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 # type: ignore |
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38 | |
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39 | try: |
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40 | from typing import Union, Dict, List, Optional |
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41 | except ImportError: |
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42 | pass |
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43 | else: |
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44 | Data = Union["Data1D", "Data2D", "SesansData"] |
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45 | |
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46 | def load_data(filename, index=0): |
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47 | # type: (str) -> Data |
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48 | """ |
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49 | Load data using a sasview loader. |
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50 | """ |
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51 | from sas.sascalc.dataloader.loader import Loader # type: ignore |
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52 | loader = Loader() |
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53 | # Allow for one part in multipart file |
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54 | if '[' in filename: |
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55 | filename, indexstr = filename[:-1].split('[') |
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56 | index = int(indexstr) |
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57 | datasets = loader.load(filename) |
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58 | if not datasets: # None or [] |
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59 | raise IOError("Data %r could not be loaded" % filename) |
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60 | if not isinstance(datasets, list): |
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61 | datasets = [datasets] |
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62 | for data in datasets: |
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63 | if hasattr(data, 'x'): |
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64 | data.qmin, data.qmax = data.x.min(), data.x.max() |
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65 | data.mask = (np.isnan(data.y) if data.y is not None |
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66 | else np.zeros_like(data.x, dtype='bool')) |
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67 | elif hasattr(data, 'qx_data'): |
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68 | data.mask = ~data.mask |
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69 | return datasets[index] if index != 'all' else datasets |
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70 | |
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71 | |
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72 | def set_beam_stop(data, radius, outer=None): |
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73 | # type: (Data, float, Optional[float]) -> None |
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74 | """ |
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75 | Add a beam stop of the given *radius*. If *outer*, make an annulus. |
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76 | """ |
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77 | from sas.sascalc.dataloader.manipulations import Ringcut |
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78 | if hasattr(data, 'qx_data'): |
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79 | data.mask = Ringcut(0, radius)(data) |
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80 | if outer is not None: |
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81 | data.mask += Ringcut(outer, np.inf)(data) |
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82 | else: |
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83 | data.mask = (data.x < radius) |
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84 | if outer is not None: |
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85 | data.mask |= (data.x >= outer) |
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86 | |
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87 | |
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88 | def set_half(data, half): |
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89 | # type: (Data, str) -> None |
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90 | """ |
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91 | Select half of the data, either "right" or "left". |
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92 | """ |
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93 | from sas.sascalc.dataloader.manipulations import Boxcut |
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94 | if half == 'right': |
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95 | data.mask += \ |
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96 | Boxcut(x_min=-np.inf, x_max=0.0, y_min=-np.inf, y_max=np.inf)(data) |
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97 | if half == 'left': |
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98 | data.mask += \ |
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99 | Boxcut(x_min=0.0, x_max=np.inf, y_min=-np.inf, y_max=np.inf)(data) |
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100 | |
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101 | |
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102 | def set_top(data, cutoff): |
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103 | # type: (Data, float) -> None |
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104 | """ |
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105 | Chop the top off the data, above *cutoff*. |
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106 | """ |
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107 | from sas.sascalc.dataloader.manipulations import Boxcut |
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108 | data.mask += \ |
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109 | Boxcut(x_min=-np.inf, x_max=np.inf, y_min=-np.inf, y_max=cutoff)(data) |
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110 | |
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111 | |
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112 | class Data1D(object): |
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113 | """ |
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114 | 1D data object. |
