def statsAndConcat(self, s, i, count, beforeAvg=True):
"""Construct array to common space for this timepoint.
This builds upon arrays from previous calls."""
if beforeAvg:
xfm = s[i].getLastXfm(s[i + 1])
else:
xfm = s[i].getLastXfm(s[i - 1])
"""Set this transform as last xfm from input to nlin and calculate nlin to s[i] stats"""
if self.nlinFH:
self.xfmToCommon.insert(0, xfm)
""" Concat transforms to get xfmToCommon and calculate statistics
Note that inverted transform, which is what we want, is calculated in
the statistics module. """
xtc = fh.createBaseName(
s[i].transformsDir,
s[i].basename + "_to_" + self.commonName + ".xfm")
xc = ma.xfmConcat(self.xfmToCommon, xtc,
fh.logFromFile(s[i].logDir, xtc))
self.p.addStage(xc)
s[i].addAndSetXfmToUse(self.nlinFH, xtc)
self.statsCalculation(s[i],
self.nlinFH,
xfm=None,
useChainStats=False)
else:
xtc = None
"""Calculate i to i+1 stats for all but final timePoint"""
if count - i > 1:
self.statsCalculation(s[i], s[i + 1], xfm=xtc, useChainStats=True)
def linAndNlinDisplacement(self):
"""The function calculates both the linear and nonlinear
portions of the displacement, in order to find
pure nonlinear. Input is the commonSpace, so the pure
nonlinear displacement will point from input to target.
This is opposite from the standard stats class, where
the common space is the target
"""
"""Calculate linear part of non-linear xfm from input to target"""
lpnl = linearPartofNlin(self.inputFH, self.targetFH)
self.p.addStage(lpnl)
self.linearXfm = lpnl.outputFiles[0]
"""Invert the transform, so we get the linear xfm from target to input."""
xi = xfmInvert(self.linearXfm, FH=self.inputFH)
self.p.addStage(xi)
"""Calculate full displacement from input to target"""
self.calcFullDisplacement()
"""Calculate pure non-linear displacement from input to target
1. Concatenate inverse linear and full input-target xfm to
get pure_nlin xfm
2. Compute mincDisplacement on this transform.
"""
nlinBase = fh.removeBaseAndExtension(self.xfm) + "_pure_nlin.xfm"
nlinXfm = fh.createBaseName(self.inputFH.tmpDir, nlinBase)
xc = xfmConcat([xi.outputFiles[0], self.xfm], nlinXfm, fh.logFromFile(self.inputFH.logDir, nlinXfm))
self.p.addStage(xc)
nlinDisp = mincDisplacement(self.inputFH, self.inputFH, nlinXfm)
self.p.addStage(nlinDisp)
self.nlinDisp = nlinDisp.outputFiles[0]
def statsAndConcat(self, s, i, count, beforeAvg=True):
"""Construct array to common space for this timepoint.
This builds upon arrays from previous calls."""
