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add idx boolean selection for member and leadtime #1183

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Merged
peanutfun merged 4 commits into from
Dec 9, 2025
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Merged

add idx boolean selection for member and leadtime #1183

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73ae20e
add idx boolean selection for member and leadtime
elianekobler Dec 9, 2025
3531c78
adapt docstrings
elianekobler Dec 9, 2025
12bf2b6
Update docstrings
peanutfun Dec 9, 2025
f0fbc7d
Add test for weird data types
peanutfun Dec 9, 2025
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32 changes: 32 additions & 0 deletions climada/util/forecast.py
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Original file line number Diff line number Diff line change
Expand Up @@ -56,3 +56,35 @@ def __init__(
)
self.member = np.asarray(member) if member is not None else np.array([])
super().__init__(**kwargs)

def idx_member(self, member: np.ndarray) -> np.ndarray:
"""Return boolean array where self.member == member using numpy.isin()

Parameters
----------
member : np.ndarray
Array of ensemble members (ints) for which to return an indexer

Returns
-------
np.ndarray
Boolean array where self.member is in member.
"""

return np.isin(self.member, member)

def idx_lead_time(self, lead_time: np.ndarray) -> np.ndarray:
"""Return boolean array where self.lead_time == lead_time using numpy.isin()

Parameters
----------
lead_time : np.ndarray
Array of lead times (numpy.timedelta64) for which to return an indexer

Returns
-------
np.ndarray
Boolean array where self.lead_time is in lead_time.
"""

return np.isin(self.lead_time, lead_time)
43 changes: 43 additions & 0 deletions climada/util/test/test_forecast.py
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Expand Up @@ -50,3 +50,46 @@ def test_forecast_init():
forecast = Forecast(lead_time=lead_times_seconds, member=[1, 2, 3])
npt.assert_array_equal(forecast.lead_time, lead_times_seconds, strict=True)
assert forecast.lead_time.dtype == np.dtype("timedelta64[ns]")


def test_idx_member():
"""Test idx_member method of Forecast class."""
forecast = Forecast(member=np.array([1, 2, 3, 4]))
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@chahank chahank Dec 9, 2025 •
edited
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What happens if I put forecast = Forecast(member=np.array([1, 1, 3, -5, '6'])) ?

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@peanutfun peanutfun Dec 9, 2025

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Works completely fine. The isin compares element-wise. Is this really necessary to test here?

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@peanutfun peanutfun Dec 9, 2025

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Added a test for unusual data


idx = forecast.idx_member(1)
npt.assert_array_equal(idx, np.array([True, False, False, False]), strict=True)

idx = forecast.idx_member(np.array([2, 4]))
npt.assert_array_equal(idx, np.array([False, True, False, True]), strict=True)

idx = forecast.idx_member([2, 4])
npt.assert_array_equal(idx, np.array([False, True, False, True]), strict=True)

idx = forecast.idx_member(None)
npt.assert_array_equal(idx, np.array([False, False, False, False]), strict=True)

# Try once with inconsitent types
forecast = Forecast(member=np.array(["1", -2, np.nan]))
npt.assert_array_equal(
forecast.idx_member([np.nan, "1"]), np.array([True, False, True]), strict=True
)


def test_idx_lead_time():
"""Test idx_lead_time method of Forecast class."""
forecast = Forecast(
lead_time=pd.timedelta_range(start="1 day", periods=4).to_numpy()
)

idx = forecast.idx_lead_time(
pd.timedelta_range(start="1 day", periods=4).to_numpy()[::2]
)
npt.assert_array_equal(idx, np.array([True, False, True, False]), strict=True)

idx = forecast.idx_lead_time(
pd.timedelta_range(start="1 day", periods=4).to_numpy()[0]
)
npt.assert_array_equal(idx, np.array([True, False, False, False]), strict=True)

idx = forecast.idx_lead_time(None)
npt.assert_array_equal(idx, np.array([False, False, False, False]), strict=True)
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