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 | import numpy as np |
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10 | from numpy import pi, cos |
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11 | |
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12 | from .generate import F64 |
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13 | |
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14 | class PyModel(object): |
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15 | """ |
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16 | Wrapper for pure python models. |
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17 | """ |
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18 | def __init__(self, model_info): |
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19 | self.info = model_info |
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20 | |
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21 | def __call__(self, q_vectors): |
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22 | q_input = PyInput(q_vectors, dtype=F64) |
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23 | kernel = self.info['Iqxy'] if q_input.is_2d else self.info['Iq'] |
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24 | return PyKernel(kernel, self.info, q_input) |
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25 | |
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26 | def release(self): |
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27 | """ |
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28 | Free resources associated with the model. |
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29 | """ |
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30 | pass |
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31 | |
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32 | class PyInput(object): |
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33 | """ |
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34 | Make q data available to the gpu. |
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35 | |
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36 | *q_vectors* is a list of q vectors, which will be *[q]* for 1-D data, |
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37 | and *[qx, qy]* for 2-D data. Internally, the vectors will be reallocated |
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38 | to get the best performance on OpenCL, which may involve shifting and |
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39 | stretching the array to better match the memory architecture. Additional |
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40 | points will be evaluated with *q=1e-3*. |
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41 | |
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42 | *dtype* is the data type for the q vectors. The data type should be |
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43 | set to match that of the kernel, which is an attribute of |
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44 | :class:`GpuProgram`. Note that not all kernels support double |
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45 | precision, so even if the program was created for double precision, |
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46 | the *GpuProgram.dtype* may be single precision. |
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47 | |
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48 | Call :meth:`release` when complete. Even if not called directly, the |
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49 | buffer will be released when the data object is freed. |
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50 | """ |
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51 | def __init__(self, q_vectors, dtype): |
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52 | self.nq = q_vectors[0].size |
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53 | self.dtype = dtype |
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54 | self.is_2d = (len(q_vectors) == 2) |
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55 | self.q_vectors = [np.ascontiguousarray(q, self.dtype) for q in q_vectors] |
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56 | self.q_pointers = [q.ctypes.data for q in self.q_vectors] |
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57 | |
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58 | def release(self): |
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59 | """ |
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60 | Free resources associated with the model inputs. |
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61 | """ |
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62 | self.q_vectors = [] |
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63 | |
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64 | class PyKernel(object): |
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65 | """ |
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66 | Callable SAS kernel. |
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67 | |
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68 | *kernel* is the DllKernel object to call. |
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69 | |
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70 | *model_info* is the module information |
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71 | |
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72 | *q_input* is the DllInput q vectors at which the kernel should be |
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73 | evaluated. |
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74 | |
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75 | The resulting call method takes the *pars*, a list of values for |
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76 | the fixed parameters to the kernel, and *pd_pars*, a list of (value,weight) |
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77 | vectors for the polydisperse parameters. *cutoff* determines the |
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78 | integration limits: any points with combined weight less than *cutoff* |
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79 | will not be calculated. |
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80 | |
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81 | Call :meth:`release` when done with the kernel instance. |
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82 | """ |
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83 | def __init__(self, kernel, model_info, q_input): |
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84 | self.info = model_info |
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85 | self.q_input = q_input |
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86 | self.res = np.empty(q_input.nq, q_input.dtype) |
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87 | dim = '2d' if q_input.is_2d else '1d' |
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88 | # Loop over q unless user promises that the kernel is vectorized by |
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89 | # taggining it with vectorized=True |
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90 | if not getattr(kernel, 'vectorized', False): |
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91 | if dim == '2d': |
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92 | def vector_kernel(qx, qy, *args): |
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93 | """ |
