%global _empty_manifest_terminate_build 0 Name: python-antspymm Version: 0.9.7 Release: 1 Summary: multi-channel/time-series medical image processing with antspyx License: Apache 2.0 URL: https://github.com/stnava/ANTsPyMM Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d9/11/551ee0bc564ba2ed0ffd8499e58173c77e5d6657f57466252fc211c8e1db/antspymm-0.9.7.tar.gz BuildArch: noarch Requires: python3-h5py Requires: python3-numpy Requires: python3-pandas Requires: python3-antspyx Requires: python3-antspyt1w Requires: python3-pathlib Requires: python3-dipy Requires: python3-nibabel Requires: python3-scipy Requires: python3-siq Requires: python3-sklearn %description # ANTsPyMM ## processing utilities for timeseries/multichannel images - mostly neuroimaging the outputs of these processes can be used for data inspection/cleaning/triage as well for interrogating hypotheses. this package also keeps track of the latest preferred algorithm variations for production environments. install the `dev` version by calling (within the source directory): ``` python setup.py install ``` or install the latest release via ``` pip install antspymm ``` # what this will do ANTsPyMM will process several types of brain MRI into tabular form as well as normalized (standard template) space. The processing includes: * T1wHier uses hierarchical processing from ANTsPyT1w organized around these measurements * CIT168 template 10.1101/211201 * Desikan Killiany Tourville (DKT) 10.3389/fnins.2012.00171 * basal forebrain (Avants et al HBM 2022 abstract) * other regions (egMTL) 10.1101/2023.01.17.23284693 * also produces jacobian data * rsfMRI: resting state functional MRI * uses 10.1016/j.conb.2012.12.009 to estimate network specific correlations * f/ALFF 10.1016/j.jneumeth.2008.04.012 * NM2DMT: neuromelanin mid-brain images * CIT168 template 10.1101/211201 * DTI: DWI diffusion weighted images organized via: * CIT168 template 10.1101/211201 * JHU atlases 10.1016/j.neuroimage.2008.07.009 10.1016/j.neuroimage.2007.07.053 * DKT for cortical to cortical tractography estimates based on DiPy * T2Flair: flair for white matter hyperintensity * https://pubmed.ncbi.nlm.nih.gov/30908194/ * https://pubmed.ncbi.nlm.nih.gov/30125711/ * https://pubmed.ncbi.nlm.nih.gov/35088930/ * T1w: voxel-based cortical thickness (DiReCT) 10.1016/j.neuroimage.2008.12.016 Results of these processes are plentiful; processing for a single subject will all modalities will take around 2 hours on an average laptop. documentation of functions [here](http://htmlpreview.github.io/?https://github.com/stnava/ANTsPyMM/blob/main/docs/antspymm/mm.html). # first time setup ```python import antspyt1w import antspymm antspyt1w.get_data(force_download=True) antspymm.get_data(force_download=True) ``` NOTE: `get_data` has a `force_download` option to make sure the latest package data is installed. NOTE: some functions in `antspynet` will download deep network model weights on the fly. if one is containerizing, then it would be worth running a test case through in the container to make sure all the relevant weights are pre-downloaded. # example processing see the latest help but this snippet gives an idea of how one might use the package: ```python import os os.environ["TF_NUM_INTEROP_THREADS"] = "8" os.environ["TF_NUM_INTRAOP_THREADS"] = "8" os.environ["ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS"] = "8" import antspymm