Re: [Numpy-discussion] Weird problem with subtracting ndarrays
Thanks for the tip. I thought it must be something simple like that. When I convert the arrays to numpy.int32 things behave normally. Another question though: a numpy.all() on the signed-int arrays shows that they are equal. So why would the subtraction of the unsigned arrays wrap around like that? Catherine On Jun 12, 2013, at 12:25 PM, <numpy-discussion-request@scipy.org> wrote:
Message: 9 Date: Wed, 12 Jun 2013 15:30:22 -0400 From: Warren Weckesser <warren.weckesser@gmail.com> Subject: Re: [Numpy-discussion] weird problem with subtracting ndarrays To: Discussion of Numerical Python <numpy-discussion@scipy.org> Message-ID: <CAGzF1udhTAmxPWwHsd9xQ386CBz2iXymmYC4kJJ3AOTRPZQzyg@mail.gmail.com> Content-Type: text/plain; charset="iso-8859-1"
On Wed, Jun 12, 2013 at 3:25 PM, Moroney, Catherine M (398D) < Catherine.M.Moroney@jpl.nasa.gov> wrote:
Hello,
I've got two arrays of the same shape that I read in from a file, and I'm trying to difference them. Very simple stuff, but I'm getting weird answers.
Here is the code:
counts1 = hfile1.read_grid_field("CFbA", "TerrainReferencedRCCMFraction_Num") counts2 = hfile2.read_grid_field("CFbA", "TerrainReferencedRCCMFraction_Num") counts1.max(), counts2.max() (13, 13) counts1.min(), counts2.min() (0, 0) numpy.all(counts1 == counts2) False diff = counts1 - counts2 diff.max() 4294967295 !! WHAT IS HAPPENING HERE ?? sum = counts1 + counts2 sum.max() 26
As you can see, the range of values in both arrays is 0 to 13, and the sum behaves normally, but the difference gives this weird number.
When I create dummy arrays, the subtraction works fine. So there must be some funny value lurking in either the counts1 or counts2 array, but the numpy.isnan() test returns False.
Any ideas for how I debug this?
Catherine
Check the dtype of the arrays. They are probably unsigned integers, and the subtraction leads to wrap-around in some cases.
For example:
In [1]: x = np.array([0, 1, 2], dtype=np.uint32)
In [2]: y = np.array([1, 1, 1], dtype=np.uint32)
In [3]: x - y Out[3]: array([4294967295, 0, 1], dtype=uint32)
Warren
On Wed, Jun 12, 2013 at 3:38 PM, Moroney, Catherine M (398D) <Catherine.M.Moroney@jpl.nasa.gov> wrote:
Thanks for the tip. I thought it must be something simple like that. When I convert the arrays to numpy.int32 things behave normally.
Another question though: a numpy.all() on the signed-int arrays shows that they are equal. So why would the subtraction of the unsigned arrays wrap around like that?
in your code
numpy.all(counts1 == counts2) False
Josef
Catherine
On Jun 12, 2013, at 12:25 PM, <numpy-discussion-request@scipy.org> wrote:
Message: 9 Date: Wed, 12 Jun 2013 15:30:22 -0400 From: Warren Weckesser <warren.weckesser@gmail.com> Subject: Re: [Numpy-discussion] weird problem with subtracting ndarrays To: Discussion of Numerical Python <numpy-discussion@scipy.org> Message-ID: <CAGzF1udhTAmxPWwHsd9xQ386CBz2iXymmYC4kJJ3AOTRPZQzyg@mail.gmail.com> Content-Type: text/plain; charset="iso-8859-1"
On Wed, Jun 12, 2013 at 3:25 PM, Moroney, Catherine M (398D) < Catherine.M.Moroney@jpl.nasa.gov> wrote:
Hello,
I've got two arrays of the same shape that I read in from a file, and I'm trying to difference them. Very simple stuff, but I'm getting weird answers.
Here is the code:
counts1 = hfile1.read_grid_field("CFbA", "TerrainReferencedRCCMFraction_Num") counts2 = hfile2.read_grid_field("CFbA", "TerrainReferencedRCCMFraction_Num") counts1.max(), counts2.max() (13, 13) counts1.min(), counts2.min() (0, 0) numpy.all(counts1 == counts2) False diff = counts1 - counts2 diff.max() 4294967295 !! WHAT IS HAPPENING HERE ?? sum = counts1 + counts2 sum.max() 26
As you can see, the range of values in both arrays is 0 to 13, and the sum behaves normally, but the difference gives this weird number.
When I create dummy arrays, the subtraction works fine. So there must be some funny value lurking in either the counts1 or counts2 array, but the numpy.isnan() test returns False.
Any ideas for how I debug this?
Catherine
Check the dtype of the arrays. They are probably unsigned integers, and the subtraction leads to wrap-around in some cases.
For example:
In [1]: x = np.array([0, 1, 2], dtype=np.uint32)
In [2]: y = np.array([1, 1, 1], dtype=np.uint32)
In [3]: x - y Out[3]: array([4294967295, 0, 1], dtype=uint32)
Warren
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Moroney, Catherine M (398D)