Thanks for the responses Paul and Marten,
I have raised an issue for the issue at:
https://github.com/numpy/numpy/issues/10227
Best regards,
Jesper
2017-12-16 22:48 GMT+01:00 Marten van Kerkwijk :
> Definitely a big! The underlying problem is:
> ```
> In [23]: np.abs(np.int16(-32768))
> Out[23
Definitely a big! The underlying problem is:
```
In [23]: np.abs(np.int16(-32768))
Out[23]: -32768
```
This is not great, but perhaps consistent with the logic that abs
should return a value of the same dtype.
It could be solved inside `masked_values` by using `np.abs(value,
dtype=xnew.dtype)`
Do
I think this is a floating point precision issue.
https://docs.python.org/3.6/tutorial/floatingpoint.html
On Fri, Dec 15, 2017 at 1:40 PM, Jesper Larsen
wrote:
> Hi numpy people,
>
> I was just wondering whether this behaviour is intended:
>
> >>> import numpy as np
> >>> np.ma.masked_values(np
Hi numpy people,
I was just wondering whether this behaviour is intended:
>>> import numpy as np
>>> np.ma.masked_values(np.array([-32768.0]), np.int16(-32768))
masked_array(data = [-32768.],
mask = False,
fill_value = -32768.0)
So the resulting masked array is not masked. On
