Chaco is a Python package for building interactive and custom 2-D plots and visualizations. Chaco facilitates writing plotting applications at all levels of complexity, from simple scripts with hard-coded data to large plotting programs with complex data interrelationships and a multitude of interactive tools. While Chaco generates attractive static plots for publication and presentation, Chaco differs from tools like Matplotlib in that it also works well for dynamic interactive data visualization and exploration.

Abjad helps composers build up complex pieces of music notation in iterative and incremental ways. Use Abjad to create a symbolic representation of all the notes, rests, chords, tuplets, beams and slurs in any score. Because Abjad extends the Python programming language, you can use Abjad to make systematic changes to music as you work. Because Abjad wraps the LilyPond music notation package, you can use Abjad to control the typographic detail of symbols on the page.

skpro is a unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in Python.

It provides scikit-learn-like, scikit-base compatible interfaces to:

• tabular supervised regressors for probabilistic prediction

• tabular probabilistic time-to-event and survival prediction

• metrics to evaluate probabilistic predictions

• reductions to turn scikit-learn regressors into probabilistic skpro regressors

• building pipelines and composite models

• symbolic probability distributions

Numba gives you the power to speed up your applications with high performance functions written directly in Python. With a few annotations, array-oriented and math-heavy Python code can be just-in-time compiled to native machine instructions, similar in performance to C, C++ and Fortran, without having to switch languages or Python interpreters.

Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool).

OSMnx is a Python library that lets you download geospatial data from OpenStreetMap and model, project, visualize, and analyze real-world street networks and any other geospatial geometries. You can download and model walkable, drivable, or bikeable urban networks with a single line of Python code then easily analyze and visualize them. You can just as easily download and work with other infrastructure types, amenities/points of interest, building footprints, elevation data, street bearings/orientations, and speed/travel time.

This package provides Python bindings to Zulip's API.

Included common integrations and bridges are located in share/zulip/integrations or in lib/<...>/site-packages/integrations:

• bridge_between_zulips

• bridge_with_irc

• bridge_with_matrix

• bridge_with_slack

• codebase

• git

• google

• hg

• jabber

• jira

• litellm

• log2zulip

• nagios

• openshift

• perforce

• rss

• svn

• trac

• trello

• twitter

• zephyr

This package provides a history of astronomy library. Current Features:

• define standard positional numeral systems with standard arithmetics (BasedReal)

• set your own precision contexts and algorithms on arithmetical operations (PrecisionContext)

• keep track of all operations

• build or import ancient astronomical tables

• perform arithmetical and statistical operations

• support for BasedReal values

• define new calendar types

• date conversions

• collection of mathematical models used for all kinds of geocentric astronomical tables

Fiona is GDAL’s neat and nimble vector API for Python programmers. Fiona is designed to be simple and dependable. It focuses on reading and writing data in standard Python IO style and relies upon familiar Python types and protocols such as files, dictionaries, mappings, and iterators instead of classes specific to OGR. Fiona can read and write real-world data using multi-layered GIS formats and zipped virtual file systems and integrates readily with other Python GIS packages such as pyproj, Rtree, and Shapely.

Mixed sync-async queue, supposed to be used for communicating between classic synchronous (threaded) code and asynchronous (in terms of asyncio) one. Like Janus god the queue object from the library has two faces: synchronous and asynchronous interface. Synchronous is fully compatible with standard queue, asynchronous one follows asyncio queue design.

PyFAI is an azimuthal integration library that tries to be fast (as fast as C and even more using OpenCL and GPU). It is based on histogramming of the 2theta/Q positions of each (center of) pixel weighted by the intensity of each pixel, but parallel version uses a SparseMatrix-DenseVector multiplication. Neighboring output bins get also a contribution of pixels next to the border thanks to pixel splitting. Finally pyFAI provides also tools to calibrate the experimental setup using Debye-Scherrer rings of a reference compound.

A wheel is a ZIP-format archive with a specially formatted filename and the .whl extension. It is designed to contain all the files for a PEP 376 compatible install in a way that is very close to the on-disk format. Many packages will be properly installed with only the Unpack step and the unpacked archive preserves enough information to Spread (copy data and scripts to their final locations) at any later time. Wheel files can be installed with a newer pip or with wheel's own command line utility.

A wheel is a ZIP-format archive with a specially formatted filename and the .whl extension. It is designed to contain all the files for a PEP 376 compatible install in a way that is very close to the on-disk format. Many packages will be properly installed with only the Unpack step and the unpacked archive preserves enough information to Spread (copy data and scripts to their final locations) at any later time. Wheel files can be installed with a newer pip or with wheel's own command line utility.

This package enables to build Probability of Detection (POD) curves from Non Destructive Test. The curves are built using parametric models: univariate linear regression, quantile regression, kriging and polynomial chaos. Analysis can be run in order to test the linear regression hypothesis.

PoD can be built from a set of data or directly from a given physical model that simulate the Non Destructive Test. In this case, the design of experiments is defined iteratively.

Sensitivity analysis can be also be performed. The aggregated Sobol indices are available as well as the perturbation law indices.

This Python library provides functionality for communicating with a Fast IDentity Online (FIDO) device over Universal Serial Bus (USB) as well as verifying attestation and assertion signatures. It aims to support the FIDO Universal 2nd Factor (U2F) and FIDO 2.0 protocols for communicating with a USB authenticator via the Client-to-Authenticator Protocol (CTAP 1 and 2). In addition to this low-level device access, classes defined in the fido2.client and fido2.server modules implement higher level operations which are useful when interfacing with an Authenticator, or when implementing a Relying Party.

This is an extended version of the Python built-in glob module which adds:

• The ability to capture the text matched by glob patterns, and return those matches alongside the file names.

