1 | """ |
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2 | Execution kernel interface |
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3 | ========================== |
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4 | |
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5 | :class:`KernelModel` defines the interface to all kernel models. |
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6 | In particular, each model should provide a :meth:`KernelModel.make_kernel` |
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7 | call which returns an executable kernel, :class:`Kernel`, that operates |
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8 | on the given set of *q_vector* inputs. On completion of the computation, |
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9 | the kernel should be released, which also releases the inputs. |
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10 | """ |
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11 | |
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12 | from __future__ import division, print_function |
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13 | |
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14 | # pylint: disable=unused-import |
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15 | try: |
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16 | from typing import List |
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17 | except ImportError: |
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18 | pass |
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19 | else: |
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20 | import numpy as np |
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21 | from .details import CallDetails |
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22 | from .modelinfo import ModelInfo |
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23 | # pylint: enable=unused-import |
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24 | |
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25 | |
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26 | class KernelModel(object): |
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27 | info = None # type: ModelInfo |
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28 | dtype = None # type: np.dtype |
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29 | def make_kernel(self, q_vectors): |
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30 | # type: (List[np.ndarray]) -> "Kernel" |
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31 | raise NotImplementedError("need to implement make_kernel") |
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32 | |
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33 | def release(self): |
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34 | # type: () -> None |
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35 | pass |
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36 | |
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37 | |
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38 | class Kernel(object): |
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39 | #: kernel dimension, either "1d" or "2d" |
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40 | dim = None # type: str |
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41 | info = None # type: ModelInfo |
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42 | results = None # type: List[np.ndarray] |
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43 | dtype = None # type: np.dtype |
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44 | |
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45 | def Iq(self, call_details, values, cutoff, magnetic): |
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46 | # type: (CallDetails, np.ndarray, np.ndarray, float, bool) -> np.ndarray |
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47 | r""" |
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48 | Returns I(q) from the polydisperse average scattering. |
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49 | |
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50 | .. math:: |
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51 | |
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52 | I(q) = \text{scale} \cdot P(q) + \text{background} |
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53 | |
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54 | With the correct choice of model and contrast, setting *scale* to |
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55 | the volume fraction $V_f$ of particles should match the measured |
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56 | absolute scattering. Some models (e.g., vesicle) have volume fraction |
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57 | built into the model, and do not need an additional scale. |
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58 | """ |
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59 | _, F2, _, shell_volume, _ = self.Fq(call_details, values, cutoff, magnetic, |
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60 | effective_radius_type=0) |
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61 | combined_scale = values[0]/shell_volume |
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62 | background = values[1] |
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63 | return combined_scale*F2 + background |
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64 | __call__ = Iq |
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65 | |
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66 | def Fq(self, call_details, values, cutoff, magnetic, effective_radius_type=0): |
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67 | # type: (CallDetails, np.ndarray, np.ndarray, float, bool, int) -> np.ndarray |
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68 | r""" |
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69 | Returns <F(q)>, <F(q)^2>, effective radius, shell volume and |
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70 | form:shell volume ratio. The <F(q)> term may be None if the |
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71 | form factor does not support direct computation of $F(q)$ |
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72 | |
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73 | $P(q) = <F^2(q)>/<V>$ is used for structure factor calculations, |
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74 | |
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75 | .. math:: |
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76 | |
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77 | I(q) = \text{scale} \cdot P(q) \cdot S(q) + \text{background} |
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78 | |
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79 | For the beta approximation, this becomes |
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80 | |
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81 | .. math:: |
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82 | |
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83 | I(q) = \text{scale} * P (1 + <F>^2/<F^2> (S - 1)) + \text{background} |
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84 | = \text{scale}/<V> (<F^2> + <F>^2 (S - 1)) + \text{background} |
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85 | |
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86 | $<F(q)>$ and $<F^2(q)>$ are averaged by polydispersity in shape |
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87 | and orientation, with each configuration $x_k$ having form factor |
