1 | """ |
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2 | SAS data representations. |
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3 | |
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4 | Plotting functions for data sets: |
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5 | |
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6 | :func:`plot_data` plots the data file. |
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7 | |
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8 | :func:`plot_theory` plots a calculated result from the model. |
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9 | |
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10 | Wrappers for the sasview data loader and data manipulations: |
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11 | |
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12 | :func:`load_data` loads a sasview data file. |
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13 | |
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14 | :func:`set_beam_stop` masks the beam stop from the data. |
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15 | |
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16 | :func:`set_half` selects the right or left half of the data, which can |
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17 | be useful for shear measurements which have not been properly corrected |
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18 | for path length and reflections. |
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19 | |
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20 | :func:`set_top` cuts the top part off the data. |
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21 | |
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22 | |
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23 | Empty data sets for evaluating models without data: |
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24 | |
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25 | :func:`empty_data1D` creates an empty dataset, which is useful for plotting |
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26 | a theory function before the data is measured. |
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27 | |
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28 | :func:`empty_data2D` creates an empty 2D dataset. |
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29 | |
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30 | Note that the empty datasets use a minimal representation of the SasView |
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31 | objects so that models can be run without SasView on the path. You could |
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32 | also use these for your own data loader. |
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33 | |
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34 | """ |
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35 | import traceback |
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36 | |
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37 | import numpy as np |
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38 | |
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39 | def load_data(filename): |
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40 | """ |
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41 | Load data using a sasview loader. |
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42 | """ |
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43 | from sas.dataloader.loader import Loader |
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44 | loader = Loader() |
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45 | data = loader.load(filename) |
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46 | if data is None: |
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47 | raise IOError("Data %r could not be loaded" % filename) |
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48 | return data |
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49 | |
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50 | |
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51 | def set_beam_stop(data, radius, outer=None): |
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52 | """ |
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53 | Add a beam stop of the given *radius*. If *outer*, make an annulus. |
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54 | """ |
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55 | from sas.dataloader.manipulations import Ringcut |
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56 | if hasattr(data, 'qx_data'): |
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57 | data.mask = Ringcut(0, radius)(data) |
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58 | if outer is not None: |
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59 | data.mask += Ringcut(outer, np.inf)(data) |
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60 | else: |
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61 | data.mask = (data.x < radius) |
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62 | if outer is not None: |
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63 | data.mask |= (data.x >= outer) |
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64 | |
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65 | |
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66 | def set_half(data, half): |
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67 | """ |
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68 | Select half of the data, either "right" or "left". |
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69 | """ |
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70 | from sas.dataloader.manipulations import Boxcut |
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71 | if half == 'right': |
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72 | data.mask += \ |
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73 | Boxcut(x_min=-np.inf, x_max=0.0, y_min=-np.inf, y_max=np.inf)(data) |
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74 | if half == 'left': |
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75 | data.mask += \ |
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76 | Boxcut(x_min=0.0, x_max=np.inf, y_min=-np.inf, y_max=np.inf)(data) |
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77 | |
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78 | |
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79 | def set_top(data, cutoff): |
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80 | """ |
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81 | Chop the top off the data, above *cutoff*. |
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82 | """ |
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83 | from sas.dataloader.manipulations import Boxcut |
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84 | data.mask += \ |
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85 | Boxcut(x_min=-np.inf, x_max=np.inf, y_min=-np.inf, y_max=cutoff)(data) |
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86 | |
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87 | |
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88 | class Data1D(object): |
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89 | """ |
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90 | 1D data object. |
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91 | |
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92 | Note that this definition matches the attributes from sasview, with |
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93 | some generic 1D data vectors and some SAS specific definitions. Some |
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94 | refactoring to allow consistent naming conventions between 1D, 2D and |
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95 | SESANS data would be helpful. |
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96 | |
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97 | **Attributes** |
