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This is a drop in replacement XS based version of Set::IntSpan::Fast. See that module for details of the interface.

The Set::IntSpan module represents sets of integers as a number of inclusive ranges, for example '1-10,19-23,45-48'. Because many of its operations involve linear searches of the list of ranges its overall performance tends to be proportional to the number of distinct ranges. This is fine for small sets but suffers compared to other possible set representations (bit vectors, hash keys) when the number of ranges grows large. Set::IntSpan::Fast tries to fix that.

Map data for the py-basemap port.

This module provides basic functions used in descriptive statistics. It borrows very heavily from Statistics::Descriptive::Full (which is included with Statistics::Descriptive) with one major difference. This module is optimized for discretized data e.g. data from an A/D conversion that has a discrete set of possible values. E.g. if your data is produced by an 8 bit A/D then you'd have only 256 possible values in your data set. Even though you might have a million data points, you'd only have 256 different values in those million points. Instead of storing the entire data set as Statistics::Descriptive does, this module only stores the values it's seen and the number of times it's seen each value. For very large data sets, this storage method results in significant speed and memory improvements. In a test case with 2.6 million data points from a real world application, Statistics::Descriptive::Discrete took 40 seconds to calculate a set of statistics instead of the 561 seconds required by Statistics::Descriptive::Full. It also required only 4MB of RAM instead of the 400MB used by Statistics::Descriptive::Full for the same data set.

This installs Math::Symbolic and a load of easily installable (i.e. pure Perl) plugins that make the module so much more powerful.

ParMETIS is an MPI-based parallel library that implements a variety of algorithms for partitioning unstructured graphs and for computing fill-reducing orderings of sparse matrices. ParMETIS extends the functionality provided by METIS and includes routines that are especially suited for parallel AMR computations and large scale numerical simulations. The algorithms implemented in ParMETIS are based on the parallel multilevel k-way graph-partitioning algorithms described in [KK95d], [KK96], [KK97], and the adaptive repartitioning algorithms described in [SKK97a], [SKK97b], [SK+98], and [SKK98].

ParMGridGen-1.0 is a highly optimized serial and parallel library for obtaining a sequence of successive coarse grids that are well suited for geometric multigrid methods. The quality of the elements of the coarse grids is optimized using a multilevel framework. The parallel library is based on MPI and is portable to a wide range of architectures.

Numarray is a reimplementation of the original Python Numeric array module that provides Python with capbilities similar to Matlab, IDL, Octave, APL and other array-based languages. This version is still in its early stages and is not yet the official replacement for Numeric though we hope it will be before long. It is not fully backwards compatible with Numeric, particularly with regard to the C API.

NZMATH is a Python based number theory oriented calculation system. The centre of development in origin is Tokyo Metropolitan University. It is freely available and distributed under the BSD license.

PRNG is a collection of portable, high-performance ANSI-C implementations of pseudorandom number generators such as linear congruential, inversive congruential, and explicit inversive congruential random number generators (LCG, ICG and EICG, respectively) created by Otmar Lendl and Josef Leydold.