David Cournapeau wrote:
2: the old implementation does not upcast the input array. If the input is int32, and min/max are float32, the function fails; if input is float32, and min/max float64, the output is still float32. Again, this seems against the expected numpy behaviour ?
The latter is expected. As discussed previously here, the types of scalars are ignored. You do get an upcast when either min or max is a float64 array. In [1]: import numpy In [2]: a = numpy.linspace(0, 10, 101) In [3]: a = numpy.linspace(0, 10, 101).astype(numpy.float32) In [4]: a.clip(numpy.float64(5), numpy.float64(6)).dtype Out[4]: dtype('float32') In [5]: a.clip(numpy.ones(101, dtype=numpy.float64), numpy.float64(6)).dtype Out[5]: dtype('float64') The int32/float32 failure is odd, though. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco