.. 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 JupyterLite or 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:: Python
# 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 3.395 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/1.4.X?urlpath=lab/tree/notebooks/auto_examples/neighbors/plot_species_kde.ipynb
:alt: Launch binder
:width: 150 px
.. container:: lite-badge
.. image:: images/jupyterlite_badge_logo.svg
:target: ../../lite/lab/?path=auto_examples/neighbors/plot_species_kde.ipynb
:alt: Launch JupyterLite
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.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_species_kde.ipynb <plot_species_kde.ipynb>`
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_species_kde.py <plot_species_kde.py>`
.. include:: plot_species_kde.recommendations
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_