Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.
API method:
GET /api/packages?search=hello&page=1&limit=20
where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned
in response headers.
If you'd like to join our channel webring send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.
Chaco is a Python package for building interactive and custom 2-D plots and visualizations. Chaco facilitates writing plotting applications at all levels of complexity, from simple scripts with hard-coded data to large plotting programs with complex data interrelationships and a multitude of interactive tools. While Chaco generates attractive static plots for publication and presentation, Chaco differs from tools like Matplotlib in that it also works well for dynamic interactive data visualization and exploration.
This package provides a flexible utility for flattening and unflattening dict-like objects in Python.
This package provides a hierarchical data modeling framework for modern science data standards.
Quantities is designed to handle arithmetic and conversions of physical quantities, which have a magnitude, dimensionality specified by various units, and possibly an uncertainty.
pathlib api extended to use fsspec backends.
Python Bindings for the NVIDIA Management Library.
Backport of pathlib ABCs.
Mayavi is a general purpose, cross-platform tool for 2-D and 3-D scientific data visualization.
Estimate and track carbon emissions from your computer, quantify and analyze their impact.
Envisage is a Python-based framework for building extensible applications, that is, applications whose functionality can be extended by adding 'plug-ins. Envisage provides a standard mechanism for features to be added to an application, whether by the original developer or by someone else. In fact, when you build an application using Envisage, the entire application consists primarily of plug-ins. In this respect, it is similar to the Eclipse and Netbeans frameworks for Java applications.
Fief is an open-source platform to manage users and authentication in your applications.
Key features:
Pre-built login and registration pages
Users management dashboard
SDK for the most popular languages and frameworks
Integrations for the most popular no-code tools
Pyface contains toolkit-independent GUI abstraction layers, used to support the TraitsUI package. Thus, you can write code in the abstraction of the Pyface API and the selected toolkit and backend take care of the details of displaying them.
ScotchPy is a python module to interface the Scotch/PT-Scotch graph partitioner library.
Dask is a flexible parallel computing library for analytics. It consists of two components: dynamic task scheduling optimized for computation, and large data collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers.
JAX is Autograd and XLA, brought together for high-performance numerical computing, including large-scale machine learning research. With its updated version of Autograd, JAX can automatically differentiate native Python and NumPy functions. It can differentiate through loops, branches, recursion, and closures, and it can take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation) via grad as well as forward-mode differentiation, and the two can be composed arbitrarily to any order.
ScotchPy is a python module to interface the Scotch/PT-Scotch graph partitioner library.
This module contains classes for the object model defined by the Static Analysis Results Interchange Format (SARIF) file format.
Dask is a flexible parallel computing library for analytics. It consists of two components: dynamic task scheduling optimized for computation, and large data collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers.
JAXopt provides hardware accelerated, batchable and differentiable optimizers in JAX.
Hardware accelerated: the implementations run on GPU and TPU, in addition to CPU.
Batchable: multiple instances of the same optimization problem can be automatically vectorized using JAX’s
vmap.Differentiable: optimization problem solutions can be differentiated with respect to their inputs either implicitly or via autodiff of unrolled algorithm iterations.
This package computes metrics and generates Interactive QC plots from the sequencing summary report generated by Oxford Nanopore technologies basecaller.
JAX is Autograd and XLA, brought together for high-performance numerical computing, including large-scale machine learning research. With its updated version of Autograd, JAX can automatically differentiate native Python and NumPy functions. It can differentiate through loops, branches, recursion, and closures, and it can take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation) via grad as well as forward-mode differentiation, and the two can be composed arbitrarily to any order.
This package provides tools for Makefile execution powered by pure Python.
Non-Metric Space Library (NMSLIB)
This is a rewrite of Dask DataFrame that includes query optimization and generally improved organization.