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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 (extract_cross_section, find_intersections, get_layer, interp, |
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interpolate_nans, log_interp, |
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nearest_intersection_idx, pressure_to_height_std, |
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reduce_point_density, resample_nn_1d) |
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from metpy.calc.tools import (_get_bound_pressure_height, _greater_or_close, _less_or_close, |
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_next_non_masked_element, delete_masked_points) |
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from metpy.testing import assert_array_almost_equal, assert_array_equal |
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from metpy.units import units |
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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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View Code Duplication |
[0.5591604, 0.5579290], [0.1284860, 0.0968003], [0.2857064, 0.3862123]]) |
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return xy |
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@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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View Code Duplication |
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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@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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def test_delete_masked_points(): |
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"""Test deleting masked points.""" |
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a = ma.masked_array(np.arange(5), mask=[False, True, False, False, False]) |
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b = ma.masked_array(np.arange(5), mask=[False, False, False, True, False]) |
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expected = np.array([0, 2, 4]) |
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a, b = delete_masked_points(a, b) |
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assert_array_equal(a, expected) |
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assert_array_equal(b, expected) |
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def test_log_interp(): |
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"""Test interpolating with log x-scale.""" |
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x_log = np.array([1e3, 1e4, 1e5, 1e6]) |
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y_log = np.log(x_log) * 2 + 3 |
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x_interp = np.array([5e3, 5e4, 5e5]) |
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y_interp_truth = np.array([20.0343863828, 24.6395565688, 29.2447267548]) |
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y_interp = log_interp(x_interp, x_log, y_log) |
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assert_array_almost_equal(y_interp, y_interp_truth, 7) |
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def test_log_interp_units(): |
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"""Test interpolating with log x-scale with units.""" |
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x_log = np.array([1e3, 1e4, 1e5, 1e6]) * units.hPa |
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y_log = (np.log(x_log.m) * 2 + 3) * units.degC |
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x_interp = np.array([5e5, 5e6, 5e7]) * units.Pa |
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y_interp_truth = np.array([20.0343863828, 24.6395565688, 29.2447267548]) * units.degC |
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y_interp = log_interp(x_interp, x_log, y_log) |
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assert_array_almost_equal(y_interp, y_interp_truth, 7) |
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@pytest.fixture |
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def get_bounds_data(): |
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"""Provide pressure and height data for testing layer bounds calculation.""" |
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pressures = np.linspace(1000, 100, 10) * units.hPa |
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heights = pressure_to_height_std(pressures) |
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return pressures, heights |
