1 | """ |
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2 | Python driver for python kernels |
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3 | |
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4 | Calls the kernel with a vector of $q$ values for a single parameter set. |
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5 | Polydispersity is supported by looping over different parameter sets and |
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6 | summing the results. The interface to :class:`PyModel` matches those for |
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7 | :class:`kernelcl.GpuModel` and :class:`kerneldll.DllModel`. |
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8 | """ |
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9 | from __future__ import division, print_function |
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10 | |
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11 | import logging |
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12 | |
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13 | import numpy as np # type: ignore |
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14 | from numpy import pi |
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15 | try: |
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16 | from numpy import cbrt |
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17 | except ImportError: |
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18 | def cbrt(x): return x ** (1.0/3.0) |
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19 | |
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20 | from .generate import F64 |
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21 | from .kernel import KernelModel, Kernel |
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22 | |
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23 | # pylint: disable=unused-import |
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24 | try: |
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25 | from typing import Union, Callable |
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26 | except ImportError: |
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27 | pass |
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28 | else: |
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29 | from . import details |
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30 | DType = Union[None, str, np.dtype] |
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31 | # pylint: enable=unused-import |
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32 | |
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33 | logger = logging.getLogger(__name__) |
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34 | |
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35 | class PyModel(KernelModel): |
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36 | """ |
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37 | Wrapper for pure python models. |
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38 | """ |
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39 | def __init__(self, model_info): |
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40 | # Make sure Iq is available and vectorized |
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41 | _create_default_functions(model_info) |
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42 | self.info = model_info |
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43 | self.dtype = np.dtype('d') |
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44 | logger.info("make python model " + self.info.name) |
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45 | |
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46 | def make_kernel(self, q_vectors): |
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47 | q_input = PyInput(q_vectors, dtype=F64) |
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48 | return PyKernel(self.info, q_input) |
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49 | |
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50 | def release(self): |
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51 | """ |
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52 | Free resources associated with the model. |
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53 | """ |
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54 | pass |
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55 | |
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56 | class PyInput(object): |
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57 | """ |
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58 | Make q data available to the gpu. |
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59 | |
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60 | *q_vectors* is a list of q vectors, which will be *[q]* for 1-D data, |
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61 | and *[qx, qy]* for 2-D data. Internally, the vectors will be reallocated |
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62 | to get the best performance on OpenCL, which may involve shifting and |
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63 | stretching the array to better match the memory architecture. Additional |
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64 | points will be evaluated with *q=1e-3*. |
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65 | |
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66 | *dtype* is the data type for the q vectors. The data type should be |
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67 | set to match that of the kernel, which is an attribute of |
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68 | :class:`GpuProgram`. Note that not all kernels support double |
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69 | precision, so even if the program was created for double precision, |
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70 | the *GpuProgram.dtype* may be single precision. |
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71 | |
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72 | Call :meth:`release` when complete. Even if not called directly, the |
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73 | buffer will be released when the data object is freed. |
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74 | """ |
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75 | def __init__(self, q_vectors, dtype): |
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76 | self.nq = q_vectors[0].size |
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77 | self.dtype = dtype |
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78 | self.is_2d = (len(q_vectors) == 2) |
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79 | if self.is_2d: |
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80 | self.q = np.empty((self.nq, 2), dtype=dtype) |
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81 | self.q[:, 0] = q_vectors[0] |
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82 | self.q[:, 1] = q_vectors[1] |
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83 | else: |
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84 | self.q = np.empty(self.nq, dtype=dtype) |
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85 | self.q[:self.nq] = q_vectors[0] |
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86 | |
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87 | def release(self): |
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88 | """ |
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89 | Free resources associated with the model inputs. |
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90 | """ |
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91 | self.q = None |
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92 | |
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93 | class PyKernel(Kernel): |
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94 | """ |
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95 | Callable SAS kernel. |
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96 | |
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97 | *kernel* is the kernel object to call. |
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98 | |
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99 | *model_info* is the module information |
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100 | |
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101 | *q_input* is the DllInput q vectors at which the kernel should be |
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102 | evaluated. |
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103 | |
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104 | The resulting call method takes the *pars*, a list of values for |
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105 | the fixed parameters to the kernel, and *pd_pars*, a list of (value,weight) |
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106 | vectors for the polydisperse parameters. *cutoff* determines the |
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107 | integration limits: any points with combined weight less than *cutoff* |
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108 | will not be calculated. |
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109 | |
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110 | Call :meth:`release` when done with the kernel instance. |
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111 | """ |
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112 | def __init__(self, model_info, q_input): |
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113 | # type: (callable, ModelInfo, List[np.ndarray]) -> None |
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114 | self.dtype = np.dtype('d') |
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115 | self.info = model_info |
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116 | self.q_input = q_input |
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117 | self.res = np.empty(q_input.nq, q_input.dtype) |
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118 | self.dim = '2d' if q_input.is_2d else '1d' |
