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The sigrok project aims at creating a portable, cross-platform, Free/Libre/Open-Source signal analysis software suite that supports various device types, such as logic analyzers, MSOs, oscilloscopes, multimeters, LCR meters, sound level meters, thermometers, hygrometers, anemometers, light meters, DAQs, dataloggers, function generators, spectrum analyzers, power supplies, GPIB interfaces, and more.

py-DendroPy is a python library for phylogenetic scripting, simulation, data processing and manipulation.

Python binding to CDO (Climate Data Operators)

Coards is a parser for time values represented using the COARDS convention.

The h5py package provides both a high- and low-level interface to the HDF5 library from Python. The low-level interface is intended to be a complete wrapping of the HDF5 1.8 API, while the high-level component supports Python-style object-oriented access to HDF5 files, datasets and groups. The goal of this package is not to provide yet another scientific data model. It is an attempt to create as straightforward a binding as possible to the existing HDF5 API and abstractions, so that Python programs can easily deal with HDF5 files and exchange data with other HDF5-aware applications.

py-hcluster library provides Python functions for agglomerative clustering. Its features include * generating hierarchical clusters from distance matrices * computing distance matrices from observation vectors * computing statistics on clusters * cutting linkages to generate flat clusters * and visualizing clusters with dendrograms. The interface is very similar to MATLAB's Statistics Toolbox API to make code easier to port from MATLAB to Python/Numpy. The core implementation of this library is in C for efficiency.

QCL is a high level, architecture independent programming language for quantum computers, with a syntax derived from classical procedural languages like C or Pascal. This allows for the complete implementation and simulation of quantum algorithms (including classical components) in one consistent formalism.

Modular toolkit for Data Processing (MDP) is a Python data processing framework. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), Gaussian Classifiers, and Restricted Boltzmann Machines.

Machine Learning PY (mlpy) is a high-performance Python package for predictive modeling. It makes extensive use of numpy (http://scipy.org) to provide fast N-dimensional array manipulation and easy integration of C code. mlpy provides high level procedures that support, with few lines of code, the design of rich Data Analysis Protocols (DAPs) for preprocessing, clustering, predictive classification and feature selection. Methods are available for feature weighting and ranking, data resampling, error evaluation and experiment landscaping.The package includes tools to measure stability in sets of ranked feature lists.

netCDF version 4 has many features not found in earlier versions of the library and is implemented on top of HDF5. This module can read and write files in both the new netCDF 4 and the old netCDF 3 format, and can create files that are readable by HDF5 clients. The API modelled after Scientific.IO.NetCDF, and should be familiar to users of that module. Many new features of netCDF 4 are implemented, such as multiple unlimited dimensions, groups and zlib data compression. All the new primitive data types (such as 64 bit and unsigned integer types) are implemented, except variable-length strings (NC_STRING). User defined data types (compound, vlen, enum etc.) are not supported.