def _createPlot(self, title, xTitle, yTitle, fnOutput, mdLabelX, mdLabelY, color='g', figure=None): xplotter = XmippPlotter(figure=figure) xplotter.plot_title_fontsize = 11 ax = xplotter.createSubPlot(title, xTitle, yTitle, 1, 1) ax.set_yscale('log') ax.set_xscale('log') #plot noise and related errorbar fnOutputN = self.protocol._defineResultsNoiseName() md = xmipp.MetaData(fnOutputN) xValueN = md.getColumnValues(xmipp.MDL_COUNT) yValueN = md.getColumnValues(xmipp.MDL_AVG) plt.plot(xValueN, yValueN, '--', color='r', label='Aligned gaussian noise') # putting error bar md = xmipp.MetaData(fnOutputN) yErrN = md.getColumnValues(xmipp.MDL_STDDEV) xValueNe = md.getColumnValues(xmipp.MDL_COUNT) yValueNe = md.getColumnValues(xmipp.MDL_AVG) plt.errorbar(xValueNe, yValueNe, yErrN, fmt='o', color='k') #plot real data-set fnOutput = self.protocol._defineResultsName() md = xmipp.MetaData(fnOutput) xValue = md.getColumnValues(xmipp.MDL_COUNT) yValue = md.getColumnValues(xmipp.MDL_AVG) plt.plot(xValue, yValue, color='g', label='Aligned particles') # putting error bar md = xmipp.MetaData(fnOutput) yErr = md.getColumnValues(xmipp.MDL_STDDEV) xValue = md.getColumnValues(xmipp.MDL_COUNT) yValue = md.getColumnValues(xmipp.MDL_AVG) plt.errorbar(xValue, yValue, yErr, fmt='o') plt.legend(loc='upper right', fontsize=11) return xplotter
def _createPlot(self, figure=None): xplotter = XmippPlotter(figure=figure) xplotter.plot_title_fontsize = 11 title = 'Validation 3D Reconstruction (Overfitting)' xTitle = '# Particles' yTitle = 'Resolution in A' ax = xplotter.createSubPlot(title, xTitle, yTitle, 1, 1) ax.set_yscale('log') ax.set_xscale('log') # plot real data-set fnOutput = self.protocol._defineResultsTxt() if exists(fnOutput): fileValues = open(fnOutput, 'r') xVal = [] yVal = [] yErr = [] for line in fileValues: values = line.split() xVal.append(float(values[0])) yVal.append(float(values[1])) yErr.append(float(values[2])) plt.plot(xVal, yVal, color='g', label='Aligned particles') plt.errorbar(xVal, yVal, yErr, fmt='o') plt.legend(loc='upper right', fontsize=11) # plot noise and related errorbar fnOutputN = self.protocol._defineResultsNoiseTxt() if exists(fnOutputN): fileNoise = open(fnOutputN, 'r') yValN = [] yErrN = [] for line in fileNoise: values = line.split() yValN.append(float(values[1])) yErrN.append(float(values[2])) plt.plot(xVal, yValN, '--', color='r', label='Aligned gaussian noise') plt.errorbar(xVal, yValN, yErrN, fmt='o', color='k') return xplotter
def _createPlotInv(self, figure=None): xplotter = XmippPlotter(figure=figure) xplotter.plot_title_fontsize = 11 title = 'Validation 3D Reconstruction (Overfitting)' xTitle = 'log10(# Particles)' yTitle = '1/Resolution^2 in 1/A^2' ax = xplotter.createSubPlot(title, xTitle, yTitle, 1, 1) # plot real data-set fnOutput = self.protocol._defineResultsTxt() if exists(fnOutput): fileValues = open(fnOutput, 'r') xValInv = [] yValInv = [] yErrInv = [] for line in fileValues: values = line.split() xValInv.append(log10(float(values[0]))) yValInv.append(float(values[3])) yErrInv.append(float(values[4])) plt.plot(xValInv, yValInv, color='g', label='Aligned particles') plt.errorbar(xValInv, yValInv, yErrInv, fmt='o') plt.legend(loc='upper right', fontsize=11) # plot noise and related errorbar fnOutputN = self.protocol._defineResultsNoiseTxt() if exists(fnOutputN): fileNoise = open(fnOutputN, 'r') yValNInv = [] yErrNInv = [] for line in fileNoise: values = line.split() yValNInv.append(float(values[3])) yErrNInv.append(float(values[4])) plt.plot(xValInv, yValNInv, '--', color='r', label='Aligned gaussian noise') plt.errorbar(xValInv, yValNInv, yErrNInv, fmt='o', color='k') return xplotter
def _createPlot(self, title, xTitle, yTitle, fnOutput, mdLabelX, mdLabelY, color = 'g', figure=None): xplotter = XmippPlotter(figure=figure) xplotter.plot_title_fontsize = 11 ax=xplotter.createSubPlot(title, xTitle, yTitle, 1, 1) ax.set_yscale('log') ax.set_xscale('log') #plot noise and related errorbar fnOutputN = self.protocol._defineResultsNoiseName() md = xmipp.MetaData(fnOutputN) xValueN = md.getColumnValues(xmipp.MDL_COUNT) yValueN = md.getColumnValues(xmipp.MDL_AVG) plt.plot(xValueN, yValueN, '--', color='r', label='Aligned gaussian noise') # putting error bar md = xmipp.MetaData(fnOutputN) yErrN = md.getColumnValues(xmipp.MDL_STDDEV) xValueNe = md.getColumnValues(xmipp.MDL_COUNT) yValueNe = md.getColumnValues(xmipp.MDL_AVG) plt.errorbar(xValueNe, yValueNe, yErrN, fmt='o', color='k') #plot real data-set fnOutput = self.protocol._defineResultsName() md = xmipp.MetaData(fnOutput) xValue = md.getColumnValues(xmipp.MDL_COUNT) yValue = md.getColumnValues(xmipp.MDL_AVG) plt.plot(xValue, yValue, color='g', label='Aligned particles') # putting error bar md = xmipp.MetaData(fnOutput) yErr = md.getColumnValues(xmipp.MDL_STDDEV) xValue = md.getColumnValues(xmipp.MDL_COUNT) yValue = md.getColumnValues(xmipp.MDL_AVG) plt.errorbar(xValue, yValue, yErr, fmt='o') plt.legend(loc='upper right' , fontsize = 11) return xplotter