Mailman 3 April 2010 - NumPy-Discussion

Is this odd?
by Shailendra April 2, 2010

April 2, 2010

Hi All, Below is some array behaviour which i think is odd >>> a=arange(10) >>> a array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> b=nonzero(a<0) >>> b (array([], dtype=int32),) >>> if not b[0]: ... print 'b[0] is false' ... b[0] is false Above case the b[0] is empty so it is fine it is considered false >>> b=nonzero(a<1) >>> b (array([0]),) >>> if not b[0]: ... print 'b[0] is false' ... b[0] is false Above case b[0] is a non-empty array. Why should this be consider false. >>> b=nonzero(a>8) >>> b (array([9]),) >>> if not b[0]: ... print 'b[0] is false' ... >>> Above case b[0] is non-empty and should be consider true.Which it does. I don't understand why non-empty array should not be considered true irrespective to what value they have. Also, please suggest the best way to differentiate between an empty array and non-empty array( irrespective to what is inside array). Thanks, Shailendra

6 8

Can this be done more efficiently using numpy?
by Vishal Rana April 1, 2010

April 1, 2010

Hi, A calculation which goes like this... n = 5 a = np.arange(1000) b = np.arange(n - 1, 1000) l = [] for i in range(b.size): # Absolute difference of n a elements and nth b element x = np.abs(a[i:i + n] - b[i]) # Average of x y = x.sum() / n l.append(y) It takes a while if I have like 200K records! Is there a more efficient way to do this using numpy? Thanks Vishal Rana

2 2

ufuncs on funny strides; also isnan, isfinite, etc on a variety of dtypes
by M Trumpis April 1, 2010

April 1, 2010

Hi all, disclaimer: pardon my vast ignorance on the subject of ufuncs, that will explain the naivety of the following questions This morning I was looking at this line of code, which was running quite slow for me and making me think data_has_nan = numpy.isnan(data_array).any() I knew that the array was of type uint8. This raised the question of how much slower would a function like isnan() run on integer typed data, and furthermore if it's integer typed data then why bother looking at the values at all? Actually I realized later that the main slow-down comes from the fact that my array was strided in fortran order (ascending strides). But from the point of view of a ufunc that is operating independently at each value, why would it need to respect striding? And a last mini question, it doesn't appear that any() is doing short circuit evaluation. It runs in appx the same time whether an array is sparsely nonzero, fully zero, or fully nonzero. Kind regards, Mike

4 4

"Match" two arrays
by Shailendra April 1, 2010

April 1, 2010

Hi All, I want to make a function which should be like this <code> cordinates1=(x1,y1) # x1 and y1 are x-cord and y-cord of a large number of points cordinates2=(x2,y2) # similar to condinates1 indices1,indices2= match_cordinates(cordinates1,cordinates2) <code> (x1[indices1],y1[indices1]) "matches" (x2[indices2],y2[indices2]) where definition of "match" is such that : If A is closest point to B and distance between A and B is less that delta than it is a "match". If A is closest point to B and distance between A and B is more that delta than there is no match. Every point has either 1 "match"(closest point) or none Also, the size of the cordinates1 and cordinates2 are quite large and "outer" should not be used. I can think of only C style code to achieve this. Can any one suggest pythonic way of doing this? Thanks, Shailendra

5 5

Re: [Numpy-discussion] Re Dealloat Numy arrays in C
by Jeremy Lewi April 1, 2010

April 1, 2010

Thanks Robert. I ended up letting Python handle the memory management. In my C-extension I just call Py_INCREF to prevent Python from deallocating it while I'm using it. I then call Py_DECREF once I'm done with it. Jeremy

1 0