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98 | |
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99 | *x*, *dx*: $q$ vector and gaussian resolution |
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100 | |
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101 | *y*, *dy*: $I(q)$ vector and measurement uncertainty |
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102 | |
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103 | *mask*: values to include in plotting/analysis |
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104 | |
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105 | *dxl*: slit widths for slit smeared data, with *dx* ignored |
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106 | |
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107 | *qmin*, *qmax*: range of $q$ values in *x* |
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108 | |
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109 | *filename*: label for the data line |
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110 | |
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111 | *_xaxis*, *_xunit*: label and units for the *x* axis |
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112 | |
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113 | *_yaxis*, *_yunit*: label and units for the *y* axis |
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114 | """ |
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115 | def __init__(self, x=None, y=None, dx=None, dy=None): |
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116 | self.x, self.y, self.dx, self.dy = x, y, dx, dy |
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117 | self.dxl = None |
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118 | self.filename = None |
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119 | self.qmin = x.min() if x is not None else np.NaN |
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120 | self.qmax = x.max() if x is not None else np.NaN |
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121 | # TODO: why is 1D mask False and 2D mask True? |
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122 | self.mask = (np.isnan(y) if y is not None |
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123 | else np.zeros_like(x, 'b') if x is not None |
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124 | else None) |
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125 | self._xaxis, self._xunit = "x", "" |
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126 | self._yaxis, self._yunit = "y", "" |
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127 | |
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128 | def xaxis(self, label, unit): |
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129 | """ |
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130 | set the x axis label and unit |
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131 | """ |
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132 | self._xaxis = label |
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133 | self._xunit = unit |
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134 | |
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135 | def yaxis(self, label, unit): |
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136 | """ |
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137 | set the y axis label and unit |
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138 | """ |
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139 | self._yaxis = label |
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140 | self._yunit = unit |
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141 | |
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142 | |
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143 | |
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144 | class Data2D(object): |
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145 | """ |
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146 | 2D data object. |
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147 | |
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148 | Note that this definition matches the attributes from sasview. Some |
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149 | refactoring to allow consistent naming conventions between 1D, 2D and |
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150 | SESANS data would be helpful. |
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151 | |
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152 | **Attributes** |
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153 | |
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154 | *qx_data*, *dqx_data*: $q_x$ matrix and gaussian resolution |
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155 | |
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156 | *qy_data*, *dqy_data*: $q_y$ matrix and gaussian resolution |
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157 | |
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158 | *data*, *err_data*: $I(q)$ matrix and measurement uncertainty |
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159 | |
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160 | *mask*: values to exclude from plotting/analysis |
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161 | |
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162 | *qmin*, *qmax*: range of $q$ values in *x* |
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163 | |
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164 | *filename*: label for the data line |
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165 | |
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166 | *_xaxis*, *_xunit*: label and units for the *x* axis |
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167 | |
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168 | *_yaxis*, *_yunit*: label and units for the *y* axis |
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169 | |
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170 | *_zaxis*, *_zunit*: label and units for the *y* axis |
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171 | |
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172 | *Q_unit*, *I_unit*: units for Q and intensity |
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173 | |
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174 | *x_bins*, *y_bins*: grid steps in *x* and *y* directions |
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175 | """ |
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176 | def __init__(self, x=None, y=None, z=None, dx=None, dy=None, dz=None): |
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177 | self.qx_data, self.dqx_data = x, dx |
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178 | self.qy_data, self.dqy_data = y, dy |
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179 | self.data, self.err_data = z, dz |
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180 | self.mask = (~np.isnan(z) if z is not None |
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181 | else np.ones_like(x) if x is not None |
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182 | else None) |
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183 | self.q_data = np.sqrt(x**2 + y**2) |
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184 | self.qmin = 1e-16 |
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185 | self.qmax = np.inf |
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186 | self.detector = [] |
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187 | self.source = Source() |
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188 | self.Q_unit = "1/A" |
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189 | self.I_unit = "1/cm" |
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190 | self.xaxis("Q_x", "1/A") |
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191 | self.yaxis("Q_y", "1/A") |
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192 | self.zaxis("Intensity", "1/cm") |
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193 | self._xaxis, self._xunit = "x", "" |
