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Split tropical_cyclone module #911

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Jul 12, 2024
Merged

Split tropical_cyclone module #911

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e13d9c4
split tropical_cyclone module
Jul 8, 2024
4f25c5f
Merge branch 'refs/heads/develop' into feature/split_TC
Jul 8, 2024
0a6c627
try fixing petals 3.11 compatibility error (petals issue #135)
Jul 9, 2024
9e33969
fix petals 3.11 compatibility error (petals issue 135)
Jul 10, 2024
32a0c9c
compute_windfields_sparse should not be a private function due to sp…
Jul 10, 2024
759ce99
Apply suggestions from code review
ThomasRoosli Jul 11, 2024
b3d85d6
Apply code review suggestions
ThomasRoosli Jul 11, 2024
0014b41
Apply suggestions from code review
ThomasRoosli Jul 11, 2024
d9a69d2
add docstring at the top of the file
Jul 11, 2024
4ca1610
Update CHANGELOG.md
peanutfun Jul 12, 2024
f8b494e
Move relative imports to the end
peanutfun Jul 12, 2024
376a8d4
Include docs of split module
peanutfun Jul 12, 2024
bad5ff8
Update CHANGELOG.md
peanutfun Jul 12, 2024
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2 changes: 2 additions & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -26,12 +26,14 @@ CLIMADA tutorials. [#872](https://github.com/CLIMADA-project/climada_python/pull
- Changed module structure: `climada.hazard.Hazard` has been split into the modules `base`, `io` and `plot` [#871](https://github.com/CLIMADA-project/climada_python/pull/871)
- `Impact.from_hdf5` now calls `str` on `event_name` data that is not strings, and issue a warning then [#894](https://github.com/CLIMADA-project/climada_python/pull/894)
- `Impact.write_hdf5` now throws an error if `event_name` is does not contain strings exclusively [#894](https://github.com/CLIMADA-project/climada_python/pull/894)
- Split `climada.hazard.trop_cyclone` module into smaller submodules without affecting module usage [#911](https://github.com/CLIMADA-project/climada_python/pull/911)

### Fixed

- Avoid an issue where a Hazard subselection would have a fraction matrix with only zeros as entries by throwing an error [#866](https://github.com/CLIMADA-project/climada_python/pull/866)
- Allow downgrading the Python bugfix version to improve environment compatibility [#900](https://github.com/CLIMADA-project/climada_python/pull/900)
- Fix broken links in `CONTRIBUTING.md` [#900](https://github.com/CLIMADA-project/climada_python/pull/900)
- When writing `TCTracks` to NetCDF, only apply compression to `float` or `int` data types. This fixes a downstream issue, see [climada_petals#135](https://github.com/CLIMADA-project/climada_petals/issues/135) [#911](https://github.com/CLIMADA-project/climada_python/pull/911)

### Added

Expand Down
20 changes: 19 additions & 1 deletion climada/hazard/tc_tracks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1383,7 +1383,10 @@ def write_hdf5(self, file_name, complevel=5):
df_attrs = pd.DataFrame([t.attrs for t in data], index=ds_combined["storm"].to_series())
ds_combined = xr.merge([ds_combined, df_attrs.to_xarray()])

encoding = {v: dict(zlib=True, complevel=complevel) for v in ds_combined.data_vars}
encoding = {
v: dict(zlib=_zlib_from_dataarray(ds_combined[v]), complevel=complevel)
for v in ds_combined.data_vars
}
LOGGER.info('Writing %d tracks to %s', self.size, file_name)
ds_combined.to_netcdf(file_name, encoding=encoding)

Expand Down Expand Up @@ -2435,3 +2438,18 @@ def set_category(max_sus_wind, wind_unit='kn', saffir_scale=None):
return (np.argwhere(max_wind < saffir_scale) - 1)[0][0]
except IndexError:
return -1

def _zlib_from_dataarray(data_var: xr.DataArray) -> bool:
"""Return true if data_var is of numerical type, return False otherwise

