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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 the `turbulence` module.""" |
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import numpy as np |
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from numpy.testing import assert_almost_equal, assert_array_equal |
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import pytest |
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from metpy.calc.turbulence import friction_velocity, get_perturbation, kinematic_flux, tke |
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# |
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# TKE Tests |
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# |
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@pytest.fixture() |
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def uvw_and_known_tke(): |
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"""Provide a set of u,v,w with a known tke value.""" |
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u = np.array([-2, -1, 0, 1, 2]) |
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v = -u |
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w = 2 * u |
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# 0.5 * sqrt(2 + 2 + 8) |
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e_true = np.sqrt(12) / 2. |
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return u, v, w, e_true |
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def test_no_tke_1d(): |
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"""Test tke calculation where the expected value is 0.""" |
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observations = 5 |
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# given all the values are the same, there should not be any tke |
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u = np.ones(observations) |
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v = np.ones(observations) |
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w = np.ones(observations) |
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e_zero = 0 |
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assert_array_equal(e_zero, tke(u, v, w)) |
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def test_no_tke_2d_axis_last(): |
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"""Test 0 tke calculation with 2D arrays; calculation axis is last.""" |
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observations = 5 |
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instruments = 2 |
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# given all the values are the same, there should not be any tke |
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u = np.ones((instruments, observations)) |
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v = np.ones((instruments, observations)) |
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w = np.ones((instruments, observations)) |
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e_zero = np.zeros(instruments) |
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assert_array_equal(e_zero, tke(u, v, w, axis=-1)) |
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def test_no_tke_2d_axis_first(): |
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"""Test 0 tke calculation with 2D arrays; calculation axis is first.""" |
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observations = 5 |
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instruments = 2 |
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# given all the values are the same, there should not be any tke |
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u = np.ones((observations, instruments)) |
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v = np.ones((observations, instruments)) |
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w = np.ones((observations, instruments)) |
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e_zero = np.zeros(instruments) |
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assert_array_equal(e_zero, tke(u, v, w, axis=0)) |
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def test_known_tke(uvw_and_known_tke): |
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"""Test basic behavior of tke with known values.""" |
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u, v, w, e_true = uvw_and_known_tke |
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assert_array_equal(e_true, tke(u, v, w)) |
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def test_known_tke_2d_axis_last(uvw_and_known_tke): |
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"""Test array with shape (3, 5) [pretend time axis is -1].""" |
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u, v, w, e_true = uvw_and_known_tke |
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u = np.array([u, u, u]) |
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v = np.array([v, v, v]) |
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w = np.array([w, w, w]) |
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e_true = e_true * np.ones(3) |
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assert_array_equal(e_true, tke(u, v, w, axis=-1)) |
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def test_known_tke_2d_axis_first(uvw_and_known_tke): |
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"""Test array with shape (5, 3) [pretend time axis is 0].""" |
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u, v, w, e_true = uvw_and_known_tke |
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u = np.array([u, u, u]).transpose() |
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v = np.array([v, v, v]).transpose() |
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w = np.array([w, w, w]).transpose() |
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e_true = e_true * np.ones(3).transpose() |
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assert_array_equal(e_true, tke(u, v, w, axis=0)) |
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assert_array_equal(e_true, tke(u, v, w, axis=0, perturbation=True)) |
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# |
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# Perturbation tests |
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# |
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@pytest.fixture() |
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def pert_zero_mean(): |
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"""Return time series with zero-mean and perturbations.""" |
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ts = np.array([-2, -1, 0, 1, 2]) |
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pert_true = ts.copy() |
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return ts, pert_true |
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@pytest.fixture() |
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def pert_nonzero_mean(): |
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"""Return time seres with non-zero-mean and perturbations.""" |
