1 | """ |
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2 | Adds a linear fit plot to the chart |
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3 | """ |
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4 | import numpy |
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5 | from PyQt4 import QtGui |
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6 | from PyQt4 import QtCore |
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7 | |
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8 | #from sas.sasgui.transform import |
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9 | from sas.qtgui.GuiUtils import formatNumber |
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10 | from sas.sasgui.plottools import fittings |
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11 | from sas.sasgui.plottools import transform |
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12 | |
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13 | from sas.sasgui.plottools.LineModel import LineModel |
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14 | |
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15 | # Local UI |
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16 | from sas.qtgui.UI.LinearFitUI import Ui_LinearFitUI |
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17 | |
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18 | class LinearFit(QtGui.QDialog, Ui_LinearFitUI): |
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19 | def __init__(self, parent=None, |
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20 | data=None, |
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21 | max_range=(0.0, 0.0), |
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22 | fit_range=(0.0, 0.0), |
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23 | xlabel="", |
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24 | ylabel=""): |
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25 | super(LinearFit, self).__init__() |
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26 | |
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27 | self.setupUi(self) |
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28 | assert(isinstance(max_range, tuple)) |
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29 | assert(isinstance(fit_range, tuple)) |
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30 | |
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31 | self.data = data |
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32 | self.parent = parent |
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33 | |
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34 | self.max_range = max_range |
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35 | self.fit_range = fit_range |
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36 | self.xLabel = xlabel |
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37 | self.yLabel = ylabel |
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38 | |
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39 | self.txtFitRangeMin.setValidator(QtGui.QDoubleValidator()) |
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40 | self.txtFitRangeMax.setValidator(QtGui.QDoubleValidator()) |
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41 | |
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42 | # Default values in the line edits |
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43 | self.txtA.setText("1") |
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44 | self.txtB.setText("1") |
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45 | self.txtAerr.setText("0") |
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46 | self.txtBerr.setText("0") |
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47 | self.txtChi2.setText("0") |
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48 | # Initial ranges |
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49 | self.txtRangeMin.setText(str(max_range[0])) |
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50 | self.txtRangeMax.setText(str(max_range[1])) |
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51 | self.txtFitRangeMin.setText(str(fit_range[0])) |
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52 | self.txtFitRangeMax.setText(str(fit_range[1])) |
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53 | |
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54 | self.lblRange.setText('Fit range of ' + self.xLabel) |
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55 | |
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56 | self.model = LineModel() |
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57 | # Display the fittings values |
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58 | self.default_A = self.model.getParam('A') |
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59 | self.default_B = self.model.getParam('B') |
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60 | self.cstA = fittings.Parameter(self.model, 'A', self.default_A) |
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61 | self.cstB = fittings.Parameter(self.model, 'B', self.default_B) |
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62 | self.transform = transform |
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63 | |
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64 | self.cmdFit.clicked.connect(self.fit) |
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65 | |
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66 | def setRangeLabel(self, label=""): |
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67 | """ |
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68 | Overwrite default fit range label to correspond to actual unit |
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69 | """ |
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70 | assert(isinstance(label, basestring)) |
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71 | self.lblRange.setText(label) |
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72 | |
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73 | def a(self): |
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74 | return (float(self.txtA.text()), float(self.txtAerr.text())) |
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75 | |
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76 | def b(self): |
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77 | return (float(self.txtB.text()), float(self.txtBerr.text())) |
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78 | |
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79 | def chi(self): |
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80 | return float(self.txtChi2.text()) |
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81 | |
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82 | def range(self): |
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83 | return (float(self.txtFitRangeMin.text()), float(self.txtFitRangeMax.text())) |
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84 | |
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85 | def fit(self, event): |
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86 | """ |
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87 | Performs the fit. Receive an event when clicking on |
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88 | the button Fit.Computes chisqr , |
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89 | A and B parameters of the best linear fit y=Ax +B |
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90 | Push a plottable to the caller |
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91 | """ |
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92 | tempx = [] |
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93 | tempy = [] |
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94 | tempdy = [] |
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95 | |
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96 | # Checks to assure data correctness |
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97 | if len(self.data.view.x) < 2: |