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@pytest.mark.parametrize('pressure, bound, hgts, interp, expected', [ |
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(get_bounds_data()[0], 900 * units.hPa, None, True, |
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(900 * units.hPa, 0.9880028 * units.kilometer)), |
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(get_bounds_data()[0], 900 * units.hPa, None, False, |
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(900 * units.hPa, 0.9880028 * units.kilometer)), |
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(get_bounds_data()[0], 870 * units.hPa, None, True, |
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(870 * units.hPa, 1.2665298 * units.kilometer)), |
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(get_bounds_data()[0], 870 * units.hPa, None, False, |
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(900 * units.hPa, 0.9880028 * units.kilometer)), |
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(get_bounds_data()[0], 0.9880028 * units.kilometer, None, True, |
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(900 * units.hPa, 0.9880028 * units.kilometer)), |
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(get_bounds_data()[0], 0.9880028 * units.kilometer, None, False, |
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(900 * units.hPa, 0.9880028 * units.kilometer)), |
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(get_bounds_data()[0], 1.2665298 * units.kilometer, None, True, |
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(870 * units.hPa, 1.2665298 * units.kilometer)), |
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(get_bounds_data()[0], 1.2665298 * units.kilometer, None, False, |
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(900 * units.hPa, 0.9880028 * units.kilometer)), |
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(get_bounds_data()[0], 900 * units.hPa, get_bounds_data()[1], True, |
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(900 * units.hPa, 0.9880028 * units.kilometer)), |
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(get_bounds_data()[0], 900 * units.hPa, get_bounds_data()[1], False, |
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(900 * units.hPa, 0.9880028 * units.kilometer)), |
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(get_bounds_data()[0], 870 * units.hPa, get_bounds_data()[1], True, |
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(870 * units.hPa, 1.2643214 * units.kilometer)), |
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(get_bounds_data()[0], 870 * units.hPa, get_bounds_data()[1], False, |
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(900 * units.hPa, 0.9880028 * units.kilometer)), |
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(get_bounds_data()[0], 0.9880028 * units.kilometer, get_bounds_data()[1], True, |
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(900 * units.hPa, 0.9880028 * units.kilometer)), |
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(get_bounds_data()[0], 0.9880028 * units.kilometer, get_bounds_data()[1], False, |
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(900 * units.hPa, 0.9880028 * units.kilometer)), |
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(get_bounds_data()[0], 1.2665298 * units.kilometer, get_bounds_data()[1], True, |
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(870.9869087 * units.hPa, 1.2665298 * units.kilometer)), |
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(get_bounds_data()[0], 1.2665298 * units.kilometer, get_bounds_data()[1], False, |
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(900 * units.hPa, 0.9880028 * units.kilometer)), |
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(get_bounds_data()[0], 0.98800289 * units.kilometer, get_bounds_data()[1], True, |
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(900 * units.hPa, 0.9880028 * units.kilometer)) |
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]) |
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def test_get_bound_pressure_height(pressure, bound, hgts, interp, expected): |
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"""Test getting bounds in layers with various parameter combinations.""" |
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bounds = _get_bound_pressure_height(pressure, bound, heights=hgts, interpolate=interp) |
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assert_array_almost_equal(bounds[0], expected[0], 5) |