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94 | Vectorized 2D kernel. |
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95 | """ |
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96 | return np.array([kernel(qxi, qyi, *args) |
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97 | for qxi, qyi in zip(qx, qy)]) |
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98 | else: |
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99 | def vector_kernel(q, *args): |
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100 | """ |
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101 | Vectorized 1D kernel. |
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102 | """ |
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103 | return np.array([kernel(qi, *args) |
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104 | for qi in q]) |
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105 | self.kernel = vector_kernel |
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106 | else: |
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107 | self.kernel = kernel |
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108 | fixed_pars = model_info['partype']['fixed-' + dim] |
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109 | pd_pars = model_info['partype']['pd-' + dim] |
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110 | vol_pars = model_info['partype']['volume'] |
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111 | |
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112 | # First two fixed pars are scale and background |
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113 | pars = [p.name for p in model_info['parameters'][2:]] |
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114 | offset = len(self.q_input.q_vectors) |
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115 | self.args = self.q_input.q_vectors + [None] * len(pars) |
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116 | self.fixed_index = np.array([pars.index(p) + offset |
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117 | for p in fixed_pars[2:]]) |
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118 | self.pd_index = np.array([pars.index(p) + offset |
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119 | for p in pd_pars]) |
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120 | self.vol_index = np.array([pars.index(p) + offset |
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121 | for p in vol_pars]) |
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122 | try: self.theta_index = pars.index('theta') + offset |
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123 | except ValueError: self.theta_index = -1 |
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124 | |
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125 | # Caller needs fixed_pars and pd_pars |
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126 | self.fixed_pars = fixed_pars |
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127 | self.pd_pars = pd_pars |
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128 | |
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129 | def __call__(self, fixed, pd, cutoff=1e-5): |
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130 | print("fixed",fixed) |
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131 | print("pd", pd) |
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132 | args = self.args[:] # grab a copy of the args |
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133 | form, form_volume = self.kernel, self.info['form_volume'] |
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134 | # First two fixed |
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135 | scale, background = fixed[:2] |
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136 | for index, value in zip(self.fixed_index, fixed[2:]): |
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137 | args[index] = float(value) |
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138 | res = _loops(form, form_volume, cutoff, scale, background, args, |
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139 | pd, self.pd_index, self.vol_index, self.theta_index) |
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140 | |
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141 | return res |
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142 | |
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143 | def release(self): |
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144 | """ |
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145 | Free resources associated with the kernel. |
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146 | """ |
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147 | self.q_input = None |
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148 | |
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149 | def _loops(form, form_volume, cutoff, scale, background, |
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150 | args, pd, pd_index, vol_index, theta_index): |
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151 | """ |
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152 | *form* is the name of the form function, which should be vectorized over |
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153 | q, but otherwise have an interface like the opencl kernels, with the |
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154 | q parameters first followed by the individual parameters in the order |
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155 | given in model.parameters (see :mod:`sasmodels.generate`). |
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156 | |
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157 | *form_volume* calculates the volume of the shape. *vol_index* gives |
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158 | the list of volume parameters |
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159 | |
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160 | *cutoff* ignores the corners of the dispersion hypercube |
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161 | |
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162 | *scale*, *background* multiplies the resulting form and adds an offset |
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163 | |
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164 | *args* is the prepopulated set of arguments to the form function, starting |
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165 | with the q vectors, and including slots for all the parameters. The |
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166 | values for the parameters will be substituted with values from the |
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167 | dispersion functions. |
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168 | |
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169 | *pd* is the list of dispersion parameters |
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170 | |
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171 | *pd_index* are the indices of the dispersion parameters in *args* |
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172 | |
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173 | *vol_index* are the indices of the volume parameters in *args* |
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174 | |
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175 | *theta_index* is the index of the theta parameter for the sasview |
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176 | spherical correction, or -1 if there is no angular dispersion |
