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.
FTP client and server for asyncio (Python 3) Library implementing FTP protocol, both client and server for Python asyncio module.
Supported commands as client: USER, PASS, ACCT, PWD, CWD, CDUP, MKD, RMD, MLSD, MLST, RNFR, RNTO, DELE, STOR, APPE, RETR, TYPE, PASV, ABOR, QUIT, REST, LIST (as fallback).
Supported commands as server: USER, PASS, QUIT, PWD, CWD, CDUP, MKD, RMD, MLSD, LIST (non-standard), MLST, RNFR, RNTO, DELE, STOR, RETR, TYPE ("I" and "A"), PASV, ABOR, APPE, REST.
folium makes it easy to visualize data that’s been manipulated in Python on an interactive leaflet map. It enables both the binding of data to a map for choropleth visualizations as well as passing rich vector/raster/HTML visualizations as markers on the map.
The library has a number of built-in tilesets from OpenStreetMap, Mapbox, and Stamen, and supports custom tilesets with Mapbox or Cloudmade API keys. It supports Image, Video, GeoJSON and TopoJSON overlays.
The Scooby package reports the following information about the currently running system:
operating system name;
hardware information including machine type (e.g., i386, x86_64, etc.), CPU count and total RAM;
Python environment (e.g., Python, IPython, etc.);
file system name;
Python version;
versions of specified Python packages.
It can generate reports as HTML tables or plain text lists.
Scooby has no required dependencies, and only few optional dependencies.
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.
This module implements the password-based key derivation function, PBKDF2, specified in RSA PKCS#5 v2.0.
PKCS#5 v2.0 Password-Based Key Derivation is a key derivation function which is part of the RSA Public Key Cryptography Standards series. The provided implementation takes a password or a passphrase and a salt value (and optionally a iteration count, a digest module, and a MAC module) and provides a file-like object from which an arbitrarily-sized key can be read.
Defcon is a set of UFO based objects optimized for use in font editing applications. The objects are built to be lightweight, fast and flexible. The objects are very bare-bones and they are not meant to be end-all, be-all objects. Rather, they are meant to provide base functionality so that you can focus on your application’s behavior, not object observing or maintaining cached data. Defcon implements UFO3 as described by the UFO font format.
ndcube is a package for manipulating, inspecting and visualizing multi-dimensional contiguous and non-contiguous coordinate-aware data arrays.
It combines data, uncertainties, units, metadata, masking, and coordinate transformations into classes with unified slicing and generic coordinate transformations and plotting/animation capabilities. It is designed to handle data of any number of dimensions and axis types (e.g. spatial, temporal, spectral, etc.) whose relationship between the array elements and the real world can be described by WCS translations.
paramz is a lightweight parameterization framework for parameterized model creation and handling. Its features include:
Easy model creation with parameters.
Fast optimized access of parameters for optimization routines.
Memory efficient storage of parameters (only one copy in memory).
Renaming of parameters.
Intuitive printing of models and parameters.
Gradient saving directly inside parameters.
Gradient checking of parameters.
Optimization of parameters.
Jupyter notebook integration.
Efficient storage of models, for reloading.
Efficient caching.
Falcon is a web API framework for building microservices, application backends and higher-level frameworks. Among its features are:
Optimized and extensible code base
Routing via URI templates and REST-inspired resource classes
Access to headers and bodies through request and response classes
Request processing via middleware components and hooks
Idiomatic HTTP error responses
Straightforward exception handling
Unit testing support through WSGI helpers and mocks
Compatible with both CPython and PyPy
Cython support for better performance when used with CPython
PyPDF2 is a pure Python PDF library capable of:
extracting document information (title, author, …)
splitting documents page by page
merging documents page by page
cropping pages
merging multiple pages into a single page
encrypting and decrypting PDF files
By being pure Python, it should run on any Python platform without any dependencies on external libraries. It can also work entirely on StringIO objects rather than file streams, allowing for PDF manipulation in memory. It is therefore a useful tool for websites that manage or manipulate PDFs.
This is a Python implementation of the zxcvbn library created at Dropbox. The original library, written for JavaScript, can be found here. This port includes features such as:
Accepts user data to be added to the dictionaries that are tested against (name, birthdate, etc.)
