1 | import wx.lib.newevent |
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2 | import matplotlib |
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3 | matplotlib.interactive(False) |
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4 | #Use the WxAgg back end. The Wx one takes too long to render |
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5 | matplotlib.use('WXAgg') |
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6 | from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg |
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7 | from matplotlib.figure import Figure |
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8 | import os |
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9 | import fittings |
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10 | import transform |
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11 | from canvas import FigureCanvas |
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12 | from matplotlib.widgets import RectangleSelector |
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13 | from pylab import gca, gcf |
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14 | from plottables import Theory1D |
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15 | #from plottables import Data1D |
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16 | #TODO: make the plottables interactive |
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17 | |
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18 | from plottables import Graph |
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19 | #(FuncFitEvent, EVT_FUNC_FIT) = wx.lib.newevent.NewEvent() |
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20 | import math,pylab |
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21 | def show_tree(obj,d=0): |
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22 | """Handy function for displaying a tree of graph objects""" |
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23 | print "%s%s" % ("-"*d,obj.__class__.__name__) |
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24 | if 'get_children' in dir(obj): |
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25 | for a in obj.get_children(): show_tree(a,d+1) |
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26 | def _rescale(lo,hi,step,pt=None,bal=None,scale='linear'): |
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27 | """ |
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28 | Rescale (lo,hi) by step, returning the new (lo,hi) |
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29 | The scaling is centered on pt, with positive values of step |
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30 | driving lo/hi away from pt and negative values pulling them in. |
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31 | If bal is given instead of point, it is already in [0,1] coordinates. |
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32 | |
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33 | This is a helper function for step-based zooming. |
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34 | """ |
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35 | # Convert values into the correct scale for a linear transformation |
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36 | # TODO: use proper scale transformers |
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37 | loprev = lo |
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38 | hiprev = hi |
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39 | ptprev = pt |
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40 | if scale=='log': |
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41 | assert lo >0 |
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42 | if lo > 0 : |
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43 | lo = math.log10(lo) |
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44 | if hi > 0 : |
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45 | hi = math.log10(hi) |
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46 | if pt is not None: pt = math.log10(pt) |
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47 | |
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48 | # Compute delta from axis range * %, or 1-% if persent is negative |
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49 | if step > 0: |
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50 | delta = float(hi-lo)*step/100 |
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51 | else: |
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52 | delta = float(hi-lo)*step/(100-step) |
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53 | |
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54 | # Add scale factor proportionally to the lo and hi values, preserving the |
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55 | # point under the mouse |
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56 | if bal is None: |
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57 | bal = float(pt-lo)/(hi-lo) |
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58 | lo = lo - bal*delta |
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59 | hi = hi + (1-bal)*delta |
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60 | |
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61 | # Convert transformed values back to the original scale |
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62 | if scale=='log': |
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63 | if (lo <= -250) or (hi >= 250): |
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64 | lo=loprev |
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65 | hi=hiprev |
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66 | print "Not possible to scale" |
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67 | |
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68 | else: |
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69 | lo,hi = math.pow(10.,lo),math.pow(10.,hi) |
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70 | #assert lo >0,"lo = %g"%lo |
