Display, filter and search log messages in your terminal

Overview

Textualog

Display, filter and search logging messages in the terminal.

screenshot

This project is powered by rich and textual.

Some of the ideas and code in this project are based on:

Installation

The easiest way to install the package is by running the pip command in the Python virtual environment of your project:

$ python -m pip install [--upgrade] textualog

Usage

The textualog app should have been installed in your environment, then run the following command:

$ textualog --log <path to the log file>

In the examples directory of this project, you can find an example log file to inspect and play with.

The main view is divided in three panels, (1) a Records panel that displays all the logging records in a colored view, (2) a Record Info panel that displays more details about the selected logging message (a message can be selected by a mouse click), and (3) a Levels panel that displays the standard logging levels. Logging levels can be switched on or off with a key press, d=debug, i=info, w=warning, e=error, c=critical. When you click inside the Record Info panel, the main view will change in a Record Details view that displays all information associated with the selected logging message. This view is mainly used when the logging message has extra multi-line information attached, and depending on the amount of information, this view is scrollable. When the selected logging message contains extra information, the Record Info panel will have an asterisk in the title. Use the Escape key to return to the main view.

The app can be terminated with the 'q' key or by pressing CTRL-C. If you need a little help on the keyboard shortcuts, press the '?' key to present the Info Help panel on the right side of the terminal. Also here use the Escape key to hide the help panel again.

Pressing the 'n' key will slide in a Namespaces panel on the left side of the Terminal. This panel is currently not functional. The idea is to allow the user to filter the logging messages by selecting one or more namespaces.

Log file formats

The current support is for a key-value type of log file. The log line shall have a fixed format, which is what I currently use in my main other projects. The following key=value pairs shall be there in the given order:

  • level=<logging level>
  • ts=<'%Y-%m-%dT%H:%M:%S,%f'>
  • process=<process name>
  • process_id=<PID>
  • caller=<calling function:lineno>
  • msg=<logging message>

In the future other formats can be supported by implementing a plugin class. Planned formats are the JSON format, ...

Roadmap

  • Display message details including extra lines that contain further information like e.g. traceback info.
  • Implement search functionality to search for strings or regular expressions and position the screen at the first match
  • Start work on filtering log messages based on their namespace
Owner
Rik Huygen
Self-educated Pythonista. Seriously trying to write clean and Pythonic code.
Rik Huygen
Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks

Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks Contributions A novel pairwise feature LSP to extract structural

31 Dec 06, 2022
Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras

Face Mask Detection Face Mask Detection System built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to detect

Chandrika Deb 1.4k Jan 03, 2023
Deploy recommendation engines with Edge Computing

RecoEdge: Bringing Recommendations to the Edge A one stop solution to build your recommendation models, train them and, deploy them in a privacy prese

NimbleEdge 131 Jan 02, 2023
PyTorch implementation of SampleRNN: An Unconditional End-to-End Neural Audio Generation Model

samplernn-pytorch A PyTorch implementation of SampleRNN: An Unconditional End-to-End Neural Audio Generation Model. It's based on the reference implem

DeepSound 261 Dec 14, 2022
An implementation of Geoffrey Hinton's paper "How to represent part-whole hierarchies in a neural network" in Pytorch.

GLOM An implementation of Geoffrey Hinton's paper "How to represent part-whole hierarchies in a neural network" for MNIST Dataset. To understand this

50 Oct 19, 2022
Matplotlib Image labeller for classifying images

mpl-image-labeller Use Matplotlib to label images for classification. Works anywhere Matplotlib does - from the notebook to a standalone gui! For more

Ian Hunt-Isaak 5 Sep 24, 2022
Improving Factual Completeness and Consistency of Image-to-text Radiology Report Generation

Improving Factual Completeness and Consistency of Image-to-text Radiology Report Generation The reference code of Improving Factual Completeness and C

46 Dec 15, 2022
Code Impementation for "Mold into a Graph: Efficient Bayesian Optimization over Mixed Spaces"

Code Impementation for "Mold into a Graph: Efficient Bayesian Optimization over Mixed Spaces" This repo contains the implementation of GEBO algorithm.

Jaeyeon Ahn 2 Mar 22, 2022
Circuit Training: An open-source framework for generating chip floor plans with distributed deep reinforcement learning

Circuit Training: An open-source framework for generating chip floor plans with distributed deep reinforcement learning. Circuit Training is an open-s

Google Research 479 Dec 25, 2022
The repository includes the code for training cell counting applications. (Keras + Tensorflow)

cell_counting_v2 The repository includes the code for training cell counting applications. (Keras + Tensorflow) Dataset can be downloaded here : http:

Weidi 113 Oct 06, 2022
Deploy a ML inference service on a budget in less than 10 lines of code.

BudgetML is perfect for practitioners who would like to quickly deploy their models to an endpoint, but not waste a lot of time, money, and effort trying to figure out how to do this end-to-end.

1.3k Dec 25, 2022
An attempt at the implementation of Glom, Geoffrey Hinton's new idea that integrates neural fields, predictive coding, top-down-bottom-up, and attention (consensus between columns)

GLOM - Pytorch (wip) An attempt at the implementation of Glom, Geoffrey Hinton's new idea that integrates neural fields, predictive coding,

Phil Wang 173 Dec 14, 2022
Official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR)

This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.

12 Jan 13, 2022
You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling

You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling Transformer-based models are widely used in natural language processi

Zhanpeng Zeng 12 Jan 01, 2023
Code and data for ACL2021 paper Cross-Lingual Abstractive Summarization with Limited Parallel Resources.

Multi-Task Framework for Cross-Lingual Abstractive Summarization (MCLAS) The code for ACL2021 paper Cross-Lingual Abstractive Summarization with Limit

Yu Bai 43 Nov 07, 2022
Multiple paper open-source codes of the Microsoft Research Asia DKI group

📫 Paper Code Collection (MSRA DKI Group) This repo hosts multiple open-source codes of the Microsoft Research Asia DKI Group. You could find the corr

Microsoft 249 Jan 08, 2023
Machine Learning Framework for Operating Systems - Brings ML to Linux kernel

KML: A Machine Learning Framework for Operating Systems & Storage Systems Storage systems and their OS components are designed to accommodate a wide v

File systems and Storage Lab (FSL) 186 Nov 24, 2022
Evaluating different engineering tricks that make RL work

Reinforcement Learning Tricks, Index This repository contains the code for the paper "Distilling Reinforcement Learning Tricks for Video Games". Short

Anssi 15 Dec 26, 2022
Autoencoder - Reducing the Dimensionality of Data with Neural Network

autoencoder Implementation of the Reducing the Dimensionality of Data with Neural Network – G. E. Hinton and R. R. Salakhutdinov paper. Notes Aim to m

Jordan Burgess 13 Nov 17, 2022