GA Custom with Multi-Point Type
------------

Another feature of `MGSurvE <https://github.com/Chipdelmal/MGSurvE>`_ is that it can handle multiple point-types 
(as we saw in our `sites and trap types tutorial <https://chipdelmal.github.io/MGSurvE/build/html/landscapeTraps.html>`_).

In this tutorial, we are going to optimize over a regular landscape with heterogeneous point-types.


Setting Landscape Up
~~~~~~~~~~~~~~~~~~~~~~

As we mentioned, we are going to use a regular grid for this landscape. We define it by running:

.. code-block:: python

    bside = math.sqrt(PTS_NUM)*11.25
    bbox = ((-bside, bside), (-bside, bside))
    xy = srv.ptsRegularGrid(int(math.sqrt(PTS_NUM)), bbox).T
    pType = np.random.choice(PTS_TYPE, xy.shape[1])
    points = pd.DataFrame({'x': xy[0], 'y': xy[1], 't': pType})

Where we assign a random point-type in the :code:`pType` variable. 

With out point-types setup, we can define the :code:`masking` matrix. 
This matrix is responsible for defining the probabilities of individuals moving from one point-type to another:

.. code-block:: python

    msk = [
        [0.100, 0.700, 0.200],
        [0.100, 0.100, 0.800],
        [0.750, 0.125, 0.125],
    ]

This means that from point-type 1, we have a set of 10%-70%-20% probabilities of landing in a point-type 1, 2, and 3 respectively.
The second row encodes the probabilities of landing in point-type 1, 2, and 3 respectively (starting from point-type 2); and so forth.

Now, with this and our traps, we are ready to setup our landscape as we usually do. The only difference being that we add our :code:`msk` to the landscape generation:


.. code-block:: python

    lnd = srv.Landscape(
        points, maskingMatrix=msk, traps=traps, trapsKernels=tker
    )


We can see the difference between a homogeneous and heterogeneous version of the landscape in the following images:


.. image:: ../../img/GA_DEMO_PT_HOM.jpg
    :width: 49%

.. image:: ../../img/GA_DEMO_PT_HET.jpg
    :width: 49%



Optimization and Results
~~~~~~~~~~~~~~~~~~~~~~

Now, the optimization part of the algorithm remains the same as in our previous examples.


Our results after running the algorithm for 500 generations are:

.. image:: ../../img/GA_DEMO_PT_HOM_TRP.jpg
    :width: 49%

.. image:: ../../img/GA_DEMO_PT_HET_TRP.jpg
    :width: 49%

where we can see the impact of encoding the point-type information into the landscape for optimization.

The full code for this demo can be found `here <https://github.com/Chipdelmal/MGSurvE/blob/main/MGSurvE/demos/Demo_pointTypes.py>`_ with the simplified version stored in `this link <https://github.com/Chipdelmal/MGSurvE/blob/main/MGSurvE/demos/Demo_pointTypes-Simple.py>`_.