# (C) British Crown Copyright 2014 - 2017, Met Office
#
# This file is part of cartopy.
#
# cartopy is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the
# Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# cartopy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with cartopy.  If not, see <https://www.gnu.org/licenses/>.

from __future__ import (absolute_import, division, print_function)

import numpy as np
from numpy.testing import assert_array_equal

import cartopy.img_transform as img_trans
import cartopy.crs as ccrs


def test_griding_data_std_range():
    # Data which exists inside the standard projection bounds i.e.
    # [-180, 180].
    target_prj = ccrs.PlateCarree()
    # create 3 data points
    lats = np.array([65, 10, -45])
    lons = np.array([-90, 0, 90])
    data = np.array([1, 2, 3])
    data_trans = ccrs.Geodetic()

    target_x, target_y, extent = img_trans.mesh_projection(target_prj, 8, 4)

    image = img_trans.regrid(data, lons, lats, data_trans, target_prj,
                             target_x, target_y,
                             mask_extrapolated=True)

    # The expected image. n.b. on a map the data is reversed in the y axis.
    expected = np.array([[3, 3, 3, 3, 3, 3, 3, 3],
                         [3, 1, 2, 2, 2, 3, 3, 3],
                         [1, 1, 1, 2, 2, 2, 3, 1],
                         [1, 1, 1, 1, 1, 1, 1, 1]], dtype=np.float64)

    expected_mask = np.array(
        [[True, True, True, True, True, True, True, True],
         [True, False, False, False, False, False, False, True],
         [True, False, False, False, False, False, False, True],
         [True, True, True, True, True, True, True, True]])

    assert_array_equal([-180, 180, -90, 90], extent)
    assert_array_equal(expected, image)
    assert_array_equal(expected_mask, image.mask)


def test_griding_data_outside_projection():
    # Data which exists outside the standard projection e.g. [0, 360] rather
    # than [-180, 180].
    target_prj = ccrs.PlateCarree()
    # create 3 data points
    lats = np.array([65, 10, -45])
    lons = np.array([120, 180, 240])
    data = np.array([1, 2, 3])
    data_trans = ccrs.Geodetic()

    target_x, target_y, extent = img_trans.mesh_projection(target_prj, 8, 4)

    image = img_trans.regrid(data, lons, lats, data_trans, target_prj,
                             target_x, target_y,
                             mask_extrapolated=True)

    # The expected image. n.b. on a map the data is reversed in the y axis.
    expected = np.array(
        [[3, 3, 3, 3, 3, 3, 3, 3],
         [3, 3, 3, 3, 3, 1, 2, 2],
         [2, 2, 3, 1, 1, 1, 1, 2],
         [1, 1, 1, 1, 1, 1, 1, 1]], dtype=np.float64)

    expected_mask = np.array(
        [[True, True, True, True, True, True, True, True],
         [False, False, True, True, True, True, False, False],
         [False, False, True, True, True, True, False, False],
         [True, True, True, True, True, True, True, True]])

    assert_array_equal([-180, 180, -90, 90], extent)
    assert_array_equal(expected, image)
    assert_array_equal(expected_mask, image.mask)
