Merge pull request #1181 from CLIMADA-project/adapt_hdf5_write_from

Adapt Hazard from_hdf5 and write_hdf5 to forecast attributes

Author: ValentinGebhart

climada/hazard/io.py

@@ -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())
@@ -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)

climada/hazard/test/test_forecast.py

@@ -114,3 +114,20 @@ 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])