def _visualizeMatrix(self, e=None):
views = []
self.isComputed()
inputCTFs = self.protocol.inputCTFs
shape = len(inputCTFs) #number of methods
_matrix = np.zeros(shape=(shape, shape))
ticksLablesMajorX = [None] * shape
ticksLablesMajorY = [None] * shape
for ctf in self.setOfCTFsConst:
m1 = int(self.extract(ctf.getAttributeValue('method1'))) - 1
m2 = int(self.extract(ctf.getAttributeValue('method2'))) - 1
_matrix[m1][m2] += 1
_matrix[m2][m1] = _matrix[m1][m2]
#rather inefficient but since outputCTF is not ordered...
if ticksLablesMajorX[m1] is None:
ticksLablesMajorX[m1] = "(%d)" % (m1 + 1)
ticksLablesMajorY[m1] = ctf.getAttributeValue('method1')
if ticksLablesMajorX[m2] is None:
ticksLablesMajorX[m2] = "(%d)" % (m2 + 1)
ticksLablesMajorY[m2] = ctf.getAttributeValue('method2')
plotter = EmPlotter(fontsize=14)
resolution = self.resolutionThreshold.get()
plotter.createSubPlot(
"# micrographs with resolution (A)\n lower than %d" % (resolution),
"Method #", "Method")
# plotter.plotMatrix(_matrix,cmap='seismic_r'
plotter.plotMatrix(_matrix,
cmap='Greens',
xticksLablesMajor=ticksLablesMajorX,
rotationX=0,
yticksLablesMajor=ticksLablesMajorY)
views.append(plotter)
return views
def visualize(self, obj, **kwargs):
model = PKPDAllometricScale()
model.load(obj.fnScale.get())
x = np.log10(np.asarray(model.X))
xlabel = "%s [%s]" % (model.predictor, model.predictorUnits)
for varName, varUnits in model.scaled_vars:
plotter = EmPlotter()
y = np.log10(np.asarray(model.Y[varName]))
ylabel = "%s [%s]" % (varName, varUnits)
ax = plotter.createSubPlot(varName, xlabel, ylabel)
ax.plot(x, y, '.', label='Species')
ax.plot(x, np.log10(model.models[varName][0])+x*model.models[varName][1],'r', label='R2=%f'%model.qualifiers[varName][0])
leg = ax.legend(loc='upper right')
if leg:
leg.draggable()
plotter.show()
for varName, varUnits in model.averaged_vars:
plotter = EmPlotter()
y = np.asarray(model.Y[varName])
ylabel = "%s [%s]" % (varName, varUnits)
ax = plotter.createSubPlot("Scatter Plot", xlabel, ylabel)
ax.plot(x, y, '.', label='Species')
ax.plot(x, model.models[varName][0]*np.ones(x.shape),'r',label='Std=%f'%model.qualifiers[varName][0])
leg = ax.legend(loc='upper right')
if leg:
leg.draggable()
plotter.show()
def _visualizeMatrix(self,e=None):
views = []
self.isComputed()
inputCTFs=self.protocol.inputCTFs
shape=len(inputCTFs)#number of methods
_matrix = np.zeros(shape=(shape, shape))
ticksLablesMajorX=[None] * shape
ticksLablesMajorY=[None] * shape
for ctf in self.setOfCTFsConst:
m1 = int(self.extract(ctf.getAttributeValue('method1')))-1
m2 = int(self.extract(ctf.getAttributeValue('method2')))-1
_matrix[m1][m2] += 1
_matrix[m2][m1] = _matrix[m1][m2]
#rather inefficient but since outputCTF is not ordered...
if ticksLablesMajorX[m1] is None:
ticksLablesMajorX[m1] = "(%d)"%(m1+1)
ticksLablesMajorY[m1] = ctf.getAttributeValue('method1')
if ticksLablesMajorX[m2] is None:
ticksLablesMajorX[m2] = "(%d)"%(m2+1)
ticksLablesMajorY[m2] = ctf.getAttributeValue('method2')
plotter = EmPlotter(fontsize=14)
resolution=self.resolutionThreshold.get()
plotter.createSubPlot("# micrographs with resolution (A)\n lower than %d"%(resolution),
"Method #", "Method")
# plotter.plotMatrix(_matrix,cmap='seismic_r'
plotter.plotMatrix(_matrix,cmap='Greens'
, xticksLablesMajor=ticksLablesMajorX,rotationX=0
, yticksLablesMajor=ticksLablesMajorY)
views.append(plotter)
return views
def _visualizeHistogram(self, e=None):
views = []
self.isComputed()
numberOfBins = self.visualizeHistogram.get()
numberOfBins = min(numberOfBins, self.setOfCTFsConst.getSize())
plotter = EmPlotter()
plotter.createSubPlot("Resolution Discrepancies histogram",
"Resolution (A)", "# of Comparisons")
resolution = [ctf.resolution.get() for ctf in self.setOfCTFsConst]
plotter.plotHist(resolution, nbins=numberOfBins)
return views.append(plotter)
def _visualizeHistogram(self, e=None):
views = []
self.isComputed()
numberOfBins = self.visualizeHistogram.get()
numberOfBins = min(numberOfBins, self.setOfCTFsConst.getSize())
plotter = EmPlotter()
plotter.createSubPlot("Resolution Discrepancies histogram",
"Resolution (A)", "# of Comparisons")
resolution = [ctf.resolution.get() for ctf in self.setOfCTFsConst]
plotter.plotHist(resolution, nbins=numberOfBins)
return views.append(plotter)
def _visualizeHistogram(self, e=None):
views = []
numberOfBins = self.visualizeHistogram.get()
numberOfBins = min(numberOfBins, self.protocol.outputCTF.getSize())
plotter = EmPlotter()
plotter.createSubPlot("Resolution Discrepancies histogram",
"Resolution (A)", "# of Comparisons")
resolution = [ctf.getResolution() for ctf in self.protocol.outputCTF]
plotter.plotHist(resolution, nbins=numberOfBins)
# ROB: why do I need this show?
plotter.show()
return views.append(plotter)
def _plotHistogram(self, param=None):
imageFile = self.protocol._getFileName(FN_RESOLMAP)
img = ImageHandler().read(imageFile)
imgData = img.getData()
imgList = imgData.flatten()
imgListNoZero = filter(lambda x: x > 0, imgList)
nbins = 30
plotter = EmPlotter(x=1, y=1, mainTitle=" ")
plotter.createSubPlot("Resolution histogram", "Resolution (A)",
"# of Counts")
fig = plotter.plotHist(imgListNoZero, nbins)
return [plotter]
def _plotHistogram(self, param=None):
""" First we parse the MSA plt:
first column: cumulative percent.
second column: iteration number.
"""
iters = []
cumPercents = []
fn = self.protocol.getOutputPlt()
with open(fn) as f:
lines_after_2 = f.readlines()[2:]
for line in lines_after_2:
values = line.split()
cumPercents.append(float(values[0]))
iters.append(int(float(values[1])))
f.close()
width = 0.85
xplotter = EmPlotter()
a = xplotter.createSubPlot(
'Behaviour of sum of eigenvalues during analysis',
'Iteration number', '%')
a.bar(iters, cumPercents, width, color='b')
return [xplotter]
def _showSSNR(self, paramName=None):
nrefs = len(self._refsList)
gridsize = self._getGridSize(nrefs)
xplotter = EmPlotter(x=gridsize[0], y=gridsize[1])
for ref3d in self._refsList:
plot_title = 'Resolution SSNR, for Class %s' % ref3d
a = xplotter.createSubPlot(plot_title,
'Angstroms^-1',
'sqrt(SSNR)',
yformat=False)
legendName = []
for it in self._iterations:
if self.protocol.IS_REFINE:
fn = self.protocol._getFileName('output_vol_par', iter=it)
else:
fn = self.protocol._getFileName('output_vol_par_class',
iter=it,
ref=ref3d)
if exists(fn):
self._plotSSNR(a, fn)
legendName.append('iter %d' % it)
xplotter.showLegend(legendName)
a.grid(True)
return [xplotter]
def _visualize(self, obj, **kwargs):
fnResults = self.protocol._getPath("results.txt")
if exists(fnResults):
X, Y, _, _ = self.protocol.getXYValues(False)
minX = min(X)
maxX = max(X)
step = (maxX - minX) / 50
xValues = np.arange(minX, maxX + step, step)
yValues = self.protocol.evalFunction(xValues)
plotter = EmPlotter()
varNameX = self.protocol.labelX.get()
varNameY = self.protocol.labelY.get()
ax = plotter.createSubPlot(
"Regression Plot", "%s [%s]" %
(varNameX,
strUnit(
self.protocol.experiment.variables[varNameX].units.unit)),
"%s [%s]" %
(varNameY,
strUnit(
self.protocol.experiment.variables[varNameY].units.unit)))
ax.plot(xValues, yValues)
ax.plot(X, Y, 'o')
return [plotter]
else:
return [
self.errorMessage("Result file '%s' not produced yet. " %
fnResults)
]
def createCtfPlot(ctfSet, ctfId):
from pyworkflow.utils.path import removeExt
ctfModel = ctfSet[ctfId]
psdFn = ctfModel.getPsdFile()
fn = removeExt(psdFn) + "_avrot.txt"
gridsize = [1, 1]
xplotter = EmPlotter(x=gridsize[0], y=gridsize[1], windowTitle='CTF Fitting')
plot_title = "CTF Fitting"
a = xplotter.createSubPlot(plot_title, 'pixels^-1', 'CTF', yformat=False)
legendName = []
for i in range(1, 5):
_plotCurve(a, i, fn)
if i == 1:
legendName.append('rotational avg. No Astg')
elif i == 2:
legendName.append('rotational avg.')
