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Change leadtime from datetime to timedelta #1172

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Merged
peanutfun merged 5 commits into from
Dec 8, 2025
+14 −16
Merged

Change leadtime from datetime to timedelta #1172

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742eaf1
change leadtime to timedelta
ValentinGebhart Dec 8, 2025
628387a
Merge branch 'forecast-class' into forecast_base_class_leadtime
ValentinGebhart Dec 8, 2025
b98deed
adapt impactforecast docstrings and tests
ValentinGebhart Dec 8, 2025
84e3cbd
Merge branch 'forecast-class' into forecast_base_class_leadtime
peanutfun Dec 8, 2025
a8b0284
Remove stray comment
peanutfun Dec 8, 2025
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8 changes: 4 additions & 4 deletions climada/engine/impact_forecast.py
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Expand Up @@ -45,9 +45,9 @@ def __init__(
Parameters
----------
lead_time : np.ndarray, optional
The lead time associated with each event entry
The lead time associated with each event entry, given as timedelta64 type
member : np.ndarray, optional
The ensemble member associated with each event entry
The ensemble member associated with each event entry, given as integers
impact_kwargs
Keyword-arguments passed to ~:py:class`climada.engine.impact.Impact`.
"""
Expand All @@ -65,9 +65,9 @@ def from_impact(
impact : climada.engine.impact.Impact
The impact object whose data to use in the forecast object
lead_time : np.ndarray, optional
The lead time associated with each event entry
The lead time associated with each event entry, given as timedelta64 type
member : np.ndarray, optional
The ensemble member associated with each event entry
The ensemble member associated with each event entry, given as integers
"""
with log_level("WARNING", "climada.engine.impact"):
return cls(
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2 changes: 1 addition & 1 deletion climada/engine/test/test_impact_forecast.py
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Expand Up @@ -52,7 +52,7 @@ def assert_impact_kwargs(impact: Impact, **kwargs):


class TestImpactForecastInit:
lead_time = pd.date_range("2000-01-01", "2000-01-02", periods=6).to_numpy()
lead_time = pd.timedelta_range(start="1 day", periods=6).to_numpy()
member = np.arange(6)

def test_impact_forecast_init(self, impact_kwargs):
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2 changes: 0 additions & 2 deletions climada/hazard/test/test_forecast.py
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Expand Up @@ -29,8 +29,6 @@
from climada.hazard.forecast import HazardForecast
from climada.hazard.test.test_base import hazard_kwargs

# --- Examples for fixtures and test organization --- #


@pytest.fixture
def haz_kwargs():
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4 changes: 2 additions & 2 deletions climada/util/forecast.py
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Expand Up @@ -28,8 +28,8 @@ class Forecast:
Attributes
----------
lead_time : np.ndarray
Array of forecast lead times, given as datetime64 objects.
Represents the time points for which forecasts are made.
Array of forecast lead times, given as timedelta64 objects.
Represents the lead times of the forecasts.
member : np.ndarray
Array of ensemble member identifiers, given as integers.
Represents different forecast ensemble members.
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14 changes: 7 additions & 7 deletions climada/util/test/test_forecast.py
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Expand Up @@ -21,6 +21,7 @@

import numpy as np
import numpy.testing as npt
import pandas as pd

from climada.util.forecast import Forecast

Expand All @@ -34,19 +35,18 @@ def test_forecast_init():
forecast = Forecast(member=np.array([1, 2]))
npt.assert_array_equal(forecast.member, np.array([1, 2]), strict=True)

forecast = Forecast(lead_time=np.array([1, 2]))
npt.assert_array_equal(forecast.lead_time, np.array([1, 2]), strict=True)
forecast = Forecast(lead_time=np.array([6, 12], dtype="timedelta64[h]"))
npt.assert_array_equal(
forecast.lead_time, np.array([6, 12], dtype="timedelta64[h]"), strict=True
)

forecast = Forecast(lead_time=np.array([1, 2]), member=[3, 4])
npt.assert_array_equal(forecast.lead_time, np.array([1, 2]), strict=True)
npt.assert_array_equal(forecast.member, np.array([3, 4]), strict=True)
assert isinstance(forecast.member, np.ndarray)

# Test with datetime64 including seconds
lead_times_seconds = np.array(
["2024-01-01T00:00:00", "2024-01-01T00:01:00", "2024-01-01"],
dtype="datetime64[s]",
)
lead_times_seconds = pd.timedelta_range(start="1 day", periods=4).to_numpy()
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("datetime64[s]")
assert forecast.lead_time.dtype == np.dtype("timedelta64[ns]")
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