| 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.zeros_like(x, dtype='bool') 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, Iq_calc=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, |
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| 346 | limits=limits, Iq_calc=Iq_calc) |
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| 347 | |
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| 348 | |
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| 349 | def protect(fn): |
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| 350 | """ |
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| 351 | Decorator to wrap calls in an exception trapper which prints the |
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| 352 | exception and continues. Keyboard interrupts are ignored. |
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| 353 | """ |
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| 354 | def wrapper(*args, **kw): |
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| 355 | """ |
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| 356 | Trap and print errors from function. |
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| 357 | """ |
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| 358 | try: |
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| 359 | return fn(*args, **kw) |
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| 360 | except KeyboardInterrupt: |
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| 361 | raise |
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| 362 | except: |
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| 363 | traceback.print_exc() |
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| 364 | |
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| 365 | return wrapper |
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| 366 | |
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| 367 | |
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| 368 | @protect |
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| 369 | def _plot_result1D(data, theory, resid, view, use_data, |
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| 370 | limits=None, Iq_calc=None): |
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| 371 | """ |
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| 372 | Plot the data and residuals for 1D data. |
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| 373 | """ |
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| 374 | import matplotlib.pyplot as plt |
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| 375 | from numpy.ma import masked_array, masked |
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| 376 | |
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| 377 | use_data = use_data and data.y is not None |
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| 378 | use_theory = theory is not None |
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| 379 | use_resid = resid is not None |
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| 380 | use_calc = use_theory and Iq_calc is not None |
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| 381 | num_plots = (use_data or use_theory) + use_calc + use_resid |
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| 382 | |
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| 383 | |
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| 384 | scale = data.x**4 if view == 'q4' else 1.0 |
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| 385 | |
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| 386 | if use_data or use_theory: |
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| 387 | if num_plots > 1: |
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| 388 | plt.subplot(1, num_plots, 1) |
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| 389 | |
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| 390 | #print(vmin, vmax) |
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| 391 | all_positive = True |
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| 392 | some_present = False |
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| 393 | if use_data: |
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| 394 | mdata = masked_array(data.y, data.mask.copy()) |
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| 395 | mdata[~np.isfinite(mdata)] = masked |
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| 396 | if view is 'log': |
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| 397 | mdata[mdata <= 0] = masked |
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| 398 | plt.errorbar(data.x, scale*mdata, yerr=data.dy, fmt='.') |
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| 399 | all_positive = all_positive and (mdata > 0).all() |
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| 400 | some_present = some_present or (mdata.count() > 0) |
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| 401 | |
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| 402 | |
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| 403 | if use_theory: |
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| 404 | mtheory = masked_array(theory, data.mask.copy()) |
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| 405 | mtheory[~np.isfinite(mtheory)] = masked |
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| 406 | if view is 'log': |
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| 407 | mtheory[mtheory <= 0] = masked |
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| 408 | plt.plot(data.x, scale*mtheory, '-', hold=True) |
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| 409 | all_positive = all_positive and (mtheory > 0).all() |
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| 410 | some_present = some_present or (mtheory.count() > 0) |
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| 411 | |
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| 412 | if limits is not None: |
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| 413 | plt.ylim(*limits) |
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| 414 | |
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| 415 | plt.xscale('linear' if not some_present else view) |
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| 416 | plt.yscale('linear' |
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| 417 | if view == 'q4' or not some_present or not all_positive |
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| 418 | else view) |
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| 419 | plt.xlabel("$q$/A$^{-1}$") |
