[Numpy-discussion] nan_to_num and bool arrays
Keith Goodman
kwgoodman@gmail....
Fri Dec 11 17:44:16 CST 2009
On Fri, Dec 11, 2009 at 2:22 PM, Robert Kern <robert.kern@gmail.com> wrote:
> On Fri, Dec 11, 2009 at 16:09, Keith Goodman <kwgoodman@gmail.com> wrote:
>> On Fri, Dec 11, 2009 at 1:14 PM, Robert Kern <robert.kern@gmail.com> wrote:
>>> On Fri, Dec 11, 2009 at 14:41, Keith Goodman <kwgoodman@gmail.com> wrote:
>>>> On Fri, Dec 11, 2009 at 12:08 PM, Bruce Southey <bsouthey@gmail.com> wrote:
>>>
>>>>> So I agree that it should leave the input untouched when a non-float
>>>>> dtype is used for some array-like input.
>>>>
>>>> Would only one line need to be changed? Would changing
>>>>
>>>> if not issubclass(t, _nx.integer):
>>>>
>>>> to
>>>>
>>>> if not issubclass(t, _nx.integer) and not issubclass(t, _nx.bool_):
>>>>
>>>> do the trick?
>>>
>>> That still leaves strings, voids, and objects. I recommend:
>>>
>>> if issubclass(t, _nx.inexact):
>>>
>>> Arguably, one should handle nan float objects in object arrays and
>>> float columns in structured arrays, but the current code does not
>>> handle either of those anyways.
>>
>> Without your change both
>>
>>>> np.nan_to_num(np.array([True, False]))
>>>> np.nan_to_num([1])
>>
>> raise exceptions. With your change:
>>
>>>> np.nan_to_num(np.array([True, False]))
>> array([ True, False], dtype=bool)
>>>> np.nan_to_num([1])
>> array([1])
>
> I think this is correct, though the latter one happens by accident.
> Lists don't have a .dtype attribute so obj2sctype(type([1])) is
> checked and happens to be object_. The latter line is intended to
> handle scalars, not sequences. I think that sequences should be
> coerced to arrays for output and this check should be more explicit
> about what it handles. [1.0] will have a problem if you don't.
That makes sense. But I'm not smart enough to implement it.
>> On a separate note, this seems a little awkward:
>>
>>>> np.nan_to_num(1.0)
>> 1.0
>>>> np.nan_to_num(1)
>> array(1)
>>>> x = np.ones(1, dtype=np.int)
>>>> np.nan_to_num(x[0])
>> 1
>
> Worth fixing.
Would this work?
def nan_to_num(x):
try:
t = x.dtype.type
except AttributeError:
t = obj2sctype(type(x))
if issubclass(t, _nx.complexfloating):
return nan_to_num(x.real) + 1j * nan_to_num(x.imag)
else:
try:
y = x.copy()
except AttributeError:
y = array(x)
if not y.shape:
y = array([x])
scalar = True
else:
scalar = False
if issubclass(t, _nx.inexact):
are_inf = isposinf(y)
are_neg_inf = isneginf(y)
are_nan = isnan(y)
maxf, minf = _getmaxmin(y.dtype.type)
y[are_nan] = 0
y[are_inf] = maxf
y[are_neg_inf] = minf
if scalar:
y = y[0]
return y
Instead of
>> nan_to_num(1.0)
1.0
>> nan_to_num(1)
array(1)
>> nan_to_num(np.array(1.0))
1.0
>> nan_to_num(np.array(1))
array(1)
it gives
>> nan_to_num(1.0)
1.0
>> nan_to_num(1)
1
>> nan_to_num(np.array(1.0))
1.0
>> nan_to_num(np.array(1))
1
I guess a lot of unit tests need to be written before nan_to_num can
be fixed. But for now, your bool fix is an improvement.
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