_rvec.pyzdoc

Go to the documentation of this file.

/**
\class ROOT::VecOps::RVec
\brief \parblock \endparblock
\htmlonly
<div class="pyrootbox">
\endhtmlonly
## PyROOT
 
The ROOT::RVec class has additional features in Python, which allow to adopt memory
from Numpy arrays and vice versa. The purpose of these features is the copyless
interfacing of Python and C++ using their most common data containers, Numpy arrays
and RVec with a std::vector interface.
 
### Conversion of RVecs to Numpy arrays
 
RVecs of fundamental types (int, float, ...) have in Python the `__array_interface__`
attribute attached. This information allows Numpy to adopt the memory of RVecs without
copying the content. You can find further documentation regarding the Numpy array interface
[here](https://numpy.org/doc/stable/reference/arrays.interface.html). The following code example
demonstrates the memory adoption mechanism using `numpy.asarray`.
 
\code{.py}
rvec = ROOT.RVec('double')((1, 2, 3))
print(rvec) # { 1.0000000, 2.0000000, 3.0000000 }
 
npy = numpy.asarray(rvec)
print(npy) # [1. 2. 3.]
 
rvec[0] = 42
print(npy) # [42. 2. 3.]
\endcode
 
### Conversion of Numpy arrays to RVecs
 
Data owned by Numpy arrays with fundamental types (int, float, ...) can be adopted by RVecs. To
create an RVec from a Numpy array, ROOT offers the facility ROOT.VecOps.AsRVec, which performs
a similar operation to `numpy.asarray`, but vice versa. A code example demonstrating the feature and
the adoption of the data owned by the Numpy array is shown below.
 
\code{.py}
npy = numpy.array([1.0, 2.0, 3.0])
print(npy) # [1. 2. 3.]
 
rvec = ROOT.VecOps.AsRVec(npy)
print(rvec) # { 1.0000000, 2.0000000, 3.0000000 }
 
npy[0] = 42
print(rvec) # { 42.000000, 2.0000000, 3.0000000 }
\endcode
 
\htmlonly
</div>
\endhtmlonly
*/