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98 | return |
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99 | if not self.checkFitValues(self.txtFitRangeMin): |
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100 | return |
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101 | |
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102 | self.xminFit, self.xmaxFit = self.range() |
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103 | |
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104 | xminView = self.xminFit |
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105 | xmaxView = self.xmaxFit |
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106 | xmin = xminView |
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107 | xmax = xmaxView |
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108 | # Set the qmin and qmax in the panel that matches the |
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109 | # transformed min and max |
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110 | self.txtRangeMin.setText(formatNumber(self.floatInvTransform(xmin))) |
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111 | self.txtRangeMax.setText(formatNumber(self.floatInvTransform(xmax))) |
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112 | |
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113 | # Store the transformed values of view x, y and dy before the fit |
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114 | xmin_check = numpy.log10(xmin) |
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115 | x = self.data.view.x |
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116 | y = self.data.view.y |
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117 | dy = self.data.view.dy |
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118 | |
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119 | if self.yLabel == "log10(y)": |
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120 | if self.xLabel == "log10(x)": |
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121 | tempy = [numpy.log10(y[i]) |
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122 | for i in range(len(x)) if x[i] >= xmin_check] |
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123 | tempdy = [transform.errToLogX(y[i], 0, dy[i], 0) |
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124 | for i in range(len(x)) if x[i] >= xmin_check] |
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125 | else: |
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126 | tempy = map(numpy.log10, y) |
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127 | tempdy = map(lambda t1,t2:transform.errToLogX(t1,0,t2,0),y,dy) |
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128 | else: |
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129 | tempy = y |
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130 | tempdy = dy |
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131 | |
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132 | if self.xLabel == "log10(x)": |
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133 | tempx = [numpy.log10(x) for x in self.data.view.x if x > xmin_check] |
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134 | else: |
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135 | tempx = self.data.view.x |
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136 | |
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137 | # Find the fitting parameters |
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138 | # Always use the same defaults, so that fit history |
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139 | # doesn't play a role! |
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140 | self.cstA = fittings.Parameter(self.model, 'A', self.default_A) |
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141 | self.cstB = fittings.Parameter(self.model, 'B', self.default_B) |
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142 | tempdy = numpy.asarray(tempdy) |
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143 | tempdy[tempdy == 0] = 1 |
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144 | |
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145 | if self.xLabel == "log10(x)": |
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146 | chisqr, out, cov = fittings.sasfit(self.model, |
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147 | [self.cstA, self.cstB], |
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148 | tempx, tempy, |
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149 | tempdy, |
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150 | numpy.log10(xmin), |
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151 | numpy.log10(xmax)) |
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152 | else: |
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153 | chisqr, out, cov = fittings.sasfit(self.model, |
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154 | [self.cstA, self.cstB], |
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155 | tempx, tempy, tempdy, |
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156 | xminView, xmaxView) |
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157 | # Use chi2/dof |
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158 | if len(tempx) > 0: |
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159 | chisqr = chisqr / len(tempx) |
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160 | |
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161 | # Check that cov and out are iterable before displaying them |
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162 | errA = numpy.sqrt(cov[0][0]) if cov is not None else 0 |
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163 | errB = numpy.sqrt(cov[1][1]) if cov is not None else 0 |
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164 | cstA = out[0] if out is not None else 0.0 |
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165 | cstB = out[1] if out is not None else 0.0 |
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166 | |
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167 | # Reset model with the right values of A and B |
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168 | self.model.setParam('A', float(cstA)) |
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169 | self.model.setParam('B', float(cstB)) |
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170 | |
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171 | tempx = [] |
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172 | tempy = [] |
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173 | y_model = 0.0 |
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174 | # load tempy with the minimum transformation |
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175 | if self.xLabel == "log10(x)": |
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176 | y_model = self.model.run(numpy.log10(xmin)) |
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177 | tempx.append(xmin) |
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178 | else: |
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179 | y_model = self.model.run(xminView) |
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180 | tempx.append(xminView) |
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181 | |
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182 | if self.yLabel == "log10(y)": |
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183 | tempy.append(numpy.power(10, y_model)) |
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184 | else: |
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185 | tempy.append(y_model) |
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186 | |
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187 | # load tempy with the maximum transformation |
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188 | if self.xLabel == "log10(x)": |
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189 | y_model = self.model.run(numpy.log10(xmax)) |
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190 | tempx.append(xmax) |
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191 | else: |
