This Python module enables remote procedure calls, clustering, and distributed-computing. For this purpose, it makes use of object-proxying, a technique that employs python's dynamic nature, to overcome the physical boundaries between processes and computers, so that remote objects can be manipulated as if they were local.

vine provides a special implementation of promises in that it can be used both for "promise of a value" and lazy evaluation. The biggest upside for this is that everything in a promise can also be a promise, e.g. filters, callbacks and errbacks can all be promises.

YAPF is a formatter for Python code. It's based off of clang-format, developed by Daniel Jasper. In essence, the algorithm takes the code and reformats it to the best formatting that conforms to the style guide, even if the original code didn't violate the style guide.

Jedi is a static analysis tool for Python that can be used in Integrated Development Environments (IDEs) and text editors. It understands Python on a deeper level than many other static analysis frameworks for Python.

Jedi understands docstrings and you can use Jedi autocompletion in your REPL as well.

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.

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.

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.

Jedi is a static analysis tool for Python that can be used in Integrated Development Environments (IDEs) and text editors. It understands Python on a deeper level than many other static analysis frameworks for Python.

Jedi understands docstrings and you can use Jedi autocompletion in your REPL as well.

Python-cbor provides an implementation of the Concise Binary Object Representation (CBOR). CBOR is comparable to JSON, has a superset of JSON's ability, but serializes to a binary format which is smaller and faster to generate and parse. The two primary functions are cbor.loads and cbor.dumps.

This package provides a range of colormaps designed for scientific use with Matplotlib. It includes perceptually uniform sequential colormaps such as abre, dusk, kepl, and octarine, as well as monochromatic sequential colormaps like blue, green, and red, and others (algae, pastel, and xray).

PYHDF4 is a python wrapper around the NCSA HDF version 4 library, which implements the SD (Scientific Dataset), VS (Vdata) and V (Vgroup) API’s. NetCDF files can also be read and modified. It is a successor of Python-HDF4 which is a fork of pyhdf.

pixy is a command-line tool for painlessly estimating average nucleotide diversity within (π) and between (dxy) populations from a VCF. In particular, pixy facilitates the use of VCFs containing invariant (monomorphic) sites, which are essential for the correct computation of π and dxy in the face of missing data (i.e. always).

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.

This tool offers a pipeline for inferring gene expression programs from scRNA-Seq. It takes a count matrix (N cells X G genes) as input and produces a (K x G) matrix of gene expression programs (GEPs) and a (N x K) matrix specifying the usage of each program for each cell in the data.

Python is a remarkably powerful dynamic programming language that is used in a wide variety of application domains. Some of its key distinguishing features include: clear, readable syntax; strong introspection capabilities; intuitive object orientation; natural expression of procedural code; full modularity, supporting hierarchical packages; exception-based error handling; and very high level dynamic data types.

Python is a remarkably powerful dynamic programming language that is used in a wide variety of application domains. Some of its key distinguishing features include: clear, readable syntax; strong introspection capabilities; intuitive object orientation; natural expression of procedural code; full modularity, supporting hierarchical packages; exception-based error handling; and very high level dynamic data types.

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).

Generalized World Coordinate System (GWCS) is an Astropy affiliated package providing tools for managing the World Coordinate System of astronomical data.

GWCS takes a general approach to the problem of expressing transformations between pixel and world coordinates. It supports a data model which includes the entire transformation pipeline from input coordinates (detector by default) to world coordinates.

Generalized World Coordinate System (GWCS) is an Astropy affiliated package providing tools for managing the World Coordinate System of astronomical data.

GWCS takes a general approach to the problem of expressing transformations between pixel and world coordinates. It supports a data model which includes the entire transformation pipeline from input coordinates (detector by default) to world coordinates.

Mypy is an optional static type checker for Python that aims to combine the benefits of dynamic typing and static typing. Mypy combines the expressive power and convenience of Python with a powerful type system and compile-time type checking. Mypy type checks standard Python programs; run them using any Python VM with basically no runtime overhead.

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.

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.

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.

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.