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177 | """ |
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178 | |
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179 | ################################################################ |
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180 | # # |
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181 | # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! # |
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182 | # !! !! # |
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183 | # !! KEEP THIS CODE CONSISTENT WITH KERNEL_TEMPLATE.C !! # |
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184 | # !! !! # |
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185 | # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! # |
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186 | # # |
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187 | ################################################################ |
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188 | |
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189 | #TODO: Wojtek's notes |
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190 | #TODO: The goal is to restructure polydispersity loop |
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191 | #so it allows fitting arbitrary polydispersity parameters |
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192 | #and it accepts cases like coupled parameters |
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193 | weight = np.empty(len(pd), 'd') |
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194 | if weight.size > 0: |
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195 | # weight vector, to be populated by polydispersity loops |
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196 | |
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197 | # identify which pd parameters are volume parameters |
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198 | vol_weight_index = np.array([(index in vol_index) for index in pd_index]) |
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199 | |
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200 | # Sort parameters in decreasing order of pd length |
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201 | Npd = np.array([len(pdi[0]) for pdi in pd], 'i') |
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202 | order = np.argsort(Npd)[::-1] |
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203 | stride = np.cumprod(Npd[order]) |
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204 | pd = [pd[index] for index in order] |
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205 | pd_index = pd_index[order] |
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206 | vol_weight_index = vol_weight_index[order] |
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207 | |
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208 | fast_value = pd[0][0] |
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209 | fast_weight = pd[0][1] |
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210 | else: |
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211 | stride = np.array([1]) |
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212 | vol_weight_index = slice(None, None) |
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213 | # keep lint happy |
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214 | fast_value = [None] |
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215 | fast_weight = [None] |
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216 | |
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217 | ret = np.zeros_like(args[0]) |
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218 | norm = np.zeros_like(ret) |
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219 | vol = np.zeros_like(ret) |
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220 | vol_norm = np.zeros_like(ret) |
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221 | for k in range(stride[-1]): |
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222 | # update polydispersity parameter values |
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223 | fast_index = k % stride[0] |
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224 | if fast_index == 0: # bottom loop complete ... check all other loops |
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225 | if weight.size > 0: |
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226 | for i, index, in enumerate(k % stride): |
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227 | args[pd_index[i]] = pd[i][0][index] |
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228 | weight[i] = pd[i][1][index] |
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229 | else: |
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230 | args[pd_index[0]] = fast_value[fast_index] |
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231 | weight[0] = fast_weight[fast_index] |
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232 | |
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233 | # Computes the weight, and if it is not sufficient then ignore this |
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234 | # parameter set. |
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235 | # Note: could precompute w1*...*wn so we only need to multiply by w0 |
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236 | w = np.prod(weight) |
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237 | if w > cutoff: |
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238 | # Note: can precompute spherical correction if theta_index is not |
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239 | # the fast index. Correction factor for spherical integration |
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240 | #spherical_correction = abs(cos(pi*args[phi_index])) if phi_index>=0 else 1.0 |
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241 | spherical_correction = (abs(cos(pi * args[theta_index])) * pi / 2 |
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242 | if theta_index >= 0 else 1.0) |
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243 | #spherical_correction = 1.0 |
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244 | |
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245 | # Call the scattering function and adds it to the total. |
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246 | I = form(*args) |
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247 | if np.isnan(I).any(): continue |
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248 | ret += w * I * spherical_correction |
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249 | norm += w |
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250 | |
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251 | # Volume normalization. |
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252 | # If there are "volume" polydispersity parameters, then these |
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253 | # will be used to call the form_volume function from the user |
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254 | # supplied kernel, and accumulate a normalized weight. |
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255 | # Note: can precompute volume norm if fast index is not a volume |
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256 | if form_volume: |
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257 | vol_args = [args[index] for index in vol_index] |
