def checkResult(self, testfiles, outDir, random):
error = ""
try:
from xmipp import FileName, Image, compareTwoFiles, compareTwoMetadataFiles
for file in testfiles:
file = join(outDir, file)
fileGoldStd = file.replace(self.fnDir, 'goldStandard')
result = True
if not exists(file):
error += file + " was NOT produced\n"
else:
if (random):
self.warning += file + " was created using a random seed, check skipped\n"
self.warningFlag = True
else:
fnGoldStd = FileName(fileGoldStd)
if fnGoldStd.isImage():
im1 = Image(fileGoldStd)
im2 = Image(file)
result = im1.equal(im2, 0.001)
elif fnGoldStd.isMetaData():
result = compareTwoMetadataFiles(file, fileGoldStd)
else:
result = compareTwoFiles(file, fileGoldStd, 0)
if not result:
error += " file '%s' and '%s' are NOT identical\n" % (
file, fileGoldStd)
except KeyboardInterrupt:
raise
except Exception, ex:
error += " Error checking results: '%s'\n" % str(ex)
def checkResult(self, testfiles, outDir, random):
error = ""
try:
from xmipp import FileName, Image, compareTwoFiles, compareTwoMetadataFiles
for file in testfiles:
file = join(outDir, file)
fileGoldStd = file.replace(self.fnDir, 'goldStandard')
result = True
if not exists(file):
error += file + " was NOT produced\n"
else:
if (random):
self.warning += file + " was created using a random seed, check skipped\n"
self.warningFlag = True
else:
fnGoldStd = FileName(fileGoldStd)
if fnGoldStd.isImage():
im1 = Image(fileGoldStd)
im2 = Image(file)
result = im1.equal(im2, 0.001)
elif fnGoldStd.isMetaData():
result = compareTwoMetadataFiles(file, fileGoldStd)
else:
result = compareTwoFiles(file, fileGoldStd, 0)
if not result:
error += " file '%s' and '%s' are NOT identical\n" % (file, fileGoldStd)
except KeyboardInterrupt:
raise
except Exception, ex:
error += " Error checking results: '%s'\n" % str(ex)
def produceAlignedImagesStep(self, volumeIsCTFCorrected, fn, images):
from numpy import array, dot
fnOut = "classes_aligned@" + fn
MDin = MetaData(images)
MDout = MetaData()
n = 1
hasCTF = MDin.containsLabel(xmipp.MDL_CTF_MODEL)
for i in MDin:
fnImg = MDin.getValue(xmipp.MDL_IMAGE, i)
fnImgRef = MDin.getValue(xmipp.MDL_IMAGE_REF, i)
maxCC = MDin.getValue(xmipp.MDL_MAXCC, i)
rot = MDin.getValue(xmipp.MDL_ANGLE_ROT, i)
tilt = MDin.getValue(xmipp.MDL_ANGLE_TILT, i)
psi = -1.0 * MDin.getValue(xmipp.MDL_ANGLE_PSI, i)
flip = MDin.getValue(xmipp.MDL_FLIP, i)
if flip:
psi = -psi
eulerMatrix = Euler_angles2matrix(0.0, 0.0, psi)
x = MDin.getValue(xmipp.MDL_SHIFT_X, i)
y = MDin.getValue(xmipp.MDL_SHIFT_Y, i)
shift = array([x, y, 0])
shiftOut = dot(eulerMatrix, shift)
[x, y, z] = shiftOut
if flip:
x = -x
id = MDout.addObject()
MDout.setValue(xmipp.MDL_IMAGE, fnImg, id)
MDout.setValue(xmipp.MDL_IMAGE_REF, fnImgRef, id)
