1 | """ |
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2 | Wrap sasmodels for direct use by bumps. |
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3 | |
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4 | :class:`Model` is a wrapper for the sasmodels kernel which defines a |
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5 | bumps *Parameter* box for each kernel parameter. *Model* accepts keyword |
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6 | arguments to set the initial value for each parameter. |
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7 | |
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8 | :class:`Experiment` combines the *Model* function with a data file loaded by the |
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9 | sasview data loader. *Experiment* takes a *cutoff* parameter controlling |
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10 | how far the polydispersity integral extends. |
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11 | |
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12 | A variety of helper functions are provided: |
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13 | |
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14 | :func:`load_data` loads a sasview data file. |
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15 | |
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16 | :func:`empty_data1D` creates an empty dataset, which is useful for plotting |
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17 | a theory function before the data is measured. |
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18 | |
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19 | :func:`empty_data2D` creates an empty 2D dataset. |
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20 | |
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21 | :func:`set_beam_stop` masks the beam stop from the data. |
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22 | |
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23 | :func:`set_half` selects the right or left half of the data, which can |
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24 | be useful for shear measurements which have not been properly corrected |
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25 | for path length and reflections. |
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26 | |
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27 | :func:`set_top` cuts the top part off the data. |
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28 | |
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29 | :func:`plot_data` plots the data file. |
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30 | |
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31 | :func:`plot_theory` plots a calculated result from the model. |
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32 | |
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33 | """ |
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34 | |
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35 | import datetime |
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36 | import warnings |
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37 | |
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38 | import numpy as np |
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39 | |
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40 | from . import sesans |
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41 | from .resolution import Perfect1D, Pinhole1D, Slit1D |
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42 | from .resolution2d import Pinhole2D |
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43 | |
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44 | # CRUFT python 2.6 |
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45 | if not hasattr(datetime.timedelta, 'total_seconds'): |
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46 | def delay(dt): |
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47 | """Return number date-time delta as number seconds""" |
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48 | return dt.days * 86400 + dt.seconds + 1e-6 * dt.microseconds |
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49 | else: |
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50 | def delay(dt): |
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51 | """Return number date-time delta as number seconds""" |
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52 | return dt.total_seconds() |
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53 | |
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54 | |
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55 | # CRUFT: old style bumps wrapper which doesn't separate data and model |
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56 | def BumpsModel(data, model, cutoff=1e-5, **kw): |
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57 | warnings.warn("Use of BumpsModel is deprecated. Use bumps_model.Experiment instead.") |
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58 | model = Model(model, **kw) |
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59 | experiment = Experiment(data=data, model=model, cutoff=cutoff) |
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60 | for k in model._parameter_names: |
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61 | setattr(experiment, k, getattr(model, k)) |
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62 | return experiment |
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63 | |
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64 | |
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65 | |
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66 | def tic(): |
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67 | """ |
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68 | Timer function. |
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69 | |
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70 | Use "toc=tic()" to start the clock and "toc()" to measure |
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71 | a time interval. |
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72 | """ |
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73 | then = datetime.datetime.now() |
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74 | return lambda: delay(datetime.datetime.now() - then) |
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75 | |
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76 | |
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77 | def load_data(filename): |
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78 | """ |
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79 | Load data using a sasview loader. |
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80 | """ |
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81 | from sas.dataloader.loader import Loader |
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82 | loader = Loader() |
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83 | data = loader.load(filename) |
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84 | if data is None: |
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85 | raise IOError("Data %r could not be loaded" % filename) |
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86 | return data |
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87 | |
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88 | def plot_data(data, view='log'): |
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89 | """ |
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90 | Plot data loaded by the sasview loader. |
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91 | """ |
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92 | if hasattr(data, 'qx_data'): |
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93 | _plot_2d_signal(data, data.data, view=view) |
