def validate(self):
errors = []
N1=getMdSize(self.Operand1)
Operation=self.Operation
checkDimensions=False
if Operation=='column' or Operation=='slice' or Operation=='row':
if not self.Operand2.isdigit():
errors.append('You should give a number for the column, slice or row')
elif Operation=='dot product':
if self.Operand2.isdigit():
errors.append('Second operand cannot be a number')
else:
checkDimensions=True
elif Operation=='plus' or Operation=='minus' or Operation=='multiply' or Operation=='divide' or Operation=='minimum' or \
Operation=='maximum':
if not self.Operand2.isdigit():
checkDimensions=True
if checkDimensions:
md1=MetaData(self.Operand1)
md2=MetaData(self.Operand2)
x1, y1, z1, _, _ = MetaDataInfo(md1)
x2, y2, z2, _, _ = MetaDataInfo(md2)
if x1!=x2 or y1!=y2 or z1!=z2:
errors.append("Image/Volume sizes in the two operands are not the same")
if md2.size()>1:
if md2.size()!=md1.size():
errors.append("The number of images/volumes in the two operands are not the same")
return errors
def summary(self):
message=ProtInitVolumeBase.summary(self)
message.append("RANSAC iterations: %d"%self.NRansac)
for n in range(self.NumVolumes):
fnBase='volumeProposed%05d'%n
fnRoot=self.workingDirPath(fnBase+".xmd")
if os.path.isfile(fnRoot):
md=MetaData(fnRoot)
if (md.size()< 5) :
message.append("Num of inliers for %s too small and equal to %d"%(fnRoot,md.size()))
message.append("Decrease the value of Inlier Threshold parameter and run again")
fnBase="ransac00000.xmd"
fnRoot=self.workingDirPath("tmp/"+fnBase)
if os.path.isfile(fnRoot):
md=MetaData(fnRoot)
if (md.size()< 5) :
message.append("Num of random samples too small and equal to %d"%(md.size()))
message.append("If the option Dimensionality reduction is on, increase the number of grids per dimension")
message.append("If the option Dimensionality reduction is off, increase the number of random samples")
if self.UseSA:
message.append("Simulated annealing used")
return message
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 coocurenceMatrix(log,RemainingClasses,WorkingDirStructure,NumVolumes,nI,CorePercentile,CorrThresh):
import numpy
mdRemaining = MetaData(RemainingClasses)
Nimgs=mdRemaining.size()
allNames=mdRemaining.getColumnValues(MDL_IMAGE)
matrixTotal = numpy.zeros([Nimgs,Nimgs])
for n in range(NumVolumes):
fnBase='proposedVolume%05d'%n
fnRoot=os.path.join(WorkingDirStructure,fnBase)
md = MetaData(fnRoot+".xmd")
size = md.size()
num=[]
corr=[]
for objId in md:
name = md.getValue(MDL_IMAGE, objId)
if name in allNames:
num.append(allNames.index(name))
corr.append(md.getValue(MDL_MAXCC, objId))
else:
print "Cannot find ",name
if size!=len(num):
print "Error when processing: ",fnRoot+".xmd"
aaa
matrix = numpy.zeros([Nimgs,Nimgs])
for i in range(size):
for j in range(size):
matrix[num[i],num[j]]=((corr[i]+corr[j])/2)
#numpy.savetxt(os.path.join(WorkingDirStructure,'coocurrenceMatrix_%05d.txt'%n), matrix)
matrixTotal=matrixTotal+matrix
matrixTotal=matrixTotal/NumVolumes
numpy.savetxt(os.path.join(WorkingDirStructure,'coocurrenceMatrix.txt'),matrixTotal)
largestComponent=procCoocurenceMatrix(matrixTotal,CorePercentile,CorrThresh)
md = MetaData()
for idx in largestComponent:
id=md.addObject()
md.setValue(MDL_IMAGE,allNames[idx],id)
md.write(os.path.join(WorkingDirStructure+"_core","imagesCore.xmd"))
if md.size()==0:
print "There are no images in the core"
aaa
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 summary(self):
message=ProtHG3DBase.summary(self)
message.append("RANSAC iterations: %d"%self.NRansacInitial)
for n in range(self.NumVolumesFinal):
fnBase='proposedVolume%05d'%n
fnRoot=self.workingDirPath(fnBase+".xmd")
if os.path.isfile(fnRoot):
md=MetaData(fnRoot)
if (md.size()< 5) :
message.append("Num of inliers for %s too small and equal to %d"%(fnRoot,md.size()))
message.append("Decrease the value of Inlier Threshold parameter and run again")
fnBase="ransac00000.xmd"
fnRoot=self.workingDirPath("tmp/"+fnBase)
if os.path.isfile(fnRoot):
md=MetaData(fnRoot)
return message
def getMdSizeEnabled(filename):
""" Return the metadata size containing only ''enabled'' lines """
md = MetaData(filename)
md.removeDisabled()
return md.size()
def wizardCL2DNumberOfClasses(gui, var):
fnSel = gui.getVarValue('InSelFile')
if exists(fnSel):
md = MetaData(fnSel)
gui.setVarValue("NumberOfReferences", int(round(md.size()/200.0)))
class ProtPreprocessVolumes(XmippProtocol):
def __init__(self, scriptname, project):
XmippProtocol.__init__(self, protDict.preprocess_volume.name, scriptname, project)
self.Import = 'from protocol_preprocess_volume import *'
self.mdIn=MetaData(self.InModel)
if self.mdIn.size()==1:
self.OutModel=self.workingDirPath("volume.vol")
self.singleVolume=True
else:
self.OutModel=self.workingDirPath("volumes.stk")
self.singleVolume=False