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71 | print "possible to scale" |
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72 | |
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73 | print "these are low and high",lo,hi |
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74 | |
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75 | return (lo,hi) |
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76 | |
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77 | |
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78 | class PlotPanel(wx.Panel): |
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79 | """ |
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80 | The PlotPanel has a Figure and a Canvas. OnSize events simply set a |
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81 | flag, and the actually redrawing of the |
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82 | figure is triggered by an Idle event. |
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83 | """ |
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84 | def __init__(self, parent, id = -1, color = None,\ |
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85 | dpi = None, **kwargs): |
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86 | wx.Panel.__init__(self, parent, id = id, **kwargs) |
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87 | self.parent = parent |
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88 | self.figure = Figure(None, dpi) |
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89 | #self.figure = pylab.Figure(None, dpi) |
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90 | #self.canvas = NoRepaintCanvas(self, -1, self.figure) |
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91 | self.canvas = FigureCanvas(self, -1, self.figure) |
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92 | self.SetColor(color) |
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93 | #self.Bind(wx.EVT_IDLE, self._onIdle) |
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94 | #self.Bind(wx.EVT_SIZE, self._onSize) |
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95 | self._resizeflag = True |
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96 | self._SetSize() |
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97 | self.subplot = self.figure.add_subplot(111) |
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98 | self.figure.subplots_adjust(left=.2, bottom=.2) |
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99 | self.yscale = 'linear' |
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100 | self.xscale = 'linear' |
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101 | sizer = wx.BoxSizer(wx.VERTICAL) |
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102 | sizer.Add(self.canvas,1,wx.EXPAND) |
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103 | self.SetSizer(sizer) |
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104 | |
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105 | # Graph object to manage the plottables |
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106 | self.graph = Graph() |
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107 | #self.Bind(EVT_FUNC_FIT, self.onFitRange) |
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108 | self.Bind(wx.EVT_CONTEXT_MENU, self.onContextMenu) |
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109 | #self.Bind(EVT_PROPERTY, self._onEVT_FUNC_PROPERTY) |
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110 | # Define some constants |
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111 | self.colorlist = ['b','g','r','c','m','y'] |
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112 | self.symbollist = ['o','x','^','v','<','>','+','s','d','D','h','H','p'] |
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113 | #User scale |
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114 | self.xLabel ="x" |
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115 | self.yLabel ="log10(y)" |
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116 | self.viewModel ="--" |
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117 | # keep track if the previous transformation of x and y in Property dialog |
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118 | self.prevXtrans =" " |
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119 | self.prevYtrans =" " |
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120 | self.canvas.mpl_connect('scroll_event',self.onWheel) |
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121 | self.axes = [self.subplot] |
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122 | # new data for the fit |
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123 | self.fit_result = Theory1D(x=[], y=[], dy=None) |
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124 | #self.fit_result = Data1D(x=[], y=[],dx=None, dy=None) |
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125 | self.fit_result.name = "Fit" |
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126 | # For fit Dialog initial display |
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127 | self.xmin=0.0 |
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128 | self.xmax=0.0 |
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129 | self.xminView=0.0 |
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130 | self.xmaxView=0.0 |
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131 | self.Avalue=None |
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132 | self.Bvalue=None |
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133 | self.ErrAvalue=None |
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134 | self.ErrBvalue=None |
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135 | self.Chivalue=None |
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136 | def resetFitView(self): |
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137 | """ |
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138 | For fit Dialog initial display |
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139 | """ |
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140 | self.xmin=0.0 |