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194 | self._yaxis, self._yunit = "y", "" |
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195 | self._zaxis, self._zunit = "z", "" |
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196 | self.x_bins, self.y_bins = None, None |
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197 | |
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198 | def xaxis(self, label, unit): |
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199 | """ |
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200 | set the x axis label and unit |
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201 | """ |
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202 | self._xaxis = label |
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203 | self._xunit = unit |
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204 | |
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205 | def yaxis(self, label, unit): |
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206 | """ |
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207 | set the y axis label and unit |
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208 | """ |
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209 | self._yaxis = label |
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210 | self._yunit = unit |
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211 | |
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212 | def zaxis(self, label, unit): |
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213 | """ |
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214 | set the y axis label and unit |
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215 | """ |
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216 | self._zaxis = label |
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217 | self._zunit = unit |
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218 | |
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219 | |
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220 | class Vector(object): |
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221 | """ |
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222 | 3-space vector of *x*, *y*, *z* |
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223 | """ |
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224 | def __init__(self, x=None, y=None, z=None): |
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225 | self.x, self.y, self.z = x, y, z |
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226 | |
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227 | class Detector(object): |
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228 | """ |
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229 | Detector attributes. |
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230 | """ |
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231 | def __init__(self, pixel_size=(None, None), distance=None): |
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232 | self.pixel_size = Vector(*pixel_size) |
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233 | self.distance = distance |
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234 | |
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235 | class Source(object): |
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236 | """ |
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237 | Beam attributes. |
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238 | """ |
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239 | def __init__(self): |
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240 | self.wavelength = np.NaN |
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241 | self.wavelength_unit = "A" |
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242 | |
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243 | |
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244 | def empty_data1D(q, resolution=0.0): |
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245 | """ |
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246 | Create empty 1D data using the given *q* as the x value. |
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247 | |
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248 | *resolution* dq/q defaults to 5%. |
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249 | """ |
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250 | |
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251 | #Iq = 100 * np.ones_like(q) |
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252 | #dIq = np.sqrt(Iq) |
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253 | Iq, dIq = None, None |
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254 | q = np.asarray(q) |
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255 | data = Data1D(q, Iq, dx=resolution * q, dy=dIq) |
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256 | data.filename = "fake data" |
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257 | return data |
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258 | |
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259 | |
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260 | def empty_data2D(qx, qy=None, resolution=0.0): |
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261 | """ |
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262 | Create empty 2D data using the given mesh. |
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263 | |
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264 | If *qy* is missing, create a square mesh with *qy=qx*. |
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265 | |
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266 | *resolution* dq/q defaults to 5%. |
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267 | """ |
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268 | if qy is None: |
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269 | qy = qx |
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270 | qx, qy = np.asarray(qx), np.asarray(qy) |
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271 | # 5% dQ/Q resolution |
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272 | Qx, Qy = np.meshgrid(qx, qy) |
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273 | Qx, Qy = Qx.flatten(), Qy.flatten() |
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274 | Iq = 100 * np.ones_like(Qx) |
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275 | dIq = np.sqrt(Iq) |
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276 | if resolution != 0: |
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277 | # https://www.ncnr.nist.gov/staff/hammouda/distance_learning/chapter_15.pdf |
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278 | # Should have an additional constant which depends on distances and |
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279 | # radii of the aperture, pixel dimensions and wavelength spread |
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280 | # Instead, assume radial dQ/Q is constant, and perpendicular matches |
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281 | # radial (which instead it should be inverse). |
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282 | Q = np.sqrt(Qx**2 + Qy**2) |
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283 | dqx = resolution * Q |
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284 | dqy = resolution * Q |
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285 | else: |
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286 | dqx = dqy = None |
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287 | |
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288 | data = Data2D(x=Qx, y=Qy, z=Iq, dx=dqx, dy=dqy, dz=dIq) |
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289 | data.x_bins = qx |
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290 | data.y_bins = qy |
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291 | data.filename = "fake data" |
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292 | |
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293 | # pixel_size in mm, distance in m |