• A recursive ** globbing syntax, akin for example to the globstar option of Bash.

• The ability to replace the file system functions used, in order to glob on virtual file systems.

• Compatible with Python 2 and Python 3 (tested with 3.3).

Glob2 currently based on the glob code from Python 3.3.1.

BBKNN is a batch effect removal tool that can be directly used in the Scanpy workflow. It serves as an alternative to scanpy.api.pp.neighbors(), with both functions creating a neighbour graph for subsequent use in clustering, pseudotime and UMAP visualisation. If technical artifacts are present in the data, they will make it challenging to link corresponding cell types across different batches. BBKNN actively combats this effect by splitting your data into batches and finding a smaller number of neighbours for each cell within each of the groups. This helps create connections between analogous cells in different batches without altering the counts or PCA space.

green is a Python test runner that describes itself as:

Clean

Low redundancy in output. Result statistics for each test is vertically aligned.

Colorful

Terminal output makes good use of color when the terminal supports it.

Fast

Tests run in independent processes (one per processor by default).

Powerful

Multi-target and auto-discovery support.

Traditional

It uses the normal unittest classes and methods.

Descriptive

Multiple verbosity levels, from just dots to full docstring output.

Convenient

Bash-completion and ZSH-completion of options and test targets.

Thorough

Built-in integration with coverage.

This Python library provides encoding and decoding for the Concise Binary Object Representation (CBOR) (RFC 8949) serialization format. The specification is fully compatible with the original RFC 7049. Among its features are:

• Simple API like the json or pickle modules.

• Support many CBOR tags with stdlib objects.

• Generic tag decoding.

• Shared value references including cyclic references.

• String references compact encoding with repeated strings replaced with indices.

• Optional C module backend tested on big- and little-endian architectures.

• Extensible tagged value handling using tag_hook and object_hook on decode and default on encode.

POPPY is a Python package that simulates physical optical propagation including diffraction. It implements a flexible framework for modeling Fraunhofer and Fresnel diffraction and point spread function formation, particularly in the context of astronomical telescopes.

POPPY was developed as part of a simulation package for the James Webb Space Telescope, but is more broadly applicable to many kinds of imaging simulations. It is not, however, a substitute for high fidelity optical design software such as Zemax or Code V, but rather is intended as a lightweight alternative for cases for which diffractive rather than geometric optics is the topic of interest, and which require portability between platforms or ease of scripting.

hissw (hiss (like a snake) + SSW) is a (VERY) lightweight (~1 file) Python package that helps one to write IDL scripts (either inline or in a separate file) which use your installed SSW packages and return the results to local Python namespace. hissw uses Jinja2 templates to generate SSW startup scripts and then runs IDL code using subprocess, i.e. the shell. Jinja2 syntax may be used to inject arguments from Python into IDL. The results are then saved to a file and then loaded back in using the amazing readsav() function in scipy.io.

This package provides a Python implementation of the SDMX 2.1 and 3.0 standards for Statistical Data and Metadata eXchange. These standards are developed and used by statistical agencies and international organisations. This package can be used to:

• Explore and retrieve data available from SDMX-REST web services operated by providers including the World Bank, International Monetary Fund, Eurostat, OECD, and United Nations;

• Read and write data and metadata in file formats including SDMX-ML (XML), SDMX-JSON, and SDMX-CSV;

• Convert data and metadata into python-pandas objects, for use with the analysis, plotting, and other tools in the Python data science ecosystem;

• Apply the SDMX information model (IM) to structure and publish your own data.

funcy is a library that provides functional tools. Examples are:

1. merge - Merges collections of the same type

2. walk - Type-preserving map

3. select - Selects a part of a collection

4. take - Takes the first n items of a collection

5. first - Takes the first item of a collection

6. remove - Predicated-removes items of a collection

7. concat - Concatenates two collections

8. flatten - Flattens a collection with subcollections

9. distinct - Returns only distinct items

10. split - Predicated-splits a collection

11. split_at - Splits a collection at a given item

12. group_by - Groups items by group

13. pairwise - Pairs off adjacent items

14. partial - Partially-applies a function

15. curry - Curries a function

16. compose - Composes functions

17. complement - Complements a predicate

18. all_fn - "all" with predicate

funcy is a library that provides functional tools. Examples are:

1. merge - Merges collections of the same type

2. walk - Type-preserving map

3. select - Selects a part of a collection

4. take - Takes the first n items of a collection

5. first - Takes the first item of a collection

6. remove - Predicated-removes items of a collection

7. concat - Concatenates two collections

8. flatten - Flattens a collection with subcollections

9. distinct - Returns only distinct items

10. split - Predicated-splits a collection

11. split_at - Splits a collection at a given item

12. group_by - Groups items by group

13. pairwise - Pairs off adjacent items

14. partial - Partially-applies a function

15. curry - Curries a function

16. compose - Composes functions

17. complement - Complements a predicate

18. all_fn - "all" with predicate

The PyPNG module implements support for PNG images. It reads and writes PNG files with all allowable bit depths (1/2/4/8/16/24/32/48/64 bits per pixel) and colour combinations: greyscale (1/2/4/8/16 bit); RGB, RGBA, LA (greyscale with alpha) with 8/16 bits per channel; colour mapped images (1/2/4/8 bit). Adam7 interlacing is supported for reading and writing. A number of optional chunks can be specified (when writing) and understood (when reading): tRNS, bKGD, gAMA.

PyPNG is not a high level toolkit for image processing (like PIL) and does not aim at being a replacement or competitor. Its strength lies in fine-grained extensive support of PNG features. It can also read and write Netpbm PAM files, with a focus on its use as an intermediate format for implementing custom PNG processing.