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141 | self.xmax=0.0 |
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142 | self.xminView=0.0 |
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143 | self.xmaxView=0.0 |
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144 | self.Avalue=None |
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145 | self.Bvalue=None |
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146 | self.ErrAvalue=None |
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147 | self.ErrBvalue=None |
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148 | self.Chivalue=None |
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149 | def onWheel(self, event): |
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150 | """ |
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151 | Process mouse wheel as zoom events |
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152 | """ |
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153 | ax = event.inaxes |
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154 | step = event.step |
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155 | |
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156 | if ax != None: |
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157 | # Event occurred inside a plotting area |
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158 | lo,hi = ax.get_xlim() |
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159 | lo,hi = _rescale(lo,hi,step,pt=event.xdata) |
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160 | ax.set_xlim((lo,hi)) |
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161 | |
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162 | lo,hi = ax.get_ylim() |
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163 | lo,hi = _rescale(lo,hi,step,pt=event.ydata) |
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164 | ax.set_ylim((lo,hi)) |
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165 | else: |
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166 | # Check if zoom happens in the axes |
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167 | xdata,ydata = None,None |
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168 | x,y = event.x,event.y |
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169 | |
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170 | for ax in self.axes: |
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171 | insidex,_ = ax.xaxis.contains(event) |
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172 | if insidex: |
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173 | xdata,_ = ax.transAxes.inverse_xy_tup((x,y)) |
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174 | print "xaxis",x,"->",xdata |
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175 | insidey,_ = ax.yaxis.contains(event) |
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176 | if insidey: |
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177 | _,ydata = ax.transAxes.inverse_xy_tup((x,y)) |
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178 | print "yaxis",y,"->",ydata |
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179 | if xdata is not None: |
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180 | lo,hi = ax.get_xlim() |
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181 | lo,hi = _rescale(lo,hi,step,bal=xdata,scale=ax.get_xscale()) |
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182 | ax.set_xlim((lo,hi)) |
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183 | if ydata is not None: |
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184 | lo,hi = ax.get_ylim() |
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185 | lo,hi = _rescale(lo,hi,step,bal=ydata,scale=ax.get_yscale()) |
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186 | ax.set_ylim((lo,hi)) |
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187 | |
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188 | self.canvas.draw_idle() |
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189 | |
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190 | |
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191 | def returnTrans(self): |
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192 | """ |
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193 | Return values and labels used by Fit Dialog |
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194 | """ |
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195 | return self.xLabel,self.yLabel, self.Avalue, self.Bvalue,\ |
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196 | self.ErrAvalue,self.ErrBvalue,self.Chivalue |
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197 | |
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198 | def setTrans(self,xtrans,ytrans): |
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199 | """ |
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200 | @param xtrans: set x transformation on Property dialog |
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201 | @param ytrans: set y transformation on Property dialog |
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202 | """ |
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203 | self.prevXtrans =xtrans |
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204 | self.prevYtrans =ytrans |
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205 | |
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206 | def onFitting(self, event): |
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207 | """ |
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208 | when clicking on linear Fit on context menu , display Fitting Dialog |
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209 | """ |
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210 | list =[] |
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211 | list = self.graph.returnPlottable() |
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212 | from fitDialog import LinearFit |
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213 | |
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214 | if len(list.keys())>0: |
