This package provides access to the
cnpy library by Carl Rogers
which provides read and write facilities for files created with (or for) the
NumPy extension for Python.
Support is provided to reading and writing of either vectors or matrices of numeric or integer types.
gzip compression can be transparently read and
written as well.
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string with (path and) filename for a
string with type 'numeric' (default) or 'integer'.
an R object, currently limited to a vector or matrix of either integer or numeric type
a boolean variable indicating whether a two-dimensional object should be transposed after reading, default is true
a one-character string indicating whether files are
appended to ("a") or written ("w", the default). In case of writing
a boolean variable indicating whether a path implied
The package uses Rcpp modules to provide R bindings
npySave() which wrap the
npy_save() functions. Currently, only one- and two-dimensional
vectors and matrices are suppported; higher-dimensional arrays could
Integer support requires access to the
long long type which is
available if the package is built using the C++11 standard; this is
the default since release 0.2.3 which came out after R 3.1.0 permitted
use of C++11 in CRAN packages.
Note that R uses only one
integer type (which uses 32 bits) and
double floating point type (which uses 64 bits). If Python
data of either type with a different bitsize is to be shared with R,
it has be cast to the corresponding width used by R first.
Dirk Eddelbuettel provided the binding to R (using the Rcpp package).
Carl Rogers wrote the underlying
cnpy library, which is
released under the MIT license.
Maintainer: Dirk Eddelbuettel <[email protected]>
Rcpp, in particular the Rcpp modules documentation.
cnpy repository: https://github.com/rogersce/cnpy
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## Not run: library(RcppCNPy) ## load NumPy file with floating-point data fmat <- npyLoad("fmat.npy") ## load NumPy file with integer data imat <- npyLoad("imat.npy", "integer") ## save floating-point data: matrix and vector M <- matrix(0:11, 3, 4, byrow=TRUE) * 1.1 v <- v <- 0:4 * 1.1 npySave("fmat.npy", M) npySave("fvec.npy", v) ## save integer data: matrix and vector M <- matrix(0:11, 3, 4, byrow=TRUE) v <- v <- 0:4 npySave("imat.npy", M) npySave("ivec.npy", v) ## End(Not run)
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