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192 | y_model = self.model.run(xmaxView) |
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193 | tempx.append(xmaxView) |
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194 | |
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195 | if self.yLabel == "log10(y)": |
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196 | tempy.append(numpy.power(10, y_model)) |
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197 | else: |
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198 | tempy.append(y_model) |
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199 | # Set the fit parameter display when FitDialog is opened again |
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200 | self.Avalue = cstA |
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201 | self.Bvalue = cstB |
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202 | self.ErrAvalue = errA |
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203 | self.ErrBvalue = errB |
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204 | self.Chivalue = chisqr |
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205 | |
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206 | # Update the widget |
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207 | self.txtA.setText(formatNumber(self.Avalue)) |
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208 | self.txtAerr.setText(formatNumber(self.ErrAvalue)) |
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209 | self.txtB.setText(formatNumber(self.Bvalue)) |
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210 | self.txtBerr.setText(formatNumber(self.ErrBvalue)) |
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211 | self.txtChi2.setText(formatNumber(self.Chivalue)) |
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212 | |
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213 | #self.parent.updatePlot.emit((tempx, tempy)) |
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214 | self.parent.emit(QtCore.SIGNAL('updatePlot'), (tempx, tempy)) |
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215 | |
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216 | def checkFitValues(self, item): |
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217 | """ |
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218 | Check the validity of input values |
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219 | """ |
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220 | flag = True |
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221 | value = item.text() |
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222 | p_white = item.palette() |
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223 | p_white.setColor(item.backgroundRole(), QtCore.Qt.white) |
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224 | p_pink = item.palette() |
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225 | p_pink.setColor(item.backgroundRole(), QtGui.QColor(255, 128, 128)) |
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226 | # Check for possible values entered |
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227 | if self.xLabel == "log10(x)": |
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228 | if float(value) > 0: |
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229 | item.setPalette(p_white) |
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230 | else: |
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231 | flag = False |
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232 | item.setPalette(p_pink) |
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233 | return flag |
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234 | |
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235 | #def checkFitRanges(self, usermin, usermax): |
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236 | # """ |
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237 | # Ensure that fields parameter contains a min and a max value |
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238 | # within x min and x max range |
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239 | # """ |
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240 | # self.mini = float(self.xminFit.GetValue()) |
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241 | # self.maxi = float(self.xmaxFit.GetValue()) |
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242 | # flag = True |
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243 | # try: |
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244 | # mini = float(usermin.GetValue()) |
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245 | # maxi = float(usermax.GetValue()) |
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246 | # if mini < maxi: |
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247 | # usermin.SetBackgroundColour(wx.WHITE) |
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248 | # usermin.Refresh() |
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249 | # else: |
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250 | # flag = False |
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251 | # usermin.SetBackgroundColour("pink") |
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252 | # usermin.Refresh() |
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253 | # except: |
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254 | # # Check for possible values entered |
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255 | # flag = False |
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256 | # usermin.SetBackgroundColour("pink") |
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257 | # usermin.Refresh() |
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258 | |
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259 | # return flag |
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260 | def floatInvTransform(self, x): |
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261 | """ |
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262 | transform a float.It is used to determine the x.View min and x.View |
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263 | max for values not in x. Also used to properly calculate RgQmin, |
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264 | RgQmax and to update qmin and qmax in the linear range boxes on the |
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265 | panel. |
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266 | |
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267 | """ |
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268 | # TODO: refactor this. This is just a hack to make the |
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269 | # functionality work without rewritting the whole code |
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270 | # with good design (which really should be done...). |
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271 | if self.xLabel == "x": |
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272 | return x |
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273 | elif self.xLabel == "x^(2)": |
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274 | return numpy.sqrt(x) |
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275 | elif self.xLabel == "x^(4)": |
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276 | return numpy.sqrt(math.sqrt(x)) |
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277 | elif self.xLabel == "log10(x)": |
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278 | return numpy.power(10, x) |
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279 | elif self.xLabel == "ln(x)": |
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280 | return numpy.exp(x) |
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281 | elif self.xLabel == "log10(x^(4))": |
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282 | return numpy.sqrt(numpy.sqrt(numpy.power(10, x))) |
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283 | return x |
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