It seems that we still need to add "#define RCPP_HAS_LONG_LONG_TYPES "
to cnpy.hotherwise it won't work, even the argument 'type' has been
specified while using npyLoad
Gong.
On 02/20/2013 12:32 PM, Gong-Yi Liao wrote:
Oh, I found it's my own fault, actually I just needed to use the
'type' argument, while specify
npyLoad('test.npy', type='integer'), the data are loaded correctly:
=== in R 2.15.2 ============
R> npyLoad(type='integer', 'test.npy')
loading int64 from .npy
[1] 103 89 114 122 81 98 89 114 88 83
========================
Gong.
On 02/20/2013 12:26 PM, Gong-Yi Liao wrote:
It seems that we have something to do with type detection, I put some
console output code to see what type of data are detected while
being loaded.
In cnpy.h, I add:
===== cnpy.h ===============
+ #define RCPP_HAS_LONG_LONG_TYPES
#ifdef RCPP_HAS_LONG_LONG_TYPES
#include <cstdint> // for std::int64_t, needs c++11 switch
#endif
============================
in cnpyMod.cpp: I sightly modified npyLoad function: (between line 61
to 71)
=== cnpyMod.cpp ===================
if (shape.size() == 1) {
if (type == "numeric") {
+ std::cout << "loading numeric (double) from .npy" <<
std::endl;
double *p = reinterpret_cast<double*>(arr.data);
ret = Rcpp::NumericVector(p, p + shape[0]);
#ifdef RCPP_HAS_LONG_LONG_TYPES
} else if (type == "integer") {
+ std::cout << "loading int64 from .npy" << std::endl;
int64_t *p = reinterpret_cast<int64_t*>(arr.data);
ret = Rcpp::IntegerVector(p, p + shape[0]);
#endif
} else {
arr.destruct();
Rf_error("Unsupported type in npyLoad");
}
===================================
In python, I generated the data:
==== in IPython 0.13 ==============
In [3]: save('test.npy', poisson(100,10))
===================================
In R, loaded data and found:
===== In R 2.15.2 =================
R> library(RcppCNPy); npyLoad('test.npy');quit('yes')
Loading required package: Rcpp
loading numeric (double) from .npy
[1] 5.088876152e-322 4.397184248e-322 5.632348363e-322
6.027600879e-322 4.001931731e-322 4.841843329e-322 4.397184248e-322
5.632348363e-322 4.347777683e-322
[10] 4.100744860e-322
=================================
It seems that problem is data type detection, could that be caused by
R itself? I think Rcpp and RcppCNPy are constrained by R's limitation.
Gong.
On 02/20/2013 07:35 AM, Dirk Eddelbuettel wrote:
Gong-Yi,
On 19 February 2013 at 19:15, Dirk Eddelbuettel wrote:
| I'll commit a fix in a bit.
There is now a candidate package 0.2.0.1 at the R-Forge page (or in
SVN)
https://r-forge.r-project.org/R/?group_id=155
Could you test that against your integer data and report back?
Thanks, Dirk
--
Gong-Yi Liao
Department of Statistics
University of Connecticut
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