if beforeAvg:
xfm = s[i].getLastXfm(s[i+1])
else:
xfm = s[i].getLastXfm(s[i-1])
"""Set this transform as last xfm from input to nlin and calculate nlin to s[i] stats"""
if self.nlinFH:
self.xfmToCommon.insert(0, xfm)
""" Concat transforms to get xfmToCommon and calculate statistics
Note that inverted transform, which is what we want, is calculated in
the statistics module. """
xtc = createBaseName(s[i].transformsDir, s[i].basename + "_to_" + self.commonName + ".xfm")
xc = ma.xfmConcat(self.xfmToCommon, xtc, fh.logFromFile(s[i].logDir, xtc))
self.p.addStage(xc)
# here in order to visually inspect the alignment with the common
# time point, we should resample this subject:
inputResampledToCommon = createBaseName(s[i].resampledDir, s[i].basename + "_to_" + self.commonName + ".mnc")
logToCommon = fh.logFromFile(s[i].logDir, inputResampledToCommon)
resampleCmd = ma.mincresample(s[i],
self.nlinFH,
likeFile=self.nlinFH,
transform=xtc,
output=inputResampledToCommon,
logFile=logToCommon,
argArray=["-sinc"])
self.p.addStage(resampleCmd)
s[i].addAndSetXfmToUse(self.nlinFH, xtc)
self.statsCalculation(s[i], self.nlinFH, xfm=None, useChainStats=False)
else:
xtc=None
"""Calculate i to i+1 stats for all but final timePoint"""
if count - i > 1:
self.statsCalculation(s[i], s[i+1], xfm=xtc, useChainStats=True)
def buildPipeline(self):
# Run lsq12 registration prior to non-linear
self.lsq12Params = mp.setLSQ12MinctraccParams(
self.fileRes,
subject_matter=self.subject_matter,
reg_protocol=self.lsq12_protocol)
lsq12reg = lsq12.LSQ12(self.inputFH,
self.targetFH,
blurs=self.lsq12Params.blurs,
step=self.lsq12Params.stepSize,
gradient=self.lsq12Params.useGradient,
simplex=self.lsq12Params.simplex,
w_translations=self.lsq12Params.w_translations,
defaultDir=self.defaultDir)
self.p.addPipeline(lsq12reg.p)
#Resample using final LSQ12 transform and reset last base volume.
res = ma.mincresample(self.inputFH,
self.targetFH,
likeFile=self.targetFH,
argArray=["-sinc"])
self.p.addStage(res)
self.inputFH.setLastBasevol(res.outputFiles[0])
lsq12xfm = self.inputFH.getLastXfm(self.targetFH)
#Get registration parameters from nlin protocol, blur and register
#Assume a SINGLE generation here.
self.nlinParams = mp.setOneGenMincANTSParams(
self.fileRes, reg_protocol=self.nlin_protocol)
for b in self.nlinParams.blurs:
for j in b:
#Note that blurs for ANTS params in an array of arrays.
if j != -1:
self.p.addStage(ma.blur(self.targetFH, j, gradient=True))
self.p.addStage(ma.blur(self.inputFH, j, gradient=True))
sp = ma.mincANTS(
self.inputFH,
self.targetFH,
defaultDir=self.defaultDir,
blur=self.nlinParams.blurs[0],
gradient=self.nlinParams.gradient[0],
similarity_metric=self.nlinParams.similarityMetric[0],
weight=self.nlinParams.weight[0],
iterations=self.nlinParams.iterations[0],
radius_or_histo=self.nlinParams.radiusHisto[0],
transformation_model=self.nlinParams.transformationModel[0],
regularization=self.nlinParams.regularization[0],
useMask=self.nlinParams.useMask[0])
self.p.addStage(sp)
nlinXfm = sp.outputFiles[0]
#Reset last base volume to original input for future registrations.
self.inputFH.setLastBasevol(setToOriginalInput=True)
#Concatenate transforms to get final lsq12 + nlin. Register volume handles naming and setting of lastXfm
output = self.inputFH.registerVolume(self.targetFH, "transforms")
xc = ma.xfmConcat([lsq12xfm, nlinXfm], output,
fh.logFromFile(self.inputFH.logDir, output))
self.p.addStage(xc)
def buildPipeline(self):
lsq12LikeFH = None
resolutionForLSQ12 = None
if self.initModel:
lsq12LikeFH = self.initModel[0]
elif self.options.lsq12_likeFile:
lsq12LikeFH = self.options.lsq12_likeFile
if lsq12LikeFH == None and self.options.lsq12_subject_matter == None:
print "\nError: the FullIterativeLSQ12Nlin module was called without specifying either an initial model, nor an lsq12_subject_matter. Currently that means that the code can not determine the resolution at which the registrations should be run. Please specify one of the two. Exiting\n"
sys.exit()
if not (lsq12LikeFH == None):
resolutionForLSQ12 = rf.returnFinestResolution(lsq12LikeFH)
lsq12module = lsq12.FullLSQ12(
self.inputs,
self.dirs.lsq12Dir,
likeFile=lsq12LikeFH,
maxPairs=self.options.lsq12_max_pairs,
lsq12_protocol=self.options.lsq12_protocol,
subject_matter=self.options.lsq12_subject_matter,
resolution=resolutionForLSQ12)
lsq12module.iterate()
self.p.addPipeline(lsq12module.p)
self.lsq12Params = lsq12module.lsq12Params
if lsq12module.lsq12AvgFH.getMask() == None:
if self.initModel:
lsq12module.lsq12AvgFH.setMask(self.initModel[0].getMask())
if not self.avgPrefix:
self.avgPrefix = self.options.pipeline_name
# same as in MBM.py:
# for now we can use the same resolution for the NLIN stages as we did for the
# LSQ12 stage. At some point we should look into the subject matter option...
nlinModule = nlin.initializeAndRunNLIN(
self.dirs.lsq12Dir,
self.inputs,
self.dirs.nlinDir,
avgPrefix=self.avgPrefix,
createAvg=False,
targetAvg=lsq12module.lsq12AvgFH,
nlin_protocol=self.options.nlin_protocol,
reg_method=self.options.reg_method,
resolution=resolutionForLSQ12)
self.p.addPipeline(nlinModule.p)
self.nlinFH = nlinModule.nlinAverages[-1]
self.nlinParams = nlinModule.nlinParams
self.initialTarget = nlinModule.initialTarget
# Now we need the full transform to go back to LSQ6 space
for i in self.inputs:
linXfm = lsq12module.lsq12AvgXfms[i]
nlinXfm = i.getLastXfm(self.nlinFH)
outXfm = st.createOutputFileName(i, nlinXfm, "transforms",
"_with_additional.xfm")
xc = ma.xfmConcat([linXfm, nlinXfm], outXfm,
fh.logFromFile(i.logDir, outXfm))
self.p.addStage(xc)
i.addAndSetXfmToUse(self.nlinFH, outXfm)
def calcNlinDisplacement(self):
"""Calculate pure non-linear displacement from target to input
1. Concatenate self.invXfm (target to input xfm) and self.linearPartOfNlinXfm
2. Compute mincDisplacement on this transform.
"""
pureNlinXfm = createOutputFileName(self.inputFH, self.invXfm, "transforms", "_pure_nlin.xfm")
xc = xfmConcat([self.invXfm, self.linearPartOfNlinXfm],
pureNlinXfm, fh.logFromFile(self.inputFH.logDir, pureNlinXfm))
self.p.addStage(xc)
nlinDisp = mincDisplacement(self.targetFH, self.inputFH, transform=pureNlinXfm)
self.p.addStage(nlinDisp)
self.nlinDisp = nlinDisp.outputFiles[0]
def calcNlinDisplacement(self):
"""Calculate pure non-linear displacement from target to input
1. Concatenate self.invXfm (target to input xfm) and self.linearPartOfNlinXfm
2. Compute mincDisplacement on this transform.
"""
pureNlinXfm = createOutputFileName(self.inputFH, self.invXfm, "transforms", "_pure_nlin.xfm")
xc = xfmConcat([self.invXfm, self.linearPartOfNlinXfm],
pureNlinXfm, fh.logFromFile(self.inputFH.logDir, pureNlinXfm))
self.p.addStage(xc)
nlinDisp = mincDisplacement(self.targetFH, self.inputFH, transform=pureNlinXfm)
self.p.addStage(nlinDisp)
self.nlinDisp = nlinDisp.outputFiles[0]
def buildPipeline(self):
# Run lsq12 registration prior to non-linear
self.lsq12Params = mp.setLSQ12MinctraccParams(self.fileRes,
subject_matter=self.subject_matter,
reg_protocol=self.lsq12_protocol)
lsq12reg = lsq12.LSQ12(self.inputFH,
self.targetFH,
blurs=self.lsq12Params.blurs,
step=self.lsq12Params.stepSize,
gradient=self.lsq12Params.useGradient,
simplex=self.lsq12Params.simplex,
w_translations=self.lsq12Params.w_translations,
defaultDir=self.defaultDir)
self.p.addPipeline(lsq12reg.p)
#Resample using final LSQ12 transform and reset last base volume.
