https://github.com/jerry-git/logplot

Awesome interactive plots from custom logs
https://github.com/jerry-git/logplot

logging matplotlib python3 visualization

Last synced: 8 days ago
JSON representation

Awesome interactive plots from custom logs

• Host: GitHub
• URL: https://github.com/jerry-git/logplot
• Owner: jerry-git
• License: mit
• Created: 2018-09-08T19:39:50.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-10-27T14:06:50.000Z (over 6 years ago)
• Last Synced: 2025-01-19T23:36:37.189Z (13 days ago)
• Topics: logging, matplotlib, python3, visualization
• Language: Python
• Homepage:
• Size: 23.6 MB
• Stars: 27
• Watchers: 4
• Forks: 3
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • Changelog: ChangeLog.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# LogPlot

[![Build Status](https://travis-ci.org/jerry-git/logplot.svg?branch=master)](https://travis-ci.org/jerry-git/logplot)

[![PyPI version](https://badge.fury.io/py/logplot.svg)](https://pypi.python.org/pypi/logplot/)

[![PyPI pyversions](https://img.shields.io/pypi/pyversions/logplot.svg)](https://pypi.python.org/pypi/logplot/)

[![codecov](https://codecov.io/gh/jerry-git/logplot/branch/master/graph/badge.svg)](https://codecov.io/gh/jerry-git/logplot)

[![license](https://img.shields.io/github/license/jerry-git/logplot.svg)](https://github.com/jerry-git/logplot/blob/master/LICENSE)

[![PRs](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)



  



## Installation

    $ pip3 install logplot

    

## Usage

### Configuration

In order to plot interesting graphs from custom logs, `logplot` needs to know what log entries are considered interesting. 

This can be done by defining `entries` and `special_entries` sections in the config file. 

The main purpose of `entries` is to plot trends, i.e. connected points. This is useful e.g. for plotting state machine states. 

`special_entries` is for plotting single, unconnected points, such as errors.

Each entry in `entries` and `special_entries` should have the following:

* `identifier`: string to be searched from log entries

* `value`: y-axis value in the plot

Ideally, one entry in `entries` should have `initial_state: true`. This is used for separating plotted series from each other.

You can define specific marker style for each entry in `special_entries`, refer to [`matplotlib` docs](https://matplotlib.org/api/markers_api.html) for available options.

In addition to the `entries` and `special-entries` sections, user can override default settings (see src/logplot/default_conf.yaml) in `general` section. 

For example, it's the place where you can define the command to be used for opening the log in your favorite editor. 

   

See doc/example/user_conf.yaml and corresponding log file (doc/example/log.txt) for full example.

### Plotting logs

    $ logplot path/to/log -c path/to/conf/file

    

For example, this:

    $ logplot doc/examples/log.txt -c doc/examples/user_conf.yaml

yields this:



  



Clicking a data point in the plot opens the log file in the correct line number in the editor defined in the configuration file.

If the `log_open_cmd` is not configured, the log will be opened with a default program defined by the OS. 

Note that `log_open_cmd` has to configured to be able to open the log in the correct line number.

`logplot` will automatically identify different trends in the plotted entries. 

You can toggle the visibility of these trends by clicking items in the legend.

Here's an example of a bit bigger log which contains 100k entries, 13 different states, and 15 different trends.

	$ logplot doc/examples/log_huge.txt -c doc/examples/user_conf_huge.yaml



  



And the same with three most occuring trends filtered out.