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SciPy Reference Guide, Release 0.8.dev 2.1.14 New complex ODE integrator scipy.integrate.ode now contains a wrapper for the ZVODE complex-valued ordinary differential equation solver (by Peter N. Brown, Alan C. Hindmarsh, and George D. Byrne). 2.1.15 New generalized symmetric and hermitian eigenvalue problem solver scipy.linalg.eigh now contains wrappers for more LAPACK symmetric and hermitian eigenvalue problem solvers. Users can now solve generalized problems, select a range of eigenvalues only, and choose to use a faster algorithm at the expense of increased memory usage. The signature of the scipy.linalg.eigh changed accordingly. 2.1.16 Bug fixes in the interpolation package The shape of return values from scipy.interpolate.interp1d used to be incorrect, if interpolated data had more than 2 dimensions and the axis keyword was set to a non-default value. This has been fixed. Moreover, interp1d returns now a scalar (0D-array) if the input is a scalar. Users of scipy.interpolate.interp1d may need to revise their code if it relies on the previous behavior. 2.1.17 Weave clean up There were numerous improvements to scipy.weave. blitz++ was relicensed by the author to be compatible with the SciPy license. wx_spec.py was removed. 2.1.18 Known problems Here are known problems with scipy 0.7.0: • weave test failures on windows: those are known, and are being revised. • weave test failure with gcc 4.3 (std::labs): this is a gcc 4.3 bug. A workaround is to add #include in scipy/weave/blitz/blitz/funcs.h (line 27). You can make the change in the installed scipy (in site-packages). 128 Chapter 2. Release Notes
3.1 Clustering package (scipy.cluster) 3.1.1 Hierarchical clustering (scipy.cluster.hierarchy) Warning: This documentation is work-in-progress and unorganized. Function Reference CHAPTER THREE REFERENCE These functions cut hierarchical clusterings into flat clusterings or find the roots of the forest formed by a cut by providing the flat cluster ids of each observation. Function Description fcluster forms flat clusters from hierarchical clusters. fclusterdata forms flat clusters directly from data. leaders singleton root nodes for flat cluster. These are routines for agglomerative clustering. Function Description linkage agglomeratively clusters original observations. single the single/min/nearest algorithm. (alias) complete the complete/max/farthest algorithm. (alias) average the average/UPGMA algorithm. (alias) weighted the weighted/WPGMA algorithm. (alias) centroid the centroid/UPGMC algorithm. (alias) median the median/WPGMC algorithm. (alias) ward the Ward/incremental algorithm. (alias) These routines compute statistics on hierarchies. Function Description cophenet computes the cophenetic distance between leaves. from_mlab_linkage converts a linkage produced by MATLAB(TM). inconsistent the inconsistency coefficients for cluster. maxinconsts the maximum inconsistency coefficient for each cluster. maxdists the maximum distance for each cluster. maxRstat the maximum specific statistic for each cluster. to_mlab_linkage converts a linkage to one MATLAB(TM) can understand. 129
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