Hi! I am confused with Numpy behavior with its scalar or 0-d arrays objects:
>>> numpy.__version__ '1.0rc2' >>> a = numpy.array((1,2,3)) >>> b = a[:2] >>> b += 1 >>> b array([2, 3]) >>> a array([2, 3, 3]) >>> type(b) <type 'numpy.ndarray'> To this point all is ok for me: subarrays share (by default) memory with their parent array. But: >>> c = a[2] >>> c += 1 >>> c 4 >>> a array([2, 3, 3]) >>> type(c) <type 'numpy.int32'> >>> id(c) 169457808 >>> c += 1 >>> id(c) 169737448 That's really confusing, because slices (from __getslice__ method) are not copies (they share memory), and items (single elements from __getitem__ ) are copies to one of the scalar objects provided by Numpy. 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? Thx for your help, Sebastien ------------------------------------------------------------------------- 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