elif i == 3:
legendName.append('CTF Fit')
elif i == 4:
legendName.append('Cross Correlation')
elif i == 5:
legendName.append('2sigma cross correlation of noise')
xplotter.showLegend(legendName)
a.grid(True)
xplotter.show()
def _plotFactorMaps(self, param=None):
# Parse the file
fn = self.protocol._getFileName('imcFile')
f = open(fn)
values = f.readline().split()
n = int(values[0]) # Number of images
nf = int(values[1]) # Number of factors
x = self.firstFactor.get()
y = self.secondFactor.get()
xFactors = []
yFactors = []
i = 0
while i < n:
imgFactors = []
while len(imgFactors) < nf:
values = f.readline().split()
imgFactors += [float(v) for v in values]
xFactors.append(imgFactors[x-1])
yFactors.append(imgFactors[y-1])
i += 1
f.close()
# Create the plot
xplotter = EmPlotter(1,1)
a = xplotter.createSubPlot("Factor %d vs %d" % (x, y),
"Factor %d" % x, "Factor %d" % y)
a.plot(xFactors, yFactors, 'o')
return [xplotter]
def visualize(self, obj, **kwargs):
prot = obj
fnProfiles = prot._getPath("profiles.txt")
fh = open(fnProfiles,"r")
state = 0
legends = ['Iliver', 'Iinlet', 'Isys']
for line in fh:
if state==0:
tokens = line.split("::")
title = tokens[1].strip()
I3=[]
state=1
elif state==1:
tokens=line.strip().split()
if len(tokens)==0:
plotter = EmPlotter()
ax = plotter.createSubPlot("Simulation", "t [h]", "[I] [mg/mL]")
t = np.asarray(I3[0],dtype=np.float64)/60
for n in range(1,len(I3)):
y = np.asarray(I3[n],dtype=np.float64)
ax.plot(t, y, label=legends[n-1])
ax.legend()
plotter.show()
state=0
else:
if len(I3)==0:
for n in range(len(tokens)):
I3.append([])
for n in range(len(tokens)):
I3[n].append(tokens[n])
fh.close()
def _visualize(self, obj, **kwargs):
fnResults = self.protocol._getPath("results.txt")
if exists(fnResults):
X, Y, _, _ = self.protocol.getXYValues(False)
minX = min(X)
maxX = max(X)
step = (maxX - minX) / 50
xValues = np.arange(minX, maxX+step, step)
yValues = self.protocol.evalFunction(xValues)
plotter = EmPlotter()
varNameX = self.protocol.labelX.get()
varNameY = self.protocol.labelY.get()
ax = plotter.createSubPlot("Regression Plot",
"%s [%s]"%(varNameX,strUnit(self.protocol.experiment.variables[varNameX].units.unit)),
"%s [%s]"%(varNameY,strUnit(self.protocol.experiment.variables[varNameY].units.unit)))
ax.plot(xValues, yValues)
ax.plot(X, Y, 'o')
return [plotter]
else:
return [self.errorMessage("Result file '%s' not produced yet. "
% fnResults)]
def _showFSC(self, paramName=None):
threshold = self.resolutionThresholdFSC.get()
nrefs = len(self._refsList)
gridsize = self._getGridSize(nrefs)
xplotter = EmPlotter(x=gridsize[0], y=gridsize[1], windowTitle='Resolution FSC')
if self.protocol.IS_REFINE:
plot_title = 'FSC'
a = xplotter.createSubPlot(plot_title, 'Angstroms^-1', 'FSC', yformat=False)
legends = []
show = False
for it in self._iterations:
parFn = self.protocol._getFileName('output_vol_par', iter=it)
if exists(parFn):
show = True
self._plotFSC(a, parFn)
legends.append('iter %d' % it)
xplotter.showLegend(legends)
if show:
if threshold < self.maxFrc:
a.plot([self.minInv, self.maxInv],[threshold, threshold], color='black', linestyle='--')
a.grid(True)
else:
raise Exception("Set a valid iteration to show its FSC")
else:
for ref3d in self._refsList:
plot_title = 'class %s' % ref3d
a = xplotter.createSubPlot(plot_title, 'Angstroms^-1', 'FSC', yformat=False)
legends = []
for it in self._iterations:
parFn = self.protocol._getFileName('output_vol_par_class', iter=it, ref=ref3d)
if exists(parFn):
show = True
self._plotFSC(a, parFn)
legends.append('iter %d' % it)
xplotter.showLegend(legends)
if show:
if threshold < self.maxFrc:
a.plot([self.minInv, self.maxInv],[threshold, threshold], color='black', linestyle='--')
a.grid(True)
else:
raise Exception("Set a valid iteration to show its FSC")
return [xplotter]
def _showFSC(self, paramName=None):
threshold = self.resolutionThresholdFSC.get()
gridsize = self._getGridSize(1)
xplotter = EmPlotter(x=gridsize[0], y=gridsize[1], windowTitle='Resolution FSC')
plot_title = 'FSC'
a = xplotter.createSubPlot(plot_title, 'Angstroms^-1', 'FSC', yformat=False)
legends = []
show = False
for it in self._iterations:
if self.resolutionPlotsFSC.get() == FSC_UNMASK:
fscUnmask = self.protocol._getFileName('fscUnmasked',run=self.protocol._getRun(), iter=it)
if os.path.exists(fscUnmask):
show = True
self._plotFSC(a, fscUnmask)
legends.append('unmasked map iter %d' % it)
xplotter.showLegend(legends)
elif self.resolutionPlotsFSC.get() == FSC_MASK:
fscMask = self.protocol._getFileName('fscMasked',run=self.protocol._getRun(), iter=it)
if os.path.exists(fscMask):
show = True
self._plotFSC(a, fscMask)
legends.append('masked map iter %d' % it)
xplotter.showLegend(legends)
elif self.resolutionPlotsFSC.get() == FSC_MASKTIGHT:
fscMaskTight = self.protocol._getFileName('fscMaskedTight',run=self.protocol._getRun(), iter=it)
if os.path.exists(fscMaskTight):
show = True
self._plotFSC(a, fscMaskTight)
legends.append('masked tight map iter %d' % it)
xplotter.showLegend(legends)
elif self.resolutionPlotsFSC.get() == FSC_ALL:
fscUnmask = self.protocol._getFileName('fscUnmasked',run=self.protocol._getRun(), iter=it)
fscMask = self.protocol._getFileName('fscMasked',run=self.protocol._getRun(), iter=it)
fscMaskTight = self.protocol._getFileName('fscMaskedTight',run=self.protocol._getRun(), iter=it)
if os.path.exists(fscUnmask):
show = True
self._plotFSC(a, fscUnmask)
legends.append('unmasked map iter %d' % it)
self._plotFSC(a, fscMask)
legends.append('masked map iter %d' % it)
self._plotFSC(a, fscMaskTight)
legends.append('masked tight map iter %d' % it)
xplotter.showLegend(legends)
if show:
if threshold < self.maxFrc:
a.plot([self.minInv, self.maxInv],[threshold, threshold], color='black', linestyle='--')
a.grid(True)
else:
raise Exception("Set a valid iteration to show its FSC")
return [xplotter]
def _showFSC(self, paramName=None):
threshold = self.resolutionThresholdFSC.get()
gridsize = self._getGridSize(1)
xplotter = EmPlotter(x=gridsize[0], y=gridsize[1], windowTitle='Resolution FSC')
plot_title = 'FSC'
a = xplotter.createSubPlot(plot_title, 'Angstroms^-1', 'FSC', yformat=False)
legends = []
show = False
for it in self._iterations:
if self.resolutionPlotsFSC.get() == FSC_UNMASK:
fscUnmask = self.protocol._getFileName('fscUnmasked',run=self.protocol._getRun(), iter=it)
if os.path.exists(fscUnmask):
show = True
self._plotFSC(a, fscUnmask)
legends.append('unmasked map iter %d' % it)
xplotter.showLegend(legends)
elif self.resolutionPlotsFSC.get() == FSC_MASK:
fscMask = self.protocol._getFileName('fscMasked',run=self.protocol._getRun(), iter=it)
if os.path.exists(fscMask):
show = True
self._plotFSC(a, fscMask)
legends.append('masked map iter %d' % it)
xplotter.showLegend(legends)
