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Merged
peanutfun merged 12 commits into from
Dec 10, 2025
+148 −39
Merged

Add select method to HazardForecast #1185

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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
c1eefdd
add HazardForecast.select and extent test
ValentinGebhart Dec 9, 2025
781cea6
Merge branch 'idx_member_leadtime' into add_sel_to_haz_and_imp_forecast
ValentinGebhart Dec 9, 2025
db1f23d
Merge branch 'forecast-class' into add_sel_to_haz_and_imp_forecast
ValentinGebhart Dec 9, 2025
49437b2
Merge branch 'forecast-class' into add_sel_to_haz_and_imp_forecast
ValentinGebhart Dec 9, 2025
64aec3d
Update docstring, reoganize tests
peanutfun Dec 9, 2025
65c1c20
Merge branch 'add_sel_to_haz_and_imp_forecast' of https://github.com/…
peanutfun Dec 9, 2025
f584008
Remove 'Self' for Py 3.10 compatibility
peanutfun Dec 9, 2025
c123dec
change variable name in select function
ValentinGebhart Dec 10, 2025
08eb263
Reorganize tests and and test for null select
peanutfun Dec 10, 2025
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61 changes: 61 additions & 0 deletions climada/hazard/forecast.py
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Original file line number Diff line number Diff line change
Expand Up @@ -104,3 +104,64 @@ 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,
member=None,
lead_time=None,
event_names=None,
event_id=None,
date=None,
orig=None,
reg_id=None,
extent=None,
reset_frequency=False,
):
"""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
-------
HazardForecast

See Also
--------
:py:meth:`~climada.hazard.base.Hazard.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_id = (
np.intersect1d(event_id, event_id_from_forecast_mask)
if event_id is not None
else event_id_from_forecast_mask
)

return super().select(
event_names=event_names,
event_id=event_id,
date=date,
orig=orig,
reg_id=reg_id,
extent=extent,
reset_frequency=reset_frequency,
)
126 changes: 87 additions & 39 deletions climada/hazard/test/test_forecast.py
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Original file line number Diff line number Diff line change
Expand Up @@ -107,46 +107,94 @@ def test_hazard_forecast_concat(haz_fc, lead_time, member):
npt.assert_array_equal(haz_fc_concat.member, np.concatenate([member, member]))


@pytest.mark.parametrize(
"var, var_select",
[("event_id", "event_id"), ("event_name", "event_names"), ("date", "date")],
)
def test_hazard_forecast_select(haz_fc, lead_time, member, haz_kwargs, var, var_select):
"""Check if Hazard.select works on the derived class"""

select_mask = np.array([3, 2])
ordered_select_mask = np.array([3, 2])
if var == "date":
# Date needs to be a valid delta
select_mask = np.array([2, 3])
ordered_select_mask = np.array([2, 3])

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})
# Note: order is preserved
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],
)
class TestSelect:

assert haz_fc_sel.centroids == haz_fc.centroids
@pytest.mark.parametrize(
"var, var_select",
[("event_id", "event_id"), ("event_name", "event_names"), ("date", "date")],
)
def test_base_class_select(
self, haz_fc, lead_time, member, haz_kwargs, var, var_select
):
"""Check if Hazard.select works on the derived class"""

select_mask = np.array([3, 2])
ordered_select_mask = np.array([3, 2])
if var == "date":
# Date needs to be a valid delta
select_mask = np.array([2, 3])
ordered_select_mask = np.array([2, 3])

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})
# Note: order is preserved
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

def test_derived_select_single(self, haz_fc, lead_time, member):
haz_fc_select = haz_fc.select(member=[3, 0])
idx = np.array([0, 3])
npt.assert_array_equal(haz_fc_select.event_id, haz_fc.event_id[idx])
npt.assert_array_equal(haz_fc_select.member, member[idx])
npt.assert_array_equal(haz_fc_select.lead_time, lead_time[idx])

haz_fc_select = haz_fc.select(lead_time=lead_time[np.array([3, 0])])
npt.assert_array_equal(haz_fc_select.event_id, haz_fc.event_id[idx])
npt.assert_array_equal(haz_fc_select.member, member[idx])
npt.assert_array_equal(haz_fc_select.lead_time, lead_time[idx])

def test_derived_select_intersections(self, haz_fc, lead_time, member, haz_kwargs):
haz_fc_select = haz_fc.select(event_id=[1, 4], member=[0, 1, 2])
npt.assert_array_equal(haz_fc_select.event_id, haz_fc.event_id[np.array([0])])

haz_fc_select = haz_fc.select(
event_id=[1, 2, 4], member=[0, 1, 2], lead_time=lead_time[1:3]
)
npt.assert_array_equal(haz_fc_select.event_id, haz_fc.event_id[np.array([1])])

# Test "outer"
haz_fc2 = HazardForecast(
lead_time=lead_time, member=np.zeros_like(member, dtype="int"), **haz_kwargs
)
haz_fc_select = haz_fc2.select(event_id=[1, 2, 4], member=[0])
npt.assert_array_equal(haz_fc_select.event_id, [1, 2, 4])
npt.assert_array_equal(haz_fc_select.member, [0, 0, 0])
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@ValentinGebhart ValentinGebhart Dec 9, 2025

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@peanutfun Should we add a test for "no match"?
Something like
haz_fc_empty = haz_fc.select(event_names = haz_fc.event_name[:2], member=member[-2:])
will raise
IndexError: arrays used as indices must be of integer (or boolean) type
that we could test for. Not sure if this is behaviour that we want, though, I would rather expect an empty HazardForecast. But it is the same behaviour as Hazard.select()

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@peanutfun peanutfun Dec 10, 2025

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You are correct, there should be a test. But we should not go further than the functionality of Hazard.select. If that's the error from the base class, I'm fine with it being raised here, too.


def test_derived_select_null(self, haz_fc, haz_kwargs):
haz_fc_select = haz_fc.select()
assert_hazard_kwargs(haz_fc_select, **haz_kwargs)

with pytest.raises(IndexError):
haz_fc.select(event_id=[-1])
with pytest.raises(IndexError):
haz_fc.select(member=[-1])
with pytest.raises(IndexError):
haz_fc.select(
lead_time=[np.timedelta64("2", "Y").astype("timedelta64[ns]")]
)


def test_write_read_hazard_forecast(haz_fc, tmp_path):
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