def _visualizeHistogram(self, e=None):
views = []
numberOfBins = self.visualizeHistogram.get()
goodScores = []
badScores = []
if hasattr(self.protocol, "outputParticles"):
goodScores += [
part._xmipp_scoreEmptiness.get()
for part in self.protocol.outputParticles
]
if hasattr(self.protocol, "eliminatedParticles"):
badScores += [
part._xmipp_scoreEmptiness.get()
for part in self.protocol.eliminatedParticles
]
plotter = EmPlotter()
plotter.createSubPlot("Emptiness Score", "Emptiness Score (a.u.)",
"# of Particles")
values = [goodScores, badScores]
labels = ["Passed particles", "Discarded particles"]
colors = ['green', 'red']
plotMultiHistogram(values, colors, labels, numberOfBins, plotter,
views)
return views
def _visualizeHistogram(self, e=None):
views = []
numberOfBins = self.visualizeHistogram.get()
if hasattr(self.protocol, "outputCTF"):
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)
views.append(plotter)
if hasattr(self.protocol, "outputCTFDiscarded"):
numberOfBins = min(numberOfBins,
self.protocol.outputCTFDiscarded.getSize())
plotter = EmPlotter()
plotter.createSubPlot(
"Resolution Discrepancies histogram (discarded)",
"Resolution (A)", "# of Comparisons")
resolution = [
ctf.getResolution() for ctf in self.protocol.outputCTFDiscarded
]
plotter.plotHist(resolution, nbins=numberOfBins)
views.append(plotter)
return views
def _plotHistogram(self, param=None):
md = MetaData()
md.read(self.protocol._getFileName(FN_METADATA_HISTOGRAM))
x_axis = []
y_axis = []
i = 0
for idx in md:
x_axis_ = md.getValue(MDL_X, idx)
if i == 0:
x0 = x_axis_
elif i == 1:
x1 = x_axis_
y_axis_ = md.getValue(MDL_COUNT, idx)
i += 1
x_axis.append(x_axis_)
y_axis.append(y_axis_)
plotter = EmPlotter()
plotter.createSubPlot("Resolutions Histogram", "Resolution (A)",
"# of Counts")
barwidth = x1 - x0
plotter.plotDataBar(x_axis, y_axis, barwidth)
return [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")
plotter.plotHist(list(imgListNoZero), nbins)
return [plotter]
def _showHistogram(self, param=None):
fn = self.protocol._getFileName('output_hist')
with open(fn) as f:
views = []
numberOfBins = 10
plotter = EmPlotter()
plotter.createSubPlot("PSF Resolution histogram", "Resolution (A)",
"Ang (str)")
resolution = [float(line.strip()) for line in f]
plotter.plotHist(resolution, nbins=numberOfBins)
plotter.show()
return views.append(plotter)
def _plotHistogram(self, param=None):
imageFile = self.protocol._getFileName(RESMAP_VOL)
img = ImageHandler().read(imageFile)
imgData = img.getData()
imgList = imgData.flatten()
imgDataMax = self.getBackGroundValue(imgList)
imgListNoZero = filter(lambda x: 0 < x < imgDataMax, imgList)
nbins = 30
plotter = EmPlotter(x=1, y=1, mainTitle=" ")
plotter.createSubPlot("Resolution histogram", "Resolution (A)",
"# of Counts")
plotter.plotHist(imgListNoZero, nbins)
return [plotter]
def plotMultiHistogram(valuesList,
colors=None,
legend=None,
numOfBins=100,
plotter=None,
views=None,
includeEmpties=False):
""" Values list must be a n-list of list,
where n is the number of the subhistograms to plot.
Multiple histograms will be plot in the same chart
If no views is passed, a new list-views will be returned with the hist.
If no plotter is passed, a new generic one will be created.
"""
if not all([isinstance(x, list) for x in valuesList]):
print("Not all items in values list are lists. Returning...")