elif self.resolutionPlotsFSC.get() == FSC_MASKTIGHT:
fscMaskTight = self.protocol._getFileName('fscMaskedTight',run=self.protocol._getRun(), iter=it)
if os.path.exists(fscMaskTight):
show = True
self._plotFSC(a, fscMaskTight)
legends.append('masked tight map iter %d' % it)
xplotter.showLegend(legends)
elif self.resolutionPlotsFSC.get() == FSC_ALL:
fscUnmask = self.protocol._getFileName('fscUnmasked',run=self.protocol._getRun(), iter=it)
fscMask = self.protocol._getFileName('fscMasked',run=self.protocol._getRun(), iter=it)
fscMaskTight = self.protocol._getFileName('fscMaskedTight',run=self.protocol._getRun(), iter=it)
if os.path.exists(fscUnmask):
show = True
self._plotFSC(a, fscUnmask)
legends.append('unmasked map iter %d' % it)
self._plotFSC(a, fscMask)
legends.append('masked map iter %d' % it)
self._plotFSC(a, fscMaskTight)
legends.append('masked tight map iter %d' % it)
xplotter.showLegend(legends)
if show:
if threshold < self.maxFrc:
a.plot([self.minInv, self.maxInv],[threshold, threshold], color='black', linestyle='--')
a.grid(True)
else:
raise Exception("Set a valid iteration to show its FSC")
return [xplotter]
def _plot(varLabelX, varLabelY, x, y, yp):
plotter = EmPlotter()
ax = plotter.createSubPlot("Plot", varLabelX, varLabelY)
xValues = _values(x, useLog=self.useTimeLog())
ax.plot(xValues, _values(y), 'x', label="Observations")
idx = np.argsort(xValues)
ypValues = _values(yp)
ax.plot(np.asarray(xValues)[idx], np.asarray(ypValues)[idx], 'g', label="Fit")
leg = ax.legend()
if leg:
leg.draggable()
plotter.show()
def _onPlotClick(self, e=None):
selection = self.tree.selection()
n = len(selection)
if n < 1 or n > 2:
self.showError("Select one or two variables to plot.")
else:
plotter = EmPlotter()
varX = self._variables[selection[0]]
xValues = self.observations[:, varX.index]
def _label(var):
return "%s [%s]" % (var.varName, var.unitStr)
if n == 1:
plotter.createSubPlot("Histogram", _label(varX), "Count")
plotter.plotHist(xValues, 50)
else: # n == 2
varY = self._variables[selection[1]]
yValues = self.observations[:, varY.index]
ax = plotter.createSubPlot("Scatter Plot", _label(varX),
_label(varY))
ax.plot(xValues, yValues, '.')
plotter.show()
def _onPlotClick(self, e=None):
selection = self.tree.selection()
n = len(selection)
if n < 1 or n > 2:
self.showError("Select one or two variables to plot.")
else:
plotter = EmPlotter()
varX = self._variables[selection[0]]
xValues = self.observations[:, varX.index]
def _label(var):
return "%s [%s]" % (var.varName, var.unitStr)
if n == 1:
plotter.createSubPlot("Histogram", _label(varX), "Count")
plotter.plotHist(xValues, 50)
else: # n == 2
varY = self._variables[selection[1]]
yValues = self.observations[:, varY.index]
ax = plotter.createSubPlot("Scatter Plot",
_label(varX), _label(varY))
ax.plot(xValues, yValues, '.')
plotter.show()
def visualize(self, obj, **kwargs):
model = PKPDAllometricScale()
model.load(obj.fnScale.get())
x = np.log10(np.asarray(model.X))
xlabel = "%s [%s]" % (model.predictor, model.predictorUnits)
for varName, varUnits in model.scaled_vars:
plotter = EmPlotter()
y = np.log10(np.asarray(model.Y[varName]))
ylabel = "%s [%s]" % (varName, varUnits)
ax = plotter.createSubPlot(varName, xlabel, ylabel)
ax.plot(x, y, '.', label='Species')
ax.plot(x,
np.log10(model.models[varName][0]) +
x * model.models[varName][1],
'r',
label='R2=%f' % model.qualifiers[varName][0])
leg = ax.legend(loc='upper right')
if leg:
leg.draggable()
plotter.show()
for varName, varUnits in model.averaged_vars:
plotter = EmPlotter()
y = np.asarray(model.Y[varName])
ylabel = "%s [%s]" % (varName, varUnits)
ax = plotter.createSubPlot("Scatter Plot", xlabel, ylabel)
ax.plot(x, y, '.', label='Species')
ax.plot(x,
model.models[varName][0] * np.ones(x.shape),
'r',
label='Std=%f' % model.qualifiers[varName][0])
leg = ax.legend(loc='upper right')
if leg:
leg.draggable()
plotter.show()
def _showGuinier(self, volume):
nrefs = len(self._refsList)
gridsize = self._getGridSize(nrefs)
guinierFn = volume + ".guinier"
d2 = self._getGuinierValue(guinierFn, 0)
legends = ["lnFweighted ln(F)", "corrected ln(F)", "model"]
xplotter = EmPlotter(*gridsize, windowTitle='Guinier Plots')
subPlot = xplotter.createSubPlot(basename(volume), 'd^-2(A^-2)', 'ln(F)', yformat=False)
for i, legend in enumerate(legends):
y = self._getGuinierValue(guinierFn, i+2)
subPlot.plot(d2, y)
xplotter.showLegend(legends)
subPlot.grid(True)
return xplotter
def _showFSC(self, paramName=None):
threshold = self.resolutionThresholdFSC.get()
iterations = self._getIterations()
groups = self._getGroups()
if self.groupFSC == 0: # group by iterations
files = [(it, self._getFinalPath('fscdoc_%02d.stk' % it))
for it in iterations]
legendPrefix = 'iter'
else:
it = iterations[-1]
legendPrefix = 'group'
def group(f): # retrieve the group number
return int(f.split('_')[-1].split('.')[0])
groupFiles = glob(self._getFinalPath('fscdoc_%02d_???.stk' % it))
groupFiles.sort()
files = [(group(f), f) for f in groupFiles if group(f) in groups]
if not files: #empty files
return [
self.errorMessage("Please select valid groups to display",
title="Wrong groups selection")
]
plotter = EmPlotter(x=1, y=1, windowTitle='Resolution FSC')
a = plotter.createSubPlot("FSC", 'Angstroms^-1', 'FSC', yformat=False)
#fscFile = self._getFinalPath('fscdoc_%02d.stk' % iterations[0])
legends = []
for it, fscFile in files:
if os.path.exists(fscFile):
self._plotFSC(a, fscFile)
legends.append('%s %d' % (legendPrefix, it))
else:
print "Missing file: ", fscFile
if threshold < self.maxfsc:
a.plot([self.minInv, self.maxInv], [threshold, threshold],
color='black',
linestyle='--')
plotter.showLegend(legends)
a.grid(True)
return [plotter]
def createCtfPlot(ctfSet, ctfId):
ctfModel = ctfSet[ctfId]
psdFn = ctfModel.getPsdFile()
fn = pwutils.removeExt(psdFn) + "_EPA.txt"
gridsize = [1, 1]
xplotter = EmPlotter(x=gridsize[0], y=gridsize[1],
windowTitle='CTF Fitting')
plot_title = "CTF Fitting"
a = xplotter.createSubPlot(plot_title, 'Resolution (Angstroms)', 'CTF',
yformat=False)
a.invert_xaxis()
for i in range(1, 5):
_plotCurve(a, i, fn)
xplotter.showLegend(['simulated CTF',
'equiphase avg.',
'equiphase avg. - bg',
'cross correlation'])
a.grid(True)
xplotter.show()
def _showGuinier(self, volume):
nrefs = len(self._refsList)
gridsize = self._getGridSize(nrefs)
guinierFn = volume + ".guinier"
d2 = self._getGuinierValue(guinierFn, 0)
legends = ["lnFweighted ln(F)", "corrected ln(F)", "model"]
xplotter = EmPlotter(*gridsize, windowTitle='Guinier Plots')
subPlot = xplotter.createSubPlot(basename(volume),
'd^-2(A^-2)',
'ln(F)',
yformat=False)
for i, legend in enumerate(legends):
y = self._getGuinierValue(guinierFn, i + 2)
subPlot.plot(d2, y)