res = ma.mincresample(self.inputFH, self.targetFH, likeFile=self.targetFH, argArray=["-sinc"])
self.p.addStage(res)
self.inputFH.setLastBasevol(res.outputFiles[0])
lsq12xfm = self.inputFH.getLastXfm(self.targetFH)
#Get registration parameters from nlin protocol, blur and register
#Assume a SINGLE generation here.
self.nlinParams = mp.setOneGenMincANTSParams(self.fileRes, reg_protocol=self.nlin_protocol)
for b in self.nlinParams.blurs:
for j in b:
#Note that blurs for ANTS params in an array of arrays.
if j != -1:
self.p.addStage(ma.blur(self.targetFH, j, gradient=True))
self.p.addStage(ma.blur(self.inputFH, j, gradient=True))
sp = ma.mincANTS(self.inputFH,
self.targetFH,
defaultDir=self.defaultDir,
blur=self.nlinParams.blurs[0],
gradient=self.nlinParams.gradient[0],
similarity_metric=self.nlinParams.similarityMetric[0],
weight=self.nlinParams.weight[0],
iterations=self.nlinParams.iterations[0],
radius_or_histo=self.nlinParams.radiusHisto[0],
transformation_model=self.nlinParams.transformationModel[0],
regularization=self.nlinParams.regularization[0],
useMask=self.nlinParams.useMask[0])
self.p.addStage(sp)
nlinXfm = sp.outputFiles[0]
#Reset last base volume to original input for future registrations.
self.inputFH.setLastBasevol(setToOriginalInput=True)
#Concatenate transforms to get final lsq12 + nlin. Register volume handles naming and setting of lastXfm
output = self.inputFH.registerVolume(self.targetFH, "transforms")
xc = ma.xfmConcat([lsq12xfm, nlinXfm], output, fh.logFromFile(self.inputFH.logDir, output))
self.p.addStage(xc)
def linAndNlinDisplacement(self):
"""
The function calculates both the linear and nonlinear
portions of the displacement, in order to find
pure nonlinear. Common space here is the target (usually
an average of some sort). We also recentre pure non linear
displacement.
"""
"""Calculate linear part of non-linear xfm from input to target"""
lpnl = linearPartofNlin(self.inputFH, self.targetFH)
self.p.addStage(lpnl)
self.linearXfm = lpnl.outputFiles[0]
"""Calculate full displacement from target to input"""
self.calcFullDisplacement()
