Sites and Trap Types
------------

So far, we have used the same point-type and just a couple of trap-types in our landscape, but MGSurvE can handle complex landscapes with high levels of heterogeneity.
We can imagine a landscape with two types of points:

*   Aquatic Habitats
*   Blood Haunts

and two types of traps:

*   Long-range, low-catch (exponential)
*   Short-range, high-catch (sigmoid)


To setup the landscape, we start by laying down the points with the :code:`(x,y)` coordinates and now, the last column :code:`t` as the point-type identifier.

.. code-block:: python

    pts = [
        [-4.0, 4.00, 0], 
        [0.25, 8.00, 1], 
        [5.00, 0.15, 0],
        [-1.0, 1.00, 0],
        [3.00, 3.00, 1]
    ]
    points = pd.DataFrame(pts, columns=['x', 'y', 't'])

Now, mosquitos don't pick their destination equally. We can assume for now, that from an aquatic habitat, they preferentially move to a blood haunt, and then back again. 
We can encode this behavior with the following "mask":

.. code-block:: python

    msk = [
        [0.3, 0.7],
        [0.7, 0.3]
    ]

Where the diagonal is the preference towards staying in the same point-type, and the off diagonals encode the probability of moving towards a different point type (in index order of point-types :code:`t`):

For our traps, we are going to use a similar setup as we did in the previous example:


.. code-block:: python

    trp = [
        [5.00, 1.00, 0, 0],
        [10.0, 0.50, 1, 0],
        [10.0, 0.00, 0, 1],
    ]
    traps = pd.DataFrame(trp, columns=['x', 'y', 't', 'f'])
    tker = {
        0: {'kernel': srv.exponentialDecay, 'params': {'A': 0.4, 'b': 2}},
        1: {'kernel': srv.sigmoidDecay,     'params': {'A': .6, 'rate': .5, 'x0': 1}}
    }


Where the column :code:`t` determines the trap kernel to be used by the trap, and the column :code:`f` if the trap is immovable (1) or movable (0) in the optimization cycle.
Finally, we can setup our landscape as follows:

.. code-block:: python

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

And that's it! We have our movable sigmoid trap (green), our two exponential-decay traps (magenta), our water sources (circles), and our blood haunts (triangles).
We can see that inter-point type transitions are more probable, as defined by our masking matrix.

.. code-block:: python

    (fig, ax) = plt.subplots(1, 2, figsize=(15, 15), sharey=False)
    lnd.plotSites(fig, ax[0])
    lnd.plotMaskedMigrationNetwork(fig, ax[0])
    lnd.plotTraps(fig, ax[0])
    lnd.plotTrapsNetwork(fig, ax[0])
    srv.plotMatrix(fig, ax[1], lnd.trapsMigration, lnd.trapsNumber)
    [srv.plotClean(fig, i, frame=False) for i in ax]

.. image:: ../../img/demo_pointTypes.jpg


The code used for this tutorial can be found `in this link <https://github.com/Chipdelmal/MGSurvE/blob/main/MGSurvE/demos/Demo_Types.py>`_.