ravetools 0.1.2
- Compatible with the latest
filearray
- Exported
grow_volume
-
mesh_from_volume
no longer throw errors if the mesh does not form a manifold
ravetools 0.1.1
CRAN release: 2023-04-19
- Fixed a precision issue that caused test failure on some machine
ravetools 0.1.0
CRAN release: 2023-02-18
- Added
fill_surface
to fill in volume based on given surface mesh - Added
mesh_from_volume
to generate mesh from volume. This function can be used together withfill_surface
to generate surface envelope - Added
register_volume
to align two imaging data using linear or non-linear registration - Added
fftw
on2D
image and3D
volume data - Added convolution for
1D
,2D
,3D
data usingFFT
ravetools 0.0.9
CRAN release: 2023-01-20
- Fixed
pwelch
frequency not starting from zero issue - Upgraded
TinyThread
using the latest pull-request toRcppParallel
ravetools 0.0.8
CRAN release: 2022-10-14
- Added
interpolate_stimulation
to detect stimulation signals within the response and interpolate with smooth signals - The package now imports
splines
- Added
fast_quantile
andfast_mvquantile
to improve the quantile/median calculation speed - Fixed the
plot_signals
plotting range too large when signals have large values (such as stimulation) - Fixed
TinyThreads
library memory leak issues - Simplified
diagnose_channel
, avoid duplicatedpwelch
calculation
ravetools 0.0.7
CRAN release: 2022-09-19
- Added signal
filter
,filtfilt
that produce the same results asMatlab
(with precision error) - Added two ways to perform band-pass filters
- Allows multiple channels through
pwelch
as a row-major matrix to speed up calculation - Added
wavelet_cycles_suggest
to provide default calculation of wavelet cycles - Added internal argument
signature
to wavelet to resolve potential cache conflicts when running in multiple processes. (This allowsRAVE
to run wavelet on multiple subjects at the same time)
ravetools 0.0.6
CRAN release: 2022-08-25
- Added decibel average in
pwelch
- Allowed
pwelch
sampling frequency to be greater than the signal length - Adjusted parameters diagnostic plot and
pwelch
plot to properly handle graph text, margin, axis - Added
plot_signals
to plot multiple functional data within the same canvas
ravetools 0.0.5
CRAN release: 2022-08-06
- Exposed
C++
code toinst/includes
so other users can dynamically link to the functions (https://github.com/dipterix/ravetools/issues/5) - Removed confusing in-place arguments in the
fftw
related code - Corrected
fftw
plans to respect the flags - Added
C++
to convert raw binary bytes touint
,int
,float
, andstring
ravetools 0.0.4
CRAN release: 2022-05-29
Parallel processes might use different temporary directory paths. To improve the performance, it is recommended to set a shared temporary directory, hence this version
- Allows temporary directories to be set via environment variable
RAVETOOLS_TEMPDIR
or optionravetools.tempdir
.
ravetools 0.0.3
CRAN release: 2022-02-16
This version fixes a memory issue reported by CRAN
check (gcc-UBSAN
).
- There is a potential integer overflow where
NA_INTEGER
is subtracted by one before being converted toR_xlen_t
type. This update fixes this issue - Removed
RcppParallel
and copied part of it intoinst/include
folder, withTBB
removed under theGPL-3
license framework.
ravetools 0.0.2
CRAN release: 2022-02-14
This is an initial version of ravetools
. Although a bare minimal set of signal processing functions are provided, it is sufficient to perform preprocess pipelines on most iEEG
signals. Some functions are added from the dipsaus
package, with considerable performance improvement. The C++
functions have been tested on all major platforms, with different architectures (ARM
, i386
, x64
).
Documentation
- Added
README
file to demonstrate basic usage - Added a
NEWS.md
file to track changes to the package.
Signal processing functions
- Re-implemented
decimate
withFIR
filters creating the same results as inMatlab
- Added
detrend
function to - Added
diagnose_channel
to visually inspect channel signals - Added
morlet_wavelet
to enable fast and memory efficient wavelet decomposition; the result agrees with existingMatlab
code with floating errors (10^-7
) - Added
multitaper
- Added
pwelch
(Welch
periodogram) - Added
notch_filter
to remove line noise
High-performance functions
The following functions are implemented in C++
parallel. They tend to be faster than normal base-R implementations, depending on the number of CPU
cores used.
- Added
collapse
to collapse arrays - Added
shift_array
to shift array along certain indices - Added
fast_cov
to calculatepearson
covariance matrix in parallel - Added
baseline_array
to calculate baseline arrays with multiple margins