.. 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:


================================================
物种分布的核密度估计
================================================
这展示了一个基于邻居查询（特别是核密度估计）的地理空间数据示例，使用基于哈弗赛因距离度量构建的球树——即经纬度点之间的距离。数据集由Phillips等人（2006）提供。
如果可用，示例使用 `basemap <https://matplotlib.org/basemap/>`_  绘制南美洲的海岸线和国界。

此示例不对数据进行任何学习（有关基于此数据集属性的分类示例，请参见:ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py` ）。它只是显示地理空间坐标中观测数据点的核密度估计。

这两种物种是：

 - `"Bradypus variegatus"
   <https://www.iucnredlist.org/species/3038/47437046>`_  ，
   棕喉树懒。

 - `"Microryzomys minutus"
   <http://www.iucnredlist.org/details/13408/0>`_  ，
   也被称为森林小稻鼠，一种生活在秘鲁、哥伦比亚、厄瓜多尔、秘鲁和委内瑞拉的啮齿动物。

参考文献
----------

 * `"物种地理分布的最大熵建模"
   <http://rob.schapire.net/papers/ecolmod.pdf>`_  
   S. J. Phillips, R. P. Anderson, R. E. Schapire - 生态建模,
   190:231-259, 2006.

.. GENERATED FROM PYTHON SOURCE LINES 28-142



.. 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


    # 作者：scikit-learn 开发者
    # SPDX-License-Identifier: BSD-3-Clause

    import matplotlib.pyplot as plt
    import numpy as np

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

    # 如果 basemap 可用，我们将使用它。
    # 否则，我们稍后将进行改进……
    try:
        from mpl_toolkits.basemap import Basemap

        basemap = True
    except ImportError:
        basemap = False


    def construct_grids(batch):
        """构建地图网格从批处理对象

    Parameters
    ----------
    batch : 批处理对象
        由 :func:`fetch_species_distributions` 返回的对象

    返回
    -------
    (xgrid, ygrid) : 一维数组
        对应于 batch.coverages 中值的网格
    """
        # 角落单元格的 x,y 坐标
        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)

        # 网格单元的 x 坐标
        xgrid = np.arange(xmin, xmax, batch.grid_size)
        # 网格单元的y坐标
        ygrid = np.arange(ymin, ymax, batch.grid_size)

        return (xgrid, ygrid)


    # 获取物种ID和位置的矩阵/数组
    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

    # 设置数据网格以绘制等高线图
    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

    # 绘制南美洲地图并标注每个物种的分布情况
    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)

        # 构建分布的核密度估计
        print(" - computing KDE in spherical coordinates")
        kde = KernelDensity(
            bandwidth=0.04, metric="haversine", kernel="gaussian", algorithm="ball_tree"
        )
        kde.fit(Xtrain[ytrain == i])

        # 仅在陆地上评估：-9999 表示海洋
        Z = np.full(land_mask.shape[0], -9999, dtype="int")
        Z[land_mask] = np.exp(kde.score_samples(xy))
        Z = Z.reshape(X.shape)

        # 绘制密度的等高线
        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 3.095 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/main?urlpath=lab/tree/notebooks/auto_examples/neighbors/plot_species_kde.ipynb
        :alt: Launch binder
        :width: 150 px

    .. 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>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_species_kde.zip <plot_species_kde.zip>`


.. include:: plot_species_kde.recommendations


.. only:: html

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

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