1 | from __future__ import print_function |
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2 | |
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3 | import numpy as np # type: ignore |
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4 | from numpy import pi, cos, sin |
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5 | |
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6 | try: |
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7 | np.meshgrid([]) |
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8 | meshgrid = np.meshgrid |
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9 | except ValueError: |
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10 | # CRUFT: np.meshgrid requires multiple vectors |
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11 | def meshgrid(*args): |
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12 | if len(args) > 1: |
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13 | return np.meshgrid(*args) |
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14 | else: |
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15 | return [np.asarray(v) for v in args] |
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16 | |
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17 | try: |
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18 | from typing import List |
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19 | except ImportError: |
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20 | pass |
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21 | else: |
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22 | from .modelinfo import ModelInfo |
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23 | |
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24 | |
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25 | class CallDetails(object): |
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26 | parts = None # type: List["CallDetails"] |
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27 | def __init__(self, model_info): |
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28 | # type: (ModelInfo) -> None |
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29 | parameters = model_info.parameters |
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30 | max_pd = parameters.max_pd |
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31 | |
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32 | # Structure of the call details buffer: |
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33 | # pd_par[max_pd] pd params in order of length |
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34 | # pd_length[max_pd] length of each pd param |
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35 | # pd_offset[max_pd] offset of pd values in parameter array |
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36 | # pd_stride[max_pd] index of pd value in loop = n//stride[k] |
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37 | # pd_prod total length of pd loop |
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38 | # pd_sum total length of the weight vector |
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39 | # num_active number of pd params |
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40 | # theta_par parameter number for theta parameter |
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41 | self.buffer = np.zeros(4*max_pd + 4, 'i4') |
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42 | |
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43 | # generate views on different parts of the array |
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44 | self._pd_par = self.buffer[0 * max_pd:1 * max_pd] |
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45 | self._pd_length = self.buffer[1 * max_pd:2 * max_pd] |
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46 | self._pd_offset = self.buffer[2 * max_pd:3 * max_pd] |
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47 | self._pd_stride = self.buffer[3 * max_pd:4 * max_pd] |
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48 | |
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49 | # theta_par is fixed |
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50 | self.theta_par = parameters.theta_offset |
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51 | |
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52 | @property |
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53 | def pd_par(self): return self._pd_par |
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54 | |
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55 | @property |
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56 | def pd_length(self): return self._pd_length |
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57 | |
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58 | @property |
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59 | def pd_offset(self): return self._pd_offset |
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60 | |
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61 | @property |
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62 | def pd_stride(self): return self._pd_stride |
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63 | |
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64 | @property |
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65 | def pd_prod(self): return self.buffer[-4] |
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66 | @pd_prod.setter |
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67 | def pd_prod(self, v): self.buffer[-4] = v |
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68 | |
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69 | @property |
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70 | def pd_sum(self): return self.buffer[-3] |
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71 | @pd_sum.setter |
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72 | def pd_sum(self, v): self.buffer[-3] = v |
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73 | |
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74 | @property |
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75 | def num_active(self): return self.buffer[-2] |
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76 | @num_active.setter |
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77 | def num_active(self, v): self.buffer[-2] = v |
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78 | |
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79 | @property |
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80 | def theta_par(self): return self.buffer[-1] |
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81 | @theta_par.setter |
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82 | def theta_par(self, v): self.buffer[-1] = v |
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83 | |
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84 | def show(self): |
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85 | print("num_active", self.num_active) |
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86 | print("pd_prod", self.pd_prod) |
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87 | print("pd_sum", self.pd_sum) |
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88 | print("theta par", self.theta_par) |
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89 | print("pd_par", self.pd_par) |
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90 | print("pd_length", self.pd_length) |
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91 | print("pd_offset", self.pd_offset) |
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92 | print("pd_stride", self.pd_stride) |
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93 | |
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94 | |
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95 | def mono_details(model_info): |
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96 | call_details = CallDetails(model_info) |
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97 | call_details.pd_prod = 1 |
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98 | call_details.pd_sum = model_info.parameters.nvalues |
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99 | call_details.pd_par[:] = np.arange(0, model_info.parameters.max_pd) |
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100 | call_details.pd_length[:] = 1 |
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101 | call_details.pd_offset[:] = np.arange(0, model_info.parameters.max_pd) |
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102 | call_details.pd_stride[:] = 1 |
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103 | return call_details |
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104 | |
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105 | |
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106 | def poly_details(model_info, weights): |
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107 | #print("weights",weights) |
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108 | #weights = weights[2:] # Skip scale and background |
