Scientific Software
Free and open source software for the scientific analysis of data, including web-based applications, software for research, data processing, data analysis, visualization, etc. Includes free, publicly available software documentation and programming tutorials. Commercial software links require sponsorship.
220 listings
Submitted May 16, 2017 to Scientific Software Auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. Auto-sklearn frees a machine learning user from algorithm selection and hyperparameter tuning. It leverages recent advantages in Bayesian optimization, meta-learning and ensemble construction.
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Submitted Apr 22, 2017 to Scientific Software Processing is a flexible software sketchbook and a language for learning how to code within the context of the visual arts. Since 2001, Processing has promoted software literacy within the visual arts and visual literacy within technology. There are tens of thousands of students, artists, designers, researchers, and hobbyists who use Processing for learning and prototyping.
» Free to download and open source » Interactive programs with 2D, 3D or PDF output » OpenGL integration for accelerated 2D and 3D » For GNU/Linux, Mac OS X, Windows, Android, and ARM » Over 100 libraries extend the core software » Well documented, with many books available |
Submitted Apr 20, 2017 to Scientific Software Google Books Ngram Viewer allows one to easily graph comma-separated phrases and view their occurrence frequency.
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Submitted Apr 17, 2017 to Scientific Software Altair is a declarative statistical visualization library for Python, based on Vega-Lite.
With Altair, you can spend more time understanding your data and its meaning. Altair’s API is simple, friendly and consistent and built on top of the powerful Vega-Lite visualization grammar. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code. |
Submitted Apr 14, 2017 to Scientific Software The nfft package is a lightweight implementation of the non-equispaced fast Fourier transform (NFFT), implemented via numpy and scipy and released under the MIT license. For information about the NFFT algorithm, see the paper Using NFFT 3 – a software library for various nonequispaced fast Fourier transforms.
The nfft package achieves comparable performance to the C package described in that paper, without any customized compiled code. Rather, it makes use of the computational building blocks available in NumPy and SciPy. For a discussion of the algorithm and this implementation, see the Implementation Walkthrough notebook. |
Submitted Apr 11, 2017 to Scientific Software ENet contains two subpackages: train and visualization. Train contains tools for training network using various architectures. It can be further used for visulaization of network's performance. This section is mainly for pixelwise segmentation and scene-parsing. Visualize can be used to view the performance of trained network on any video/image as an overlay.
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Submitted Apr 11, 2017 to Scientific Software Sonnet is a library built on top of TensorFlow for building complex neural networks. The main principle of Sonnet is to first construct Python objects which represent some part of a neural network, and then separately connect these objects into the TensorFlow computation graph.
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Submitted Apr 07, 2017 to Scientific Software Chainer is a flexible framework of neural networks for deep learning. This repo is dedicated to improving Chainer performance on CPU, especially in Intel® Xeon® and Intel® Xeon Phi™ processors.
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Submitted Apr 07, 2017 to Scientific Software The goal of the Open Quantum Safe (OQS) project is to support the development and prototyping quantum-resistant cryptography. OQS consists of two main lines of work: liboqs, an open source C library for quantum-resistant cryptographic algorithms, and prototype integrations into protocols and applications, including the widely used OpenSSL library.
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Submitted Apr 06, 2017 to Scientific Software Google Earth Enterprise Open Source (Open GEE) is an open source, production-ready geospatial solution with over 12 years of development behind it, a vibrant community, and loads of features. With Open GEE you can build your own, private Google Earth and store and process petabytes of imagery, terrain, and vector data on your own server infrastructure.
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Submitted Apr 04, 2017 to Scientific Software Spatial data plus the power of the ggplot2 framework means easier mapping when input data are already in the form of Spatial* objects (most spatial data in R).
The ggspatial package provides several functions to convert spatial objects to ggplot2 layers. There are three cases: vector data (geom_spatial() or ggspatial()), raster data (geom_spatial() or ggraster()), and tiled basemap (geom_osm() or ggosm()). Their usage is almost identical to normal ggplot geom_* functions, except that the mapping and the data arguments are switched (usually mapping comes before data, but in this context, where the type of the object determines the method that gets called, it makes more sense for data to come first). |
Submitted Apr 02, 2017 to Scientific Software This is the code for the paper Perceptual Losses for Real-Time Style Transfer and Super-Resolution, Justin Johnson, Alexandre Alahi, Li Fei-Fei.
The paper builds on A Neural Algorithm of Artistic Style by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge by training feedforward neural networks that apply artistic styles to images. After training, our feedforward networks can stylize images hundreds of times faster than the optimization-based method presented by Gatys et al. This repository also includes an implementation of instance normalization as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization by Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. This simple trick significantly improves the quality of feedforward style transfer models. |
Submitted Apr 01, 2017 to Scientific Software PyScatWave is a CuPy/PyTorch Scattering implementation. A scattering network is a Convolutional Network with filters predefined to be wavelets that are not learned and it can be used in vision task such as classification of images. The scattering transform can drastically reduce the spatial resolution of the input (e.g. 224x224->14x14) with demonstrably neglible loss in dicriminative power.
The software uses PyTorch + NumPy FFT on CPU, and PyTorch + CuPy + CuFFT on GPU. |
Submitted Mar 30, 2017 to Scientific Software TopoJSON is an extension to GeoJSON that encodes topology created by Mike Bostock.
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Submitted Mar 27, 2017 to Scientific Software TextRank is an implementation for text summarization and keyword extraction in Python. TextRank also offers text modeling with graph and gexf exportation.
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Submitted Mar 27, 2017 to Scientific Software Doxygen is the de facto standard tool for generating documentation from annotated C++ sources, but it also supports other popular programming languages such as C, Objective-C, C#, PHP, Java, Python, IDL (Corba, Microsoft, and UNO/OpenOffice flavors), Fortran, VHDL, Tcl, and to some extent D.
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Submitted Mar 27, 2017 to Scientific Software MeTA is a modern C++ data sciences toolkit featuring
- text tokenization, including deep semantic features like parse trees - inverted and forward indexes with compression and various caching strategies - a collection of ranking functions for searching the indexes - topic models - classification algorithms - graph algorithms - language models - CRF implementation (POS-tagging, shallow parsing) - wrappers for liblinear and libsvm (including libsvm dataset parsers) - UTF8 support for analysis on various languages - multithreaded algorithms |
Submitted Mar 27, 2017 to Scientific Software ZeroMQ \zero-em-queue\, \ØMQ\:
Ø Connect your code in any language, on any platform. Ø Carries messages across inproc, IPC, TCP, TIPC, multicast. Ø Smart patterns like pub-sub, push-pull, and router-dealer. Ø High-speed asynchronous I/O engines, in a tiny library. Ø Backed by a large and active open source community. Ø Supports every modern language and platform. Ø Build any architecture: centralized, distributed, small, or large. Ø Free software with full commercial support. |
Submitted Mar 27, 2017 to Scientific Software Graphify is a Neo4j unmanaged extension used for document and text classification using graph-based hierarchical pattern recognition.
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Submitted Mar 27, 2017 to Scientific Software This is an experimental graphical user interface intended to enable the interactive exploration of Adaptable Seismic Data Format (ASDF) files. It can mostly deal with earthquake event data sets and is currently not really suited to explore large noise data sets but we will add this capability in the near future.
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