marmaduke woodman

PyPI wheel installs incorrect architecture for macOS on M1

2

A `pip install tvb-gdist` on macOS in an `arm64` environment, works but unable to import, ``` --------------------------------------------------------------------------- ImportError Traceback (most recent call last) /var/folders/22/7cxnw94d2bq3cw98w7px74w40000gn/T/ipykernel_2570/3461558562.py in ----> 1 import gdist ImportError:...

WIP Parallelize gdist calculation

3

This enable parallelization of the gdist calculation across CPU cores (6-core CPU drops expected runtime from 8 hours to 1h30). It also enables a progress bar and keyboard interrupt support.

Use Cython parallel

2

Speeding this up should be fairly easy with Cython's support for OpenMP. It's worth trying and testing the results are identical.

Commit C source [ctypes]

3

Cython is a largish, dev-time dependency in many cases. It would be an added convenience to commit the C sources to Git repo, also pushing them to PyPI.

Gain matrices map source space (neural mass state variables) to sensor space (MEG, EEG and sEEG recordings). They are required for simulating MEG, EEG and sEEG; they are also required...

see https://github.com/the-virtual-brain/tvb-recon/pull/33#issuecomment-283004761

NumPy style docstrings

1

As a project in the scientific community, docstrings are expected to be in [NumPy format](https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt). PyCharm defaults to `:param:` style, but it will do NumPy for you, just take a...

show_aparc_aseg_with_new_values fails w/ -1 index

1

Testing locally, I see a failure in `test_show_aparc_aseg_with_new_values()`, where in ```python for i in xrange(aparc_aseg_matrix.shape[0]): for j in xrange(aparc_aseg_matrix.shape[1]): if aparc_aseg_matrix[i][j] > 0: if fs_to_conn_indices_mapping.has_key(aparc_aseg_matrix[i][j]): aparc_aseg_matrix[i][j] = conn_measure[ fs_to_conn_indices_mapping.get(aparc_aseg_matrix[i][j])] else:...

I usually avoided enhancements over standard `ndarray`, but I think xarray [1] makes a good case, in combining both Pandas like convenience with labeled dimensions. Specifically, it seems like a...