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Merged
peanutfun merged 7 commits into from
Dec 9, 2025
+87 −12
Merged

Forecast/select extended tests #1182

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396239a
Add extended select impact forecast test
Dec 9, 2025
260dfc2
Add extended hazard forecast test
Dec 9, 2025
d7084ce
Update climada/hazard/test/test_forecast.py
chahank Dec 9, 2025
bf57de1
Update climada/hazard/test/test_forecast.py
chahank Dec 9, 2025
9249e01
Update climada/engine/test/test_impact_forecast.py
chahank Dec 9, 2025
dd78fac
Rewrite select test with test parameters
Dec 9, 2025
4b7d4bc
Merge remote-tracking branch 'origin/forecast-class' into forecast/se…
Dec 9, 2025
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47 changes: 41 additions & 6 deletions climada/engine/test/test_impact_forecast.py
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Original file line number Diff line number Diff line change
Expand Up @@ -86,14 +86,49 @@ def test_impact_forecast_from_impact(

def test_impact_forecast_select(impact_forecast, lead_time, member, impact_kwargs):
"""Check if Impact.select works on the derived class"""
event_ids = impact_kwargs["event_id"][np.array([2, 0])]
impact_fc = impact_forecast.select(event_ids=event_ids)
# NOTE: Events keep their original order
select_mask = np.array([1, 2])
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ordered_select_mask = np.array([1, 2])
vars_to_select = {
"event_id": "event_ids",
"event_name": "event_names",
"date": "dates",
}
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for var, var_select in vars_to_select.items():
var_value = np.array(impact_kwargs[var])[select_mask]
# event_name is a list, convert to numpy array for indexing
impact_fc = impact_forecast.select(**{var_select: var_value})
# NOTE: Events keep their original order
npt.assert_array_equal(
impact_fc.event_id,
impact_forecast.event_id[ordered_select_mask],
)
npt.assert_array_equal(
impact_fc.event_name,
np.array(impact_forecast.event_name)[ordered_select_mask],
)
npt.assert_array_equal(
impact_fc.date, impact_forecast.date[ordered_select_mask]
)
npt.assert_array_equal(
impact_fc.frequency, impact_forecast.frequency[ordered_select_mask]
)
npt.assert_array_equal(impact_fc.member, member[ordered_select_mask])
npt.assert_array_equal(impact_fc.lead_time, lead_time[ordered_select_mask])
npt.assert_array_equal(
impact_fc.imp_mat.todense(),
impact_forecast.imp_mat.todense()[ordered_select_mask],
)

exp_col = 0
select_mask = np.array([exp_col])
coord_exp = impact_kwargs["coord_exp"][select_mask]
impact_fc = impact_forecast.select(coord_exp=coord_exp)
npt.assert_array_equal(impact_fc.member, member)
npt.assert_array_equal(impact_fc.lead_time, lead_time)
npt.assert_array_equal(
impact_fc.event_id, impact_forecast.event_id[np.array([0, 2])]
impact_fc.imp_mat.todense(), impact_forecast.imp_mat.todense()[:, exp_col]
)
npt.assert_array_equal(impact_fc.member, member[np.array([0, 2])])
npt.assert_array_equal(impact_fc.lead_time, lead_time[np.array([0, 2])])
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@pytest.mark.skip("Concat from base class does not work")
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49 changes: 43 additions & 6 deletions climada/hazard/test/test_forecast.py
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Expand Up @@ -100,10 +100,47 @@ def test_hazard_forecast_concat(haz_fc, lead_time, member):
npt.assert_array_equal(haz_fc_concat.member, np.concatenate([member, member]))


def test_hazard_forecast_select(haz_fc, lead_time, member):
def test_hazard_forecast_select(haz_fc, lead_time, member, haz_kwargs):
"""Check if Hazard.select works on the derived class"""
haz_fc_select = haz_fc.select(event_id=[4, 1])
# NOTE: Events keep their original order
npt.assert_array_equal(haz_fc_select.event_id, haz_fc.event_id[np.array([3, 0])])
npt.assert_array_equal(haz_fc_select.member, member[np.array([3, 0])])
npt.assert_array_equal(haz_fc_select.lead_time, lead_time[np.array([3, 0])])
# haz_fc_select = haz_fc.select(event_id=[4, 1])
# # NOTE: Events keep their original order
# npt.assert_array_equal(haz_fc_select.event_id, haz_fc.event_id[np.array([3, 0])])
# npt.assert_array_equal(haz_fc_select.member, member[np.array([3, 0])])
# npt.assert_array_equal(haz_fc_select.lead_time, lead_time[np.array([3, 0])])
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select_mask = np.array([2, 3])
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ordered_select_mask = np.array([2, 3])
vars_to_select = {
"event_id": "event_id",
"event_name": "event_names",
"date": "date",
}
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for var, var_select in vars_to_select.items():
var_value = np.array(haz_kwargs[var])[select_mask]
# event_name is a list, convert to numpy array for indexing
haz_fc_sel = haz_fc.select(**{var_select: var_value})
npt.assert_array_equal(
haz_fc_sel.event_id,
haz_fc.event_id[ordered_select_mask],
)
npt.assert_array_equal(
haz_fc_sel.event_name,
np.array(haz_fc.event_name)[ordered_select_mask],
)
npt.assert_array_equal(haz_fc_sel.date, haz_fc.date[ordered_select_mask])
npt.assert_array_equal(
haz_fc_sel.frequency, haz_fc.frequency[ordered_select_mask]
)
npt.assert_array_equal(haz_fc_sel.member, member[ordered_select_mask])
npt.assert_array_equal(haz_fc_sel.lead_time, lead_time[ordered_select_mask])
npt.assert_array_equal(
haz_fc_sel.intensity.todense(),
haz_fc.intensity.todense()[ordered_select_mask],
)
npt.assert_array_equal(
haz_fc_sel.fraction.todense(),
haz_fc.fraction.todense()[ordered_select_mask],
)

assert haz_fc_sel.centroids == haz_fc.centroids
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