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ts = np.array([-2, 0, 2, 4, 6]) |
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# ts.mean() = 2 |
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pert_true = np.array([-4, -2, 0, 2, 4]) |
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return ts, pert_true |
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def test_no_perturbation_1d(): |
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"""Test with uniform data in 1D.""" |
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observations = 5 |
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# given all the values are the same, there should not be perturbations |
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ts = np.ones(observations) |
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pert_zero = 0 |
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assert_array_equal(pert_zero, get_perturbation(ts)) |
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def test_no_perturbation_2d_axis_last(): |
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"""Test with uniform data in 2D along the last axis.""" |
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observations = 5 |
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instruments = 2 |
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# given all the values are the same, there should not be perturbations |
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ts = np.ones((instruments, observations)) |
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pert_zero = np.zeros((instruments, observations)) |
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assert_array_equal(pert_zero, get_perturbation(ts, axis=-1)) |
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def test_no_perturbation_2d_axis_first(): |
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"""Test with uniform data in 2D along the first axis.""" |
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observations = 5 |
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instruments = 2 |
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# given all the values are the same, there should not be perturbations |
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ts = np.ones((observations, instruments)) |
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pert_zero = np.zeros((observations, instruments)) |
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assert_array_equal(pert_zero, get_perturbation(ts, axis=0)) |
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def test_known_perturbation_zero_mean_1d(pert_zero_mean): |
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"""Test with zero-mean data in 1D.""" |
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ts, pert_known = pert_zero_mean |
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assert_array_equal(pert_known, get_perturbation(ts)) |
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def test_known_perturbation_zero_mean_2d_axis_last(pert_zero_mean): |
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"""Test with zero-mean data in 2D along the last axis.""" |
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ts, pert_known = pert_zero_mean |
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ts = np.array([ts, ts, ts]) |
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pert_known = np.array([pert_known, pert_known, pert_known]) |
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assert_array_equal(pert_known, get_perturbation(ts, axis=-1)) |
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def test_known_perturbation_zero_mean_2d_axis_first(pert_zero_mean): |
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"""Test with zero-mean data in 2D along the first axis.""" |
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ts, pert_known = pert_zero_mean |
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ts = np.array([ts, ts, ts]).transpose() |
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pert_known = np.array([pert_known, pert_known, pert_known]).transpose() |
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assert_array_equal(pert_known, get_perturbation(ts, axis=0)) |
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def test_known_perturbation_nonzero_mean_1d(pert_nonzero_mean): |
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"""Test with non-zero-mean data in 1D.""" |
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ts, pert_known = pert_nonzero_mean |
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assert_array_equal(pert_known, get_perturbation(ts)) |
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def test_known_perturbation_nonzero_mean_2d_axis_last(pert_nonzero_mean): |
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"""Test with non-zero-mean data in 2D along the last axis.""" |
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ts, pert_known = pert_nonzero_mean |
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ts = np.array([ts, ts, ts]) |
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pert_known = np.array([pert_known, pert_known, pert_known]) |
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assert_array_equal(pert_known, get_perturbation(ts, axis=-1)) |
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def test_known_perturbation_nonzero_mean_2d_axis_first(pert_nonzero_mean): |
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"""Test with non-zero-mean data in 2D along the first axis.""" |
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ts, pert_known = pert_nonzero_mean |
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ts = np.array([ts, ts, ts]).transpose() |
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pert_known = np.array([pert_known, pert_known, pert_known]).transpose() |
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assert_array_equal(pert_known, get_perturbation(ts, axis=0)) |
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# |
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# Kinematic Flux Tests |
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# |
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@pytest.fixture() |
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def uvw_and_known_kf_zero_mean(): |
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"""Return components and kinematic flux for zero-mean time series.""" |
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u = np.array([-2, -1, 0, 1, 2]) |
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v = -u |
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w = 2 * u |
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kf_true = {'uv': -2, 'uw': 4, 'vw': -4} |
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return u, v, w, kf_true |
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@pytest.fixture() |
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def uvw_and_known_kf_nonzero_mean(): |