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115 | |
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116 | Note that this definition matches the attributes from sasview, with |
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117 | some generic 1D data vectors and some SAS specific definitions. Some |
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118 | refactoring to allow consistent naming conventions between 1D, 2D and |
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119 | SESANS data would be helpful. |
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120 | |
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121 | **Attributes** |
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122 | |
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123 | *x*, *dx*: $q$ vector and gaussian resolution |
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124 | |
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125 | *y*, *dy*: $I(q)$ vector and measurement uncertainty |
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126 | |
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127 | *mask*: values to include in plotting/analysis |
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128 | |
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129 | *dxl*: slit widths for slit smeared data, with *dx* ignored |
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130 | |
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131 | *qmin*, *qmax*: range of $q$ values in *x* |
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132 | |
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133 | *filename*: label for the data line |
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134 | |
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135 | *_xaxis*, *_xunit*: label and units for the *x* axis |
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136 | |
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137 | *_yaxis*, *_yunit*: label and units for the *y* axis |
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138 | """ |
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139 | def __init__(self, x=None, y=None, dx=None, dy=None): |
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140 | # type: (Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]) -> None |
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141 | self.x, self.y, self.dx, self.dy = x, y, dx, dy |
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142 | self.dxl = None |
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143 | self.filename = None |
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144 | self.qmin = x.min() if x is not None else np.NaN |
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145 | self.qmax = x.max() if x is not None else np.NaN |
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146 | # TODO: why is 1D mask False and 2D mask True? |
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147 | self.mask = (np.isnan(y) if y is not None |
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148 | else np.zeros_like(x, 'b') if x is not None |
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149 | else None) |
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150 | self._xaxis, self._xunit = "x", "" |
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151 | self._yaxis, self._yunit = "y", "" |
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152 | |
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153 | def xaxis(self, label, unit): |
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154 | # type: (str, str) -> None |
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155 | """ |
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156 | set the x axis label and unit |
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157 | """ |
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158 | self._xaxis = label |
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159 | self._xunit = unit |
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160 | |
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161 | def yaxis(self, label, unit): |
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162 | # type: (str, str) -> None |
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163 | """ |
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164 | set the y axis label and unit |
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165 | """ |
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166 | self._yaxis = label |
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167 | self._yunit = unit |
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168 | |
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169 | class SesansData(Data1D): |
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170 | """ |
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171 | SESANS data object. |
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172 | |
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173 | This is just :class:`Data1D` with a wavelength parameter. |
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174 | |
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175 | *x* is spin echo length and *y* is polarization (P/P0). |
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176 | """ |
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177 | def __init__(self, **kw): |
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178 | Data1D.__init__(self, **kw) |
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179 | self.lam = None # type: Optional[np.ndarray] |
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180 | |
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181 | class Data2D(object): |
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182 | """ |
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183 | 2D data object. |
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184 | |
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185 | Note that this definition matches the attributes from sasview. Some |
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186 | refactoring to allow consistent naming conventions between 1D, 2D and |
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187 | SESANS data would be helpful. |
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188 | |
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189 | **Attributes** |
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190 | |
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191 | *qx_data*, *dqx_data*: $q_x$ matrix and gaussian resolution |
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192 | |
