What was new in previous releases?¶
Version 0.2¶
Notable changes¶
Added the
HilbertDetector
and optimized its performance on long recordings
Detailed list of changes¶
This release is the first one under the mne.tools
umbrella. We introduced three core detectors that
we found were used and cited in the literature: Line Length, RMS and Hilbert detectors. We have organized
a roadmap for what future improvements would entail. In addition, we have added tutorials that are rendered
as jupyter notebooks, which walk through usage of the package to: i) load data from BIDS, ii) run a detection and
then iii) evaluate the efficacy using tools from scikit-learn
.
Enhancements¶
Added
mne_hfo.compute_chs_hfo_rates()
to compute HFO rates per unit of time for every channel, by Adam Li) (#13)Added
mne_hfo.io.create_events_df()
to generate a DataFrame of HFO events fromRaw
object, or dictionary of lists of HFO endpoints, by Adam Li (#7)Added
mne_hfo.find_coincident_events()
to compare two dicts that contain event information by Patrick Myers (#10)Added notebook to demo use of detection algorithms by Patrick Myers (#10)
Vectorized detection overlap check to enhance scoring speed by Patrick Myers (#15)
Added notebook to demo use of GridSearchCV to optimize detector performance by Patrick Myers (#15)
Added module to compare detections and notebook to demo usage by Patrick Myers (#22)
Added initial implementation of HilbertDetector by Patrick Myers (#23)
Improve memory utilization by allowing parallelization of the entire workflow per channel by Patrick Myers (#38)
API changes¶
Added
mne_hfo.io.events_to_annotations()
to go from*events.tsv
to*annotations.tsv
files, by Adam Li (#10)Added
mne_hfo.sklearn.make_Xy_sklearn()
to format data into scikit-learn compatible data structures for the sake of running hyper-parameter searches withSearchCV
functions, by Adam Li (#15)Separated postprocessing step into two discrete steps _threshold_statistic and _post_process_ch_hfos by Patrick Myers (#23)
Requirements¶
Bug fixes¶
Fixed channel name issue introduced by redundant type checks when using fit_and_predict by Patrick Myers (#15)