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"""Return components and kinematic flux for non-zero-mean time series.""" |
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u = np.array([-2, -1, 0, 1, 5]) |
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v = -u |
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w = 2 * u |
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kf_true = {'uv': -5.84, 'uw': 11.68, 'vw': -11.68} |
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return u, v, w, kf_true |
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def test_kf_1d_zero_mean(uvw_and_known_kf_zero_mean): |
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"""Test kinematic flux calculation in 1D with zero-mean time series.""" |
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u, v, w, kf_true = uvw_and_known_kf_zero_mean |
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assert_array_equal(kinematic_flux(u, v, perturbation=False), |
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kf_true['uv']) |
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assert_array_equal(kinematic_flux(u, w, perturbation=False), |
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kf_true['uw']) |
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assert_array_equal(kinematic_flux(v, w, perturbation=False), |
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kf_true['vw']) |
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# given u, v, and w have a zero mean, the kf computed with |
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# perturbation=True and perturbation=False should be the same |
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assert_array_equal(kinematic_flux(u, v, perturbation=False), |
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kinematic_flux(u, v, perturbation=True)) |
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assert_array_equal(kinematic_flux(u, w, perturbation=False), |
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kinematic_flux(u, w, perturbation=True)) |
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assert_array_equal(kinematic_flux(v, w, perturbation=False), |
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kinematic_flux(v, w, perturbation=True)) |
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def test_kf_1d_nonzero_mean(uvw_and_known_kf_nonzero_mean): |
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"""Test kinematic flux calculation in 1D with non-zero-mean time series.""" |
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u, v, w, kf_true = uvw_and_known_kf_nonzero_mean |
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assert_array_equal(kinematic_flux(u, v, perturbation=False), |
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kf_true['uv']) |
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assert_array_equal(kinematic_flux(u, w, perturbation=False), |
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kf_true['uw']) |
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assert_array_equal(kinematic_flux(v, w, perturbation=False), |
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kf_true['vw']) |
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View Code Duplication |
def test_kf_2d_axis_last_zero_mean(uvw_and_known_kf_zero_mean): |
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"""Test kinematic flux calculation in 2D with zero-mean time series along last axis.""" |
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u, v, w, kf_true = uvw_and_known_kf_zero_mean |
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u = np.array([u, u, u]) |
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v = np.array([v, v, v]) |
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w = np.array([w, w, w]) |
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for key in kf_true.keys(): |
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tmp = kf_true[key] |
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kf_true[key] = np.array([tmp, tmp, tmp]) |
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assert_array_equal(kinematic_flux(u, v, perturbation=False, axis=-1), |
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kf_true['uv']) |
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assert_array_equal(kinematic_flux(u, w, perturbation=False, axis=-1), |
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kf_true['uw']) |
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assert_array_equal(kinematic_flux(v, w, perturbation=False, axis=-1), |
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kf_true['vw']) |
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# given u, v, and w have a zero mean, the kf computed with |
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# perturbation=True and perturbation=False should be the same |
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assert_array_equal(kinematic_flux(u, v, perturbation=False, axis=-1), |
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kinematic_flux(u, v, perturbation=True, axis=-1)) |
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assert_array_equal(kinematic_flux(u, w, perturbation=False, axis=-1), |
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kinematic_flux(u, w, perturbation=True, axis=-1)) |
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assert_array_equal(kinematic_flux(v, w, perturbation=False, axis=-1), |
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kinematic_flux(v, w, perturbation=True, axis=-1)) |
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View Code Duplication |
def test_kf_2d_axis_last_nonzero_mean(uvw_and_known_kf_nonzero_mean): |
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"""Test kinematic flux calculation in 2D with non-zero-mean time series along last axis.""" |
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u, v, w, kf_true = uvw_and_known_kf_nonzero_mean |
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u = np.array([u, u, u]) |
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v = np.array([v, v, v]) |
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w = np.array([w, w, w]) |
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for key in kf_true.keys(): |
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tmp = kf_true[key] |
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kf_true[key] = np.array([tmp, tmp, tmp]) |
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assert_array_equal(kinematic_flux(u, v, perturbation=False, axis=-1), |
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kf_true['uv']) |
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assert_array_equal(kinematic_flux(u, w, perturbation=False, axis=-1), |
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kf_true['uw']) |
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assert_array_equal(kinematic_flux(v, w, perturbation=False, axis=-1), |
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kf_true['vw']) |
