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@@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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- "execution_count": 5,
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+ "execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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@@ -25,6 +25,7 @@
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"import re\n",
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"import config\n",
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"import getData\n",
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+ "import analysis\n",
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"\n",
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"sys.path.append(os.path.join(os.path.expanduser('~'),'software','src','nixSuite','wrapper'))\n",
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"import nixWrapper\n",
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@@ -32,336 +33,7 @@
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"import labkeyInterface\n",
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"import labkeyFileBrowser\n",
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"import labkeyDatabaseBrowser\n",
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- "\n",
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- "def calculateCenters(db,setup,nclass,nr):\n",
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- " #path of scripts is ../scripts\n",
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- " #one up\n",
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- " baseDir=os.path.dirname(os.getcwd())\n",
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- " #scriptPath\n",
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- " scriptPath=os.path.join(baseDir,'scripts','generateCenters.sh')\n",
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- " rows=getData.getPatients(db,setup)\n",
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- " for r in rows:\n",
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- " #download \n",
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- " code=config.getCode(r,setup)\n",
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- " for nc in nclass:\n",
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- " print('centers: {}/{},{}'.format(code,nc,nr))\n",
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- " print(subprocess.run([scriptPath,code,str(nc),str(nr)], \\\n",
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- " check=True, stdout=subprocess.PIPE).stdout)\n",
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- " \n",
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- "def doAnalysis(db,setup,nclass,nr,mode):\n",
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- " baseDir=os.path.dirname(os.getcwd())#one up\n",
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- " #this is if IVF is inferred together with k1 fits\n",
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- " #runScript='doAnalysis.sh'\n",
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- " #this is if IVF is taken from previous cluster fits\n",
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- " #runScript='doAnalysisIVF.sh'\n",
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- " \n",
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- " \n",
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- " if mode=='global':\n",
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- " runScript='doAnalysis.sh'\n",
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- " analysisType=''\n",
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- " if mode=='IVF':\n",
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- " runScript='doAnalysisIVF.sh'\n",
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- " analysisType='IVF_'\n",
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- " \n",
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- " try:\n",
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- " print('Setting runScript to {}'.format(runScript))\n",
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- " except NameError:\n",
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- " print('Mode can be one of (global,IVF))')\n",
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- " return\n",
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- " \n",
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- " #either doAnalysis or doAnalysisIVF.sh\n",
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- " scriptPath=os.path.join(baseDir,'scripts',runScript)\n",
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- "\n",
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- " rows=getData.getPatients(db,setup)\n",
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- " for r in rows:\n",
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- " dataPath=config.getLocalDir(r,setup)\n",
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- " code=config.getCode(r,setup)\n",
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- " for nc in nclass:\n",
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- " for rId in numpy.arange(nr):\n",
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- " print('{} [{} {}/{}]'.format(code,nc,rId+1,nr))\n",
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- " #this is a duplicate of path generated in fitCenters.m\n",
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- " fName=os.path.join(dataPath,'{}_{}_{}_{}fitParFinal.txt'.format(code,nc,rId+1,analysisType))\n",
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- " if os.path.isfile(fName):\n",
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- " print('Skipping; {} available.'.format(fName))\n",
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- " continue\n",
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- " subprocess.run([scriptPath,code,str(nc),str(rId+1)],\\\n",
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- " check=True, stdout=subprocess.PIPE)\n",
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- "\n",
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- "def doPixelAnalysis(db,setup,sigma2,mode='IVF'):\n",
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- " \n",
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- " baseDir=os.path.dirname(os.getcwd())#one up\n",
