A Divendres 20 Octubre 2006 11:42, Sebastien Bardeau va escriure: [snip] > I can understand that numpy.scalars do not provide inplace operations > (like Python standard scalars, they are immutable), so I'd like to use > > 0-d Numpy.ndarrays. But: > >>> d = numpy.array(a[2],copy=False) > >>> d += 1 > >>> d > > array(4) > > >>> a > > array([2, 3, 3]) > > >>> type(d) > > <type 'numpy.ndarray'> > > >>> d.shape > > () > > >>> id(d) > > 169621280 > > >>> d += 1 > >>> id(d) > > 169621280 > > This is not a solution because d is a copy since construction time... > My question is: is there a way to get a single element of an array into > a 0-d array which shares memory with its parent array?
One possible solution (there can be more) is using ndarray: In [47]: a=numpy.array([1,2,3], dtype="i4") In [48]: n=1 # the position that you want to share In [49]: b=numpy.ndarray(buffer=a[n:n+1], shape=(), dtype="i4") In [50]: a Out[50]: array([1, 2, 3]) In [51]: b Out[51]: array(2) In [52]: b += 1 In [53]: b Out[53]: array(3) In [54]: a Out[54]: array([1, 3, 3]) Cheers, -- >0,0< Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data "-" ------------------------------------------------------------------------- Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
