def transform_vol(invol, xfm, space_defining):
    invol = bg.unzip_file(invol)  # in case zipped
    xfm = bg.unzip_file(xfm)  # in case zipped
    space_defining = bg.unzip_file(space_defining)  # in case zipped
    pp.apply_transform_onefile(xfm, invol)
    pp.reslice(space_defining, invol)
    rinvol = pp.prefix_filename(invol, prefix='r')
    bg.remove_files([invol])
    bg.zip_files([space_defining])
    return rinvol
def transform_vol(invol, xfm, space_defining):
    invol = bg.unzip_file(invol)# in case zipped
    xfm =  bg.unzip_file(xfm)# in case zipped
    space_defining = bg.unzip_file(space_defining)# in case zipped
    pp.apply_transform_onefile(xfm, invol)
    pp.reslice(space_defining, invol)
    rinvol = pp.prefix_filename(invol, prefix='r')
    bg.remove_files([invol])
    bg.zip_files([space_defining])
    return rinvol
        ### XXXXXXXXXXXXXXXXX
        ##  set_up_dir
        outdirs = pp.set_up_dir(root, subid, tracer)

        ## set up MRI
        brainmask = os.path.join(outdirs["anatomydir"][0], "brainmask.nii")
        fsmri = pp.find_single_file(os.path.join(mridir, subid, "mri/brainmask.mgz"))
        if os.path.isfile(brainmask):
            logging.warning("%s has existing anatomy," "skipping" % (brainmask))
        elif fsmri is None:
            logging.error("%s NO MRI: %s" % (subid, fsmri))

        else:
            fsmri = bg.copy_file(fsmri, outdirs["anatomydir"][0])
            brainmask = bg.convert(fsmri, brainmask)
            bg.remove_files([fsmri])
            # copy aseg+aparc
        aparcnii = os.path.join(outdirs["anatomydir"][0], "%s_aparc_aseg.nii.gz" % subid)
        aparc = pp.find_single_file(os.path.join(mridir, subid, "mri/aparc+aseg.mgz"))
        if os.path.isfile(aparcnii):
            logging.warning("%s has existing anatomy," "skipping" % (aparcnii))
        elif aparc is None:
            logging.error("%s NO APARC ASEG: %s" % (subid, aparc))

        else:
            aparc = bg.copy_file(aparc, outdirs["anatomydir"][0])
            aparcnii = bg.convert(aparc, aparcnii)
            bg.remove_files([aparc])
        # make pons
        refdir, _ = outdirs["refdir"]
        brainstem = os.path.join(refdir, "brainstem.nii.gz")
예제 #4
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        outdirs = pp.set_up_dir(root, subid, tracer)

        ## set up MRI
        brainmask = os.path.join(outdirs['anatomydir'][0], 'brainmask.nii')
        fsmri = pp.find_single_file(
            os.path.join(mridir, subid, 'mri/brainmask.mgz'))
        if os.path.isfile(brainmask):
            logging.warning('%s has existing anatomy,'\
                            'skipping'%(brainmask))
        elif fsmri is None:
            logging.error('NO MRI: %s' % (fsmri))

        else:
            fsmri = bg.copy_file(fsmri, outdirs['anatomydir'][0])
            brainmask = bg.convert(fsmri, brainmask)
            bg.remove_files([fsmri])
            # copy aseg+aparc
        aparcnii = os.path.join(outdirs['anatomydir'][0],
                                '%s_aparc_aseg.nii.gz' % subid)
        aparc = pp.find_single_file(
            os.path.join(mridir, subid, 'mri/aparc+aseg.mgz'))
        if os.path.isfile(aparcnii):
            logging.warning('%s has existing anatomy,'\
                            'skipping'%(aparcnii))
        elif aparc is None:
            logging.error('NO APARC ASEG: %s' % aparc)

        else:
            aparc = bg.copy_file(aparc, outdirs['anatomydir'][0])
            aparcnii = bg.convert(aparc, aparcnii)
            bg.remove_files([aparc])
        ## set up MRI
        brainmask = os.path.join(outdirs['anatomydir'][0],
                                 'brainmask.nii')
        fsmri = pp.find_single_file(os.path.join(mridir,
                                                 subid,
                                                 'mri/brainmask.mgz'))
        if os.path.isfile(brainmask):
            logging.warning('%s has existing anatomy,'\
                            'skipping'%(brainmask))
        elif fsmri is None:
            logging.error('%s NO MRI: %s'%(subid,fsmri)) 
            
