def createPlot2D(self, volPrefix, md):
import xmipp
figurePath = self._getExtraPath(volPrefix +
'softAlignmentValidation2D.png')
figureSize = (8, 6)
#alignedMovie = mic.alignMetaData
plotter = Plotter(*figureSize)
figure = plotter.getFigure()
ax = figure.add_subplot(111)
ax.grid()
ax.set_title('Soft alignment validation map')
ax.set_xlabel('Angular Precision')
ax.set_ylabel('Angular Accuracy')
for objId in md:
x = md.getValue(xmipp.MDL_SCORE_BY_ALIGNABILITY_PRECISION, objId)
y = md.getValue(xmipp.MDL_SCORE_BY_ALIGNABILITY_ACCURACY, objId)
ax.plot(x, y, 'r.', markersize=1)
ax.grid(True, which='both')
ax.autoscale_view(True, True, True)
plotter.savefig(figurePath)
plotter.show()
return plotter
def movieCreatePlot(mic, saveFig):
import xmipp
meanX = []
meanY = []
figureSize = (8, 6)
#alignedMovie = mic.alignMetaData
md = xmipp.MetaData(mic.alignMetaData)
plotter = Plotter(*figureSize)
figure = plotter.getFigure()
preX = 0.0
preY = 0.0
meanX.append(0.0)
meanY.append(0.0)
ax = figure.add_subplot(111)
ax.grid()
ax.set_title('Cartesian representation')
ax.set_xlabel('Drift x (pixels)')
ax.set_ylabel('Drift y (pixels)')
ax.plot(0, 0, 'yo-')
for objId in md:
preX += md.getValue(xmipp.MDL_OPTICALFLOW_MEANX, objId)
preY += md.getValue(xmipp.MDL_OPTICALFLOW_MEANY, objId)
meanX.append(preX)
meanY.append(preY)
ax.plot(preX, preY, 'yo-')
ax.text(preX-0.02, preY+0.01, str(objId+1))
ax.plot(np.asarray(meanX), np.asarray(meanY))
if saveFig:
plotter.savefig(mic.plotCart)
return plotter
def movieCreatePlot(mic, saveFig):
import xmipp
meanX = []
meanY = []
figureSize = (8, 6)
#alignedMovie = mic.alignMetaData
md = xmipp.MetaData(mic.alignMetaData)
plotter = Plotter(*figureSize)
figure = plotter.getFigure()
preX = 0.0
preY = 0.0
meanX.append(0.0)
meanY.append(0.0)
ax = figure.add_subplot(111)
ax.grid()
ax.set_title('Cartesian representation')
ax.set_xlabel('Drift x (pixels)')
ax.set_ylabel('Drift y (pixels)')
ax.plot(0, 0, 'yo-')
for objId in md:
preX += md.getValue(xmipp.MDL_OPTICALFLOW_MEANX, objId)
preY += md.getValue(xmipp.MDL_OPTICALFLOW_MEANY, objId)
meanX.append(preX)
meanY.append(preY)
ax.plot(preX, preY, 'yo-')
ax.text(preX - 0.02, preY + 0.01, str(objId + 1))
ax.plot(np.asarray(meanX), np.asarray(meanY))
if saveFig:
plotter.savefig(mic.plotCart)
return plotter
def createPlot2D(self,volPrefix,md):
import xmipp
figurePath = self._getExtraPath(volPrefix + 'softAlignmentValidation2D.png')
figureSize = (8, 6)
#alignedMovie = mic.alignMetaData
plotter = Plotter(*figureSize)
figure = plotter.getFigure()
ax = figure.add_subplot(111)
ax.grid()
ax.set_title('Soft alignment validation map')
ax.set_xlabel('Angular Precision')
ax.set_ylabel('Angular Accuracy')
for objId in md:
x = md.getValue(xmipp.MDL_SCORE_BY_ALIGNABILITY_PRECISION, objId)
y = md.getValue(xmipp.MDL_SCORE_BY_ALIGNABILITY_ACCURACY, objId)
ax.plot(x, y, 'r.',markersize=1)
ax.grid(True, which='both')
ax.autoscale_view(True,True,True)
plotter.savefig(figurePath)
plotter.show()
return plotter
def _createSpectraPlot(self, label, array, index, objId=None):
if objId is None:
objId = index
# Number of harmonics calculated
plotter = Plotter()
a = plotter.createSubPlot('Spectrum for %s %d' % (label, objId),
