Adding recent scripts, particularly generateFigures.py and updates to segmentationIRAEMM_ONKO.json

pythonScripts/generateFigures.py

@@ -0,0 +1,229 @@
+import nibabel
+import os
+import json
+import sys
+import numpy
+import matplotlib.pyplot
+#import chardet
+
+def buildPath(server,project,imageDir,patientCode,visitCode,imageType):
+    path='/'.join([server,'labkey/_webdav',project,'@files',imageDir,patientCode,visitCode])
+    tail='_notCropped_2mmVoxel'
+    if imageType=='Segm':
+        tail='_v5'
+    path+='/'+patientCode+'-'+visitCode+'_'+imageType+tail+'.nii.gz'
+    return path
+
+
+def getCOWAxis(seg,val,axis):
+#returns center of weight for segmentation image where val is selected
+    if axis==0:
+        #2,1 or 1,1
+        i1=2
+        i2=1
+    if axis==1:
+        #0,1 or 2,0
+        i1=2
+        i2=0
+    if axis==2:
+        #0,0 or 1,0
+        i1=1
+        i2=0
+    
+    s=numpy.sum(numpy.sum(seg==val,i1),i2)
+    x=numpy.arange(len(s))
+    s0=0
+    for i in x:
+        if s[i]==0:
+            continue
+        s0=i
+        break
+    s1=len(s)
+    for i in numpy.arange(s0,len(s)):
+        if s[i]>0:
+            continue
+        s1=i
+        break
+    return [s0,numpy.average(x,weights=s),s1]
+
+def getGeometry(seg,val):
+#return center of weight of segmentation seg for segment val as a 3D vector
+    return [getCOWAxis(seg,val,x) for x in [0,1,2]]
+
+def getCOW(geom):
+    return [x[1] for x in geom]
+
+def getRange(geom):
+    return [[x[0],x[2]] for x in geom]
+
+
+def plot(imgs,t,val,tempBase):
+
+
+    segColors=[[0,0,0],[0.1,0.1,0.1],[0,0.2,0],[1,0,0],[0,0,1],[1,0,1]]
+
+#3-lungs 4-thyroid 5-bowel
+    delta=20
+    if val==4:
+        delta=40
+    window=350
+    level=40
+    geo=getGeometry(imgs['Segm'],val)
+    cowF=getCOW(geo)
+    rng=getRange(geo)
+    #print(rng)
+    cowI=[int(x) for x in cowF]
+    segment=imgs['Segm']==val
+
+    i0=rng[0][0]-delta
+    if i0<0:
+        i0=0
+    i1=rng[0][1]+delta
+    if i1>imgs['CT'].shape[0]:
+        i1=imgs['CT'].shape[0]
+    k0=rng[2][0]-delta
+    if k0<0:
+        k0=0
+    k1=rng[2][1]+delta
+    if k1>imgs['CT'].shape[2]:
+        k1=imgs['CT'].shape[2]
+
+    if t=='CT':
+        v0=level-0.5*window
+        v1=v0+window
+        matplotlib.pyplot.imshow(imgs['CT'][i0:i1,cowI[1],k0:k1].transpose(),cmap='gray',vmin=v0,vmax=v1)
+    if t=='PET':
+        matplotlib.pyplot.imshow(imgs['PET'][i0:i1,cowI[1],k0:k1].transpose(),cmap='inferno')
+        #blueish
+
+    if t=='CT':
+        rgb=segColors[val]
+    if t=='PET':
+        rgb=[1,1,1]
+
+    colors = [rgb+[c] for c in numpy.linspace(0,1,100)]
+                                                                                    
+    cmap = matplotlib.colors.LinearSegmentedColormap.from_list('mycmap', colors, N=5)
+
+    matplotlib.pyplot.imshow(segment[i0:i1,cowI[1],k0:k1].transpose(), cmap=cmap, alpha=0.2)
+    matplotlib.pyplot.gca().invert_yaxis()
+    outfile=os.path.join(tempBase,'slice{}_{}.png'.format(t,val))
+    matplotlib.pyplot.savefig(outfile)
+    return outfile
+
+
+
+def main(parameterFile):
+#mask for segmentations
+
+    setupJSON=os.path.join(os.path.expanduser('~'),'.labkey','setup.json')
+    with open(setupJSON) as f:
+        setup=json.load(f)
+            
+    sys.path.insert(0,setup["paths"]["nixWrapper"])
+    import nixWrapper 
+             
+    nixWrapper.loadLibrary("labkeyInterface")
+
+    import labkeyInterface
+    import labkeyDatabaseBrowser
+    import labkeyFileBrowser
+
+    fconfig=os.path.join(os.path.expanduser('~'),'.labkey','network.json')
+                 
+    net=labkeyInterface.labkeyInterface()
+    net.init(fconfig)
+    db=labkeyDatabaseBrowser.labkeyDB(net)
+    fb=labkeyFileBrowser.labkeyFileBrowser(net)
+
+    tempBase=os.path.join(os.path.expanduser('~'),'temp')
+    
+    with open(parameterFile) as f:
+        pars=json.load(f)
+
+    project=pars['project']
+    dataset=pars['targetQuery']
+    schema=pars['targetSchema']
+
+    reportSchema=pars['reportSchema']
+    reportQuery=pars['reportQuery']
+    participantField=pars['participantField']
+
+    #all images from database
+    ds=db.selectRows(project,schema,dataset,[])
+    
+    #input
+    imageResampledField={"CT":"ctResampled","PET":"petResampled","patientmask":"ROImask"}
+
+    rows=ds['rows']
+    rows=[ds['rows'][0]]
+
+    for r in rows:
+        print(r)
+        iTypes=['CT','PET','Segm']
+        needToCalculate=False
+        for t in ['CT','PET']:
+            idFilter={'variable':participantField,'value':r[participantField],'oper':'eq'}
+            visitFilter={'variable':'visitCode','value':r['visitCode'],'oper':'eq'}
+            verFilter={'variable':'version','value':pars['version'],'oper':'eq'}
+            typeFilter={'variable':'type','value':t,'oper':'eq'}
+            
+            ds2=db.selectRows(project,reportSchema,reportQuery,[idFilter,visitFilter,verFilter,typeFilter])
+            
+            if len(ds2['rows'])==0:
+                #skip if row is present
+                #there are in fact multiple rows for multiple organs...
+                needToCalculate=True
+                break
+
+        if not needToCalculate:
+            continue
+        imgs={} 
+        for t in iTypes:
+            
+            try:
+                imagePath=r['_labkeyurl_'+imageResampledField[t]]
+            except KeyError:
+                ds1=db.selectRows(project,pars['segmentationSchema'],pars['segmentationQuery'],\
+                        [idFilter,visitFilter,verFilter])
+                imagePath=ds1['rows'][0]['_labkeyurl_segmentation']
+
+            localPath=os.path.join(tempBase,'image'+t+'.nii.gz')
+            if os.path.isfile(localPath):
+                os.remove(localPath)
+            fb.readFileToFile(imagePath,localPath)
+            img=nibabel.load(localPath)
+            imgs[t]=img.get_fdata()
+        print('Loading completed')
+        for t in ['CT','PET']:
+            for val in [3,4,5]:
+                outfile=plot(imgs,t,val,tempBase)
+                remoteDir=fb.buildPathURL(project,[pars['imageDir'],r['patientCode'],r['visitCode']])
+                imageFile=r['patientCode']+'-'+r['visitCode']+'_'+t+'_{}'.format(val)+'_'+pars['version']+'.png'
+                remoteFile='/'.join([remoteDir,imageFile])
+                fb.writeFileToFile(outfile,remoteFile)
+                print('Uploaded {}'.format(remoteFile))
+                os.remove(outfile)  
+                organFilter={'variable':'organ','value':'{}'.format(val),'oper':'eq'}
+                typeFilter['value']=t
+                ds3=db.selectRows(project,reportSchema,reportQuery,\
+                        [idFilter,visitFilter,verFilter,organFilter,typeFilter])
+                if len(ds3['rows'])>0:
+                    mode='update'
+                    frow=ds3['rows'][0]
+                else:
+                    mode='insert'
+                    frow={}
+                    for f in [participantField,'patientCode','visitCode']:
+                        frow[f]=r[f]
+                frow['organ']='{}'.format(val)
+                frow['type']=t
+                frow['version']=pars['version']
+                frow['file']=imageFile
+                db.modifyRows(mode,project,reportSchema,reportQuery,[frow])
+        print('Images uploaded')
+
+
+if __name__ == '__main__':
+    main(sys.argv[1])
+