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193 | *qy_data*, *dqy_data*: $q_y$ matrix and gaussian resolution |
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194 | |
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195 | *data*, *err_data*: $I(q)$ matrix and measurement uncertainty |
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196 | |
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197 | *mask*: values to exclude from plotting/analysis |
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198 | |
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199 | *qmin*, *qmax*: range of $q$ values in *x* |
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200 | |
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201 | *filename*: label for the data line |
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202 | |
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203 | *_xaxis*, *_xunit*: label and units for the *x* axis |
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204 | |
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205 | *_yaxis*, *_yunit*: label and units for the *y* axis |
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206 | |
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207 | *_zaxis*, *_zunit*: label and units for the *y* axis |
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208 | |
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209 | *Q_unit*, *I_unit*: units for Q and intensity |
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210 | |
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211 | *x_bins*, *y_bins*: grid steps in *x* and *y* directions |
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212 | """ |
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213 | def __init__(self, x=None, y=None, z=None, dx=None, dy=None, dz=None): |
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214 | # type: (Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]) -> None |
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215 | self.qx_data, self.dqx_data = x, dx |
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216 | self.qy_data, self.dqy_data = y, dy |
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217 | self.data, self.err_data = z, dz |
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218 | self.mask = (np.isnan(z) if z is not None |
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219 | else np.zeros_like(x, dtype='bool') if x is not None |
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220 | else None) |
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221 | self.q_data = np.sqrt(x**2 + y**2) |
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222 | self.qmin = 1e-16 |
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223 | self.qmax = np.inf |
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224 | self.detector = [] |
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225 | self.source = Source() |
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226 | self.Q_unit = "1/A" |
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227 | self.I_unit = "1/cm" |
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228 | self.xaxis("Q_x", "1/A") |
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229 | self.yaxis("Q_y", "1/A") |
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230 | self.zaxis("Intensity", "1/cm") |
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231 | self._xaxis, self._xunit = "x", "" |
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232 | self._yaxis, self._yunit = "y", "" |
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233 | self._zaxis, self._zunit = "z", "" |
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234 | self.x_bins, self.y_bins = None, None |
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235 | self.filename = None |
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236 | |
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237 | def xaxis(self, label, unit): |
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238 | # type: (str, str) -> None |
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239 | """ |
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240 | set the x axis label and unit |
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241 | """ |
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242 | self._xaxis = label |
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243 | self._xunit = unit |
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244 | |
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245 | def yaxis(self, label, unit): |
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246 | # type: (str, str) -> None |
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247 | """ |
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248 | set the y axis label and unit |
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249 | """ |
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250 | self._yaxis = label |
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251 | self._yunit = unit |
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252 | |
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253 | def zaxis(self, label, unit): |
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254 | # type: (str, str) -> None |
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255 | """ |
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256 | set the y axis label and unit |
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257 | """ |
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258 | self._zaxis = label |
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259 | self._zunit = unit |
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260 | |
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261 | |
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262 | class Vector(object): |
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263 | """ |
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264 | 3-space vector of *x*, *y*, *z* |
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265 | """ |
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266 | def __init__(self, x=None, y=None, z=None): |
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267 | # type: (float, float, Optional[float]) -> None |
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268 | self.x, self.y, self.z = x, y, z |
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269 | |
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270 | class Detector(object): |
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271 | """ |
