def wizardBrowseCTF2(gui, var):
error = None
vList = ['LowResolCutoff', 'HighResolCutoff']
freqs = gui.getVarlistValue(vList)
importRunName = gui.getVarValue('ImportRun')
prot = gui.project.getProtocolFromRunName(importRunName)
path = prot.WorkingDir
if path and exists(path):
mdPath = prot.getFilename('micrographs')
if exists(mdPath):
from xmipp import MetaData, MDL_MICROGRAPH
md = MetaData(mdPath)
if md.size():
image = md.getValue(MDL_MICROGRAPH, md.firstObject())
if image:
filterExt = "*" + splitext(image)[1]
value = gui.getVarValue('DownsampleFactor')
results = wizardHelperSetDownsampling(gui, var, path, filterExt, value, freqs, md)
if results:
gui.setVarlistValue(vList, results[1:])
else:
error = "Not micrograph found in metadata <%s>" % mdPath
#gui.setVarValue('LowResolCutoff', results[1])
#gui.setVarValue('HighResolCutoff', results[2])
else:
error = "Micrograph metadata <%s> is empty" % mdPath
else:
error = "Micrograph metadata <%s> doesn't exists" % mdPath
else:
error = "Import run <%s> doesn't exists" % str(path)
if error:
showWarning("Select Downsampling Wizard", error, gui.master)
return None
else:
return results
def wizardChooseSizeToExtract(gui, var):
from xmipp import MDL_PICKING_PARTICLE_SIZE, MDL_CTF_MODEL, MDL_SAMPLINGRATE, MDL_SAMPLINGRATE_ORIGINAL
from protlib_gui_ext import ListboxDialog
pickingRun = gui.getVarValue('PickingRun')
pickingProt = gui.project.getProtocolFromRunName(pickingRun)
fnConfig = pickingProt.getFilename("config")
if not exists(fnConfig):
showWarning("Warning", "No elements to select", parent=gui.master)
return
md = MetaData(fnConfig)
particleSize = md.getValue(MDL_PICKING_PARTICLE_SIZE, md.firstObject())
type = gui.getVarValue("DownsampleType")
mdAcquisition = MetaData(pickingProt.getFilename('acquisition'))
objId = mdAcquisition.firstObject()
tsOriginal = tsPicking = mdAcquisition.getValue(MDL_SAMPLINGRATE, objId)
if mdAcquisition.containsLabel(MDL_SAMPLINGRATE_ORIGINAL):
tsOriginal = mdAcquisition.getValue(MDL_SAMPLINGRATE_ORIGINAL, objId)
if type == "same as picking":
factor = 1
else:
factor = tsPicking / tsOriginal;
if type == "other":
try:
factor /= float(gui.getVarValue("DownsampleFactor"))
except Exception, e:
showWarning("Warning", "Please select valid downsample factor", parent=gui.master)
return
def visualize(self):
if self.getRunState() == SqliteDb.RUN_FINISHED:
from protlib_utils import runShowJ
runShowJ("classes@"+self.workingDirPath("classes.xmd"), extraParams=" --columns %d" % self.SomXdim)
else:
from protlib_gui_ext import showWarning
showWarning("Warning", "The algorithm has not finished yet", parent=self.master)
def visualize(self):
summaryFile = self.getFilename('micrographs')
if not exists(summaryFile): # Try to create partial summary file
summaryFile = summaryFile.replace(self.WorkingDir, self.TmpDir)
buildSummaryMetadata(self.WorkingDir, self.Input['micrographs'], summaryFile, self.MDL_TYPE)
if exists(summaryFile):
self.regenerateSummary(summaryFile)
runShowJ(summaryFile, extraParams = "--mode metadata --render psd psdEnhanced image1 image2 --order psd psdEnhanced image1 image2 --zoom 50")
else:
showWarning('Warning', 'There are not results yet',self.master)
def visualize(self):
from protlib_gui_figure import XmippArrayPlotter1D, XmippArrayPlotter2D, XmippArrayPlotter3D
components = self.DisplayRawDeformation.split()
dim = len(components)
if dim > 0:
modeList = []
modeNameList = []
# Get modes
MD = MetaData(self.Modesfile)
MD.removeDisabled()
for modeComponent in components:
mode = int(modeComponent)
if mode > MD.size():
from protlib_gui_ext import showWarning
showWarning("Warning", "You don't have so many modes", parent=self.master)
else:
mode -= 1
currentMode = 0
modeName = ""
for id in MD:
modeName = MD.getValue(MDL_NMA_MODEFILE, id)
currentMode += 1
if currentMode > mode:
break
modeNameList.append(modeName)
modeList.append(mode)
# Actually plot
if dim == 1:
XmippArrayPlotter1D(
self.extraPath("deformations.txt"),
modeList[0],
"Histogram for mode %s" % modeNameList[0],
"Deformation value",
"Number of images",
)
elif dim == 2:
XmippArrayPlotter2D(
self.extraPath("deformations.txt"), modeList[0], modeList[1], "", modeNameList[0], modeNameList[1]
)
elif dim == 3:
XmippArrayPlotter3D(
self.extraPath("deformations.txt"),
modeList[0],
modeList[1],
