[Numpy-discussion] nan_to_num and bool arrays
Keith Goodman
kwgoodman@gmail....
Fri Dec 11 22:27:50 CST 2009
On Fri, Dec 11, 2009 at 6:38 PM, Robert Kern <robert.kern@gmail.com> wrote:
> On Fri, Dec 11, 2009 at 18:38, Keith Goodman <kwgoodman@gmail.com> wrote:
>
>> That seems to work. To avoid changing the input
>>
>>>> x = np.array(1)
>>>> x.shape
>> ()
>>>> y = nan_to_num(x)
>>>> x.shape
>> (1,)
>>
>> I moved y = x.copy() further up and switched x's to y's. Here's what
>> it looks like:
>>
>> def nan_to_num(x):
>> is_scalar = False
>> if not isinstance(x, _nx.ndarray):
>> x = asarray(x)
>> if x.shape == ():
>> # Must return this as a scalar later.
>> is_scalar = True
>> y = x.copy()
>> old_shape = y.shape
>> if y.shape == ():
>> # We need element access.
>> y.shape = (1,)
>> t = y.dtype.type
>> if issubclass(t, _nx.complexfloating):
>> return nan_to_num(y.real) + 1j * nan_to_num(y.imag)
>
> Almost! You need to handle the shape restoration in this branch, too.
>
> In [9]: nan_to_num(array(1+1j))
> Out[9]: array([ 1.+1.j])
Taking care of my imaginary bug has the nice side effect of leaving us
with only one return statement. I changed
return nan_to_num(y.real) + 1j * nan_to_num(y.imag)
to
y = nan_to_num(y.real) + 1j * nan_to_num(y.imag)
And changed the if on the next line to elif.
def nan_to_num(x):
is_scalar = False
if not isinstance(x, _nx.ndarray):
x = asarray(x)
if x.shape == ():
# Must return this as a scalar later.
is_scalar = True
y = x.copy()
old_shape = y.shape
if y.shape == ():
# We need element access.
y.shape = (1,)
t = y.dtype.type
if issubclass(t, _nx.complexfloating):
y = nan_to_num(y.real) + 1j * nan_to_num(y.imag)
elif 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 is_scalar:
y = y[0]
else:
y.shape = old_shape
return y
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