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assert_array_almost_equal(bounds[1], expected[1], 5) |
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def test_get_bound_invalid_bound_units(): |
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"""Test that value error is raised with invalid bound units.""" |
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p = np.arange(900, 300, -100) * units.hPa |
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with pytest.raises(ValueError): |
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_get_bound_pressure_height(p, 100 * units.degC) |
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251
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252
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253
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def test_get_bound_pressure_out_of_range(): |
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254
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"""Test when bound is out of data range in pressure.""" |
|
255
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p = np.arange(900, 300, -100) * units.hPa |
|
256
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|
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with pytest.raises(ValueError): |
|
257
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_get_bound_pressure_height(p, 100 * units.hPa) |
|
258
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with pytest.raises(ValueError): |
|
259
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_get_bound_pressure_height(p, 1000 * units.hPa) |
|
260
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261
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262
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def test_get_bound_height_out_of_range(): |
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263
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"""Test when bound is out of data range in height.""" |
|
264
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p = np.arange(900, 300, -100) * units.hPa |
|
265
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h = np.arange(1, 7) * units.kilometer |
|
266
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with pytest.raises(ValueError): |
|
267
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_get_bound_pressure_height(p, 8 * units.kilometer, heights=h) |
|
268
|
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with pytest.raises(ValueError): |
|
269
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_get_bound_pressure_height(p, 100 * units.meter, heights=h) |
|
270
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271
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272
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def test_get_layer_float32(): |
|
273
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"""Test that get_layer works properly with float32 data.""" |
|
274
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p = np.asarray([940.85083008, 923.78851318, 911.42022705, 896.07220459, |
|
275
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876.89404297, 781.63330078], np.float32) * units('hPa') |
|
276
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hgt = np.asarray([563.671875, 700.93817139, 806.88098145, 938.51745605, |
|
277
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1105.25854492, 2075.04443359], dtype=np.float32) * units.meter |
|
278
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279
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true_p_layer = np.asarray([940.85083008, 923.78851318, 911.42022705, 896.07220459, |
|
280
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876.89404297, 831.86472819], np.float32) * units('hPa') |
|
281
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true_hgt_layer = np.asarray([563.671875, 700.93817139, 806.88098145, 938.51745605, |
|
282
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1105.25854492, 1549.8079], dtype=np.float32) * units.meter |
|
283
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284
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p_layer, hgt_layer = get_layer(p, hgt, heights=hgt, depth=1000. * units.meter) |
|
285
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assert_array_almost_equal(p_layer, true_p_layer, 4) |
|
286
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assert_array_almost_equal(hgt_layer, true_hgt_layer, 4) |
|
287
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288
