Nipype provides a uniform interface to existing neuroimaging software and facilitates interaction between these packages within a single workflow. Nipype provides an environment that encourages interactive exploration of algorithms from different packages.
Convert GeoJSON to WKT/WKB (Well-Known Text/Binary) or GeoPackage Binary, and vice versa. Extended WKB/WKT are also supported. Conversion functions are exposed through idiomatic load/loads/dump/dumps interfaces.
Plotly's Python graphing library makes interactive, publication-quality graphs online. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts.
NumPy is the fundamental package for scientific computing with Python. It contains among other things: a powerful N-dimensional array object, sophisticated (broadcasting) functions, tools for integrating C/C++ and Fortran code, useful linear algebra, Fourier transform, and random number capabilities.
Pydbus provides a pythonic interface to the D-Bus message bus system. Pydbus can be used to access remote objects and also for object publication. It is based on PyGI, the Python GObject Introspection bindings, which is the recommended way to use GLib from Python.
The Python Imaging Library adds image processing capabilities to your Python interpreter. This library provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities. The core image library is designed for fast access to data stored in a few basic pixel formats. It should provide a solid foundation for a general image processing tool.
The Python Imaging Library adds image processing capabilities to your Python interpreter. This library provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities. The core image library is designed for fast access to data stored in a few basic pixel formats. It should provide a solid foundation for a general image processing tool.
NumPy is the fundamental package for scientific computing with Python. It contains among other things: a powerful N-dimensional array object, sophisticated (broadcasting) functions, tools for integrating C/C++ and Fortran code, useful linear algebra, Fourier transform, and random number capabilities. Version 1.8 is the last one to contain the numpy.oldnumeric API that includes the compatibility layer numpy.oldnumeric with NumPy's predecessor Numeric.
JAXopt provides hardware accelerated, batchable and differentiable optimizers in JAX.
Hardware accelerated: the implementations run on GPU and TPU, in addition to CPU.
Batchable: multiple instances of the same optimization problem can be automatically vectorized using JAX’s
vmap.Differentiable: optimization problem solutions can be differentiated with respect to their inputs either implicitly or via autodiff of unrolled algorithm iterations.
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.
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Python inotify.
Video editing with Python
Implementation of semantic version
Awesome List used GitHub stars.
Documentation at https://melpa.org/#/pythonic
Multidimensional data visualization across files.
Python client library for Core API.
Function decoration for backoff and retry
Python implementation of Jean Meeus astronomical routines
Python bindings for the remote Jenkins API.
Embedding of configuration information in Python code.
Python Gettext po to mo file compiler.
Utility functions for writing pythonic emacs package.