automatic import of python-antspymm

3 files changed, 1054 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..e7ecba0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/antspymm-0.9.7.tar.gz
diff --git a/python-antspymm.spec b/python-antspymm.spec
new file mode 100644
index 0000000..d520ba1
--- /dev/null
+++ b/python-antspymm.spec
@@ -0,0 +1,1052 @@
+%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 <Python_Bot@openeuler.org> - 0.9.7-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..c90da65
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+467ebb20172cb222026942c6d66a976e  antspymm-0.9.7.tar.gz