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215 | first_item = list.keys()[0] |
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216 | dlg = LinearFit( None, first_item, self.onFitDisplay,self.returnTrans, -1, 'Fitting') |
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217 | |
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218 | if (self.xmin !=0.0 )and ( self.xmax !=0.0)\ |
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219 | and(self.xminView !=0.0 )and ( self.xmaxView !=0.0): |
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220 | dlg.setFitRange(self.xminView,self.xmaxView,self.xmin,self.xmax) |
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221 | dlg.ShowModal() |
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222 | |
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223 | def _onProperties(self, event): |
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224 | """ |
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225 | when clicking on Properties on context menu ,The Property dialog is displayed |
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226 | The user selects a transformation for x or y value and a new plot is displayed |
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227 | """ |
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228 | list =[] |
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229 | list = self.graph.returnPlottable() |
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230 | if len(list.keys())>0: |
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231 | first_item = list.keys()[0] |
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232 | if first_item.x !=[]: |
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233 | from PropertyDialog import Properties |
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234 | dial = Properties(self, -1, 'Properties') |
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235 | dial.setValues( self.prevXtrans, self.prevYtrans,self.viewModel ) |
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236 | if dial.ShowModal() == wx.ID_OK: |
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237 | self.xLabel, self.yLabel,self.viewModel = dial.getValues() |
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238 | if self.viewModel =="Guinier lny vs x^(2)": |
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239 | self.xLabel="x^(2)" |
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240 | self.yLabel="ln(y)" |
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241 | self.viewModel = "--" |
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242 | dial.setValues( self.xLabel, self.yLabel,self.viewModel ) |
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243 | self._onEVT_FUNC_PROPERTY() |
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244 | dial.Destroy() |
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245 | |
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246 | |
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247 | def set_yscale(self, scale='linear'): |
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248 | """ |
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249 | Set the scale on Y-axis |
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250 | @param scale: the scale of y-axis |
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251 | """ |
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252 | self.subplot.set_yscale(scale) |
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253 | self.yscale = scale |
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254 | |
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255 | def get_yscale(self): |
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256 | """ |
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257 | @return: Y-axis scale |
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258 | """ |
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259 | return self.yscale |
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260 | |
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261 | def set_xscale(self, scale='linear'): |
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262 | """ |
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263 | Set the scale on x-axis |
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264 | @param scale: the scale of x-axis |
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265 | """ |
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266 | self.subplot.set_xscale(scale) |
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267 | self.xscale = scale |
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268 | |
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269 | def get_xscale(self): |
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270 | """ |
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271 | @return: x-axis scale |
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272 | """ |
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273 | return self.xscale |
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274 | |
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275 | def SetColor(self, rgbtuple): |
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276 | """Set figure and canvas colours to be the same""" |
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277 | if not rgbtuple: |
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278 | rgbtuple = wx.SystemSettings.GetColour(wx.SYS_COLOUR_BTNFACE).Get() |
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279 | col = [c/255.0 for c in rgbtuple] |
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280 | self.figure.set_facecolor(col) |
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281 | self.figure.set_edgecolor(col) |
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282 | self.canvas.SetBackgroundColour(wx.Colour(*rgbtuple)) |
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283 | |
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284 | def _onSize(self, event): |
