1 | #TODO: Use simview to generate P(r) and I(q) pairs in sansview. |
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2 | # Make sure the option of saving each curve is available |
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3 | # Use the I(q) curve as input and compare the output to P(r) |
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4 | |
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5 | import os |
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6 | import sys |
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7 | import wx |
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8 | import logging |
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9 | from sans.guitools.plottables import Data1D, Theory1D |
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10 | from sans.guicomm.events import NewPlotEvent, StatusEvent |
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11 | import math, numpy |
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12 | from sans.pr.invertor import Invertor |
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13 | from DataLoader.loader import Loader |
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14 | |
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15 | PR_FIT_LABEL = r"$P_{fit}(r)$" |
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16 | PR_LOADED_LABEL = r"$P_{loaded}(r)$" |
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17 | IQ_DATA_LABEL = r"$I_{obs}(q)$" |
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18 | IQ_FIT_LABEL = r"$I_{fit}(q)$" |
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19 | IQ_SMEARED_LABEL = r"$I_{smeared}(q)$" |
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20 | |
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21 | import wx.lib |
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22 | (NewPrFileEvent, EVT_PR_FILE) = wx.lib.newevent.NewEvent() |
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23 | |
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24 | |
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25 | class Plugin: |
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26 | |
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27 | DEFAULT_ALPHA = 0.0001 |
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28 | DEFAULT_NFUNC = 10 |
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29 | DEFAULT_DMAX = 140.0 |
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30 | |
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31 | def __init__(self): |
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32 | ## Plug-in name |
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33 | self.sub_menu = "Pr inversion" |
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34 | |
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35 | ## Reference to the parent window |
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36 | self.parent = None |
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37 | |
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38 | ## Simulation window manager |
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39 | self.simview = None |
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40 | |
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41 | ## List of panels for the simulation perspective (names) |
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42 | self.perspective = [] |
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43 | |
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44 | ## State data |
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45 | self.alpha = self.DEFAULT_ALPHA |
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46 | self.nfunc = self.DEFAULT_NFUNC |
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47 | self.max_length = self.DEFAULT_DMAX |
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48 | self.q_min = None |
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49 | self.q_max = None |
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50 | self.has_bck = False |
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51 | self.slit_height = 0 |
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52 | self.slit_width = 0 |
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53 | ## Remember last plottable processed |
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54 | self.last_data = "sphere_60_q0_2.txt" |
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55 | self._current_file_data = None |
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56 | ## Time elapsed for last computation [sec] |
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57 | # Start with a good default |
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58 | self.elapsed = 0.022 |
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59 | self.iq_data_shown = False |
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60 | |
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61 | ## Current invertor |
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62 | self.invertor = None |
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63 | self.pr = None |
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64 | # Copy of the last result in case we need to display it. |
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65 | self._last_pr = None |
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66 | self._last_out = None |
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67 | self._last_cov = None |
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68 | ## Calculation thread |
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69 | self.calc_thread = None |
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70 | ## Estimation thread |
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71 | self.estimation_thread = None |
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72 | ## Result panel |
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73 | self.control_panel = None |
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74 | ## Currently views plottable |
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75 | self.current_plottable = None |
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76 | ## Number of P(r) points to display on the output plot |
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77 | self._pr_npts = 51 |
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78 | ## Flag to let the plug-in know that it is running standalone |
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79 | self.standalone = True |
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80 | self._normalize_output = False |
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81 | self._scale_output_unity = False |
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82 | |
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83 | ## List of added P(r) plots |
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84 | self._added_plots = {} |
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85 | self._default_Iq = {} |
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86 | |
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87 | # Log startup |
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88 | logging.info("Pr(r) plug-in started") |
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89 | |
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90 | |
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91 | |
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92 | def populate_menu(self, id, owner): |
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93 | """ |
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94 | Create a menu for the plug-in |
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95 | """ |
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96 | return [] |
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97 | |
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98 | def help(self, evt): |
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99 | """ |
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100 | Show a general help dialog. |
