Suggested change for NaN, Infs float->int conversion
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)
ints = np.array([1]) ints.dtype
However, setting NaNs into integer arrays gives a value of 0 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
2009/1/25 Matthew Brett <matthew.brett@gmail.com>:
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
I find this behaviour surprising too. There is a ticket at http://projects.scipy.org/scipy/numpy/ticket/980 Cheers Stéfan
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
participants (3)
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josef.pktd＠gmail.com -
Matthew Brett -
Stéfan van der Walt