xplotter.showLegend(legends)
subPlot.grid(True)
return xplotter
def createCtfPlot(ctfSet, ctfId):
ctfModel = ctfSet[ctfId]
psdFn = ctfModel.getPsdFile()
fn = removeExt(psdFn) + "_avrot.txt"
gridsize = [1, 1]
xplotter = EmPlotter(x=gridsize[0], y=gridsize[1], windowTitle='CTF Fitting')
plot_title = "CTF Fitting"
a = xplotter.createSubPlot(plot_title, 'pixels^-1', 'CTF', yformat=False)
legendName = ['rotational avg. No Astg',
'rotational avg.',
'CTF Fit',
'Cross Correlation',
'2sigma cross correlation of noise']
for i in range(1, 6):
_plotCurve(a, i, fn)
xplotter.showLegend(legendName)
a.grid(True)
xplotter.show()
def createCtfPlot(ctfSet, ctfId):
ctfModel = ctfSet[ctfId]
psdFn = ctfModel.getPsdFile()
fn = removeExt(psdFn) + "_avrot.txt"
gridsize = [1, 1]
xplotter = EmPlotter(x=gridsize[0],
y=gridsize[1],
windowTitle='CTF Fitting')
plot_title = "CTF Fitting"
a = xplotter.createSubPlot(plot_title, 'pixels^-1', 'CTF', yformat=False)
legendName = [
'rotational avg. No Astg', 'rotational avg.', 'CTF Fit',
'Cross Correlation', '2sigma cross correlation of noise'
]
for i in range(1, 6):
_plotCurve(a, i, fn)
xplotter.showLegend(legendName)
a.grid(True)
xplotter.show()
def _showSSNR(self, paramName=None):
nrefs = len(self._refsList)
gridsize = self._getGridSize(nrefs)
xplotter = EmPlotter(x=gridsize[0], y=gridsize[1])
for ref3d in self._refsList:
plot_title = 'Resolution SSNR, for Class %s' % ref3d
a = xplotter.createSubPlot(plot_title, 'Angstroms^-1', 'sqrt(SSNR)', yformat=False)
legendName = []
for it in self._iterations:
if self.protocol.IS_REFINE:
fn = self.protocol._getFileName('output_vol_par', iter=it)
else:
fn = self.protocol._getFileName('output_vol_par_class', iter=it, ref=ref3d)
if exists(fn):
self._plotSSNR(a, fn)
legendName.append('iter %d' % it)
xplotter.showLegend(legendName)
a.grid(True)
return [xplotter]
def _onPlotSummaryClick(self, e=None):
sampleKeys = self.samplesTree.selection()
n = len(sampleKeys)
if n == 1:
self.showInfo("Please select several samples to plot.")
else:
if n > 1:
samples = [self.experiment.samples[k] for k in sampleKeys]
else:
samples = self.experiment.samples.values()
dataDict = {} # key will be time values
for s in samples:
xValues, yValues = self.getPlotValues(s)
for x, y in izip(xValues, yValues):
if x in dataDict:
dataDict[x].append(y)
else:
dataDict[x] = [y]
sortedTime = sorted(dataDict.keys())
# We will store five values (min, 25%, 50%, 75%, max)
# for each of the time entries computed
percentileList = [0, 25, 50, 75, 100]
Y = np.zeros((len(sortedTime), 5))
for i, t in enumerate(sortedTime):
Y[i,:] = np.percentile(dataDict[t], percentileList)
plotter = EmPlotter()
ax = plotter.createSubPlot("Summary Plot", self.getTimeLabel(),
self.getMeasureLabel())
ax.plot(sortedTime, Y[:, 0], 'r--', label="Minimum")
ax.plot(sortedTime, Y[:, 1], 'b--', label="25%")
ax.plot(sortedTime, Y[:, 2], 'g', label="50% (Median)")
ax.plot(sortedTime, Y[:, 3], 'b--', label="75%")
ax.plot(sortedTime, Y[:, 4], 'r--', label="Maximum")
ax.legend()
plotter.show()
def _showFSC(self, paramName=None):
threshold = self.resolutionThresholdFSC.get()
iterations = self._getIterations()
groups = self._getGroups()
if self.groupFSC == 0: # group by iterations
files = [(it, self._getFinalPath('fscdoc_%02d.stk' % it)) for it in iterations]
legendPrefix = 'iter'
else:
it = iterations[-1]
legendPrefix = 'group'
def group(f): # retrieve the group number
return int(f.split('_')[-1].split('.')[0])
groupFiles = glob(self._getFinalPath('fscdoc_%02d_???.stk' % it))
groupFiles.sort()
files = [(group(f), f) for f in groupFiles if group(f) in groups]
if not files: #empty files
return [self.errorMessage("Please select valid groups to display",
title="Wrong groups selection")]
plotter = EmPlotter(x=1, y=1, windowTitle='Resolution FSC')
a = plotter.createSubPlot("FSC", 'Angstroms^-1', 'FSC', yformat=False)
#fscFile = self._getFinalPath('fscdoc_%02d.stk' % iterations[0])
legends = []
for it, fscFile in files:
if os.path.exists(fscFile):
self._plotFSC(a, fscFile)
legends.append('%s %d' % (legendPrefix, it))
else:
print "Missing file: ", fscFile
if threshold < self.maxfsc:
a.plot([self.minInv, self.maxInv],[threshold, threshold],
color='black', linestyle='--')
plotter.showLegend(legends)
a.grid(True)
return [plotter]
def createCtfPlot(ctfSet, ctfId):
ctfModel = ctfSet[ctfId]
psdFn = ctfModel.getPsdFile()
fn = pwutils.removeExt(psdFn) + "_EPA.txt"
gridsize = [1, 1]
xplotter = EmPlotter(x=gridsize[0],
y=gridsize[1],
windowTitle='CTF Fitting')
plot_title = "CTF Fitting"
a = xplotter.createSubPlot(plot_title,
'Resolution (Angstroms)',
'CTF',
yformat=False)
a.invert_xaxis()
for i in range(1, 5):
_plotCurve(a, i, fn)
xplotter.showLegend([
'simulated CTF', 'equiphase avg.', 'equiphase avg. - bg',
'cross correlation'
])
a.grid(True)
xplotter.show()
def _plotHistogram(self, param=None):
""" First we parse the MSA plt:
first column: cumulative percent.
second column: iteration number.
"""
iters = []
cumPercents = []
fn = self.protocol.getOutputPlt()
with open(fn) as f:
lines_after_2 = f.readlines()[2:]
for line in lines_after_2:
values = line.split()
cumPercents.append(float(values[0]))
iters.append(int(float(values[1])))
f.close()
width = 0.85
xplotter = EmPlotter()
a = xplotter.createSubPlot('Behaviour of sum of eigenvalues during analysis', 'Iteration number', '%')
a.bar(iters, cumPercents, width, color='b')
return [xplotter]
def _plotHistogram(self, param=None):
""" First we parse the cas_EIG file and we read:
first line: take the number of eigen values.
then one line per factor and we read the percent and cumulative percent.
"""
from numpy import arange
from matplotlib.ticker import FormatStrFormatter
fn = self.protocol._getFileName('eigFile')
f = open(fn)
values = f.readline().split()
n = int(values[0]) # Number of factors
factors = arange(1, n+1)
percents = []
cumPercents = []
for i in factors:
values = f.readline().split()
percents.append(float(values[1]))
cumPercents.append(float(values[2]))
f.close()
width = 0.85
xplotter = EmPlotter()
a = xplotter.createSubPlot('Eigenvalues histogram', 'Eigenvalue number', '%')
a.set_xticks(factors + 0.45)
a.xaxis.set_major_formatter(FormatStrFormatter('%1.0f'))
bars = a.bar(factors, percents, width, color='b')
for i, rect in enumerate(bars):
h = rect.get_height()
a.text(rect.get_x()+rect.get_width()/2., h+0.3, '%d' % cumPercents[i],
ha='center', va='bottom')
a.set_ylim([0, percents[0] + 5])
return [xplotter]
class PKPDResponsiveDialog(dialog.Dialog):
def __init__(self, parent, title, **kwargs):
""" From kwargs:
message: message tooltip to show when browsing.
selected: the item that should be selected.
validateSelectionCallback: a callback function to validate selected items.
