sigma_clip: change default from np.median to np.ma.median

`np.median` does not respect masked arrays:
```
In [18]: a = np.ma.masked_array(np.arange(10))

In [19]: a[[0, 1, 2, 9]] = np.ma.masked

In [20]: np.ma.median(a), np.median(a)
Out[20]: (5.5, 4.5)

In [21]: np.ma.median(a), np.median(a), np.ma.median(a.data)
Out[21]: (5.5, 4.5, 4.5)
```
The current default for `stats.sigma_clip` is to use `np.median` to estimate the center of a distribution. Since this function does not care about masking, the center of the distribution does not move even if many more values are masked.
Insert `print cenfunc(filtered_data), np.sqrt(varfunc(filtered_data) * sig ** 2), filtered_data.count()` into `sigma_clip` and you will see that the center of the distribution is always constant for every step in the iteration.
Obviously, that is not what we want.
I tried to come up with a short example to shows this, but did not find a good one. So, here is the real data where I encountered the problem: http://pastebin.com/SXh8VUuV

`sigma_clip(data, iters=None)` returns only zeros, while `sigma_clip(data, iters=None, cenfunc=np.ma.median)` works as expected. Thus, we should definitely make `np.ma.median` the default.