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94 | else: |
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95 | # Note: kind of weird using the _plot_result1D to plot just the |
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96 | # data, but it handles the masking and graph markup already, so |
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97 | # do not repeat. |
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98 | _plot_result1D(data, None, None, view) |
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99 | |
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100 | def plot_theory(data, theory, view='log'): |
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101 | if hasattr(data, 'qx_data'): |
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102 | _plot_2d_signal(data, theory, view=view) |
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103 | else: |
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104 | _plot_result1D(data, theory, None, view, include_data=False) |
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105 | |
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106 | |
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107 | def empty_data1D(q, resolution=0.05): |
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108 | """ |
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109 | Create empty 1D data using the given *q* as the x value. |
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110 | |
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111 | *resolution* dq/q defaults to 5%. |
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112 | """ |
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113 | |
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114 | from sas.dataloader.data_info import Data1D |
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115 | |
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116 | Iq = 100 * np.ones_like(q) |
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117 | dIq = np.sqrt(Iq) |
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118 | data = Data1D(q, Iq, dx=resolution * q, dy=dIq) |
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119 | data.filename = "fake data" |
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120 | data.qmin, data.qmax = q.min(), q.max() |
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121 | data.mask = np.zeros(len(Iq), dtype='bool') |
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122 | return data |
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123 | |
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124 | |
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125 | def empty_data2D(qx, qy=None, resolution=0.05): |
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126 | """ |
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127 | Create empty 2D data using the given mesh. |
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128 | |
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129 | If *qy* is missing, create a square mesh with *qy=qx*. |
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130 | |
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131 | *resolution* dq/q defaults to 5%. |
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132 | """ |
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133 | from sas.dataloader.data_info import Data2D, Detector |
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134 | |
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135 | if qy is None: |
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136 | qy = qx |
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137 | Qx, Qy = np.meshgrid(qx, qy) |
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138 | Qx, Qy = Qx.flatten(), Qy.flatten() |
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139 | Iq = 100 * np.ones_like(Qx) |
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140 | dIq = np.sqrt(Iq) |
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141 | mask = np.ones(len(Iq), dtype='bool') |
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142 | |
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143 | data = Data2D() |
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144 | data.filename = "fake data" |
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145 | data.qx_data = Qx |
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146 | data.qy_data = Qy |
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147 | data.data = Iq |
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148 | data.err_data = dIq |
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149 | data.mask = mask |
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150 | data.qmin = 1e-16 |
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151 | data.qmax = np.inf |
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152 | |
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153 | # 5% dQ/Q resolution |
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154 | if resolution != 0: |
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155 | # https://www.ncnr.nist.gov/staff/hammouda/distance_learning/chapter_15.pdf |
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156 | # Should have an additional constant which depends on distances and |
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157 | # radii of the aperture, pixel dimensions and wavelength spread |
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158 | # Instead, assume radial dQ/Q is constant, and perpendicular matches |
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159 | # radial (which instead it should be inverse). |
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160 | Q = np.sqrt(Qx**2 + Qy**2) |
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161 | data.dqx_data = resolution * Q |
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162 | data.dqy_data = resolution * Q |
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163 | |
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164 | detector = Detector() |
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165 | detector.pixel_size.x = 5 # mm |
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166 | detector.pixel_size.y = 5 # mm |
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167 | detector.distance = 4 # m |
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168 | data.detector.append(detector) |
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169 | data.xbins = qx |
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170 | data.ybins = qy |
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171 | data.source.wavelength = 5 # angstroms |
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172 | data.source.wavelength_unit = "A" |
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173 | data.Q_unit = "1/A" |
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174 | data.I_unit = "1/cm" |
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175 | data.q_data = np.sqrt(Qx ** 2 + Qy ** 2) |
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176 | data.xaxis("Q_x", "A^{-1}") |
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177 | data.yaxis("Q_y", "A^{-1}") |
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178 | data.zaxis("Intensity", r"\text{cm}^{-1}") |
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179 | return data |
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180 | |
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181 | |
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182 | def set_beam_stop(data, radius, outer=None): |
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183 | """ |
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184 | Add a beam stop of the given *radius*. If *outer*, make an annulus. |
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185 | """ |