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- " \n",
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- " #in global mode, IVF parameters are inferred together with fits to classes\n",
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- " #this is essentially repeat of the above, except that classes are taken as\n",
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- " #time-response curves from pixels in the sigma2-defined neighborhood of\n",
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- " #target pixels\n",
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- " if mode=='global':\n",
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- " runScript='doPixelAnalysis.sh'\n",
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- " analysisType=''\n",
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- " #in IVF mode, the parameters of input function are taken from the cluster fit \n",
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- " #(doAnalysis above, with mode=general). The rest is the same as for global mode\n",
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- " if mode=='IVF':\n",
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- " runScript='doPixelIVFAnalysis.sh'\n",
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- " analysisType='IVF_'\n",
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- " \n",
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- " #either doPixelAnalysis or doPixelAnalysisIVF.sh\n",
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- " scriptPath=os.path.join(baseDir,'scripts',runScript)\n",
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- "\n",
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- "\n",
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- " try:\n",
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- " print('Setting runScript to {}'.format(runScript))\n",
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- " except NameError:\n",
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- " print('Mode can be one of (global,IVF))')\n",
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- " return\n",
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- " \n",
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- " rows=getData.getPatients(db,setup)\n",
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- " for r in rows:\n",
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- " dataPath=config.getLocalDir(r,setup)\n",
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- " code=config.getCode(r,setup)\n",
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- " sName=os.path.join(dataPath,'{}_Segmentation.txt'.format(code))\n",
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- " sFile=os.path.join(dataPath,sName)\n",
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- " x=numpy.loadtxt(sFile)\n",
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- " nc=x.shape[0]\n",
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- " for s2 in sigma2:\n",
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- " sigmaCode='{:.2f}'.format(s2)\n",
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- " sigmaCode=re.sub('\\.','p',sigmaCode)\n",
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- " fName=os.path.join(dataPath,'{}_{}_{}_Pixel{}_fitParFinal.txt'.format(code,nc,sigmaCode,analysisType))\n",
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- " if os.path.isfile(fName):\n",
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- " print('Skipping; {} available.'.format(fName))\n",
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- " continue\n",
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- " subprocess.run([scriptPath,code,str(s2)], \\\n",
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- " check=True, stdout=subprocess.PIPE)\n",
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- " \n",
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- " \n",
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- "def getIWeights(r,setup,nclass,realizationId,ic):\n",
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- " locDir=config.getLocalDir(r,setup)\n",
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- " code=config.getCode(r,setup)\n",
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- " fname='{}_{}_{}_center{}_centerWeigth.nrrd'.\\\n",
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- " format(code,nclass,realizationId+1,ic+1)\n",
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- " uFile=os.path.join(locDir,fname)\n",
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- " \n",
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- " imU=SimpleITK.ReadImage(uFile)\n",
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- " return SimpleITK.GetArrayFromImage(imU)\n",
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- "\n",
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- "def getGaussianWeight(nU,pt,sigma2,na):\n",
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- " #gaussian weighted summation of surrounding pixels\n",
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- " \n",
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- " #find point closest to the target point\n",
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- " idx=[int(x) for x in pt]\n",
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- " #running offset \n",
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- " fidx=numpy.zeros(3)\n",
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- " #half of the neighborhood\n",
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- " na2=0.5*(na-1)\n",
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- " \n",
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- " fs=0\n",
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- " fWeight=0\n",
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- " for i in numpy.arange(na):\n",
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- " fidx[0]=idx[0]+(i-na2)\n",
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- " for j in numpy.arange(na):\n",
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- " fidx[1]=idx[1]+(j-na2)\n",
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- " for k in numpy.arange(na):\n",
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- " fidx[2]=idx[2]+(k-na2)\n",
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- " fidx=[int(x) for x in fidx]\n",