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88 | $F(q, x_k)$, weight $w_k$ and volume $V_k$. The result is: |
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89 | |
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90 | .. math:: |
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91 | |
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92 | P(q) = \frac{\sum w_k F^2(q, x_k) / \sum w_k}{\sum w_k V_k / \sum w_k} |
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93 | |
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94 | The form factor itself is scaled by volume and contrast to compute the |
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95 | total scattering. This is then squared, and the volume weighted |
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96 | F^2 is then normalized by volume F. For a given density, the number |
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97 | of scattering centers is assumed to scale linearly with volume. Later |
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98 | scaling the resulting $P(q)$ by the volume fraction of particles |
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99 | gives the total scattering on an absolute scale. Most models |
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100 | incorporate the volume fraction into the overall scale parameter. An |
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101 | exception is vesicle, which includes the volume fraction parameter in |
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102 | the model itself, scaling $F$ by $\surd V_f$ so that the math for |
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103 | the beta approximation works out. |
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104 | |
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105 | By scaling $P(q)$ by total weight $\sum w_k$, there is no need to make |
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106 | sure that the polydisperisity distributions normalize to one. In |
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107 | particular, any distibution values $x_k$ outside the valid domain |
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108 | of $F$ will not be included, and the distribution will be implicitly |
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109 | truncated. This is controlled by the parameter limits defined in the |
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110 | model (which truncate the distribution before calling the kernel) as |
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111 | well as any region excluded using the *INVALID* macro defined within |
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112 | the model itself. |
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113 | |
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114 | The volume used in the polydispersity calculation is the form volume |
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115 | for solid objects or the shell volume for hollow objects. Shell |
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116 | volume should be used within $F$ so that the normalizing scale |
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117 | represents the volume fraction of the shell rather than the entire |
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118 | form. This corresponds to the volume fraction of shell-forming |
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119 | material added to the solvent. |
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120 | |
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121 | The calculation of $S$ requires the effective radius and the |
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122 | volume fraction of the particles. The model can have several |
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123 | different ways to compute effective radius, with the |
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124 | *effective_radius_type* parameter used to select amongst them. The |
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125 | volume fraction of particles should be determined from the total |
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126 | volume fraction of the form, not just the shell volume fraction. |
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127 | This makes a difference for hollow shapes, which need to scale |
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128 | the volume fraction by the returned volume ratio when computing $S$. |
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129 | For solid objects, the shell volume is set to the form volume so |
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130 | this scale factor evaluates to one and so can be used for both |
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131 | hollow and solid shapes. |
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132 | """ |
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133 | self._call_kernel(call_details, values, cutoff, magnetic, effective_radius_type) |
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134 | #print("returned",self.q_input.q, self.result) |
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135 | nout = 2 if self.info.have_Fq and self.dim == '1d' else 1 |
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136 | total_weight = self.result[nout*self.q_input.nq + 0] |
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137 | if total_weight == 0.: |
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138 | total_weight = 1. |
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139 | # Note: shell_volume == form_volume for solid objects |
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140 | form_volume = self.result[nout*self.q_input.nq + 1]/total_weight |
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141 | shell_volume = self.result[nout*self.q_input.nq + 2]/total_weight |
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142 | effective_radius = self.result[nout*self.q_input.nq + 3]/total_weight |
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143 | if shell_volume == 0.: |
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144 | shell_volume = 1. |
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145 | F1 = self.result[1:nout*self.q_input.nq:nout]/total_weight if nout == 2 else None |
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146 | F2 = self.result[0:nout*self.q_input.nq:nout]/total_weight |
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147 | return F1, F2, effective_radius, shell_volume, form_volume/shell_volume |
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148 | |
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149 | def release(self): |
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150 | # type: () -> None |
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151 | pass |
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152 | |
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153 | def _call_kernel(self, call_details, values, cutoff, magnetic, effective_radius_type): |
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154 | # type: (CallDetails, np.ndarray, np.ndarray, float, bool, int) -> np.ndarray |
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155 | """ |
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156 | Call the kernel. Subclasses defining kernels for particular execution |
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157 | engines need to provide an implementation for this. |
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158 | """ |
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159 | raise NotImplementedError() |
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