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# Copyright (c) 2017 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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"""Test the `indices` module.""" |
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from datetime import datetime |
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import warnings |
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from metpy.calc import (bulk_shear, bunkers_storm_motion, mean_pressure_weighted, |
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precipitable_water, significant_tornado, supercell_composite) |
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from metpy.deprecation import MetpyDeprecationWarning |
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from metpy.io import get_upper_air_data |
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from metpy.io.upperair import UseSampleData |
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from metpy.testing import assert_almost_equal, assert_array_equal |
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from metpy.units import concatenate, units |
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import numpy as np |
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warnings.simplefilter('ignore', MetpyDeprecationWarning) |
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def test_precipitable_water(): |
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"""Test precipitable water with observed sounding.""" |
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with UseSampleData(): |
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data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC', source='wyoming') |
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pw = precipitable_water(data.variables['dewpoint'][:], data.variables['pressure'][:], |
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top=400 * units.hPa) |
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truth = (0.8899441949243486 * units('inches')).to('millimeters') |
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assert_array_equal(pw, truth) |
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def test_precipitable_water_no_bounds(): |
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"""Test precipitable water with observed sounding and no bounds given.""" |
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with UseSampleData(): |
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data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC', source='wyoming') |
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dewpoint = data.variables['dewpoint'][:] |
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pressure = data.variables['pressure'][:] |
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inds = pressure >= 400 * units.hPa |
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pw = precipitable_water(dewpoint[inds], pressure[inds]) |
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truth = (0.8899441949243486 * units('inches')).to('millimeters') |
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assert_array_equal(pw, truth) |
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def test_precipitable_water_bound_error(): |
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"""Test with no top bound given and data that produced floating point issue #596.""" |
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p = np.array([993., 978., 960.5, 927.6, 925., 895.8, 892., 876., 45.9, 39.9, 36., 36., |
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34.3]) * units.hPa |
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Td = np.array([25.5, 24.1, 23.1, 21.2, 21.1, 19.4, 19.2, 19.2, -87.1, -86.5, -86.5, -86.5, |
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-88.1]) * units.degC |
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pw = precipitable_water(Td, p) |
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truth = 89.86955998646951 * units('millimeters') |
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assert_array_equal(pw, truth) |
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def test_mean_pressure_weighted(): |
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"""Test pressure-weighted mean wind function with vertical interpolation.""" |
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View Code Duplication |
with UseSampleData(): |
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data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC', source='wyoming') |
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u, v = mean_pressure_weighted(data.variables['pressure'][:], |
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data.variables['u_wind'][:], |
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data.variables['v_wind'][:], |
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heights=data.variables['height'][:], |
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depth=6000 * units('meter')) |
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assert_almost_equal(u, 6.0208700094534775 * units('m/s'), 7) |
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assert_almost_equal(v, 7.966031839967931 * units('m/s'), 7) |
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def test_mean_pressure_weighted_elevated(): |
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"""Test pressure-weighted mean wind function with a base above the surface.""" |
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with UseSampleData(): |
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data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC', source='wyoming') |
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u, v = mean_pressure_weighted(data.variables['pressure'][:], |
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data.variables['u_wind'][:], |
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data.variables['v_wind'][:], |
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heights=data.variables['height'][:], |
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depth=3000 * units('meter'), |
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bottom=data.variables['height'][0] + 3000 * units('meter')) |
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assert_almost_equal(u, 8.270829843626476 * units('m/s'), 7) |
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assert_almost_equal(v, 1.7392601775853547 * units('m/s'), 7) |
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def test_bunkers_motion(): |
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View Code Duplication |
"""Test Bunkers storm motion with observed sounding.""" |
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with UseSampleData(): |
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data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC', source='wyoming') |
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motion = concatenate(bunkers_storm_motion(data.variables['pressure'][:], |
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data.variables['u_wind'][:], data.variables['v_wind'][:], |
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data.variables['height'][:])) |
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truth = [1.4537892577864744, 2.0169333025630616, 10.587950761120482, 13.915130377372801, |
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6.0208700094534775, 7.9660318399679308] * units('m/s') |
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assert_almost_equal(motion.flatten(), truth, 8) |
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def test_bulk_shear(): |
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View Code Duplication |
"""Test bulk shear with observed sounding.""" |
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with UseSampleData(): |
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data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC', source='wyoming') |
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u, v = bulk_shear(data.variables['pressure'][:], data.variables['u_wind'][:], |
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data.variables['v_wind'][:], heights=data.variables['height'][:], |
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depth=6000 * units('meter')) |
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truth = [29.899581266946115, -14.389225800205509] * units('knots') |
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assert_almost_equal(u.to('knots'), truth[0], 8) |
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assert_almost_equal(v.to('knots'), truth[1], 8) |
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def test_bulk_shear_no_depth(): |
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"""Test bulk shear with observed sounding and no depth given. Issue #568.""" |
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with UseSampleData(): |
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data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC', source='wyoming') |
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u, v = bulk_shear(data.variables['pressure'][:], data.variables['u_wind'][:], |
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data.variables['v_wind'][:], heights=data.variables['height'][:]) |
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truth = [20.225018939, 22.602359692] * units('knots') |
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assert_almost_equal(u.to('knots'), truth[0], 8) |
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assert_almost_equal(v.to('knots'), truth[1], 8) |
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def test_bulk_shear_elevated(): |
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"""Test bulk shear with observed sounding and a base above the surface.""" |
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with UseSampleData(): |
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data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC', source='wyoming') |
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u, v = bulk_shear(data.variables['pressure'][:], data.variables['u_wind'][:], |
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data.variables['v_wind'][:], heights=data.variables['height'][:], |
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bottom=data.variables['height'][0] + 3000 * units('meter'), |
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depth=3000 * units('meter')) |
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truth = [0.9655943923302139, -3.8405428777944466] * units('m/s') |
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assert_almost_equal(u, truth[0], 8) |
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assert_almost_equal(v, truth[1], 8) |
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def test_supercell_composite(): |
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"""Test supercell composite function.""" |
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mucape = [2000., 1000., 500., 2000.] * units('J/kg') |
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esrh = [400., 150., 45., 45.] * units('m^2/s^2') |
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ebwd = [30., 15., 5., 5.] * units('m/s') |
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truth = [16., 2.25, 0., 0.] |
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supercell_comp = supercell_composite(mucape, esrh, ebwd) |
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assert_array_equal(supercell_comp, truth) |
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def test_sigtor(): |
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"""Test significant tornado parameter function.""" |
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sbcape = [2000., 2000., 2000., 2000., 3000, 4000] * units('J/kg') |
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sblcl = [3000., 1500., 500., 1500., 1500, 800] * units('meter') |
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srh1 = [200., 200., 200., 200., 300, 400] * units('m^2/s^2') |
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shr6 = [20., 5., 20., 35., 20., 35] * units('m/s') |
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truth = [0., 0, 1.777778, 1.333333, 2., 10.666667] |
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sigtor = significant_tornado(sbcape, sblcl, srh1, shr6) |
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assert_almost_equal(sigtor, truth, 6) |
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Unidata /
MetPy
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