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289
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|
def test_get_layer_ragged_data(): |
|
290
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|
"""Tests that error is raised for unequal length pressure and data arrays.""" |
|
291
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|
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p = np.arange(10) * units.hPa |
|
292
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|
|
y = np.arange(9) * units.degC |
|
293
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|
|
with pytest.raises(ValueError): |
|
294
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get_layer(p, y) |
|
295
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|
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|
|
296
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|
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|
297
|
|
|
def test_get_layer_invalid_depth_units(): |
|
298
|
|
|
"""Tests that error is raised when depth has invalid units.""" |
|
299
|
|
|
p = np.arange(10) * units.hPa |
|
300
|
|
|
y = np.arange(9) * units.degC |
|
301
|
|
|
with pytest.raises(ValueError): |
|
302
|
|
|
get_layer(p, y, depth=400 * units.degC) |
|
303
|
|
|
|
|
304
|
|
|
|
|
305
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|
|
@pytest.fixture |
|
306
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|
|
def layer_test_data(): |
|
307
|
|
|
"""Provide test data for testing of layer bounds.""" |
|
308
|
|
|
pressure = np.arange(1000, 10, -100) * units.hPa |
|
309
|
|
|
temperature = np.linspace(25, -50, len(pressure)) * units.degC |
|
310
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|
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return pressure, temperature |
|
311
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|
312
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|
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|
|
313
|
|
|
@pytest.mark.parametrize('pressure, variable, heights, bottom, depth, interp, expected', [ |
|
314
|
|
|
(layer_test_data()[0], layer_test_data()[1], None, None, 150 * units.hPa, True, |
|
315
|
|
|
(np.array([1000, 900, 850]) * units.hPa, |
|
316
|
|
|
np.array([25.0, 16.666666, 12.62262]) * units.degC)), |
|
317
|
|
|
(layer_test_data()[0], layer_test_data()[1], None, None, 150 * units.hPa, False, |
|
318
|
|
|
(np.array([1000, 900]) * units.hPa, np.array([25.0, 16.666666]) * units.degC)), |
|
319
|
|
|
(layer_test_data()[0], layer_test_data()[1], None, 2 * units.km, 3 * units.km, True, |
|
320
|
|
|
(np.array([794.85264282, 700., 600., 540.01696548]) * units.hPa, |
|
321
|
|
|
np.array([7.93049516, 0., -8.33333333, -13.14758845]) * units.degC)) |
|
322
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|
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]) |
|
323
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|
View Code Duplication |
def test_get_layer(pressure, variable, heights, bottom, depth, interp, expected): |
|
|
|
|
|
|
324
|
|
|
"""Tests get_layer functionality.""" |
|
325
|
|
|
p_layer, y_layer = get_layer(pressure, variable, heights=heights, bottom=bottom, |
|
326
|
|
|
depth=depth, interpolate=interp) |
|
327
|
|
|
assert_array_almost_equal(p_layer, expected[0], 5) |
|
328
|
|
|
assert_array_almost_equal(y_layer, expected[1], 5) |
|
329
|
|
|
|
|
330
|
|
|
|
|
331
|
|
|
def test_log_interp_2d(): |
|
332
|
|
|
"""Test interpolating with log x-scale in 2 dimensions.""" |
|
333
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|
View Code Duplication |
x_log = np.array([[1e3, 1e4, 1e5, 1e6], [1e3, 1e4, 1e5, 1e6]]) |
|
|
|
|
|
|
334
|
|
|
y_log = np.log(x_log) * 2 + 3 |
|
335
|
|
|
x_interp = np.array([5e3, 5e4, 5e5]) |
|
336
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|
|
y_interp_truth = np.array([20.0343863828, 24.6395565688, 29.2447267548]) |
|
337
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|
|
y_interp = log_interp(x_interp, x_log, y_log, axis=1) |
|
338
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assert_array_almost_equal(y_interp[1], y_interp_truth, 7) |
|
339
|
|
|
|
|
340
|
|
|
|
|
341
|
|
|
def test_log_interp_3d(): |
|
342
|
|
|
"""Test interpolating with log x-scale 3 dimensions along second axis.""" |
|
343
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|
View Code Duplication |
x_log = np.ones((3, 4, 3)) * np.array([1e3, 1e4, 1e5, 1e6]).reshape(-1, 1) |
|
|
|
|
|
|
344
|
|
|
y_log = np.log(x_log) * 2 + 3 |
|
345
|
|