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101 | TODO: replace the text with a nice image |
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102 | """ |
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103 | from inversion_panel import HelpDialog |
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104 | dialog = HelpDialog(None, -1) |
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105 | if dialog.ShowModal() == wx.ID_OK: |
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106 | dialog.Destroy() |
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107 | else: |
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108 | dialog.Destroy() |
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109 | |
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110 | def _fit_pr(self, evt): |
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111 | from sans.pr.invertor import Invertor |
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112 | import numpy |
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113 | import pylab |
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114 | import math |
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115 | from sans.guicomm.events import NewPlotEvent |
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116 | from sans.guitools.plottables import Data1D, Theory1D |
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117 | |
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118 | # Generate P(r) for sphere |
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119 | radius = 60.0 |
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120 | d_max = 2*radius |
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121 | |
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122 | |
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123 | r = pylab.arange(0.01, d_max, d_max/51.0) |
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124 | M = len(r) |
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125 | y = numpy.zeros(M) |
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126 | pr_err = numpy.zeros(M) |
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127 | |
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128 | sum = 0.0 |
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129 | for j in range(M): |
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130 | value = self.pr_theory(r[j], radius) |
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131 | sum += value |
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132 | y[j] = value |
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133 | pr_err[j] = math.sqrt(y[j]) |
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134 | |
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135 | |
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136 | y = y/sum*d_max/len(r) |
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137 | |
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138 | |
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139 | |
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140 | # Perform fit |
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141 | pr = Invertor() |
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142 | pr.d_max = d_max |
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143 | pr.alpha = 0 |
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144 | pr.x = r |
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145 | pr.y = y |
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146 | pr.err = pr_err |
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147 | out, cov = pr.pr_fit() |
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148 | for i in range(len(out)): |
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149 | print "%g +- %g" % (out[i], math.sqrt(cov[i][i])) |
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150 | |
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151 | |
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152 | # Show input P(r) |
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153 | new_plot = Data1D(pr.x, pr.y, dy=pr.err) |
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154 | new_plot.name = "P_{obs}(r)" |
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155 | new_plot.xaxis("\\rm{r}", 'A') |
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156 | new_plot.yaxis("\\rm{P(r)} ","cm^{-3}") |
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157 | wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title="Pr")) |
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158 | |
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159 | # Show P(r) fit |
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160 | self.show_pr(out, pr) |
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161 | |
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162 | # Show I(q) fit |
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163 | q = pylab.arange(0.001, 0.1, 0.01/51.0) |
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164 | self.show_iq(out, pr, q) |
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165 | |
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166 | |
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167 | def show_shpere(self, x, radius=70.0, x_range=70.0): |
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168 | import numpy |
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169 | import pylab |
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170 | import math |
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171 | from sans.guicomm.events import NewPlotEvent |
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172 | from sans.guitools.plottables import Data1D, Theory1D |
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173 | # Show P(r) |
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174 | y_true = numpy.zeros(len(x)) |
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175 | |
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176 | sum_true = 0.0 |
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177 | for i in range(len(x)): |
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178 | y_true[i] = self.pr_theory(x[i], radius) |
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179 | sum_true += y_true[i] |
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180 | |
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181 | y_true = y_true/sum_true*x_range/len(x) |
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182 | |
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183 | # Show the theory P(r) |
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184 | new_plot = Theory1D(x, y_true) |
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185 | new_plot.name = "P_{true}(r)" |
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186 | new_plot.xaxis("\\rm{r}", 'A') |
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187 | new_plot.yaxis("\\rm{P(r)} ","cm^{-3}") |
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188 | |
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189 | |
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190 | #Put this call in plottables/guitools |
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191 | wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title="Sphere P(r)")) |
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192 | |
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193 | def get_npts(self): |
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194 | """ |
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195 | Returns the number of points in the I(q) data |
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196 | """ |
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197 | try: |
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198 | return len(self.pr.x) |
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199 | except: |
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200 | return 0 |
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201 | |
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202 | def show_iq(self, out, pr, q=None): |
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203 | import numpy |
