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# Copyright (c) 2008-2015 MetPy Developers. |
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# Distributed under the terms of the BSD 3-Clause License. |
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# SPDX-License-Identifier: BSD-3-Clause |
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"""Tests for `calc.tools` module.""" |
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import numpy as np |
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import numpy.ma as ma |
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import pytest |
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from metpy.calc import (find_intersections, interpolate_nans, nearest_intersection_idx, |
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reduce_point_density, resample_nn_1d) |
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from metpy.calc.tools import _next_non_masked_element |
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from metpy.testing import assert_array_almost_equal, assert_array_equal |
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def test_resample_nn(): |
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"""Test 1d nearest neighbor functionality.""" |
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a = np.arange(5.) |
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b = np.array([2, 3.8]) |
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truth = np.array([2, 4]) |
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assert_array_equal(truth, resample_nn_1d(a, b)) |
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def test_nearest_intersection_idx(): |
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"""Test nearest index to intersection functionality.""" |
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x = np.linspace(5, 30, 17) |
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y1 = 3 * x**2 |
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y2 = 100 * x - 650 |
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truth = np.array([2, 12]) |
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assert_array_equal(truth, nearest_intersection_idx(y1, y2)) |
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@pytest.mark.parametrize('direction, expected', [ |
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('all', np.array([[8.88, 24.44], [238.84, 1794.53]])), |
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('increasing', np.array([[24.44], [1794.53]])), |
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('decreasing', np.array([[8.88], [238.84]])) |
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]) |
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def test_find_intersections(direction, expected): |
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"""Test finding the intersection of two curves functionality.""" |
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x = np.linspace(5, 30, 17) |
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y1 = 3 * x**2 |
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y2 = 100 * x - 650 |
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# Note: Truth is what we will get with this sampling, not the mathematical intersection |
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assert_array_almost_equal(expected, find_intersections(x, y1, y2, direction=direction), 2) |
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def test_find_intersections_no_intersections(): |
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"""Test finding the intersection of two curves with no intersections.""" |
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x = np.linspace(5, 30, 17) |
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y1 = 3 * x + 0 |
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y2 = 5 * x + 5 |
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# Note: Truth is what we will get with this sampling, not the mathematical intersection |
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truth = np.array([[], |
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[]]) |
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assert_array_equal(truth, find_intersections(x, y1, y2)) |
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def test_find_intersections_invalid_direction(): |
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"""Test exception if an invalid direction is given.""" |
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x = np.linspace(5, 30, 17) |
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y1 = 3 * x ** 2 |
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y2 = 100 * x - 650 |
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with pytest.raises(ValueError): |
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find_intersections(x, y1, y2, direction='increaing') |
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def test_interpolate_nan_linear(): |
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"""Test linear interpolation of arrays with NaNs in the y-coordinate.""" |
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x = np.linspace(0, 20, 15) |
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y = 5 * x + 3 |
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nan_indexes = [1, 5, 11, 12] |
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y_with_nan = y.copy() |
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y_with_nan[nan_indexes] = np.nan |
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assert_array_almost_equal(y, interpolate_nans(x, y_with_nan), 2) |
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def test_interpolate_nan_log(): |
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"""Test log interpolation of arrays with NaNs in the y-coordinate.""" |
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x = np.logspace(1, 5, 15) |
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y = 5 * np.log(x) + 3 |
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nan_indexes = [1, 5, 11, 12] |
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y_with_nan = y.copy() |
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y_with_nan[nan_indexes] = np.nan |
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assert_array_almost_equal(y, interpolate_nans(x, y_with_nan, kind='log'), 2) |
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def test_interpolate_nan_invalid(): |
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"""Test log interpolation with invalid parameter.""" |
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x = np.logspace(1, 5, 15) |
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y = 5 * np.log(x) + 3 |
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with pytest.raises(ValueError): |
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interpolate_nans(x, y, kind='loog') |
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@pytest.mark.parametrize('mask, expected_idx, expected_element', [ |
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([False, False, False, False, False], 1, 1), |
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([False, True, True, False, False], 3, 3), |
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([False, True, True, True, True], None, None) |
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]) |
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def test_non_masked_elements(mask, expected_idx, expected_element): |
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"""Test with a valid element.""" |
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a = ma.masked_array(np.arange(5), mask=mask) |
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idx, element = _next_non_masked_element(a, 1) |
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assert idx == expected_idx |
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assert element == expected_element |
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@pytest.fixture |
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def thin_point_data(): |
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r"""Provide scattered points for testing.""" |
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xy = np.array([[0.8793620, 0.9005706], [0.5382446, 0.8766988], [0.6361267, 0.1198620], |
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[0.4127191, 0.0270573], [0.1486231, 0.3121822], [0.2607670, 0.4886657], |
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[0.7132257, 0.2827587], [0.4371954, 0.5660840], [0.1318544, 0.6468250], |
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[0.6230519, 0.0682618], [0.5069460, 0.2326285], [0.1324301, 0.5609478], |
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[0.7975495, 0.2109974], [0.7513574, 0.9870045], [0.9305814, 0.0685815], |
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[0.5271641, 0.7276889], [0.8116574, 0.4795037], [0.7017868, 0.5875983], |
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[0.5591604, 0.5579290], [0.1284860, 0.0968003], [0.2857064, 0.3862123]]) |
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return xy |
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View Code Duplication |
@pytest.mark.parametrize('radius, truth', |
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[(2.0, np.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.bool)), |
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(1.0, np.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
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0, 0, 0, 0, 0, 0, 0, 0, 1, 0], dtype=np.bool)), |
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(0.3, np.array([1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, |
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0, 0, 0, 0, 0, 1, 0, 0, 0, 0], dtype=np.bool)), |
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(0.1, np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, |
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0, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=np.bool)) |
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]) |
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def test_reduce_point_density(thin_point_data, radius, truth): |
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r"""Test that reduce_point_density works.""" |
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assert_array_equal(reduce_point_density(thin_point_data, radius=radius), truth) |
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View Code Duplication |
@pytest.mark.parametrize('radius, truth', |
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[(2.0, np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
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0, 0, 0, 0, 0, 0, 0, 0, 0, 1], dtype=np.bool)), |
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(0.7, np.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
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0, 0, 0, 1, 0, 0, 0, 0, 0, 1], dtype=np.bool)), |
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(0.3, np.array([1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, |
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0, 0, 0, 1, 0, 0, 0, 1, 0, 1], dtype=np.bool)), |
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(0.1, np.array([1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, |
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0, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=np.bool)) |
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]) |
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def test_reduce_point_density_priority(thin_point_data, radius, truth): |
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r"""Test that reduce_point_density works properly with priority.""" |
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key = np.array([8, 6, 2, 8, 6, 4, 4, 8, 8, 6, 3, 4, 3, 0, 7, 4, 3, 2, 3, 3, 9]) |
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assert_array_equal(reduce_point_density(thin_point_data, radius, key), truth) |
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def test_reduce_point_density_1d(): |
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r"""Test that reduce_point_density works with 1D points.""" |
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x = np.array([1, 3, 4, 8, 9, 10]) |
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assert_array_equal(reduce_point_density(x, 2.5), |
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np.array([1, 0, 1, 1, 0, 0], dtype=np.bool)) |
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Unidata /
MetPy
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