Month: July 2021

Astropy

Python library for ODE integration via Taylor’s method and LLVM

heyoka.py heyoka.py is a Python library for the integration of ordinary differential equations (ODEs) via Taylor’s method. Notable features include: support for both double-precision and extended-precision floating-point types (80-bit and 128-bit), the ability to maintain machine precision accuracy over tens of billions of timesteps, high-precision zero-cost dense output, accurate and reliable event detection, batch mode integration to harness the power of modern SIMD instruction sets, interoperability with SymPy, a high-performance implementation of Taylor’s method based on automatic differentiation techniques and […]

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Automation

Scientific measurement library for instruments, experiments, and live-plotting

PyMeasure scientific package PyMeasure makes scientific measurements easy to set up and run. The package contains a repository of instrument classes and a system for running experiment procedures, which provides graphical interfaces for graphing live data and managing queues of experiments. Both parts of the package are independent, and when combined provide all the necessary requirements for advanced measurements with only limited coding. PyMeasure is currently under active development, so please report any issues you experience to our Issues page. […]

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Automation

A Tool for automatically reordering python imports

reorder_python_imports Rewrites source to reorder python imports. Tool for automatically reordering python imports. Similar to isort but uses static analysis more. Installation pip install reorder-python-imports Console scripts Consult reorder-python-imports –help for the full set of options. reorder-python-imports takes filenames as positional arguments Common options: –py##-plus: see below. –add-import / –remove-import: see below. –replace-import: see below. –application-directories: by default, reorder-python-imports assumesyour project is rooted at .. If this isn’t true, tell it where yourimport roots live. For example, when using the […]

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Automation

Fully automatic light management based on conditions like motion

ad-automoli Fully automatic light management based on conditions like motion, illuminance, humidity, and other clever features. Fully automatic light management based on motion as AppDaemon app. 🕓 multiple daytimes to define different scenes for morning, noon, …💡 supports Hue (for Hue Rooms/Groups) & Home Assistant scenes🔌 switches lights and plugs (with lights)☀️ supports illumination sensors to switch the light just if needed💦 supports humidity sensors as blocker (the “shower case“)🔍 automatic discovery of lights and sensors⛰️ stable and tested by […]

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Bot

Telegram Userbot built with Pyrogram

Pyrogram Userbot A Telegram Userbot based on Pyrogram This repository contains the source code of a Telegram Userbot and the instructions for running a copy yourself. Beside its main purpose, the bot is featuring Pyrogram Asyncio and Smart Plugins; feel free to explore the source code to learn more about these topics. I assume you will read this whole README.md file before continuing. Development in progress. Requirements You’re gonna need to get the following programs and services either installed on […]

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Machine Learning

Python package for covariance matrices manipulation and Biosignal classification

pyRiemann pyRiemann is a python package for covariance matrices manipulation and classification through Riemannian geometry. The primary target is classification of multivariate biosignals, like EEG, MEG or EMG. This is work in progress … stay tuned. This code is BSD-licenced (3 clause). Documentation The documentation is available on http://pyriemann.readthedocs.io/en/latest/ Install Using PyPI pip install pyriemann or using pip+git for the latest version of the code : pip install git+https://github.com/pyRiemann/pyRiemann Anaconda is not currently supported, if you want to use anaconda, […]

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Neural Machine Translation (NMT), NMT Leave a comment

Machine Translation Weekly 86: The Wisdom of the WMT Crowd

Most of the papers that I comment on and review here present novel and cool ideas on how to improve something in machine translation or multilingual NLP. On the other hand, the WMT submissions are different. People want to get the best translation quality and value efficiency, and simplicity. Novelty and prettiness of the ideas are secondary. WMT organizes annual competitions in machine translation quality (and other tasks related to machine translation) where dozens of companies and universities participate. Each […]

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Deep Learning

Advanced Deep Learning with TensorFlow 2 and Keras

dvanced-Deep-Learning-with-Keras This is the code repository for Advanced Deep Learning with TensoFlow 2 and Keras, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish. Please note that the code examples have been updated to support TensorFlow 2.0 Keras API only. About the Book Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available […]

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Book

Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide

Deep Learning with PyTorch Step-by-Step This is the official repository of my book “Deep Learning with PyTorch Step-by-Step“. Here you will find one Jupyter notebook for every chapter in the book. Each notebook contains all the code shown in its corresponding chapter, and you should be able torun its cells in sequence to get the same outputs as shown in the book. I strongly believe that being able to reproduce the results brings confidence to the reader. There are three […]

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Matplotlib

Creates clean and beautiful plots that work on light and dark backgrounds

dufte This package creates clean and beautiful plots that work on light and dark backgrounds. Inspired by the work of Edward Tufte. To use, simply select the dufte style: import dufte import matplotlib.pyplot as plt # global setting: plt.style.use(dufte.style) # with a context manager: with plt.style.context(dufte.style_bar): # … pass Check out dufte.legend(), dufte.ylabel(), and dufte.show_bar_values() for more duftiness. Comparison with default Matplotlib See here for how to create the below plots. matplotlib dufte with dufte.legend() matplotlib dufte dufte with dufte.show_bar_values() […]

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