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258 | vol_weight = np.prod(weight[vol_weight_index]) |
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259 | vol += vol_weight * form_volume(*vol_args) |
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260 | vol_norm += vol_weight |
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261 | |
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262 | positive = (vol * vol_norm != 0.0) |
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263 | ret[positive] *= vol_norm[positive] / vol[positive] |
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264 | result = scale * ret / norm + background |
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265 | return result |
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266 | ======= |
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267 | """ |
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268 | Python driver for python kernels |
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269 | |
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270 | Calls the kernel with a vector of $q$ values for a single parameter set. |
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271 | Polydispersity is supported by looping over different parameter sets and |
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272 | summing the results. The interface to :class:`PyModel` matches those for |
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273 | :class:`kernelcl.GpuModel` and :class:`kerneldll.DllModel`. |
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274 | """ |
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275 | import numpy as np |
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276 | from numpy import pi, cos |
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277 | |
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278 | from .generate import F64 |
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279 | |
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280 | class PyModel(object): |
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281 | """ |
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282 | Wrapper for pure python models. |
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283 | """ |
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284 | def __init__(self, model_info): |
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285 | self.info = model_info |
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286 | |
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287 | def __call__(self, q_vectors): |
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288 | q_input = PyInput(q_vectors, dtype=F64) |
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289 | kernel = self.info['Iqxy'] if q_input.is_2d else self.info['Iq'] |
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290 | return PyKernel(kernel, self.info, q_input) |
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291 | |
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292 | def release(self): |
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293 | """ |
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294 | Free resources associated with the model. |
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295 | """ |
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296 | pass |
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297 | |
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298 | class PyInput(object): |
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299 | """ |
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300 | Make q data available to the gpu. |
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301 | |
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302 | *q_vectors* is a list of q vectors, which will be *[q]* for 1-D data, |
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303 | and *[qx, qy]* for 2-D data. Internally, the vectors will be reallocated |
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304 | to get the best performance on OpenCL, which may involve shifting and |
---|
305 | stretching the array to better match the memory architecture. Additional |
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306 | points will be evaluated with *q=1e-3*. |
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307 | |
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308 | *dtype* is the data type for the q vectors. The data type should be |
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309 | set to match that of the kernel, which is an attribute of |
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310 | :class:`GpuProgram`. Note that not all kernels support double |
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311 | precision, so even if the program was created for double precision, |
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312 | the *GpuProgram.dtype* may be single precision. |
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313 | |
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314 | Call :meth:`release` when complete. Even if not called directly, the |
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315 | buffer will be released when the data object is freed. |
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316 | """ |
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317 | def __init__(self, q_vectors, dtype): |
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318 | self.nq = q_vectors[0].size |
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319 | self.dtype = dtype |
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320 | self.is_2d = (len(q_vectors) == 2) |
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321 | self.q_vectors = [np.ascontiguousarray(q, self.dtype) for q in q_vectors] |
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322 | self.q_pointers = [q.ctypes.data for q in self.q_vectors] |
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323 | |
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324 | def release(self): |
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325 | """ |
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326 | Free resources associated with the model inputs. |
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327 | """ |
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328 | self.q_vectors = [] |
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329 | |
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330 | class PyKernel(object): |
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331 | """ |
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332 | Callable SAS kernel. |
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333 | |
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334 | *kernel* is the DllKernel object to call. |
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335 | |
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336 | *model_info* is the module information |
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337 | |
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338 | *q_input* is the DllInput q vectors at which the kernel should be |
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339 | evaluated. |
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340 | |
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341 | The resulting call method takes the *pars*, a list of values for |
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342 | the fixed parameters to the kernel, and *pd_pars*, a list of (value,weight) |
