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Add select to ImpactForecast #1188

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peanutfun merged 5 commits into from
Dec 10, 2025
Merged

Add select to ImpactForecast #1188

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574a3a7
first draft impact forecast select
ValentinGebhart Dec 9, 2025
00cfde0
add test and docstrings
ValentinGebhart Dec 9, 2025
b8dc687
change variable name in select
ValentinGebhart Dec 10, 2025
acaf55f
Apply suggestion from @peanutfun
peanutfun Dec 10, 2025
96a3f84
Reorganize tests and add tests for empty selections
peanutfun Dec 10, 2025
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56 changes: 56 additions & 0 deletions climada/engine/impact_forecast.py
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Original file line number Diff line number Diff line change
Expand Up @@ -184,3 +184,59 @@ def _check_sizes(self):
num_entries = len(self.event_id)
size(exp_len=num_entries, var=self.member, var_name="Forecast.member")
size(exp_len=num_entries, var=self.lead_time, var_name="Forecast.lead_time")

def select(
self,
event_ids=None,
event_names=None,
dates=None,
coord_exp=None,
reset_frequency=False,
member=None,
lead_time=None,
):
"""Select entries based on the parameters and return a new instance.
The selection will contain the intersection of all given parameters.

Parameters
----------
member : Sequence of ints
Ensemble members to select
lead_time : Sequence of numpy.timedelta64
Lead times to select

Returns
-------
ImpactForecast

See Also
--------
:py:meth:`~climada.engine.impact.Impact.select`
"""
if member is not None or lead_time is not None:
mask_member = (
self.idx_member(member)
if member is not None
else np.full_like(self.member, True, dtype=bool)
)
mask_lead_time = (
self.idx_lead_time(lead_time)
if lead_time is not None
else np.full_like(self.lead_time, True, dtype=bool)
)
event_id_from_forecast_mask = np.asarray(self.event_id)[
(mask_member & mask_lead_time)
]
event_ids = (
np.intersect1d(event_ids, event_id_from_forecast_mask)
if event_ids is not None
else event_id_from_forecast_mask
)

return super().select(
event_ids=event_ids,
event_names=event_names,
dates=dates,
coord_exp=coord_exp,
reset_frequency=reset_frequency,
)
152 changes: 104 additions & 48 deletions climada/engine/test/test_impact_forecast.py
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Original file line number Diff line number Diff line change
Expand Up @@ -92,58 +92,114 @@ def test_impact_forecast_from_impact(
self.assert_impact_kwargs(impact_forecast, **impact_kwargs)


@pytest.mark.parametrize(
"var, var_select",
[("event_id", "event_ids"), ("event_name", "event_names"), ("date", "dates")],
)
def test_impact_forecast_select_events(
impact_forecast, lead_time, member, impact_kwargs, var, var_select
):
"""Check if Impact.select works on the derived class"""
select_mask = np.array([2, 1])
ordered_select_mask = np.array([1, 2])
if var == "date":
# Date needs to be a valid delta
select_mask = np.array([1, 2])
ordered_select_mask = np.array([1, 2])
class TestSelect:

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],
@pytest.mark.parametrize(
"var, var_select",
[("event_id", "event_ids"), ("event_name", "event_names"), ("date", "dates")],
)
def test_base_class_select(
self, impact_forecast, lead_time, member, impact_kwargs, var, var_select
):
"""Check if Impact.select works on the derived class"""
select_mask = np.array([2, 1])
ordered_select_mask = np.array([1, 2])
if var == "date":
# Date needs to be a valid delta
select_mask = np.array([1, 2])
ordered_select_mask = np.array([1, 2])

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],
)

def test_impact_forecast_select_exposure(
self, impact_forecast, lead_time, member, impact_kwargs
):
"""Check if Impact.select works on the derived class"""
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.imp_mat.todense(), impact_forecast.imp_mat.todense()[:, exp_col]
)

def test_impact_forecast_select_exposure(
impact_forecast, lead_time, member, impact_kwargs
):
"""Check if Impact.select works on the derived class"""
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.imp_mat.todense(), impact_forecast.imp_mat.todense()[:, exp_col]
)
def test_derived_select_single(self, impact_forecast, lead_time, member):
imp_fc_select = impact_forecast.select(member=[2, 0])
idx = np.array([0, 2])
npt.assert_array_equal(imp_fc_select.event_id, impact_forecast.event_id[idx])
npt.assert_array_equal(imp_fc_select.member, member[idx])
npt.assert_array_equal(imp_fc_select.lead_time, lead_time[idx])

imp_fc_select = impact_forecast.select(lead_time=lead_time[np.array([2, 0])])
npt.assert_array_equal(imp_fc_select.event_id, impact_forecast.event_id[idx])
npt.assert_array_equal(imp_fc_select.member, member[idx])
npt.assert_array_equal(imp_fc_select.lead_time, lead_time[idx])

def test_derived_select_intersections(
self, impact_forecast, lead_time, member, impact_kwargs
):
imp_fc_select = impact_forecast.select(event_ids=[10, 14], member=[0, 1, 2])
npt.assert_array_equal(
imp_fc_select.event_id, impact_forecast.event_id[np.array([0])]
)

imp_fc_select = impact_forecast.select(
event_ids=[10, 11, 13], member=[0, 1, 2], lead_time=lead_time[1:3]
)
npt.assert_array_equal(
imp_fc_select.event_id, impact_forecast.event_id[np.array([1])]
)

# Test "outer"
impact_forecast2 = ImpactForecast(
lead_time=lead_time,
member=np.zeros_like(member, dtype="int"),
**impact_kwargs,
)
imp_fc_select = impact_forecast2.select(event_ids=[10, 11, 13], member=[0])
npt.assert_array_equal(imp_fc_select.event_id, [10, 11, 13])
npt.assert_array_equal(imp_fc_select.member, [0, 0, 0])

def test_no_select(self, impact_forecast, impact_kwargs):
imp_fc_select = impact_forecast.select()
npt.assert_array_equal(
imp_fc_select.imp_mat.todense(), impact_forecast.imp_mat.todense()
)

num_centroids = len(impact_kwargs["coord_exp"])
imp_fc_select = impact_forecast.select(event_names=["aaaaa", "foo"])
assert imp_fc_select.imp_mat.shape == (0, num_centroids)
imp_fc_select = impact_forecast.select(event_ids=[-1, 1002])
assert imp_fc_select.imp_mat.shape == (0, num_centroids)
imp_fc_select = impact_forecast.select(member=[-1])
assert imp_fc_select.imp_mat.shape == (0, num_centroids)
imp_fc_select = impact_forecast.select(np.timedelta64("3", "Y"))
assert imp_fc_select.imp_mat.shape == (0, num_centroids)


@pytest.mark.skip("Concat from base class does not work")
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