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204 | import pylab |
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205 | import math |
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206 | from sans.guicomm.events import NewPlotEvent |
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207 | from sans.guitools.plottables import Data1D, Theory1D |
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208 | |
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209 | qtemp = pr.x |
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210 | if not q==None: |
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211 | qtemp = q |
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212 | |
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213 | # Make a plot |
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214 | maxq = -1 |
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215 | for q_i in qtemp: |
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216 | if q_i>maxq: |
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217 | maxq=q_i |
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218 | |
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219 | minq = 0.001 |
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220 | |
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221 | # Check for user min/max |
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222 | if not pr.q_min==None: |
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223 | minq = pr.q_min |
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224 | if not pr.q_max==None: |
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225 | maxq = pr.q_max |
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226 | |
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227 | x = pylab.arange(minq, maxq, maxq/301.0) |
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228 | y = numpy.zeros(len(x)) |
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229 | err = numpy.zeros(len(x)) |
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230 | for i in range(len(x)): |
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231 | value = pr.iq(out, x[i]) |
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232 | y[i] = value |
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233 | try: |
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234 | err[i] = math.sqrt(math.fabs(value)) |
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235 | except: |
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236 | err[i] = 1.0 |
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237 | print "Error getting error", value, x[i] |
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238 | |
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239 | new_plot = Theory1D(x, y) |
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240 | new_plot.name = IQ_FIT_LABEL |
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241 | new_plot.xaxis("\\rm{Q}", 'A^{-1}') |
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242 | new_plot.yaxis("\\rm{Intensity} ","cm^{-1}") |
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243 | #new_plot.group_id = "test group" |
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244 | wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title="I(q)")) |
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245 | |
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246 | # If we have used slit smearing, plot the smeared I(q) too |
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247 | if pr.slit_width>0 or pr.slit_height>0: |
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248 | x = pylab.arange(minq, maxq, maxq/301.0) |
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249 | y = numpy.zeros(len(x)) |
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250 | err = numpy.zeros(len(x)) |
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251 | for i in range(len(x)): |
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252 | value = pr.iq_smeared(out, x[i]) |
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253 | y[i] = value |
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254 | try: |
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255 | err[i] = math.sqrt(math.fabs(value)) |
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256 | except: |
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257 | err[i] = 1.0 |
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258 | print "Error getting error", value, x[i] |
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259 | |
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260 | new_plot = Theory1D(x, y) |
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261 | new_plot.name = IQ_SMEARED_LABEL |
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262 | new_plot.xaxis("\\rm{Q}", 'A^{-1}') |
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263 | new_plot.yaxis("\\rm{Intensity} ","cm^{-1}") |
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264 | #new_plot.group_id = "test group" |
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265 | wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title="I(q)")) |
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266 | |
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267 | |
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268 | def _on_pr_npts(self, evt): |
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269 | """ |
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270 | Redisplay P(r) with a different number of points |
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271 | """ |
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272 | from inversion_panel import PrDistDialog |
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273 | dialog = PrDistDialog(None, -1) |
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274 | dialog.set_content(self._pr_npts) |
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275 | if dialog.ShowModal() == wx.ID_OK: |
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276 | self._pr_npts= dialog.get_content() |
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277 | dialog.Destroy() |
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278 | self.show_pr(self._last_out, self._last_pr, self._last_cov) |
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279 | else: |
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280 | dialog.Destroy() |
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281 | |
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282 | |
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283 | def show_pr(self, out, pr, cov=None): |
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284 | import numpy |
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285 | import pylab |
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286 | import math |
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287 | from sans.guicomm.events import NewPlotEvent |
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288 | from sans.guitools.plottables import Data1D, Theory1D |
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289 | |
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290 | # Show P(r) |
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291 | x = pylab.arange(0.0, pr.d_max, pr.d_max/self._pr_npts) |
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292 | |
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293 | y = numpy.zeros(len(x)) |
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294 | dy = numpy.zeros(len(x)) |
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295 | y_true = numpy.zeros(len(x)) |
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296 | |
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297 | sum = 0.0 |
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298 | pmax = 0.0 |
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299 | cov2 = numpy.ascontiguousarray(cov) |
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300 | |
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301 | for i in range(len(x)): |
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302 | if cov2==None: |