MDout.setValue(xmipp.MDL_IMAGE1, "%05d@%s" % (n, self._getExtraPath("diff.stk")), id)
if hasCTF:
fnCTF = MDin.getValue(xmipp.MDL_CTF_MODEL, i)
MDout.setValue(xmipp.MDL_CTF_MODEL, fnCTF, id)
MDout.setValue(xmipp.MDL_MAXCC, maxCC, id)
MDout.setValue(xmipp.MDL_ANGLE_ROT, rot, id)
MDout.setValue(xmipp.MDL_ANGLE_TILT, tilt, id)
MDout.setValue(xmipp.MDL_ANGLE_PSI, psi, id)
MDout.setValue(xmipp.MDL_SHIFT_X, x, id)
MDout.setValue(xmipp.MDL_SHIFT_Y, y, id)
MDout.setValue(xmipp.MDL_FLIP, flip, id)
MDout.setValue(xmipp.MDL_ENABLED, 1, id)
n += 1
MDout.write(fnOut, xmipp.MD_APPEND)
# Actually create the differences
img = Image()
imgRef = Image()
if hasCTF and volumeIsCTFCorrected:
Ts = MDin.getValue(xmipp.MDL_SAMPLINGRATE, MDin.firstObject())
for i in MDout:
img.readApplyGeo(MDout, i)
imgRef.read(MDout.getValue(xmipp.MDL_IMAGE_REF, i))
if hasCTF and volumeIsCTFCorrected:
fnCTF = MDout.getValue(xmipp.MDL_CTF_MODEL, i)
imgRef.applyCTF(fnCTF, Ts)
img.convert2DataType(DT_DOUBLE)
imgDiff = img - imgRef
imgDiff.write(MDout.getValue(xmipp.MDL_IMAGE1, i))
def preRun(self):
self.ImportProtocol()
self.Iteration_Working_Directory = os.path.join(self.pmprotWorkingDir,'Iter_00'+ str(self.iterationNo))
self.subtractionDir = self.workingDirPath(self.RunName)
self.volsDir = self.workingDirPath(self.volsDir)
self.referenceDir = self.workingDirPath(self.referenceDir)
self.subImgsDir = self.workingDirPath(self.subImgsDir)
self.scaledImages = self.workingDirPath(self.scaledImages)
self.resultsImagesName = self.workingDirPath(self.resultsImagesName)
self.localFilenameCurrentAngles = self.workingDirPath(self.localCurrentAngles)
self.CtfGroupDirectory = self.workingDirPath(self.CtfGroupDirectoryName)
tmpCTFname = join(self.CtfGroupDirectoryName,self.localStackCTFs)
self.projmatchStackCTFs = join(self.pmprotWorkingDir,tmpCTFname)
self.localStackCTFs = self.workingDirPath(tmpCTFname)
tmpCTFname = join(self.CtfGroupDirectoryName,self.localDocCTFs)
self.projmatchDocCTFs = join(self.pmprotWorkingDir,tmpCTFname)
self.localDocCTFs = self.workingDirPath(tmpCTFname)
if(self.MaxChangeInAngles > 100):
self.MaxChangeInAngles=-1
tmpFilename = self.current_angles
self.filename_currentAngles = os.path.join(self.Iteration_Working_Directory,tmpFilename)
if(self.doScaleImages):
md = MetaData(self.filename_currentAngles)
img = Image()
img.readApplyGeo(md, 1, False, DATA, ALL_IMAGES,False)
x=y=z=n=0
(x,y,z,n) = img.getDimensions()
factorX = x / self.dimX
if (self.dimY<0):
factorY = y / self.dimX
else:
factorY = y / self.dimY
self.dRradiusMax = round(self.dRradiusMax/factorX)
self.dRradiusMin = round(self.dRradiusMin/factorY)
def produceAlignedImagesStep(self, volumeIsCTFCorrected, fn, images):
from numpy import array, dot