return
if colors is None:
from matplotlib import colors
from random import shuffle
colors = colors.cnames.keys()
shuffle(colors)
if any([len(x) for x in valuesList]):
if plotter is None:
plotter = EmPlotter()
plotter.createSubPlot("Histogram", "Score", "# of Items")
w1 = None
finalLegend = []
for idx, values in enumerate(valuesList):
if values or includeEmpties:
if w1 is None:
w1 = (max(values) - min(values)) / numOfBins
else:
numOfBins = int((max(values) - min(values)) / w1)
plotter.plotHist(values, nbins=numOfBins, color=colors[idx])
if legend:
finalLegend.append(legend[idx])
if finalLegend:
plotter.legend(labels=finalLegend)
if views is None:
views = [plotter]
else:
views.append(plotter)
return views
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 plot1D(self, ctfSet, ctfId):
ctfModel = ctfSet[ctfId]
psdFn = ctfModel.getPsdFile()
fn = os.path.join(
pwutils.removeExt(psdFn).replace("_ctf", "") + '_EPA.log')
xplotter = EmPlotter(windowTitle='GCTF results')
plot_title = '%s # %d\n' % (ctfSet.getTsId(),
ctfId) + getPlotSubtitle(ctfModel)
a = xplotter.createSubPlot(plot_title, 'Resolution (Angstroms)', 'CTF')
a.invert_xaxis()
version = Plugin.getActiveVersion()
curves = [1, 4, 5] if version == '1.18' else [1, 3, 4]
for i in curves:
_plotCurve(a, i, fn)
xplotter.showLegend([
'simulated CTF',
# 'equiphase avg.',
# 'bg', # only for v1.18
'equiphase avg. - bg',
'cross correlation'
])
a.grid(True)
return xplotter
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 _showMollweide(self, param=None):
""" This plot script is based on two scripts by their respective authors:
- PlotOD.py from cryoEF package
- https://github.com/PirateFernandez/python3_rln_scripts/blob/main/rln_star_2_mollweide_any_star.py
"""
import numpy as np
from matplotlib import spines
from scipy.stats import gaussian_kde
views = []
xplotter = EmPlotter(
windowTitle="Mollweide projection plot of orientation distribution"
)
fn = np.genfromtxt(self.protocol._getFileName('anglesFn'),
delimiter=' ')
phi = fn[:, 0]
theta = fn[:, 1]
# Convert degrees to radians and obey angular range conventions
x = phi / 180 * np.pi # x is the phi angle (longitude)
y = theta / 180 * np.pi # y is the theta angle (latitude)
y = -1 * y + np.pi / 2 # The convention in RELION is [0, 180] for theta,
# whereas for the projection function it is [90, -90], so this conversion is required.
vertical_rad = np.vstack([y, x])
m = gaussian_kde(vertical_rad)(vertical_rad)
ax = xplotter.createSubPlot('', 'phi', 'theta', projection="mollweide")
# Plot your points on the projection
#ax.plot(x, y, ',', alpha=0.5, color='#64B5F6') # alpha - transparency (from 0 to 1), color - specify hex code
a = ax.scatter(x, y, cmap='plasma', c=m, s=2, alpha=0.4)
# Draw the horizontal and the vertical grid lines. Can add more grid lines if required.
major_ticks_x = [-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi]
major_ticks_y = [-np.pi / 2, -np.pi / 4, 0, np.pi / 4, np.pi / 2]
ax.set_xticks(major_ticks_x)
ax.set_yticks(major_ticks_y)
ax.set_xticklabels([
'-180$^\circ$', '-90$^\circ$', '0$^\circ$', '90$^\circ$',
'180$^\circ$'
],
color='grey')
ax.set_yticklabels([
'-90$^\circ$', '-45$^\circ$', '0$^\circ$', '45$^\circ$',
'90$^\circ$'
],
color='grey')
# Set the color and the thickness of the grid lines
ax.grid(which='both', linestyle='--', linewidth=1, color='#555F61')
# Set the color and the thickness of the outlines
for child in ax.get_children():
if isinstance(child, spines.Spine):
child.set_color('#555F61')
xplotter.getColorBar(a)
xplotter.tightLayout()
xplotter.show()
return views.append(xplotter)
def _plotHistogram(self, param=None):
md = MetaData()
md.read(self.protocol._getExtraPath(FN_METADATA_HISTOGRAM))
x_axis = []
y_axis = []
for idx in md:
x_axis_ = md.getValue(MDL_X, idx)
y_axis_ = md.getValue(MDL_COUNT, idx)
x_axis.append(x_axis_)
y_axis.append(y_axis_)
_plotter = EmPlotter()
_plotter.createSubPlot("Resolutions Histogram",
"Resolution (A)", "# of Counts")
barwidth = (x_axis[-1] - x_axis[0]) / len(x_axis)
_plotter.plotDataBar(x_axis[:-2], y_axis[:-2], barwidth)
return [_plotter]
def getCTFViews(self, ctfSet):