"""
self.values = []
self.plotter = None
self.targetProtocol = kwargs['targetProtocol']
self.experiment = self.targetProtocol.experiment
self.varNameX = kwargs['varNameX']
self.varNameY = kwargs['varNameY']
self.provider = SamplesTreeProvider(self.experiment)
self.model = self.loadModel()
self.validateSelectionCallback = kwargs.get(
'validateSelectionCallback', None)
self.setLabels()
dialog.Dialog.__init__(self,
parent,
title,
buttons=[('Select', dialog.RESULT_YES),
('Cancel', dialog.RESULT_CANCEL)])
def setLabels(self):
pass
def loadModel(self):
model = self.targetProtocol.createModel()
model.setExperiment(self.experiment)
# if hasattr(self.protODE, "deltaT"):
# model.deltaT = self.protODE.deltaT.get()
model.setXVar(self.varNameX)
model.setYVar(self.varNameY)
return model
def body(self, bodyFrame):
bodyFrame.config(bg='white')
gui.configureWeigths(bodyFrame)
self._createSamplesFrame(bodyFrame)
self._createSlidersFrame(bodyFrame)
self._createLogsFrame(bodyFrame)
def _createSamplesFrame(self, content):
frame = tk.Frame(content, bg='white')
#frame = tk.LabelFrame(content, text='General')
lfSamples = tk.LabelFrame(frame, text="Samples", bg='white')
gui.configureWeigths(frame)
lfSamples.grid(row=0, column=0, sticky='news', padx=5, pady=5)
self.samplesTree = self._addBoundTree(lfSamples, self.provider, 10)
self.samplesTree.itemClick = self._onSampleChanged
frame.grid(row=0, column=0, sticky='news', padx=5, pady=5)
def _createSlidersFrame(self, content):
frame = tk.Frame(content, bg='white')
lfBounds = tk.LabelFrame(frame, text="Parameter Bounds", bg='white')
gui.configureWeigths(frame)
i = 0
self.sliders = {}
paramUnits = self.targetProtocol.parameterUnits
for paramName, bounds in self.targetProtocol.getParameterBounds(
).iteritems():
bounds = bounds or (0, 1)
slider = MinMaxSlider(lfBounds,
"%s [%s]" %
(paramName, strUnit(paramUnits[i])),
bounds[0],
bounds[1],
callback=self._onVarChanged)
slider.grid(row=i, column=0, padx=5, pady=5)
self.sliders[paramName] = slider
i += 1
lfBounds.grid(row=0, column=0, sticky='news', padx=5, pady=5)
frame.grid(row=0, column=1, sticky='news', padx=5, pady=5)
def _createLogsFrame(self, content):
frame = tk.Frame(content)
def addVar(text, col, varName):
varFrame = tk.Frame(frame)
varFrame.grid(row=0, column=col, sticky='new')
label = tk.Label(varFrame, text=text) #, font=self.fontBold)
label.grid(row=0, column=0, padx=5, pady=2, sticky='nw')
combo = tk.Label(varFrame, text=varName, width=10)
combo.grid(row=0, column=1, sticky='nw', padx=5, pady=5)
radioVar = tk.IntVar()
radio = tk.Checkbutton(varFrame, text='Log10', variable=radioVar)
radio.grid(row=0, column=2, sticky='nw', padx=5, pady=5)
return combo, radio, radioVar
self.timeWidget = addVar('Time variable', 0, self.varNameX)
self.timeWidget[2].trace('w', self._onLogChanged)
self.measureWidget = addVar('Measure variable', 1, self.varNameY)
measureVar = self.measureWidget[2]
measureVar.set(True)
measureVar.trace('w', self._onLogChanged)
frame.grid(row=1,
column=0,
columnspan=2,
sticky='news',
padx=5,
pady=5)
def _addBoundTree(self, parent, provider, height):
bt = BoundTree(parent, provider, height=height)
bt.grid(row=0, column=0, sticky='news', padx=5, pady=5)
gui.configureWeigths(parent)
return bt
def apply(self):
self.values = []
def _onVarChanged(self, *args):
sampleKeys = self.samplesTree.selection()
if sampleKeys:
self.computeFit()
self.plotResults()
else:
dialog.showInfo("Warning", "Please select some sample(s) to plot.",
self)
def computeFit(self):
currentParams = []
for paramName in self.targetProtocol.getParameterNames():
currentParams.append(self.sliders[paramName].getValue())
self.targetProtocol.setParameters(currentParams)
self.ypValues = self.targetProtocol.forwardModel(
currentParams, self.xpValues)
def getBoundsList(self):
boundList = []
for paramName in self.targetProtocol.getParameterNames():
boundList.append(self.sliders[paramName].getMinMax())
return boundList
def useTimeLog(self):
return self.timeWidget[2].get()
def useMeasureLog(self):
return self.measureWidget[2].get()
def getUnits(self, varName):
return self.experiment.variables[varName].getUnitsString()
def getLabel(self, varName, useLog):
varLabel = '%s [%s]' % (varName, self.getUnits(varName))
if useLog:
varLabel = "log10(%s)" % varLabel
return varLabel
def getTimeLabel(self):
return self.getLabel(self.varNameX, self.useTimeLog())
def getMeasureLabel(self):
return self.getLabel(self.varNameY, self.useMeasureLog())
def computePlotValues(self, xValues, yValues):
useMeasureLog = self.useMeasureLog()
useTimeLog = self.useTimeLog()
if not (useMeasureLog or useTimeLog):
newXValues = xValues
newYValues = yValues
else:
# If log will be used either for time or measure var
# we need to filter elements larger than 0
newXValues = []
newYValues = []
def _value(v, useLog):
if useLog:
return math.log10(v) if v > 0 else None
return v
for x, y in izip(xValues, yValues):
x = _value(x, useTimeLog)
y = _value(y, useMeasureLog)
if x is not None and y is not None:
newXValues.append(x)
newYValues.append(y)
return newXValues, newYValues
def _updateModel(self):
""" This function should be called whenever the sample changes """
pass
def _onLogChanged(self, *args):
# We will treat a log change as a sample change to plot
self._onSampleChanged()
def _onSampleChanged(self, e=None):
sampleKeys = self.samplesTree.selection()
if sampleKeys:
# When the sample is changed we need to re-compute (with log or not)
# the x, y values
self.sample = self.experiment.samples[sampleKeys[0]]
self.xValues, self.yValues = self.sample.getXYValues(
self.varNameX, self.varNameY)
self.newXValues, self.newYValues = self.computePlotValues(
self.xValues[0], self.yValues[0])
self._updateModel()
self.computeFit()
self.plotResults()
else:
dialog.showInfo("Warning", "Please select some sample(s) to plot.",
self)
def plotResults(self):
if self.plotter is None or self.plotter.isClosed():
self.plotter = EmPlotter()
doShow = True
else:
doShow = False
ax = self.plotter.getLastSubPlot()
self.plotter.clear()
ax = self.plotter.createSubPlot("Sample: %s" % self.sample.sampleName,
self.getTimeLabel(),
self.getMeasureLabel())
self.newXPValues, self.newYPValues = self.computePlotValues(
self.xpValues[0], self.ypValues[0])
ax.plot(self.newXValues, self.newYValues, 'x', label="Observations")
ax.plot(self.newXPValues, self.newYPValues, label="Fit")
ax.legend()
if doShow:
self.plotter.show()
else:
self.plotter.draw()
def destroy(self):
"""Destroy the window"""
if not (self.plotter is None or self.plotter.isClosed()):
self.plotter.close()
dialog.Dialog.destroy(self)
class PKPDResponsiveDialog(dialog.Dialog):
def __init__(self, parent, title, **kwargs):
""" From kwargs:
message: message tooltip to show when browsing.
selected: the item that should be selected.
validateSelectionCallback: a callback function to validate selected items.
