Thanks Didrik!
On Thu, Apr 1, 2010 at 1:21 AM, Didrik Pinte <li...@dipole-consulting.com>wrote: > On Wed, 2010-03-31 at 23:13 -0700, Vishal Rana wrote: > > 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? > > Like this ? > > import numpy > from numpy.lib.stride_tricks import as_strided > n = 5 > a = numpy.arange(1000) > b = numpy.arange(n - 1, 1000) > > > def sum_1(n, a, b): > # original method > l = [] > for i in range(b.size): > # Absolute difference of n a elements and nth b element > x = numpy.abs(a[i:i + n] - b[i]) > > # Average of x > y = x.sum() / n > > l.append(y) > return l > > def sum_numpy(n, a, b): > ast = as_strided(a, shape=(1000-n+1, n), strides=(8,8)) > diff = numpy.abs(ast - b[:,None]) > y = diff.sum(axis=1) /n > return y > > test1 = sum_1(n,a,b) > test2 = sum_numpy(n,a,b) > > assert(numpy.alltrue(test2 == test1)) > > -- Didrik > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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