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- " fd=numpy.array(fidx)-numpy.array(pt)\n",
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- " dist2=numpy.sum(fd*fd)\n",
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- " fw=numpy.exp(-0.5*dist2/sigma2);\n",
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- " fs+=fw\n",
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- " fWeight+=fw*nU[tuple(fidx)]\n",
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- " #print('\\t{}/{}: {}/{:.2f} {:.2g} {:.3g} {:.2g}'.format(idx,fidx,numpy.sqrt(dist2),fw,nU[tuple(fidx)],fs,fWeight))\n",
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- " fWeight/=fs\n",
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- " return fWeight\n",
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- " \n",
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- "\n",
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- "\n",
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- "#gets weights by class for a particular realization and sigma2 averaging\n",
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- "def getWeights(db,r,setup,nclass,realizationId,sigma2,na):\n",
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- " #for w1, classes are in 0 to nclass-1 space\n",
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- " #na is the size of the neighborhood\n",
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- " idFilter=config.getIdFilter(r,setup)\n",
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- " visitFilter=config.getVisitFilter(r,setup)\n",
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- " code=config.getCode(r,setup)\n",
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- " rows=getData.getSegmentation(db,setup,[idFilter,visitFilter])\n",
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- " pts={r['regionId']:[float(x) for x in [r['x'],r['y'],r['z']]] for r in rows}\n",
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- " \n",
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- " w={region:numpy.zeros(nclass) for region in pts}\n",
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- " na2=0.5*(na-1)\n",
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- " \n",
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- " for c in numpy.arange(nclass):\n",
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- " nU=getIWeights(r,setup,nclass,realizationId,c)\n",
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- " \n",
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- " for region in w:\n",
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- " #print(pts[region])\n",
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- " #print('{} {}/{} {}'.format(code,c,nclass,region))\n",
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- " #gaussian weighted summation of surrounding pixels\n",
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- " w[region][c]=getGaussianWeight(nU,pts[region],sigma2,na)\n",
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- " \n",
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- " \n",
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- " return w\n",
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- "\n",
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- "\n",
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- "#gets fitPar for a particular realization in [0..nr-1] range\n",
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- "def getFitPar(r,setup,nclass,realizationId,mode=''):\n",
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- " #fitGoodnes A tau alpha delay [k1 BVF k2 delay]xNcenters\n",
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- " allowedModes=['','IVF','Pixel','PixelIVF']\n",
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- " if mode not in allowedModes:\n",
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- " print('Mode should be one of {}'.format(allowedModes))\n",
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- " return []\n",
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- " \n",
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- " if mode=='PixelIVF':\n",
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- " #4th parameter is sigma, not realizationId\n",
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- " rCode='{:.2f}'.format(realizationId)\n",
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- " rCode=re.sub('\\.','p',rCode)\n",
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- " else:\n",
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- " #add one to match matlab 1..N array indexing\n",
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- " rCode='{}'.format(realizationId+1)\n",
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- " \n",
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- " if len(mode)>0:\n",
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- " mode=mode+'_'\n",
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- " \n",
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- " code=config.getCode(r,setup)\n",
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- " fname='{}_{}_{}_{}fitParFinal.txt'.format(code,nclass,rCode,mode)\n",
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- " locDir=config.getLocalDir(r,setup)\n",
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- " uFile=os.path.join(locDir,fname)\n",
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- " return numpy.genfromtxt(uFile,delimiter='\\t')\n",
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- "\n",
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- "def getK1(fitPar,iclass):\n",
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- " #fitGoodnes A tau alpha delay [k1 BVF k2 delay]xNcenters\n",
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- " #0 to nclass-1 space\n",
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- " return fitPar[4*iclass+5]\n",
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- "\n",
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- "def calculateK1(w,fitPar):\n",
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- " #calculateK1 for region weights\n",
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- " #return the k1 that belongs to the \n",
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- " #maximum class in region (M) and \n",