---|
343 | vectors for the polydisperse parameters. *cutoff* determines the |
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344 | integration limits: any points with combined weight less than *cutoff* |
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345 | will not be calculated. |
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346 | |
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347 | Call :meth:`release` when done with the kernel instance. |
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348 | """ |
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349 | def __init__(self, kernel, model_info, q_input): |
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350 | self.info = model_info |
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351 | self.q_input = q_input |
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352 | self.res = np.empty(q_input.nq, q_input.dtype) |
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353 | dim = '2d' if q_input.is_2d else '1d' |
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354 | # Loop over q unless user promises that the kernel is vectorized by |
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355 | # taggining it with vectorized=True |
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356 | if not getattr(kernel, 'vectorized', False): |
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357 | if dim == '2d': |
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358 | def vector_kernel(qx, qy, *args): |
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359 | """ |
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360 | Vectorized 2D kernel. |
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361 | """ |
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362 | return np.array([kernel(qxi, qyi, *args) |
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363 | for qxi, qyi in zip(qx, qy)]) |
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364 | else: |
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365 | def vector_kernel(q, *args): |
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366 | """ |
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367 | Vectorized 1D kernel. |
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368 | """ |
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369 | return np.array([kernel(qi, *args) |
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370 | for qi in q]) |
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371 | self.kernel = vector_kernel |
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372 | else: |
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373 | self.kernel = kernel |
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374 | fixed_pars = model_info['partype']['fixed-' + dim] |
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375 | pd_pars = model_info['partype']['pd-' + dim] |
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376 | vol_pars = model_info['partype']['volume'] |
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377 | |
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378 | # First two fixed pars are scale and background |
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379 | pars = [p.name for p in model_info['parameters'][2:]] |
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380 | offset = len(self.q_input.q_vectors) |
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381 | self.args = self.q_input.q_vectors + [None] * len(pars) |
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382 | self.fixed_index = np.array([pars.index(p) + offset |
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383 | for p in fixed_pars[2:]]) |
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384 | self.pd_index = np.array([pars.index(p) + offset |
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385 | for p in pd_pars]) |
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386 | self.vol_index = np.array([pars.index(p) + offset |
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387 | for p in vol_pars]) |
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388 | try: self.theta_index = pars.index('theta') + offset |
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389 | except ValueError: self.theta_index = -1 |
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390 | |
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391 | # Caller needs fixed_pars and pd_pars |
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392 | self.fixed_pars = fixed_pars |
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393 | self.pd_pars = pd_pars |
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394 | |
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395 | def __call__(self, fixed, pd, cutoff=1e-5): |
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396 | #print("fixed",fixed) |
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397 | #print("pd", pd) |
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398 | args = self.args[:] # grab a copy of the args |
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399 | form, form_volume = self.kernel, self.info['form_volume'] |
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400 | # First two fixed |
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401 | scale, background = fixed[:2] |
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402 | for index, value in zip(self.fixed_index, fixed[2:]): |
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403 | args[index] = float(value) |
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404 | res = _loops(form, form_volume, cutoff, scale, background, args, |
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405 | pd, self.pd_index, self.vol_index, self.theta_index) |
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406 | |
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407 | return res |
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408 | |
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409 | def release(self): |
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410 | """ |
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411 | Free resources associated with the kernel. |
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412 | """ |
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413 | self.q_input = None |
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414 | |
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415 | def _loops(form, form_volume, cutoff, scale, background, |
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416 | args, pd, pd_index, vol_index, theta_index): |
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417 | """ |
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418 | *form* is the name of the form function, which should be vectorized over |
---|
419 | q, but otherwise have an interface like the opencl kernels, with the |
---|
420 | q parameters first followed by the individual parameters in the order |
---|
421 | given in model.parameters (see :mod:`sasmodels.generate`). |
---|
422 | |
---|
423 | *form_volume* calculates the volume of the shape. *vol_index* gives |
---|
424 | the list of volume parameters |
---|
425 | |
---|
426 | *cutoff* ignores the corners of the dispersion hypercube |
---|
427 | |
---|
428 | *scale*, *background* multiplies the resulting form and adds an offset |
---|
429 | |
---|
430 | *args* is the prepopulated set of arguments to the form function, starting |
---|
431 | with the q vectors, and including slots for all the parameters. The |
