basic.py 文件源码

def _scal_elemwise_with_nfunc(nfunc, nin, nout):
    """
    Replace a symbol definition with an elementwise version of the
    corresponding scalar Op.  If it is not None, the nfunc argument
    should be a string such that getattr(numpy, nfunc) implements
    a vectorized version of the elemwise operation. nin is the number
    of inputs expected by that function, and nout is the number of
    **destination** inputs it takes. That is, the function should
    take nin+nout inputs. nout == 0 means that the numpy function
    does not take a numpy array argument to put its result in.

    """
    def construct(symbol):
        symbolname = symbol.__name__
        inplace = symbolname.endswith('_inplace')
        if inplace:
            msg = "inplace"
        else:
            msg = "no_inplace"

        n = "Elemwise{%s,%s}" % (symbolname, msg)

        if inplace:
            scalar_op = getattr(scal, symbolname[:-len('_inplace')])
            inplace_scalar_op = scalar_op.__class__(scal.transfer_type(0))
            rval = elemwise.Elemwise(inplace_scalar_op, {0: 0}, name=n,
                                     nfunc_spec=(nfunc and (nfunc, nin, nout)))
        else:
            scalar_op = getattr(scal, symbolname)
            rval = elemwise.Elemwise(scalar_op, name=n,
                                     nfunc_spec=(nfunc and (nfunc, nin, nout)))

        if getattr(symbol, '__doc__', False):
            rval.__doc__ = symbol.__doc__ + '\n' + rval.__doc__

        # for the meaning of this see the ./epydoc script
        # it makes epydoc display rval as if it were a function, not an object
        rval.__epydoc_asRoutine = symbol
        rval.__module__ = 'tensor'

        pprint.assign(rval, printing.FunctionPrinter(symbolname))

        return rval
    return construct