species in scattering_lengths: 

116 for isotope in scattering_lengths[species]: 

117 for fld in 'b_c b_i'.split(): 

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

119 if 'None' in val: 

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

121 elif 'j' in val: 

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

123 else: 

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

125 data.append( 

126 dict( 

127 species=species, 

128 isotope=int(isotope), 

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

130 / 100, # % -> fraction 

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

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

133 ) 

134 ) 

135 scattering_lengths = DataFrame.from_dict(data) 

136 return scattering_lengths