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@@ 333-340 (lines=8) @@
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| 330 |
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def test_log_interp_2d(): |
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"""Test interpolating with log x-scale in 2 dimensions.""" |
| 332 |
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x_log = np.array([[1e3, 1e4, 1e5, 1e6], [1e3, 1e4, 1e5, 1e6]]) |
| 333 |
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y_log = np.log(x_log) * 2 + 3 |
| 334 |
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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, axis=1) |
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assert_array_almost_equal(y_interp[1], y_interp_truth, 7) |
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| 339 |
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| 340 |
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def test_log_interp_3d(): |
| 341 |
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"""Test interpolating with log x-scale 3 dimensions along second axis.""" |
| 342 |
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x_log = np.ones((3, 4, 3)) * np.array([1e3, 1e4, 1e5, 1e6]).reshape(-1, 1) |
| 343 |
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y_log = np.log(x_log) * 2 + 3 |
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@@ 323-330 (lines=8) @@
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| 320 |
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np.array([7.93049516, 0., -8.33333333, -13.14758845]) * units.degC)) |
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]) |
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def test_get_layer(pressure, variable, heights, bottom, depth, interp, expected): |
| 323 |
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"""Tests get_layer functionality.""" |
| 324 |
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p_layer, y_layer = get_layer(pressure, variable, heights=heights, bottom=bottom, |
| 325 |
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depth=depth, interpolate=interp) |
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assert_array_almost_equal(p_layer, expected[0], 5) |
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assert_array_almost_equal(y_layer, expected[1], 5) |
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| 329 |
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|
| 330 |
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def test_log_interp_2d(): |
| 331 |
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"""Test interpolating with log x-scale in 2 dimensions.""" |
| 332 |
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x_log = np.array([[1e3, 1e4, 1e5, 1e6], [1e3, 1e4, 1e5, 1e6]]) |
| 333 |
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y_log = np.log(x_log) * 2 + 3 |
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@@ 343-352 (lines=10) @@
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| 340 |
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def test_log_interp_3d(): |
| 341 |
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"""Test interpolating with log x-scale 3 dimensions along second axis.""" |
| 342 |
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x_log = np.ones((3, 4, 3)) * np.array([1e3, 1e4, 1e5, 1e6]).reshape(-1, 1) |
| 343 |
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y_log = np.log(x_log) * 2 + 3 |
| 344 |
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x_interp = np.array([5e3, 5e4, 5e5]) |
| 345 |
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y_interp_truth = np.array([20.0343863828, 24.6395565688, 29.2447267548]) |
| 346 |
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y_interp = log_interp(x_interp, x_log, y_log, axis=1) |
| 347 |
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assert_array_almost_equal(y_interp[0, :, 0], y_interp_truth, 7) |
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| 349 |
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| 350 |
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def test_log_interp_4d(): |
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"""Test interpolating with log x-scale 4 dimensions.""" |
| 352 |
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x_log = np.ones((2, 2, 3, 4)) * np.array([1e3, 1e4, 1e5, 1e6]) |
| 353 |
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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]) |
Unidata /
MetPy
