================
Plotting outputs
================

You should now have a ``results.csv.bz2`` file in the ``output`` directory,
which contains the results of four *model runs* of the outbreak of the
lurgy that was seeded in London.

You can plot graphs of the result using the
:doc:`metawards-plot <../../metawards_plot_help>` command.
To run this, type;

.. code-block:: bash

   metawards-plot --input output/results.csv.bz2

Understanding the graphs
------------------------

This will create two sets of graphs;

* ``output/overview.pdf``
    This is an overview of the **E**, **I**, **IW** and **R** values
    from each day of the model outbreak for each of the four *model runs*.
    Your graph should look something like this;

.. image:: ../../images/tutorial_1_3_overview.jpg
   :alt: Overview image of the outbreak of the lurgy

* ``output/average.pdf``
    This shows the average of the **E**, **I**, **IW** and **R** values,
    with the standard deviation shown as the error bars. Your graph should
    look something like this;

.. image:: ../../images/tutorial_1_3_average.jpg
   :alt: Overview image of the outbreak of the lurgy

The **E** graph shows the total number of latent infections. It should be
slightly ahead of and of a similar shape to the **I** graph, which shows
the total number of infections.

The **IW** graph shows the number of wards with at least one infection.
This cannot grow to more than the total number of wards, hence why
you see this graph topping out at ~8588, as this is the number of wards.

The **R** graph shows the number of individuals removed from the epidemic
(e.g. as they may have recovered). This should have an "S" shape,
showing exponential growth in the initial stage of the epidemic that
tails off as the number of individuals susceptible to infection is
reduced (e.g. as immunity in the population is built up).

.. note::

    Your graphs may look a little different in the exact numbers, but should
    be similar in shape. The purpose of this type of modelling is not to
    make exact numerical predictions, but to instead understand trends
    and timelines.

Jupyter notebooks
-----------------

The ``metawards-plot`` command can be used for quick-and-simple plots.
If you want something more complex then you can take a look at the functions
in the `metawards.analysis` module. In addition, we have a
:download:`Jupyter notebook <../../notebooks/1_3_plotting.ipynb>`
which you can look at which breaks down exactly how ``metawards-plot``
uses pandas and matplotlib to render these two graphs.