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def test_kf_2d_axis_first_zero_mean(uvw_and_known_kf_zero_mean): |
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"""Test kinematic flux calculation in 2D with zero-mean time series along first axis.""" |
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u, v, w, kf_true = uvw_and_known_kf_zero_mean |
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u = np.array([u, u, u]).transpose() |
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v = np.array([v, v, v]).transpose() |
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w = np.array([w, w, w]).transpose() |
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for key in kf_true.keys(): |
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tmp = kf_true[key] |
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kf_true[key] = np.array([tmp, tmp, tmp]).transpose() |
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assert_array_equal(kinematic_flux(u, v, perturbation=False, axis=0), |
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kf_true['uv']) |
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assert_array_equal(kinematic_flux(u, w, perturbation=False, axis=0), |
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kf_true['uw']) |
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assert_array_equal(kinematic_flux(v, w, perturbation=False, axis=0), |
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kf_true['vw']) |
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# given u, v, and w have a zero mean, the kf computed with |
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# perturbation=True and perturbation=False should be the same |
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assert_array_equal(kinematic_flux(u, v, perturbation=False, axis=0), |
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kinematic_flux(u, v, perturbation=True, axis=0)) |
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assert_array_equal(kinematic_flux(u, w, perturbation=False, axis=0), |
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kinematic_flux(u, w, perturbation=True, axis=0)) |
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assert_array_equal(kinematic_flux(v, w, perturbation=False, axis=0), |
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kinematic_flux(v, w, perturbation=True, axis=0)) |
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View Code Duplication |
def test_kf_2d_axis_first_nonzero_mean(uvw_and_known_kf_nonzero_mean): |
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"""Test kinematic flux in 2D with non-zero-mean time series along first axis.""" |
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u, v, w, kf_true = uvw_and_known_kf_nonzero_mean |
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u = np.array([u, u, u]).transpose() |
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v = np.array([v, v, v]).transpose() |
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w = np.array([w, w, w]).transpose() |
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for key in kf_true.keys(): |
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tmp = kf_true[key] |
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kf_true[key] = np.array([tmp, tmp, tmp]).transpose() |
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assert_array_equal(kinematic_flux(u, v, perturbation=False, axis=0), |
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kf_true['uv']) |
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assert_array_equal(kinematic_flux(u, w, perturbation=False, axis=0), |
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kf_true['uw']) |
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assert_array_equal(kinematic_flux(v, w, perturbation=False, axis=0), |
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kf_true['vw']) |
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# |
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# Friction Velocity Tests |
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# |
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@pytest.fixture() |
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def uvw_and_known_u_star_zero_mean(): |
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"""Return components and friction velocity for a zero-mean time series.""" |
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u = np.array([-2, -1, 0, 1, 2]) |
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v = -u |
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w = 2 * u |
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u_star_true = {'uw': 2.0, 'uwvw': 2.3784142300054421} |
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return u, v, w, u_star_true |
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@pytest.fixture() |
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def uvw_and_known_u_star_nonzero_mean(): |
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"""Return components and friction velocity for a non-zero-mean time series.""" |
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u = np.array([-2, -1, 0, 1, 5]) |
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v = -u |
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w = 2 * u |
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u_star_true = {'uw': 3.4176014981270124, 'uwvw': 4.0642360178166017} |
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return u, v, w, u_star_true |
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def test_u_star_1d_zero_mean(uvw_and_known_u_star_zero_mean): |
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"""Test friction velocity in 1D with a zero-mean time series.""" |
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u, v, w, u_star_true = uvw_and_known_u_star_zero_mean |
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assert_almost_equal(friction_velocity(u, w, perturbation=False), |
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u_star_true['uw']) |
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assert_almost_equal(friction_velocity(u, w, v=v, perturbation=False), |
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u_star_true['uwvw']) |
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def test_u_star_1d_nonzero_mean(uvw_and_known_u_star_nonzero_mean): |
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"""Test friction velocity in 1D with a non-zero-mean time series.""" |
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u, v, w, u_star_true = uvw_and_known_u_star_nonzero_mean |
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assert_almost_equal(friction_velocity(u, w, perturbation=False), |
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u_star_true['uw']) |
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assert_almost_equal(friction_velocity(u, w, v=v, perturbation=False), |
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u_star_true['uwvw']) |
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View Code Duplication |