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294 | detector = Detector(pixel_size=(5, 5), distance=4) |
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295 | data.detector.append(detector) |
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296 | data.source.wavelength = 5 # angstroms |
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297 | data.source.wavelength_unit = "A" |
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298 | return data |
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299 | |
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300 | |
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301 | def plot_data(data, view='log', limits=None): |
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302 | """ |
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303 | Plot data loaded by the sasview loader. |
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304 | |
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305 | *data* is a sasview data object, either 1D, 2D or SESANS. |
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306 | |
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307 | *view* is log or linear. |
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308 | |
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309 | *limits* sets the intensity limits on the plot; if None then the limits |
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310 | are inferred from the data. |
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311 | """ |
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312 | # Note: kind of weird using the plot result functions to plot just the |
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313 | # data, but they already handle the masking and graph markup already, so |
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314 | # do not repeat. |
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315 | if hasattr(data, 'lam'): |
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316 | _plot_result_sesans(data, None, None, use_data=True, limits=limits) |
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317 | elif hasattr(data, 'qx_data'): |
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318 | _plot_result2D(data, None, None, view, use_data=True, limits=limits) |
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319 | else: |
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320 | _plot_result1D(data, None, None, view, use_data=True, limits=limits) |
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321 | |
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322 | |
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323 | def plot_theory(data, theory, resid=None, view='log', |
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324 | use_data=True, limits=None): |
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325 | """ |
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326 | Plot theory calculation. |
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327 | |
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328 | *data* is needed to define the graph properties such as labels and |
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329 | units, and to define the data mask. |
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330 | |
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331 | *theory* is a matrix of the same shape as the data. |
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332 | |
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333 | *view* is log or linear |
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334 | |
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335 | *use_data* is True if the data should be plotted as well as the theory. |
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336 | |
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337 | *limits* sets the intensity limits on the plot; if None then the limits |
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338 | are inferred from the data. |
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339 | """ |
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340 | if hasattr(data, 'lam'): |
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341 | _plot_result_sesans(data, theory, resid, use_data=True, limits=limits) |
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342 | elif hasattr(data, 'qx_data'): |
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343 | _plot_result2D(data, theory, resid, view, use_data, limits=limits) |
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344 | else: |
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345 | _plot_result1D(data, theory, resid, view, use_data, limits=limits) |
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346 | |
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347 | |
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348 | def protect(fn): |
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349 | """ |
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350 | Decorator to wrap calls in an exception trapper which prints the |
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351 | exception and continues. Keyboard interrupts are ignored. |
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352 | """ |
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353 | def wrapper(*args, **kw): |
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354 | """ |
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355 | Trap and print errors from function. |
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356 | """ |
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357 | try: |
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358 | return fn(*args, **kw) |
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359 | except KeyboardInterrupt: |
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360 | raise |
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361 | except: |
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362 | traceback.print_exc() |
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363 | |
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364 | return wrapper |
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365 | |
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366 | |
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367 | @protect |
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368 | def _plot_result1D(data, theory, resid, view, use_data, limits=None): |
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369 | """ |
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370 | Plot the data and residuals for 1D data. |
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371 | """ |
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372 | import matplotlib.pyplot as plt |
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373 | from numpy.ma import masked_array, masked |
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374 | |
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375 | use_data = use_data and data.y is not None |
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376 | use_theory = theory is not None |
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377 | use_resid = resid is not None |
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378 | num_plots = (use_data or use_theory) + use_resid |
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379 | |
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380 | scale = data.x**4 if view == 'q4' else 1.0 |
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381 | |
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382 | if use_data or use_theory: |
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383 | #print(vmin, vmax) |
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384 | all_positive = True |
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385 | some_present = False |
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386 | if use_data: |
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387 | mdata = masked_array(data.y, data.mask.copy()) |
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388 | mdata[~np.isfinite(mdata)] = masked |
