Should numpy.sqrt(-1) return 1j rather than nan?

[pearu at cens.ioc.ee]

On Wed, 11 Oct 2006, Travis Oliphant wrote:

> >Interestingly, in worst cases numpy.sqrt is approximately ~3 times slower
> >than scipy.sqrt on negative input but ~2 times faster on positive input:
> >
> >In [47]: pos_input = numpy.arange(1,100,0.001)
> >
> >In [48]: %timeit -n 1000 b=numpy.sqrt(pos_input)
> >1000 loops, best of 3: 4.68 ms per loop
> >
> >In [49]: %timeit -n 1000 b=scipy.sqrt(pos_input)
> >1000 loops, best of 3: 10 ms per loop
> >  
> >
> 
> This is the one that concerns me.  Slowing everybody down who knows they 
> have positive values just for people that don't seems problematic.

I think the code in scipy.sqrt can be optimized from

  def _fix_real_lt_zero(x):
      x = asarray(x)
      if any(isreal(x) & (x<0)):
          x = _tocomplex(x)
      return x

  def sqrt(x):
      x = _fix_real_lt_zero(x)
      return nx.sqrt(x)

to (untested)

  def _fix_real_lt_zero(x):
      x = asarray(x)
      if not isinstance(x,(nt.csingle,nt.cdouble)) and any(x<0):
          x = _tocomplex(x)
      return x

  def sqrt(x):
      x = _fix_real_lt_zero(x)
      return nx.sqrt(x)

or

  def sqrt(x):
      old = nx.seterr(invalid='raises')
      try:
          r = nx.sqrt(x)
      except FloatingPointError:
          x = _tocomplex(x)
          r = nx.sqrt(x)
      nx.seterr(**old)
      return r

I haven't timed these cases yet..

Pearu


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