"""Calculate pure non-linear displacement from target to input
1. Concatenate linear and inverse target to input transform to
get pure_nlin xfm
2. Compute mincDisplacement on this transform.
"""
nlinXfm = createPureNlinXfmName(self.inputFH, self.invXfm)
xc = xfmConcat([self.linearXfm, self.invXfm], nlinXfm, fh.logFromFile(self.inputFH.logDir, nlinXfm))
self.p.addStage(xc)
nlinDisp = mincDisplacement(self.targetFH, self.inputFH, transform=nlinXfm)
self.p.addStage(nlinDisp)
self.nlinDisp = nlinDisp.outputFiles[0]
"""Calculate average displacement and re-center non-linear displacement
if an array of input file handlers was specified on instantiation. """
if self.dispToAvg:
"""Calculate average inverse displacement"""
avgOutput = abspath(self.targetFH.basedir) + "/" + "average_inv_pure_displacement.mnc"
logBase = fh.removeBaseAndExtension(avgOutput)
avgLog = fh.createLogFile(self.targetFH.basedir, logBase)
avg = mincAverageDisp(self.dispToAvg, avgOutput, logFile=avgLog)
self.p.addStage(avg)
"""Centre pure nlin displacement by subtracting average from existing"""
centredBase = fh.removeBaseAndExtension(self.nlinDisp).split("_displacement")[0]
centredOut = fh.createBaseName(self.inputFH.statsDir,
centredBase + "_centred_displacement.mnc")
cmd = ["mincmath", "-clobber", "-sub", InputFile(self.nlinDisp),
InputFile(avgOutput), OutputFile(centredOut)]
centredDisp = CmdStage(cmd)
centredDisp.setLogFile(LogFile(fh.logFromFile(self.inputFH.logDir, centredOut)))
self.p.addStage(centredDisp)
"""Reset centred displacement to be self.nlinDisp"""
self.nlinDisp = centredOut
def calcFullDisplacement(self):
"""Calculate full displacement from target to input. If an
additionaXfm is specified, it is concatenated to self.xfm here """
if self.additionalXfm:
outXfm = createOutputFileName(self.inputFH, self.xfm, "transforms", "_with_additional.xfm")
xc = xfmConcat([self.additionalXfm, self.xfm], outXfm, fh.logFromFile(self.inputFH.logDir, outXfm))
self.p.addStage(xc)
xi = xfmInvert(xc.outputFiles[0], FH=self.inputFH)
self.p.addStage(xi)
fullDisp = mincDisplacement(self.targetFH, self.inputFH, transform=xi.outputFiles[0])
else:
fullDisp = mincDisplacement(self.targetFH, self.inputFH, transform=self.invXfm)
self.p.addStage(fullDisp)
self.fullDisp = fullDisp.outputFiles[0]
def calcFullDisplacement(self):
"""Calculate full displacement from target to input. If an
additionaXfm is specified, it is concatenated to self.xfm here """
if self.additionalXfm:
outXfm = createOutputFileName(self.inputFH, self.xfm, "transforms", "_with_additional.xfm")
xc = xfmConcat([self.additionalXfm, self.xfm], outXfm, fh.logFromFile(self.inputFH.logDir, outXfm))
self.p.addStage(xc)
xi = xfmInvert(xc.outputFiles[0], FH=self.inputFH)
self.p.addStage(xi)
fullDisp = mincDisplacement(self.targetFH, self.inputFH, transform=xi.outputFiles[0])
else:
fullDisp = mincDisplacement(self.targetFH, self.inputFH, transform=self.invXfm)
self.p.addStage(fullDisp)
self.fullDisp = fullDisp.outputFiles[0]
def calcNlinDisplacement(self):
"""Calculate pure non-linear displacement from input to target
1. Invert the linear transform, so we get the linear xfm from target to input.
2. Concatenate the full non-linear (input to target) transform with the
linear target to input transform.
3. Calculate the displacement on this transform. """
xi = xfmInvert(self.linearPartOfNlinXfm, FH=self.inputFH)
self.p.addStage(xi)
pureNlinXfm = createOutputFileName(self.inputFH, self.xfm, "transforms", "_pure_nlin.xfm")
xc = xfmConcat([self.xfm, xi.outputFiles[0]],
pureNlinXfm, fh.logFromFile(self.inputFH.logDir, pureNlinXfm))
self.p.addStage(xc)
nlinDisp = mincDisplacement(self.inputFH, self.inputFH, transform=pureNlinXfm)
self.p.addStage(nlinDisp)
self.nlinDisp = nlinDisp.outputFiles[0]
def calcNlinDisplacement(self):
"""Calculate pure non-linear displacement from input to target
1. Invert the linear transform, so we get the linear xfm from target to input.
2. Concatenate the full non-linear (input to target) transform with the
linear target to input transform.