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303 | value = pr.pr(out, x[i]) |
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304 | else: |
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305 | (value, dy[i]) = pr.pr_err(out, cov2, x[i]) |
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306 | sum += value*pr.d_max/len(x) |
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307 | |
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308 | # keep track of the maximum P(r) value |
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309 | if value>pmax: |
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310 | pmax = value |
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311 | |
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312 | y[i] = value |
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313 | |
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314 | if self._normalize_output==True: |
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315 | y = y/sum |
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316 | dy = dy/sum |
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317 | elif self._scale_output_unity==True: |
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318 | y = y/pmax |
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319 | dy = dy/pmax |
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320 | |
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321 | if cov2==None: |
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322 | new_plot = Theory1D(x, y) |
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323 | else: |
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324 | new_plot = Data1D(x, y, dy=dy) |
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325 | new_plot.name = PR_FIT_LABEL |
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326 | new_plot.xaxis("\\rm{r}", 'A') |
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327 | new_plot.yaxis("\\rm{P(r)} ","cm^{-3}") |
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328 | |
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329 | wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title="P(r) fit")) |
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330 | |
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331 | return x, pr.d_max |
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332 | |
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333 | |
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334 | def choose_file(self): |
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335 | """ |
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336 | |
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337 | """ |
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338 | #TODO: this should be in a common module |
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339 | return self.parent.choose_file() |
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340 | |
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341 | |
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342 | def load(self, path): |
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343 | """ |
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344 | Load data. This will eventually be replaced |
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345 | by our standard DataLoader class. |
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346 | """ |
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347 | class FileData: |
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348 | x = None |
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349 | y = None |
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350 | err = None |
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351 | path = None |
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352 | |
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353 | def __init__(self, path): |
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354 | self.path = path |
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355 | |
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356 | self._current_file_data = FileData(path) |
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357 | |
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358 | # Use data loader to load file |
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359 | dataread = Loader().load(path) |
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360 | x = None |
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361 | y = None |
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362 | err = None |
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363 | if dataread.__class__.__name__ == 'Data1D': |
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364 | x = dataread.x |
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365 | y = dataread.y |
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366 | err = dataread.dy |
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367 | else: |
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368 | raise RuntimeError, "This tool can only read 1D data" |
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369 | |
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370 | self._current_file_data.x = x |
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371 | self._current_file_data.y = y |
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372 | self._current_file_data.err = err |
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373 | return x, y, err |
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374 | |
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375 | def load_columns(self, path = "sphere_60_q0_2.txt"): |
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376 | """ |
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377 | Load 2- or 3- column ascii |
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378 | """ |
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379 | import numpy, math, sys |
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380 | # Read the data from the data file |
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381 | data_x = numpy.zeros(0) |
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382 | data_y = numpy.zeros(0) |
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383 | data_err = numpy.zeros(0) |
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384 | scale = None |
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385 | min_err = 0.0 |
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386 | if not path == None: |
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387 | input_f = open(path,'r') |
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388 | buff = input_f.read() |
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389 | lines = buff.split('\n') |
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390 | for line in lines: |
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391 | try: |
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392 | toks = line.split() |
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393 | x = float(toks[0]) |
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394 | y = float(toks[1]) |
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395 | if len(toks)>2: |
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396 | err = float(toks[2]) |
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397 | else: |
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398 | if scale==None: |
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399 | scale = 0.05*math.sqrt(y) |
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400 | #scale = 0.05/math.sqrt(y) |
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401 | min_err = 0.01*y |
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402 | err = scale*math.sqrt(y)+min_err |
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403 | #err = 0 |
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404 | |
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405 | data_x = numpy.append(data_x, x) |
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406 | data_y = numpy.append(data_y, y) |
