
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/neighbors/plot_species_kde.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_neighbors_plot_species_kde.py>`
        to download the full example code or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_neighbors_plot_species_kde.py:


================================================
Kernel Density Estimate of Species Distributions
================================================
This shows an example of a neighbors-based query (in particular a kernel
density estimate) on geospatial data, using a Ball Tree built upon the
Haversine distance metric -- i.e. distances over points in latitude/longitude.
The dataset is provided by Phillips et. al. (2006).
If available, the example uses
`basemap <https://matplotlib.org/basemap/>`_
to plot the coast lines and national boundaries of South America.

This example does not perform any learning over the data
(see :ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py` for
an example of classification based on the attributes in this dataset).  It
simply shows the kernel density estimate of observed data points in
geospatial coordinates.

The two species are:

 - `"Bradypus variegatus"
   <https://www.iucnredlist.org/species/3038/47437046>`_ ,
   the Brown-throated Sloth.

 - `"Microryzomys minutus"
   <http://www.iucnredlist.org/details/13408/0>`_ ,
   also known as the Forest Small Rice Rat, a rodent that lives in Peru,
   Colombia, Ecuador, Peru, and Venezuela.

References
----------

 * `"Maximum entropy modeling of species geographic distributions"
   <http://rob.schapire.net/papers/ecolmod.pdf>`_
   S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,
   190:231-259, 2006.

.. GENERATED FROM PYTHON SOURCE LINES 38-153



.. image-sg:: /auto_examples/neighbors/images/sphx_glr_plot_species_kde_001.png
   :alt: Bradypus Variegatus, Microryzomys Minutus
   :srcset: /auto_examples/neighbors/images/sphx_glr_plot_species_kde_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

     - computing KDE in spherical coordinates
     - plot coastlines from coverage
     - computing KDE in spherical coordinates
     - plot coastlines from coverage






|

.. code-block:: default


    # Author: Jake Vanderplas <jakevdp@cs.washington.edu>
    #
    # License: BSD 3 clause

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.datasets import fetch_species_distributions
    from sklearn.neighbors import KernelDensity

    # if basemap is available, we'll use it.
    # otherwise, we'll improvise later...
    try:
        from mpl_toolkits.basemap import Basemap

        basemap = True
    except ImportError:
        basemap = False


    def construct_grids(batch):
        """Construct the map grid from the batch object

        Parameters
        ----------
        batch : Batch object
            The object returned by :func:`fetch_species_distributions`

        Returns
        -------
        (xgrid, ygrid) : 1-D arrays
            The grid corresponding to the values in batch.coverages
        """
        # x,y coordinates for corner cells
        xmin = batch.x_left_lower_corner + batch.grid_size
        xmax = xmin + (batch.Nx * batch.grid_size)
        ymin = batch.y_left_lower_corner + batch.grid_size
        ymax = ymin + (batch.Ny * batch.grid_size)

        # x coordinates of the grid cells
        xgrid = np.arange(xmin, xmax, batch.grid_size)
        # y coordinates of the grid cells
        ygrid = np.arange(ymin, ymax, batch.grid_size)

        return (xgrid, ygrid)


    # Get matrices/arrays of species IDs and locations
    data = fetch_species_distributions()
    species_names = ["Bradypus Variegatus", "Microryzomys Minutus"]

    Xtrain = np.vstack([data["train"]["dd lat"], data["train"]["dd long"]]).T
    ytrain = np.array(
        [d.decode("ascii").startswith("micro") for d in data["train"]["species"]],
        dtype="int",
    )
    Xtrain *= np.pi / 180.0  # Convert lat/long to radians

    # Set up the data grid for the contour plot
    xgrid, ygrid = construct_grids(data)
    X, Y = np.meshgrid(xgrid[::5], ygrid[::5][::-1])
    land_reference = data.coverages[6][::5, ::5]
    land_mask = (land_reference > -9999).ravel()

    xy = np.vstack([Y.ravel(), X.ravel()]).T
    xy = xy[land_mask]
    xy *= np.pi / 180.0

    # Plot map of South America with distributions of each species
    fig = plt.figure()
    fig.subplots_adjust(left=0.05, right=0.95, wspace=0.05)

    for i in range(2):
        plt.subplot(1, 2, i + 1)

        # construct a kernel density estimate of the distribution
        print(" - computing KDE in spherical coordinates")
        kde = KernelDensity(
            bandwidth=0.04, metric="haversine", kernel="gaussian", algorithm="ball_tree"
        )
        kde.fit(Xtrain[ytrain == i])

        # evaluate only on the land: -9999 indicates ocean
        Z = np.full(land_mask.shape[0], -9999, dtype="int")
        Z[land_mask] = np.exp(kde.score_samples(xy))
        Z = Z.reshape(X.shape)

        # plot contours of the density
        levels = np.linspace(0, Z.max(), 25)
        plt.contourf(X, Y, Z, levels=levels, cmap=plt.cm.Reds)

        if basemap:
            print(" - plot coastlines using basemap")
            m = Basemap(
                projection="cyl",
                llcrnrlat=Y.min(),
                urcrnrlat=Y.max(),
                llcrnrlon=X.min(),
                urcrnrlon=X.max(),
                resolution="c",
            )
            m.drawcoastlines()
            m.drawcountries()
        else:
            print(" - plot coastlines from coverage")
            plt.contour(
                X, Y, land_reference, levels=[-9998], colors="k", linestyles="solid"
            )
            plt.xticks([])
            plt.yticks([])

        plt.title(species_names[i])

    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 2.712 seconds)


.. _sphx_glr_download_auto_examples_neighbors_plot_species_kde.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.3.X?urlpath=lab/tree/notebooks/auto_examples/neighbors/plot_species_kde.ipynb
        :alt: Launch binder
        :width: 150 px



    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_species_kde.py <plot_species_kde.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_species_kde.ipynb <plot_species_kde.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