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186 | from sas.dataloader.manipulations import Ringcut |
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187 | if hasattr(data, 'qx_data'): |
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188 | data.mask = Ringcut(0, radius)(data) |
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189 | if outer is not None: |
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190 | data.mask += Ringcut(outer, np.inf)(data) |
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191 | else: |
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192 | data.mask = (data.x >= radius) |
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193 | if outer is not None: |
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194 | data.mask &= (data.x < outer) |
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195 | |
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196 | |
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197 | def set_half(data, half): |
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198 | """ |
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199 | Select half of the data, either "right" or "left". |
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200 | """ |
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201 | from sas.dataloader.manipulations import Boxcut |
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202 | if half == 'right': |
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203 | data.mask += \ |
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204 | Boxcut(x_min=-np.inf, x_max=0.0, y_min=-np.inf, y_max=np.inf)(data) |
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205 | if half == 'left': |
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206 | data.mask += \ |
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207 | Boxcut(x_min=0.0, x_max=np.inf, y_min=-np.inf, y_max=np.inf)(data) |
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208 | |
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209 | |
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210 | def set_top(data, cutoff): |
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211 | """ |
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212 | Chop the top off the data, above *cutoff*. |
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213 | """ |
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214 | from sas.dataloader.manipulations import Boxcut |
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215 | data.mask += \ |
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216 | Boxcut(x_min=-np.inf, x_max=np.inf, y_min=-np.inf, y_max=cutoff)(data) |
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217 | |
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218 | |
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219 | def _plot_result1D(data, theory, resid, view, include_data=True): |
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220 | """ |
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221 | Plot the data and residuals for 1D data. |
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222 | """ |
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223 | import matplotlib.pyplot as plt |
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224 | from numpy.ma import masked_array, masked |
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225 | #print "not a number",sum(np.isnan(data.y)) |
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226 | #data.y[data.y<0.05] = 0.5 |
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227 | mdata = masked_array(data.y, data.mask) |
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228 | mdata[~np.isfinite(mdata)] = masked |
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229 | if view is 'log': |
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230 | mdata[mdata <= 0] = masked |
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231 | |
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232 | scale = data.x**4 if view == 'q4' else 1.0 |
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233 | if resid is not None: |
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234 | plt.subplot(121) |
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235 | |
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236 | if include_data: |
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237 | plt.errorbar(data.x, scale*mdata, yerr=data.dy, fmt='.') |
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238 | if theory is not None: |
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239 | mtheory = masked_array(theory, mdata.mask) |
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240 | plt.plot(data.x, scale*mtheory, '-', hold=True) |
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241 | plt.xscale(view) |
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242 | plt.yscale('linear' if view == 'q4' else view) |
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243 | plt.xlabel('Q') |
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244 | plt.ylabel('I(Q)') |
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245 | if resid is not None: |
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246 | mresid = masked_array(resid, mdata.mask) |
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247 | plt.subplot(122) |
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248 | plt.plot(data.x, mresid, 'x') |
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249 | plt.ylabel('residuals') |
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250 | plt.xlabel('Q') |
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251 | plt.xscale(view) |
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252 | |
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253 | # pylint: disable=unused-argument |
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254 | def _plot_sesans(data, theory, resid, view): |
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255 | import matplotlib.pyplot as plt |
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256 | plt.subplot(121) |
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257 | plt.errorbar(data.x, data.y, yerr=data.dy) |
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258 | plt.plot(data.x, theory, '-', hold=True) |
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259 | plt.xlabel('spin echo length (nm)') |
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260 | plt.ylabel('polarization (P/P0)') |
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261 | plt.subplot(122) |
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262 | plt.plot(data.x, resid, 'x') |
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263 | plt.xlabel('spin echo length (nm)') |
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264 | plt.ylabel('residuals (P/P0)') |
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265 | |
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266 | def _plot_result2D(data, theory, resid, view): |
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267 | """ |
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268 | Plot the data and residuals for 2D data. |
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269 | """ |
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270 | import matplotlib.pyplot as plt |
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271 | target = data.data[~data.mask] |
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272 | if view == 'log': |
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273 | vmin = min(target[target>0].min(), theory[theory>0].min()) |
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274 | vmax = max(target.max(), theory.max()) |
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275 | else: |
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276 | vmin = min(target.min(), theory.min()) |
