Turning algs for old NumPy modules into numpy code I suffer from this: Upon further processing of returns of numpy calculations, lots of data in an apps object tree will become elementary numpy types. First there is some inefficiency in calculations. And then you get data inflation and questionable dependencies - e.g. with pickle,ZODB,mpi's ... :
>>> l=array((1.,0)) >>> l.prod() 0.0 >>> cPickle.dumps(_) "cnumpy.core.multiarray\nscalar\np1\n(cnumpy\ndtype\np2\n(S'f8'\nI0\nI1\ntRp3\n(I2\nS'<'\nNNNI-1\nI-1\ntbS'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00'\ntRp4\n." >>> cPickle.dumps(0.0) 'F0\n.' >>> l=array((1,0)) >>> l.prod() 0 >>> cPickle.dumps(_) "cnumpy.core.multiarray\nscalar\np1\n(cnumpy\ndtype\np2\n(S'i4'\nI0\nI1\ntRp3\n(I2\nS'<'\nNNNI-1\nI-1\ntbS'\\x00\\x00\\x00\\x00'\ntRp4\n." >>> cPickle.dumps(0) 'I0\n.' >>> type(l.prod()) <type 'numpy.int32'> To avoid this you'd need a type cast in Python code everywhere you get scalars from numpy into a python variable. Error prone task. Or check/re-render your whole object tree. Wouldn't it be much better if numpy would return Python scalars for float64 (maybe even for float32) and int32, int64 ... where possible? (as numarray and Numeric did) I suppose numpy knows internally very quickly how to cast. Or is there maybe a config-setting to turn numpy this way? Robert -- http://mail.python.org/mailman/listinfo/python-list