# This could be used by any CTF viewer to show CTF plus, phaseShift plot
# if applies.
# Return phaseShift plot if apply
firstCtf = ctfSet.getFirstItem()
if firstCtf.hasPhaseShift():
phase_shift = []
for ctf in ctfSet.iterItems():
phShift = ctf.getPhaseShift()
phase_shift.append(phShift)
plotter = EmPlotter()
plotter.createSubPlot("Phase Shift estimation", "Number of CTFs",
"Phase Shift")
plotter.plotData(np.arange(0, len(phase_shift)), phase_shift)
self._views.append(plotter)
# Return Standard CTF view (showJ)
self._views.append(CtfView(self._project, ctfSet))
def _plotDendrogram(self, e=None):
xplotter = EmPlotter()
self.plt = xplotter.createSubPlot("Dendrogram", "", "")
self.step = 0.25
self.rightMost = 0.0 # Used to arrange leaf nodes at the bottom
node = self.protocol.buildDendrogram()
self.plotNode(node, self.minHeight.get())
self.plt.set_xlim(0., self.rightMost + self.step)
self.plt.set_ylim(-10, 105)
return [xplotter]
def _visualizeHistogram(self, e=None):
views = []
numberOfBins = self.visualizeHistogram.get()
outCoords = self.protocol.getOutput()
if outCoords:
mdLabel = emlib.MDL_GOOD_REGION_SCORE
if getXmippAttribute(outCoords.getFirstItem(), mdLabel):
plotter = EmPlotter()
plotter.createSubPlot("Deep micrograph score",
"Deep micrograph score",
"Number of Coordinates")
cScores = [getXmippAttribute(coord, mdLabel).get()
for coord in outCoords]
plotter.plotHist(cScores, nbins=numberOfBins)
views.append(plotter)
else:
print(" > 'outputCoordinates' don't have 'xmipp_zScoreDeepLearning2' label.")
else:
print(" > Output not ready yet.")
return views
def visualize(self, obj, **kwargs):
self.prot = obj
# Experimental
x = np.asarray([float(xi.strip()) for xi in self.prot.x.get().split(',')])
p = np.asarray([float(xi.strip()) for xi in self.prot.p.get().split(',')])
if self.prot.descending:
x=np.flip(x,0)
p=np.flip(p,0)
logx = np.log(x)
logx = np.insert(logx, 0, np.log(np.min(x)/2))
p=p/np.sum(p)
barx = []
bary = p
widths = []
locx = []
labels = []
for i in range (0,p.shape[0]):
barx.append(0.5*(logx[i]+logx[i+1]))
widths.append(logx[i+1]-logx[i])
locx.append(np.log(x[i]))
labels.append("log(%4.2f)"%x[i])
plotter =EmPlotter(style='seaborn-whitegrid')
ax = plotter.createSubPlot("Particle size distribution", "log(Particle size)", "Fraction")
ax.bar(barx, bary, width=widths, linewidth=1, label="Experimental", edgecolor="black")
plt.xticks(locx, labels)
# Theoretical
fhSummary = open(self.prot._getPath("summary.txt"))
lineno=0
for line in fhSummary.readlines():
if lineno==0:
mu = float((line.split()[2]).split('=')[1])
elif lineno==1:
sigma = float((line.split()[2]).split('=')[1])
lineno+=1
fhSummary.close()
logx = np.arange(np.min(logx),np.max(logx),(np.max(logx)-np.min(logx))/100)
theox = norm.pdf(logx,mu,sigma)
ax.plot(logx,theox/np.max(theox)*np.max(bary), color='red', label='Theoretical (log-normal)')
# General
ax.legend()
ax.grid(True)
plotter.show()
def createCtfPlot(ctfSet, ctfId):
""" Create EmPlotter instance. """
ctfModel = ctfSet[ctfId]
psdFn = ctfModel.getPsdFile()
fn = removeExt(psdFn) + "_avrot.txt"
xplotter = EmPlotter(windowTitle='CTFFind results')
plot_title = getPlotSubtitle(ctfModel)
a = xplotter.createSubPlot(plot_title, 'Spacial frequency (1/A)',
'Amplitude (or cross-correlation)')
legendName = ['Amplitude spectrum', 'CTF Fit', 'Quality of fit']
_plotCurves(a, fn)
xplotter.showLegend(legendName, loc='upper right')
a.set_ylim([-0.1, 1.1])
a.grid(True)
xplotter.show()
def show(self, form, *args):
prot = form.protocol
defocusGroups = prot.createDefocusGroups()
print(defocusGroups)
plotter = EmPlotter(windowTitle='%d Defocus Groups' %
len(defocusGroups),
figsize=(8, 6))
ax = plotter.createSubPlot("", "defocus (A)", "count", 1, 1)
for group in defocusGroups:
ax.bar(group.minDefocus,
group.count,
group.maxDefocus - group.minDefocus,
align='edge')