3. Calculate the displacement on this transform. """
xi = xfmInvert(self.linearPartOfNlinXfm, FH=self.inputFH)
self.p.addStage(xi)
pureNlinXfm = createOutputFileName(self.inputFH, self.xfm, "transforms", "_pure_nlin.xfm")
xc = xfmConcat([self.xfm, xi.outputFiles[0]],
pureNlinXfm, fh.logFromFile(self.inputFH.logDir, pureNlinXfm))
self.p.addStage(xc)
nlinDisp = mincDisplacement(self.inputFH, self.inputFH, transform=pureNlinXfm)
self.p.addStage(nlinDisp)
self.nlinDisp = nlinDisp.outputFiles[0]
def buildPipeline(self):
lsq12LikeFH = None
if self.initModel:
lsq12LikeFH = self.initModel[0]
elif self.options.lsq12_likeFile:
lsq12LikeFH = self.options.lsq12_likeFile
lsq12module = lsq12.FullLSQ12(self.inputs,
self.dirs.lsq12Dir,
likeFile=lsq12LikeFH,
maxPairs=self.options.lsq12_max_pairs,
lsq12_protocol=self.options.lsq12_protocol,
subject_matter=self.options.lsq12_subject_matter)
lsq12module.iterate()
self.p.addPipeline(lsq12module.p)
self.lsq12Params = lsq12module.lsq12Params
if lsq12module.lsq12AvgFH.getMask()== None:
if self.initModel:
lsq12module.lsq12AvgFH.setMask(self.initModel[0].getMask())
if not self.avgPrefix:
self.avgPrefix = self.options.pipeline_name
nlinModule = nlin.initializeAndRunNLIN(self.dirs.lsq12Dir,
self.inputs,
self.dirs.nlinDir,
avgPrefix=self.avgPrefix,
createAvg=False,
targetAvg=lsq12module.lsq12AvgFH,
nlin_protocol=self.options.nlin_protocol,
reg_method=self.options.reg_method)
self.p.addPipeline(nlinModule.p)
self.nlinFH = nlinModule.nlinAverages[-1]
self.nlinParams = nlinModule.nlinParams
self.initialTarget = nlinModule.initialTarget
# Now we need the full transform to go back to LSQ6 space
for i in self.inputs:
linXfm = lsq12module.lsq12AvgXfms[i]
nlinXfm = i.getLastXfm(self.nlinFH)
outXfm = st.createOutputFileName(i, nlinXfm, "transforms", "_with_additional.xfm")
xc = ma.xfmConcat([linXfm, nlinXfm], outXfm, fh.logFromFile(i.logDir, outXfm))
self.p.addStage(xc)
i.addAndSetXfmToUse(self.nlinFH, outXfm)
def statsAndConcat(self, s, i, count, beforeAvg=True):
"""Construct array to common space for this timepoint.
This builds upon arrays from previous calls."""
if beforeAvg:
xfm = s[i].getLastXfm(s[i+1])
else:
xfm = s[i].getLastXfm(s[i-1])
"""Set this transform as last xfm from input to nlin and calculate nlin to s[i] stats"""
if self.nlinFH:
self.xfmToCommon.insert(0, xfm)
""" Concat transforms to get xfmToCommon and calculate statistics
Note that inverted transform, which is what we want, is calculated in
the statistics module. """
xtc = fh.createBaseName(s[i].transformsDir, s[i].basename + "_to_" + self.commonName + ".xfm")
xc = ma.xfmConcat(self.xfmToCommon, xtc, fh.logFromFile(s[i].logDir, xtc))
self.p.addStage(xc)
s[i].addAndSetXfmToUse(self.nlinFH, xtc)
self.statsCalculation(s[i], self.nlinFH, xfm=None, useChainStats=False)
else:
xtc=None
"""Calculate i to i+1 stats for all but final timePoint"""
if count - i > 1:
self.statsCalculation(s[i], s[i+1], xfm=xtc, useChainStats=True)
def buildPipeline(self):
lsq12LikeFH = None
resolutionForLSQ12 = None
if self.initModel:
lsq12LikeFH = self.initModel[0]