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277 | vmax = max(target.max(), theory.max()) |
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278 | #print vmin, vmax |
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279 | plt.subplot(131) |
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280 | _plot_2d_signal(data, target, view=view, vmin=vmin, vmax=vmax) |
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281 | plt.title('data') |
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282 | plt.colorbar() |
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283 | plt.subplot(132) |
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284 | _plot_2d_signal(data, theory, view=view, vmin=vmin, vmax=vmax) |
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285 | plt.title('theory') |
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286 | plt.colorbar() |
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287 | plt.subplot(133) |
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288 | _plot_2d_signal(data, resid, view='linear') |
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289 | plt.title('residuals') |
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290 | plt.colorbar() |
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291 | |
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292 | def _plot_2d_signal(data, signal, vmin=None, vmax=None, view='log'): |
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293 | """ |
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294 | Plot the target value for the data. This could be the data itself, |
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295 | the theory calculation, or the residuals. |
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296 | |
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297 | *scale* can be 'log' for log scale data, or 'linear'. |
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298 | """ |
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299 | import matplotlib.pyplot as plt |
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300 | from numpy.ma import masked_array |
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301 | |
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302 | image = np.zeros_like(data.qx_data) |
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303 | image[~data.mask] = signal |
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304 | valid = np.isfinite(image) |
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305 | if view == 'log': |
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306 | valid[valid] = (image[valid] > 0) |
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307 | image[valid] = np.log10(image[valid]) |
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308 | elif view == 'q4': |
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309 | image[valid] *= (data.qx_data[valid]**2+data.qy_data[valid]**2)**2 |
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310 | image[~valid | data.mask] = 0 |
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311 | #plottable = Iq |
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312 | plottable = masked_array(image, ~valid | data.mask) |
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313 | xmin, xmax = min(data.qx_data), max(data.qx_data) |
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314 | ymin, ymax = min(data.qy_data), max(data.qy_data) |
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315 | # TODO: fix vmin, vmax so it is shared for theory/resid |
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316 | vmin = vmax = None |
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317 | try: |
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318 | if vmin is None: vmin = image[valid & ~data.mask].min() |
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319 | if vmax is None: vmax = image[valid & ~data.mask].max() |
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320 | except: |
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321 | vmin, vmax = 0, 1 |
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322 | #print vmin,vmax |
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323 | plt.imshow(plottable.reshape(128, 128), |
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324 | interpolation='nearest', aspect=1, origin='upper', |
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325 | extent=[xmin, xmax, ymin, ymax], vmin=vmin, vmax=vmax) |
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326 | |
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327 | |
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328 | class Model(object): |
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329 | def __init__(self, kernel, **kw): |
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330 | from bumps.names import Parameter |
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331 | |
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332 | self.kernel = kernel |
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333 | partype = kernel.info['partype'] |
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334 | |
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335 | pars = [] |
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336 | for p in kernel.info['parameters']: |
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337 | name, default, limits = p[0], p[2], p[3] |
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338 | value = kw.pop(name, default) |
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339 | setattr(self, name, Parameter.default(value, name=name, limits=limits)) |
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340 | pars.append(name) |
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341 | for name in partype['pd-2d']: |
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342 | for xpart, xdefault, xlimits in [ |
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343 | ('_pd', 0, limits), |
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344 | ('_pd_n', 35, (0, 1000)), |
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345 | ('_pd_nsigma', 3, (0, 10)), |
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346 | ('_pd_type', 'gaussian', None), |
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347 | ]: |
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348 | xname = name + xpart |
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349 | xvalue = kw.pop(xname, xdefault) |
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350 | if xlimits is not None: |
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351 | xvalue = Parameter.default(xvalue, name=xname, limits=xlimits) |
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352 | pars.append(xname) |
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353 | setattr(self, xname, xvalue) |
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354 | self._parameter_names = pars |
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355 | if kw: |
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356 | raise TypeError("unexpected parameters: %s" |
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357 | % (", ".join(sorted(kw.keys())))) |
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358 | |
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359 | def parameters(self): |
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360 | """ |
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361 | Return a dictionary of parameters |
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362 | """ |
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363 | return dict((k, getattr(self, k)) for k in self._parameter_names) |
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364 | |
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365 | class Experiment(object): |
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366 | """ |