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285 | self._resizeflag = True |
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286 | |
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287 | def _onIdle(self, evt): |
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288 | if self._resizeflag: |
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289 | self._resizeflag = False |
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290 | self._SetSize() |
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291 | self.draw() |
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292 | |
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293 | def _SetSize(self, pixels = None): |
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294 | """ |
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295 | This method can be called to force the Plot to be a desired size, which defaults to |
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296 | the ClientSize of the panel |
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297 | """ |
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298 | if not pixels: |
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299 | pixels = self.GetClientSize() |
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300 | self.canvas.SetSize(pixels) |
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301 | self.figure.set_size_inches(pixels[0]/self.figure.get_dpi(), |
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302 | pixels[1]/self.figure.get_dpi()) |
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303 | |
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304 | def draw(self): |
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305 | """Where the actual drawing happens""" |
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306 | self.figure.canvas.draw_idle() |
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307 | |
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308 | |
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309 | |
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310 | |
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311 | |
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312 | def onSaveImage(self, evt): |
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313 | #figure.savefig |
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314 | #print "Save image not implemented" |
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315 | path = None |
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316 | dlg = wx.FileDialog(self, "Choose a file", os.getcwd(), "", "*.png", wx.SAVE) |
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317 | if dlg.ShowModal() == wx.ID_OK: |
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318 | path = dlg.GetPath() |
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319 | mypath = os.path.basename(path) |
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320 | print path |
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321 | dlg.Destroy() |
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322 | if not path == None: |
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323 | self.subplot.figure.savefig(path,dpi=300, facecolor='w', edgecolor='w', |
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324 | orentation='portrait', papertype=None, format='png') |
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325 | |
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326 | def onContextMenu(self, event): |
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327 | """ |
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328 | Default context menu for a plot panel |
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329 | """ |
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330 | # Slicer plot popup menu |
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331 | slicerpop = wx.Menu() |
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332 | slicerpop.Append(313,'&Save image', 'Save image as PNG') |
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333 | wx.EVT_MENU(self, 313, self.onSaveImage) |
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334 | |
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335 | slicerpop.Append(316, '&Load 1D data file') |
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336 | wx.EVT_MENU(self, 316, self._onLoad1DData) |
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337 | |
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338 | slicerpop.AppendSeparator() |
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339 | slicerpop.Append(315, '&Properties') |
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340 | wx.EVT_MENU(self, 315, self._onProperties) |
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341 | |
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342 | slicerpop.AppendSeparator() |
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343 | slicerpop.Append(317, '&Linear Fit') |
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344 | wx.EVT_MENU(self, 317, self.onFitting) |
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345 | |
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346 | slicerpop.AppendSeparator() |
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347 | slicerpop.Append(318, '&Reset Graph') |
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348 | wx.EVT_MENU(self, 318, self.onResetGraph) |
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349 | |
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350 | pos = event.GetPosition() |
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351 | pos = self.ScreenToClient(pos) |
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352 | self.PopupMenu(slicerpop, pos) |
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353 | |
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354 | ## The following is plottable functionality |
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355 | |
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356 | |
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357 | def properties(self,prop): |