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407 | data_err = numpy.append(data_err, err) |
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408 | except: |
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409 | pass |
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410 | |
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411 | if not scale==None: |
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412 | message = "The loaded file had no error bars, statistical errors are assumed." |
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413 | wx.PostEvent(self.parent, StatusEvent(status=message)) |
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414 | else: |
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415 | wx.PostEvent(self.parent, StatusEvent(status='')) |
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416 | |
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417 | return data_x, data_y, data_err |
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418 | |
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419 | def load_abs(self, path): |
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420 | """ |
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421 | Load an IGOR .ABS reduced file |
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422 | @param path: file path |
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423 | @return: x, y, err vectors |
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424 | """ |
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425 | import numpy, math, sys |
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426 | # Read the data from the data file |
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427 | data_x = numpy.zeros(0) |
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428 | data_y = numpy.zeros(0) |
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429 | data_err = numpy.zeros(0) |
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430 | scale = None |
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431 | min_err = 0.0 |
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432 | |
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433 | data_started = False |
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434 | if not path == None: |
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435 | input_f = open(path,'r') |
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436 | buff = input_f.read() |
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437 | lines = buff.split('\n') |
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438 | for line in lines: |
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439 | if data_started==True: |
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440 | try: |
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441 | toks = line.split() |
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442 | x = float(toks[0]) |
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443 | y = float(toks[1]) |
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444 | if len(toks)>2: |
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445 | err = float(toks[2]) |
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446 | else: |
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447 | if scale==None: |
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448 | scale = 0.05*math.sqrt(y) |
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449 | #scale = 0.05/math.sqrt(y) |
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450 | min_err = 0.01*y |
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451 | err = scale*math.sqrt(y)+min_err |
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452 | #err = 0 |
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453 | |
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454 | data_x = numpy.append(data_x, x) |
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455 | data_y = numpy.append(data_y, y) |
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456 | data_err = numpy.append(data_err, err) |
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457 | except: |
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458 | pass |
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459 | elif line.find("The 6 columns")>=0: |
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460 | data_started = True |
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461 | |
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462 | if not scale==None: |
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463 | message = "The loaded file had no error bars, statistical errors are assumed." |
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464 | wx.PostEvent(self.parent, StatusEvent(status=message)) |
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465 | else: |
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466 | wx.PostEvent(self.parent, StatusEvent(status='')) |
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467 | |
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468 | return data_x, data_y, data_err |
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469 | |
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470 | |
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471 | |
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472 | def pr_theory(self, r, R): |
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473 | """ |
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474 | |
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475 | """ |
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476 | if r<=2*R: |
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477 | return 12.0* ((0.5*r/R)**2) * ((1.0-0.5*r/R)**2) * ( 2.0 + 0.5*r/R ) |
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478 | else: |
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479 | return 0.0 |
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480 | |
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481 | def get_context_menu(self, graph=None): |
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482 | """ |
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483 | Get the context menu items available for P(r) |
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484 | @param graph: the Graph object to which we attach the context menu |
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485 | @return: a list of menu items with call-back function |
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486 | """ |
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487 | # Look whether this Graph contains P(r) data |
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488 | #if graph.selected_plottable==IQ_DATA_LABEL: |
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489 | for item in graph.plottables: |
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490 | if item.name==PR_FIT_LABEL: |
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491 | m_list = [["Add P(r) data", "Load a data file and display it on this plot", self._on_add_data], |
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492 | ["Change number of P(r) points", "Change the number of points on the P(r) output", self._on_pr_npts]] |
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493 | |
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494 | if self._scale_output_unity==True or self._normalize_output==True: |
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495 | m_list.append(["Disable P(r) scaling", |
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496 | "Let the output P(r) keep the scale of the data", |
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497 | self._on_disable_scaling]) |
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498 | |
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499 | if self._scale_output_unity==False: |
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500 | m_list.append(["Scale P_max(r) to unity", |
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501 | "Scale P(r) so that its maximum is 1", |
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502 | self._on_scale_unity]) |