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272 | Detector attributes. |
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273 | """ |
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274 | def __init__(self, pixel_size=(None, None), distance=None): |
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275 | # type: (Tuple[float, float], float) -> None |
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276 | self.pixel_size = Vector(*pixel_size) |
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277 | self.distance = distance |
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278 | |
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279 | class Source(object): |
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280 | """ |
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281 | Beam attributes. |
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282 | """ |
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283 | def __init__(self): |
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284 | # type: () -> None |
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285 | self.wavelength = np.NaN |
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286 | self.wavelength_unit = "A" |
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287 | |
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288 | |
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289 | def empty_data1D(q, resolution=0.0): |
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290 | # type: (np.ndarray, float) -> Data1D |
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291 | """ |
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292 | Create empty 1D data using the given *q* as the x value. |
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293 | |
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294 | *resolution* dq/q defaults to 5%. |
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295 | """ |
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296 | |
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297 | #Iq = 100 * np.ones_like(q) |
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298 | #dIq = np.sqrt(Iq) |
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299 | Iq, dIq = None, None |
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300 | q = np.asarray(q) |
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301 | data = Data1D(q, Iq, dx=resolution * q, dy=dIq) |
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302 | data.filename = "fake data" |
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303 | return data |
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304 | |
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305 | |
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306 | def empty_data2D(qx, qy=None, resolution=0.0): |
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307 | # type: (np.ndarray, Optional[np.ndarray], float) -> Data2D |
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308 | """ |
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309 | Create empty 2D data using the given mesh. |
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310 | |
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311 | If *qy* is missing, create a square mesh with *qy=qx*. |
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312 | |
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313 | *resolution* dq/q defaults to 5%. |
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314 | """ |
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315 | if qy is None: |
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316 | qy = qx |
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317 | qx, qy = np.asarray(qx), np.asarray(qy) |
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318 | # 5% dQ/Q resolution |
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319 | Qx, Qy = np.meshgrid(qx, qy) |
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320 | Qx, Qy = Qx.flatten(), Qy.flatten() |
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321 | Iq = 100 * np.ones_like(Qx) # type: np.ndarray |
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322 | dIq = np.sqrt(Iq) |
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323 | if resolution != 0: |
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324 | # https://www.ncnr.nist.gov/staff/hammouda/distance_learning/chapter_15.pdf |
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325 | # Should have an additional constant which depends on distances and |
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326 | # radii of the aperture, pixel dimensions and wavelength spread |
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327 | # Instead, assume radial dQ/Q is constant, and perpendicular matches |
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328 | # radial (which instead it should be inverse). |
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329 | Q = np.sqrt(Qx**2 + Qy**2) |
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330 | dqx = resolution * Q |
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331 | dqy = resolution * Q |
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332 | else: |
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333 | dqx = dqy = None |
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334 | |
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335 | data = Data2D(x=Qx, y=Qy, z=Iq, dx=dqx, dy=dqy, dz=dIq) |
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336 | data.x_bins = qx |
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337 | data.y_bins = qy |
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338 | data.filename = "fake data" |
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339 | |
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340 | # pixel_size in mm, distance in m |
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341 | detector = Detector(pixel_size=(5, 5), distance=4) |
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342 | data.detector.append(detector) |
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343 | data.source.wavelength = 5 # angstroms |
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344 | data.source.wavelength_unit = "A" |
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345 | return data |
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346 | |
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347 | |
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348 | def plot_data(data, view='log', limits=None): |
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349 | # type: (Data, str, Optional[Tuple[float, float]]) -> None |
