In the documentation built from master branch, there are two examples with missing thumbnails. These were imported from Nistats, and they are missing there as well.

We need to fix this before rolling out the next Nilearn stable.

The two examples are:

9.5.2. Example of a events.tsv file generation: the neurospin/localizer events
04_glm/first-level-models-write-events-file.py

9.8.9. D

Anatomical data can be viewed using the built-in identity transform in pycortex:

import cortex
nifti = cortex.db.get_anat('S1')
arr = nifti.get_data().T
vol = cortex.Volume(arr, 'S1', 'identity')
cortex.webgl.show(vol)

Right now the top-level ToC is:

• Installation
• Usage Notes
• Running fMRIPrep via Docker containers
• Running fMRIPrep via Singularity containers
• Processing pipeline details
• Susceptibility Distortion Correction (SDC)
• Outputs of fMRIPrep
• Defining standard and nonstandard spaces where data will be resampled
• FAQ, Tips, and Tricks
• Contributing to

Some is already in .gitignore, but some is not. We should check all examples.

https://scalablebrainatlas.incf.org/mouse/ABA_v3

We should swap our current code where we loop over flattened arrays in Brain_Data.similarity() to scipy.spatial.distance.cdist which is 2 orders of magnitude faster.

For example, correlating a 5,000 x 50,000 Brain with a 11 x 50,000 Brain takes ~2:30 min, but with cdist takes about ~ 12 seconds.

• Should be done automatically by the code
• In the code we should catch right click on a hemi and select the closest ROI (partially done by snap cursor)

Ideally I want to use this wonderful toolbox to generate the overview plots given in the interactive window but do the actual marking of these images externally.

Is there an option to generate individual plots without the interactive window (and preferably without the need for tkmedit) as I would like to run this as a batch job on our cluster and that wouldn't allow any graphical windows.

Copy pasting the first few lines of the tutorial
https://teneto.readthedocs.io/en/latest/tutorial/networkrepresentation.html#temporalnetwork-object
yields an error at tnet.network.head() because tnet created with
tnet.generatenetwork('rand_binomial',size=(5,3), prob=0.5)
is a Numpy ndarray and not a Pandas DataFrame.

I haven't tried other ways of creating tnet, so I'm not sure if `t

There are a lot of commands (in bin folder) that should be remove (at least pyhrf_maketests and pyhrf_validate -> use only nose command)

Hi @damaggu - could you please use the NumPy docstring style for docstrings?
This will make the docstrings more transparent to people working with the scientific python stack, and we already have some experience with troubleshooting Sphinx docs generation from this format.

https://github.com/IBT-FMI/SAMRI/blob/2ebfe09

This line https://github.com/SIMEXP/niak/blob/master/bricks/fmri_preprocess/niak_brick_regress_confounds.m#L234

should say "Adding motion parameters..."

and not "Adding high frequency noise..."

The description of the functional preprocessing pipeline is not up to date anymore and needs to be updated according to the fmriflows publication and the workflow graphs under https://github.com/miykael/fmriflows/tree/master/static

We want the web-app to automatically generate a method section.

We therefore need to have some template or boilerplate method section with empty placeholders to be filled in by the different options chosen by users when they fill the list.

This requires writing those boilerplate texts for each activity of the checklist.

• This might need identifying which set of items can be grouped tog