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119 | |
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120 | partable = model_info.parameters |
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121 | #kernel_parameters = (partable.iqxy_parameters if q_input.is_2d |
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122 | # else partable.iq_parameters) |
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123 | kernel_parameters = partable.iq_parameters |
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124 | volume_parameters = partable.form_volume_parameters |
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125 | |
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126 | # Create an array to hold the parameter values. There will be a |
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127 | # single array whose values are updated as the calculator goes |
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128 | # through the loop. Arguments to the kernel and volume functions |
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129 | # will use views into this vector, relying on the fact that a |
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130 | # an array of no dimensions acts like a scalar. |
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131 | parameter_vector = np.empty(len(partable.call_parameters)-2, 'd') |
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132 | |
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133 | # Create views into the array to hold the arguments |
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134 | offset = 0 |
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135 | kernel_args, volume_args = [], [] |
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136 | for p in partable.kernel_parameters: |
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137 | if p.length == 1: |
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138 | # Scalar values are length 1 vectors with no dimensions. |
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139 | v = parameter_vector[offset:offset+1].reshape(()) |
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140 | else: |
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141 | # Vector values are simple views. |
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142 | v = parameter_vector[offset:offset+p.length] |
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143 | offset += p.length |
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144 | if p in kernel_parameters: |
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145 | kernel_args.append(v) |
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146 | if p in volume_parameters: |
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147 | volume_args.append(v) |
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148 | |
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149 | # Hold on to the parameter vector so we can use it to call kernel later. |
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150 | # This may also be required to preserve the views into the vector. |
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151 | self._parameter_vector = parameter_vector |
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152 | |
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153 | # Generate a closure which calls the kernel with the views into the |
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154 | # parameter array. |
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155 | if q_input.is_2d: |
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156 | form = model_info.Iqxy |
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157 | qx, qy = q_input.q[:, 0], q_input.q[:, 1] |
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158 | self._form = lambda: form(qx, qy, *kernel_args) |
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159 | else: |
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160 | form = model_info.Iq |
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161 | q = q_input.q |
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162 | self._form = lambda: form(q, *kernel_args) |
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163 | |
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164 | # Generate a closure which calls the form_volume if it exists. |
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165 | self._volume_args = volume_args |
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166 | volume = model_info.form_volume |
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167 | shell = model_info.shell_volume |
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168 | radius = model_info.effective_radius |
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169 | self._volume = ((lambda: (shell(*volume_args), volume(*volume_args))) if shell and volume |
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170 | else (lambda: [volume(*volume_args)]*2) if volume |
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171 | else (lambda: (1.0, 1.0))) |
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172 | self._radius = ((lambda mode: radius(mode, *volume_args)) if radius |
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173 | else (lambda mode: cbrt(0.75/pi*volume(*volume_args))) if volume |
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174 | else (lambda mode: 1.0)) |
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175 | |
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176 | |
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177 | |
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178 | def _call_kernel(self, call_details, values, cutoff, magnetic, effective_radius_type): |
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179 | # type: (CallDetails, np.ndarray, np.ndarray, float, bool) -> np.ndarray |
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180 | if magnetic: |
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181 | raise NotImplementedError("Magnetism not implemented for pure python models") |
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182 | #print("Calling python kernel") |
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183 | #call_details.show(values) |
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184 | radius = ((lambda: 0.0) if effective_radius_type == 0 |
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185 | else (lambda: self._radius(effective_radius_type))) |
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186 | self.result = _loops( |
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187 | self._parameter_vector, self._form, self._volume, radius, |
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188 | self.q_input.nq, call_details, values, cutoff) |
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189 | |
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190 | def release(self): |
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191 | # type: () -> None |
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192 | """ |
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193 | Free resources associated with the kernel. |
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194 | """ |
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195 | self.q_input.release() |
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196 | self.q_input = None |
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197 | |
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198 | def _loops(parameters, # type: np.ndarray |
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199 | form, # type: Callable[[], np.ndarray] |
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200 | form_volume, # type: Callable[[], float] |
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201 | form_radius, # type: Callable[[], float] |
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202 | nq, # type: int |
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203 | call_details, # type: details.CallDetails |
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204 | values, # type: np.ndarray |
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205 | cutoff, # type: float |
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206 | ): |
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207 | # type: (...) -> None |
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208 | ################################################################ |
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209 | # # |
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210 | # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! # |
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211 | # !! !! # |
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212 | # !! KEEP THIS CODE CONSISTENT WITH KERNEL_TEMPLATE.C !! # |
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213 | # !! !! # |
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214 | # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! # |
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215 | # # |
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216 | ################################################################ |
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217 | |
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218 | # WARNING: Trickery ahead |
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219 | # The parameters[] vector is embedded in the closures for form(), |
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220 | # form_volume() and form_radius(). We set the initial vector from |
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221 | # the values for the model parameters. As we loop through the polydispesity |
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222 | # mesh, we update the components with the polydispersity values before |
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223 | # calling the respective functions. |
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224 | n_pars = len(parameters) |
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225 | parameters[:] = values[2:n_pars+2] |
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226 | |