Gives a score to the password, from 0 (terrible) to 4 (great).
Provides feedback on the password and ways to improve it.
Returns time estimates on how long it would take to guess the password in different situations.
Blosc2 is a high performance compressor optimized for binary data. It has been designed to transmit data to the processor cache faster than the traditional, non-compressed, direct memory fetch approach via a memcpy() system call.
Python-Blosc2 wraps the C-Blosc2 library, and it aims to leverage its new API so as to support super-chunks, multi-dimensional arrays, serialization and other features introduced in C-Blosc2.
Python-Blosc2 also reproduces the API of Python-Blosc and is meant to be able to access its data, so it can be used as a drop-in replacement.
PyEGA3 is a tool for viewing and downloading files from authorized EGA datasets. It uses the EGA data API and has several key features:
Files are transferred over secure https connections and received unencrypted, so no need for decryption after download.
Downloads resume from where they left off in the event that the connection is interrupted.
Supports file segmenting and parallelized download of segments, improving overall performance.
After download completes, file integrity is verified using checksums.
Implements the GA4GH-compliant htsget protocol for download of genomic ranges for data files with accompanying index files.
Locust is a performance testing tool that aims to be easy to use, scriptable and scalable. The test scenarios are described in plain Python. It provides a web-based user interface to visualize the results in real-time, but can also be run non-interactively. Locust is primarily geared toward testing HTTP-based applications or services, but it can be customized to test any system or protocol.
Note: Locust will complain if the available open file descriptors limit for the user is too low. To raise such limit on a Guix System, refer to info guix --index-search=pam-limits-service-type.
ikarus is a stepwise machine learning pipeline that tries to cope with a task of distinguishing tumor cells from normal cells. Leveraging multiple annotated single cell datasets it can be used to define a gene set specific to tumor cells. First, the latter gene set is used to rank cells and then to train a logistic classifier for the robust classification of tumor and normal cells. Finally, sensitivity is increased by propagating the cell labels based on a custom cell-cell network. ikarus is tested on multiple single cell datasets to ascertain that it achieves high sensitivity and specificity in multiple experimental contexts.
Python-daemon is a library that assists a Python program to turn itself into a well-behaved Unix daemon process, as specified in PEP 3143.
This library provides a DaemonContext class that manages the following important tasks for becoming a daemon process:
Detach the process into its own process group.
Set process environment appropriate for running inside a chroot.
Renounce suid and sgid privileges.
Close all open file descriptors.
Change the working directory, uid, gid, and umask.
Set appropriate signal handlers.
Open new file descriptors for stdin, stdout, and stderr.
Manage a specified PID lock file.
Register cleanup functions for at-exit processing.
This package implements functionality for simulating X-ray emission from astrophysical sources.
X-rays probe the high-energy universe, from hot galaxy clusters to compact objects such as neutron stars and black holes and many interesting sources in between. pyXSIM makes it possible to generate synthetic X-ray observations of these sources from a wide variety of models, whether from grid-based simulation codes such as FLASH, Enzo, and Athena, to particle-based codes such as Gadget and AREPO, and even from datasets that have been created 'by hand', such as from NumPy arrays. pyXSIM also provides facilities for manipulating the synthetic observations it produces in various ways, as well as ways to export the simulated X-ray events to other software packages to simulate the end products of specific X-ray observatories.
This package implements sparse arrays of arbitrary dimension on top of numpy and scipy.sparse. Sparse array is a matrix in which most of the elements are zero. python-sparse generalizes the scipy.sparse.coo_matrix and scipy.sparse.dok_matrix layouts, but extends beyond just rows and columns to an arbitrary number of dimensions. Additionally, this project maintains compatibility with the numpy.ndarray interface rather than the numpy.matrix interface used in scipy.sparse. These differences make this project useful in certain situations where scipy.sparse matrices are not well suited, but it should not be considered a full replacement. It lacks layouts that are not easily generalized like compressed sparse row/column(CSR/CSC) and depends on scipy.sparse for some computations.
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Python inotify.
Video editing with Python
Implementation of semantic version
Ed25519 public-key signatures