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109 | |
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110 | # Decreasing list of polydispersity lengths |
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111 | #print([p.id for p in model_info.parameters.call_parameters]) |
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112 | pd_length = np.array([len(w) for w in weights[2:2+model_info.parameters.npars]]) |
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113 | num_active = np.sum(pd_length>1) |
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114 | max_pd = model_info.parameters.max_pd |
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115 | if num_active > max_pd: |
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116 | raise ValueError("Too many polydisperse parameters") |
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117 | |
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118 | pd_offset = np.cumsum(np.hstack((0, pd_length))) |
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119 | #print(", ".join(str(i)+"-"+p.id for i,p in enumerate(model_info.parameters.call_parameters))) |
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120 | #print("len:",pd_length) |
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121 | #print("off:",pd_offset) |
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122 | # Note: the reversing view, x[::-1], does not require a copy |
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123 | idx = np.argsort(pd_length)[::-1][:max_pd] |
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124 | pd_stride = np.cumprod(np.hstack((1, pd_length[idx]))) |
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125 | |
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126 | call_details = CallDetails(model_info) |
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127 | call_details.pd_par[:max_pd] = idx |
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128 | call_details.pd_length[:max_pd] = pd_length[idx] |
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129 | call_details.pd_offset[:max_pd] = pd_offset[idx] |
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130 | call_details.pd_stride[:max_pd] = pd_stride[:-1] |
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131 | call_details.pd_prod = pd_stride[-1] |
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132 | call_details.pd_sum = sum(len(w) for w in weights) |
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133 | call_details.num_active = num_active |
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134 | #call_details.show() |
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135 | return call_details |
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136 | |
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137 | |
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138 | def dispersion_mesh(model_info, pars): |
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139 | """ |
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140 | Create a mesh grid of dispersion parameters and weights. |
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141 | |
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142 | Returns [p1,p2,...],w where pj is a vector of values for parameter j |
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143 | and w is a vector containing the products for weights for each |
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144 | parameter set in the vector. |
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145 | """ |
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146 | value, weight = zip(*pars) |
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147 | weight = [w if w else [1.] for w in weight] |
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148 | weight = np.vstack([v.flatten() for v in meshgrid(*weight)]) |
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149 | weight = np.prod(weight, axis=0) |
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150 | value = [v.flatten() for v in meshgrid(*value)] |
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151 | lengths = [par.length for par in model_info.parameters.kernel_parameters |
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152 | if par.type == 'volume'] |
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153 | if any(n > 1 for n in lengths): |
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154 | pars = [] |
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155 | offset = 0 |
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156 | for n in lengths: |
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157 | pars.append(np.vstack(value[offset:offset+n]) if n > 1 else value[offset]) |
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158 | offset += n |
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159 | value = pars |
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160 | return value, weight |
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161 | |
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162 | |
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163 | |
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164 | def build_details(kernel, pairs): |
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165 | # type: (Kernel, Tuple[List[np.ndarray], List[np.ndarray]]) -> Tuple[CallDetails, np.ndarray, bool] |
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166 | """ |
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167 | Converts (value, weight) pairs into parameters for the kernel call. |
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168 | |
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169 | Returns a CallDetails object indicating the polydispersity, a data object |
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170 | containing the different values, and the magnetic flag indicating whether |
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171 | any magnetic magnitudes are non-zero. Magnetic vectors (M0, phi, theta) are |
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172 | converted to rectangular coordinates (mx, my, mz). |
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173 | """ |
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174 | values, weights = zip(*pairs) |
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175 | scalars = [v[0] for v in values] |
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176 | if all(len(w)==1 for w in weights): |
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177 | call_details = mono_details(kernel.info) |
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178 | data = np.array(scalars+scalars+[1]*len(scalars), dtype=kernel.dtype) |
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179 | else: |
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180 | call_details = poly_details(kernel.info, weights) |
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181 | data = np.hstack(scalars+list(values)+list(weights)).astype(kernel.dtype) |
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182 | is_magnetic = convert_magnetism(kernel.info.parameters, data) |
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183 | #call_details.show() |
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184 | return call_details, data, is_magnetic |
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185 | |
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186 | def convert_magnetism(parameters, values): |
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187 | """ |
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188 | Convert magnetism in value table from polar to rectangular coordinates. |
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189 | |
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190 | Returns True if any magnetism is present. |
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191 | """ |
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192 | mag = values[parameters.nvalues-3*parameters.nmagnetic:parameters.nvalues] |
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193 | mag = mag.reshape(-1, 3) |
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194 | M0 = mag[:,0] |
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195 | if np.any(M0): |
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196 | theta, phi = mag[:,1]*pi/180., mag[:,2]*pi/180. |
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197 | cos_theta = cos(theta) |
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198 | mx = M0*cos_theta*cos(phi) |
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199 | my = M0*sin(theta) |
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200 | mz = -M0*cos_theta*sin(phi) |
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201 | mag[:,0], mag[:,1], mag[:,2] = mx, my, mz |
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202 | return True |
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203 | else: |
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204 | return False |
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