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389 | if view is 'log': |
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390 | mdata[mdata <= 0] = masked |
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391 | plt.errorbar(data.x/10, scale*mdata, yerr=data.dy, fmt='.') |
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392 | all_positive = all_positive and (mdata > 0).all() |
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393 | some_present = some_present or (mdata.count() > 0) |
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394 | |
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395 | |
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396 | if use_theory: |
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397 | mtheory = masked_array(theory, data.mask.copy()) |
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398 | mtheory[~np.isfinite(mtheory)] = masked |
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399 | if view is 'log': |
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400 | mtheory[mtheory <= 0] = masked |
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401 | plt.plot(data.x/10, scale*mtheory, '-', hold=True) |
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402 | all_positive = all_positive and (mtheory > 0).all() |
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403 | some_present = some_present or (mtheory.count() > 0) |
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404 | |
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405 | if limits is not None: |
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406 | plt.ylim(*limits) |
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407 | |
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408 | if num_plots > 1: |
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409 | plt.subplot(1, num_plots, 1) |
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410 | plt.xscale('linear' if not some_present else view) |
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411 | plt.yscale('linear' |
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412 | if view == 'q4' or not some_present or not all_positive |
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413 | else view) |
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414 | plt.xlabel("$q$/nm$^{-1}$") |
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415 | plt.ylabel('$I(q)$') |
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416 | |
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417 | if use_resid: |
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418 | mresid = masked_array(resid, data.mask.copy()) |
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419 | mresid[~np.isfinite(mresid)] = masked |
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420 | some_present = (mresid.count() > 0) |
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421 | |
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422 | if num_plots > 1: |
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423 | plt.subplot(1, num_plots, (use_data or use_theory) + 1) |
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424 | plt.plot(data.x/10, mresid, '-') |
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425 | plt.xlabel("$q$/nm$^{-1}$") |
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426 | plt.ylabel('residuals') |
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427 | plt.xscale('linear' if not some_present else view) |
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428 | |
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429 | |
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430 | @protect |
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431 | def _plot_result_sesans(data, theory, resid, use_data, limits=None): |
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432 | """ |
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433 | Plot SESANS results. |
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434 | """ |
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435 | import matplotlib.pyplot as plt |
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436 | use_data = use_data and data.y is not None |
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437 | use_theory = theory is not None |
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438 | use_resid = resid is not None |
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439 | num_plots = (use_data or use_theory) + use_resid |
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440 | |
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441 | if use_data or use_theory: |
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442 | if num_plots > 1: |
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443 | plt.subplot(1, num_plots, 1) |
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444 | if use_data: |
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445 | plt.errorbar(data.x, data.y, yerr=data.dy) |
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446 | if theory is not None: |
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447 | plt.plot(data.x, theory, '-', hold=True) |
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448 | if limits is not None: |
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449 | plt.ylim(*limits) |
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450 | plt.xlabel('spin echo length (nm)') |
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451 | plt.ylabel('polarization (P/P0)') |
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452 | |
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453 | if resid is not None: |
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454 | if num_plots > 1: |
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455 | plt.subplot(1, num_plots, (use_data or use_theory) + 1) |
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456 | plt.plot(data.x, resid, 'x') |
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457 | plt.xlabel('spin echo length (nm)') |
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458 | plt.ylabel('residuals (P/P0)') |
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459 | |
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460 | |
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461 | @protect |
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462 | def _plot_result2D(data, theory, resid, view, use_data, limits=None): |
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463 | """ |
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464 | Plot the data and residuals for 2D data. |
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465 | """ |
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466 | import matplotlib.pyplot as plt |
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467 | use_data = use_data and data.data is not None |
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468 | use_theory = theory is not None |
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469 | use_resid = resid is not None |
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470 | num_plots = use_data + use_theory + use_resid |
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471 | |
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472 | # Put theory and data on a common colormap scale |
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473 | vmin, vmax = np.inf, -np.inf |
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474 | if use_data: |
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475 | target = data.data[~data.mask] |
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476 | datamin = target[target > 0].min() if view == 'log' else target.min() |
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477 | datamax = target.max() |
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478 | vmin = min(vmin, datamin) |
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479 | vmax = max(vmax, datamax) |
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480 | if use_theory: |
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481 | theorymin = theory[theory > 0].min() if view == 'log' else theory.min() |