elif self.options.lsq12_likeFile:
lsq12LikeFH = self.options.lsq12_likeFile
if lsq12LikeFH == None and self.options.lsq12_subject_matter == None and self.providedResolution == None:
print("\nError: the FullIterativeLSQ12Nlin module was called without specifying either an initial model, nor an lsq12_subject_matter. Currently that means that the code can not determine the resolution at which the registrations should be run. Please specify one of the two. Exiting\n")
sys.exit()
if not (lsq12LikeFH == None):
resolutionForLSQ12 = rf.returnFinestResolution(lsq12LikeFH)
if resolutionForLSQ12 == None and self.providedResolution == None:
print("\nError: the resolution at which the LSQ12 and the NLIN registration should be run could not be determined from either the initial model nor the LSQ12 like file. Please provide the fileResolution to the FullIterativeLSQ12Nlin module. Exiting\n")
sys.exit()
if resolutionForLSQ12 == None and self.providedResolution:
resolutionForLSQ12 = self.providedResolution
lsq12module = lsq12.FullLSQ12(self.inputs,
self.dirs.lsq12Dir,
queue_type=self.options.queue_type,
likeFile=lsq12LikeFH,
maxPairs=self.options.lsq12_max_pairs,
lsq12_protocol=self.options.lsq12_protocol,
subject_matter=self.options.lsq12_subject_matter,
resolution=resolutionForLSQ12)
lsq12module.iterate()
self.p.addPipeline(lsq12module.p)
self.lsq12Params = lsq12module.lsq12Params
if lsq12module.lsq12AvgFH.getMask()== None:
if self.initModel:
lsq12module.lsq12AvgFH.setMask(self.initModel[0].getMask())
if not self.avgPrefix:
self.avgPrefix = self.options.pipeline_name
# same as in MBM.py:
# for now we can use the same resolution for the NLIN stages as we did for the
# LSQ12 stage. At some point we should look into the subject matter option...
nlinModule = nlin.initializeAndRunNLIN(self.dirs.lsq12Dir,
self.inputs,
self.dirs.nlinDir,
avgPrefix=self.avgPrefix,
createAvg=False,
targetAvg=lsq12module.lsq12AvgFH,
nlin_protocol=self.options.nlin_protocol,
reg_method=self.options.reg_method,
resolution=resolutionForLSQ12)
self.p.addPipeline(nlinModule.p)
self.nlinFH = nlinModule.nlinAverages[-1]
self.nlinParams = nlinModule.nlinParams
self.initialTarget = nlinModule.initialTarget
# Now we need the full transform to go back to LSQ6 space
for i in self.inputs:
linXfm = lsq12module.lsq12AvgXfms[i]
nlinXfm = i.getLastXfm(self.nlinFH)
outXfm = st.createOutputFileName(i, nlinXfm, "transforms", "_with_additional.xfm")
xc = ma.xfmConcat([linXfm, nlinXfm], outXfm, fh.logFromFile(i.logDir, outXfm))
self.p.addStage(xc)
i.addAndSetXfmToUse(self.nlinFH, outXfm)
def calcDetAndLogDet(self, useFullDisp=False):
#Lots of repetition here--let's see if we can't make some functions.
"""useFullDisp indicates whether or not to use full displacement field or non-linear component only"""
if useFullDisp:
dispToUse = self.fullDisp
else:
dispToUse = self.nlinDisp
"""Insert -1 at beginning of blurs array to include the calculation of unblurred jacobians."""
self.blurs.insert(0,-1)
for b in self.blurs:
"""Calculate default output filenames and set input for determinant calculation."""