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350 | """ |
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351 | Plot data loaded by the sasview loader. |
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352 | |
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353 | *data* is a sasview data object, either 1D, 2D or SESANS. |
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354 | |
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355 | *view* is log or linear. |
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356 | |
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357 | *limits* sets the intensity limits on the plot; if None then the limits |
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358 | are inferred from the data. |
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359 | """ |
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360 | # Note: kind of weird using the plot result functions to plot just the |
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361 | # data, but they already handle the masking and graph markup already, so |
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362 | # do not repeat. |
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363 | if hasattr(data, 'isSesans') and data.isSesans: |
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364 | _plot_result_sesans(data, None, None, use_data=True, limits=limits) |
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365 | elif hasattr(data, 'qx_data'): |
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366 | _plot_result2D(data, None, None, view, use_data=True, limits=limits) |
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367 | else: |
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368 | _plot_result1D(data, None, None, view, use_data=True, limits=limits) |
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369 | |
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370 | |
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371 | def plot_theory(data, theory, resid=None, view='log', |
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372 | use_data=True, limits=None, Iq_calc=None): |
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373 | # type: (Data, Optional[np.ndarray], Optional[np.ndarray], str, bool, Optional[Tuple[float,float]], Optional[np.ndarray]) -> None |
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374 | """ |
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375 | Plot theory calculation. |
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376 | |
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377 | *data* is needed to define the graph properties such as labels and |
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378 | units, and to define the data mask. |
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379 | |
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380 | *theory* is a matrix of the same shape as the data. |
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381 | |
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382 | *view* is log or linear |
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383 | |
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384 | *use_data* is True if the data should be plotted as well as the theory. |
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385 | |
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386 | *limits* sets the intensity limits on the plot; if None then the limits |
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387 | are inferred from the data. |
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388 | |
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389 | *Iq_calc* is the raw theory values without resolution smearing |
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390 | """ |
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391 | if hasattr(data, 'isSesans') and data.isSesans: |
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392 | _plot_result_sesans(data, theory, resid, use_data=True, limits=limits) |
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393 | elif hasattr(data, 'qx_data'): |
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394 | _plot_result2D(data, theory, resid, view, use_data, limits=limits) |
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395 | else: |
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396 | _plot_result1D(data, theory, resid, view, use_data, |
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397 | limits=limits, Iq_calc=Iq_calc) |
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398 | |
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399 | |
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400 | def protect(func): |
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401 | # type: (Callable) -> Callable |
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402 | """ |
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403 | Decorator to wrap calls in an exception trapper which prints the |
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404 | exception and continues. Keyboard interrupts are ignored. |
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405 | """ |
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406 | def wrapper(*args, **kw): |
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407 | """ |
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408 | Trap and print errors from function. |
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409 | """ |
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410 | try: |
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411 | return func(*args, **kw) |
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412 | except Exception: |
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413 | traceback.print_exc() |
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414 | |
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415 | return wrapper |
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416 | |
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417 | |
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418 | @protect |
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419 | def _plot_result1D(data, theory, resid, view, use_data, |
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420 | limits=None, Iq_calc=None): |
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421 | # type: (Data1D, Optional[np.ndarray], Optional[np.ndarray], str, bool, Optional[Tuple[float, float]], Optional[np.ndarray]) -> None |
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422 | """ |