def defineSteps(self):
self.insertStep('createAcquisition',WorkingDir=self.WorkingDir,Ts=self.FinalTs)
if self.InitialTs!=self.FinalTs or self.FinalSize!=-1:
if self.InitialTs!=self.FinalTs and self.FinalSize==-1:
x, _, _, _, _ = MetaDataInfo(self.mdIn)
self.FinalSize=floor(x/(self.FinalTs/self.InitialTs))
self.insertStep("changeSamplingRateAndOrBox",InModel=self.InModel,OutModel=self.OutModel,
SingleVolume=self.singleVolume, InitialTs=self.InitialTs,FinalTs=self.FinalTs,Size=self.FinalSize)
else:
if self.singleVolume:
self.insertStep('copyFile',source=self.InModel,dest=self.OutModel)
else:
self.insertRunJobStep('xmipp_image_convert', "-i %s -o %s --save_metadata_stack --track_origin"%(self.InModel,self.OutModel),
verifyFiles=[self.OutModel])
if self.DoChangehand:
self.insertStep("changeHand",OutModel=self.OutModel)
if self.DoRandomize:
self.insertStep("randomize",OutModel=self.OutModel,Ts=self.FinalTs,MaxResolution=self.MaxResolutionRandomize)
if self.DoFilter:
self.insertStep("filter",OutModel=self.OutModel,Ts=self.FinalTs,MaxResolution=self.MaxResolution)
if self.DoSymmetrize:
self.insertStep("symmetrize",OutModel=self.OutModel,Symmetry=self.Symmetry, SymmetryAggregation=self.SymmetryAggregation)
if self.DoMask:
self.insertStep("mask",OutModel=self.OutModel,MaskRadius=self.MaskRadius)
if self.DoAdjust:
self.insertStep("adjust",OutModel=self.OutModel,SetOfImages=self.SetOfImages)
if self.DoNormalize:
self.insertStep("normalize",OutModel=self.OutModel,MaskRadius=self.MaskRadiusNormalize)
if self.DoThreshold:
self.insertStep("threshold",OutModel=self.OutModel,Threshold=self.Threshold)
if self.DoSegment:
self.insertStep("segment",OutModel=self.OutModel,SegmentationType=self.SegmentationType,SegmentationMass=self.SegmentationMass,
Ts=self.FinalTs)
def validate(self):
errors = []
if self.InitialTs<0:
errors.append("Initial sampling rate must be provided")
if self.FinalTs<0:
errors.append("Initial sampling rate must be provided")
maxFreq=2.0*self.FinalTs
if self.DoRandomize and self.MaxResolutionRandomize<maxFreq:
errors.append("Phase randomization cannot be performed beyond %f A (Nyquist)"%maxFreq)
if self.DoFilter and self.MaxResolutionRandomize<maxFreq:
errors.append("Low pass filtering cannot be performed beyond %f A (Nyquist)"%maxFreq)
if self.DoAdjust and not self.singleVolume:
errors.append("Gray adjusting is meant only for single volumes")
if self.DoSegment and not self.singleVolume:
errors.append("Segmentation is meant only for single volumes")
return errors
def summary(self):
messages = []
messages.append("Input model: [%s]" % self.InModel)
messages.append("Output: [%s]" % self.OutModel)
messages.append("Operations: ")
if self.InitialTs!=self.FinalTs:
messages.append(" Sampling rate changed from %f to %f"%(float(self.InitialTs),float(self.FinalTs)))
if self.FinalSize>0:
messages.append(" Volume boxed to %dx%dx%d voxels"%(int(self.FinalSize),int(self.FinalSize),int(self.FinalSize)))
if self.DoChangehand:
messages.append(" Hand changed")
if self.DoRandomize:
messages.append(" Phases randomized beyond %f A"%float(self.MaxResolutionRandomize))
if self.DoFilter:
messages.append(" Filtered to %f A"%float(self.MaxResolution))
if self.DoSymmetrize:
messages.append(" Symmetrized %s"%self.Symmetry)
if self.DoMask:
if self.MaskRadius>0:
messages.append(" Masked within a sphere of radius %d"%self.MaskRadius)
else:
messages.append(" Masked within the maximal sphere fitting in its box")
if self.DoAdjust:
messages.append(" Gray values adjusted to fit [%s]"%self.SetOfImages)
if self.DoNormalize:
if self.MaskRadiusNormalize>0:
messages.append(" Normalized with background beyond radius %d"%self.MaskRadiusNormalize)
else:
messages.append(" Normalized with background beyond the maximal fitting sphere")
if self.DoThreshold:
messages.append(" Thresholded below %f"%(float(self.Threshold)))
if self.DoSegment:
if self.SegmentationType=="Automatic":
messages.append(" Automatically segmented")
else:
m=" Segmented to a mass of "
if self.SegmentationType=="Voxel mass":
m+="%d voxels"%(int(SegmentationMass))
elif self.SegmentationType=="Aminoacid mass":
m+="%d aminoacids"%(int(SegmentationMass))
elif self.SegmentationType=="Dalton mass":
m+="%d daltons"%(int(SegmentationMass))
messages.append(m)
return messages
def papers(self):
papers=[]
if self.DoNormalize:
papers.append('Sorzano, Ultramic (2004) [http://www.ncbi.nlm.nih.gov/pubmed/15450658]')
if self.InitialTs!=self.FinalTs:
papers.append('Sorzano, IEEE WISP (2009) [http://ieeexplore.ieee.org/xpl/login.jsp?arnumber=5286563]')
if self.DoRandomize:
papers.append('Chen, Ultramic (2013) [http://www.ncbi.nlm.nih.gov/pubmed/23872039]')
return papers
def visualize(self):
from protlib_utils import runShowJ
if os.path.exists(self.OutModel):
runShowJ(self.OutModel)
