Neurocaps

🚀 New/Added
- Ability to project CAPs onto surface plots.

🚀 New/Added
- Added "Custom" as a valid keyword for `parcel_approach` in the `TimeseriesExtractor` and `CAP` classes to support
custom parcellation with bilateral nodes (nodes that have a left and right hemisphere version). Timeseries extraction,
CAPs extraction, and all visualization methods are available for custom parcellations.
- Added `exclude_niftis` parameter to `TimeseriesExtractor.get_bold` to skip over specific files during timeseries
extraction.
- Added `fd_threshold` parameter to `TimeseriesExtractor` to scrub frames that exceed a specific threshold after
nuisance regression is done.
- Added options to flush print statements during timeseries extraction.
- Added additional **kwargs for `CAP.visualize_caps`.

♻ Changed
- Changed `network` parameter in `TimeseriesExtractor.visualize_bold` to `region`.
- Changed "networks" option in `visual_scope` parameter in `CAP.visualize_caps` to "regions".

🐛 Fixes
- Fixed reference before assignment when specifying the repetition time (TR) when using the `tr` parameter in
`TimeseriesExtractor.get_bold`. Prior only extracting the TR from the metadata files, which is done if the `tr`
parameter was not specified worked.
- Allow bids datasets that do not specify run id or session id in their file names to be ran instead of producing an
error. Prior, only bids datasets that included "ses-" and "run-" in the file names worked. Files that do not have
"run-" in it's name will include a default run-id in their sub-key to maintain the structure of the
`TimeseriesExtractor.subject_timeseries` dictionary". This default id is "run-1".
- Fixed error in `CAP.visualize_caps` when plotting "outer products" plot without subplots.

🚀 New/Added
- Support Windows by only allowing install of pybids if system is not Windows. On Windows machines
`TimeseriesExtractor` cannot be used but `CAP` and all other functions can be used.

🚀 New/Added
- Added `merge_dicts` to be able to combine different subject_timeseries and only return shared subjects.
- Print names of confounds used for each subject and run when extracting timeseries for transparency.
- Ability to extract timeseries using the AAL or Schaefer parcellation.
- Ability to use multiprocessing to speed up timeseries extraction.
- Can be used to extract task (entire task timeseries or a single specific condition) or resting-state data.
- Ability to denoise data during extraction using band pass filtering, confounds, detrending, and removing dummy scans.
- Can visualize the extracted timeseries at the node or network level.
- Ability to perform Co-activation Patterns (CAPs) analysis on separate groups or all subjects.
- Can use silhouette method or elbow method to determine optimal cluster size and the optimal kmeans model will be
saved.
- Can visualize kneed plots for elbow method.
- Can visualize CAPs using heatmaps or outer product plots at the network or node level of the Schaefer or AAL atlas.
- Can calculate temporal frequency, persistence, counts, and transition frequency. As well as save each as a csv file.