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

This package computes metrics and generates Interactive QC plots from the sequencing summary report generated by Oxford Nanopore technologies basecaller.

This package provides tools for Makefile execution powered by pure Python.

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

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

Morfessor

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

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.

Optax is a gradient processing and optimization library for JAX.

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).

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

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

JAXopt provides hardware accelerated, batchable and differentiable optimizers in JAX.

1. Hardware accelerated: the implementations run on GPU and TPU, in addition to CPU.

2. Batchable: multiple instances of the same optimization problem can be automatically vectorized using JAX’s vmap.

3. Differentiable: optimization problem solutions can be differentiated with respect to their inputs either implicitly or via autodiff of unrolled algorithm iterations.

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

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

This package provides a Stream and Optional class.

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.

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.

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

ProseMirror plugin for cursors at normally impossible-to-reach positions

Like JSON.stringify, but doesn't blow up on circular refs.

JSON-RPC 1.0/2.0 compliant server and client