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367 | Return a bumps wrapper for a SAS model. |
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368 | |
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369 | *data* is the data to be fitted. |
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370 | |
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371 | *model* is the SAS model from :func:`core.load_model`. |
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372 | |
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373 | *cutoff* is the integration cutoff, which avoids computing the |
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374 | the SAS model where the polydispersity weight is low. |
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375 | |
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376 | Model parameters can be initialized with additional keyword |
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377 | arguments, or by assigning to model.parameter_name.value. |
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378 | |
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379 | The resulting bumps model can be used directly in a FitProblem call. |
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380 | """ |
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381 | def __init__(self, data, model, cutoff=1e-5): |
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382 | |
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383 | # remember inputs so we can inspect from outside |
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384 | self.data = data |
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385 | self.model = model |
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386 | self.cutoff = cutoff |
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387 | if hasattr(data, 'lam'): |
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388 | self.data_type = 'sesans' |
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389 | elif hasattr(data, 'qx_data'): |
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390 | self.data_type = 'Iqxy' |
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391 | else: |
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392 | self.data_type = 'Iq' |
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393 | |
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394 | # interpret data |
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395 | partype = model.kernel.info['partype'] |
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396 | if self.data_type == 'sesans': |
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397 | q = sesans.make_q(data.sample.zacceptance, data.Rmax) |
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398 | self.index = slice(None, None) |
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399 | self.Iq = data.y |
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400 | self.dIq = data.dy |
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401 | #self._theory = np.zeros_like(q) |
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402 | q_vectors = [q] |
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403 | elif self.data_type == 'Iqxy': |
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404 | q = np.sqrt(data.qx_data**2 + data.qy_data**2) |
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405 | qmin = getattr(data, 'qmin', 1e-16) |
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406 | qmax = getattr(data, 'qmax', np.inf) |
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407 | accuracy = getattr(data, 'accuracy', 'Low') |
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408 | self.index = (~data.mask) & (~np.isnan(data.data)) \ |
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409 | & (q >= qmin) & (q <= qmax) |
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410 | self.Iq = data.data[self.index] |
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411 | self.dIq = data.err_data[self.index] |
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412 | self.resolution = Pinhole2D(data=data, index=self.index, |
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413 | nsigma=3.0, accuracy=accuracy) |
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414 | #self._theory = np.zeros_like(self.Iq) |
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415 | if not partype['orientation'] and not partype['magnetic']: |
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416 | raise ValueError("not 2D without orientation or magnetic parameters") |
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417 | #qx,qy = self.resolution.q_calc |
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418 | #q_vectors = [np.sqrt(qx**2 + qy**2)] |
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419 | else: |
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420 | q_vectors = self.resolution.q_calc |
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421 | elif self.data_type == 'Iq': |
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422 | self.index = (data.x >= data.qmin) & (data.x <= data.qmax) & ~np.isnan(data.y) |
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423 | self.Iq = data.y[self.index] |
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424 | self.dIq = data.dy[self.index] |
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425 | if getattr(data, 'dx', None) is not None: |
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426 | q, dq = data.x[self.index], data.dx[self.index] |
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427 | if (dq>0).any(): |
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428 | self.resolution = Pinhole1D(q, dq) |
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429 | else: |
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430 | self.resolution = Perfect1D(q) |
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431 | elif (getattr(data, 'dxl', None) is not None and |
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432 | getattr(data, 'dxw', None) is not None): |
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433 | q = data.x[self.index] |
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434 | width = data.dxh[self.index] # Note: dx |
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435 | self.resolution = Slit1D(data.x[self.index], |
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436 | width=data.dxh[self.index], |
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437 | height=data.dxw[self.index]) |
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438 | else: |
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439 | self.resolution = Perfect1D(data.x[self.index]) |
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440 | |
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441 | #self._theory = np.zeros_like(self.Iq) |
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442 | q_vectors = [self.resolution.q_calc] |
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443 | else: |
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444 | raise ValueError("Unknown data type") # never gets here |
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445 | |
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446 | # Remember function inputs so we can delay loading the function and |
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447 | # so we can save/restore state |
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448 | self._fn_inputs = [v for v in q_vectors] |
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449 | self._fn = None |
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450 | |
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451 | self.update() |
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452 | |
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453 | def update(self): |
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454 | self._cache = {} |
