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.

PyRuSH is the python implementation of RuSH, which is originally developed using Java. RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and eliminates the effect of rule order on accuracy.

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.

Cesium is an end-to-end machine learning platform for time-series, from calculation of features to model-building to predictions. Cesium has two main components - a Python library, and a web application platform that allows interactive exploration of machine learning pipelines. Take control over the workflow in a Python terminal or Jupyter notebook with the Cesium library, or upload your time-series files, select your machine learning model, and watch Cesium do feature extraction and evaluation right in your browser with the web application.

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.

AlgoPy provides a functionality to differentiate functions implemented as computer programs by using Algorithmic Differentiation (AD) techniques in the forward and reverse mode.

The forward mode propagates univariate Taylor polynomials of arbitrary order. Hence it is also possible to use AlgoPy to evaluate higher-order derivative tensors. The reverse mode is also known as backpropagation and can be found in similar form in tools like PyTorch. Speciality of AlgoPy is the possibility to differentiate functions that contain matrix functions as +,-,*,/, dot, solve, qr, eigh, cholesky.

Conflict-free Replicated Data Types (CRDTs) allow creating shared documents that can automatically merge changes made concurrently on different copies of the data. When the data lives on different machines, they make it possible to build distributed systems that work with local data, leaving the synchronization and conflict resolution with remote data to the CRDT algorithm, which ensures that all data replicas eventually converge to the same state.

This is a Python CRDT library that provides bindings for Yrs, the Rust port of the Yjs framework.

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

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:

1. Accepts user data to be added to the dictionaries that are tested against (name, birthdate, etc.)

2. Gives a score to the password, from 0 (terrible) to 4 (great).

3. Provides feedback on the password and ways to improve it.

4. 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.

This package provides methods for simulation and gradient-based parameter estimation in the context of geophysical applications.

The vision is to create a package for finite volume simulation with applications to geophysical imaging and subsurface flow. To enable the understanding of the many different components, this package has the following features:

• modular with respect to the spacial discretization, optimization routine, and geophysical problem

• built with the inverse problem in mind

• provides a framework for geophysical and hydrogeologic problems

• supports 1D, 2D and 3D problems

• designed for large-scale inversions

PyBVRF is a Python package for working with BVRF files.

The package includes the following features:

• Support for multi-participant recordings

• Seamless integration with MNE-Python

• Convenient access to metadata (including the original YAML header)

• Support for markers and impedance data

A BVRF recording consists of multiple files which are expected to be available in the same directory. The required files are:

• <fname>.bvrh (header file)

• <fname>.bvrd (data file)

• <fname>.bvrm (marker file)

Optionally, <fname>.bvri (impedance file) may also be present.

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:

1. Detach the process into its own process group.

2. Set process environment appropriate for running inside a chroot.

3. Renounce suid and sgid privileges.

4. Close all open file descriptors.

5. Change the working directory, uid, gid, and umask.

6. Set appropriate signal handlers.

7. Open new file descriptors for stdin, stdout, and stderr.

8. Manage a specified PID lock file.

9. Register cleanup functions for at-exit processing.

This package provides NASA's Coordinated Data Analysis System Web Service Client Library.

The Coordinated Data Analysis System (CDAS) supports simultaneous multi-mission, multi-instrument selection and comparison of science data among a wide range of current space missions. While CDAWeb provides access to this data through an HTML-based user interface, these Web services provides a (Web) application programmming interface (API) to CDAS. If you are not a software developer and simply want to use the existing web (HTML) interface to CDAS, then return to the main CDAWeb page. If you are developing software that requires science data from any of the CDAWeb datasets, then the CDAS Web services will provide access to the data without having to explicitly find,download, and read the data files.

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.

Aurora is an open-source package that robustly estimates single station and remote reference electromagnetic TFs from MT time series. Aurora is part of an open-source processing workflow that leverages the self-describing data container MTH5, which in turn leverages the general mt-metadata framework to manage metadata. These pre-existing packages simplify the processing by providing managed data structures, transfer functions to be generated with only a few lines of code. The processing depends on two inputs -- a table defining the data to use for TF estimation, and a JSON file specifying the processing parameters, both of which are generated automatically, and can be modified if desired. Output TFs are returned as mt-metadata objects, and can be exported to a variety of common formats for plotting, modeling and inversion.

pynose is a maintained successor of deprecated nose unittest runner. Changes over nose:

• fixes AttributeError: module 'collections' has no attribute 'Callable'

• fixes AttributeError: module 'inspect' has no attribute 'getargspec'

• fixes ImportError: cannot import name '_TextTestResult' from 'unittest'

• fixes RuntimeWarning: TestResult has no addDuration method

• fixes DeprecationWarning: pkg_resources is deprecated as an API

• fixes all flake8 issues from the original nose

• replaces the imp module with the newer importlib module

• the default logging level now hides INFO logs for less noise

• adds --capture-logs for hiding output from all logging levels

• adds --logging-init to use logging.basicConfig(level)

• the -s option is always active to see the output of print()

• adds --capture-output for hiding the output of print()

• adds --co as a shortcut to using --collect-only

Python driver for MongoDB.

Video editing with Python

Documentation at https://melpa.org/#/pythonic