ANN: PyIMSL Studio 1.5 now available at no charge for non-commercial use

[Steve Lang]

Visual Numerics, a Rogue Wave Software Company, is making PyIMSL Studio 1.5 available for download at no charge for non-commercial use or for commercial evaluation.

Learn more about PyIMSL Studio and download at:  http://www.vni.com/campaigns/pyimslstudioeval

PyIMSL Studio contains both open source and proprietary components that create a fully supported and documented platform for analytic prototyping and production development.

- For prototyping, a number of open source tools including Python, NumPy, Eclipse, matplotlib and commercial components from Visual Numerics, Inc. are available for Python, including Python wrappers to the mathematics and statistics algorithms in the IMSL Numerical Library which are incorporated in the distribution. This combination of tools provides a rich environment for prototype development.

- For production deployment, commercial users of PyIMSL Studio also have access to the IMSL C Library to allow the development of native C implementations of algorithms for high performance production code. Using the IMSL C Library provides parity between prototype and production code.

The IMSL Numerical Libraries have been the cornerstone of high-performance and desktop computing as well as predictive analytics applications in science, technical and business environments for well over three decades. Functional areas include:

Mathematics
    * Matrix Operations
    * Linear Algebra
    * Eigensystems
    * Interpolation & Approximation
    * Numerical Quadrature
    * Differential Equations
    * Transforms
    * Nonlinear Equations
    * Optimization
    * Special Functions
    * Finance & Bond Calculations

Statistics
    * Basic Statistics
    * Time Series & Forecasting
    * Multivariate Analysis
    * Nonparametric Tests
    * Correlation & Covariance
    * Regression
    * Analysis of Variance and Designed Experiments
    * Categorical and Discrete Data Analysis
    * Survival and Reliability Analysis
    * Goodness of Fit
    * Distribution Functions
    * Random Number Generation
    * Neural Networks
    * Genetic Algorithm
    * Naïve Bayes