"""
self.values = []
self.plotter = None
self.targetProtocol = kwargs['targetProtocol']
self.experiment = self.targetProtocol.experiment
self.varNameX = kwargs['varNameX']
self.varNameY = kwargs['varNameY']
self.provider = SamplesTreeProvider(self.experiment)
self.model = self.loadModel()
self.validateSelectionCallback = kwargs.get('validateSelectionCallback', None)
self.setLabels()
dialog.Dialog.__init__(self, parent, title,
buttons=[('Select', dialog.RESULT_YES),
('Cancel', dialog.RESULT_CANCEL)])
def setLabels(self):
pass
def loadModel(self):
model = self.targetProtocol.createModel()
model.setExperiment(self.experiment)
# if hasattr(self.protODE, "deltaT"):
# model.deltaT = self.protODE.deltaT.get()
model.setXVar(self.varNameX)
model.setYVar(self.varNameY)
return model
def body(self, bodyFrame):
bodyFrame.config(bg='white')
gui.configureWeigths(bodyFrame)
self._createSamplesFrame(bodyFrame)
self._createSlidersFrame(bodyFrame)
self._createLogsFrame(bodyFrame)
def _createSamplesFrame(self, content):
frame = tk.Frame(content, bg='white')
#frame = tk.LabelFrame(content, text='General')
lfSamples = tk.LabelFrame(frame, text="Samples", bg='white')
gui.configureWeigths(frame)
lfSamples.grid(row=0, column=0, sticky='news', padx=5, pady=5)
self.samplesTree = self._addBoundTree(lfSamples,
self.provider, 10)
self.samplesTree.itemClick = self._onSampleChanged
frame.grid(row=0, column=0, sticky='news', padx=5, pady=5)
def _createSlidersFrame(self, content):
frame = tk.Frame(content, bg='white')
lfBounds = tk.LabelFrame(frame, text="Parameter Bounds", bg='white')
gui.configureWeigths(frame)
i = 0
self.sliders = {}
paramUnits = self.targetProtocol.parameterUnits
for paramName, bounds in self.targetProtocol.getParameterBounds().iteritems():
bounds = bounds or (0, 1)
slider = MinMaxSlider(lfBounds, "%s [%s]"%(paramName,strUnit(paramUnits[i])),
bounds[0], bounds[1],
callback=self._onVarChanged)
slider.grid(row=i, column=0, padx=5, pady=5)
self.sliders[paramName] = slider
i += 1
lfBounds.grid(row=0, column=0, sticky='news', padx=5, pady=5)
frame.grid(row=0, column=1, sticky='news', padx=5, pady=5)
def _createLogsFrame(self, content):
frame = tk.Frame(content)
def addVar(text, col, varName):
varFrame = tk.Frame(frame)
varFrame.grid(row=0, column=col, sticky='new')
label = tk.Label(varFrame, text=text)#, font=self.fontBold)
label.grid(row=0, column=0, padx=5, pady=2, sticky='nw')
combo = tk.Label(varFrame, text=varName, width=10)
combo.grid(row=0, column=1, sticky='nw', padx=5, pady=5)
radioVar = tk.IntVar()
radio = tk.Checkbutton(varFrame, text='Log10', variable=radioVar)
radio.grid(row=0, column=2, sticky='nw', padx=5, pady=5)
return combo, radio, radioVar
self.timeWidget = addVar('Time variable', 0, self.varNameX)
self.timeWidget[2].trace('w', self._onLogChanged)
self.measureWidget = addVar('Measure variable', 1, self.varNameY)
measureVar = self.measureWidget[2]
measureVar.set(True)
measureVar.trace('w', self._onLogChanged)
frame.grid(row=1, column=0, columnspan=2, sticky='news', padx=5, pady=5)
def _addBoundTree(self, parent, provider, height):
bt = BoundTree(parent, provider, height=height)
bt.grid(row=0, column=0, sticky='news', padx=5, pady=5)
gui.configureWeigths(parent)
return bt
def apply(self):
self.values = []
def _onVarChanged(self, *args):
sampleKeys = self.samplesTree.selection()
if sampleKeys:
self.computeFit()
self.plotResults()
else:
dialog.showInfo("Warning","Please select some sample(s) to plot.",self)
def computeFit(self):
currentParams = []
for paramName in self.targetProtocol.getParameterNames():
currentParams.append(self.sliders[paramName].getValue())
self.targetProtocol.setParameters(currentParams)
self.ypValues = self.targetProtocol.forwardModel(currentParams, self.xpValues)
def getBoundsList(self):
boundList = []
for paramName in self.targetProtocol.getParameterNames():
boundList.append(self.sliders[paramName].getMinMax())
return boundList
def useTimeLog(self):
return self.timeWidget[2].get()
def useMeasureLog(self):
return self.measureWidget[2].get()
def getUnits(self, varName):
return self.experiment.variables[varName].getUnitsString()
def getLabel(self, varName, useLog):
varLabel = '%s [%s]' % (varName, self.getUnits(varName))
if useLog:
varLabel = "log10(%s)" % varLabel
return varLabel
def getTimeLabel(self):
return self.getLabel(self.varNameX, self.useTimeLog())
def getMeasureLabel(self):
return self.getLabel(self.varNameY, self.useMeasureLog())
def computePlotValues(self, xValues, yValues):
useMeasureLog = self.useMeasureLog()
useTimeLog = self.useTimeLog()
if not (useMeasureLog or useTimeLog):
newXValues = xValues
newYValues = yValues
else:
# If log will be used either for time or measure var
# we need to filter elements larger than 0
newXValues = []
newYValues = []
def _value(v, useLog):
if useLog:
return math.log10(v) if v > 0 else None
return v
for x, y in izip(xValues, yValues):
x = _value(x, useTimeLog)
y = _value(y, useMeasureLog)
if x is not None and y is not None:
newXValues.append(x)
newYValues.append(y)
return newXValues, newYValues
def _updateModel(self):
""" This function should be called whenever the sample changes """
pass
def _onLogChanged(self, *args):
# We will treat a log change as a sample change to plot
self._onSampleChanged()
def _onSampleChanged(self, e=None):
sampleKeys = self.samplesTree.selection()
if sampleKeys:
# When the sample is changed we need to re-compute (with log or not)
# the x, y values
self.sample = self.experiment.samples[sampleKeys[0]]
self.xValues, self.yValues = self.sample.getXYValues(self.varNameX,
self.varNameY)
self.newXValues, self.newYValues = self.computePlotValues(self.xValues[0],
self.yValues[0])
self._updateModel()
self.computeFit()
self.plotResults()
else:
dialog.showInfo("Warning","Please select some sample(s) to plot.",self)
def plotResults(self):
if self.plotter is None or self.plotter.isClosed():
self.plotter = EmPlotter()
doShow = True
else:
doShow = False
ax = self.plotter.getLastSubPlot()
self.plotter.clear()
ax = self.plotter.createSubPlot("Sample: %s" % self.sample.sampleName,
self.getTimeLabel(),
self.getMeasureLabel())
self.newXPValues, self.newYPValues = self.computePlotValues(self.xpValues[0],
self.ypValues[0])
ax.plot(self.newXValues, self.newYValues, 'x', label="Observations")
ax.plot(self.newXPValues, self.newYPValues, label="Fit")
ax.legend()
if doShow:
self.plotter.show()
else:
self.plotter.draw()
def destroy(self):
"""Destroy the window"""
if not (self.plotter is None or self.plotter.isClosed()):
self.plotter.close()
dialog.Dialog.destroy(self)
def _showFSC(self, paramName=None):
threshold = self.resolutionThresholdFSC.get()
nrefs = len(self._refsList)
gridsize = self._getGridSize(nrefs)
xplotter = EmPlotter(x=gridsize[0],
y=gridsize[1],
windowTitle='Resolution FSC')
if self.protocol.IS_REFINE:
plot_title = 'FSC'
a = xplotter.createSubPlot(plot_title,
'Angstroms^-1',
'FSC',
yformat=False)
legends = []
show = False
for it in self._iterations:
parFn = self.protocol._getFileName('output_vol_par', iter=it)
if exists(parFn):
show = True
self._plotFSC(a, parFn)
legends.append('iter %d' % it)
xplotter.showLegend(legends)
if show:
if threshold < self.maxFrc:
a.plot([self.minInv, self.maxInv], [threshold, threshold],
color='black',
linestyle='--')
a.grid(True)
else:
raise Exception("Set a valid iteration to show its FSC")
else:
for ref3d in self._refsList:
plot_title = 'class %s' % ref3d
a = xplotter.createSubPlot(plot_title,
'Angstroms^-1',
'FSC',
yformat=False)
legends = []
for it in self._iterations:
parFn = self.protocol._getFileName('output_vol_par_class',
iter=it,
ref=ref3d)
if exists(parFn):
show = True
self._plotFSC(a, parFn)
legends.append('iter %d' % it)
xplotter.showLegend(legends)
if show:
if threshold < self.maxFrc:
a.plot([self.minInv, self.maxInv],
[threshold, threshold],
color='black',
linestyle='--')
a.grid(True)
else:
raise Exception("Set a valid iteration to show its FSC")
return [xplotter]
def visualize(self, obj, **kwargs):
prot = obj
fnProfiles = prot._getPath("profiles.txt")
fh = open(fnProfiles,"r")
Rtype = []
Rlegends = []
R = []
state = 0
for line in fh:
if state==0:
tokens = line.split("::")
Rtype.append(tokens[0])
Rlegends.append(tokens[1].strip())
Ri=[]
state=1
elif state==1:
tokens=line.strip().split()
if len(tokens)==0:
R.append(Ri)
state=0
else:
if len(Ri)==0:
for n in range(len(tokens)):
Ri.append([])
for n in range(len(tokens)):
Ri[n].append(tokens[n])
fh.close()
plotter = None
previousType = ""
for legend, Ri, Rtypei in izip(Rlegends, R, Rtype):
if plotter is None or Rtypei!=previousType:
if previousType!="":
self.addLimits(plotter,previousType,minX,maxX)
plotter = EmPlotter()
doShow = True
if Rtypei=="ReversibleLiver" or Rtypei=="TimeDependentLiver" or Rtypei=="InductionLiver" or Rtypei=="StaticLiver" or Rtypei=="TransporterLiver":
Ilabel="[Ih] [uM]"
elif Rtypei=="ReversibleGut" or Rtypei=="TimeDependentGut" or Rtypei=="InductionGut" or Rtypei=="StaticGut" or Rtypei=="TransporterGut":
Ilabel="[Ig] [uM]"
elif Rtypei=="TransporterRenal":
Ilabel="[Cmax] [uM]"
ax = plotter.createSubPlot("Plot", Ilabel, "R")
previousType = Rtypei
minX = None
maxX = None
else:
doShow = False
ax = plotter.getLastSubPlot()
x = np.asarray(Ri[0],dtype=np.float64)
if len(Ri)==2:
y=np.asarray(Ri[1],dtype=np.float64)
else:
y=np.asarray(Ri[2],dtype=np.float64)
ax.plot(x, y, label=legend)
minXi = np.min(x)
maxXi = np.max(x)
if minX==None:
minX=minXi
maxX=maxXi
minX=min(minXi,minX)
maxX=min(maxXi,maxX)
leg = ax.legend()
if leg:
leg.draggable()
if doShow:
plotter.show()
else:
plotter.draw()
self.addLimits(plotter,previousType,minX,maxX)
class ExperimentWindow(gui.Window):
""" This class creates a Window that will display some Point's
contained in a Data object.