Parameters
----------
data_var : xr.DataArray

Returns
-------
bool
"""
if np.issubdtype(data_var.dtype, float) or np.issubdtype(data_var.dtype, int):
return True
return False
243 changes: 4 additions & 239 deletions climada/hazard/test/test_trop_cyclone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,25 +26,21 @@

import numpy as np
from scipy import sparse
import xarray as xr

import climada.hazard.test as hazard_test
from climada.util import ureg
from climada.test import get_test_file
from climada.hazard.tc_tracks import TCTracks
from climada.hazard.trop_cyclone import (
TropCyclone, get_close_centroids, _vtrans, _B_holland_1980, _bs_holland_2008,
_v_max_s_holland_2008, _x_holland_2010, _stat_holland_1980, _stat_holland_2010, _stat_er_2011,
tctrack_to_si, MBAR_TO_PA, KM_TO_M, H_TO_S,
)
from climada.hazard.trop_cyclone.trop_cyclone import (
TropCyclone, )
from climada.hazard.centroids.centr import Centroids
import climada.hazard.test as hazard_test


DATA_DIR = Path(hazard_test.__file__).parent.joinpath('data')

TEST_TRACK = DATA_DIR.joinpath("trac_brb_test.csv")
TEST_TRACK_SHORT = DATA_DIR.joinpath("trac_short_test.csv")


CENTR_TEST_BRB = Centroids.from_hdf5(get_test_file('centr_test_brb', file_format='hdf5'))


Expand Down Expand Up @@ -294,236 +290,6 @@ def test_two_files_pass(self):
self.assertEqual(tc_haz.fraction.nonzero()[0].size, 0)
self.assertEqual(tc_haz.intensity.nonzero()[0].size, 0)

class TestWindfieldHelpers(unittest.TestCase):
"""Test helper functions of TC wind field model"""

def test_get_close_centroids_pass(self):
"""Test get_close_centroids function."""
si_track = xr.Dataset({
"lat": ("time", np.array([0, -0.5, 0])),
"lon": ("time", np.array([0.9, 2, 3.2])),
}, attrs={"mid_lon": 0.0})
centroids = np.array([
[0, -0.2], [0, 0.9], [-1.1, 1.2], [1, 2.1], [0, 4.3], [0.6, 3.8], [0.9, 4.1],
])
centroids_close, mask_close, mask_close_alongtrack = (
get_close_centroids(si_track, centroids, 112.0)
)
self.assertEqual(centroids_close.shape[0], mask_close.sum())
self.assertEqual(mask_close_alongtrack.shape[0], si_track.sizes["time"])
self.assertEqual(mask_close_alongtrack.shape[1], centroids_close.shape[0])
np.testing.assert_equal(mask_close_alongtrack.any(axis=0), True)
np.testing.assert_equal(mask_close, np.array(
[False, True, True, False, False, True, False]
))
np.testing.assert_equal(mask_close_alongtrack, np.array([
[True, False, False],
[False, True, False],
[False, False, True],
]))
np.testing.assert_equal(centroids_close, centroids[mask_close])

# example where antimeridian is crossed
si_track = xr.Dataset({
"lat": ("time", np.linspace(-10, 10, 11)),
"lon": ("time", np.linspace(170, 200, 11)),
}, attrs={"mid_lon": 180.0})
centroids = np.array([[-11, 169], [-7, 176], [4, -170], [10, 170], [-10, -160]])
centroids_close, mask_close, mask_close_alongtrack = (
get_close_centroids(si_track, centroids, 600.0)
)
self.assertEqual(centroids_close.shape[0], mask_close.sum())
self.assertEqual(mask_close_alongtrack.shape[0], si_track.sizes["time"])
self.assertEqual(mask_close_alongtrack.shape[1], centroids_close.shape[0])
np.testing.assert_equal(mask_close_alongtrack.any(axis=0), True)
np.testing.assert_equal(mask_close, np.array([True, True, True, False, False]))
np.testing.assert_equal(centroids_close, np.array([
# the longitudinal coordinate of the third centroid is normalized
[-11, 169], [-7, 176], [4, 190],
]))

def test_B_holland_1980_pass(self):
"""Test _B_holland_1980 function."""
si_track = xr.Dataset({
"pdelta": ("time", MBAR_TO_PA * np.array([15, 30])),
"vgrad": ("time", [35, 40]),
"rho_air": ("time", [1.15, 1.15])
})
_B_holland_1980(si_track)
np.testing.assert_array_almost_equal(si_track["hol_b"], [2.5, 1.667213])

si_track = xr.Dataset({
"pdelta": ("time", MBAR_TO_PA * np.array([4.74, 15, 30, 40])),
"vmax": ("time", [np.nan, 22.5, 25.4, 42.5]),
"rho_air": ("time", [1.2, 1.2, 1.2, 1.2])
})
_B_holland_1980(si_track, gradient_to_surface_winds=0.9)
np.testing.assert_allclose(si_track["hol_b"], [np.nan, 1.101, 0.810, 1.473], atol=1e-3)