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358 | """Set some properties of the graph. |
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359 | |
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360 | The set of properties is not yet determined. |
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361 | """ |
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362 | # The particulars of how they are stored and manipulated (e.g., do |
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363 | # we want an inventory internally) is not settled. I've used a |
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364 | # property dictionary for now. |
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365 | # |
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366 | # How these properties interact with a user defined style file is |
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367 | # even less clear. |
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368 | |
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369 | # Properties defined by plot |
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370 | self.subplot.set_xlabel(r"$%s$" % prop["xlabel"]) |
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371 | self.subplot.set_ylabel(r"$%s$" % prop["ylabel"]) |
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372 | self.subplot.set_title(prop["title"]) |
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373 | |
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374 | # Properties defined by user |
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375 | #self.axes.grid(True) |
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376 | |
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377 | def clear(self): |
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378 | """Reset the plot""" |
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379 | |
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380 | # TODO: Redraw is brutal. Render to a backing store and swap in |
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381 | # TODO: rather than redrawing on the fly. |
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382 | self.subplot.clear() |
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383 | self.subplot.hold(True) |
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384 | |
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385 | def render(self): |
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386 | """Commit the plot after all objects are drawn""" |
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387 | # TODO: this is when the backing store should be swapped in. |
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388 | from matplotlib.font_manager import FontProperties |
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389 | self.subplot.legend(prop=FontProperties(size=10)) |
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390 | #self.subplot.legend() |
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391 | pass |
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392 | |
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393 | def xaxis(self,label,units): |
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394 | """xaxis label and units. |
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395 | |
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396 | Axis labels know about units. |
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397 | |
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398 | We need to do this so that we can detect when axes are not |
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399 | commesurate. Currently this is ignored other than for formatting |
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400 | purposes. |
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401 | """ |
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402 | if units != "": label = label + " (" + units + ")" |
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403 | self.subplot.set_xlabel(label) |
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404 | pass |
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405 | |
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406 | def yaxis(self,label,units): |
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407 | """yaxis label and units.""" |
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408 | if units != "": label = label + " (" + units + ")" |
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409 | self.subplot.set_ylabel(label) |
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410 | pass |
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411 | |
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412 | def _connect_to_xlim(self,callback): |
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413 | """Bind the xlim change notification to the callback""" |
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414 | def process_xlim(axes): |
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415 | lo,hi = subplot.get_xlim() |
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416 | callback(lo,hi) |
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417 | self.subplot.callbacks.connect('xlim_changed',process_xlim) |
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418 | |
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419 | #def connect(self,trigger,callback): |
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420 | # print "PlotPanel.connect???" |
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421 | # if trigger == 'xlim': self._connect_to_xlim(callback) |
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422 | |
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423 | def points(self,x,y,dx=None,dy=None,color=0,symbol=0,label=None): |
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424 | """Draw markers with error bars""" |
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425 | self.subplot.set_yscale('linear') |
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426 | self.subplot.set_xscale('linear') |
