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