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This package provides tools for Makefile execution powered by pure Python.
This is a rewrite of Dask DataFrame that includes query optimization and generally improved organization.
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 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.
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.
Dask.distributed is a lightweight library for distributed computing in Python. It extends both the concurrent.futures and dask APIs to moderate sized clusters.
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.
This package provides procedures to calculate statistics for Oxford Nanopore sequencing data and alignments.
This package contains functions to extract information from Oxford Nanopore sequencing data and alignments.
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.
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.
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.
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.
Protocol buffers are a language-neutral, platform-neutral extensible mechanism for serializing structured data.
Morfessor
This package provides a Stream and Optional class.
This package provides a small utility for simplifying and cleaning up argument parsing scripts.
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.
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.
ScotchPy is a python module to interface the Scotch/PT-Scotch graph partitioner library.
ScotchPy is a python module to interface the Scotch/PT-Scotch graph partitioner library.
ScotchPy is a python module to interface the Scotch/PT-Scotch graph partitioner library.
This package contains a few simple math function for other Oxford Nanopore processing scripts.