def test_bs_holland_2008_pass(self):
"""Test _bs_holland_2008 function. Compare to MATLAB reference."""
si_track = xr.Dataset({
"tstep": ("time", H_TO_S * np.array([1.0, 1.0, 1.0])),
"lat": ("time", [12.299999504631234, 12.299999504631343, 12.299999279463769]),
"pdelta": ("time", MBAR_TO_PA * np.array([4.74, 4.73, 4.73])),
"cen": ("time", MBAR_TO_PA * np.array([1005.2585, 1005.2633, 1005.2682])),
"vtrans_norm": ("time", [np.nan, 5.241999541820597, 5.123882725120426]),
})
_bs_holland_2008(si_track)
np.testing.assert_allclose(
si_track["hol_b"], [np.nan, 1.27, 1.27], atol=1e-2
)

def test_v_max_s_holland_2008_pass(self):
"""Test _v_max_s_holland_2008 function."""
# Numbers analogous to test_B_holland_1980_pass
si_track = xr.Dataset({
"pdelta": ("time", MBAR_TO_PA * np.array([15, 30])),
"hol_b": ("time", [2.5, 1.67]),
"rho_air": ("time", [1.15, 1.15]),
})
_v_max_s_holland_2008(si_track)
np.testing.assert_array_almost_equal(si_track["vmax"], [34.635341, 40.033421])

def test_holland_2010_pass(self):
"""Test Holland et al. 2010 wind field model."""
# The parameter "x" is designed to be exactly 0.5 inside the radius of max wind (RMW) and
# to increase or decrease linearly outside of it in radial direction.
#
# An increase (decrease) of "x" outside of the RMW is for cases where the max wind is very
# high (low), but the RMW is still comparably large (small). This means, wind speeds need
# to decay very sharply (only moderately) outside of the RMW to reach the low prescribed
# peripheral wind speeds.
#
# The "hol_b" parameter tunes the meaning of a "comparably" large or small RMW.
si_track = xr.Dataset({
# four test cases:
# - low vmax, moderate RMW: x decreases moderately
# - large hol_b: x decreases sharply
# - very low vmax: x decreases so much, it needs to be clipped at 0
# - large vmax, large RMW: x increases
"rad": ("time", KM_TO_M * np.array([75, 75, 75, 90])),
"vmax": ("time", [35.0, 35.0, 16.0, 90.0]),
"hol_b": ("time", [1.75, 2.5, 1.9, 1.6]),
})
d_centr = KM_TO_M * np.array([
# first column is for locations within the storm eye
# second column is for locations at or close to the radius of max wind
# third column is for locations outside the storm eye
# fourth column is for locations exactly at the peripheral radius
# fifth column is for locations outside the peripheral radius
[0., 75, 220, 300, 490],
[30, 74, 170, 300, 501],
[21, 76, 230, 300, 431],
[32, 91, 270, 300, 452],
], dtype=float)
close_centr = np.array([
# note that we set one of these to "False" for testing
[True, True, True, True, True],
[True, True, True, True, False],
[True, True, True, True, True],
[True, True, True, True, True],
], dtype=bool)
hol_x = _x_holland_2010(si_track, d_centr, close_centr)
np.testing.assert_array_almost_equal(hol_x, [
[0.5, 0.500000, 0.485077, 0.476844, 0.457291],
[0.5, 0.500000, 0.410997, 0.400000, 0.000000],
[0.5, 0.497620, 0.400000, 0.400000, 0.400000],
[0.5, 0.505022, 1.403952, 1.554611, 2.317948],
])

v_ang_norm = _stat_holland_2010(si_track, d_centr, close_centr, hol_x)
np.testing.assert_allclose(v_ang_norm, [
# first column: converge to 0 when approaching storm eye
# second column: vmax at RMW
# fourth column: peripheral speed (17 m/s) at peripheral radius (unless x is clipped!)
[ 0.000000, 35.000000, 21.181497, 17.000000, 12.1034610],
[ 1.296480, 34.990037, 21.593755, 12.891313, 0.0000000],
[ 0.321952, 15.997500, 9.712006, 8.087240, 6.2289690],
[24.823469, 89.992938, 24.381965, 17.000000, 1.9292020],
], atol=1e-6)

