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=============================
Species distribution dataset
=============================

This dataset represents the geographic distribution of species.
The dataset is provided by Phillips et. al. (2006).

The two species are:

 - `"Bradypus variegatus"
   <http://www.iucnredlist.org/details/3038/0>`_ ,
   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.

Notes
-----

For an example of using this dataset, see
:ref:`examples/applications/plot_species_distribution_modeling.py
<sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py>`.
    N)BytesIO)makedirsremove)exists   )Bunch)validate_params   )get_data_home)RemoteFileMetadata_fetch_remote_pkl_filepathzsamples.zipz.https://ndownloader.figshare.com/files/5976075Z@abb07ad284ac50d9e6d20f1c4211e0fd3c098f7f85955e89d321ee8efe37ac28)filenameurlZchecksumzcoverages.zipz.https://ndownloader.figshare.com/files/5976078Z@4d862674d72e79d6cee77e63b98651ec7926043ba7d39dcb31329cf3f6073807zspecies_coverage.pkz   c                    sb    fddt |D }dd tfdd|D }tj |d}t|d }|dkr^d||< |S )	zjLoad a coverage file from an open file object.

    This will return a numpy array of the given dtype
    c                    s   g | ]}   qS  )readline).0_)Fr   f/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/sklearn/datasets/_species_distributions.py
<listcomp>P   s     z"_load_coverage.<locals>.<listcomp>c                 S   s   |   d t|   d fS )Nr   r	   )splitfloat)tr   r   r   <lambda>Q       z _load_coverage.<locals>.<lambda>c                    s   g | ]} |qS r   r   )r   line)
make_tupler   r   r   R   s     dtypes   NODATA_valuei)rangedictnploadtxtint)r   header_lengthr    headerMZnodatar   )r   r   r   _load_coverageK   s    r)   c                 C   s6   |   d d}tj| dddd}||j_|S )zLoad csv file.

    Parameters
    ----------
    F : file object
        CSV file open in byte mode.

    Returns
    -------
    rec : np.ndarray
        record array representing the data
    ascii,r   z	a22,f4,f4)Zskiprows	delimiterr    )r   decodestripr   r#   r$   r    names)r   r/   Zrecr   r   r   	_load_csv[   s    r0   c                 C   s`   | j | j }|| j| j  }| j| j }|| j| j  }t||| j}t||| j}||fS )a%  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
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  Loader for species distribution dataset from Phillips et. al. (2006).

    Read more in the :ref:`User Guide <datasets>`.

    Parameters
    ----------
    data_home : str, default=None
        Specify another download and cache folder for the datasets. By default
        all scikit-learn data is stored in '~/scikit_learn_data' subfolders.

    download_if_missing : bool, default=True
        If False, raise an OSError if the data is not locally available
        instead of trying to download the data from the source site.

    Returns
    -------
    data : :class:`~sklearn.utils.Bunch`
        Dictionary-like object, with the following attributes.

        coverages : array, shape = [14, 1592, 1212]
            These represent the 14 features measured
            at each point of the map grid.
            The latitude/longitude values for the grid are discussed below.
            Missing data is represented by the value -9999.
        train : record array, shape = (1624,)
            The training points for the data.  Each point has three fields:

            - train['species'] is the species name
            - train['dd long'] is the longitude, in degrees
            - train['dd lat'] is the latitude, in degrees
        test : record array, shape = (620,)
            The test points for the data.  Same format as the training data.
        Nx, Ny : integers
            The number of longitudes (x) and latitudes (y) in the grid
        x_left_lower_corner, y_left_lower_corner : floats
            The (x,y) position of the lower-left corner, in degrees
        grid_size : float
            The spacing between points of the grid, in degrees

    Notes
    -----

    This dataset represents the geographic distribution of species.
    The dataset is provided by Phillips et. al. (2006).

    The two species are:

    - `"Bradypus variegatus"
      <http://www.iucnredlist.org/details/3038/0>`_ ,
      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.

    - For an example of using this dataset with scikit-learn, see
      :ref:`examples/applications/plot_species_distribution_modeling.py
      <sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py>`.

    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.
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