Neurocaps

- Still in beta but stable
- Allow Windows install to do CAP analysis and timeseries visualization if pickled subject timeseries is transferred to a Windows system but raise System error if the TimerseriesExtractor.get_bold() method is used on Windows system (only development version)
- Allow subject_timeseries to be set in TimeseriesExtractor and create check to ensure it is is the structure needed for methods to be able to work on it

- Add options to flush print statements during timeseries extraction
- Fix reference before assignment when specifying tr to not only rely on tr extraction from the metadata
- Session no longer required to be specified in the event a bids directory doesn't specify sessions

- Still in beta but stable
- Added new parameter to `merge_dicts` to return all dictionaries used for creating the combined dictionary with only the ids available in the combined dictionary
- Prints names of confounds used for each subject and run when extracting timeseries for transparency
- Add error when attempting to plot CAPs if the number of labels/rois in `parcel_approach` do not match the number of labels/rois used when the CAPs where estimated
- Move warning up earlier if using the incorrect `parcel_approach` when attempting to plot CAPs

- Still in beta but stable
- Assumes the use of fMRIPrep
- Ability to extract timeseries using AAL or Schaefer atlas.
- 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.