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| 420 | plt.ylabel('$I(q)$') |
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| 421 | |
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| 422 | if use_calc: |
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| 423 | # Only have use_calc if have use_theory |
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| 424 | plt.subplot(1, num_plots, 2) |
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| 425 | qx, qy, Iqxy = Iq_calc |
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| 426 | plt.pcolormesh(qx, qy, np.log10(Iqxy)) |
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| 427 | plt.xlabel("$q_x$/A$^{-1}$") |
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| 428 | plt.xlabel("$q_y$/A$^{-1}$") |
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| 429 | #plt.axis('equal') |
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| 430 | |
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| 431 | if use_resid: |
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| 432 | mresid = masked_array(resid, data.mask.copy()) |
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| 433 | mresid[~np.isfinite(mresid)] = masked |
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| 434 | some_present = (mresid.count() > 0) |
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| 435 | |
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| 436 | if num_plots > 1: |
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| 437 | plt.subplot(1, num_plots, use_calc + 2) |
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| 438 | plt.plot(data.x, mresid, '-') |
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| 439 | plt.xlabel("$q$/A$^{-1}$") |
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| 440 | plt.ylabel('residuals') |
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| 441 | plt.xscale('linear' if not some_present else view) |
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| 442 | |
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| 443 | |
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| 444 | @protect |
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| 445 | def _plot_result_sesans(data, theory, resid, use_data, limits=None): |
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| 446 | """ |
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| 447 | Plot SESANS results. |
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| 448 | """ |
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| 449 | import matplotlib.pyplot as plt |
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| 450 | use_data = use_data and data.y is not None |
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| 451 | use_theory = theory is not None |
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| 452 | use_resid = resid is not None |
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| 453 | num_plots = (use_data or use_theory) + use_resid |
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| 454 | |
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| 455 | if use_data or use_theory: |
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| 456 | is_tof = np.any(data.lam!=data.lam[0]) |
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| 457 | if num_plots > 1: |
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| 458 | plt.subplot(1, num_plots, 1) |
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| 459 | if use_data: |
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| 460 | if is_tof: |
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| 461 | plt.errorbar(data.x, np.log(data.y)/(data.lam*data.lam), yerr=data.dy/data.y/(data.lam*data.lam)) |
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| 462 | else: |
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| 463 | plt.errorbar(data.x, data.y, yerr=data.dy) |
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| 464 | if theory is not None: |
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| 465 | if is_tof: |
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| 466 | plt.plot(data.x, np.log(theory)/(data.lam*data.lam), '-', hold=True) |
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| 467 | else: |
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| 468 | plt.plot(data.x, theory, '-', hold=True) |
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| 469 | if limits is not None: |
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| 470 | plt.ylim(*limits) |
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| 471 | |
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| 472 | plt.xlabel('spin echo length ({})'.format(data._xunit)) |
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| 473 | if is_tof: |
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| 474 | plt.ylabel('(Log (P/P$_0$))/$\lambda^2$') |
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| 475 | else: |
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| 476 | plt.ylabel('polarization (P/P0)') |
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| 477 | |
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| 478 | |
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| 479 | if resid is not None: |
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| 480 | if num_plots > 1: |
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| 481 | plt.subplot(1, num_plots, (use_data or use_theory) + 1) |
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| 482 | plt.plot(data.x, resid, 'x') |
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| 483 | plt.xlabel('spin echo length ({})'.format(data._xunit)) |
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| 484 | plt.ylabel('residuals (P/P0)') |
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| 485 | |
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| 486 | |
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| 487 | @protect |
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| 488 | def _plot_result2D(data, theory, resid, view, use_data, limits=None): |
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| 489 | """ |
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| 490 | Plot the data and residuals for 2D data. |
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| 491 | """ |
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| 492 | import matplotlib.pyplot as plt |
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| 493 | use_data = use_data and data.data is not None |
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| 494 | use_theory = theory is not None |
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| 495 | use_resid = resid is not None |
