Open Source Python Scientific/Engineering Software - Page 62

As of 2012, the source code is hosted in GitHub https://github.com/tessonec/PySPG PySPG is both, a set of python modules and a program that allows the user to run a program systematically changing the parameters that them receive. Originally conceived to generate parameters for running simulations in computational physics.

A module containing python wrappers for the SUite of Non-linear DIfferential/ALgebraic Solvers (SUNDIALS), using ctypes.

PySpline is a graphical, cross-platform program for processing X-ray absorption spectroscopy (XAS) and Extended X-ray absorption fine structure (EXAFS) data. It allows processing parameters to be adjusted and observe the effect on R-space data.

A lightweight hyperspectral imaging library that provides developers with spectral algorithms for the Python programming language. New for v0.14.x: a scikit-learn bridge (alpha and partial). The functions and classes are organized by topics: * abundance maps: FCLS, NNLS, UCLS * classification: AbundanceClassification, NormXCorr, KMeans SAM, SID, SVC * detection: ACE, CEM, GLRT, MatchedFilter, OSP * distance: chebychev, NormXCorr, SAM, SID * endmembers extraction: ATGP, FIPPI, NFINDR, PPI * material count: HfcVd, HySime * noise: Savitzky Golay, MNF, whiten * sigproc: bilateral * sklearn: HyperEstimatorCrossVal, HyperSVC and others * spectro: convex hull quotient, features extraction (tetracorder style), USGS06 lib interface * util: load_ENVI_file, load_ENVI_spec_lib, corr, cov and others The library do an extensive use of the numpy numeric library and can achieve good speed. The library is mature enough and is very usable even if the development is at a beta step.

A set of components based upon the PyGTK architecture for interactively displaying time-sampled data in the form of "strip charts".

A python class and GUI for visualization of data generated by the medical treatment planning software TRiP. Project has been moved to http://github.com/pytrip/pytrip on 15.06.2016

PyTom is a toolbox developed for interpreting cryo electron tomography data. All steps from reconstruction, localization, alignment and classification are covered with standard and improved methods. Please sign up to our mailing list to keep up with the most recent updates and versions.

It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. We have outsourced a lot of functionality of PyTorch Geometric to other packages, which needs to be additionally installed. These packages come with their own CPU and GPU kernel implementations based on C++/CUDA extensions. We do not recommend installation as root user on your system python. Please setup an Anaconda/Miniconda environment or create a Docker image. We provide pip wheels for all major OS/PyTorch/CUDA combinations.

The library consists of various dynamic and temporal geometric deep learning, embedding, and Spatio-temporal regression methods from a variety of published research papers. Moreover, it comes with an easy-to-use dataset loader, train-test splitter and temporal snaphot iterator for dynamic and temporal graphs. The framework naturally provides GPU support. It also comes with a number of benchmark datasets from the epidemiological forecasting, sharing economy, energy production and web traffic management domains. Finally, you can also create your own datasets. The package interfaces well with Pytorch Lightning which allows training on CPUs, single and multiple GPUs out-of-the-box. PyTorch Geometric Temporal makes implementing Dynamic and Temporal Graph Neural Networks quite easy - see the accompanying tutorial. Head over to our documentation to find out more about installation, creation of datasets and a full list of implemented methods and available datasets.

Developed on Python to leverage state-of-the-art extensibility and easily integrated plugins Able to use low cost robots with the accuracy and dexterity of much more expensive robots, achieved in part by: a) Ability to drive movement by inexpensive DC gear head motors, including the inherent ability to specify movement by applied force rather than strict space/time control that steppers impose. b) Arms and linkages don't have to have super tight tolerances, because accuracy is achieved by tightly integrated visual feedback that's easy to program and low complexity processing. Ability to program easily with a GUI and visual interface that sees things from the robots' camera perspective. Flexible use of coordinate spaces that make programming arms with 6 or more degrees of freedom super easy. Got a bot arm with 13 linkages that can reach around corners? Easy. Simple arm with only 2 degrees of freedom, with one polar and one cartesian? Any arm geometry works.

PyVib2 is a program for analyzing vibrational motion and vibrational spectra. It is an open source "all-in-one" solution for scientists working in the field of vibrational spectroscopy (Raman and IR) and vibrational optical activity (ROA and VCD).

Tk-based standalone graphing system and extensible graphing library with a huge range of 2d modes (maybe 3d soon?) written in Python (Python/Tk are 100% cross platform)

PyVision is a object-oriented Computer Vision Toolkit for researchers that contains vision and machine learning algorithms and algorithm analysis and easily interfaces with scipy/numpy, PIL, opencv and other computer and machine learning libraries.

A Python interface to the Wordnet database of word meanings and lexical relationships. allows the user to type expressions such as N['dog'], hyponyms(N['dog'][0]), and closure(ADJ['red'], SYNONYM) to query the database for lexical relationships.

PyXPA is a Python interface to the XPA library (http://hea-www.harvard.edu/RD/xpa/ developed by the SAO/HEAD R&D Group (http://hea-www.harvard.edu/RD/)).

Python extension module based on C-XSC (C++ Language eXtensions for Scientific Computation) and the associated Toolbox. Provides a real and complex interval arithmetic with verified results for numerical applications.

PyZgoubi is an interface to the Zgoubi particle tracking code written in python. It aims to ease the use of Zgoubi by providing a simple interface to create beam line elements and particles, and to automate running and analysing of simulations.

pyanpha is a shell-based program, which applies the multi-wavelength anomalous diffraction technique to Surface Diffraction. It enables one to calculate the phases of the structure factors of crystal truncation rods when anomalous scatterers are present in the thin film. Functionality extends to standalone simulations. It was coined pyanpha which is an acronym for PYthon ANomalous PHAsing and it is written in python.

A collection of modules and enduser tools designed to help design, test and use system models. While my research (hence, the examples) is focused on hydro-ecological systems, the program can be used on any system characterized by ODEs (and soon PDEs?)

Pyclamp is Python package used to extract and analyse electrophysiological data. A graphical user interface has been developed to allow a user to run the pack- age without requiring any knowledge of Python code. Presently, Pyclamp is designed to perform very specific forms of analysis on evoked synaptic responses: Data analysis (under development) : This is a highly user-interactive en-vironment that can be used to discriminate synaptic events, obtain various measures of their kinetics and size, and output the results. Quantal analysis : This part of the package performs simple variance-mean analysis and Bayesian quantal analysis to estimate the quantal sizea dn number of release sites among other measures.

Basic and intermediate examples to read, modify and write DICOM files with Python code using Jupyter - To install Jupyter - https://jupyter.org/install ====== All examples are based on Pydicom. An open source library - https://pydicom.github.io/

Pydusa is a package for parallel programming using Python. It contains a module for doing MPI programming in Python. We have added parallel solver packages such as Parallel SuperLU for solving sparse linear systems.

Allows one to program fablab (factory and/or laboratory) in the same way as we program computer. Imagine commands like: "dim screw12 as new screw" or "mold(form_piston_081212, brass)" or ''gear(3)=lasercut(steel2,"biggear.gc")". This approach will let us program highly automated and self-deploying or even self-replicating factories. I am more engineer than programmer and just beginner in python. So it looks like community help will be needed :)

This repository is outdated. Please visit https://github.com/RegulatoryGenomicsUPF/pyicoteo/tags

Pylero is an open-source Python-based text generator.