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This package provides cubic spline interpolation of numeric data. The data is passed as references to two arrays containing the x and y ordinates. It may be used as an exporter of the numerical functions or, more easily as a class module.

This module is an extension to the Math::Symbolic module. A basic familiarity with that module is required. Math::SymbolicX::Inline allows easy creation of Perl functions from symbolic expressions in the context of Math::Symbolic. That means you can define arbitrary Math::Symbolic trees (including derivatives) and let this module compile them to package subroutines.

The BitVector class for a memory-efficient packed representation of bit arrays and for logical operations on such arrays. The core idea used in this Python script for bin packing is based on an internet posting by Josiah Carlson to the Pyrex mailing list.

Qhull computes convex hulls, Delaunay triangulations, halfspace intersections about a point, Voronoi diagrams, furthest-site Delaunay triangulations, and furthest-site Voronoi diagrams. It runs in 2-d, 3-d, 4-d, and higher dimensions. It implements the Quickhull algorithm for computing the convex hull. Qhull handles roundoff errors from floating point arithmetic. It computes volumes, surface areas, and approximations to the convex hull.

Qhull computes convex hulls, Delaunay triangulations, halfspace intersections about a point, Voronoi diagrams, furthest-site Delaunay triangulations, and furthest-site Voronoi diagrams. It runs in 2-d, 3-d, 4-d, and higher dimensions. It implements the Quickhull algorithm for computing the convex hull. Qhull handles roundoff errors from floating point arithmetic. It computes volumes, surface areas, and approximations to the convex hull.

Gnuplot.py is a Python package that interfaces to gnuplot, the popular plotting program. It allows you to use gnuplot from within Python to plot arrays of data from memory, data files, or mathematical functions. If you use Python to perform computations or as `glue' for numerical programs, you can use this package to plot data on the fly as they are computed. And the combination with Python makes it is easy to automate things, including to create crude `animations' by plotting different datasets one after another. Commands are communicated to gnuplot through a pipe and data either through the same pipe (as "inline" data) or through temporary files. It has been written and tested on a Unix computer. This package has an object-oriented design that allows the user flexibility to set plot options and to run multiple gnuplot sessions simultaneously. If you are more ambitious, it is not difficult to add entirely new types of plottable items by deriving from the `PlotItem' class. For a demonstration, run the python file by typing `python demo.py'.

This module extends Python with a Graph class which is capable of handling arbitrary directed and undirected graphs with thousands of nodes and millions of edges. Since the module makes use of the open source igraph library written in almost 100% pure C, it is blazing fast and outperforms most other pure Python-based packages around.

pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.

SpeedCrunch is a multiplatform desktop calculator for power users. It is designed to be enjoyed using keyboard. Result is shown in scrollable display, history of expressions is available with up and down arrow. Some other features: optional keypad, syntax highlight, matched parenthesis indicator, just-in-time calculation (show result even before you finish typing) and autocomplete for variables.

Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models.