---|
432 | values for the parameters will be substituted with values from the |
---|
433 | dispersion functions. |
---|
434 | |
---|
435 | *pd* is the list of dispersion parameters |
---|
436 | |
---|
437 | *pd_index* are the indices of the dispersion parameters in *args* |
---|
438 | |
---|
439 | *vol_index* are the indices of the volume parameters in *args* |
---|
440 | |
---|
441 | *theta_index* is the index of the theta parameter for the sasview |
---|
442 | spherical correction, or -1 if there is no angular dispersion |
---|
443 | """ |
---|
444 | |
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445 | ################################################################ |
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446 | # # |
---|
447 | # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! # |
---|
448 | # !! !! # |
---|
449 | # !! KEEP THIS CODE CONSISTENT WITH KERNEL_TEMPLATE.C !! # |
---|
450 | # !! !! # |
---|
451 | # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! # |
---|
452 | # # |
---|
453 | ################################################################ |
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454 | |
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455 | weight = np.empty(len(pd), 'd') |
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456 | if weight.size > 0: |
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457 | # weight vector, to be populated by polydispersity loops |
---|
458 | |
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459 | # identify which pd parameters are volume parameters |
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460 | vol_weight_index = np.array([(index in vol_index) for index in pd_index]) |
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461 | |
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462 | # Sort parameters in decreasing order of pd length |
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463 | Npd = np.array([len(pdi[0]) for pdi in pd], 'i') |
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464 | order = np.argsort(Npd)[::-1] |
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465 | stride = np.cumprod(Npd[order]) |
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466 | pd = [pd[index] for index in order] |
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467 | pd_index = pd_index[order] |
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468 | vol_weight_index = vol_weight_index[order] |
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469 | |
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470 | fast_value = pd[0][0] |
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471 | fast_weight = pd[0][1] |
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472 | else: |
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473 | stride = np.array([1]) |
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474 | vol_weight_index = slice(None, None) |
---|
475 | # keep lint happy |
---|
476 | fast_value = [None] |
---|
477 | fast_weight = [None] |
---|
478 | |
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479 | ret = np.zeros_like(args[0]) |
---|
480 | norm = np.zeros_like(ret) |
---|
481 | vol = np.zeros_like(ret) |
---|
482 | vol_norm = np.zeros_like(ret) |
---|
483 | for k in range(stride[-1]): |
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484 | # update polydispersity parameter values |
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485 | fast_index = k % stride[0] |
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486 | if fast_index == 0: # bottom loop complete ... check all other loops |
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487 | if weight.size > 0: |
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488 | for i, index, in enumerate(k % stride): |
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489 | args[pd_index[i]] = pd[i][0][index] |
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490 | weight[i] = pd[i][1][index] |
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491 | else: |
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492 | args[pd_index[0]] = fast_value[fast_index] |
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493 | weight[0] = fast_weight[fast_index] |
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494 | |
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495 | # Computes the weight, and if it is not sufficient then ignore this |
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496 | # parameter set. |
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497 | # Note: could precompute w1*...*wn so we only need to multiply by w0 |
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498 | w = np.prod(weight) |
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499 | if w > cutoff: |
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500 | # Note: can precompute spherical correction if theta_index is not |
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501 | # the fast index. Correction factor for spherical integration |
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502 | #spherical_correction = abs(cos(pi*args[phi_index])) if phi_index>=0 else 1.0 |
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503 | spherical_correction = (abs(cos(pi * args[theta_index])) * pi / 2 |
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504 | if theta_index >= 0 else 1.0) |
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505 | #spherical_correction = 1.0 |
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506 | |
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507 | # Call the scattering function and adds it to the total. |
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508 | I = form(*args) |
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509 | if np.isnan(I).any(): continue |
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510 | ret += w * I * spherical_correction |
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511 | norm += w |
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512 | |
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513 | # Volume normalization. |
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514 | # If there are "volume" polydispersity parameters, then these |
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515 | # will be used to call the form_volume function from the user |
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516 | # supplied kernel, and accumulate a normalized weight. |
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517 | # Note: can precompute volume norm if fast index is not a volume |
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518 | if form_volume: |
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519 | vol_args = [args[index] for index in vol_index] |
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520 | vol_weight = np.prod(weight[vol_weight_index]) |
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521 | vol += vol_weight * form_volume(*vol_args) |
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522 | vol_norm += vol_weight |
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523 | |
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524 | positive = (vol * vol_norm != 0.0) |
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525 | ret[positive] *= vol_norm[positive] / vol[positive] |
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526 | result = scale * ret / norm + background |
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527 | return result |
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