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503 | |
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504 | if self._normalize_output==False: |
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505 | m_list.append(["Normalize P(r) to unity", |
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506 | "Normalize the integral of P(r) to 1", |
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507 | self._on_normalize]) |
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508 | |
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509 | return m_list |
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510 | #return [["Add P(r) data", "Load a data file and display it on this plot", self._on_add_data], |
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511 | # ["Change number of P(r) points", "Change the number of points on the P(r) output", self._on_pr_npts]] |
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512 | |
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513 | elif item.name==graph.selected_plottable: |
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514 | return [["Compute P(r)", "Compute P(r) from distribution", self._on_context_inversion]] |
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515 | |
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516 | return [] |
---|
517 | |
---|
518 | def _on_disable_scaling(self, evt): |
---|
519 | """ |
---|
520 | Disable P(r) scaling |
---|
521 | @param evt: Menu event |
---|
522 | """ |
---|
523 | self._normalize_output = False |
---|
524 | self._scale_output_unity = False |
---|
525 | self.show_pr(self._last_out, self._last_pr, self._last_cov) |
---|
526 | |
---|
527 | # Now replot the original added data |
---|
528 | for plot in self._added_plots: |
---|
529 | self._added_plots[plot].y = numpy.copy(self._default_Iq[plot]) |
---|
530 | wx.PostEvent(self.parent, NewPlotEvent(plot=self._added_plots[plot], |
---|
531 | title=self._added_plots[plot].name, |
---|
532 | update=True)) |
---|
533 | |
---|
534 | # Need the update flag in the NewPlotEvent to protect against |
---|
535 | # the plot no longer being there... |
---|
536 | |
---|
537 | def _on_normalize(self, evt): |
---|
538 | """ |
---|
539 | Normalize the area under the P(r) curve to 1. |
---|
540 | This operation is done for all displayed plots. |
---|
541 | |
---|
542 | @param evt: Menu event |
---|
543 | """ |
---|
544 | self._normalize_output = True |
---|
545 | self._scale_output_unity = False |
---|
546 | |
---|
547 | self.show_pr(self._last_out, self._last_pr, self._last_cov) |
---|
548 | |
---|
549 | # Now scale the added plots too |
---|
550 | for plot in self._added_plots: |
---|
551 | sum = numpy.sum(self._added_plots[plot].y) |
---|
552 | npts = len(self._added_plots[plot].x) |
---|
553 | sum *= self._added_plots[plot].x[npts-1]/npts |
---|
554 | y = self._added_plots[plot].y/sum |
---|
555 | |
---|
556 | new_plot = Theory1D(self._added_plots[plot].x, y) |
---|
557 | new_plot.name = self._added_plots[plot].name |
---|
558 | new_plot.xaxis("\\rm{r}", 'A') |
---|
559 | new_plot.yaxis("\\rm{P(r)} ","cm^{-3}") |
---|
560 | |
---|
561 | wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, update=True, |
---|
562 | title=self._added_plots[plot].name)) |
---|
563 | |
---|
564 | |
---|
565 | |
---|
566 | def _on_scale_unity(self, evt): |
---|
567 | """ |
---|
568 | Scale the maximum P(r) value on each displayed plot to 1. |
---|
569 | |
---|
570 | @param evt: Menu event |
---|
571 | """ |
---|
572 | self._scale_output_unity = True |
---|
573 | self._normalize_output = False |
---|
574 | |
---|
575 | self.show_pr(self._last_out, self._last_pr, self._last_cov) |
---|
576 | |
---|
577 | # Now scale the added plots too |
---|
578 | for plot in self._added_plots: |
---|
579 | _max = 0 |
---|
580 | for y in self._added_plots[plot].y: |
---|
581 | if y>_max: |
---|
582 | _max = y |
---|
583 | y = self._added_plots[plot].y/_max |
---|
584 | |
---|
585 | new_plot = Theory1D(self._added_plots[plot].x, y) |
---|
586 | new_plot.name = self._added_plots[plot].name |
---|
587 | new_plot.xaxis("\\rm{r}", 'A') |
---|
588 | new_plot.yaxis("\\rm{P(r)} ","cm^{-3}") |
---|
589 | |
---|
590 | wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, update=True, |
---|
591 | title=self._added_plots[plot].name)) |
---|
592 | |
---|
593 | |
---|
594 | def _on_add_data(self, evt): |
---|
595 | """ |
---|
596 | Add a data curve to the plot |
---|
597 | WARNING: this will be removed once guiframe.plotting has its full functionality |
---|
598 | """ |
---|
599 | path = self.choose_file() |
---|
600 | if path==None: |
---|
601 | return |
---|
602 | |
---|
603 | #x, y, err = self.parent.load_ascii_1D(path) |
---|
604 | # Use data loader to load file |
---|
605 | try: |
---|
606 | dataread = Loader().load(path) |
---|
607 | x = None |
---|
608 | y = None |
---|
609 | err = None |
---|
610 | if dataread.__class__.__name__ == 'Data1D': |
---|
611 | x = dataread.x |
---|
612 | y = dataread.y |
---|
613 | err = dataread.dy |
---|
614 | else: |
---|
615 | wx.PostEvent(self.parent, StatusEvent(status="This tool can only read 1D data")) |
---|
616 | return |
---|
617 | except: |
---|
618 | wx.PostEvent(self.parent, StatusEvent(status=sys.exc_value)) |
---|
619 | return |
---|
620 | |
---|
621 | filename = os.path.basename(path) |
---|
622 | |
---|
623 | #new_plot = Data1D(x, y, dy=err) |
---|
624 | new_plot = Theory1D(x, y) |
---|
625 | new_plot.name = filename |
---|
626 | new_plot.xaxis("\\rm{r}", 'A') |
---|
627 | new_plot.yaxis("\\rm{P(r)} ","cm^{-3}") |
---|
628 | |
---|
629 | # Store a ref to the plottable for later use |
---|
630 | self._added_plots[filename] = new_plot |
---|
631 | self._default_Iq[filename] = numpy.copy(y) |
---|
632 | |
---|
633 | wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title=filename)) |
---|
634 | |
---|
635 | |
---|
636 | |
---|
637 | def start_thread(self): |
---|
638 | from pr_thread import CalcPr |
---|
639 | from copy import deepcopy |
---|
640 | |
---|
641 | # If a thread is already started, stop it |
---|
642 | if self.calc_thread != None and self.calc_thread.isrunning(): |
---|
643 | self.calc_thread.stop() |
---|
644 | |
---|
645 | pr = self.pr.clone() |
---|
646 | self.calc_thread = CalcPr(pr, self.nfunc, error_func=self._thread_error, completefn=self._completed, updatefn=None) |
---|
647 | self.calc_thread.queue() |
---|
648 | self.calc_thread.ready(2.5) |
---|
649 | |
---|
650 | def _thread_error(self, error): |
---|
651 | wx.PostEvent(self.parent, StatusEvent(status=error)) |
---|
652 | |
---|
653 | def _estimate_completed(self, alpha, message, elapsed): |
---|
654 | """ |
---|
655 | Parameter estimation completed, |
---|
656 | display the results to the user |
---|
657 | @param alpha: estimated best alpha |
---|
658 | @param elapsed: computation time |
---|
659 | """ |
---|
660 | # Save useful info |
---|
661 | self.elapsed = elapsed |
---|
662 | self.control_panel.alpha_estimate = alpha |
---|
663 | if not message==None: |
---|
664 | wx.PostEvent(self.parent, StatusEvent(status=str(message))) |
---|
665 | |
---|
666 | self.perform_estimateNT() |
---|
667 | |
---|
668 | |
---|
669 | |
---|
670 | def _estimateNT_completed(self, nterms, alpha, message, elapsed): |
---|
671 | """ |
---|
672 | Parameter estimation completed, |
---|
673 | display the results to the user |
---|
674 | @param alpha: estimated best alpha |
---|
675 | @param nterms: estimated number of terms |
---|
676 | @param elapsed: computation time |
---|
677 | """ |
---|
678 | # Save useful info |
---|
679 | self.elapsed = elapsed |
---|
680 | self.control_panel.nterms_estimate = nterms |
---|
681 | self.control_panel.alpha_estimate = alpha |
---|
682 | if not message==None: |
---|
683 | wx.PostEvent(self.parent, StatusEvent(status=str(message))) |
---|
684 | |
---|
685 | def _completed(self, out, cov, pr, elapsed): |
---|
686 | """ |
---|
687 | Method called with the results when the inversion |
---|
688 | is done |
---|
689 | |
---|
690 | @param out: output coefficient for the base functions |
---|
691 | @param cov: covariance matrix |
---|
692 | @param pr: Invertor instance |
---|
693 | @param elapsed: time spent computing |
---|
694 | """ |
---|
695 | from copy import deepcopy |