fnOut = 'classes_aligned@' + fn
MDin = MetaData(images)
MDout = MetaData()
n = 1
hasCTF = MDin.containsLabel(xmipp.MDL_CTF_MODEL)
for i in MDin:
fnImg = MDin.getValue(xmipp.MDL_IMAGE, i)
fnImgRef = MDin.getValue(xmipp.MDL_IMAGE_REF, i)
maxCC = MDin.getValue(xmipp.MDL_MAXCC, i)
rot = MDin.getValue(xmipp.MDL_ANGLE_ROT, i)
tilt = MDin.getValue(xmipp.MDL_ANGLE_TILT, i)
psi = -1. * MDin.getValue(xmipp.MDL_ANGLE_PSI, i)
flip = MDin.getValue(xmipp.MDL_FLIP, i)
if flip:
psi = -psi
eulerMatrix = Euler_angles2matrix(0., 0., psi)
x = MDin.getValue(xmipp.MDL_SHIFT_X, i)
y = MDin.getValue(xmipp.MDL_SHIFT_Y, i)
shift = array([x, y, 0])
shiftOut = dot(eulerMatrix, shift)
[x, y, z] = shiftOut
if flip:
x = -x
id = MDout.addObject()
MDout.setValue(xmipp.MDL_IMAGE, fnImg, id)
MDout.setValue(xmipp.MDL_IMAGE_REF, fnImgRef, id)
MDout.setValue(xmipp.MDL_IMAGE1,
"%05d@%s" % (n, self._getExtraPath("diff.stk")), id)
if hasCTF:
fnCTF = MDin.getValue(xmipp.MDL_CTF_MODEL, i)
MDout.setValue(xmipp.MDL_CTF_MODEL, fnCTF, id)
MDout.setValue(xmipp.MDL_MAXCC, maxCC, id)
MDout.setValue(xmipp.MDL_ANGLE_ROT, rot, id)
MDout.setValue(xmipp.MDL_ANGLE_TILT, tilt, id)
MDout.setValue(xmipp.MDL_ANGLE_PSI, psi, id)
MDout.setValue(xmipp.MDL_SHIFT_X, x, id)
MDout.setValue(xmipp.MDL_SHIFT_Y, y, id)
MDout.setValue(xmipp.MDL_FLIP, flip, id)
MDout.setValue(xmipp.MDL_ENABLED, 1, id)
n += 1
MDout.write(fnOut, xmipp.MD_APPEND)
# Actually create the differences
img = Image()
imgRef = Image()
if hasCTF and volumeIsCTFCorrected:
Ts = MDin.getValue(xmipp.MDL_SAMPLINGRATE, MDin.firstObject())
for i in MDout:
img.readApplyGeo(MDout, i)
imgRef.read(MDout.getValue(xmipp.MDL_IMAGE_REF, i))
if hasCTF and volumeIsCTFCorrected:
fnCTF = MDout.getValue(xmipp.MDL_CTF_MODEL, i)
imgRef.applyCTF(fnCTF, Ts)
img.convert2DataType(DT_DOUBLE)
imgDiff = img - imgRef
imgDiff.write(MDout.getValue(xmipp.MDL_IMAGE1, i))
if fn.isMetaData():
md = MetaData(fn)
fileList = []
for objId in md:
fileList.append(md.getValue(MDL_IMAGE, objId))
if len(fileList) > 10:
break
fn = getFilename()
if fn is None:
return
# if radius = -1 then we take half of the image dimension
if outerRadius < 0:
if fn.isImage():
img = Image()
#ROB: must convert to str first or PyString_AsString will complain
img.read(fn, HEADER)
xdim = img.getDimensions()[0]
outerRadius = xdim / 2
if unit == 'angstrom':
outerRadius *= sampling
gui.setVarValue(outerVarName, outerRadius)
from protlib_gui_ext import XmippBrowserMask
results = showBrowseDialog(parent=gui.master, browser=XmippBrowserMask, title="Select mask radius", allowFilter=False,
extra={'fileList': fileList, 'outerRadius': outerRadius,
'innerRadius': innerRadius, 'showInner': showInner,
'unit':unit, 'sampling':sampling})
if results:
# if (unit == 'angstrom'):
