Description Usage Arguments Details Author(s) References See Also Examples
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.
Files with gzip
compression can be transparently read and
written as well.
1 2 3 
filename 
string with (path and) filename for a 
type 
string with type 'numeric' (default) or 'integer'. 
object 
an R object, currently limited to a vector or matrix of either integer or numeric type 
dotranspose 
a boolean variable indicating whether a twodimensional object should be transposed after reading, default is true 
mode 
a onecharacter string indicating whether files are
appended to ("a") or written ("w", the default). In case of writing

checkPath 
a boolean variable indicating whether a path implied
in the 
The package uses Rcpp modules to provide R bindings npyLoad()
and npySave()
which wrap the npy_load()
and
npy_save()
functions. Currently, only one and twodimensional
vectors and matrices are suppported; higherdimensional arrays could
be added.
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
one 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.
The cnpy
repository: https://github.com/rogersce/cnpy
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  ## Not run:
library(RcppCNPy)
## load NumPy file with floatingpoint data
fmat < npyLoad("fmat.npy")
## load NumPy file with integer data
imat < npyLoad("imat.npy", "integer")
## save floatingpoint 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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