[ANN] python-blosc 1.0.5 released
============================= Announcing python-blosc 1.0.5 ============================= What is it? =========== A Python wrapper for the Blosc compression library. Blosc (http://blosc.pytables.org) is a high performance compressor optimized for binary data. It has been designed to transmit data to the processor cache faster than the traditional, non-compressed, direct memory fetch approach via a memcpy() OS call. Blosc works well for compressing numerical arrays that contains data with relatively low entropy, like sparse data, time series, grids with regular-spaced values, etc. python-blosc is a Python package that wraps it. What is new? ============ - Upgraded to latest Blosc 1.1.4. - Better handling of condition errors, and improved memory releasing in case of errors (thanks to Valentin Haenel and Han Genuit). - Better handling of types (should compile without warning now, at least with GCC). For more info, you can see the release notes in: https://github.com/FrancescAlted/python-blosc/wiki/Release-notes More docs and examples are available in the Quick User's Guide wiki page: https://github.com/FrancescAlted/python-blosc/wiki/Quick-User's-Guide Download sources ================ Go to: http://github.com/FrancescAlted/python-blosc and download the most recent release from there. Blosc is distributed using the MIT license, see LICENSES/BLOSC.txt for details. Mailing list ============ There is an official mailing list for Blosc at: blosc@googlegroups.com http://groups.google.es/group/blosc -- Francesc Alted
Hello All, I have a bit of code that nicely accomplishes what I need for a course I am teaching. I'd like to extend this for larger 3D grids, and I suspect that the looping will be a brutal performance hit. Even if my suspicions are not confirmed, I still would like to know if it's possible to vectorize the following code: from scipy import shape, prod, array, zeros,ravel, reshape,sin, mgrid from scipy.misc import derivative def gradient2D_vect(func,x,y): the_shape = shape(x) x1=ravel(x) y1=ravel(y) N = prod(the_shape) the_result = zeros([N,2]) for k in range(N): func_x=lambda x: func(x,y1[k]) func_y=lambda y: func(x1[k],y) the_result[k,:]= array([derivative(func_x,x1[k],dx=.01, order=5), derivative(func_y,y1[k],dx=.01, order=5)]) if prod(shape(the_shape))==1: return the_result else: return reshape(the_result,[the_shape[0],the_shape[1],2]) fxy = lambda x,y: sin(x*y) #just a little test x,y=mgrid[0:5,0:4] the_gradient = gradient2D_vect(fxy, x,y) Cheers, Eric Carlson
participants (2)
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Eric Carlson
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Francesc Alted