---|
696 | # Save useful info |
---|
697 | self.elapsed = elapsed |
---|
698 | # Keep a copy of the last result |
---|
699 | self._last_pr = pr.clone() |
---|
700 | self._last_out = out |
---|
701 | self._last_cov = cov |
---|
702 | |
---|
703 | # Save Pr invertor |
---|
704 | self.pr = pr |
---|
705 | |
---|
706 | #message = "Computation completed in %g seconds [chi2=%g]" % (elapsed, pr.chi2) |
---|
707 | #wx.PostEvent(self.parent, StatusEvent(status=message)) |
---|
708 | |
---|
709 | cov = numpy.ascontiguousarray(cov) |
---|
710 | |
---|
711 | # Show result on control panel |
---|
712 | self.control_panel.chi2 = pr.chi2 |
---|
713 | self.control_panel.elapsed = elapsed |
---|
714 | self.control_panel.oscillation = pr.oscillations(out) |
---|
715 | #print "OSCILL", pr.oscillations(out) |
---|
716 | #print "PEAKS:", pr.get_peaks(out) |
---|
717 | self.control_panel.positive = pr.get_positive(out) |
---|
718 | self.control_panel.pos_err = pr.get_pos_err(out, cov) |
---|
719 | self.control_panel.rg = pr.rg(out) |
---|
720 | self.control_panel.iq0 = pr.iq0(out) |
---|
721 | self.control_panel.bck = pr.background |
---|
722 | |
---|
723 | if False: |
---|
724 | for i in range(len(out)): |
---|
725 | try: |
---|
726 | print "%d: %g +- %g" % (i, out[i], math.sqrt(math.fabs(cov[i][i]))) |
---|
727 | except: |
---|
728 | print sys.exc_value |
---|
729 | print "%d: %g +- ?" % (i, out[i]) |
---|
730 | |
---|
731 | # Make a plot of I(q) data |
---|
732 | new_plot = Data1D(self.pr.x, self.pr.y, dy=self.pr.err) |
---|
733 | new_plot.name = IQ_DATA_LABEL |
---|
734 | new_plot.xaxis("\\rm{Q}", 'A^{-1}') |
---|
735 | new_plot.yaxis("\\rm{Intensity} ","cm^{-1}") |
---|
736 | #new_plot.group_id = "test group" |
---|
737 | wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title="Iq")) |
---|
738 | |
---|
739 | # Show I(q) fit |
---|
740 | self.show_iq(out, self.pr) |
---|
741 | |
---|
742 | # Show P(r) fit |
---|
743 | x_values, x_range = self.show_pr(out, self.pr, cov) |
---|
744 | |
---|
745 | # Popup result panel |
---|
746 | #result_panel = InversionResults(self.parent, -1, style=wx.RAISED_BORDER) |
---|
747 | |
---|
748 | def show_data(self, path=None, reset=False): |
---|
749 | """ |
---|
750 | Show data read from a file |
---|
751 | @param path: file path |
---|
752 | @param reset: if True all other plottables will be cleared |
---|
753 | """ |
---|
754 | |
---|
755 | |
---|
756 | if path is not None: |
---|
757 | try: |
---|
758 | pr = self._create_file_pr(path) |
---|
759 | self.pr = pr |
---|
760 | except: |
---|
761 | wx.PostEvent(self.parent, StatusEvent(status=sys.exc_value)) |
---|
762 | return |
---|
763 | |
---|
764 | # Make a plot of I(q) data |
---|
765 | if self.pr.err==None: |
---|
766 | new_plot = Theory1D(self.pr.x, self.pr.y) |
---|
767 | else: |
---|
768 | new_plot = Data1D(self.pr.x, self.pr.y, dy=self.pr.err) |
---|
769 | new_plot.name = IQ_DATA_LABEL |
---|
770 | new_plot.xaxis("\\rm{Q}", 'A^{-1}') |
---|
771 | new_plot.yaxis("\\rm{Intensity} ","cm^{-1}") |
---|
772 | new_plot.interactive = True |
---|
773 | #new_plot.group_id = "test group" |
---|
774 | wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title="I(q)", reset=reset)) |
---|
775 | |
---|
776 | # Get Q range |
---|
777 | self.control_panel.q_min = self.pr.x.min() |
---|
778 | self.control_panel.q_max = self.pr.x.max() |
---|
779 | |
---|
780 | |
---|
781 | |
---|
782 | def setup_plot_inversion(self, alpha, nfunc, d_max, q_min=None, q_max=None, |
---|
783 | bck=False, height=0, width=0): |
---|
784 | self.alpha = alpha |
---|
785 | self.nfunc = nfunc |
---|
786 | self.max_length = d_max |
---|
787 | self.q_min = q_min |
---|
788 | self.q_max = q_max |
---|
789 | self.has_bck = bck |
---|
790 | self.slit_height = height |
---|
791 | self.slit_width = width |
---|
792 | |
---|
793 | try: |
---|
794 | pr = self._create_plot_pr() |
---|
795 | if not pr==None: |
---|
796 | self.pr = pr |
---|
797 | self.perform_inversion() |
---|
798 | except: |
---|
799 | wx.PostEvent(self.parent, StatusEvent(status=sys.exc_value)) |
---|
800 | |
---|
801 | def estimate_plot_inversion(self, alpha, nfunc, d_max, q_min=None, q_max=None, |
---|
802 | bck=False, height=0, width=0): |
---|
803 | self.alpha = alpha |
---|
804 | self.nfunc = nfunc |
---|
805 | self.max_length = d_max |
---|
806 | self.q_min = q_min |
---|
807 | self.q_max = q_max |
---|
808 | self.has_bck = bck |
---|
809 | self.slit_height = height |
---|
810 | self.slit_width = width |
---|
811 | |
---|
812 | try: |
---|
813 | pr = self._create_plot_pr() |
---|
814 | if not pr==None: |
---|
815 | self.pr = pr |
---|
816 | self.perform_estimate() |
---|
817 | except: |
---|
818 | wx.PostEvent(self.parent, StatusEvent(status=sys.exc_value)) |
---|
819 | |
---|
820 | def _create_plot_pr(self, estimate=False): |
---|
821 | """ |
---|
822 | Create and prepare invertor instance from |
---|
823 | a plottable data set. |
---|
824 | @param path: path of the file to read in |
---|
825 | """ |
---|
826 | # Get the data from the chosen data set and perform inversion |
---|
827 | pr = Invertor() |
---|
828 | pr.d_max = self.max_length |
---|
829 | pr.alpha = self.alpha |
---|
830 | pr.q_min = self.q_min |
---|
831 | pr.q_max = self.q_max |
---|
832 | pr.x = self.current_plottable.x |
---|
833 | pr.y = self.current_plottable.y |
---|
834 | pr.has_bck = self.has_bck |
---|
835 | pr.slit_height = self.slit_height |
---|
836 | pr.slit_width = self.slit_width |
---|
837 | |
---|
838 | # Fill in errors if none were provided |
---|
839 | if self.current_plottable.dy == None: |
---|
840 | print "no error", self.current_plottable.name |
---|
841 | y = numpy.zeros(len(pr.y)) |
---|
842 | for i in range(len(pr.y)): |
---|
843 | y[i] = math.sqrt(pr.y[i]) |
---|
844 | pr.err = y |
---|
845 | else: |
---|
846 | pr.err = self.current_plottable.dy |
---|
847 | |
---|
848 | #self.pr = pr |
---|
849 | return pr |
---|
850 | |
---|
851 | |
---|
852 | def setup_file_inversion(self, alpha, nfunc, d_max, path, q_min=None, q_max=None, |
---|
853 | bck=False, height=0, width=0): |
---|
854 | self.alpha = alpha |
---|
855 | self.nfunc = nfunc |
---|
856 | self.max_length = d_max |
---|
857 | self.q_min = q_min |
---|
858 | self.q_max = q_max |
---|
859 | self.has_bck = bck |
---|
860 | self.slit_height = height |
---|
861 | self.slit_width = width |
---|
862 | |
---|
863 | try: |
---|
864 | pr = self._create_file_pr(path) |
---|
865 | if not pr==None: |
---|
866 | self.pr = pr |
---|
867 | self.perform_inversion() |
---|
868 | except: |
---|
869 | wx.PostEvent(self.parent, StatusEvent(status=sys.exc_value)) |
---|
870 | |
---|
871 | def estimate_file_inversion(self, alpha, nfunc, d_max, path, q_min=None, q_max=None, |
---|
872 | bck=False, height=0, width=0): |
---|
873 | self.alpha = alpha |
---|
874 | self.nfunc = nfunc |
---|
875 | self.max_length = d_max |
---|
876 | self.q_min = q_min |
---|
877 | self.q_max = q_max |
---|
878 | self.has_bck = bck |
---|
879 | self.slit_height = height |
---|
880 | self.slit_width = width |
---|
881 | |
---|
882 | try: |
---|
883 | pr = self._create_file_pr(path) |
---|
884 | if not pr==None: |
---|
885 | self.pr = pr |
---|
886 | self.perform_estimate() |
---|
887 | except: |
---|
888 | wx.PostEvent(self.parent, StatusEvent(status=sys.exc_value)) |
---|
889 | |
---|
890 | |
---|
891 | def _create_file_pr(self, path): |
---|
892 | """ |
---|
893 | Create and prepare invertor instance from |
---|
894 | a file data set. |
---|
895 | @param path: path of the file to read in |
---|
896 | """ |
---|
897 | # Load data |
---|
898 | if os.path.isfile(path): |
---|
899 | |
---|
900 | if self._current_file_data is not None \ |
---|
901 | and self._current_file_data.path==path: |
---|
902 | # Protect against corrupted data from |
---|
903 | # previous failed load attempt |
---|
904 | if self._current_file_data.x is None: |
---|
905 | return None |
---|
906 | x = self._current_file_data.x |
---|
907 | y = self._current_file_data.y |
---|
908 | err = self._current_file_data.err |
---|
909 | else: |
---|
910 | x, y, err = self.load(path) |
---|
911 | |
---|
912 | # If we have not errors, add statistical errors |
---|
913 | if err==None and y is not None: |
---|
914 | err = numpy.zeros(len(y)) |
