Open Source Python Scientific/Engineering Software - Page 61

Pysolar is a Python library for calculating the position of the sun relative to the earth as a function of latitude, longitude, and time. There is also code included for other problems related to the development of photovoltaic systems. Still in alpha.

Python & NOVAS. Use Python to calculate the position of Sun, Moon, Planets and Stars with unprecedented precision. PyNOVAS can be used by (Amateur) Astronomers, Sailors and other people interested in Celestial Navigation, Astronomy and Astrometry.

Python Arrays is a project to reimplement the existing Python Numeric module which adds the ability to efficiently manipulate large numeric arrays in ways similar to Matlab and IDL.

This program is a User Interface to run command-line applications in any python-enabled system. Batch execution is available for files in a folder or in subfolders.

This is a Python package for general Bayesian inference.

Functions to convert to and from decimal to Canonical Signed Digit representation. Handles fractional numbers. Useful for multiplierless hardware blocks on ASICs, FPGAs etc.

Made like in one day, using PyQT5, QT Designer, and PyInstaller

Python Champollion Electronic utilities

A lightweight, browsing-based, 100% Python, federated data integration framework. Users may create custom schemas for disparate sources, query and expand results across sources to find related data; for use in fields such as bioinformatics and datamining

Moved to Google Code, see http://code.google.com/p/pythonequations/downloads/list

Python FFTW as the name suggests are Python bindings to the FFTW3 C-library from http://www.fftw.org. They use numpy and python ctypes, and often offer about twice the performance compared to the fft routines included in numpy.

A python library for handling and analyzing biological sequence annotations as described in GFF files (General Feature Format). The library is intended to be a complete framework for this file format.

The Python Genetic Programming Project implements a Genetic Programming System a la J Koza in Python.

A Python library for easy creation and manipulation of Google Earth KML and KMZ placemark files. Please get your copy from http://pykml.cvs.sourceforge.net/viewvc/pykml/pykml/?view=tar

A toolkit of Python software useful in a laboratory data acquisition and analysis environment. Includes support for such protocols as VXI-11 (and its extension, LXI), Vernier LabPro (now very old), and National Instruments DSTP (now very old). Also includes data analysis and modelling tidbits. Python3 updates are on the way in the very near future for the biggest packages. the vxi11 package is fully up-to-date, although see the blog post about python 3.13

The project is a suite of utilities to manage grammars, monolingual dictionaries and translation manuals for as many as possible languages.

A python package for interpreting METAR coded weather reports

A python module aimed to automate routine lab calculating and formatting tasks.

py-rrdtool is a Python module provides an interface to RRDTool, the well known graphing/logging tool.

The Serial Data Acquisition is a lightweight data acquisition system able to parse a vast majority of mostly unidirectional streams. Results are saved in a SQLite DB and accessible over XML-RPC or plain HTTP. Its design is modular and easily extendable.

PyExp is a Python framework to develop programs driving physical experiments, dealing with Physical Elements in the high layer and translating this into actions on devices or groups of devices on the low layer.

This project is a python module containing functions to do with matricies. The functions allow all of the standard matrix operations plus a few extras.

A python API for Google's KML file. Added doc Added description to PlaceMark

A Python Script I made to split a starfield image into several layers. To be able to use the script, PixInsight with an installed Gaia data catalogue is required, as it needs the exported astrometry data text file.

Torch Points 3D is a framework for developing and testing common deep learning models to solve tasks related to unstructured 3D spatial data i.e. Point Clouds. The framework currently integrates some of the best-published architectures and it integrates the most common public datasets for ease of reproducibility. It heavily relies on Pytorch Geometric and Facebook Hydra library thanks for the great work! We aim to build a tool that can be used for benchmarking SOTA models, while also allowing practitioners to efficiently pursue research into point cloud analysis, with the end goal of building models which can be applied to real-life applications. Task driven implementation with dynamic model and dataset resolution from arguments. Core implementation of common components for point cloud deep learning - greatly simplifying the creation of new models. 4 Base Convolution base classes to simplify the implementation of new convolutions. Each base class supports a different data format.