pythonScripts/modifyDataset.py

@@ -0,0 +1,61 @@
+#a script to modify a patient from list. Current implementation deletes 
+#all data for patients identified by study regulatory number from study
+
+#basic python
+import os
+import subprocess
+import re
+import datetime
+import sys
+import json
+
+
+def main(parameterFile):
+    fhome=os.path.expanduser('~')
+
+    with open(os.path.join(fhome,".labkey","setup.json")) as f:
+        setup=json.load(f)
+
+    sys.path.insert(0,setup["paths"]["nixWrapper"])
+    import nixWrapper
+
+    nixWrapper.loadLibrary("labkeyInterface")
+
+    import labkeyInterface
+    import labkeyDatabaseBrowser
+    import labkeyFileBrowser
+
+
+    net=labkeyInterface.labkeyInterface()
+    net.init(os.path.join(fhome,'.labkey','network.json'))
+
+    db=labkeyDatabaseBrowser.labkeyDB(net)
+
+    #by default uses .labkey/Remote.json configuration
+    with open(parameterFile) as f:
+        pars=json.load(f)
+
+
+    project=pars['project']
+    schema=pars['schema']
+    query=pars['query']
+    
+
+    #study section ################
+
+
+
+#select patients enroled under regulatory number
+    
+    ds=db.selectRows(project,schema,query,[])
+
+    for r in ds['rows']:
+        r['View']='[VIEW]'
+        db.modifyRows('update',project,schema,query,[r])
+
+    print("Done")
+
+if __name__ == '__main__':
+    main(sys.argv[1])
+
+

templates/segmentationIRAEMM_ONKO.json

@@ -1,7 +1,7 @@
 {
  "setVariables":["__tempBase__","__segBase__","__roiFile__","__petFile__","__ctFile__","__segFile__","__modelName__"],
  "setVariablesComment":"this variables will get updated with local values like home and can be used to set variables further on",
- "__tempBase__":"__home__/temp/segmentationdm",
+ "__tempBase__":"__home__/temp/iraemm",
  "__segBase__":"/home/studen/software/src/iraemm/segmentation",
  "__roiFile__":"testMask.nii.gz",
  "__ctFile__":"testCT.nii.gz",
@@ -17,6 +17,9 @@
  "participantField":"ParticipantId",
  "segmentationSchema":"study",
  "segmentationQuery":"Segmentations",
+ "reportQuery":"reportImages",
+ "reportSchema":"lists",
+ "percentileQuery":"SUVQuantiles",
  "imageDir":"preprocessedImages",
  "version":"v5",
  "versionNumber":"5",