        else:
            fsmri = bg.copy_file(fsmri, outdirs['anatomydir'][0])
            brainmask = bg.convert(fsmri, brainmask)
            bg.remove_files([fsmri])
            # copy aseg+aparc
        aparcnii = os.path.join(outdirs['anatomydir'][0],
                                '%s_aparc_aseg.nii.gz'%subid) 
        aparc = pp.find_single_file(os.path.join(mridir,
                                                 subid,
                                                 'mri/aparc+aseg.mgz'))
        if os.path.isfile(aparcnii):
            logging.warning('%s has existing anatomy,'\
                            'skipping'%(aparcnii))
        elif aparc is None:
            logging.error('%s NO APARC ASEG: %s'%(subid, aparc))

        else:
            aparc = bg.copy_file(aparc, outdirs['anatomydir'][0])
            aparcnii = bg.convert(aparc, aparcnii)     
예제 #6
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        outdirs = pp.set_up_dir(root, subid, tracer)

        ## set up MRI
        brainmask = os.path.join(outdirs['anatomydir'][0], 'brainmask.nii')
        fsmri = pp.find_single_file(
            os.path.join(mridir, subid, 'mri/brainmask.mgz'))
        if os.path.isfile(brainmask):
            logging.warning('%s has existing anatomy,'\
                            'skipping'%(brainmask))
        elif fsmri is None:
            logging.error('%s NO MRI: %s' % (subid, fsmri))

        else:
            fsmri = bg.copy_file(fsmri, outdirs['anatomydir'][0])
            brainmask = bg.convert(fsmri, brainmask)
            bg.remove_files([fsmri])
            # copy aseg+aparc
        aparcnii = os.path.join(outdirs['anatomydir'][0],
                                '%s_aparc_aseg.nii.gz' % subid)
        aparc = pp.find_single_file(
            os.path.join(mridir, subid, 'mri/aparc+aseg.mgz'))
        if os.path.isfile(aparcnii):
            logging.warning('%s has existing anatomy,'\
                            'skipping'%(aparcnii))
        elif aparc is None:
            logging.error('%s NO APARC ASEG: %s' % (subid, aparc))

        else:
            aparc = bg.copy_file(aparc, outdirs['anatomydir'][0])
            aparcnii = bg.convert(aparc, aparcnii)
            bg.remove_files([aparc])
예제 #7
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        crg_out = pp.simple_coregister(mean_img, sum1_5, newnifti[:5])
        if crg_out.runtime.returncode is not 0:
            logging.error('Failed to coreg 1-5 to mean for  %s' % subid)
            continue
        # grab all realigned files
        allrealigned = crg_out.outputs.coregistered_files + \
                       rlgnout.outputs.realigned_files
        # make new mean files(s) based on fully realigned files
        # 1. first 20 mins for coreg (frames 1-23)
        # 2. 40-60 mins for possible SUVR (frames 28-31)
        mean_20min = pp.make_mean_20min(allrealigned)
        mean_40_60min = pp.make_mean_40_60(allrealigned)

        # clean up
        # remove copied unrealigned frames
        bg.remove_files(newnifti)

        # QA
        # make 4d for QA
        qadir, exists = qa.make_qa_dir(realigndir, name='data_QA')
        data4d = qa.make_4d_nibabel(allrealigned, outdir=qadir)
        #snrimg = qa.gen_sig2noise_img(data4d,qadir)
        #artout = qa.run_artdetect(data4d,tmpparameterfile)
        #qa.screen_data_dirnme(data4d, qadir)
        qa.plot_movement(tmpparameterfile, subid)
        qa.calc_robust_median_diff(data4d)
        qa.screen_pet(data4d)

        # Coregister cerebellum, brainmask and aparc_aseg to pet space
        #
        logging.info('Coreg %s' % (subid))
        ## set up MRI
        brainmask = os.path.join(outdirs['anatomydir'][0],
                                 'brainmask.nii')
        fsmri = pp.find_single_file(os.path.join(mridir,
                                                 subid,
                                                 'mri/brainmask.mgz'))
        if os.path.isfile(brainmask):
            logging.warning('%s has existing anatomy,'\
                            'skipping'%(brainmask))
        elif fsmri is None:
            logging.error('%s NO MRI: %s'%(subid,fsmri)) 
            