def test_stat_holland_1980(self):
"""Test _stat_holland_1980 function. Compare to MATLAB reference."""
d_centr = KM_TO_M * np.array([
[299.4501244109841, 291.0737897183741, 292.5441003235722, 40.665454622610511],
[293.6067129546862, 1000.0, 298.2652319413182, 70.0],
])
si_track = xr.Dataset({
"rad": ("time", KM_TO_M * np.array([40.665454622610511, 75.547902916671745])),
"hol_b": ("time", [1.486076257880692, 1.265551666104679]),
"pdelta": ("time", MBAR_TO_PA * np.array([39.12, 4.73])),
"lat": ("time", [-14.089110370469488, 12.299999279463769]),
"cp": ("time", [3.54921922e-05, 3.10598285e-05]),
"rho_air": ("time", [1.15, 1.15]),
})
mask = np.array([[True, True, True, True], [True, False, True, True]], dtype=bool)
v_ang_norm = _stat_holland_1980(si_track, d_centr, mask)
np.testing.assert_allclose(
v_ang_norm, [[11.28, 11.68, 11.61, 42.41], [5.38, 0, 5.28, 12.76]], atol=1e-2,
)

# without Coriolis force, values are higher, esp. far away from the center:
v_ang_norm = _stat_holland_1980(si_track, d_centr, mask, cyclostrophic=True)
np.testing.assert_allclose(
v_ang_norm, [[15.72, 16.04, 15.98, 43.13], [8.84, 0, 8.76, 13.81]], atol=1e-2,
)

d_centr = np.array([[], []])
mask = np.ones_like(d_centr, dtype=bool)
v_ang_norm = _stat_holland_1980(si_track, d_centr, mask)
np.testing.assert_array_equal(v_ang_norm, np.array([[], []]))

def test_er_2011_pass(self):
"""Test Emanuel and Rotunno 2011 wind field model."""
# test at centroids within and outside of radius of max wind
d_centr = KM_TO_M * np.array([[35, 70, 75, 220], [30, 150, 1000, 300]], dtype=float)
si_track = xr.Dataset({
"rad": ("time", KM_TO_M * np.array([75.0, 40.0])),
"vmax": ("time", [35.0, 40.0]),
"lat": ("time", [20.0, 27.0]),
"cp": ("time", [4.98665369e-05, 6.61918149e-05]),
})
mask = np.array([[True, True, True, True], [True, False, True, True]], dtype=bool)
v_ang_norm = _stat_er_2011(si_track, d_centr, mask)
np.testing.assert_array_almost_equal(v_ang_norm,
[[28.258025, 36.782418, 36.869995, 22.521237],
[39.670883, 0, 3.300626, 10.827206]])

def test_vtrans_pass(self):
"""Test _vtrans function. Compare to MATLAB reference."""
tc_track = TCTracks.from_processed_ibtracs_csv(TEST_TRACK)
tc_track.equal_timestep()
track_ds = tc_track.data[0]

si_track = tctrack_to_si(track_ds)
_vtrans(si_track)

to_kn = (1.0 * ureg.meter / ureg.second).to(ureg.knot).magnitude

self.assertEqual(si_track["vtrans_norm"].size, track_ds["time"].size)
self.assertEqual(si_track["vtrans_norm"].values[0], 0)
self.assertAlmostEqual(si_track["vtrans_norm"].values[1] * to_kn, 10.191466246)

def testtctrack_to_si(self):
""" Test tctrack_to_si should create the same vmax output independent of the input unit """
tc_track = TCTracks.from_processed_ibtracs_csv(TEST_TRACK_SHORT).data[0]

tc_track_kmph = tc_track.copy(deep=True)
tc_track_kmph['max_sustained_wind'] *= (
(1.0 * ureg.knot).to(ureg.km / ureg.hour).magnitude
)
tc_track_kmph.attrs['max_sustained_wind_unit'] = 'km/h'

si_track = tctrack_to_si(tc_track)
si_track_from_kmph = tctrack_to_si(tc_track_kmph)

np.testing.assert_array_almost_equal(si_track["vmax"], si_track_from_kmph["vmax"])

tc_track.attrs['max_sustained_wind_unit'] = 'elbows/fortnight'
with self.assertRaises(ValueError):
tctrack_to_si(tc_track)


class TestClimateSce(unittest.TestCase):
def test_apply_criterion_track(self):
Expand Down Expand Up @@ -682,7 +448,6 @@ def tearDown(self):

if __name__ == "__main__":
TESTS = unittest.TestLoader().loadTestsFromTestCase(TestReader)
TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestWindfieldHelpers))
TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestClimateSce))
TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestDumpReloadCycle))
unittest.TextTestRunner(verbosity=2).run(TESTS)
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