274 lines
9.0 KiB
Python
274 lines
9.0 KiB
Python
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"""
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=============================
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Species distribution dataset
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=============================
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This dataset represents the geographic distribution of species.
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The dataset is provided by Phillips et. al. (2006).
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The two species are:
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- `"Bradypus variegatus"
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<http://www.iucnredlist.org/details/3038/0>`_ ,
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the Brown-throated Sloth.
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- `"Microryzomys minutus"
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<http://www.iucnredlist.org/details/13408/0>`_ ,
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also known as the Forest Small Rice Rat, a rodent that lives in Peru,
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Colombia, Ecuador, Peru, and Venezuela.
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References
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----------
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`"Maximum entropy modeling of species geographic distributions"
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<http://rob.schapire.net/papers/ecolmod.pdf>`_ S. J. Phillips,
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R. P. Anderson, R. E. Schapire - Ecological Modelling, 190:231-259, 2006.
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Notes
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-----
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For an example of using this dataset, see
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:ref:`examples/applications/plot_species_distribution_modeling.py
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<sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py>`.
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"""
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# Authors: Peter Prettenhofer <peter.prettenhofer@gmail.com>
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# Jake Vanderplas <vanderplas@astro.washington.edu>
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#
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# License: BSD 3 clause
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import logging
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from io import BytesIO
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from os import PathLike, makedirs, remove
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from os.path import exists
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import joblib
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import numpy as np
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from ..utils import Bunch
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from ..utils._param_validation import validate_params
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from . import get_data_home
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from ._base import RemoteFileMetadata, _fetch_remote, _pkl_filepath
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# The original data can be found at:
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# https://biodiversityinformatics.amnh.org/open_source/maxent/samples.zip
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SAMPLES = RemoteFileMetadata(
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filename="samples.zip",
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url="https://ndownloader.figshare.com/files/5976075",
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checksum="abb07ad284ac50d9e6d20f1c4211e0fd3c098f7f85955e89d321ee8efe37ac28",
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)
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# The original data can be found at:
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# https://biodiversityinformatics.amnh.org/open_source/maxent/coverages.zip
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COVERAGES = RemoteFileMetadata(
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filename="coverages.zip",
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url="https://ndownloader.figshare.com/files/5976078",
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checksum="4d862674d72e79d6cee77e63b98651ec7926043ba7d39dcb31329cf3f6073807",
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)
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DATA_ARCHIVE_NAME = "species_coverage.pkz"
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logger = logging.getLogger(__name__)
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def _load_coverage(F, header_length=6, dtype=np.int16):
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"""Load a coverage file from an open file object.
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This will return a numpy array of the given dtype
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"""
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header = [F.readline() for _ in range(header_length)]
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make_tuple = lambda t: (t.split()[0], float(t.split()[1]))
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header = dict([make_tuple(line) for line in header])
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M = np.loadtxt(F, dtype=dtype)
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nodata = int(header[b"NODATA_value"])
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if nodata != -9999:
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M[nodata] = -9999
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return M
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def _load_csv(F):
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"""Load csv file.
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Parameters
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----------
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F : file object
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CSV file open in byte mode.
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Returns
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-------
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rec : np.ndarray
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record array representing the data
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"""
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names = F.readline().decode("ascii").strip().split(",")
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rec = np.loadtxt(F, skiprows=0, delimiter=",", dtype="S22,f4,f4")
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rec.dtype.names = names
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return rec
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def construct_grids(batch):
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"""Construct the map grid from the batch object
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Parameters
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----------
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batch : Batch object
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The object returned by :func:`fetch_species_distributions`
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Returns
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-------
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(xgrid, ygrid) : 1-D arrays
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The grid corresponding to the values in batch.coverages
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"""
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# x,y coordinates for corner cells
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xmin = batch.x_left_lower_corner + batch.grid_size
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xmax = xmin + (batch.Nx * batch.grid_size)
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ymin = batch.y_left_lower_corner + batch.grid_size
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ymax = ymin + (batch.Ny * batch.grid_size)
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# x coordinates of the grid cells
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xgrid = np.arange(xmin, xmax, batch.grid_size)
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# y coordinates of the grid cells
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ygrid = np.arange(ymin, ymax, batch.grid_size)
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return (xgrid, ygrid)
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@validate_params(
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{"data_home": [str, PathLike, None], "download_if_missing": ["boolean"]},
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prefer_skip_nested_validation=True,
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)
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def fetch_species_distributions(*, data_home=None, download_if_missing=True):
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"""Loader for species distribution dataset from Phillips et. al. (2006).
