| NEWS | R Documentation |
The reticulate vignette has new section on 3d-arrays.
Added clarification to the manual page that the default types are 32-bit integer and 64-bit double (as we are working with R here).
Several updates have been made to the CI system; it now runs r-ci.
The README.md was updated with some new badges.
The vignettes are now pre-made to avoid any external dependencies.
The vignettes have been updated using ‘collapse’ mode and edited.
The README.md now refers to reticulate as an alternative and points to the “Using reticulate” vignette.
The file src/RcppExports.cpp is used for package
registration instead of src/init.c.
The npySave function has a new option to check the path
in the given filename.
A new vignette was added showing how the reticulate package can be used instead.
Vignette sets knitr option python.reticulate=FALSE to
avoid another dependency just for the vignette [CRAN request]
Vignette updated to Rmd and use of pinp package
File src/init.c added for dynamic registration
Expanded documentation in README.md
Added examples to help page
Added CITATION file for JOSS paper
Synchronized code with the cnpy repository
Updated vignette
Expanded DESCRIPTION
Support for saving integer objects was not correct and has been fixed
Support for loading and saving on 'big endian' systems was incomplete, has been greatly expanded and corrected, thanks in large part to very diligent testing as well as patching by Wush Wu
The implementation now uses const iterators, thanks to a pull request by Romain Francois
The vignette no longer assumes that one can call gzip
via system as the world's leading consumer OS may disagree.
src/Makevars now sets CXX_STD = CXX11 which also
provides the long long type on all platforms, so integer file
support is no longer conditional
Consequently, code conditional on RCPP_HAS_LONG_LONG_TYPES
has been simplified and is no longer conditional.
The package now depends on R 3.1.0 or later to allow this.
The vignette has been updated and refreshed to reflect this.
Switched to using the result from the compile-time configuration
for R to determine big or little endian (as needed for the NPy
headers)
Added a new test (and test validation result file) for a complete save-reload cycle and comparison
Synchronized code with the cnpy repository
Added new function to test from R whether integers supported
Updated tests for integer support, if available
Updated vignette and discussion about need for rebuilding only
RcppCNPy with the -std=c++11 flag if integer support is
desired
Updated tests for integer support, if available
Updated THANKS file
Ensure that inclusion of Rinternal.h does not affect
other headers by adding RF_NO_REMAP
Support for writing of gzip-ed npy files has
been added.
A new option dotranspose has been added to
npyLoad() to support data sets that do not need to be
transposed to be used in R.
A memory leak in reading files has been corrected.
Added automatic use of transpose to automagically account for Fortran-vs-C major storage defaults between Python and R.
Support for integer types in dependent on the int64_t
type which is available only when the -std=c++0x switch is
used at build-time (and CRAN still discourages use of it)
Added support for reading gzip'ed files ending in ".npy.gz"
Added regression tests in directory tests/
Added a vignette describing the package
Added a timing benchmark in demo/timings.R
Initial version, as a straightforward Rcpp modules wrap around
the cpny library by Carl Rogers (on github under a MIT
license).
At present, npy files can be read and written for
vectors and matrices of either numeric or integer type.
Note however that matrices are currently transposed because
of the default Fortran ordering done by numpy.