Dependency Updates - Update `scipy.binom_test` -> `scipy.binomtest` in code-base for compatibility with current `scipy` versions
Testing Updates - Fixed a testing issue on filepath checking on windows - Forced numpy random seed to 0 for all `pytest` fixtures that use `numpy.random` to generate test data (avoids random test failures due to random data) - Updated stock GA for setting up conda to v3 - Added m1 macOS runners to grid as experimental (not currently working due to missing hdf5 install on OS) - Set python 3.11 on macOS runner as experimental due to upstream joblib and 3.11 [issue](https://github.com/joblib/joblib/issues/1544) - Set docs building to experimental until upstream issues are resolved (docs are outdated with respect to this release!)
0.5.0
Changes - Drop support for python 3.7 and add support up to 3.11 - Switch from `deepdish` to `h5py` for loading and saving hdf5 `Brain_Data` and `Adjacency` files, with backwards compatible support for hdf5 files created on older versions of nltools
0.4.7
Changes
Fixes - `nltools.stats.regression` now returns the standard errors of beta estimates - The canonical output order is: `b`, `se`, `t`, `p`, `df`, `res` - `Brain_Data` and `Adjacency` will now correctly store the standard error estimates in `stats['sigma']`
0.4.6
Changes
Fixes - Fixed warnings from `onsets_to_dm` as error-checking wasn't quite right - Fixed deprecated `nilearn` warnings - Fixed deprecated `nibabel` `.get_affine()` -> `.affine`
New - `Brain_Data.similarity` should be dramatically faster and now supports rank correlation: https://github.com/cosanlab/nltools/issues/308 https://github.com/cosanlab/nltools/issues/316 https://github.com/cosanlab/nltools/issues/404 - `Design_Matrix.clean` will raise an error if there are duplicate column name - Loading `.h5` objects in `Brain_Data` now respects the `mask` argument:
User loads h5 that contains mask so that mask is used instead of the default MNI mask
Brain_Data('brain.h5')
User loads h5 that contains mask but also sets mask argument. Now mask value takes precedence over whatever mask is in h5 so we issue a warning to the user letting them know on load
User loads h5 that does NOT contain a mask and doesnt set the mask argument so the default MNI mask is used, similar to nifti files This is an implicit fallback just like with niftis
Brain_Data('brain_nomask.h5')
User loads h5 that does NOT contain mask but also sets mask argument Mask value is used to learn transformation like niftis No need to warn them about anything
General - No longer pin `pandas` and `deepdish` versions - Works with the latest version of `sklearn` - Remove documentation build artifacts from version control
0.4.4
General - Removed `mne` as a dependency - Clarify doctstring for `Adjacency.distance_to_similarity` to note we currently only support euclidean and correlation distance
Bug Fixes - `Path` objects now reliably work for `Brain_Data` and `Adjacency` classes with passing tests - Fixed major bug in `isps` where hilbert trasform was being applied to the wrong axises
New Features Adjacency - new `generate_permutations` method which acts as python _generator_ that can be used for iteration - new `.cluster_summary` method to summarize with and between cluster distances - new `.sum` method to add adjacency matrices - new `.fisher_z_r` method to invert `.fisher_r_z`
Stats - new `align_states` function that implements the Hungarian Algorithm - `isps` gains a new `pairwise` argument