PeachPy is a Python framework for writing high-performance assembly kernels. PeachPy aims to simplify writing optimized assembly kernels while preserving all optimization opportunities of traditional assembly.

PeachPy can generate ELF, MS-COFF, Mach-O object files, and assembly listings for the Go language tool chain; it adapts to different calling conventions and application binary interfaces (ABIs); it takes care of register allocation; it supports x86_64 instructions up to AVX-512 and SHA.

HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection. HDBSCAN is ideal for exploratory data analysis; it's a fast and robust algorithm that you can trust to return meaningful clusters (if there are any).

This package implements a functionality to create project files for supported tools and run them in batch or GUI mode. All EDA tools such as Icarus, Yosys, ModelSim, Vivado, Verilator, GHDL, Quartus etc get input HDL files (Verilog and VHDL) and some tool-specific files (constraint files,memory initialization files, IP description files etc). Together with the files, perhaps a couple of Verilog `defines, some top-level parameters/generics or some tool-specific options are set.

Pytools is a bag of things that are ``missing'' from the Python standard library:

• small helper functions such as len_iterable, argmin, tuple generation, permutation generation, ASCII table pretty printing, GvR's monkeypatch_xxx hack, the elusive flatten, and much more.

• Michele Simionato's decorator module

• A time-series logging module, pytools.log.

• Batch job submission, pytools.batchjob.

• A lexer, pytools.lex.

This package provides wrappers for the commands of Python 3.x such that they can also be invoked under their usual names---e.g., python instead of python3 or pip instead of pip3.

To function properly, this package should not be installed together with the python package: this package uses the python package as a propagated input, so installing this package already makes both the versioned and the unversioned commands available.

This package provides wrappers for the commands of Python 3.x such that they can also be invoked under their usual names---e.g., python instead of python3 or pip instead of pip3.

To function properly, this package should not be installed together with the python package: this package uses the python package as a propagated input, so installing this package already makes both the versioned and the unversioned commands available.

Netcdf4-python is a Python interface to the netCDF C library. netCDF version 4 has many features not found in earlier versions of the library and is implemented on top of HDF5. This module can read and write files in both the new netCDF 4 and the old netCDF 3 format, and can create files that are readable by HDF5 clients. The API is modelled after Scientific.IO.NetCDF, and should be familiar to users of that module.

MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors.

mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point and collective communications of any picklable Python object as well as optimized communications of Python objects (such as NumPy arrays) that expose a buffer interface.

The drizzle library is a Python package for combining dithered images into a single image. This library is derived from code used in DrizzlePac. Like DrizzlePac, most of the code is implemented in the C language. The biggest change from DrizzlePac is that this code passes an array that maps the input to output image into the C code, while the DrizzlePac code computes the mapping by using a Python callback. Switching to using an array allowed the code to be greatly simplified.

python-astroid provides a common base representation of Python source code for projects such as pychecker, pyreverse, pylint, etc. It provides a compatible representation which comes from the _ast module. It rebuilds the tree generated by the builtin _ast module by recursively walking down the AST and building an extended ast. The new node classes have additional methods and attributes for different usages. They include some support for static inference and local name scopes. Furthermore, astroid builds partial trees by inspecting living objects.

Mutagen is a Python module to handle audio metadata. It supports ASF, FLAC, M4A, Monkey’s Audio, MP3, Musepack, Ogg FLAC, Ogg Speex, Ogg Theora, Ogg Vorbis, True Audio, WavPack and OptimFROG audio files. All versions of ID3v2 are supported, and all standard ID3v2.4 frames are parsed. It can read Xing headers to accurately calculate the bitrate and length of MP3s. ID3 and APEv2 tags can be edited regardless of audio format. It can also manipulate Ogg streams on an individual packet/page level.

Boltons is a set of over 230 pure-Python utilities in the same spirit as — and yet conspicuously missing from — the standard library, including:

• Atomic file saving, bolted on with fileutils

• A highly-optimized OrderedMultiDict, in dictutils

• Two types of PriorityQueue, in queueutils

• Chunked and windowed iteration, in iterutils

• Recursive data structure iteration and merging, with iterutils.remap

• Exponential backoff functionality, including jitter, through iterutils.backoff

• A full-featured TracebackInfo type, for representing stack traces, in tbutils

This project documents instruction sets in a format convenient for tools development. An instruction set is represented by three files:

• an XML file that describes instructions;

• an XSD file that describes the structure of the XML file;

• a Python module that reads the XML file and represents it as a set of Python objects;

It currently provides descriptions for most user-mode x86, x86_64, and k1om instructions up to AVX-512 and SHA (including 3dnow!+, XOP, FMA3, FMA4, TBM and BMI2).

This package is a Cython wrapper for khash-sets/maps. It brings functionality of khash to Python and Cython and can be used seamlessly in numpy or pandas. Numpy's world is lacking the concept of a (hash-)set. This shortcoming is fixed and efficient (memory- and speedwise compared to pandas) unique and isin are implemented. Python-set/dict have a big memory-footprint. For some datatypes the overhead can be reduced by using khash by factor 4-8.

Natsort lets you apply natural sorting on lists instead of lexicographical. If you use the built-in sorted method in python on a list such as [a20, a9, a1, a4, a10], it would be returned as [a1, a10, a20, a4, a9]. Natsort provides a function natsorted that identifies numbers and sorts them separately from strings. It can also sort version numbers, real numbers, mixed types and more, and comes with a shell command natsort that exposes this functionality in the command line.

MOFA is a factor analysis model that provides a general framework for the integration of multi-omic data sets in an unsupervised fashion. Intuitively, MOFA can be viewed as a versatile and statistically rigorous generalization of principal component analysis to multi-omics data. Given several data matrices with measurements of multiple -omics data types on the same or on overlapping sets of samples, MOFA infers an interpretable low-dimensional representation in terms of a few latent factors. These learnt factors represent the driving sources of variation across data modalities, thus facilitating the identification of cellular states or disease subgroups.

This Python module can be used to generate and parse RFC 5451/7001/7601 Authentication-Results email headers. It supports extensions such as:

• RFC 5617 DKIM/ADSP

• RFC 6008 DKIM signature identification (header.b)

• RFC 6212 VBR

• RFC 6577 SPF

• RFC 7281 Authentication-Results registration for S/MIME

• RFC 7293 The Require-Recipient-Valid-Since header field

• RFC 7489 DMARC

• ARC (draft-ietf-dmarc-arc-protocol-08)

This package provides a simple Python test runner for unittest that outputs Test Anything Protocol (TAP) results to standard output. Contrary to other TAP runners for Python, pycotap...

• prints TAP (and only TAP) to standard output instead of to a separate file, allowing you to pipe it directly to TAP pretty printers and processors;

• only contains a TAP reporter, so no parsers, no frameworks, no dependencies, etc;

• is configurable: you can choose how you want the test output and test result diagnostics to end up in your TAP output (as TAP diagnostics, YAML blocks, or attachments).

This is the reference implementation of the CWL standards. The CWL open standards are for describing analysis workflows and tools in a way that makes them portable and scalable across a variety of software and hardware environments, from workstations to cluster, cloud, and high performance computing (HPC) environments. CWL is designed to meet the needs of data-intensive science, such as Bioinformatics, Medical Imaging, Astronomy, Physics, and Chemistry. The cwltool is intended to be feature complete and to provide comprehensive validation of CWL files as well as provide other tools related to working with CWL descriptions.

Pyogrio provides a GeoPandas-oriented API to OGR vector data sources, such as ESRI Shapefile, GeoPackage, and GeoJSON. Vector data sources have geometries, such as points, lines, or polygons, and associated records with potentially many columns worth of data. Pyogrio uses a vectorized approach for reading and writing GeoDataFrames to and from OGR vector data sources in order to give you faster interoperability. It uses pre-compiled bindings for GDAL/OGR so that the performance is primarily limited by the underlying I/O speed of data source drivers in GDAL/OGR rather than multiple steps of converting to and from Python data types within Python.

python-pandera provides a flexible and expressive API for performing data validation on dataframe-like objects to make data processing pipelines more readable and robust. Dataframes contain information that python-pandera explicitly validates at runtime. This is useful in production-critical data pipelines or reproducible research settings. With python-pandera, you can:

• Define a schema once and use it to validate different dataframe types.

• Check the types and properties of columns.

• Perform more complex statistical validation like hypothesis testing.

• Seamlessly integrate with existing data pipelines via function decorators.

• Define dataframe models with the class-based API with pydantic-style syntax.

• Synthesize data from schema objects for property-based testing.

• Lazily validate dataframes so that all validation rules are executed.

• Integrate with a rich ecosystem of tools like python-pydantic, python-fastapi and python-mypy.

Cheetah is a text-based template engine and Python code generator.

Cheetah can be used as a standalone templating utility or referenced as a library from other Python applications. It has many potential uses, but web developers looking for a viable alternative to ASP, JSP, PHP and PSP are expected to be its principle user group.

Features:

1. Generates HTML, SGML, XML, SQL, Postscript, form email, LaTeX, or any other text-based format.

2. Cleanly separates content, graphic design, and program code.

3. Blends the power and flexibility of Python with a simple template language that non-programmers can understand.

4. Gives template writers full access to any Python data structure, module, function, object, or method in their templates.

5. Makes code reuse easy by providing an object-orientated interface to templates that is accessible from Python code or other Cheetah templates. One template can subclass another and selectively reimplement sections of it.

6. Provides a simple, yet powerful, caching mechanism that can dramatically improve the performance of a dynamic website.

7. Compiles templates into optimized, yet readable, Python code.

Python humanize utilities

Python Netlink library.