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423 | Plot the data and residuals for 1D data. |
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424 | """ |
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425 | import matplotlib.pyplot as plt # type: ignore |
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426 | from numpy.ma import masked_array, masked # type: ignore |
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427 | |
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428 | use_data = use_data and data.y is not None |
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429 | use_theory = theory is not None |
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430 | use_resid = resid is not None |
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431 | use_calc = use_theory and Iq_calc is not None |
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432 | num_plots = (use_data or use_theory) + use_calc + use_resid |
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433 | non_positive_x = (data.x <= 0.0).any() |
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434 | |
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435 | scale = data.x**4 if view == 'q4' else 1.0 |
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436 | |
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437 | if use_data or use_theory: |
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438 | if num_plots > 1: |
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439 | plt.subplot(1, num_plots, 1) |
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440 | |
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441 | #print(vmin, vmax) |
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442 | all_positive = True |
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443 | some_present = False |
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444 | if use_data: |
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445 | mdata = masked_array(data.y, data.mask.copy()) |
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446 | mdata[~np.isfinite(mdata)] = masked |
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447 | if view is 'log': |
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448 | mdata[mdata <= 0] = masked |
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449 | plt.errorbar(data.x, scale*mdata, yerr=data.dy, fmt='.') |
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450 | all_positive = all_positive and (mdata > 0).all() |
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451 | some_present = some_present or (mdata.count() > 0) |
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452 | |
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453 | |
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454 | if use_theory: |
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455 | # Note: masks merge, so any masked theory points will stay masked, |
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456 | # and the data mask will be added to it. |
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457 | mtheory = masked_array(theory, data.mask.copy()) |
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458 | mtheory[~np.isfinite(mtheory)] = masked |
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459 | if view is 'log': |
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460 | mtheory[mtheory <= 0] = masked |
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461 | plt.plot(data.x, scale*mtheory, '-') |
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462 | all_positive = all_positive and (mtheory > 0).all() |
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463 | some_present = some_present or (mtheory.count() > 0) |
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464 | |
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465 | if limits is not None: |
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466 | plt.ylim(*limits) |
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467 | |
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468 | plt.xscale('linear' if not some_present or non_positive_x |
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469 | else view if view is not None |
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470 | else 'log') |
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471 | plt.yscale('linear' |
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472 | if view == 'q4' or not some_present or not all_positive |
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473 | else view if view is not None |
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474 | else 'log') |
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475 | plt.xlabel("$q$/A$^{-1}$") |
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476 | plt.ylabel('$I(q)$') |
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477 | title = ("data and model" if use_theory and use_data |
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478 | else "data" if use_data |
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479 | else "model") |
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480 | plt.title(title) |
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481 | |
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482 | if use_calc: |
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483 | # Only have use_calc if have use_theory |
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484 | plt.subplot(1, num_plots, 2) |
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485 | qx, qy, Iqxy = Iq_calc |
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486 | plt.pcolormesh(qx, qy[qy > 0], np.log10(Iqxy[qy > 0, :])) |
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487 | plt.xlabel("$q_x$/A$^{-1}$") |
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488 | plt.xlabel("$q_y$/A$^{-1}$") |
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489 | plt.xscale('log') |
---|
490 | plt.yscale('log') |
---|
491 | #plt.axis('equal') |
---|
492 | |
---|
493 | if use_resid: |
---|
494 | mresid = masked_array(resid, data.mask.copy()) |
---|
495 | mresid[~np.isfinite(mresid)] = masked |
---|
496 | some_present = (mresid.count() > 0) |
---|
497 | |
---|
498 | if num_plots > 1: |
---|
499 | plt.subplot(1, num_plots, use_calc + 2) |
---|
500 | plt.plot(data.x, mresid, '.') |
---|
501 | plt.xlabel("$q$/A$^{-1}$") |
---|
502 | plt.ylabel('residuals') |
---|
503 | plt.xscale('linear') |
---|
504 | plt.title('(model - Iq)/dIq') |
---|
505 | |
---|
506 | |
---|
507 | @protect |
---|
508 | def _plot_result_sesans(data, theory, resid, use_data, limits=None): |
---|