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427 | # Convert tuple (lo,hi) to array [(x-lo),(hi-x)] |
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428 | if dx != None and type(dx) == type(()): |
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429 | dx = nx.vstack((x-dx[0],dx[1]-x)).transpose() |
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430 | if dy != None and type(dy) == type(()): |
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431 | dy = nx.vstack((y-dy[0],dy[1]-y)).transpose() |
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432 | |
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433 | if dx==None and dy==None: |
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434 | h = self.subplot.plot(x,y,color=self._color(color), |
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435 | marker=self._symbol(symbol),linestyle='',label=label) |
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436 | else: |
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437 | self.subplot.errorbar(x, y, yerr=dy, xerr=None, |
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438 | ecolor=self._color(color), capsize=2,linestyle='', barsabove=False, |
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439 | marker=self._symbol(symbol), |
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440 | lolims=False, uplims=False, |
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441 | xlolims=False, xuplims=False,label=label) |
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442 | |
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443 | self.subplot.set_yscale(self.yscale) |
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444 | self.subplot.set_xscale(self.xscale) |
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445 | |
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446 | def curve(self,x,y,dy=None,color=0,symbol=0,label=None): |
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447 | """Draw a line on a graph, possibly with confidence intervals.""" |
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448 | c = self._color(color) |
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449 | self.subplot.set_yscale('linear') |
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450 | self.subplot.set_xscale('linear') |
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451 | |
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452 | hlist = self.subplot.plot(x,y,color=c,marker='',linestyle='-',label=label) |
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453 | |
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454 | self.subplot.set_yscale(self.yscale) |
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455 | self.subplot.set_xscale(self.xscale) |
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456 | |
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457 | def _color(self,c): |
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458 | """Return a particular colour""" |
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459 | return self.colorlist[c%len(self.colorlist)] |
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460 | |
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461 | def _symbol(self,s): |
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462 | """Return a particular symbol""" |
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463 | return self.symbollist[s%len(self.symbollist)] |
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464 | |
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465 | def _onEVT_FUNC_PROPERTY(self): |
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466 | """ |
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467 | Receive the x and y transformation from myDialog,Transforms x and y in View |
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468 | and set the scale |
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469 | """ |
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470 | list =[] |
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471 | list = self.graph.returnPlottable() |
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472 | self.fit_result.x =[] |
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473 | self.fit_result.y =[] |
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474 | self.fit_result.dx=None |
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475 | self.fit_result.dy=None |
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476 | |
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477 | for item in list: |
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478 | item.setLabel(self.xLabel,self.yLabel) |
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479 | if ( self.xLabel=="x" ): |
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480 | item.transformX(transform.toX,transform.errToX) |
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481 | self.set_xscale("linear") |
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482 | name, units = item.get_xaxis() |
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483 | self.graph.xaxis("%s" % name, "%s^{-1}" % units) |
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484 | |
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485 | if ( self.xLabel=="x^(2)" ): |
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486 | item.transformX(transform.toX2,transform.errToX2) |
---|
487 | self.set_xscale('linear') |
---|
488 | name, units = item.get_xaxis() |
---|
489 | self.graph.xaxis("%s^{2}" % name, "%s^{-2}" % units) |
---|
490 | |
---|
491 | if (self.xLabel=="log10(x)" ): |
---|
492 | item.transformX(transform.toX,transform.errToX) |
---|
493 | self.set_xscale("log") |
---|
494 | name, units = item.get_xaxis() |
---|
495 | self.graph.xaxis("\log_{10}\ \ %s" % name, "%s^{-1}" % units) |
---|
496 | |
---|
497 | if ( self.yLabel=="ln(y)" ): |
---|
498 | item.transformY(transform.toLogX,transform.errToLogX) |
---|
499 | self.set_yscale("linear") |
---|
500 | name, units = item.get_yaxis() |
---|
501 | self.graph.yaxis("log\ \ %s" % name, "%s^{-1}" % units) |
---|
502 | |
---|
503 | if ( self.yLabel=="y" ): |
---|
