The h5py package provides both a high- and low-level interface to the HDF5 library from Python. The low-level interface is intended to be a complete wrapping of the HDF5 API, while the high-level component supports access to HDF5 files, datasets and groups using established Python and NumPy concepts.

This package provides a Python wrapper for Ofir Pele and Michael Werman's implementation of the Earth Mover's Distance.

This library implements support for mixed precision training in JAX. It provides two key abstractions. These abstractions are mixed precision policies and loss scaling.

This package provides a Stream and Optional class.

This package contains functions to extract information from Oxford Nanopore sequencing data and alignments.

TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of probabilistic methods with deep networks, gradient-based inference via automatic differentiation, and scalability to large datasets and models via hardware acceleration (e.g., GPUs) and distributed computation.

Protocol Buffers are a way of encoding structured data in an efficient yet extensible format. Google uses Protocol Buffers for almost all of its internal RPC protocols and file formats.

pybind11 is a lightweight header-only library that exposes C++ types in Python and vice versa, mainly to create Python bindings of existing C++ code. Its goals and syntax are similar to the Boost.Python library: to minimize boilerplate code in traditional extension modules by inferring type information using compile-time introspection.

ScotchPy is a python module to interface the Scotch/PT-Scotch graph partitioner library.

Morfessor

ScotchPy is a python module to interface the Scotch/PT-Scotch graph partitioner library.

Chex is a library of utilities for helping to write reliable JAX code. This includes utils to help:

• Instrument your code (e.g. assertions)

• Debug (e.g. transforming pmaps in vmaps within a context manager).

• Test JAX code across many variants (e.g. jitted vs non-jitted).

This package provides a small utility for simplifying and cleaning up argument parsing scripts.

This package provides a Python library for topic modelling, document indexing and similarity retrieval with large corpora. The target audience is the natural language processing and information retrieval community.

This package contains a few simple math function for other Oxford Nanopore processing scripts.

Optax is a gradient processing and optimization library for JAX.

TensorFlow is a flexible platform for building and training machine learning models. It provides a library for high performance numerical computation and includes high level Python APIs, including both a sequential API for beginners that allows users to build models quickly by plugging together building blocks and a subclassing API with an imperative style for advanced research.

This package provides procedures to calculate statistics for Oxford Nanopore sequencing data and alignments.

NumPyro is a lightweight probabilistic programming library that provides a NumPy backend for Pyro. It relies on JAX for automatic differentiation and JIT compilation to GPU / CPU.

Protocol buffers are a language-neutral, platform-neutral extensible mechanism for serializing structured data.

TensorFlow is a flexible platform for building and training machine learning models. It provides a library for high performance numerical computation and includes high level Python APIs, including both a sequential API for beginners that allows users to build models quickly by plugging together building blocks and a subclassing API with an imperative style for advanced research.

Dask.distributed is a lightweight library for distributed computing in Python. It extends both the concurrent.futures and dask APIs to moderate sized clusters.

This is a collection of independent Python modules providing utilities for various projects.

Basic cache object for storing key-value pairs.