modeList[2],
"",
modeNameList[0],
modeNameList[1],
modeNameList[2],
)
def wizardSelectFromList(master, frame, list):
'''Helper function to select elements from a list '''
L=len(list)
if L == 0:
showWarning("Warning", "No elements to select", parent=master)
return
if L == 1:
return list[0]
from protlib_gui_ext import ListboxDialog
d = ListboxDialog(frame, list, selectmode=tk.SINGLE)
if len(d.result) > 0:
index = d.result[0]
return(list[index])
else:
return None
def wizardHelperFilter(gui, browser, title, **args):
extra = {'previewLabel': 'Image', 'computingMessage': 'Applying filter...'}
extra.update(args)
varName = args.get('varName', 'InSelFile')
selfile = gui.getVarValue(varName)
path, filename = split(selfile)
if not exists(selfile):
showWarning("Warning", "The input metadata is not a valid file", parent=gui.master)
return
# Select the first 10 particles to display the filter
md = MetaData(selfile)
fileList = []
for i, objId in enumerate(md):
if i == 10:
break
fileList.append(md.getValue(MDL_IMAGE, objId))
extra['fileList'] = fileList
return showBrowseDialog(path=path, parent=gui.master, browser=browser,title=title,
seltype="file", selmode="browse", filter=filename, previewDim=256, extra=extra)
def visualize(self):
if not os.path.exists(self.outputFile):
from protlib_gui_ext import showWarning
showWarning("Error", "There is no result yet",self.master)
else:
runShowJ(self.outputFile)
def wizardShowJ(gui, var):
value = var.getTkValue().strip()
if len(value):
openFile(var.getTkValue())
else:
showWarning("Empty file", "Please select a file to visualize", parent=gui.master)
def visualize(self):
summaryFile = self.getFilename('micrographs')
if exists(summaryFile):
runShowJ(summaryFile, extraParams = "--mode metadata")
else:
showWarning('Warning', 'There are not results yet',self.master)
def launchML2DPlots(protML, selectedPlots):
''' Launch some plot for an ML2D protocol run '''
#import matplotlib
import numpy as np
from protlib_gui_figure import XmippPlotter
protML._plot_count = 0
lastIter = lastIteration(protML)
if lastIter == 0:
return
refs = protML.getFilename('iter_refs', iter=lastIter)
# if not exists(refs):
# return
# blocks = getBlocksInMetaDataFile(refs)
# lastBlock = blocks[-1]
def doPlot(plotName):
return plotName in selectedPlots
# Remove 'mirror' from list if DoMirror is false
if doPlot('DoShowMirror') and not protML.DoMirror:
selectedPlots.remove('DoShowMirror')
n = len(selectedPlots)
if n == 0:
showWarning("ML2D plots", "Nothing to plot", protML.master)
return
elif n == 1:
gridsize = [1, 1]
elif n == 2:
gridsize = [2, 1]
else:
gridsize = [2, 2]
xplotter = XmippPlotter(*gridsize)
# Create data to plot
iters = range(1, lastIter+1)
ll = []
pmax = []
for iter in iters:
logs = protML.getFilename('iter_logs', iter=iter)
md = MetaData(logs)
id = md.firstObject()
ll.append(md.getValue(MDL_LL, id))
pmax.append(md.getValue(MDL_PMAX, id))
if doPlot('DoShowLL'):
a = xplotter.createSubPlot('Log-likelihood (should increase)', 'iterations', 'LL', yformat=True)
a.plot(iters, ll)
#Create plot of mirror for last iteration
if doPlot('DoShowMirror'):
from numpy import arange
from matplotlib.ticker import FormatStrFormatter
md = MetaData(refs)
mirrors = [md.getValue(MDL_MIRRORFRAC, id) for id in md]
nrefs = len(mirrors)
ind = arange(1, nrefs + 1)
width = 0.85
a = xplotter.createSubPlot('Mirror fractions on last iteration', 'references', 'mirror fraction')
a.set_xticks(ind + 0.45)
a.xaxis.set_major_formatter(FormatStrFormatter('%1.0f'))
a.bar(ind, mirrors, width, color='b')
a.set_ylim([0, 1.])
a.set_xlim([0.8, nrefs + 1])
if doPlot('DoShowPmax'):
a = xplotter.createSubPlot('Probabilities distribution', 'iterations', 'Pmax/Psum')
a.plot(iters, pmax, color='green')
if doPlot('DoShowSignalChange'):
md = MetaData()
for iter in iters:
fn = protML.getFilename('iter_refs', iter=iter)
md2 = MetaData(fn)
md2.fillConstant(MDL_ITER, str(iter))
md.unionAll(md2)
# 'iter(.*[1-9].*)@2D/ML2D/run_004/ml2d_iter_refs.xmd')
#a = plt.subplot(gs[1, 1])
#print "md:", md
md2 = MetaData()
md2.aggregate(md, AGGR_MAX, MDL_ITER, MDL_SIGNALCHANGE, MDL_MAX)
signal_change = [md2.getValue(MDL_MAX, id) for id in md2]
xplotter.createSubPlot('Maximum signal change', 'iterations', 'signal change')
xplotter.plot(iters, signal_change, color='green')
return xplotter