504 | item.transformY(transform.toX,transform.errToX) |
---|
505 | self.set_yscale("linear") |
---|
506 | name, units = item.get_yaxis() |
---|
507 | self.graph.yaxis("%s" % name, "%s^{-1}" % units) |
---|
508 | |
---|
509 | if ( self.yLabel=="log10(y)" ): |
---|
510 | item.transformY(transform.toX,transform.errToX) |
---|
511 | self.set_yscale("log") |
---|
512 | name, units = item.get_yaxis() |
---|
513 | self.graph.yaxis("\log_{10}\ \ %s" % name, "%s^{-1}" % units) |
---|
514 | |
---|
515 | if ( self.yLabel=="y^(2)" ): |
---|
516 | item.transformY( transform.toX2,transform.errToX2 ) |
---|
517 | self.set_yscale("linear") |
---|
518 | name, units = item.get_yaxis() |
---|
519 | self.graph.yaxis("%s^{2}" % name, "%s^{-2}" % units) |
---|
520 | |
---|
521 | if ( self.yLabel =="1/y"): |
---|
522 | item.transformY(transform.toOneOverX,transform.errOneOverX ) |
---|
523 | self.set_yscale("linear") |
---|
524 | name, units = item.get_yaxis() |
---|
525 | self.graph.yaxis("%s" % name, "\ \%s" % units) |
---|
526 | |
---|
527 | if ( self.yLabel =="1/sqrt(y)" ): |
---|
528 | item.transformY(transform.toOneOverSqrtX,transform.errOneOverSqrtX ) |
---|
529 | self.set_yscale("linear") |
---|
530 | name, units = item.get_yaxis() |
---|
531 | self.graph.yaxis("\sqrt{%s}" %name, "%s" % units) |
---|
532 | |
---|
533 | if ( self.yLabel =="ln(y*x)"): |
---|
534 | item.transformY( transform.toLogXY,transform.errToLogXY) |
---|
535 | self.set_yscale("linear") |
---|
536 | yname, yunits = item.get_yaxis() |
---|
537 | xname, xunits = item.get_xaxis() |
---|
538 | self.graph.yaxis("log\ %s %s" % (yname,xname), "%s^{-1}%s^{-1}" % (yunits,xunits)) |
---|
539 | |
---|
540 | if ( self.yLabel =="ln(y*x^(2))"): |
---|
541 | item.transformY( transform.toLogYX2,transform.errToLogYX2) |
---|
542 | self.set_yscale("linear") |
---|
543 | yname, yunits = item.get_yaxis() |
---|
544 | xname, xunits = item.get_xaxis() |
---|
545 | self.graph.yaxis("Log %s%s^{2}" % (yname,xname), "%s^{-1}%s^{-2}" % (yunits,xunits)) |
---|
546 | |
---|
547 | if ( self.yLabel =="ln(y*x^(4))"): |
---|
548 | item.transformY(transform.toLogYX4,transform.errToLogYX4) |
---|
549 | self.set_yscale("linear") |
---|
550 | yname, yunits = item.get_yaxis() |
---|
551 | xname, xunits = item.get_xaxis() |
---|
552 | self.graph.yaxis("Log %s%s^{4}" % (yname,xname), "%s^{-1}%s^{-4}" % (yunits,xunits)) |
---|
553 | |
---|
554 | if ( self.viewModel == "Guinier lny vs x^(2)"): |
---|
555 | |
---|
556 | item.transformX(transform.toX2,transform.errToX2) |
---|
557 | self.set_xscale('linear') |
---|
558 | name, units = item.get_xaxis() |
---|
559 | self.graph.xaxis("%s^{2}" % name, "%s^{-2}" % units) |
---|
560 | |
---|
561 | item.transformY(transform.toLogX,transform.errToLogX ) |
---|
562 | self.set_yscale("linear") |
---|
563 | name, units = item.get_yaxis() |
---|
564 | self.graph.yaxis("$Log %s$" % name, "%s^{-1}" % units) |
---|
565 | |
---|
566 | item.transformView() |
---|
567 | |
---|
568 | #item.name = self.yLabel+" vs " +self.xLabel |
---|
569 | self.resetFitView() |
---|
570 | self.prevXtrans = self.xLabel |
---|
571 | self.prevYtrans = self.yLabel |
---|
572 | self.graph.render(self) |
---|
573 | self.subplot.figure.canvas.draw_idle() |
---|
574 | |
---|
575 | def onFitDisplay(self, tempx,tempy,xminView,xmaxView,xmin,xmax,func): |
---|
576 | """ |
---|
577 | Add a new plottable into the graph .In this case this plottable will be used |
---|
578 | to fit some data |
---|
579 | @param tempx: The x data of fit line |
---|
580 | @param tempy: The y data of fit line |
---|
581 | @param xminView: the lower bound of fitting range |
---|
582 | @param xminView: the upper bound of fitting range |
---|
583 | @param xmin: the lowest value of data to fit to the line |
---|
584 | @param xmax: the highest value of data to fit to the line |
---|
585 | """ |
---|
586 | # Saving value to redisplay in Fit Dialog when it is opened again |
---|
587 | self.Avalue,self.Bvalue,self.ErrAvalue,self.ErrBvalue,self.Chivalue=func |
---|
588 | self.xminView=xminView |
---|
589 | self.xmaxView=xmaxView |
---|
590 | self.xmin= xmin |
---|
591 | self.xmax= xmax |
---|
592 | #In case need to change the range of data plotted |
---|
593 | list =[] |
---|
594 | list = self.graph.returnPlottable() |
---|
595 | for item in list: |
---|
596 | #item.onFitRange(xminView,xmaxView) |
---|
597 | item.onFitRange(None,None) |
---|
598 | |
---|
599 | # Create new data plottable with result |
---|
600 | self.fit_result.x =[] |
---|
601 | self.fit_result.y =[] |
---|
602 | self.fit_result.x =tempx |
---|
603 | self.fit_result.y =tempy |
---|
604 | self.fit_result.dx=None |
---|
605 | self.fit_result.dy=None |
---|
606 | #Load the view with the new values |
---|
607 | self.fit_result.reset_view() |
---|
608 | # Add the new plottable to the graph |
---|
609 | self.graph.add(self.fit_result) |
---|
610 | self.graph.render(self) |
---|
611 | self.subplot.figure.canvas.draw_idle() |
---|
612 | |
---|
613 | |
---|
614 | def onResetGraph(self,event): |
---|
615 | """ |
---|
616 | Reset the graph by plotting the full range of data |
---|
617 | """ |
---|
618 | list =[] |
---|
619 | list = self.graph.returnPlottable() |
---|
620 | for item in list: |
---|
621 | item.onReset() |
---|
622 | self.graph.render(self) |
---|
623 | self.subplot.figure.canvas.draw_idle() |
---|
624 | |
---|
625 | class NoRepaintCanvas(FigureCanvasWxAgg): |
---|
626 | """We subclass FigureCanvasWxAgg, overriding the _onPaint method, so that |
---|
627 | the draw method is only called for the first two paint events. After that, |
---|
628 | the canvas will only be redrawn when it is resized. |
---|
629 | """ |
---|
630 | def __init__(self, *args, **kwargs): |
---|
631 | FigureCanvasWxAgg.__init__(self, *args, **kwargs) |
---|
632 | self._drawn = 0 |
---|
633 | |
---|
634 | def _onPaint(self, evt): |
---|
635 | """ |
---|
636 | Called when wxPaintEvt is generated |
---|
637 | """ |
---|
638 | if not self._isRealized: |
---|
639 | self.realize() |
---|
640 | if self._drawn < 2: |
---|
641 | self.draw(repaint = False) |
---|
642 | self._drawn += 1 |
---|
643 | self.gui_repaint(drawDC=wx.PaintDC(self)) |
---|
644 | |
---|