It will allow to launch 1D, 2D and 3D plots by selecting any
combination of the x1, x2...xn from the Point dimension.
Points can be selected by either Click and Drag in the Scatter plot or..
by creating an Expression.
Finally, there is a button 'Create Cluster' that will call a callback
fuction to take care of it.
"""
def __init__(self, **kwargs):
gui.Window.__init__(self, minsize=(420, 200), **kwargs)
self.experiment = kwargs.get('experiment')
self.fitting = kwargs.get('fitting', None)
self.callback = kwargs.get('callback', None)
content = tk.Frame(self.root)
self._createContent(content)
content.grid(row=0, column=0, sticky='news')
content.columnconfigure(0, weight=1)
content.rowconfigure(0, weight=1)
self.plotter = None
def _createContent(self, content):
# Create and fill the frame containing the Experiment
# info, variables and doses
p = tk.PanedWindow(content, orient=tk.VERTICAL, bg='white')
p.grid(row=0, column=0, sticky='news', padx=5, pady=5)
self._createTopFrame(p)
# Create the middle frame containing the Samples Box
self._createSamplesFrame(p)
# Create the last frame with the buttons
self._createButtonsFrame(content)
#self._updateSelectionLabel()
def _createTopFrame(self, content):
frame = tk.Frame(content)
frame.columnconfigure(0, weight=1)
frame.rowconfigure(0, weight=1)
tab = ttk.Notebook(frame)
tab.grid(row=0, column=0, sticky='news', padx=5, pady=5)
def addTab(label):
lf = tk.Frame(tab)
tab.add(lf, text=label)
return lf
lfGeneral = addTab('General')
self._addLabel(lfGeneral, 'Title', 0, 0)
self._titleVar = tk.StringVar()
self._titleVar.set(self.experiment.general['title'])
titleEntry = tk.Entry(lfGeneral, width=26, textvariable=self._titleVar,
bg='white')
titleEntry.grid(row=0, column=1, sticky='nw', padx=5, pady=(5, 0))
self._addLabel(lfGeneral, 'Comment', 1, 0)
commentText = gui.text.Text(lfGeneral, width=30, height=3, bg='white')
commentText.setText(self.experiment.general['comment'])
commentText.grid(row=1, column=1, sticky='nw', padx=5, pady=(5, 0))
self._commentText = commentText
lfVars = addTab('Variables')
self.varsTree = self._addBoundTree(lfVars, VariablesTreeProvider, 5)
lfVias = addTab('Vias')
self.viasTree = self._addBoundTree(lfVias, ViasTreeProvider, 5)
lfDoses = addTab('Doses')
self.dosesTree = self._addBoundTree(lfDoses, DosesTreeProvider, 5)
lfGroups = addTab('Groups')
self.groupsTree = self._addBoundTree(lfGroups, GroupsTreeProvider, 5)
# Fitting tab
if self.fitting:
tabFitting = addTab('Fitting')
t = TaggedText(tabFitting, width=80, height=10, bg='white')
t.grid(row=0, column=0, sticky='nw', padx=10, pady=10)
t.setText('Select one of the samples to see more information.')
t.setReadOnly(True)
self.fittingText = t
# Buttons for fitting
buttonsFrame = self._createFittingButtonsFrame(tabFitting)
buttonsFrame.grid(row=0, column=1, sticky='news', padx=5, pady=5)
#frame.grid(row=0, column=0, sticky='news', padx=5, pady=(10, 5))
content.add(frame, sticky='news', padx=5, pady=5)
def _createSamplesFrame(self, content):
frame = tk.Frame(content)
#frame = tk.LabelFrame(content, text='General')
lfSamples = tk.LabelFrame(frame, text='Samples')
gui.configureWeigths(frame)
lfSamples.grid(row=0, column=0, sticky='news', padx=5, pady=5)
self.samplesTree = self._addBoundTree(lfSamples, SamplesTreeProvider,
10, fitting=self.fitting)
self.samplesTree.itemDoubleClick = self._onSampleDoubleClick
self.samplesTree.itemClick = self._onSampleClick
plotFrame = tk.Frame(lfSamples)
plotFrame.grid(row=1, column=0, sticky='ws', padx=5, pady=5)
# Add a combobox with the variable for time
timeVars = [v.varName for v in self.experiment.variables.values()
if v.role == v.ROLE_TIME]
timeVars += [v.varName for v in self.experiment.variables.values()
if v.role == v.ROLE_MEASUREMENT]
measureVars = [v.varName for v in self.experiment.variables.values()
if v.role == v.ROLE_MEASUREMENT]
def addVar(text, col, choices):
varFrame = tk.Frame(plotFrame)
varFrame.grid(row=0, column=col, sticky='new')
label = tk.Label(varFrame, text=text, font=self.fontBold)
label.grid(row=0, column=0, padx=5, pady=2, sticky='nw')
combo = ComboBox(varFrame, choices, width=10)
combo.grid(row=0, column=1, sticky='nw', padx=5, pady=5)
radioVar = tk.IntVar()
radio = tk.Checkbutton(varFrame, text='Log10', variable=radioVar)
radio.grid(row=0, column=2, sticky='nw', padx=5, pady=5)
return combo, radio, radioVar
self.timeWidget = addVar('Time variable', 0, timeVars)
self.measureWidget = addVar('Measure variable', 1, measureVars)
self.measureWidget[2].set(True)
self.plotButton = Button(plotFrame, ' Plot ', font=self.fontBold,
command=self._onPlotClick,
tooltip='Select one or more samples to plot '
'their measures of the selected '
'variables (optionally in log).')
self.plotButton.grid(row=0, column=2, sticky='ne', padx=5)
self.plotSummaryButton = Button(plotFrame, ' Summary Plot ',
font=self.fontBold,
command=self._onPlotSummaryClick,
tooltip='Select several samples to plot'
' their statistics'
' (min, max, avg and std).')