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Read more in the :ref:`User Guide <species_distribution_dataset>`.
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Parameters
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----------
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data_home : str or path-like, default=None
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Specify another download and cache folder for the datasets. By default
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all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
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download_if_missing : bool, default=True
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If False, raise an OSError if the data is not locally available
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instead of trying to download the data from the source site.
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Returns
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-------
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data : :class:`~sklearn.utils.Bunch`
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Dictionary-like object, with the following attributes.
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coverages : array, shape = [14, 1592, 1212]
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These represent the 14 features measured
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at each point of the map grid.
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The latitude/longitude values for the grid are discussed below.
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Missing data is represented by the value -9999.
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train : record array, shape = (1624,)
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The training points for the data. Each point has three fields:
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- train['species'] is the species name
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- train['dd long'] is the longitude, in degrees
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- train['dd lat'] is the latitude, in degrees
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test : record array, shape = (620,)
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The test points for the data. Same format as the training data.
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Nx, Ny : integers
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The number of longitudes (x) and latitudes (y) in the grid
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x_left_lower_corner, y_left_lower_corner : floats
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The (x,y) position of the lower-left corner, in degrees
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grid_size : float
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The spacing between points of the grid, in degrees
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Notes
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-----
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This dataset represents the geographic distribution of species.
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The dataset is provided by Phillips et. al. (2006).
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The two species are:
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- `"Bradypus variegatus"
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<http://www.iucnredlist.org/details/3038/0>`_ ,
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the Brown-throated Sloth.
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- `"Microryzomys minutus"
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<http://www.iucnredlist.org/details/13408/0>`_ ,
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also known as the Forest Small Rice Rat, a rodent that lives in Peru,
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Colombia, Ecuador, Peru, and Venezuela.
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- For an example of using this dataset with scikit-learn, see
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:ref:`examples/applications/plot_species_distribution_modeling.py
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<sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py>`.
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References
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----------
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* `"Maximum entropy modeling of species geographic distributions"
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<http://rob.schapire.net/papers/ecolmod.pdf>`_
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S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,
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190:231-259, 2006.
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Examples
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--------
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>>> from sklearn.datasets import fetch_species_distributions
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>>> species = fetch_species_distributions()
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>>> species.train[:5]
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array([(b'microryzomys_minutus', -64.7 , -17.85 ),
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(b'microryzomys_minutus', -67.8333, -16.3333),
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(b'microryzomys_minutus', -67.8833, -16.3 ),
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(b'microryzomys_minutus', -67.8 , -16.2667),
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(b'microryzomys_minutus', -67.9833, -15.9 )],
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dtype=[('species', 'S22'), ('dd long', '<f4'), ('dd lat', '<f4')])
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"""
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data_home = get_data_home(data_home)
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if not exists(data_home):
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makedirs(data_home)
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# Define parameters for the data files. These should not be changed
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# unless the data model changes. They will be saved in the npz file
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# with the downloaded data.
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extra_params = dict(
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x_left_lower_corner=-94.8,
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Nx=1212,
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y_left_lower_corner=-56.05,
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Ny=1592,
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grid_size=0.05,
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)
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dtype = np.int16
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archive_path = _pkl_filepath(data_home, DATA_ARCHIVE_NAME)
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if not exists(archive_path):
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if not download_if_missing:
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raise OSError("Data not found and `download_if_missing` is False")
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logger.info("Downloading species data from %s to %s" % (SAMPLES.url, data_home))
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samples_path = _fetch_remote(SAMPLES, dirname=data_home)
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with np.load(samples_path) as X: # samples.zip is a valid npz
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for f in X.files:
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fhandle = BytesIO(X[f])
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if "train" in f:
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train = _load_csv(fhandle)
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if "test" in f:
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test = _load_csv(fhandle)
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remove(samples_path)
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logger.info(
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"Downloading coverage data from %s to %s" % (COVERAGES.url, data_home)
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)
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coverages_path = _fetch_remote(COVERAGES, dirname=data_home)
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with np.load(coverages_path) as X: # coverages.zip is a valid npz
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coverages = []
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for f in X.files:
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fhandle = BytesIO(X[f])
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logger.debug(" - converting {}".format(f))
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coverages.append(_load_coverage(fhandle))
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coverages = np.asarray(coverages, dtype=dtype)
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remove(coverages_path)
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bunch = Bunch(coverages=coverages, test=test, train=train, **extra_params)
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joblib.dump(bunch, archive_path, compress=9)
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else:
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bunch = joblib.load(archive_path)
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return bunch
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