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 _createScatterPlot(self, rmax, colorzaxis=False):
gridsize = self._getGridSize(1)
xplotter = EmPlotter(x=gridsize[0],
y=gridsize[1],
windowTitle='Tilt geometry plot')
plot_title = 'Tilt pair parameter plot'
a = xplotter.createSubPlot(plot_title,
'Tilt axis',
'Tilt angle',
projection='polar')
datap, r, theta, zaxis = self._getValues()
if colorzaxis:
a.scatter(theta, r, c=zaxis)
else:
a.scatter(theta, r)
a.set_rmax(rmax)
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]
def createCtfPlot(ctfSet, ctfId):
ctfModel = ctfSet[ctfId]
psdFn = ctfModel.getPsdFile()
fn = pwutils.removeExt(psdFn) + "_EPA.log"
xplotter = EmPlotter(windowTitle='CTF Fitting')
plot_title = getPlotSubtitle(ctfModel)
a = xplotter.createSubPlot(plot_title, 'Resolution (Angstroms)', 'CTF')
a.invert_xaxis()
version = Plugin.getActiveVersion()
curves = [1, 4, 5] if version == '1.18' else [1, 3, 4]
for i in curves:
_plotCurve(a, i, fn)
xplotter.showLegend([
'simulated CTF',
# 'equiphase avg.',
# 'bg', # only for v1.18
'equiphase avg. - bg',
'cross correlation'
])
a.grid(True)
xplotter.show()
def _showVolumeColorSlices(self, param=None):
imageFile = self.protocol._getFileName(RESMAP_VOL)
imgData, _, _, _ = self.getImgData(imageFile)
xplotter = EmPlotter(x=2,
y=2,
mainTitle="Local Resolution Slices "
"along %s-axis." % self._getAxis())
# The slices to be shown are close to the center. Volume size is divided
# in segments, the fourth central ones are selected i.e. 3,4,5,6
for i in list(range(3, 7)):
sliceNumber = self.getSlice(i, imgData)
a = xplotter.createSubPlot("Slice %s" % (sliceNumber + 1), '', '')
matrix = self.getSliceImage(imgData, sliceNumber, self._getAxis())
plot = xplotter.plotMatrix(a,
matrix,
self.lowest.get(),
self.highest.get(),
cmap=self.getColorMap(),
interpolation="nearest")
xplotter.getColorBar(plot)
return [xplotter]
def _showOneColorslice(self, param=None):
imageFile = self.protocol._getFileName(RESMAP_VOL)
imgData, _, _, volDims = self.getImgData(imageFile)
print(volDims)
xplotter = EmPlotter(x=1,
y=1,
mainTitle="Local Resolution Slices "
"along %s-axis." % self._getAxis())
sliceNumber = self.sliceNumber.get()
if sliceNumber < 0:
sliceNumber = volDims[0] / 2
else:
sliceNumber -= 1
# sliceNumber has no sense to start in zero
a = xplotter.createSubPlot("Slice %s" % (sliceNumber + 1), '', '')
matrix = self.getSliceImage(imgData, sliceNumber, self._getAxis())
plot = xplotter.plotMatrix(a,
matrix,
self.lowest.get(),
self.highest.get(),
cmap=self.getColorMap(),
interpolation="nearest")
xplotter.getColorBar(plot)
return [xplotter]
def _showFSC(self, paramName=None):
threshold = self.resolutionThresholdFSC.get()
iterations = self._getIterations()
groups = self._getGroups()
if self.isGoldStdProt():
template = 'fscdoc_m_%02d.stk'
title = 'Masked FSC'
else:
template = 'fscdoc_%02d.stk'
title = 'FSC'
if self.groupFSC == 0: # group by iterations
files = [(it, self._getFinalPath(template % it))
for it in iterations]
legendPrefix = 'iter'
else:
it = iterations[-1] # show only last iteration
legendPrefix = 'group'
def group(f): # retrieve the group number
return int(f.split('_')[-1].split('.')[0])
groupFiles = glob(self._getFinalPath('ofscdoc_%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(title, 'Angstroms^-1', 'FSC', yformat=False)
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)
# plot final FSC curve (from BP)
if self.groupFSC == 0 and not self.isGoldStdProt():
lastIter = self.protocol._getLastIterNumber()
if lastIter in iterations:
fscFinalFile = self._getFinalPath('ofscdoc_%02d.stk' %
lastIter)
if os.path.exists(fscFinalFile):
self._plotFSC(a, fscFinalFile)
legends.append('final')
if threshold < self.maxfsc:
a.plot([self.minInv, self.maxInv], [threshold, threshold],
color='black',
linestyle='--')
plotter.showLegend(legends)
a.grid(True)
return [plotter]