|
x_interp = np.array([5e3, 5e4, 5e5]) |
|
346
|
|
|
y_interp_truth = np.array([20.0343863828, 24.6395565688, 29.2447267548]) |
|
347
|
|
|
y_interp = log_interp(x_interp, x_log, y_log, axis=1) |
|
348
|
|
|
assert_array_almost_equal(y_interp[0, :, 0], y_interp_truth, 7) |
|
349
|
|
|
|
|
350
|
|
|
|
|
351
|
|
|
def test_log_interp_4d(): |
|
352
|
|
|
"""Test interpolating with log x-scale 4 dimensions.""" |
|
353
|
|
|
x_log = np.ones((2, 2, 3, 4)) * np.array([1e3, 1e4, 1e5, 1e6]) |
|
354
|
|
|
y_log = np.log(x_log) * 2 + 3 |
|
355
|
|
|
x_interp = np.array([5e3, 5e4, 5e5]) |
|
356
|
|
|
y_interp_truth = np.array([20.0343863828, 24.6395565688, 29.2447267548]) |
|
357
|
|
|
y_interp = log_interp(x_interp, x_log, y_log, axis=3) |
|
358
|
|
|
assert_array_almost_equal(y_interp[0, 0, 0, :], y_interp_truth, 7) |
|
359
|
|
|
|
|
360
|
|
|
|
|
361
|
|
|
def test_log_interp_2args(): |
|
362
|
|
|
"""Test interpolating with log x-scale with 2 arguments.""" |
|
363
|
|
|
x_log = np.array([1e3, 1e4, 1e5, 1e6]) |
|
364
|
|
|
y_log = np.log(x_log) * 2 + 3 |
|
365
|
|
|
y_log2 = np.log(x_log) * 2 + 3 |
|
366
|
|
|
x_interp = np.array([5e3, 5e4, 5e5]) |
|
367
|
|
|
y_interp_truth = np.array([20.0343863828, 24.6395565688, 29.2447267548]) |
|
368
|
|
|
y_interp = log_interp(x_interp, x_log, y_log, y_log2) |
|
369
|
|
|
assert_array_almost_equal(y_interp[1], y_interp_truth, 7) |
|
370
|
|
|
assert_array_almost_equal(y_interp[0], y_interp_truth, 7) |
|
371
|
|
|
|
|
372
|
|
|
|
|
373
|
|
|
def test_log_interp_set_nan_above(): |
|
374
|
|
|
"""Test interpolating with log x-scale setting out of bounds above data to nan.""" |
|
375
|
|
|
x_log = np.array([1e3, 1e4, 1e5, 1e6]) |
|
376
|
|
|
y_log = np.log(x_log) * 2 + 3 |
|
377
|
|
|
x_interp = np.array([1e7]) |
|
378
|
|
|
y_interp_truth = np.nan |
|
379
|
|
|
with pytest.warns(Warning): |
|
380
|
|
|
y_interp = log_interp(x_interp, x_log, y_log) |
|
381
|
|
|
assert_array_almost_equal(y_interp, y_interp_truth, 7) |
|
382
|
|
|
|
|
383
|
|
|
|
|
384
|
|
|
def test_log_interp_no_extrap(): |
|
385
|
|
|
"""Test interpolating with log x-scale setting out of bounds value error.""" |
|
386
|
|
|
x_log = np.array([1e3, 1e4, 1e5, 1e6]) |
|
387
|
|
|
y_log = np.log(x_log) * 2 + 3 |
|
388
|
|
|
x_interp = np.array([1e7]) |
|
389
|
|
|
with pytest.raises(ValueError): |
|
390
|
|
|
log_interp(x_interp, x_log, y_log, fill_value=None) |
|
391
|
|
|
|
|
392
|
|
|
|
|
393
|
|
|
def test_log_interp_set_nan_below(): |
|
394
|
|
|
"""Test interpolating with log x-scale setting out of bounds below data to nan.""" |
|
395
|
|
|
x_log = np.array([1e3, 1e4, 1e5, 1e6]) |
|
396
|
|
|
y_log = np.log(x_log) * 2 + 3 |
|
397
|
|
|
x_interp = 1e2 |
|
398
|
|
|
y_interp_truth = np.nan |
|
399
|
|
|
with pytest.warns(Warning): |
|
400
|
|
|
y_interp = log_interp(x_interp, x_log, y_log) |
|
401
|
|
|
assert_array_almost_equal(y_interp, y_interp_truth, 7) |
|
402
|
|
|
|
|
403
|
|
|
|
|
404
|
|
|
def test_interp_2args(): |
|
405
|
|
|
"""Test interpolation with 2 arguments.""" |
|
406
|
|
|
x = np.array([1., 2., 3., 4.]) |
|
407
|
|
|
y = x |
|
408
|
|
|
y2 = x |
|
409
|
|
|
x_interp = np.array([2.5000000, 3.5000000]) |
|
410
|
|
|
y_interp_truth = np.array([2.5000000, 3.5000000]) |
|
411
|
|
|
y_interp = interp(x_interp, x, y, y2) |
|
412
|
|
|
assert_array_almost_equal(y_interp[0], y_interp_truth, 7) |
|
413
|
|
|
assert_array_almost_equal(y_interp[1], y_interp_truth, 7) |
|
414
|
|
|
|
|
415
|
|
|
|
|
416
|
|
|
def test_interp_decrease(): |
|
417
|
|
|
"""Test interpolation with decreasing interpolation points.""" |
|
418
|
|
|
x = np.array([1., 2., 3., 4.]) |
|
419
|
|
|
y = x |
|
420
|
|
|
x_interp = np.array([3.5000000, 2.5000000]) |
|
421
|
|
|
y_interp_truth = np.array([3.5000000, 2.5000000]) |
|
422
|
|
|
y_interp = interp(x_interp, x, y) |
|
423
|
|
|
assert_array_almost_equal(y_interp, y_interp_truth, 7) |
|
424
|
|
|
|
|
425
|
|
|
|
|
426
|
|
|
def test_interp_decrease_xp(): |
|
427
|
|
|
"""Test interpolation with decreasing order.""" |
|
428
|
|
|
x = np.array([4., 3., 2., 1.]) |
|
429
|
|
|
y = x |
|
430
|
|
|
x_interp = np.array([3.5000000, 2.5000000]) |
|
431
|
|
|
y_interp_truth = np.array([3.5000000, 2.5000000]) |
|
432
|
|
|
y_interp = interp(x_interp, x, y) |
|
433
|
|
|
assert_array_almost_equal(y_interp, y_interp_truth, 7) |
|
434
|
|
|
|
|
435
|
|
|
|
|
436
|
|
|
def test_interp_end_point(): |
|
437
|
|
|
"""Test interpolation with point at data endpoints.""" |
|
438
|
|
|
x = np.array([1., 2., 3., 4.]) |
|
439
|
|
|
y = x |
|
440
|
|
|