        else:
            fsmri = bg.copy_file(fsmri, outdirs['anatomydir'][0])
            brainmask = bg.convert(fsmri, brainmask)
            bg.remove_files([fsmri])
            # copy aseg+aparc
        aparcnii = os.path.join(outdirs['anatomydir'][0],
                                '%s_aparc_aseg.nii.gz'%subid) 
        aparc = pp.find_single_file(os.path.join(mridir,
                                                 subid,
                                                 'mri/aparc+aseg.mgz'))
        if os.path.isfile(aparcnii):
            logging.warning('%s has existing anatomy,'\
                            'skipping'%(aparcnii))
        elif aparc is None:
            logging.error('%s NO APARC ASEG: %s'%(subid, aparc))

        else:
            aparc = bg.copy_file(aparc, outdirs['anatomydir'][0])
            aparcnii = bg.convert(aparc, aparcnii)     
예제 #9
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        crg_out = pp.simple_coregister(mean_img, sum1_5, newnifti[:5])
        if crg_out.runtime.returncode is not 0:
            logging.error('Failed to coreg 1-5 to mean for  %s' % subid)
            continue
        # grab all realigned files
        allrealigned = crg_out.outputs.coregistered_files + \
                       rlgnout.outputs.realigned_files
        # make new mean files(s) based on fully realigned files
        # 1. first 20 mins for coreg (frames 1-23)
        # 2. 40-60 mins for possible SUVR (frames 28-31)
        mean_20min = pp.make_mean_20min(allrealigned)
        mean_40_60min = pp.make_mean_40_60(allrealigned)

        # clean up
        # remove copied unrealigned frames
        bg.remove_files(newnifti)
        

        # QA
        # make 4d for QA
        qadir, exists = qa.make_qa_dir(realigndir, name='data_QA')
        data4d = qa.make_4d_nibabel(allrealigned, outdir=qadir)
        #snrimg = qa.gen_sig2noise_img(data4d,qadir)
        #artout = qa.run_artdetect(data4d,tmpparameterfile)
        #qa.screen_data_dirnme(data4d, qadir)
        qa.plot_movement(tmpparameterfile, subid)
        qa.calc_robust_median_diff(data4d)
        qa.screen_pet(data4d)

        # Coregister cerebellum, brainmask and aparc_aseg to pet space
        #
예제 #10
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        if not hasqa:
            logging.info("qa %s" % subid)
            qa.plot_movement(tmpparameterfile, subid)
            # get rid of NAN in files
            no_nanfiles = pp.clean_nan(tmprealigned)
            # make 4d volume to visualize movement
            img4d = qa.make_4d_nibabel(no_nanfiles)
            bg.zip_files(tmprealigned)
            # save qa image
            # qa.save_qa_img(img4d)
            qa.plot_movement(tmpparameterfile, subid)
            qa.calc_robust_median_diff(img4d)
            qa.screen_pet(img4d)
            # remove tmpfiles

            bg.remove_files(no_nanfiles)
            bg.remove_files(newnifti)

        # coreg pons to pet
        # find PONS
        pons_searchstr = "%s/ref_region/pons_tu.nii*" % tracerdir
        pons = pp.find_single_file(pons_searchstr)
        if "gz" in pons:
            pons = bg.unzip_file(pons)
        if pons is None:
            logging.warning("no pons_tu found for %s" % (subid))
            continue
        # find MRI
        searchstring = "%s/anatomy/brainmask.nii" % sub
        mri = pp.find_single_file(searchstring)
        if mri is None:
예제 #11
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        if not hasqa:
            logging.info('qa %s' % subid)
            qa.plot_movement(tmpparameterfile, subid)
            # get rid of NAN in files
            no_nanfiles = pp.clean_nan(tmprealigned)
            #make 4d volume to visualize movement
            img4d = qa.make_4d_nibabel(no_nanfiles)
            bg.zip_files(tmprealigned)
            #save qa image
            #qa.save_qa_img(img4d)
            qa.plot_movement(tmpparameterfile, subid)
            qa.calc_robust_median_diff(img4d)
            qa.screen_pet(img4d)
            #remove tmpfiles

            bg.remove_files(no_nanfiles)
            bg.remove_files(newnifti)

        # coreg pons to pet
        # find PONS
        pons_searchstr = '%s/ref_region/pons_tu.nii*' % tracerdir
        pons = pp.find_single_file(pons_searchstr)
        if 'gz' in pons:
            pons = bg.unzip_file(pons)
        if pons is None:
            logging.warning('no pons_tu found for %s' % (subid))
            continue
        # find MRI
        searchstring = '%s/anatomy/brainmask.nii' % sub
        mri = pp.find_single_file(searchstring)
        if mri is None: