This Python package implements Ted Dunning's t-digest data structure, which is designed around computing accurate estimates such as percentiles, quantiles and trimmed means from streaming or distributed data. Two t-digests can be added, making the data structure ideal for map-reduce settings, and can be serialized into much less than 10 kB, instead of storing the entire list of data.

This package provides Kullback-Leibler projections for Bayesian model selection. Variable selection refers to the process of identifying the most relevant variables in a model from a larger set of predictors. When performing this process, we usually assume that variables contribute unevenly to the outcome, and we want to identify the most important ones. Sometimes we also care about the order in which variables are included in the model.

This package provides some extra widgets for PyQt/PySide:

• Multi-handle slider for float values

• Spinbox with arbitrarily large integers

• Magnitude combined with unit dropdown

• Label that will elide text

• Searchable ComboBox populated from Enum

• Searchable List

• Searchable Tree

• Color ComboBox

• Colormap ComboBox

• Toggle switch

• Collapsible widget to hide and unhide child widgets

• Flow layout

CoolBox is a toolkit for visual analysis of genomics data. It aims to be highly compatible with the Python ecosystem, easy to use and highly customizable with a well-designed user interface. It can be used in various visualization situations, for example, to produce high-quality genome track plots or fetch common used genomic data files with a Python script or command line, interactively explore genomic data within Jupyter environment or web browser.

This package can 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.

The socksio Python module is a client-side sans-I/O SOCKS proxy implementation. It supports SOCKS4, SOCKS4A, and SOCKS5. socksio is a sans-I/O library similar to h11 or h2; this means the library itself does not handle the actual sending of the bytes through the network, it only deals with the implementation details of the SOCKS protocols. It can be paired with any I/O library.

This package implements a functionality to produce map based simulations for the Simons Observatory or other CMB experiments. It creates simulated maps in HEALPix and CAR pixelization based on:

• foreground models included in PySM

• custom foregrounds models from the so_pysm_models package

• precomputed Cosmic Microwave Background simulations

• noise simulations based on expected performance and simulated hitmaps

• effect of gaussian beam convolution

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).

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.

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.

Python implementation of Parker wind models for planetary atmospheres. p-winds produces simplified, 1-D models of the upper atmosphere of a planet, and perform radiative transfer to calculate observable spectral signatures.

The scalable implementation of 1D models allows for atmospheric retrievals to calculate atmospheric escape rates and temperatures. In addition, the modular implementation allows for a smooth plugging-in of more complex descriptions to forward model their corresponding spectral signatures (e.g., self-consistent or 3D models).

Python implementation of Parker wind models for planetary atmospheres. p-winds produces simplified, 1-D models of the upper atmosphere of a planet, and perform radiative transfer to calculate observable spectral signatures.

The scalable implementation of 1D models allows for atmospheric retrievals to calculate atmospheric escape rates and temperatures. In addition, the modular implementation allows for a smooth plugging-in of more complex descriptions to forward model their corresponding spectral signatures (e.g., self-consistent or 3D models).

The electromagnetic modeller empymod can model electric or magnetic responses due to a three-dimensional electric or magnetic source in a layered-earth model with vertical transverse isotropic (VTI) resistivity, VTI electric permittivity, and VTI magnetic permeability, from very low frequencies (DC) to very high frequencies (GPR). The computation is carried out in the wavenumber-frequency domain, and various Hankel- and Fourier-transform methods are included to transform the responses into the space-frequency and space-time domains.

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).

Gammapy is an Python package for gamma-ray astronomy built on Numpy, Scipy and Astropy. It is used as core library for the Science Analysis tools of the Cherenkov Telescope Array (CTA), recommended by the H.E.S.S. collaboration to be used for Science publications, and is already widely used in the analysis of existing gamma-ray instruments, such as MAGIC VERITAS and HAWC.

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.

This package provides a Pythonic Jupyter-friendly Python API for the HepMC3 library.

pyhepmc has been optimised for safety, usability, and efficiency by a human expert, something that an automatic tool cannot provide. It brings these unique features:

• Python idioms are supported where appropriate.

• Simple IO with pyhepmc.open.

• An alternative Numpy API whih accelerates event processing.

• The public API is fully documented with Python docstrings.

• Objects are inspectable in Jupyter notebooks.

• Events render as graphs in Jupyter notebooks.