---|
915 | scale = None |
---|
916 | min_err = 0.0 |
---|
917 | for i in range(len(y)): |
---|
918 | # Scale the error so that we can fit over several decades of Q |
---|
919 | if scale==None: |
---|
920 | scale = 0.05*math.sqrt(y[i]) |
---|
921 | min_err = 0.01*y[i] |
---|
922 | err[i] = scale*math.sqrt( math.fabs(y[i]) ) + min_err |
---|
923 | message = "The loaded file had no error bars, statistical errors are assumed." |
---|
924 | wx.PostEvent(self.parent, StatusEvent(status=message)) |
---|
925 | else: |
---|
926 | wx.PostEvent(self.parent, StatusEvent(status='')) |
---|
927 | |
---|
928 | try: |
---|
929 | # Get the data from the chosen data set and perform inversion |
---|
930 | pr = Invertor() |
---|
931 | pr.d_max = self.max_length |
---|
932 | pr.alpha = self.alpha |
---|
933 | pr.q_min = self.q_min |
---|
934 | pr.q_max = self.q_max |
---|
935 | pr.x = x |
---|
936 | pr.y = y |
---|
937 | pr.err = err |
---|
938 | pr.has_bck = self.has_bck |
---|
939 | pr.slit_height = self.slit_height |
---|
940 | pr.slit_width = self.slit_width |
---|
941 | return pr |
---|
942 | except: |
---|
943 | wx.PostEvent(self.parent, StatusEvent(status="Problem reading data: %s" % sys.exc_value)) |
---|
944 | return None |
---|
945 | |
---|
946 | def perform_estimate(self): |
---|
947 | from pr_thread import EstimatePr |
---|
948 | from copy import deepcopy |
---|
949 | |
---|
950 | # If a thread is already started, stop it |
---|
951 | if self.estimation_thread != None and self.estimation_thread.isrunning(): |
---|
952 | self.estimation_thread.stop() |
---|
953 | |
---|
954 | pr = self.pr.clone() |
---|
955 | self.estimation_thread = EstimatePr(pr, self.nfunc, error_func=self._thread_error, |
---|
956 | completefn = self._estimate_completed, |
---|
957 | updatefn = None) |
---|
958 | self.estimation_thread.queue() |
---|
959 | self.estimation_thread.ready(2.5) |
---|
960 | |
---|
961 | def perform_estimateNT(self): |
---|
962 | from pr_thread import EstimateNT |
---|
963 | from copy import deepcopy |
---|
964 | |
---|
965 | # If a thread is already started, stop it |
---|
966 | if self.estimation_thread != None and self.estimation_thread.isrunning(): |
---|
967 | self.estimation_thread.stop() |
---|
968 | |
---|
969 | pr = self.pr.clone() |
---|
970 | # Skip the slit settings for the estimation |
---|
971 | # It slows down the application and it doesn't change the estimates |
---|
972 | pr.slit_height = 0.0 |
---|
973 | pr.slit_width = 0.0 |
---|
974 | self.estimation_thread = EstimateNT(pr, self.nfunc, error_func=self._thread_error, |
---|
975 | completefn = self._estimateNT_completed, |
---|
976 | updatefn = None) |
---|
977 | self.estimation_thread.queue() |
---|
978 | self.estimation_thread.ready(2.5) |
---|
979 | |
---|
980 | def perform_inversion(self): |
---|
981 | |
---|
982 | # Time estimate |
---|
983 | #estimated = self.elapsed*self.nfunc**2 |
---|
984 | #message = "Computation time may take up to %g seconds" % self.elapsed |
---|
985 | #wx.PostEvent(self.parent, StatusEvent(status=message)) |
---|
986 | |
---|
987 | # Start inversion thread |
---|
988 | self.start_thread() |
---|
989 | return |
---|
990 | |
---|
991 | out, cov = self.pr.lstsq(self.nfunc) |
---|
992 | |
---|
993 | # Save useful info |
---|
994 | self.elapsed = self.pr.elapsed |
---|
995 | |
---|
996 | for i in range(len(out)): |
---|
997 | try: |
---|
998 | print "%d: %g +- %g" % (i, out[i], math.sqrt(math.fabs(cov[i][i]))) |
---|
999 | except: |
---|
1000 | print "%d: %g +- ?" % (i, out[i]) |
---|
1001 | |
---|
1002 | |
---|
1003 | |
---|
1004 | # Make a plot of I(q) data |
---|
1005 | new_plot = Data1D(self.pr.x, self.pr.y, dy=self.pr.err) |
---|
1006 | new_plot.name = "I_{obs}(q)" |
---|
1007 | new_plot.xaxis("\\rm{Q}", 'A^{-1}') |
---|
1008 | new_plot.yaxis("\\rm{Intensity} ","cm^{-1}") |
---|
1009 | wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title="Iq")) |
---|
1010 | |
---|
1011 | # Show I(q) fit |
---|
1012 | self.show_iq(out, self.pr) |
---|
1013 | |
---|
1014 | # Show P(r) fit |
---|
1015 | x_values, x_range = self.show_pr(out, self.pr, cov=cov) |
---|
1016 | |
---|
1017 | |
---|
1018 | |
---|
1019 | def _on_context_inversion(self, event): |
---|
1020 | panel = event.GetEventObject() |
---|
1021 | |
---|
1022 | from inversion_panel import InversionDlg |
---|
1023 | |
---|
1024 | # If we have more than one displayed plot, make the user choose |
---|
1025 | if len(panel.plots)>1 and panel.graph.selected_plottable in panel.plots: |
---|
1026 | dataset = panel.graph.selected_plottable |
---|
1027 | if False: |
---|
1028 | dialog = InversionDlg(None, -1, "P(r) Inversion", panel.plots, pars=False) |
---|
1029 | dialog.set_content(self.last_data, self.nfunc, self.alpha, self.max_length) |
---|
1030 | if dialog.ShowModal() == wx.ID_OK: |
---|
1031 | dataset = dialog.get_content() |
---|
1032 | dialog.Destroy() |
---|
1033 | else: |
---|
1034 | dialog.Destroy() |
---|
1035 | return |
---|
1036 | elif len(panel.plots)==1: |
---|
1037 | dataset = panel.plots.keys()[0] |
---|
1038 | else: |
---|
1039 | print "Error: No data is available" |
---|
1040 | return |
---|
1041 | |
---|
1042 | # Store a reference to the current plottable |
---|
1043 | # If we have a suggested value, use it. |
---|
1044 | try: |
---|
1045 | estimate = float(self.control_panel.alpha_estimate) |
---|
1046 | self.control_panel.alpha = estimate |
---|
1047 | except: |
---|
1048 | self.control_panel.alpha = self.alpha |
---|
1049 | print "No estimate yet" |
---|
1050 | pass |
---|
1051 | try: |
---|
1052 | estimate = int(self.control_panel.nterms_estimate) |
---|
1053 | self.control_panel.nfunc = estimate |
---|
1054 | except: |
---|
1055 | self.control_panel.nfunc = self.nfunc |
---|
1056 | print "No estimate yet" |
---|
1057 | pass |
---|
1058 | |
---|
1059 | self.current_plottable = panel.plots[dataset] |
---|
1060 | self.control_panel.plotname = dataset |
---|
1061 | #self.control_panel.nfunc = self.nfunc |
---|
1062 | self.control_panel.d_max = self.max_length |
---|
1063 | self.parent.set_perspective(self.perspective) |
---|
1064 | self.control_panel._on_invert(None) |
---|
1065 | |
---|
1066 | def get_panels(self, parent): |
---|
1067 | """ |
---|
1068 | Create and return a list of panel objects |
---|
1069 | """ |
---|
1070 | from inversion_panel import InversionControl |
---|
1071 | |
---|
1072 | self.parent = parent |
---|
1073 | self.control_panel = InversionControl(self.parent, -1, |
---|
1074 | style=wx.RAISED_BORDER, |
---|
1075 | standalone=self.standalone) |
---|
1076 | self.control_panel.set_manager(self) |
---|
1077 | self.control_panel.nfunc = self.nfunc |
---|
1078 | self.control_panel.d_max = self.max_length |
---|
1079 | self.control_panel.alpha = self.alpha |
---|
1080 | |
---|
1081 | self.perspective = [] |
---|
1082 | self.perspective.append(self.control_panel.window_name) |
---|
1083 | |
---|
1084 | self.parent.Bind(EVT_PR_FILE, self._on_new_file) |
---|
1085 | |
---|
1086 | return [self.control_panel] |
---|
1087 | |
---|
1088 | def _on_new_file(self, evt): |
---|
1089 | """ |
---|
1090 | Called when the application manager posted an |
---|
1091 | EVT_PR_FILE event. Just prompt the control |
---|
1092 | panel to load a new data file. |
---|
1093 | """ |
---|
1094 | self.control_panel._change_file(None) |
---|
1095 | |
---|
1096 | def get_perspective(self): |
---|
1097 | """ |
---|
1098 | Get the list of panel names for this perspective |
---|
1099 | """ |
---|
1100 | return self.perspective |
---|
1101 | |
---|
1102 | def on_perspective(self, event): |
---|
1103 | """ |
---|
1104 | Call back function for the perspective menu item. |
---|
1105 | We notify the parent window that the perspective |
---|
1106 | has changed. |
---|
1107 | """ |
---|
1108 | self.parent.set_perspective(self.perspective) |
---|
1109 | |
---|
1110 | def post_init(self): |
---|
1111 | """ |
---|
1112 | Post initialization call back to close the loose ends |
---|
1113 | [Somehow openGL needs this call] |
---|
1114 | """ |
---|
1115 | self.parent.set_perspective(self.perspective) |
---|
1116 | |
---|
1117 | if __name__ == "__main__": |
---|
1118 | i = Plugin() |
---|
1119 | print i.perform_estimateNT() |
---|
1120 | |
---|
1121 | |
---|
1122 | |
---|
1123 | |
---|