gcvb (generate compute validate benchmark) is a Python 3 module aiming at facilitating non-regression, validation and benchmarking of simulation codes. gcvb is not a complete tool of continuous integration (CI). It is rather a component of the testing part of a CI workflow. It can compare the different metrics of your computation with references that can be a file, depends of the 'configuration' or are absolute. This is the fork of Marek Felšöci.

Core component suite for Dash.

gcvb (generate compute validate benchmark) is a Python 3 module aiming at facilitating non-regression, validation and benchmarking of simulation codes. gcvb is not a complete tool of continuous integration (CI). It is rather a component of the testing part of a CI workflow. It can compare the different metrics of your computation with references that can be a file, depends of the 'configuration' or are absolute. This is a minimal version without the dashboard functionality. This is the fork of Marek Felšöci.

Deferring loading of JS files until after React loads.

This package provides a dash component for specifying raw HTML.

Python client library for visual regression testing with Percy (https://percy.io).

Dash table.

Bootstrap themed components for use in Plotly Dash.

Export data as binary VTK files

This library provides ordinary differential equation (ODE) solvers implemented in PyTorch. Backpropagation through ODE solutions is supported using the adjoint method for constant memory cost. For usage of ODE solvers in deep learning applications.

As the solvers are implemented in PyTorch, algorithms in this repository are fully supported to run on the GPU.

image and video datasets and models for torch deep learning

The Python Imaging Library adds image processing capabilities to your Python interpreter. This library provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities. The core image library is designed for fast access to data stored in a few basic pixel formats. It should provide a solid foundation for a general image processing tool.

Python implementation of the Tensor Train (TT) toolbox. It contains several important packages for working with the TT-format in Python. It is able to do TT-interpolation, solve linear systems, eigenproblems, solve dynamical problems. Several computational routines are done in Fortran (which can be used separately), and are wrapped with the f2py tool.

Access dict values as attributes (works recursively).

Generates LaTeX source from Python functions.

This package provides a complete GCC tool chain for Fortran development to be installed in user profiles. This includes gfortran, as well as libc (headers and binaries, plus debugging symbols in the debug output), and binutils.

This package provides a complete GCC tool chain for Fortran development to be installed in user profiles. This includes gfortran, as well as libc (headers and binaries, plus debugging symbols in the debug output), and binutils.

GCC is the GNU Compiler Collection. It provides compiler front-ends for several languages, including C, C++, Objective-C, Fortran, Ada, and Go. It also includes runtime support libraries for these languages.

Expect tests are a method of writing tests where instead of hard-coding the expected output of a test, you run the test to get the output, and the test framework automatically populates the expected output.

GUIX package for the SZ compressor.

FTI stands for Fault Tolerance Interface and is a library that aims to give computational scientists the means to perform fast and efficient multilevel checkpointing in large scale supercomputers.

GUIX package for the SZ3 compressor.

zfp is a compressed number format for multi-dimensional arrays. zfp provides compressed-array classes (e.g., for in-memory storage) and high-speed, parallel data compression (e.g., for offline storage). zfp supports both lossy and lossless compression and fine-grained user control over accuracy and storage size.

This package has been developed for computing exact distances, without heuristics, between all pairs of reads of a NGS sample. This is a first step for supervised or unsupervised clustering of reads in an environmental sample.