import antspyt1w import antspynet import ants ... i/o code here ... tabPro, normPro = antspymm.mm( t1, hier, nm_image_list = mynm, rsf_image = rsf, dw_image = dwi, bvals = bval_fname, bvecs = bvec_fname, flair_image = flair, do_tractography=False, do_kk=False, do_normalization=True, verbose=True ) antspymm.write_mm( '/tmp/test_output', t1wide, tabPro, normPro ) ``` ## blind quality control this package also provides tools to identify the *best* multi-modality image set at a given visit. the code below provides guidance on how to automatically qc, filter and match multiple modality images at each time point. these tools are based on standard unsupervised approaches and are not perfect so we recommend using the associated plotting/visualization techniques to check the quality characterizations for each modality. ```python ## run the qc on all images - requires a relatively large sample per modality to be effective ## then aggregate qcdf=pd.DataFrame() for fn in fns: qcdf=pd.concat( [qcdf,antspymm.blind_image_assessment(fn)], axis=0) qcdfa=antspymm.average_blind_qc_by_modality(qcdf,verbose=True) ## reduce the time series qc qcdfaol=antspymm.outlierness_by_modality(qcdfa) # estimate outlier scores print( qcdfaol.shape ) print( qcdfaol.keys ) matched_mm_data=antspymm.match_modalities( qcdfaol ) ``` or just get modality-specific outlierness "by hand" then match `mm`: ```python import antspymm import pandas as pd mymods = antspymm.get_valid_modalities( ) alldf = pd.DataFrame() for n in range(len(mymods)): m=mymods[n] jj=antspymm.collect_blind_qc_by_modality("qc/*"+m+"*csv") jjj=antspymm.average_blind_qc_by_modality(jj,verbose=False).dropna(axis=1) ## reduce the time series qc jjj=antspymm.outlierness_by_modality( jjj, verbose=False) alldf = pd.concat( [alldf, jjj ], axis=0 ) jjj.to_csv( "mm_outlierness_"+m+".csv") print(m+" done") # write the joined data out alldf.to_csv( "mm_outlierness.csv", index=False ) # find the best mm collection matched_mm_data=antspymm.match_modalities( alldf, verbose=True ) matched_mm_data.to_csv( "matched_mm_data.csv", index=False ) matched_mm_data['negative_outlier_factor'] = 1.0 - matched_mm_data['ol_loop'].astype("float") matched_mm_data2 = antspymm.highest_quality_repeat( matched_mm_data, 'subjectID', 'date', qualityvar='negative_outlier_factor') matched_mm_data2.to_csv( "matched_mm_data2.csv", index=False ) ``` ## an example on open neuro (BIDS) data from : [ANT PD](https://openneuro.org/datasets/ds001907/versions/3.0.2) ``` imagesBIDS/ └── ANTPD └── sub-RC4125 └── ses-1 ├── anat │   ├── sub-RC4125_ses-1_T1w.json │   └── sub-RC4125_ses-1_T1w.nii.gz ├── dwi │   ├── sub-RC4125_ses-1_dwi.bval │   ├── sub-RC4125_ses-1_dwi.bvec │   ├── sub-RC4125_ses-1_dwi.json │   └── sub-RC4125_ses-1_dwi.nii.gz └── func ├── sub-RC4125_ses-1_task-ANT_run-1_bold.json ├── sub-RC4125_ses-1_task-ANT_run-1_bold.nii.gz └── sub-RC4125_ses-1_task-ANT_run-1_events.tsv ``` ```python import antspymm import pandas as pd import glob as glob fns = glob.glob("imagesBIDS/ANTPD/sub-RC4125/ses-*/*/*gz") fns.sort() randid='000' # BIDS does not have unique image ids - so we assign one studycsv = antspymm.generate_mm_dataframe( 'ANTPD', 'sub-RC4125', 'ses-1', randid, 'T1w', '/Users/stnava/data/openneuro/imagesBIDS/', '/Users/stnava/data/openneuro/processed/', t1_filename=fns[0], dti_filenames=[fns[1]], rsf_filenames=[fns[2]]) studycsv2 = studycsv.dropna(axis=1) mmrun = antspymm.mm_csv( studycsv2, mysep='_' ) ``` ## NRG example NRG format details [here](https://htmlpreview.github.io/?https://github.com/stnava/biomedicalDataOrganization/blob/master/src/nrg_data_organization_summary.html) ``` imagesNRG/ └── ANTPD └── sub-RC4125 └── ses-1 ├── DTI │   └── 000 │   ├── ANTPD_sub-RC4125_ses-1_DTI_000.bval │   ├── ANTPD_sub-RC4125_ses-1_DTI_000.bvec │   ├── ANTPD_sub-RC4125_ses-1_DTI_000.json │   └── ANTPD_sub-RC4125_ses-1_DTI_000.nii.gz ├── T1w │   └── 000 │   └── ANTPD_sub-RC4125_ses-1_T1w_000.nii.gz └── rsfMRI └── 000 └── ANTPD_sub-RC4125_ses-1_rsfMRI_000.nii.gz ``` ```python import antspymm import pandas as pd import glob as glob t1fn=glob.glob("imagesNRG/ANTPD/sub-RC4125/ses-*/*/*/*T1w*gz")[0] # flair also takes a single image dtfn=glob.glob("imagesNRG/ANTPD/sub-RC4125/ses-*/*/*/*DTI*gz") rsfn=glob.glob("imagesNRG/ANTPD/sub-RC4125/ses-*/*/*/*rsfMRI*gz") studycsv = antspymm.generate_mm_dataframe( 'ANTPD', 'sub-RC4125', 'ses-1', '000', 'T1w', '/Users/stnava/data/openneuro/imagesNRG/', '/Users/stnava/data/openneuro/processed/', t1fn, rsf_filenames=rsfn, dti_filenames=dtfn ) studycsv2 = studycsv.dropna(axis=1) mmrun = antspymm.mm_csv( studycsv2, mysep='_' ) ``` ## useful tools for converting dicom to nifti * [dcm2niix](https://github.com/rordenlab/dcm2niix) * [dicom2nifti](https://dicom2nifti.readthedocs.io/en/latest/) ```python import dicom2nifti dicom2nifti.convert_directory(dicom_directory, output_folder, compression=True, reorient=True) ``` * [simpleitk](https://pypi.org/project/SimpleITK/) ```python import SimpleITK as sitk import sys import os import glob as glob import ants dd='dicom' oo='dicom2nifti' folders=glob.glob('dicom/*') k=0 for f in folders: print(f) reader = sitk.ImageSeriesReader() ff=glob.glob(f+"/*") dicom_names = reader.GetGDCMSeriesFileNames(ff[0]) if len(ff) > 0: fnout='dicom2nifti/image_'+str(k).zfill(4)+'.nii.gz' if not exists(fnout): failed=False reader.SetFileNames(dicom_names) try: image = reader.Execute() except: failed=True pass if not failed: size = image.GetSpacing() print( image.GetMetaDataKeys( ) ) print( size ) sitk.WriteImage(image, fnout ) img=ants.image_read( fnout ) img=ants.iMath(img,'TruncateIntensity',0.02,0.98) ants.plot( img, nslices=21,ncol=7,axis=2, crop=True ) else: print(f+ ": "+'empty') k=k+1 ``` ## build docs ``` pdoc -o ./docs antspymm --html ``` ## to publish a release ``` rm -r -f build/ antspymm.egg-info/ dist/ python3 setup.py sdist bdist_wheel python3 -m twine upload -u username -p password dist/* ``` %package -n python3-antspymm Summary: multi-channel/time-series medical image processing with antspyx Provides: python-antspymm BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-antspymm # ANTsPyMM ## processing utilities for timeseries/multichannel images - mostly neuroimaging the outputs of these processes can be used for data inspection/cleaning/triage as well for interrogating hypotheses. this package also keeps track of the latest preferred algorithm variations for production environments. install the `dev` version by calling (within the source directory): ``` python setup.py install ``` or install the latest release via ``` pip install antspymm ``` # what this will do ANTsPyMM will process several types of brain MRI into tabular form as well as normalized (standard template) space. The processing includes: * T1wHier uses hierarchical processing from ANTsPyT1w organized around these measurements * CIT168 template 10.1101/211201 * Desikan Killiany Tourville (DKT) 10.3389/fnins.2012.00171 * basal forebrain (Avants et al HBM 2022 abstract) * other regions (egMTL) 10.1101/2023.01.17.23284693 * also produces jacobian data * rsfMRI: resting state functional MRI * uses 10.1016/j.conb.2012.12.009 to estimate network specific correlations * f/ALFF 10.1016/j.jneumeth.2008.04.012 * NM2DMT: neuromelanin mid-brain images * CIT168 template 10.1101/211201 * DTI: DWI diffusion weighted images organized via: * CIT168 template 10.1101/211201 * JHU atlases 10.1016/j.neuroimage.2008.07.009 10.1016/j.neuroimage.2007.07.053 * DKT for cortical to cortical tractography estimates based on DiPy * T2Flair: flair for white matter hyperintensity * https://pubmed.ncbi.nlm.nih.gov/30908194/ * https://pubmed.ncbi.nlm.nih.gov/30125711/ * https://pubmed.ncbi.nlm.nih.gov/35088930/ * T1w: voxel-based cortical thickness (DiReCT) 10.1016/j.neuroimage.2008.12.016 Results of these processes are plentiful; processing for a single subject will all modalities will take around 2 hours on an average laptop. documentation of functions [here](http://htmlpreview.github.io/?https://github.com/stnava/ANTsPyMM/blob/main/docs/antspymm/mm.html). # first time setup ```python import antspyt1w import antspymm antspyt1w.get_data(force_download=True) antspymm.get_data(force_download=True) ``` NOTE: `get_data` has a `force_download` option to make sure the latest package data is installed. NOTE: some functions in `antspynet` will download deep network model weights on the fly. if one is containerizing, then it would be worth running a test case through in the container to make sure all the relevant weights are pre-downloaded. # example processing see the latest help but this snippet gives an idea of how one might use the package: ```python import os os.environ["TF_NUM_INTEROP_THREADS"] = "8" os.environ["TF_NUM_INTRAOP_THREADS"] = "8" os.environ["ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS"] = "8" import antspymm import antspyt1w import antspynet import ants ... i/o code here ... tabPro, normPro = antspymm.mm( t1, hier, nm_image_list = mynm, rsf_image = rsf, dw_image = dwi, bvals = bval_fname, bvecs = bvec_fname, flair_image = flair, do_tractography=False, do_kk=False, do_normalization=True, verbose=True ) antspymm.write_mm( '/tmp/test_output', t1wide, tabPro, normPro ) ``` ## blind quality control this package also provides tools to identify the *best* multi-modality image set at a given visit. the code below provides guidance on how to automatically qc, filter and match multiple modality images at each time point. these tools are based on standard unsupervised approaches and are not perfect so we recommend using the associated plotting/visualization techniques to check the quality characterizations for each modality. ```python ## run the qc on all images - requires a relatively large sample per modality to be effective ## then aggregate qcdf=pd.DataFrame() for fn in fns: qcdf=pd.concat( [qcdf,antspymm.blind_image_assessment(fn)], axis=0) qcdfa=antspymm.average_blind_qc_by_modality(qcdf,verbose=True) ## reduce the time series qc qcdfaol=antspymm.outlierness_by_modality(qcdfa) # estimate outlier scores print( qcdfaol.shape ) print( qcdfaol.keys ) matched_mm_data=antspymm.match_modalities( qcdfaol ) ``` or just get modality-specific outlierness "by hand" then match `mm`: ```python import antspymm import pandas as pd mymods = antspymm.get_valid_modalities( ) alldf = pd.DataFrame() for n in range(len(mymods)): m=mymods[n] jj=antspymm.collect_blind_qc_by_modality("qc/*"+m+"*csv") jjj=antspymm.average_blind_qc_by_modality(jj,verbose=False).dropna(axis=1) ## reduce the time series qc jjj=antspymm.outlierness_by_modality( jjj, verbose=False) alldf = pd.concat( [alldf, jjj ], axis=0 ) jjj.to_csv( "mm_outlierness_"+m+".csv") print(m+" done") # write the joined data out alldf.to_csv( "mm_outlierness.csv", index=False ) # find the best mm collection matched_mm_data=antspymm.match_modalities( alldf, verbose=True ) matched_mm_data.to_csv( "matched_mm_data.csv", index=False ) matched_mm_data['negative_outlier_factor'] = 1.0 - matched_mm_data['ol_loop'].astype("float") matched_mm_data2 = antspymm.highest_quality_repeat( matched_mm_data, 'subjectID', 'date', qualityvar='negative_outlier_factor') matched_mm_data2.to_csv( "matched_mm_data2.csv", index=False ) ``` ## an example on open neuro (BIDS) data from : [ANT PD](https://openneuro.org/datasets/ds001907/versions/3.0.2) ``` imagesBIDS/ └── ANTPD └── sub-RC4125 └── ses-1 ├── anat │   ├── sub-RC4125_ses-1_T1w.json │   └── sub-RC4125_ses-1_T1w.nii.gz ├── dwi │   ├── sub-RC4125_ses-1_dwi.bval │   ├── sub-RC4125_ses-1_dwi.bvec │   ├── sub-RC4125_ses-1_dwi.json │   └── sub-RC4125_ses-1_dwi.nii.gz └── func ├── sub-RC4125_ses-1_task-ANT_run-1_bold.json ├── sub-RC4125_ses-1_task-ANT_run-1_bold.nii.gz └── sub-RC4125_ses-1_task-ANT_run-1_events.tsv ``` ```python import antspymm import pandas as pd import glob as glob fns = glob.glob("imagesBIDS/ANTPD/sub-RC4125/ses-*/*/*gz") fns.sort() randid='000' # BIDS does not have unique image ids - so we assign one studycsv = antspymm.generate_mm_dataframe( 'ANTPD', 'sub-RC4125', 'ses-1', randid, 'T1w', '/Users/stnava/data/openneuro/imagesBIDS/', '/Users/stnava/data/openneuro/processed/', t1_filename=fns[0], dti_filenames=[fns[1]], rsf_filenames=[fns[2]]) studycsv2 = studycsv.dropna(axis=1) mmrun = antspymm.mm_csv( studycsv2, mysep='_' ) ``` ## NRG example NRG format details [here](https://htmlpreview.github.io/?https://github.com/stnava/biomedicalDataOrganization/blob/master/src/nrg_data_organization_summary.html) ``` imagesNRG/ └── ANTPD └── sub-RC4125 └── ses-1 ├── DTI │   └── 000 │   ├── ANTPD_sub-RC4125_ses-1_DTI_000.bval │   ├── ANTPD_sub-RC4125_ses-1_DTI_000.bvec │   ├── ANTPD_sub-RC4125_ses-1_DTI_000.json │   └── ANTPD_sub-RC4125_ses-1_DTI_000.nii.gz ├── T1w │   └── 000 │   └── ANTPD_sub-RC4125_ses-1_T1w_000.nii.gz └── rsfMRI └── 000 └── ANTPD_sub-RC4125_ses-1_rsfMRI_000.nii.gz ``` ```python import antspymm import pandas as pd import glob as glob t1fn=glob.glob("imagesNRG/ANTPD/sub-RC4125/ses-*/*/*/*T1w*gz")[0] # flair also takes a single image dtfn=glob.glob("imagesNRG/ANTPD/sub-RC4125/ses-*/*/*/*DTI*gz") rsfn=glob.glob("imagesNRG/ANTPD/sub-RC4125/ses-*/*/*/*rsfMRI*gz") studycsv = antspymm.generate_mm_dataframe( 'ANTPD', 'sub-RC4125', 'ses-1', '000', 'T1w', '/Users/stnava/data/openneuro/imagesNRG/', '/Users/stnava/data/openneuro/processed/', t1fn, rsf_filenames=rsfn, dti_filenames=dtfn ) studycsv2 = studycsv.dropna(axis=1) mmrun = antspymm.mm_csv( studycsv2, mysep='_' ) ``` ## useful tools for converting dicom to nifti * [dcm2niix](https://github.com/rordenlab/dcm2niix) * [dicom2nifti](https://dicom2nifti.readthedocs.io/en/latest/) ```python