OHBM 2019 posters from the AFNI group

AFNI Message Board
1

The following posters from members of the AFNI group are being presented at OHBM, 2019:

Notizia dell’AFNI: Meet afni_proc.py–Your New Best Friend
https://afni.nimh.nih.gov/pub/dist/HBM2019/Cox_Poster_OHBM2019.pdf

Notizia dell’AFNI! AFNI now makes templates from your subjects easily!
https://afni.nimh.nih.gov/pub/dist/HBM2019/GlenEtal_ohbm_2019_dask_template_toolv2.pdf

Notizia dell’AFNI: Even easier FMRI QC, with afni_proc.py’s automatic HTML review
https://afni.nimh.nih.gov/pub/dist/HBM2019/TaylorEtal_OHBM2019_poster_apqc.pdf

Notizia dell’AFNI: Efficient Inter-Subject Correlation Analysis for Naturalistic data via Region-Based Multilevel Modeling
https://afni.nimh.nih.gov/pub/dist/HBM2019/ChenEtal_OHBM2019b_MBA.pdf

Notizia dell’AFNI: Do We Have to Deal with Multiple Comparisons in Neuroimaging?
https://afni.nimh.nih.gov/pub/dist/HBM2019/ChenEtal_OHBM2019a_RBA.pdf

Notizia dell’AFNI: Adapting to future needs in human neuroimaging.
https://afni.nimh.nih.gov/pub/dist/HBM2019/LeeEtal_ohbm_rome_2019.pdf

Enjoy!

–pt

2

Hi Paul/Daniel/John,

I saw the poster for make_template_dask.py at HBM and wanted to try it out but it seems to not be released yet(?) - was wondering if there’s any planned release date or if it’s available and I’m missing it?

Thank you!
James

3

It’s still not in the regular release, but here are some instructions for using the beta version.

==================================================================================================
INSTALLATION INSTRUCTIONS

  1. Install Anaconda using their downloadable scripts for your environment. On linux systems:

wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
./Miniconda3-latest-Linux-x86_64.sh

Or use one of the installers here. On a Mac, I’ve downloaded the Python 2.7 installer - you can still use Python 3.6 later in your Anaconda environment- from the Anaconda download page.
https://www.anaconda.com/download

  1. The installation program should ask you to put these lines in your .bashrc file. (not sure why there is a space after the first dot here).
    You can source .bashrc or start a new bash shell. These lines are different than previous recent versions,
    but conda lets you know to put these lines in.

. /home/glend/miniconda3/etc/profile.d/conda.sh
conda activate

  1. Start a new bash shell
    bash

  2. Make a new conda environment for Dask that we call “dask” below. This should work with python2 or python3.
    conda create -y -c conda-forge -n dask python=3 dask
    conda activate dask

  3. Install dask-jobqueue using pip:
    pip install dask_jobqueue

  4. Install this special version of afni python code (this is a beta version, not integrated into the AFNI code completely yet)
    https://afni.nimh.nih.gov/sscc/staff/glend/python_template_beta1.tgz
    and then pip install the package.
    The local pip install must be done at the directory above the afni_python directory, and contains the setup.py file that specifies afni_python. Traverse down to the “python_scripts” directory. (this step lets python know where to look for other python files to import)

mkdir afni_template_beta
cd afni_template_beta
wget https://afni.nimh.nih.gov/sscc/staff/glend/python_template_beta1.tgz
tar xzvf python_template_beta1.tgz
cd python_scripts
pip install -e .

  1. Test out the template script to see the help. This is one level down from the previous directory in the “afni_python” directory.
    cd afni_python
    ./make_template_dask.py

  2. Add the full path to the afni_python directory to path (put absolute path in .bashrc file) This allows you to call the python programs from the command line.

export PATH=pwd:$PATH

and add this to your .bashrc file with something like this:
echo export PATH = pwd:‘$PATH’ >> ~/.bashrc

  1. There are three new python files needed here that are not yet in the standard AFNI python distribution:
    make_template_dask.py - the controlling script. Configures Dask and checks options
    construct_template_graph.py - the “meat” of the processing. Loops across subjects to create various mean templates and does the image processing.
    regwrap.py - configuration of options and simple utility functions

The other python scripts are similar to the standard distribution except python imports for afni are all from the pipinstalled afni_python package.

  1. Running this script with data and options (for debugging, precede this with "python -m pdb "… )

make_template_dask.py -ok_to_exist -dsets /data/DSST/template_making/testdata/sub-*_T1w.nii.gz
-init_base /usr/local/apps/afni/current/linux_openmp_64/MNI152_2009_template.nii.gz
-bokeh_port 8790 -dask_mode localcluster -anisosmooth
-final_space SLU_elderly1.0

The localcluster is used here, but on a SLURM cluster, we use “-dask_mode SLURM”. Also most importantly, you need to come up with a name for the output space name of the data.

  1. Bokeh graphs and monitoring using port number used above.
    start browser (firefox) http://localhost:8790

\mkdir -p $outdir

cd $outdir

------------- RUUUUUUuuuuuuuuuuuuuuuuuunnnn!..

export OMP_NUM_THREADS=1

#python -m pdb which make_template_dask.py \

#make_template_dask.py \

python -m pdb which make_template_dask.py \

-ok_to_exist                            \

-dsets `cat ${inilist}|tr '\n' ' '`     \

-init_base $inibase                     \

-bokeh_port 8791                        \

-no_strip -anisosmooth                  \

-aniso_iters 1                          \

-max_threads 2                          \

-aff_vol_rsz  ${vol_affx}               \

-cluster_walltime "71:59:00"            \

-dask_mode localcluster                 \

-findtypical_final                      \

-final_space MYSPACENAME
4

Dear Daniel + colleagues,

Have there been any news on make_template_dask since 2019? I’ve looked for it on AFNI’s GitHub and in the current source code for afni_python but couldn’t find it, so I assume it’s still in beta? Have there been any updates?

Thanks a lot for your fabulous work on this and all the other cool AFNI functions!

All the best,
Alexander

5

No, unfortunately, all is still the same since that post. It does work well, but integrating it with the rest of AFNI is more difficult. I can go over it with you personally if you are interested.

6

Thanks so much for the quick reply! I will follow the steps in your tutorial then and would get back to you if I get stuck.

All the best,
Alexander