509 | # type: (SesansData, Optional[np.ndarray], Optional[np.ndarray], bool, Optional[Tuple[float, float]]) -> None |
---|
510 | """ |
---|
511 | Plot SESANS results. |
---|
512 | """ |
---|
513 | import matplotlib.pyplot as plt # type: ignore |
---|
514 | use_data = use_data and data.y is not None |
---|
515 | use_theory = theory is not None |
---|
516 | use_resid = resid is not None |
---|
517 | num_plots = (use_data or use_theory) + use_resid |
---|
518 | |
---|
519 | if use_data or use_theory: |
---|
520 | is_tof = (data.lam != data.lam[0]).any() |
---|
521 | if num_plots > 1: |
---|
522 | plt.subplot(1, num_plots, 1) |
---|
523 | if use_data: |
---|
524 | if is_tof: |
---|
525 | plt.errorbar(data.x, np.log(data.y)/(data.lam*data.lam), |
---|
526 | yerr=data.dy/data.y/(data.lam*data.lam)) |
---|
527 | else: |
---|
528 | plt.errorbar(data.x, data.y, yerr=data.dy) |
---|
529 | if theory is not None: |
---|
530 | if is_tof: |
---|
531 | plt.plot(data.x, np.log(theory)/(data.lam*data.lam), '-') |
---|
532 | else: |
---|
533 | plt.plot(data.x, theory, '-') |
---|
534 | if limits is not None: |
---|
535 | plt.ylim(*limits) |
---|
536 | |
---|
537 | plt.xlabel('spin echo length ({})'.format(data._xunit)) |
---|
538 | if is_tof: |
---|
539 | plt.ylabel(r'(Log (P/P$_0$))/$\lambda^2$') |
---|
540 | else: |
---|
541 | plt.ylabel('polarization (P/P0)') |
---|
542 | |
---|
543 | |
---|
544 | if resid is not None: |
---|
545 | if num_plots > 1: |
---|
546 | plt.subplot(1, num_plots, (use_data or use_theory) + 1) |
---|
547 | plt.plot(data.x, resid, 'x') |
---|
548 | plt.xlabel('spin echo length ({})'.format(data._xunit)) |
---|
549 | plt.ylabel('residuals (P/P0)') |
---|
550 | |
---|
551 | |
---|
552 | @protect |
---|
553 | def _plot_result2D(data, theory, resid, view, use_data, limits=None): |
---|
554 | # type: (Data2D, Optional[np.ndarray], Optional[np.ndarray], str, bool, Optional[Tuple[float,float]]) -> None |
---|
555 | """ |
---|
556 | Plot the data and residuals for 2D data. |
---|
557 | """ |
---|
558 | import matplotlib.pyplot as plt # type: ignore |
---|
559 | use_data = use_data and data.data is not None |
---|
560 | use_theory = theory is not None |
---|
561 | use_resid = resid is not None |
---|
562 | num_plots = use_data + use_theory + use_resid |
---|
563 | |
---|
564 | # Put theory and data on a common colormap scale |
---|
565 | vmin, vmax = np.inf, -np.inf |
---|
566 | target = None # type: Optional[np.ndarray] |
---|
567 | if use_data: |
---|
568 | target = data.data[~data.mask] |
---|
569 | datamin = target[target > 0].min() if view == 'log' else target.min() |
---|
570 | datamax = target.max() |
---|
571 | vmin = min(vmin, datamin) |
---|
572 | vmax = max(vmax, datamax) |
---|
573 | if use_theory: |
---|
574 | theorymin = theory[theory > 0].min() if view == 'log' else theory.min() |
---|
575 | theorymax = theory.max() |
---|
576 | vmin = min(vmin, theorymin) |
---|
577 | vmax = max(vmax, theorymax) |
---|
578 | |
---|
579 | # Override data limits from the caller |
---|
580 | if limits is not None: |
---|
581 | vmin, vmax = limits |
---|
582 | |
---|
583 | # Plot data |
---|
584 | if use_data: |
---|
585 | if num_plots > 1: |
---|
586 | plt.subplot(1, num_plots, 1) |
---|
587 | _plot_2d_signal(data, target, view=view, vmin=vmin, vmax=vmax) |
---|
588 | plt.title('data') |
---|
589 | h = plt.colorbar() |
---|
590 | h.set_label('$I(q)$') |
---|
591 | |
---|
592 | # plot theory |
---|
593 | if use_theory: |
---|
594 | if num_plots > 1: |
---|
595 | plt.subplot(1, num_plots, use_data+1) |
---|
596 | _plot_2d_signal(data, theory, view=view, vmin=vmin, vmax=vmax) |
---|
597 | plt.title('theory') |
---|
598 | h = plt.colorbar() |
---|
599 | h.set_label(r'$\log_{10}I(q)$' if view == 'log' |
---|
600 | else r'$q^4 I(q)$' if view == 'q4' |
---|
601 | else '$I(q)$') |
---|
602 | |
---|
603 | # plot resid |
---|
604 | if use_resid: |
---|
605 | if num_plots > 1: |
---|
606 | plt.subplot(1, num_plots, use_data+use_theory+1) |
---|
607 | _plot_2d_signal(data, resid, view='linear') |
---|
608 | plt.title('residuals') |
---|
609 | h = plt.colorbar() |
---|
610 | h.set_label(r'$\Delta I(q)$') |
---|
611 | |
---|
612 | |
---|
613 | @protect |
---|
614 | def _plot_2d_signal(data, signal, vmin=None, vmax=None, view='log'): |
---|
615 | # type: (Data2D, np.ndarray, Optional[float], Optional[float], str) -> Tuple[float, float] |
---|
616 | """ |
---|
617 | Plot the target value for the data. This could be the data itself, |
---|
618 | the theory calculation, or the residuals. |
---|
619 | |
---|
620 | *scale* can be 'log' for log scale data, or 'linear'. |
---|
621 | """ |
---|
622 | import matplotlib.pyplot as plt # type: ignore |
---|
623 | from numpy.ma import masked_array # type: ignore |
---|
624 | |
---|
625 | image = np.zeros_like(data.qx_data) |
---|
626 | image[~data.mask] = signal |
---|
627 | valid = np.isfinite(image) |
---|
628 | if view == 'log': |
---|
629 | valid[valid] = (image[valid] > 0) |
---|
630 | if vmin is None: vmin = image[valid & ~data.mask].min() |
---|
631 | if vmax is None: vmax = image[valid & ~data.mask].max() |
---|
632 | image[valid] = np.log10(image[valid]) |
---|
633 | elif view == 'q4': |
---|
634 | image[valid] *= (data.qx_data[valid]**2+data.qy_data[valid]**2)**2 |
---|
635 | if vmin is None: vmin = image[valid & ~data.mask].min() |
---|
636 | if vmax is None: vmax = image[valid & ~data.mask].max() |
---|
637 | else: |
---|
638 | if vmin is None: vmin = image[valid & ~data.mask].min() |
---|
639 | if vmax is None: vmax = image[valid & ~data.mask].max() |
---|
640 | |
---|
641 | image[~valid | data.mask] = 0 |
---|
642 | #plottable = Iq |
---|
643 | plottable = masked_array(image, ~valid | data.mask) |
---|
644 | # Divide range by 10 to convert from angstroms to nanometers |
---|
645 | xmin, xmax = min(data.qx_data), max(data.qx_data) |
---|
646 | ymin, ymax = min(data.qy_data), max(data.qy_data) |
---|
647 | if view == 'log': |
---|
648 | vmin, vmax = np.log10(vmin), np.log10(vmax) |
---|
649 | plt.imshow(plottable.reshape(len(data.x_bins), len(data.y_bins)), |
---|
650 | interpolation='nearest', aspect=1, origin='lower', |
---|
651 | extent=[xmin, xmax, ymin, ymax], vmin=vmin, vmax=vmax) |
---|
652 | plt.xlabel("$q_x$/A$^{-1}$") |
---|
653 | plt.ylabel("$q_y$/A$^{-1}$") |
---|
654 | return vmin, vmax |
---|
655 | |
---|
656 | def demo(): |
---|
657 | # type: () -> None |
---|
658 | """ |
---|
659 | Load and plot a SAS dataset. |
---|
660 | """ |
---|
661 | data = load_data('DEC07086.DAT') |
---|
662 | set_beam_stop(data, 0.004) |
---|
663 | plot_data(data) |
---|
664 | import matplotlib.pyplot as plt # type: ignore |
---|
665 | plt.show() |
---|
666 | |
---|
667 | |
---|
668 | if __name__ == "__main__": |
---|
669 | demo() |
---|