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482 | theorymax = theory.max() |
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483 | vmin = min(vmin, theorymin) |
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484 | vmax = max(vmax, theorymax) |
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485 | |
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486 | # Override data limits from the caller |
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487 | if limits is not None: |
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488 | vmin, vmax = limits |
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489 | |
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490 | # Plot data |
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491 | if use_data: |
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492 | if num_plots > 1: |
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493 | plt.subplot(1, num_plots, 1) |
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494 | _plot_2d_signal(data, target, view=view, vmin=vmin, vmax=vmax) |
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495 | plt.title('data') |
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496 | h = plt.colorbar() |
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497 | h.set_label('$I(q)$') |
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498 | |
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499 | # plot theory |
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500 | if use_theory: |
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501 | if num_plots > 1: |
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502 | plt.subplot(1, num_plots, use_data+1) |
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503 | _plot_2d_signal(data, theory, view=view, vmin=vmin, vmax=vmax) |
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504 | plt.title('theory') |
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505 | h = plt.colorbar() |
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506 | h.set_label(r'$\log_{10}I(q)$' if view == 'log' |
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507 | else r'$q^4 I(q)$' if view == 'q4' |
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508 | else '$I(q)$') |
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509 | |
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510 | # plot resid |
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511 | if use_resid: |
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512 | if num_plots > 1: |
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513 | plt.subplot(1, num_plots, use_data+use_theory+1) |
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514 | _plot_2d_signal(data, resid, view='linear') |
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515 | plt.title('residuals') |
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516 | h = plt.colorbar() |
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517 | h.set_label(r'$\Delta I(q)$') |
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518 | |
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519 | |
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520 | @protect |
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521 | def _plot_2d_signal(data, signal, vmin=None, vmax=None, view='log'): |
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522 | """ |
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523 | Plot the target value for the data. This could be the data itself, |
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524 | the theory calculation, or the residuals. |
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525 | |
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526 | *scale* can be 'log' for log scale data, or 'linear'. |
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527 | """ |
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528 | import matplotlib.pyplot as plt |
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529 | from numpy.ma import masked_array |
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530 | |
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531 | image = np.zeros_like(data.qx_data) |
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532 | image[~data.mask] = signal |
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533 | valid = np.isfinite(image) |
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534 | if view == 'log': |
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535 | valid[valid] = (image[valid] > 0) |
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536 | if vmin is None: vmin = image[valid & ~data.mask].min() |
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537 | if vmax is None: vmax = image[valid & ~data.mask].max() |
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538 | image[valid] = np.log10(image[valid]) |
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539 | elif view == 'q4': |
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540 | image[valid] *= (data.qx_data[valid]**2+data.qy_data[valid]**2)**2 |
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541 | if vmin is None: vmin = image[valid & ~data.mask].min() |
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542 | if vmax is None: vmax = image[valid & ~data.mask].max() |
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543 | else: |
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544 | if vmin is None: vmin = image[valid & ~data.mask].min() |
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545 | if vmax is None: vmax = image[valid & ~data.mask].max() |
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546 | |
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547 | image[~valid | data.mask] = 0 |
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548 | #plottable = Iq |
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549 | plottable = masked_array(image, ~valid | data.mask) |
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550 | # Divide range by 10 to convert from angstroms to nanometers |
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551 | xmin, xmax = min(data.qx_data)/10, max(data.qx_data)/10 |
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552 | ymin, ymax = min(data.qy_data)/10, max(data.qy_data)/10 |
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553 | if view == 'log': |
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554 | vmin, vmax = np.log10(vmin), np.log10(vmax) |
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555 | plt.imshow(plottable.reshape(len(data.x_bins), len(data.y_bins)), |
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556 | interpolation='nearest', aspect=1, origin='upper', |
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557 | extent=[xmin, xmax, ymin, ymax], vmin=vmin, vmax=vmax) |
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558 | plt.xlabel("$q_x$/nm$^{-1}$") |
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559 | plt.ylabel("$q_y$/nm$^{-1}$") |
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560 | return vmin, vmax |
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561 | |
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562 | def demo(): |
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563 | """ |
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564 | Load and plot a SAS dataset. |
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565 | """ |
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566 | data = load_data('DEC07086.DAT') |
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567 | set_beam_stop(data, 0.004) |
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568 | plot_data(data) |
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569 | import matplotlib.pyplot as plt; plt.show() |
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570 | |
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571 | |
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572 | if __name__ == "__main__": |
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573 | demo() |
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