x_interp = np.array([1.0, 4.0]) |
|
441
|
|
|
y_interp_truth = np.array([1.0, 4.0]) |
|
442
|
|
|
y_interp = interp(x_interp, x, y) |
|
443
|
|
|
assert_array_almost_equal(y_interp, y_interp_truth, 7) |
|
444
|
|
|
|
|
445
|
|
|
|
|
446
|
|
|
def test_greater_or_close(): |
|
447
|
|
|
"""Test floating point greater or close to.""" |
|
448
|
|
|
x = np.array([0.0, 1.0, 1.49999, 1.5, 1.5000, 1.7]) |
|
449
|
|
|
comparison_value = 1.5 |
|
450
|
|
|
truth = np.array([False, False, True, True, True, True]) |
|
451
|
|
|
res = _greater_or_close(x, comparison_value) |
|
452
|
|
|
assert_array_equal(res, truth) |
|
453
|
|
|
|
|
454
|
|
|
|
|
455
|
|
|
def test_less_or_close(): |
|
456
|
|
|
"""Test floating point less or close to.""" |
|
457
|
|
|
x = np.array([0.0, 1.0, 1.49999, 1.5, 1.5000, 1.7]) |
|
458
|
|
|
comparison_value = 1.5 |
|
459
|
|
|
truth = np.array([True, True, True, True, True, False]) |
|
460
|
|
|
res = _less_or_close(x, comparison_value) |
|
461
|
|
|
assert_array_equal(res, truth) |
|
462
|
|
|
|
|
463
|
|
|
|
|
464
|
|
|
def test_cross_section(): |
|
465
|
|
|
"""Test extracting cross section from model data.""" |
|
466
|
|
|
lat = np.array([85., 84.5, 84., 83.5, 83.]) |
|
467
|
|
|
lon = np.array([215., 215.5, 216., 216.5, 217.]) |
|
468
|
|
|
tmp = np.array([[[241.5, 241.6000061, 241.6000061, 241.6000061, 241.6000061], |
|
469
|
|
|
[240.80000305, 240.80000305, 240.80000305, 240.80000305, 240.8999939], |
|
470
|
|
|
[240.1000061, 240.1000061, 240.19999695, 240.19999695, 240.19999695], |
|
471
|
|
|
[239.3999939, 239.5, 239.5, 239.5, 239.6000061], |
|
472
|
|
|
[238.8999939, 238.8999939, 239., 239., 239.]], |
|
473
|
|
|
[[244., 244.1000061, 244.1000061, 244.1000061, 244.19999695], |
|
474
|
|
|
[243.5, 243.5, 243.5, 243.6000061, 243.6000061], |
|
475
|
|
|
[242.8999939, 243., 243., 243.1000061, 243.1000061], |
|
476
|
|
|
[242.5, 242.6000061, 242.6000061, 242.6000061, 242.69999695], |
|
477
|
|
|
[242.30000305, 242.3999939, 242.3999939, 242.3999939, 242.5]], |
|
478
|
|
|
[[246.19999695, 246.19999695, 246.19999695, 246.19999695, 246.30000305], |
|
479
|
|
|
[245.69999695, 245.80000305, 245.80000305, 245.80000305, 245.8999939], |
|
480
|
|
|
[245.30000305, 245.30000305, 245.3999939, 245.3999939, 245.5], |
|
481
|
|
|
[244.80000305, 244.8999939, 244.8999939, 245., 245.], |
|
482
|
|
|
[244.6000061, 244.6000061, 244.69999695, 244.80000305, 244.80000305]], |
|
483
|
|
|
[[247.80000305, 247.80000305, 247.80000305, 247.8999939, 247.8999939], |
|
484
|
|
|
[247.3999939, 247.3999939, 247.5, 247.5, 247.6000061], |
|
485
|
|
|
[246.8999939, 246.8999939, 246.8999939, 247., 247.], |
|
486
|
|
|
[246.30000305, 246.30000305, 246.3999939, 246.3999939, 246.5], |
|
487
|
|
|
[245.80000305, 245.80000305, 245.8999939, 245.8999939, 246.]], |
|
488
|
|
|
[[248.80000305, 248.8999939, 248.8999939, 248.8999939, 249.], |
|
489
|
|
|
[248.30000305, 248.3999939, 248.3999939, 248.5, 248.5], |
|
490
|
|
|
[247.80000305, 247.80000305, 247.80000305, 247.8999939, 247.8999939], |
|
491
|
|
|
[247., 247.1000061, 247.1000061, 247.1000061, 247.19999695], |
|
492
|
|
|
[246.5, 246.5, 246.5, 246.6000061, 246.6000061]]]) |
|
493
|
|
|
cross_section = extract_cross_section([1, 1], [3, 3], lat, lon, tmp, num=5) |
|
494
|
|
|
cross_section_truth = np.array([[240.80000305, 240.19999695, 240.19999695, 240.19999695, |
|
495
|
|
|
239.5], |
|
496
|
|
|
[243.5, 243., 243., 243, 242.6000061], |
|
497
|
|
|
[245.80000305, 245.3999939, 245.3999939, 245.3999939, |
|
498
|
|
|
245.], |
|
499
|
|
|
[247.3999939, 246.8999939, 246.8999939, 246.8999939, |
|
500
|
|
|
246.3999939], |
|
501
|
|
|
[248.3999939, 247.80000305, 247.80000305, 247.80000305, |
|
502
|
|
|
247.1000061]]) |
|
503
|
|
|
assert_array_almost_equal(cross_section[2], cross_section_truth, 4) |
|
504
|
|
|
lat_truth = np.array([84.5, 84., 84., 84., 83.5]) |
|
505
|
|
|
assert_array_almost_equal(cross_section[0], lat_truth, 4) |
|
506
|
|
|
lon_truth = np.array([215.5, 216., 216., 216., 216.5]) |
|
507
|
|
|
assert_array_almost_equal(cross_section[1], lon_truth, 4) |
|
508
|
|
|
|
|
509
|
|
|
|
|
510
|
|
|
def test_cross_section_non_3d(): |
|
511
|
|
|
"""Tests that error is given if x and y are not 1 or 2 dimensions.""" |
|
512
|
|
|
lat = np.array([85., 84.5, 84., 83.5, 83.]) |
|
513
|
|
|
lon = np.ones((3, 2, 4)) |
|
514
|
|
|
tmp = np.ones(5) |
|
515
|
|
|
with pytest.raises(ValueError): |
|
516
|
|
|
extract_cross_section([1, 1], [3, 3], lat, lon, tmp) |
|
517
|
|
|
|
Unidata /
MetPy
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