 |
| ||||
SciPy is handy, easy to use package of tools specially designed for science and engineering in Python. It includes modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, ODE solvers, and more.
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.
Requirements:
· Python
What's New in This Release: [ read full changelog ]< br />
New features:
``scipy.spatial`` improvements:
· cKDTree feature-complete - Cython version of KDTree, cKDTree, is now feature-complete. Most operations (construction, query, query_ball_point, query_pairs, count_neighbors and sparse_distance_matrix) are between 200 and 1000 times faster in cKDTree than in KDTree. With very minor caveats, cKDTree has exactly the same interface as KDTree, and can be used as a drop-in replacement.
· Voronoi diagrams and convex hulls - `scipy.spatial` now contains functionality for computing Voronoi diagrams and convex hulls using the Qhull library. (Delaunay triangulation was available since Scipy 0.9.0.)
· Delaunay improvements - It's now possible to pass in custom Qhull options in Delaunay triangulation. Coplanar points are now also recorded, if present. Incremental construction of Delaunay triangulations is now also possible
· Spectral estimators (``scipy.signal``) - The functions ``scipy.signal.periodogram`` and ``scipy.signal.welch`` were added, providing DF...
Via: SciPy 0.11.0 / 0.12.0 Beta 1