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| 496 | num_plots = use_data + use_theory + use_resid |
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| 497 | |
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| 498 | # Put theory and data on a common colormap scale |
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| 499 | vmin, vmax = np.inf, -np.inf |
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| 500 | if use_data: |
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| 501 | target = data.data[~data.mask] |
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| 502 | datamin = target[target > 0].min() if view == 'log' else target.min() |
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| 503 | datamax = target.max() |
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| 504 | vmin = min(vmin, datamin) |
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| 505 | vmax = max(vmax, datamax) |
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| 506 | if use_theory: |
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| 507 | theorymin = theory[theory > 0].min() if view == 'log' else theory.min() |
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| 508 | theorymax = theory.max() |
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| 509 | vmin = min(vmin, theorymin) |
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| 510 | vmax = max(vmax, theorymax) |
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| 511 | |
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| 512 | # Override data limits from the caller |
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| 513 | if limits is not None: |
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| 514 | vmin, vmax = limits |
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| 515 | |
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| 516 | # Plot data |
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| 517 | if use_data: |
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| 518 | if num_plots > 1: |
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| 519 | plt.subplot(1, num_plots, 1) |
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| 520 | _plot_2d_signal(data, target, view=view, vmin=vmin, vmax=vmax) |
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| 521 | plt.title('data') |
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| 522 | h = plt.colorbar() |
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| 523 | h.set_label('$I(q)$') |
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| 524 | |
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| 525 | # plot theory |
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| 526 | if use_theory: |
|---|
| 527 | if num_plots > 1: |
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| 528 | plt.subplot(1, num_plots, use_data+1) |
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| 529 | _plot_2d_signal(data, theory, view=view, vmin=vmin, vmax=vmax) |
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| 530 | plt.title('theory') |
|---|
| 531 | h = plt.colorbar() |
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| 532 | h.set_label(r'$\log_{10}I(q)$' if view == 'log' |
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| 533 | else r'$q^4 I(q)$' if view == 'q4' |
|---|
| 534 | else '$I(q)$') |
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| 535 | |
|---|
| 536 | # plot resid |
|---|
| 537 | if use_resid: |
|---|
| 538 | if num_plots > 1: |
|---|
| 539 | plt.subplot(1, num_plots, use_data+use_theory+1) |
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| 540 | _plot_2d_signal(data, resid, view='linear') |
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| 541 | plt.title('residuals') |
|---|
| 542 | h = plt.colorbar() |
|---|
| 543 | h.set_label(r'$\Delta I(q)$') |
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| 544 | |
|---|
| 545 | |
|---|
| 546 | @protect |
|---|
| 547 | def _plot_2d_signal(data, signal, vmin=None, vmax=None, view='log'): |
|---|
| 548 | """ |
|---|
| 549 | Plot the target value for the data. This could be the data itself, |
|---|
| 550 | the theory calculation, or the residuals. |
|---|
| 551 | |
|---|
| 552 | *scale* can be 'log' for log scale data, or 'linear'. |
|---|
| 553 | """ |
|---|
| 554 | import matplotlib.pyplot as plt |
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| 555 | from numpy.ma import masked_array |
|---|
| 556 | |
|---|
| 557 | image = np.zeros_like(data.qx_data) |
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| 558 | image[~data.mask] = signal |
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| 559 | valid = np.isfinite(image) |
|---|
| 560 | if view == 'log': |
|---|
| 561 | valid[valid] = (image[valid] > 0) |
|---|
| 562 | if vmin is None: vmin = image[valid & ~data.mask].min() |
|---|
| 563 | if vmax is None: vmax = image[valid & ~data.mask].max() |
|---|
| 564 | image[valid] = np.log10(image[valid]) |
|---|
| 565 | elif view == 'q4': |
|---|
| 566 | image[valid] *= (data.qx_data[valid]**2+data.qy_data[valid]**2)**2 |
|---|
| 567 | if vmin is None: vmin = image[valid & ~data.mask].min() |
|---|
| 568 | if vmax is None: vmax = image[valid & ~data.mask].max() |
|---|
| 569 | else: |
|---|
| 570 | if vmin is None: vmin = image[valid & ~data.mask].min() |
|---|
| 571 | if vmax is None: vmax = image[valid & ~data.mask].max() |
|---|
| 572 | |
|---|
| 573 | image[~valid | data.mask] = 0 |
|---|
| 574 | #plottable = Iq |
|---|
| 575 | plottable = masked_array(image, ~valid | data.mask) |
|---|
| 576 | # Divide range by 10 to convert from angstroms to nanometers |
|---|
| 577 | xmin, xmax = min(data.qx_data), max(data.qx_data) |
|---|
| 578 | ymin, ymax = min(data.qy_data), max(data.qy_data) |
|---|
| 579 | if view == 'log': |
|---|
| 580 | vmin, vmax = np.log10(vmin), np.log10(vmax) |
|---|
| 581 | plt.imshow(plottable.reshape(len(data.x_bins), len(data.y_bins)), |
|---|
| 582 | interpolation='nearest', aspect=1, origin='lower', |
|---|
| 583 | extent=[xmin, xmax, ymin, ymax], vmin=vmin, vmax=vmax) |
|---|
| 584 | plt.xlabel("$q_x$/A$^{-1}$") |
|---|
| 585 | plt.ylabel("$q_y$/A$^{-1}$") |
|---|
| 586 | return vmin, vmax |
|---|
| 587 | |
|---|
| 588 | def demo(): |
|---|
| 589 | """ |
|---|
| 590 | Load and plot a SAS dataset. |
|---|
| 591 | """ |
|---|
| 592 | data = load_data('DEC07086.DAT') |
|---|
| 593 | set_beam_stop(data, 0.004) |
|---|
| 594 | plot_data(data) |
|---|
| 595 | import matplotlib.pyplot as plt; plt.show() |
|---|
| 596 | |
|---|
| 597 | |
|---|
| 598 | if __name__ == "__main__": |
|---|
| 599 | demo() |
|---|
