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Library of web access log analysis

Project description

.. raw:: html

<p align="center">
<img alt="lala Logo" title="lala Logo" src="https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/lala/master/docs/_static/images/logo.png" width="300">
<br /><br />
</p>

.. image:: https://travis-ci.org/Edinburgh-Genome-Foundry/lala.svg?branch=master
:target: https://travis-ci.org/Edinburgh-Genome-Foundry/lala
:alt: Travis CI build status

Lala is a Python library for access log analysis (right now it only supports NGINX standard logs). It provides a set of methods to retrieve, parse and analyze access logs, in particular for plotting geo-localization or time-series data. Think of it as a poor nam's Google Analytics, that can help you vizualise the requests to your server.

Installation
-------------

(Soon) You can install lala through PIP

.. code::

sudo pip install python-lala

Alternatively, you can unzip the sources in a folder and type

.. code::

sudo python setup.py install

Usage
-----

.. code:: python

import lala

with open('access_logs.txt'), 'r') as f:
entries, errored_lines = lala.logs_lines_to_dataframe(f)

Similarly, to fetch logs on a distant server (for which you have access keys)
you would write:

.. code:: python

logs= lala.get_remote_file_content(
host="cuba.genomefoundry.org", user='root',
filename='/var/log/nginx_cuba/access.log'
)
entries, errors = lala.logs_lines_to_dataframe(logs.split('\n'))

Now ``entries`` is a `Pandas <https://pandas.pydata.org/>`_ dataframe where each row is one server access, with fields such as ``IP``, ``date``, ``referrer``, ``country_name``, etc. The data can be analyzed using Pandas' built-in filtering and plotting functions.

For practicality, Lala provides a few methods, such as a pie-chart plotter::

.. code:: python

ax, country_values = lala.plot_piechart(entries, 'country_name')

.. raw:: html

<p align="center">
<img src="https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/Proglog/master/examples/basic_example_piechart.png" width="500">
</p>

Next we plot the location (cities) providing the most connexions:

.. code:: python

ax = lala.countries_colormap(country_values.iteritems(), figsize=(12, 8))
lala.plot_geo_positions(entries, ax)

.. raw:: html

<p align="center">
<img src="https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/Proglog/master/examples/basic_example_worldmap.png" width="500">
</p>

Finally, we restrict the entries to the UK, and we plot a timeline of connexions:

.. code:: python

uk_entries = entries[entries.country_name == 'United Kingdom']
ax = lala.plot_entries_in_time(uk_entries, bins_per_day=2)

.. raw:: html

<p align="center">
<img src="https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/Proglog/master/examples/basic_example_timeline.png" width="500">
</p>


License = MIT
--------------

lala is an open-source software originally written at the `Edinburgh Genome Foundry <http://genomefoundry.org>`_ by `Zulko <https://github.com/Zulko>`_ and `released on Github <https://github.com/Edinburgh-Genome-Foundry/lala>`_ under the MIT licence (¢ Edinburg Genome Foundry).

Everyone is welcome to contribute !

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