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- " #a weighted average (W)\n",
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- " k1={region:{'M':0,'W':0} for region in w}\n",
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- " for region in w:\n",
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- " cmax=numpy.argmax(w[region])\n",
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- " k1[region]['M']=getK1(fitPar,cmax)\n",
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- " fs=0\n",
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- " for c in numpy.arange(len(w[region])):\n",
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- " fs+=w[region][c]*getK1(fitPar,c)\n",
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- " k1[region]['W']=fs\n",
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- " return k1\n",
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- "\n",
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- "def getPatientValuesByNclass(db,r,setup,nclass,nrealizations,sigma2,na):\n",
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- " #summary script\n",
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- " #db is for database; needs segmentations\n",
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- " #r,setup identify patient\n",
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- " #nclass and nrealizations select strategy\n",
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- " #sigma2 is for combining output from adjacent pixels\n",
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- " #na is neighborhood size where smoothing/combination is done\n",
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- " k1={}\n",
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- " for rId in numpy.arange(nrealizations):\n",
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- " w=getWeights(db,r,setup,nclass,rId,sigma2,na)\n",
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- " fitPar=getFitPar(r,setup,nclass,rId,'IVF')\n",
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- " qk1=calculateK1(w,fitPar)\n",
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- " for region in w:\n",
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- " for type in qk1[region]:\n",
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- " try:\n",
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- " k1[region][type].append(qk1[region][type])\n",
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- " except KeyError:\n",
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- " k1={region:{type:[] for type in qk1[region]} for region in w}\n",
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- " print(type)\n",
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- " k1[region][type].append(qk1[region][type])\n",
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- " print('[{}] {}/{}'.format(nclass,rId+1,nrealizations))\n",
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- " return k1 \n",
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- "\n",
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- "def getSummaryPatientValuesByNclass(db,r,setup,nclass,nrealizations,sigma2,na):\n",
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- " #summary script, same arguments as above\n",
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- " #also return deviation over realization\n",
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- " k1=getPatientValuesByNclass(db,r,setup,nclass,nrealizations,sigma2,na)\n",
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- " avgType=['M','W']\n",
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- " summaryK1={type:{region:{\n",
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- " 'mean':numpy.mean(k1[region][type]), \n",
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- " 'std':numpy.std(k1[region][type]), \n",
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- " 'median':numpy.median(k1[region][type])} for region in k1}\n",
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- " for type in avgType}\n",
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- " \n",
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- " return summaryK1\n",
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- "\n",
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- "def fullSummary(db,setup,classes,nr,sigma2,na):\n",
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- " rows=getData.getPatients(db,setup)\n",
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- " return \\\n",
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- " {config.getCode(r,setup):\\\n",
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- " {c:getSummaryPatientValuesByNclass(db,r,setup,c,nr,sigma2,na) for c in classes} for r in rows}\n",
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- " \n",
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- "def storeSummary(db,setup,summary,sigma2,na):\n",
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- " #dsM=db.selectRows(project,'study','Imaging',[])\n",
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- " for rCode in summary:\n",
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- " r=config.decode(rCode,setup)\n",
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- " idFilter=config.getIdFilter(r,setup)\n",
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- " visitFilter=config.getVisitFilter(r,setup)\n",
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- " for c in summary[rCode]:\n",
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- " cFilter={'variable':'nclass','value':str(c),'oper':'eq'}\n",
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- " for t in summary[rCode][c]:\n",
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- " tFilter={'variable':'option','value':t,'oper':'eq'}\n",
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- " for rId in summary[rCode][c][t]:\n",
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- " rFilter={'variable':'regionId','value':str(rId),'oper':'eq'}\n",
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- " rows=getData.getSummary(db,setup,[idFilter,visitFilter,cFilter,tFilter,rFilter])\n",
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- " if len(rows)>0:\n",
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- " qrow=rows[0]\n",
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- " mode='update'\n",
