"""
=============================
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>`.
"""

# Authors: Peter Prettenhofer <peter.prettenhofer@gmail.com>
#          Jake Vanderplas <vanderplas@astro.washington.edu>
#
# License: BSD 3 clause

import logging
from io import BytesIO
from numbers import Integral, Real
from os import PathLike, makedirs, remove
from os.path import exists

import joblib
import numpy as np

from ..utils import Bunch
from ..utils._param_validation import Interval, validate_params
from . import get_data_home
from ._base import RemoteFileMetadata, _fetch_remote, _pkl_filepath

# The original data can be found at:
# https://biodiversityinformatics.amnh.org/open_source/maxent/samples.zip
SAMPLES = RemoteFileMetadata(
    filename="samples.zip",
    url="https://ndownloader.figshare.com/files/5976075",
    checksum="abb07ad284ac50d9e6d20f1c4211e0fd3c098f7f85955e89d321ee8efe37ac28",
)

# The original data can be found at:
# https://biodiversityinformatics.amnh.org/open_source/maxent/coverages.zip
COVERAGES = RemoteFileMetadata(
    filename="coverages.zip",
    url="https://ndownloader.figshare.com/files/5976078",
    checksum="4d862674d72e79d6cee77e63b98651ec7926043ba7d39dcb31329cf3f6073807",
)

DATA_ARCHIVE_NAME = "species_coverage.pkz"


logger = logging.getLogger(__name__)


def _load_coverage(F, header_length=6, dtype=np.int16):
    """Load a coverage file from an open file object.

    This will return a numpy array of the given dtype
    """
    header = [F.readline() for _ in range(header_length)]
    make_tuple = lambda t: (t.split()[0], float(t.split()[1]))
    header = dict([make_tuple(line) for line in header])

    M = np.loadtxt(F, dtype=dtype)
    nodata = int(header[b"NODATA_value"])
    if nodata != -9999:
        M[nodata] = -9999
    return M


def _load_csv(F):
    """Load csv file.

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

    Returns
    -------
    rec : np.ndarray
        record array representing the data
    """
    names = F.readline().decode("ascii").strip().split(",")

    rec = np.loadtxt(F, skiprows=0, delimiter=",", dtype="S22,f4,f4")
    rec.dtype.names = names
    return rec


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)


@validate_params(
    {
        "data_home": [str, PathLike, None],
        "download_if_missing": ["boolean"],
        "n_retries": [Interval(Integral, 1, None, closed="left")],
        "delay": [Interval(Real, 0.0, None, closed="neither")],
    },
    prefer_skip_nested_validation=True,
)
def fetch_species_distributions(
    *,
    data_home=None,
    download_if_missing=True,
    n_retries=3,
    delay=1.0,
):
    """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.

    - 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.

    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')])
    """
    data_home = get_data_home(data_home)
    if not exists(data_home):
        makedirs(data_home)

    # Define parameters for the data files.  These should not be changed
    # unless the data model changes.  They will be saved in the npz file
    # with the downloaded data.
    extra_params = dict(
        x_left_lower_corner=-94.8,
        Nx=1212,
        y_left_lower_corner=-56.05,
        Ny=1592,
        grid_size=0.05,
    )
    dtype = np.int16

    archive_path = _pkl_filepath(data_home, DATA_ARCHIVE_NAME)

    if not exists(archive_path):
        if not download_if_missing:
            raise OSError("Data not found and `download_if_missing` is False")
        logger.info("Downloading species data from %s to %s" % (SAMPLES.url, data_home))
        samples_path = _fetch_remote(
            SAMPLES, dirname=data_home, n_retries=n_retries, delay=delay
        )
        with np.load(samples_path) as X:  # samples.zip is a valid npz
            for f in X.files:
                fhandle = BytesIO(X[f])
                if "train" in f:
                    train = _load_csv(fhandle)
                if "test" in f:
                    test = _load_csv(fhandle)
        remove(samples_path)

        logger.info(
            "Downloading coverage data from %s to %s" % (COVERAGES.url, data_home)
        )
        coverages_path = _fetch_remote(
            COVERAGES, dirname=data_home, n_retries=n_retries, delay=delay
        )
        with np.load(coverages_path) as X:  # coverages.zip is a valid npz
            coverages = []
            for f in X.files:
                fhandle = BytesIO(X[f])
                logger.debug(" - converting {}".format(f))
                coverages.append(_load_coverage(fhandle))
            coverages = np.asarray(coverages, dtype=dtype)
        remove(coverages_path)

        bunch = Bunch(coverages=coverages, test=test, train=train, **extra_params)
        joblib.dump(bunch, archive_path, compress=9)
    else:
        bunch = joblib.load(archive_path)

    return bunch