import dicom2nifti dicom2nifti.convert_directory(dicom_directory, output_folder, compression=True, reorient=True) ``` * [simpleitk](https://pypi.org/project/SimpleITK/) ```python import SimpleITK as sitk import sys import os import glob as glob import ants dd='dicom' oo='dicom2nifti' folders=glob.glob('dicom/*') k=0 for f in folders: print(f) reader = sitk.ImageSeriesReader() ff=glob.glob(f+"/*") dicom_names = reader.GetGDCMSeriesFileNames(ff[0]) if len(ff) > 0: fnout='dicom2nifti/image_'+str(k).zfill(4)+'.nii.gz' if not exists(fnout): failed=False reader.SetFileNames(dicom_names) try: image = reader.Execute() except: failed=True pass if not failed: size = image.GetSpacing() print( image.GetMetaDataKeys( ) ) print( size ) sitk.WriteImage(image, fnout ) img=ants.image_read( fnout ) img=ants.iMath(img,'TruncateIntensity',0.02,0.98) ants.plot( img, nslices=21,ncol=7,axis=2, crop=True ) else: print(f+ ": "+'empty') k=k+1 ``` ## build docs ``` pdoc -o ./docs antspymm --html ``` ## to publish a release ``` rm -r -f build/ antspymm.egg-info/ dist/ python3 setup.py sdist bdist_wheel python3 -m twine upload -u username -p password dist/* ``` %package help Summary: Development documents and examples for antspymm Provides: python3-antspymm-doc %description help # ANTsPyMM ## processing utilities for timeseries/multichannel images - mostly neuroimaging the outputs of these processes can be used for data inspection/cleaning/triage as well for interrogating hypotheses. this package also keeps track of the latest preferred algorithm variations for production environments. install the `dev` version by calling (within the source directory): ``` python setup.py install ``` or install the latest release via ``` pip install antspymm ``` # what this will do ANTsPyMM will process several types of brain MRI into tabular form as well as normalized (standard template) space. The processing includes: * T1wHier uses hierarchical processing from ANTsPyT1w organized around these measurements * CIT168 template 10.1101/211201 * Desikan Killiany Tourville (DKT) 10.3389/fnins.2012.00171 * basal forebrain (Avants et al HBM 2022 abstract) * other regions (egMTL) 10.1101/2023.01.17.23284693 * also produces jacobian data * rsfMRI: resting state functional MRI * uses 10.1016/j.conb.2012.12.009 to estimate network specific correlations * f/ALFF 10.1016/j.jneumeth.2008.04.012 * NM2DMT: neuromelanin mid-brain images * CIT168 template 10.1101/211201 * DTI: DWI diffusion weighted images organized via: * CIT168 template 10.1101/211201 * JHU atlases 10.1016/j.neuroimage.2008.07.009 10.1016/j.neuroimage.2007.07.053 * DKT for cortical to cortical tractography estimates based on DiPy * T2Flair: flair for white matter hyperintensity * https://pubmed.ncbi.nlm.nih.gov/30908194/ * https://pubmed.ncbi.nlm.nih.gov/30125711/ * https://pubmed.ncbi.nlm.nih.gov/35088930/ * T1w: voxel-based cortical thickness (DiReCT) 10.1016/j.neuroimage.2008.12.016 Results of these processes are plentiful; processing for a single subject will all modalities will take around 2 hours on an average laptop. documentation of functions [here](http://htmlpreview.github.io/?https://github.com/stnava/ANTsPyMM/blob/main/docs/antspymm/mm.html). # first time setup ```python import antspyt1w import antspymm antspyt1w.get_data(force_download=True) antspymm.get_data(force_download=True) ``` NOTE: `get_data` has a `force_download` option to make sure the latest package data is installed. NOTE: some functions in `antspynet` will download deep network model weights on the fly. if one is containerizing, then it would be worth running a test case through in the container to make sure all the relevant weights are pre-downloaded. # example processing see the latest help but this snippet gives an idea of how one might use the package: ```python import os os.environ["TF_NUM_INTEROP_THREADS"] = "8" os.environ["TF_NUM_INTRAOP_THREADS"] = "8" os.environ["ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS"] = "8" import antspymm import antspyt1w import antspynet import ants ... i/o code here ... tabPro, normPro = antspymm.mm( t1, hier, nm_image_list = mynm, rsf_image = rsf, dw_image = dwi, bvals = bval_fname, bvecs = bvec_fname, flair_image = flair, do_tractography=False, do_kk=False, do_normalization=True, verbose=True ) antspymm.write_mm( '/tmp/test_output', t1wide, tabPro, normPro ) ``` ## blind quality control this package also provides tools to identify the *best* multi-modality image set at a given visit. the code below provides guidance on how to automatically qc, filter and match multiple modality images at each time point. these tools are based on standard unsupervised approaches and are not perfect so we recommend using the associated plotting/visualization techniques to check the quality characterizations for each modality. ```python ## run the qc on all images - requires a relatively large sample per modality to be effective ## then aggregate qcdf=pd.DataFrame() for fn in fns: qcdf=pd.concat( [qcdf,antspymm.blind_image_assessment(fn)], axis=0) qcdfa=antspymm.average_blind_qc_by_modality(qcdf,verbose=True) ## reduce the time series qc qcdfaol=antspymm.outlierness_by_modality(qcdfa) # estimate outlier scores print( qcdfaol.shape ) print( qcdfaol.keys ) matched_mm_data=antspymm.match_modalities( qcdfaol ) ``` or just get modality-specific outlierness "by hand" then match `mm`: ```python import antspymm import pandas as pd mymods = antspymm.get_valid_modalities( ) alldf = pd.DataFrame() for n in range(len(mymods)): m=mymods[n] jj=antspymm.collect_blind_qc_by_modality("qc/*"+m+"*csv") jjj=antspymm.average_blind_qc_by_modality(jj,verbose=False).dropna(axis=1) ## reduce the time series qc jjj=antspymm.outlierness_by_modality( jjj, verbose=False) alldf = pd.concat( [alldf, jjj ], axis=0 ) jjj.to_csv( "mm_outlierness_"+m+".csv") print(m+" done") # write the joined data out alldf.to_csv( "mm_outlierness.csv", index=False ) # find the best mm collection matched_mm_data=antspymm.match_modalities( alldf, verbose=True ) matched_mm_data.to_csv( "matched_mm_data.csv", index=False ) matched_mm_data['negative_outlier_factor'] = 1.0 - matched_mm_data['ol_loop'].astype("float") matched_mm_data2 = antspymm.highest_quality_repeat( matched_mm_data, 'subjectID', 'date', qualityvar='negative_outlier_factor') matched_mm_data2.to_csv( "matched_mm_data2.csv", index=False ) ``` ## an example on open neuro (BIDS) data from : [ANT PD](https://openneuro.org/datasets/ds001907/versions/3.0.2) ``` imagesBIDS/ └── ANTPD └── sub-RC4125 └── ses-1 ├── anat │   ├── sub-RC4125_ses-1_T1w.json │   └── sub-RC4125_ses-1_T1w.nii.gz ├── dwi │   ├── sub-RC4125_ses-1_dwi.bval │   ├── sub-RC4125_ses-1_dwi.bvec │   ├── sub-RC4125_ses-1_dwi.json │   └── sub-RC4125_ses-1_dwi.nii.gz └── func ├── sub-RC4125_ses-1_task-ANT_run-1_bold.json ├── sub-RC4125_ses-1_task-ANT_run-1_bold.nii.gz └── sub-RC4125_ses-1_task-ANT_run-1_events.tsv ``` ```python import antspymm import pandas as pd import glob as glob fns = glob.glob("imagesBIDS/ANTPD/sub-RC4125/ses-*/*/*gz") fns.sort() randid='000' # BIDS does not have unique image ids - so we assign one studycsv = antspymm.generate_mm_dataframe( 'ANTPD', 'sub-RC4125', 'ses-1', randid, 'T1w', '/Users/stnava/data/openneuro/imagesBIDS/', '/Users/stnava/data/openneuro/processed/', t1_filename=fns[0], dti_filenames=[fns[1]], rsf_filenames=[fns[2]]) studycsv2 = studycsv.dropna(axis=1) mmrun = antspymm.mm_csv( studycsv2, mysep='_' ) ``` ## NRG example NRG format details [here](https://htmlpreview.github.io/?https://github.com/stnava/biomedicalDataOrganization/blob/master/src/nrg_data_organization_summary.html) ``` imagesNRG/ └── ANTPD └── sub-RC4125 └── ses-1 ├── DTI │   └── 000 │   ├── ANTPD_sub-RC4125_ses-1_DTI_000.bval │   ├── ANTPD_sub-RC4125_ses-1_DTI_000.bvec │   ├── ANTPD_sub-RC4125_ses-1_DTI_000.json │   └── ANTPD_sub-RC4125_ses-1_DTI_000.nii.gz ├── T1w │   └── 000 │   └── ANTPD_sub-RC4125_ses-1_T1w_000.nii.gz └── rsfMRI └── 000 └── ANTPD_sub-RC4125_ses-1_rsfMRI_000.nii.gz ``` ```python import antspymm import pandas as pd import glob as glob t1fn=glob.glob("imagesNRG/ANTPD/sub-RC4125/ses-*/*/*/*T1w*gz")[0] # flair also takes a single image dtfn=glob.glob("imagesNRG/ANTPD/sub-RC4125/ses-*/*/*/*DTI*gz") rsfn=glob.glob("imagesNRG/ANTPD/sub-RC4125/ses-*/*/*/*rsfMRI*gz") studycsv = antspymm.generate_mm_dataframe( 'ANTPD', 'sub-RC4125', 'ses-1', '000', 'T1w', '/Users/stnava/data/openneuro/imagesNRG/', '/Users/stnava/data/openneuro/processed/', t1fn, rsf_filenames=rsfn, dti_filenames=dtfn ) studycsv2 = studycsv.dropna(axis=1) mmrun = antspymm.mm_csv( studycsv2, mysep='_' ) ``` ## useful tools for converting dicom to nifti * [dcm2niix](https://github.com/rordenlab/dcm2niix) * [dicom2nifti](https://dicom2nifti.readthedocs.io/en/latest/) ```python import dicom2nifti dicom2nifti.convert_directory(dicom_directory, output_folder, compression=True, reorient=True) ``` * [simpleitk](https://pypi.org/project/SimpleITK/) ```python import SimpleITK as sitk import sys import os import glob as glob import ants dd='dicom' oo='dicom2nifti' folders=glob.glob('dicom/*') k=0 for f in folders: print(f) reader = sitk.ImageSeriesReader() ff=glob.glob(f+"/*") dicom_names = reader.GetGDCMSeriesFileNames(ff[0]) if len(ff) > 0: fnout='dicom2nifti/image_'+str(k).zfill(4)+'.nii.gz' if not exists(fnout): failed=False reader.SetFileNames(dicom_names) try: image = reader.Execute() except: failed=True pass if not failed: size = image.GetSpacing() print( image.GetMetaDataKeys( ) ) print( size ) sitk.WriteImage(image, fnout ) img=ants.image_read( fnout ) img=ants.iMath(img,'TruncateIntensity',0.02,0.98) ants.plot( img, nslices=21,ncol=7,axis=2, crop=True ) else: print(f+ ": "+'empty') k=k+1 ``` ## build docs ``` pdoc -o ./docs antspymm --html ``` ## to publish a release ``` rm -r -f build/ antspymm.egg-info/ dist/ python3 setup.py sdist bdist_wheel python3 -m twine upload -u username -p password dist/* ``` %prep %autosetup -n antspymm-0.9.7 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-antspymm -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon May 29 2023 Python_Bot - 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