[Numpy-discussion] Suggested change for NaN, Infs float->int conversion
josef.pktd@gmai...
josef.pktd@gmai...
Sun Jan 25 07:04:27 CST 2009
On Sun, Jan 25, 2009 at 4:35 AM, Matthew Brett <matthew.brett@gmail.com> wrote:
> Hi,
>
> When converting arrays from float to ints, I notice that NaNs, Infs,
> and -Infs all get the minimum integer value:
>
>>>> flts = np.array([np.nan, np.inf, -np.inf])
>>>> flts.astype(np.int16)
> array([-32768, -32768, -32768], dtype=int16)
>
> However, setting NaNs into integer arrays gives a value of 0
>
>>>> ints = np.array([1])
>>>> ints.dtype
> dtype('int32')
>>>> ints[0] = np.nan
>>>> ints
> array([0])
>
> whereas Inf or -Inf raise an error (as Josef pointed out recently):
>
>>> ints[0] = np.inf
> ------------------------------------------------------------
> Traceback (most recent call last):
> File "<ipython console>", line 1, in <module>
> OverflowError: cannot convert float infinity to long
>
>>>> ints[0] = -np.inf
> ------------------------------------------------------------
> Traceback (most recent call last):
> File "<ipython console>", line 1, in <module>
> OverflowError: cannot convert float infinity to long
>
> Matlab seems more consistent and sensible here:
>
>>> flts = [NaN Inf -Inf];
>>> int32(flts)
>
> ans =
>
> 0 2147483647 -2147483648
>
>>> ints = int32([1 1 1]);
>>> ints(:) = [NaN Inf -Inf]
>
> ints =
>
> 0 2147483647 -2147483648
>
> Is there a route to change towards the matlab behavior? Or at least
> make numpy behavior self-consistent?
>
> Best,
>
> Matthew
As we discussed in another thread, I think that the silent conversion
of nan to zero should not be done, since it is too much a source of
errors. Users should be forced to set nans to a valid number
explicitly.
Josef
More information about the Numpy-discussion
mailing list