xlabel='Harmonics', ylabel='Energy')
n = self.spectraHighHarmonic.get() - self.spectraLowHarmonic.get() + 1
i1 = (index-1) * n
i2 = i1 + n
xdata = range(self.spectraLowHarmonic.get(), self.spectraHighHarmonic.get()+1)
ydata = array[i1:i2]
a.plot(xdata, ydata)
plotFile = join(self.plotsDir, 'spectra_%s%06d.png' % (label, objId))
plotter.savefig(plotFile)
return plotFile
def _createSpectraPlot(self, label, array, index, objId=None):
if objId is None:
objId = index
# Number of harmonics calculated
plotter = Plotter()
a = plotter.createSubPlot('Spectrum for %s %d' % (label, objId),
xlabel='Harmonics', ylabel='Energy')
n = self.spectraHighHarmonic.get() - self.spectraLowHarmonic.get() + 1
i1 = (index-1) * n
i2 = i1 + n
xdata = range(self.spectraLowHarmonic.get(), self.spectraHighHarmonic.get()+1)
ydata = array[i1:i2]
a.plot(xdata, ydata)
plotFile = join(self.plotsDir, 'spectra_%s%06d.png' % (label, objId))
plotter.savefig(plotFile)
return plotFile
def movieCreatePlot(plotType, movie, saveFig):
import xmipp
meanX = []
meanY = []
stdX = []
stdY = []
colors = []
gr = 255.0
# if movie is None:
# return [self.errorMessage("Invalid movie id *%d*" % movieId,
# title="Invalid input")]
alignedMovie = movie.alignMetaData
md = xmipp.MetaData(alignedMovie)
colorDist = 255 / md.size()
cartPosition = None
polarPosition = None
colorBarPosition = 122
figureSize = (8, 6)
if plotType == PLOT_CART:
cartPosition = 121
elif plotType == PLOT_POLAR:
polarPosition = 121
elif plotType == PLOT_POLARCART:
cartPosition = 132
polarPosition = 131
colorBarPosition = 133
plotter = Plotter(*figureSize)
figure = plotter.getFigure()
# Plot the color bar
ax = figure.add_subplot(colorBarPosition, aspect="equal", xlim=[0, 6])
ax.set_xticklabels([])
ax.set_yticklabels([])
colorBarX = np.array([1, 1])
colorBarY = np.array([2, 4])
# Read the shifts information from the Metadata
for objId in md:
meanX.append(md.getValue(xmipp.MDL_OPTICALFLOW_MEANX, objId))
meanY.append(md.getValue(xmipp.MDL_OPTICALFLOW_MEANY, objId))
stdX.append(md.getValue(xmipp.MDL_OPTICALFLOW_STDY, objId))
stdY.append(md.getValue(xmipp.MDL_OPTICALFLOW_STDY, objId))
colors.append((1, gr / 255.0, 0))
ax.plot(colorBarX, colorBarY, c=(1, gr / 255.0, 0), linewidth=10)
ax.text(2, np.mean(colorBarY), str(objId) + "-" + str(objId + 1))
colorBarY += 2
gr -= colorDist
area = (np.sqrt(np.power(np.asarray(stdX), 2) + np.power(np.asarray(stdY), 2))) * 700
# Plot in polar if needed
if polarPosition:
r = np.sqrt(np.power(np.asarray(meanX), 2) + np.power(np.asarray(meanY), 2))
theta = np.arctan2(meanY, meanX) * 180 / np.pi
ax = figure.add_subplot(polarPosition, projection="polar")
ax.set_title("Polar representation")
c = ax.scatter(theta, r, c=colors, s=area, cmap=plt.cm.hsv)
c.set_alpha(0.75)
ax.plot(theta, r, "-^")
if saveFig:
plotter.savefig(movie.plotPolar)
# Plot in cartesian if needed
if cartPosition:
ax = figure.add_subplot(cartPosition)
ax.grid()
ax.grid()
ax.set_title("Cartesian representation")
c = ax.scatter(np.asarray(meanX), np.asarray(meanY), c=colors, s=area, cmap=plt.cm.hsv)
c.set_alpha(0.75)
ax.plot(np.asarray(meanX), np.asarray(meanY), "-^")
if saveFig:
plotter.savefig(movie.plotCart)
return plotter
