
    /ii$                        d Z ddlZddlmZ ddlmZmZ ddlmZm	Z	 ddl
mZ ddlZddlZddlmZ ddlmZmZmZ dd	lmZ dd
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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.
    N)BytesIO)IntegralReal)PathLikeremove)exists)get_data_home)RemoteFileMetadata_fetch_remote_pkl_filepath)Bunch)Intervalvalidate_paramszsamples.zipz.https://ndownloader.figshare.com/files/5976075@abb07ad284ac50d9e6d20f1c4211e0fd3c098f7f85955e89d321ee8efe37ac28)filenameurlchecksumzcoverages.zipz.https://ndownloader.figshare.com/files/5976078@4d862674d72e79d6cee77e63b98651ec7926043ba7d39dcb31329cf3f6073807zspecies_coverage.pkz   c                       fdt          |          D             }d t          fd|D                       }t          j         |          }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                 8    g | ]}                                 S  )readline).0_Fs     m/var/www/html/bet.cuttalo.com/ml/venv/lib/python3.11/site-packages/sklearn/datasets/_species_distributions.py
<listcomp>z"_load_coverage.<locals>.<listcomp>H   s!    999qajjll999    c                     |                                  d         t          |                                  d                   fS )Nr      )splitfloat)ts    r   <lambda>z _load_coverage.<locals>.<lambda>I   s+    AGGIIaL%		!*=*=> r   c                 &    g | ]} |          S r   r   )r   line
make_tuples     r   r   z"_load_coverage.<locals>.<listcomp>J   s#    777::d##777r   dtypes   NODATA_valuei)rangedictnploadtxtint)r   header_lengthr*   headerMnodatar(   s   `     @r   _load_coverager4   C   s    
 :999E-$8$8999F>>J777777788F

1E"""A())F&	Hr   c                     |                                                      d                                                              d          }t	          j        | 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	S22,f4,f4)skiprows	delimiterr*   )r   decodestripr"   r-   r.   r*   names)r   r<   recs      r   	_load_csvr>   S   s_     JJLL((..0066s;;E
*Qc
E
E
ECCIOJr   c                     | j         | j        z   }|| j        | j        z  z   }| j        | j        z   }|| j        | j        z  z   }t          j        ||| j                  }t          j        ||| 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
    )x_left_lower_corner	grid_sizeNxy_left_lower_cornerNyr-   arange)batchxminxmaxyminymaxxgridygrids          r   construct_gridsrM   g   s~     $u6D58eo-.D$u6D58eo-.D IdD%/22EIdD%/22E5>r   booleanr!   left)closedg        neither)	data_homedownload_if_missing	n_retriesdelayT)prefer_skip_nested_validation   g      ?c                    t          |           } t          ddddd          }t          j        }t	          | t
                    }t          |          s|st          d          t          	                    dt          j        d	|            t          t          | ||
          }t          j        |          5 }|j        D ]=}	t          ||	                   }
d|	v rt!          |
          }d|	v rt!          |
          }>	 ddd           n# 1 swxY w Y   t#          |           t          	                    dt$          j        d	|            t          t$          | ||
          }t          j        |          5 }g }|j        D ]f}	t          ||	                   }
t                              d                    |	                     |                    t-          |
                     gt          j        ||          }ddd           n# 1 swxY w Y   t#          |           t1          d|||d|}t3          j        ||d           nt3          j        |          }|S )a  Loader for species distribution dataset from Phillips et. al. (2006).

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

    Parameters
    ----------
    data_home : str or path-like, 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.

    n_retries : int, default=3
        Number of retries when HTTP errors are encountered.

        .. versionadded:: 1.5

    delay : float, default=1.0
        Number of seconds between retries.

        .. versionadded:: 1.5

    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.

    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.

    Examples
    --------
    >>> from sklearn.datasets import fetch_species_distributions
    >>> species = fetch_species_distributions()
    >>> species.train[:5]
    array([(b'microryzomys_minutus', -64.7   , -17.85  ),
           (b'microryzomys_minutus', -67.8333, -16.3333),
           (b'microryzomys_minutus', -67.8833, -16.3   ),
           (b'microryzomys_minutus', -67.8   , -16.2667),
           (b'microryzomys_minutus', -67.9833, -15.9   )],
          dtype=[('species', 'S22'), ('dd long', '<f4'), ('dd lat', '<f4')])

    For a more extended example,
    see :ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py`
    g33333Wi  gfffffLi8  g?)r@   rB   rC   rD   rA   z1Data not found and `download_if_missing` is FalsezDownloading species data from z to )dirnamerT   rU   traintestNzDownloading coverage data from z - converting {}r)   )	coveragesr[   rZ   	   )compressr   )r	   r,   r-   int16r   DATA_ARCHIVE_NAMEr   OSErrorloggerinfoSAMPLESr   r   loadfilesr   r>   r   	COVERAGESdebugformatappendr4   asarrayr   joblibdump)rR   rS   rT   rU   extra_paramsr*   archive_pathsamples_pathXffhandlerZ   r[   coverages_pathr\   bunchs                   r   fetch_species_distributionsrv      s   R i((I
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 W^$$ 	;IW : :!!A$--/66q99:::  !8!89999
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   r   r   sklearn.utilsr   sklearn.utils._param_validationr   r   rd   rg   r`   	getLogger__name__rb   r_   r4   r>   rM   strrv   r   r   r   <module>r      s    <        " " " " " " " "                    * * * * * * S S S S S S S S S S       E E E E E E E E 
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