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Adapt Hazard from_hdf5 and write_hdf5 to forecast attributes #1181

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Merged
ValentinGebhart merged 3 commits into from
Dec 9, 2025
+26 −1
Merged

Adapt Hazard from_hdf5 and write_hdf5 to forecast attributes #1181

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34612c5
adapt from_hdf5 and write_hdf5
ValentinGebhart Dec 9, 2025
e123b7b
implement code review
ValentinGebhart Dec 9, 2025
9ce7f73
add comment to test
ValentinGebhart Dec 9, 2025
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10 changes: 9 additions & 1 deletion climada/hazard/io.py
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Original file line number Diff line number Diff line change
Expand Up @@ -917,6 +917,10 @@ def write_hdf5(self, file_name, todense=False):
# Centroids have their own write_hdf5 method,
# which is invoked at the end of this method (s.b.)
continue
elif var_name == "lead_time":
hf_data.create_dataset(
var_name, data=var_val.astype("timedelta64[ns]").astype("int64")
)
elif isinstance(var_val, sparse.csr_matrix):
if todense:
hf_data.create_dataset(var_name, data=var_val.toarray())
Expand Down Expand Up @@ -987,7 +991,11 @@ def from_hdf5(cls, file_name):
continue
if var_name == "centroids":
continue
if isinstance(var_val, np.ndarray) and var_val.ndim == 1:
if var_name == "lead_time":
hazard_kwargs[var_name] = np.array(hf_data.get(var_name)).astype(
"timedelta64[ns]"
)
elif isinstance(var_val, np.ndarray) and var_val.ndim == 1:
hazard_kwargs[var_name] = np.array(hf_data.get(var_name))
elif isinstance(var_val, sparse.csr_matrix):
hf_csr = hf_data.get(var_name)
Expand Down
17 changes: 17 additions & 0 deletions climada/hazard/test/test_forecast.py
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Expand Up @@ -107,3 +107,20 @@ def test_hazard_forecast_select(haz_fc, lead_time, member):
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])])


def test_write_read_hazard_forecast(haz_fc, tmp_path):

file_name = tmp_path / "test_hazard_forecast.h5"

haz_fc.write_hdf5(file_name)
haz_fc_read = HazardForecast.from_hdf5(file_name)

assert haz_fc_read.lead_time.dtype.kind == np.dtype("timedelta64").kind

for key in haz_fc.__dict__.keys():
if key in ["intensity", "fraction"]:
(haz_fc.__dict__[key] != haz_fc_read.__dict__[key]).nnz == 0
else:
# npt.assert_array_equal also works for comparing int, float or list
npt.assert_array_equal(haz_fc.__dict__[key], haz_fc_read.__dict__[key])
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