outputBase = fh.removeBaseAndExtension(dispToUse).split("_displacement")[0]
inputDet = dispToUse
outputDet = fh.createBaseName(self.inputFH.tmpDir, outputBase + "_determinant.mnc")
outDetShift = fh.createBaseName(self.inputFH.tmpDir, outputBase + "_det_plus1.mnc")
outLogDet = fh.createBaseName(self.inputFH.statsDir, outputBase + "_log_determinant.mnc")
outLogDetScaled = fh.createBaseName(self.inputFH.statsDir, outputBase + "_log_determinant_scaled.mnc")
"""Calculate smoothed deformation field for all blurs other than -1"""
if b != -1:
fwhm = "--fwhm=" + str(b)
outSmooth = fh.createBaseName(self.inputFH.tmpDir,
outputBase + "_smooth_displacement_fwhm" + str(b) + ".mnc")
cmd = ["smooth_vector", "--clobber", "--filter", fwhm,
InputFile(dispToUse), OutputFile(outSmooth)]
smoothVec = CmdStage(cmd)
smoothVec.setLogFile(LogFile(fh.logFromFile(self.inputFH.logDir, outSmooth)))
self.p.addStage(smoothVec)
"""Override file name defaults for each blur and set input for determinant calculation."""
inputDet = outSmooth
outputDet = fh.createBaseName(self.inputFH.tmpDir,
outputBase + "_determinant_fwhm" + str(b) + ".mnc")
outDetShift = fh.createBaseName(self.inputFH.tmpDir,
outputBase + "_det_plus1_fwhm" + str(b) + ".mnc")
outLogDet = fh.createBaseName(self.inputFH.statsDir,
outputBase + "_log_determinant_fwhm" + str(b) + ".mnc")
outLogDetScaled = fh.createBaseName(self.inputFH.statsDir,
outputBase + "_log_determinant_scaled_fwhm" + str(b) + ".mnc")
"""Calculate the determinant, then add 1 (per mincblob weirdness)"""
cmd = ["mincblob", "-clobber", "-determinant", InputFile(inputDet), OutputFile(outputDet)]
det = CmdStage(cmd)
det.setLogFile(LogFile(fh.logFromFile(self.inputFH.logDir, outputDet)))
self.p.addStage(det)
cmd = ["mincmath", "-clobber", "-2", "-const", str(1), "-add",
InputFile(outputDet), OutputFile(outDetShift)]
det = CmdStage(cmd)
det.setLogFile(LogFile(fh.logFromFile(self.inputFH.logDir, outDetShift)))
self.p.addStage(det)
"""Calculate log determinant (jacobian) and add to statsGroup."""
cmd = ["mincmath", "-clobber", "-2", "-log", InputFile(outDetShift), OutputFile(outLogDet)]
det = CmdStage(cmd)
det.setLogFile(LogFile(fh.logFromFile(self.inputFH.logDir, outLogDet)))
self.p.addStage(det)
self.statsGroup.jacobians[b] = outLogDet
"""If self.linearXfm present, calculate scaled log determinant (scaled jacobian) and add to statsGroup"""
if not useFullDisp:
"""
If self.scaleFactor is specified, then concatenate this additional transform
with self.linearXfm. Typically, this will come from an LSQ12 registration, but
may come from another alignment.
"""
if self.scalingFactor:
toConcat = [self.scalingFactor, self.linearXfm]
self.fullLinearXfm = fh.createBaseName(self.inputFH.transformsDir, self.inputFH.basename + "_full_linear.xfm")
logFile=fh.logFromFile(self.inputFH.logDir, fh.removeBaseAndExtension(self.fullLinearXfm))
concat = xfmConcat(toConcat, self.fullLinearXfm, logFile=logFile)
self.p.addStage(concat)
else:
self.fullLinearXfm = self.linearXfm
cmd = ["scale_voxels", "-clobber", "-invert", "-log",
InputFile(self.fullLinearXfm), InputFile(outLogDet), OutputFile(outLogDetScaled)]
det = CmdStage(cmd)
det.setLogFile(LogFile(fh.logFromFile(self.inputFH.logDir, outLogDetScaled)))
self.p.addStage(det)
self.statsGroup.scaledJacobians[b] = outLogDetScaled
else:
self.statsGroup.scaledJacobians = None