def test_u_star_2d_axis_last_zero_mean(uvw_and_known_u_star_zero_mean): |
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"""Test friction velocity in 2D with a zero-mean time series along the last axis.""" |
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u, v, w, u_star_true = uvw_and_known_u_star_zero_mean |
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u = np.array([u, u, u]) |
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v = np.array([v, v, v]) |
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w = np.array([w, w, w]) |
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for key in u_star_true.keys(): |
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tmp = u_star_true[key] |
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u_star_true[key] = np.array([tmp, tmp, tmp]) |
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assert_almost_equal(friction_velocity(u, w, perturbation=False, |
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axis=-1), u_star_true['uw']) |
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assert_almost_equal(friction_velocity(u, w, v=v, perturbation=False, |
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axis=-1), u_star_true['uwvw']) |
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376
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View Code Duplication |
def test_u_star_2d_axis_last_nonzero_mean(uvw_and_known_u_star_nonzero_mean): |
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"""Test friction velocity in 2D with a non-zero-mean time series along the last axis.""" |
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u, v, w, u_star_true = uvw_and_known_u_star_nonzero_mean |
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u = np.array([u, u, u]) |
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v = np.array([v, v, v]) |
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w = np.array([w, w, w]) |
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for key in u_star_true.keys(): |
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tmp = u_star_true[key] |
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u_star_true[key] = np.array([tmp, tmp, tmp]) |
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assert_almost_equal(friction_velocity(u, w, perturbation=False, |
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axis=-1), u_star_true['uw']) |
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387
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assert_almost_equal(friction_velocity(u, w, v=v, perturbation=False, |
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axis=-1), u_star_true['uwvw']) |
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389
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390
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391
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View Code Duplication |
def test_u_star_2d_axis_first_zero_mean(uvw_and_known_u_star_zero_mean): |
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392
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"""Test friction velocity in 2D with a zero-mean time series along the first axis.""" |
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393
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u, v, w, u_star_true = uvw_and_known_u_star_zero_mean |
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u = np.array([u, u, u]).transpose() |
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395
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v = np.array([v, v, v]).transpose() |
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396
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w = np.array([w, w, w]).transpose() |
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397
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for key in u_star_true.keys(): |
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398
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tmp = u_star_true[key] |
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399
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u_star_true[key] = np.array([tmp, tmp, tmp]).transpose() |
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400
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assert_almost_equal(friction_velocity(u, w, perturbation=False, |
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401
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axis=0), u_star_true['uw']) |
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402
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assert_almost_equal(friction_velocity(u, w, v=v, perturbation=False, |
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403
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axis=0), u_star_true['uwvw']) |
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404
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405
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406
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View Code Duplication |
def test_u_star_2d_axis_first_nonzero_mean(uvw_and_known_u_star_nonzero_mean): |
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407
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"""Test friction velocity in 2D with a non-zero-mean time series along the first axis.""" |
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408
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u, v, w, u_star_true = uvw_and_known_u_star_nonzero_mean |
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u = np.array([u, u, u]).transpose() |
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410
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v = np.array([v, v, v]).transpose() |
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411
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w = np.array([w, w, w]).transpose() |
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412
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for key in u_star_true.keys(): |
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413
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tmp = u_star_true[key] |
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414
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u_star_true[key] = np.array([tmp, tmp, tmp]).transpose() |
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415
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assert_almost_equal(friction_velocity(u, w, perturbation=False, |
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416
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axis=0), u_star_true['uw']) |
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417
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assert_almost_equal(friction_velocity(u, w, v=v, perturbation=False, |
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418
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axis=0), u_star_true['uwvw']) |
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419
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