self.plotSummaryButton.grid(row=1, column=2, sticky='ne',
padx=5, pady=5)
#frame.grid(row=1, column=0, sticky='news', padx=5, pady=5)
content.add(frame, sticky='news', padx=5, pady=5)
def _createButtonsFrame(self, content):
frame = tk.Frame(content)
gui.configureWeigths(frame)
buttonsFrame = tk.Frame(frame)
buttonsFrame.grid(row=0, column=0, sticky='ne')
closeButton = Button(buttonsFrame, 'Close', command=self.close,
imagePath='fa-times.png')
closeButton.grid(row=0, column=0, sticky='ne', padx=5)
self.newButton = HotButton(buttonsFrame, ' New Experiment ',
command=self._onCreateClick,
tooltip='Create a new experiment with the '
'selected samples. You can also edit'
'title and comment.')
self.newButton.grid(row=0, column=1, sticky='ne', padx=5)
frame.grid(row=1, column=0, sticky='news', padx=5, pady=5)
def _createFittingButtonsFrame(self, content):
buttonsFrame = tk.Frame(content)
buttonsFrame.grid(row=0, column=0, sticky='ne')
plotValuesButton = Button(buttonsFrame, 'Plot Fit',
command=self._onPlotFitClick)
plotValuesButton.grid(row=0, column=0, sticky='sew', padx=5, pady=5)
openValuesButton = Button(buttonsFrame, 'Open Fit',
command=self._onOpenFitClick)
openValuesButton.grid(row=1, column=0, sticky='sew', padx=5, pady=5)
return buttonsFrame
def _addLabel(self, parent, text, r, c):
label = tk.Label(parent, text=text, font=self.fontBold)
label.grid(row=r, column=c, padx=5, pady=5, sticky='ne')
return label
def _addBoundTree(self, parent, ProviderClass, height, **kwargs):
bt = BoundTree(parent, ProviderClass(self.experiment, **kwargs),
height=height)
bt.grid(row=0, column=0, sticky='news', padx=5, pady=5)
gui.configureWeigths(parent)
return bt
def getUnits(self, varName):
return self.experiment.variables[varName].getUnitsString()
def getLabel(self, varName, useLog):
varLabel = '%s [%s]' % (varName, self.getUnits(varName))
if useLog:
varLabel = "log10(%s)" % varLabel
return varLabel
def getTimeVarName(self):
return self.timeWidget[0].getText()
def useTimeLog(self):
return self.timeWidget[2].get()
def getTimeLabel(self):
return self.getLabel(self.getTimeVarName(), self.useTimeLog())
def getMeasureVarName(self):
return self.measureWidget[0].getText()
def useMeasureLog(self):
return self.measureWidget[2].get()
def getMeasureLabel(self):
return self.getLabel(self.getMeasureVarName(), self.useMeasureLog())
def getPlotValues(self, sample):
xValues, yValues = sample.getXYValues(self.getTimeVarName(),
self.getMeasureVarName())
xValues=xValues[0] # From [array(...)] to array(...)
yValues=yValues[0]
useMeasureLog = self.useMeasureLog()
useTimeLog = self.useTimeLog()
if not (useMeasureLog or useTimeLog):
return xValues, yValues
# If log will be used either for time or measure var
# we need to filter elements larger than 0
newXValues = []
newYValues = []
def _value(v, useLog):
if useLog:
return math.log10(v) if v > 0 else None
return v
for x, y in izip(xValues, yValues):
x = _value(x, useTimeLog)
y = _value(y, useMeasureLog)
if x is not None and y is not None:
newXValues.append(x)
newYValues.append(y)
return newXValues, newYValues
def _onPlotClick(self, e=None):
sampleKeys = self.samplesTree.selection()
if sampleKeys:
if self.plotter is None or self.plotter.isClosed():
self.plotter = EmPlotter()
doShow = True
ax = self.plotter.createSubPlot("Plot", self.getTimeLabel(),
self.getMeasureLabel())
self.plotDict = {}
else:
doShow = False
ax = self.plotter.getLastSubPlot()
samples = [self.experiment.samples[k] for k in sampleKeys]
for s in samples:
if not s.sampleName in self.plotDict:
x, y = self.getPlotValues(s)
ax.plot(x, y, label=s.sampleName)
self.plotDict[s.sampleName] = True
leg = ax.legend()
if leg:
leg.draggable()
if doShow:
self.plotter.show()
else:
self.plotter.draw()
else:
self.showInfo("Please select some sample(s) to plot.")
def _onPlotSummaryClick(self, e=None):
sampleKeys = self.samplesTree.selection()
n = len(sampleKeys)
if n == 1:
self.showInfo("Please select several samples to plot.")
else:
if n > 1:
samples = [self.experiment.samples[k] for k in sampleKeys]
else:
samples = self.experiment.samples.values()
dataDict = {} # key will be time values
for s in samples:
xValues, yValues = self.getPlotValues(s)
for x, y in izip(xValues, yValues):
if x in dataDict:
dataDict[x].append(y)
else:
dataDict[x] = [y]
sortedTime = sorted(dataDict.keys())
# We will store five values (min, 25%, 50%, 75%, max)
# for each of the time entries computed
percentileList = [0, 25, 50, 75, 100]
Y = np.zeros((len(sortedTime), 5))
for i, t in enumerate(sortedTime):
Y[i,:] = np.percentile(dataDict[t], percentileList)
plotter = EmPlotter()
ax = plotter.createSubPlot("Summary Plot", self.getTimeLabel(),
self.getMeasureLabel())
ax.plot(sortedTime, Y[:, 0], 'r--', label="Minimum")
ax.plot(sortedTime, Y[:, 1], 'b--', label="25%")
ax.plot(sortedTime, Y[:, 2], 'g', label="50% (Median)")
ax.plot(sortedTime, Y[:, 3], 'b--', label="75%")
ax.plot(sortedTime, Y[:, 4], 'r--', label="Maximum")
ax.legend()
plotter.show()
def _onCreateClick(self, e=None):
sampleKeys = self.samplesTree.selection()
if sampleKeys and self.callback:
self.callback()
else:
self.showInfo("Please select some sample(s) to create a new experiment.")
def _onSampleClick(self, obj):
# In fitting mode we need to display some information for
# the given sample fit
if self.fitting:
selection = self.samplesTree.selection()
if selection:
sampleKey = selection[0]
sampleFit = self.fitting.getSampleFit(sampleKey)
textMsg = sampleFit.getBasicInfo()
else:
textMsg = 'Select one of the samples to see more information.'
self.fittingText.setReadOnly(False)
self.fittingText.setText(textMsg)
self.fittingText.setReadOnly(True)
if not (self.plotter is None or self.plotter.isClosed()):
self._onPlotClick()
def _onSampleDoubleClick(self, obj):
sampleKeys = self.samplesTree.selection()
if sampleKeys:
MeasureWindow(masterWindow=self,
sample=self.experiment.samples[sampleKeys[0]]).show()
def _onPlotFitClick(self, e=None):
sampleKeys = self.samplesTree.selection()
if sampleKeys:
def _value(v, useLog):
if useLog:
return math.log10(v) if v > 0 else None
return v
def _values(values, useLog=self.useMeasureLog()):
return [_value(float(x), useLog) for x in values]
def _plot(varLabelX, varLabelY, x, y, yp):
plotter = EmPlotter()
ax = plotter.createSubPlot("Plot", varLabelX, varLabelY)
xValues = _values(x, useLog=self.useTimeLog())
ax.plot(xValues, _values(y), 'x', label="Observations")
idx = np.argsort(xValues)
ypValues = _values(yp)
ax.plot(np.asarray(xValues)[idx], np.asarray(ypValues)[idx], 'g', label="Fit")
leg = ax.legend()
if leg:
leg.draggable()
plotter.show()
# Get first selected element
fit = self.fitting.getSampleFit(sampleKeys[0])
if fit==None:
return
varLabelX = self.getLabel(self.fitting.predictor.varName,
self.useTimeLog())
if type(self.fitting.predicted)==list:
i=0
for v in self.fitting.predicted:
varLabelY = self.getLabel(v.varName, self.useMeasureLog())
_plot(varLabelX, varLabelY, fit.x[i], fit.y[i], fit.yp[i])
i+=1
else:
varLabelY = self.getLabel(self.fitting.predicted.varName,
self.useMeasureLog())
_plot(varLabelX, varLabelY, fit.x[0], fit.y[0], fit.yp[0])
else:
self.showInfo("Please select some sample(s) to plot.")
def _onOpenFitClick(self, e=None):
sampleKeys = self.samplesTree.selection()
if sampleKeys:
# Get first selected element
fit = self.fitting.getSampleFit(sampleKeys[0])
FitWindow(masterWindow=self,
fitting=self.fitting, sampleFit=fit).show()
else:
self.showInfo("Please select some sample(s) to plot.")
def _onClosing(self):
if self.plotter:
self.plotter.close()
gui.Window._onClosing(self)