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455 | |
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456 | def numpoints(self): |
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457 | """ |
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458 | Return the number of points |
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459 | """ |
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460 | return len(self.Iq) |
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461 | |
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462 | def parameters(self): |
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463 | """ |
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464 | Return a dictionary of parameters |
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465 | """ |
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466 | return self.model.parameters() |
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467 | |
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468 | def theory(self): |
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469 | if 'theory' not in self._cache: |
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470 | if self._fn is None: |
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471 | q_input = self.model.kernel.make_input(self._fn_inputs) |
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472 | self._fn = self.model.kernel(q_input) |
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473 | |
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474 | fixed_pars = [getattr(self.model, p).value for p in self._fn.fixed_pars] |
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475 | pd_pars = [self._get_weights(p) for p in self._fn.pd_pars] |
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476 | #print fixed_pars,pd_pars |
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477 | Iq_calc = self._fn(fixed_pars, pd_pars, self.cutoff) |
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478 | #self._theory[:] = self._fn.eval(pars, pd_pars) |
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479 | if self.data_type == 'sesans': |
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480 | result = sesans.hankel(self.data.x, self.data.lam * 1e-9, |
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481 | self.data.sample.thickness / 10, |
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482 | self._fn_inputs[0], Iq_calc) |
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483 | self._cache['theory'] = result |
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484 | else: |
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485 | Iq = self.resolution.apply(Iq_calc) |
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486 | self._cache['theory'] = Iq |
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487 | return self._cache['theory'] |
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488 | |
---|
489 | def residuals(self): |
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490 | #if np.any(self.err ==0): print "zeros in err" |
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491 | return (self.theory() - self.Iq) / self.dIq |
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492 | |
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493 | def nllf(self): |
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494 | delta = self.residuals() |
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495 | #if np.any(np.isnan(R)): print "NaN in residuals" |
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496 | return 0.5 * np.sum(delta ** 2) |
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497 | |
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498 | #def __call__(self): |
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499 | # return 2 * self.nllf() / self.dof |
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500 | |
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501 | def plot(self, view='log'): |
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502 | """ |
---|
503 | Plot the data and residuals. |
---|
504 | """ |
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505 | data, theory, resid = self.data, self.theory(), self.residuals() |
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506 | if self.data_type == 'Iq': |
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507 | _plot_result1D(data, theory, resid, view) |
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508 | elif self.data_type == 'Iqxy': |
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509 | _plot_result2D(data, theory, resid, view) |
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510 | elif self.data_type == 'sesans': |
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511 | _plot_sesans(data, theory, resid, view) |
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512 | else: |
---|
513 | raise ValueError("Unknown data type") |
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514 | |
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515 | def simulate_data(self, noise=None): |
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516 | theory = self.theory() |
---|
517 | if noise is not None: |
---|
518 | self.dIq = theory*noise*0.01 |
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519 | dy = self.dIq |
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520 | y = theory + np.random.randn(*dy.shape) * dy |
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521 | self.Iq = y |
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522 | if self.data_type == 'Iq': |
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523 | self.data.dy[self.index] = dy |
---|
524 | self.data.y[self.index] = y |
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525 | elif self.data_type == 'Iqxy': |
---|
526 | self.data.data[self.index] = y |
---|
527 | elif self.data_type == 'sesans': |
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528 | self.data.y[self.index] = y |
---|
529 | else: |
---|
530 | raise ValueError("Unknown model") |
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531 | |
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532 | def save(self, basename): |
---|
533 | pass |
---|
534 | |
---|
535 | def _get_weights(self, par): |
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536 | """ |
---|
537 | Get parameter dispersion weights |
---|
538 | """ |
---|
539 | from . import weights |
---|
540 | |
---|
541 | relative = self.model.kernel.info['partype']['pd-rel'] |
---|
542 | limits = self.model.kernel.info['limits'] |
---|
543 | disperser, value, npts, width, nsigma = [ |
---|
544 | getattr(self.model, par + ext) |
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545 | for ext in ('_pd_type', '', '_pd_n', '_pd', '_pd_nsigma')] |
---|
546 | value, weight = weights.get_weights( |
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547 | disperser, int(npts.value), width.value, nsigma.value, |
---|
548 | value.value, limits[par], par in relative) |
---|
549 | return value, weight / np.sum(weight) |
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550 | |
---|
551 | def __getstate__(self): |
---|
552 | # Can't pickle gpu functions, so instead make them lazy |
---|
553 | state = self.__dict__.copy() |
---|
554 | state['_fn'] = None |
---|
555 | return state |
---|
556 | |
---|
557 | def __setstate__(self, state): |
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558 | # pylint: disable=attribute-defined-outside-init |
---|
559 | self.__dict__ = state |
---|
560 | |
---|
561 | |
---|
562 | def demo(): |
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563 | data = load_data('DEC07086.DAT') |
---|
564 | set_beam_stop(data, 0.004) |
---|
565 | plot_data(data) |
---|
566 | import matplotlib.pyplot as plt; plt.show() |
---|
567 | |
---|
568 | |
---|
569 | if __name__ == "__main__": |
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570 | demo() |
---|