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- " else:\n",
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- " qrow={qr:r[qr] for qr in r}\n",
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- " qrow['nclass']=c\n",
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- " qrow['option']=t\n",
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- " qrow['regionId']=rId\n",
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- " seqNum=config.getTargetSeqNum(r,setup)\n",
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- " qrow['SequenceNum']=100+seqNum+c+0.001*rId\n",
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- " if t=='M':\n",
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- " qrow['SequenceNum']+=0.0001\n",
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- " mode='insert'\n",
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- " for v in summary[rCode][c][t][rId]:\n",
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- " qrow[v]=summary[rCode][c][t][rId][v]\n",
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- " qrow['sigma2']=sigma2\n",
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- " qrow['na']=na\n",
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- " getData.updateSummary(db,setup,mode,[qrow])\n",
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- " \n",
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- "def summaryPixelIVF(db,setup,sigma2):\n",
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|
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- " #for second type of analysis (pixel based regions)\n",
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- " rows=getData.getPatients(db,setup)\n",
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- " return \\\n",
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- " {config.getCode(r,setup):\\\n",
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- " {s2:getPixelIVF(db,r,setup,s2) for s2 in sigma2} for r in rows}\n",
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- "\n",
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- " \n",
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- "def storeIVF(db,setup,summary):\n",
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- " for rCode in summary:\n",
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- " r=config.decode(rCode,setup)\n",
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- " idFilter=config.getIdFilter(r,setup)\n",
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- " visitFilter=config.getVisitFilter(r,setup)\n",
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- " for s2 in summary[rCode]:\n",
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- " sigmaFilter={'variable':'sigma2','value':str(s2),'oper':'eq'}\n",
|
|
|
- " nr=len(summary[rCode][s2])\n",
|
|
|
- " for rId in summary[rCode][s2]:\n",
|
|
|
- " rFilter={'variable':'regionId','value':str(rId),'oper':'eq'}\n",
|
|
|
- " typeFilter={'variable':'option','value':'D','oper':'eq'}\n",
|
|
|
- " rows=getData.getSummary(db,setup,[idFilter,visitFilter,sigmaFilter,rFilter,typeFilter])\n",
|
|
|
- " if len(rows)>0:\n",
|
|
|
- " qrow=rows[0]\n",
|
|
|
- " mode='update'\n",
|
|
|
- " else:\n",
|
|
|
- " qrow={qr:r[qr] for qr in r}\n",
|
|
|
- " qrow['sigma2']=s2\n",
|
|
|
- " qrow['regionId']=rId\n",
|
|
|
- " seqNum=config.getTargetSeqNum(r,setup)\n",
|
|
|
- " qrow['SequenceNum']=140+seqNum+0.01*rId+0.001*s2\n",
|
|
|
- " mode='insert'\n",
|
|
|
- " qrow['mean']=summary[rCode][s2][rId]\n",
|
|
|
- " qrow['na']=7\n",
|
|
|
- " qrow['nclass']=nr\n",
|
|
|
- " qrow['option']='D'\n",
|
|
|
- " getData.updateSummary(db,setup,mode,[qrow])\n",
|
|
|
- " \n",
|
|
|
- "def getPixelIVF(db,r,setup,sigma2):\n",
|
|
|
- " idFilter=config.getIdFilter(r,setup)\n",
|
|
|
- " visitFilter=config.getVisitFilter(r,setup)\n",
|
|
|
- " rows=getData.getSegmentation(db,setup,[idFilter,visitFilter])\n",
|
|
|
- " nclassIVF=len(rows)\n",
|
|
|
- " fitPar=getFitPar(r,setup,nclassIVF,sigma2,'PixelIVF')\n",
|
|
|
- " k1={r['regionId']:getK1(fitPar,r['regionId']) for r in rows}\n",
|
|
|
- " return k1"
|
|
|
+ "\n"
|
|
|
]
|
|
|
},
|
|
|
{
|
|
|
@@ -412,33 +84,40 @@
|
|
|
"#takes multiple hours for nr=20, nclass=[10,20,30]\n",
|
|
|
"#runs matlab - maybe the first time licensing will be checked and error will be returned\n",
|
|
|
"#just run once\n",
|
|
|
- "#calculateCenters(db,setup,nclass,nr)\n",
|
|
|
+ "#analysis.calculateCenters(db,setup,nclass,nr)\n",
|
|
|
"\n",
|
|
|
"#3.) now do analysis - global gets the average IVF fit (see point one of the slides for both branches of analysis)\n",
|
|
|
"#this again takes multiple hours for four images\n",
|
|
|
"#from what I see this could be shortend (since only global parameters are sought)\n",
|
|
|
- "#doAnalysis(db,setup,nclass,nr,'global')\n",
|
|
|
+ "#analysis.doAnalysis(db,setup,nclass,nr,'global')\n",
|
|
|
"\n",
|
|
|
"#4.) do cluster analysis (bullets two and three under ML analysis)\n",
|
|
|
- "#doAnalysis(db,setup,nclass,nr,'IVF')\n",
|
|
|
+ "#analysis.doAnalysis(db,setup,nclass,nr,'IVF')\n",
|
|
|
"\n",
|
|
|
"#5.) sort out the segmentation points -> segmentation.ipynb and generate Segmentation.txt files read by pixelAnalysis\n",
|
|
|
"\n",
|
|
|
"#6.) run doPixelAnalysisIVF to calculate parameters from locations\n",
|
|
|
"sigma2=[0.1,1,4]\n",
|
|
|
- "#doPixelAnalysis(db,setup,sigma2)\n",
|
|
|
+ "#analysis.doPixelAnalysis(db,setup,sigma2)\n",
|
|
|
"\n",
|
|
|
"#7.) store summary to database\n",
|
|
|
"na=3\n",
|
|
|
"s2=0.1\n",
|
|
|
- "#summary=fullSummary(db,setup,nclass,nr,s2,na)\n",
|
|
|
- "#storeSummary(db,setup,summary,s2,na)\n",
|
|
|
+ "#summary=analysis.fullSummary(db,setup,nclass,nr,s2,na)\n",
|
|
|
+ "#analysis.storeSummary(db,setup,summary,s2,na)\n",
|
|
|
"\n",
|
|
|
- "#summaryIVF=summaryPixelIVF(db,setup,sigma2)\n",
|
|
|
- "#storeIVF(db,setup,summaryIVF)\n",
|
|
|
+ "summaryIVF=analysis.summaryPixelIVF(db,setup,sigma2)\n",
|
|
|
+ "analysis.storeIVF(db,setup,summaryIVF)\n",
|
|
|
"\n",
|
|
|
- "#8.) Plot regions through chart wizard on labkey\n"
|
|
|
+ "#8.) Plot regions through chart wizard on labkey, see also Summary R Report for error-plots on multi-class analysis\n"
|
|
|
]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "code",
|
|
|
+ "execution_count": null,
|
|
|
+ "metadata": {},
|
|
|
+ "outputs": [],
|
|
|
+ "source": []
|
|
|
}
|
|
|
],
|
|
|
"metadata": {
|
