# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause

# isort: off
import warnings

warnings.filterwarnings("ignore", category=DeprecationWarning)
# isort: on

import numpy as np
import scipy.interpolate as interpolate
import unittest

from omni.isaac.lab.app import run_tests


class TestScipyOperations(unittest.TestCase):
    """Tests for assuring scipy related operations used in Isaac Lab."""

    def test_interpolation(self):
        """Test scipy interpolation 2D method."""
        # parameters
        size = (10.0, 12.0)
        horizontal_scale = 0.1
        vertical_scale = 0.005
        downsampled_scale = 0.2
        noise_range = (-0.02, 0.1)
        noise_step = 0.02
        # switch parameters to discrete units
        # -- horizontal scale
        width_pixels = int(size[0] / horizontal_scale)
        length_pixels = int(size[1] / horizontal_scale)
        # -- downsampled scale
        width_downsampled = int(size[0] / downsampled_scale)
        length_downsampled = int(size[1] / downsampled_scale)
        # -- height
        height_min = int(noise_range[0] / vertical_scale)
        height_max = int(noise_range[1] / vertical_scale)
        height_step = int(noise_step / vertical_scale)

        # create range of heights possible
        height_range = np.arange(height_min, height_max + height_step, height_step)
        # sample heights randomly from the range along a grid
        height_field_downsampled = np.random.choice(height_range, size=(width_downsampled, length_downsampled))
        # create interpolation function for the sampled heights
        x = np.linspace(0, size[0] * horizontal_scale, width_downsampled)
        y = np.linspace(0, size[1] * horizontal_scale, length_downsampled)

        # interpolate the sampled heights to obtain the height field
        x_upsampled = np.linspace(0, size[0] * horizontal_scale, width_pixels)
        y_upsampled = np.linspace(0, size[1] * horizontal_scale, length_pixels)
        # -- method 1: interp2d (this will be deprecated in the future 1.12 release)
        func_interp2d = interpolate.interp2d(y, x, height_field_downsampled, kind="cubic")
        z_upsampled_interp2d = func_interp2d(y_upsampled, x_upsampled)
        # -- method 2: RectBivariateSpline (alternate to interp2d)
        func_RectBiVariate = interpolate.RectBivariateSpline(x, y, height_field_downsampled)
        z_upsampled_RectBivariant = func_RectBiVariate(x_upsampled, y_upsampled)
        # -- method 3: RegularGridInterpolator (recommended from scipy but slow!)
        # Ref: https://github.com/scipy/scipy/issues/18010
        func_RegularGridInterpolator = interpolate.RegularGridInterpolator(
            (x, y), height_field_downsampled, method="cubic"
        )
        xx_upsampled, yy_upsampled = np.meshgrid(x_upsampled, y_upsampled, indexing="ij", sparse=True)
        z_upsampled_RegularGridInterpolator = func_RegularGridInterpolator((xx_upsampled, yy_upsampled))

        # check if the interpolated height field is the same as the sampled height field
        np.testing.assert_allclose(z_upsampled_interp2d, z_upsampled_RectBivariant, atol=1e-14)
        np.testing.assert_allclose(z_upsampled_RectBivariant, z_upsampled_RegularGridInterpolator, atol=1e-14)
        np.testing.assert_allclose(z_upsampled_RegularGridInterpolator, z_upsampled_interp2d, atol=1e-14)


if __name__ == "__main__":
    run_tests()
