Metadata-Version: 2.1
Name: python-lala
Version: 0.1.4
Summary: Library of web access log analysis
Home-page: UNKNOWN
Author: Zulko
License: MIT
Description: .. image:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/lala/master/docs/_static/images/logo.png
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        .. 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
        
        .. image:: https://coveralls.io/repos/github/Edinburgh-Genome-Foundry/lala/badge.svg?branch=master
           :target: https://coveralls.io/github/Edinburgh-Genome-Foundry/lala?branch=master
        
        
        Lala is a Python library for access log analysis. It provides a set of methods to retrieve, parse and analyze access logs (only from NGINX for now), and makes it easy to plot geo-localization or time-series data. Think of it as a simpler, Python-automatable version of Google Analytics, to make reports like this:
        
        .. image:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/lala/master/docs/_static/images/report.jpeg
           :width: 550 px
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        Usage
        -----
        
        .. code:: python
        
            from lala import WebLogs
            weblogs, errored_lines = WebLogs.from_nginx_weblogs('access_logs.txt')
        
        Similarly, to fetch logs on a distant server (for which you have access keys)
        you would write:
        
        .. code:: python
        
            from lala import get_remote_file_content, WebLogs
        
            logs= lala.get_remote_file_content(
                host="cuba.genomefoundry.org", user='root',
                filename='/var/log/nginx_cuba/access.log'
            )
            weblogs, errors = WebLogs.from_nginx_weblogs(logs.split('\n'))
        
        Now ``weblogs`` is a scpecial kind of `Pandas <https://pandas.pydata.org/>`_ dataframe where each row is one server access, with fields such as ``IP``, ``date``, ``referrer``, ``country_name``, etc.
        
        .. image:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/lala/master/docs/_static/images/dataframe_example.png
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        The web logs can therefore be analyzed using any of Pandas' built-in filtering and plotting functions. The ``WebLogs`` class also provides additional methods which are particularly useful to analyse web logs, for instance to plot pie-charts:
        
        .. code:: python
        
            ax, country_values = weblogs.plot_piechart('country_name')
        
        .. image:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/lala/master/examples/basic_example_piechart.png
           :width: 300 px
           :alt: alternate text
           :align: center
        
        Next we plot the location (cities) providing the most connexions:
        
        .. code:: python
        
            ax = weblogs.plot_geo_positions()
        
        .. image:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/lala/master/examples/basic_example_worldmap.png
           :width: 700 px
           :alt: alternate text
           :align: center
        
        We can also restrict the entries to the UK, and plot a timeline of connexions:
        
        .. code:: python
        
            uk_entries = weblogs[weblogs.country_name == 'United Kingdom']
            ax = uk_entries.plot_timeline(bins_per_day=2)
        
        .. image:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/lala/master/examples/basic_example_timeline.png
           :width: 700 px
           :alt: alternate text
           :align: center
        
        Here is how to get the visitors a list of visitors and visits, sort out the most frequent visitors, find their locations, and plot it all:
        
        .. code:: python
        
            visitors = weblogs.visitors_and_visits()
            visitors_locations = weblogs.visitors_locations()
            frequent_visitors = weblogs.most_frequent_visitors(n_visitors=5)
            ax = weblogs.plot_most_frequent_visitors(n_visitors=5)
        
        .. image:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/lala/master/examples/basic_example_frequent_visitors.png
           :width: 450 px
           :alt: alternate text
           :align: center
        
        Lala can do more, such as identifying the domain name of the visitors, which can be used to filter out the robots of search engines:
        
        
        .. code:: python
        
            weblogs.identify_ips_domains()
            filtered_entries = weblogs.filter_by_text_search(
                terms=['googlebot', 'spider.yandex', 'baidu', 'msnbot'],
                not_in='domain'
            )
        
        Lala also plays nicely with the `PDF Reports <https://github.com/Edinburgh-Genome-Foundry/pdf_reports>`_ library to let you define report templates such as `this one <https://github.com/Edinburgh-Genome-Foundry/lala/blob/master/examples/data/example_template.pug>`_ (written in Pug), and then generate `this PDF report <https://github.com/Edinburgh-Genome-Foundry/lala/blob/master/examples/report_example.pdf>`_ with the following code:
        
        .. code:: python
        
            weblogs.write_report(template_path="path/to/template.pug",
                                 target="report_example.pdf")
        
        Installation
        -------------
        
        You can install lala through PIP
        
        .. code:: bash
        
            sudo pip install python-lala
        
        Alternatively, you can unzip the sources in a folder and type
        
        .. code:: bash
        
            sudo python setup.py install
        
        For plotting maps you will need Cartopy which is not always easy to install - it may depend on your system. If you are on Ubuntu 16+, first install the dependencies with:
        
        .. code:: bash
        
            sudo apt-get install libproj-dev proj-bin proj-data libgeos-dev
            sudo pip install cython
        
        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 (Copyright 2018 Edinburgh Genome Foundry).
        
        Everyone is welcome to contribute!
        
Keywords: access log analysis website webservice stats
Platform: UNKNOWN
Description-Content-Type: text/x-rst
