SciPy includes modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, genetic algorithms, ODE solvers, and more.
The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization.
Together, they run on all popular operating systems, are quick to install, and are free of charge. SciPy and NumPy are easy to use, but powerful enough to be depended upon by some of the world's leading engineers and scientists.
If you need to manipulate numbers on a computer and display or publish the results, give SciPy a try!
Requirements:
· Numpy
· Python 2.4 or later
What's New in This Release: [ read full changelog ]
· #835: stats: nan propagation in stats.distributions
· #1202: io: netcdf segfault
· #1531: optimize: make curve_fit work with method as callable.
· #1560: linalg: fixed mistake in eig_banded documentation.
· #1565: ndimage: bug in ndimage.variance
· #1457: ndimage: standard_deviation does not work with sequence of indexes
· #1562: cluster: segfault in linkage function
· #1568: stats: One-sided fisher_exact() returns `p` < 1 for 0 successful attempts - #1575: stats: zscore and zmap handle the axis keyword incorrectly
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