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227 | if call_details.num_active == 0: |
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228 | total = form() |
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229 | weight_norm = 1.0 |
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230 | weighted_shell, weighted_form = form_volume() |
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231 | weighted_radius = form_radius() |
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232 | |
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233 | else: |
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234 | pd_value = values[2+n_pars:2+n_pars + call_details.num_weights] |
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235 | pd_weight = values[2+n_pars + call_details.num_weights:] |
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236 | |
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237 | weight_norm = 0.0 |
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238 | weighted_form = 0.0 |
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239 | weighted_shell = 0.0 |
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240 | weighted_radius = 0.0 |
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241 | partial_weight = np.NaN |
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242 | weight = np.NaN |
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243 | |
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244 | p0_par = call_details.pd_par[0] |
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245 | p0_length = call_details.pd_length[0] |
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246 | p0_index = p0_length |
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247 | p0_offset = call_details.pd_offset[0] |
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248 | |
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249 | pd_par = call_details.pd_par[:call_details.num_active] |
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250 | pd_offset = call_details.pd_offset[:call_details.num_active] |
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251 | pd_stride = call_details.pd_stride[:call_details.num_active] |
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252 | pd_length = call_details.pd_length[:call_details.num_active] |
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253 | |
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254 | total = np.zeros(nq, 'd') |
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255 | for loop_index in range(call_details.num_eval): |
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256 | # update polydispersity parameter values |
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257 | if p0_index == p0_length: |
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258 | pd_index = (loop_index//pd_stride)%pd_length |
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259 | parameters[pd_par] = pd_value[pd_offset+pd_index] |
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260 | partial_weight = np.prod(pd_weight[pd_offset+pd_index][1:]) |
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261 | p0_index = loop_index%p0_length |
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262 | |
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263 | weight = partial_weight * pd_weight[p0_offset + p0_index] |
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264 | parameters[p0_par] = pd_value[p0_offset + p0_index] |
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265 | p0_index += 1 |
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266 | if weight > cutoff: |
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267 | # Call the scattering function |
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268 | # Assume that NaNs are only generated if the parameters are bad; |
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269 | # exclude all q for that NaN. Even better would be to have an |
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270 | # INVALID expression like the C models, but that is expensive. |
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271 | Iq = np.asarray(form(), 'd') |
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272 | if np.isnan(Iq).any(): |
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273 | continue |
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274 | |
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275 | # update value and norm |
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276 | total += weight * Iq |
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277 | weight_norm += weight |
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278 | shell, form = form_volume() |
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279 | weighted_form += weight * form |
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280 | weighted_shell += weight * shell |
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281 | weighted_radius += weight * form_radius() |
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282 | |
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283 | result = np.hstack((total, weight_norm, weighted_form, weighted_shell, weighted_radius)) |
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284 | return result |
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285 | |
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286 | |
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287 | def _create_default_functions(model_info): |
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288 | """ |
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289 | Autogenerate missing functions, such as Iqxy from Iq. |
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290 | |
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291 | This only works for Iqxy when Iq is written in python. :func:`make_source` |
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292 | performs a similar role for Iq written in C. This also vectorizes |
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293 | any functions that are not already marked as vectorized. |
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294 | """ |
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295 | # Note: must call create_vector_Iq before create_vector_Iqxy |
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296 | _create_vector_Iq(model_info) |
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297 | _create_vector_Iqxy(model_info) |
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298 | |
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299 | |
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300 | def _create_vector_Iq(model_info): |
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301 | """ |
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302 | Define Iq as a vector function if it exists. |
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303 | """ |
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304 | Iq = model_info.Iq |
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305 | if callable(Iq) and not getattr(Iq, 'vectorized', False): |
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306 | #print("vectorizing Iq") |
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307 | def vector_Iq(q, *args): |
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308 | """ |
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309 | Vectorized 1D kernel. |
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310 | """ |
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311 | return np.array([Iq(qi, *args) for qi in q]) |
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312 | vector_Iq.vectorized = True |
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313 | model_info.Iq = vector_Iq |
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314 | |
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315 | |
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316 | def _create_vector_Iqxy(model_info): |
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317 | """ |
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318 | Define Iqxy as a vector function if it exists, or default it from Iq(). |
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319 | """ |
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320 | Iqxy = getattr(model_info, 'Iqxy', None) |
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321 | if callable(Iqxy): |
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322 | if not getattr(Iqxy, 'vectorized', False): |
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323 | #print("vectorizing Iqxy") |
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324 | def vector_Iqxy(qx, qy, *args): |
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325 | """ |
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326 | Vectorized 2D kernel. |
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327 | """ |
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328 | return np.array([Iqxy(qxi, qyi, *args) for qxi, qyi in zip(qx, qy)]) |
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329 | vector_Iqxy.vectorized = True |
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330 | model_info.Iqxy = vector_Iqxy |
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331 | else: |
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332 | #print("defaulting Iqxy") |
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333 | # Iq is vectorized because create_vector_Iq was already called. |
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334 | Iq = model_info.Iq |
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335 | def default_Iqxy(qx, qy, *args): |
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336 | """ |
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337 | Default 2D kernel. |
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338 | """ |
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339 | return Iq(np.sqrt(qx**2 + qy**2), *args) |
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340 | default_Iqxy.vectorized = True |
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341 | model_info.Iqxy = default_Iqxy |
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