Hazard forecast class (#1171)

* Create hazardForecast base class
* Update datetime to timedelta for lead_time
* Add tests

---------

Co-authored-by: luseverin <[email protected]>
Co-authored-by: Lukas Riedel <[email protected]>

Author: luseverin

climada/hazard/forecast.py

@@ -18,3 +18,74 @@
 
 Define Forecast variant of Hazard.
 """
+
+import logging
+
+import numpy as np
+
+from climada.hazard.base import Hazard
+from climada.util.forecast import Forecast
+
+LOGGER = logging.getLogger(__name__)
+
+
+class HazardForecast(Forecast, Hazard):
+    """A hazard object with forecast information"""
+
+    def __init__(
+        self,
+        lead_time: np.ndarray | None = None,
+        member: np.ndarray | None = None,
+        **hazard_kwargs,
+    ):
+        """
+        Initialize a HazardForecast object.
+
+        Parameters
+        ----------
+        lead_time : np.ndarray of np.timedelta64 or None, optional
+            Forecast lead times. Default is empty array.
+        member : np.ndarray or None, optional
+            Ensemble member identifiers as integers. Default is empty array.
+        **hazard_kwargs
+            keyword arguments to pass to :py:class:`~climada.hazard.base.Hazard` See
+            py:meth`~climada.hazard.base.Hazard.__init__` for details.
+        """
+        super().__init__(lead_time=lead_time, member=member, **hazard_kwargs)
+
+    @classmethod
+    def from_hazard(cls, hazard: Hazard, lead_time: np.ndarray, member: np.ndarray):
+        """
+        Create a HazardForecast object from a Hazard object.
+
+        Parameters
+        ----------
+        hazard : climada.hazard.base.Hazard
+            Hazard object to convert into a HazardForecast.
+        lead_time : np.ndarray of np.timedelta64 or None, optional
+            Forecast lead times. Default is empty array.
+        member : np.ndarray or None, optional
+            Ensemble member identifiers as integers. Default is empty array.
+
+        Returns
+        -------
+        HazardForecast
+            A HazardForecast object with the same attributes as the input hazard,
+            but with lead_time and member attributes set from instance of HazardForecast.
+        """
+        return cls(
+            lead_time=lead_time,
+            member=member,
+            haz_type=hazard.haz_type,
+            pool=hazard.pool,
+            units=hazard.units,
+            centroids=hazard.centroids,
+            event_id=hazard.event_id,
+            frequency=hazard.frequency,
+            frequency_unit=hazard.frequency_unit,
+            event_name=hazard.event_name,
+            date=hazard.date,
+            orig=hazard.orig,
+            intensity=hazard.intensity,
+            fraction=hazard.fraction,
+        )

climada/hazard/test/test_base.py

@@ -46,27 +46,29 @@
 """
 
 
+def hazard_kwargs():
+    return {
+        "haz_type": "TC",
+        "pool": None,
+        "intensity": sparse.csr_matrix(
+            [[0.2, 0.3, 0.4], [0.1, 0.1, 0.01], [4.3, 2.1, 1.0], [5.3, 0.2, 0.0]]
+        ),
+        "fraction": sparse.csr_matrix(
+            [[0.02, 0.03, 0.04], [0.01, 0.01, 0.01], [0.3, 0.1, 0.0], [0.3, 0.2, 0.0]]
+        ),
+        "centroids": Centroids(lat=np.array([1, 3, 5]), lon=np.array([2, 4, 6])),
+        "event_id": np.array([1, 2, 3, 4]),
+        "event_name": ["ev1", "ev2", "ev3", "ev4"],
+        "date": np.array([1, 2, 3, 4]),
+        "orig": np.array([True, False, False, True]),
+        "frequency": np.array([0.1, 0.5, 0.5, 0.2]),
+        "frequency_unit": "1/week",
+        "units": "m/s",
+    }
+
+
 def dummy_hazard():
-    fraction = sparse.csr_matrix(
-        [[0.02, 0.03, 0.04], [0.01, 0.01, 0.01], [0.3, 0.1, 0.0], [0.3, 0.2, 0.0]]
-    )
-    intensity = sparse.csr_matrix(
-        [[0.2, 0.3, 0.4], [0.1, 0.1, 0.01], [4.3, 2.1, 1.0], [5.3, 0.2, 0.0]]
-    )
-
-    return Hazard(
-        "TC",
-        intensity=intensity,
-        fraction=fraction,
-        centroids=Centroids(lat=np.array([1, 3, 5]), lon=np.array([2, 4, 6])),
-        event_id=np.array([1, 2, 3, 4]),
-        event_name=["ev1", "ev2", "ev3", "ev4"],
-        date=np.array([1, 2, 3, 4]),
-        orig=np.array([True, False, False, True]),
-        frequency=np.array([0.1, 0.5, 0.5, 0.2]),
-        frequency_unit="1/week",
-        units="m/s",
-    )
+    return Hazard(**hazard_kwargs())
 
 
 class TestLoader(unittest.TestCase):

climada/hazard/test/test_forecast.py

@@ -19,28 +19,72 @@
 Tests for Hazard Forecast.
 """
 
+import numpy as np
+import numpy.testing as npt
+import pandas as pd
 import pytest
+from scipy.sparse import csr_matrix
 
-from climada.hazard import Hazard
+from climada.hazard.base import Hazard
+from climada.hazard.forecast import HazardForecast
+from climada.hazard.test.test_base import hazard_kwargs
 
 # --- Examples for fixtures and test organization --- #
 
 
 @pytest.fixture
-def hazard():
-    return Hazard()
+def haz_kwargs():
+    return hazard_kwargs()
 
 
-def test_empty_hazard(hazard):
-    assert hazard.size == 0
-    assert hazard.haz_type == ""
+@pytest.fixture
+def hazard(haz_kwargs):
+    return Hazard(**haz_kwargs)
+
+
+@pytest.fixture
+def lead_time():
+    return pd.timedelta_range("1h", periods=6).to_numpy()
 
 
-class TestSomething:
+@pytest.fixture
+def member():
+    return np.arange(6)
 
-    @pytest.fixture(autouse=True)
-    def haz_type(self, hazard):
-        hazard.haz_type = "foo"
 
-    def test_haz_type(self, hazard):
-        assert hazard.haz_type == "foo"
+@pytest.fixture
+def haz_fc(lead_time, member, haz_kwargs):
+    return HazardForecast(
+        lead_time=lead_time,
+        member=member,
+        **haz_kwargs,
+    )
+
+
+def assert_hazard_kwargs(hazard: Hazard, **kwargs):
+    for key, value in kwargs.items():
+        attr = getattr(hazard, key)
+        if isinstance(value, (np.ndarray, list)):
+            npt.assert_array_equal(attr, value)
+        elif isinstance(value, csr_matrix):
+            npt.assert_array_equal(attr.todense(), value.todense())
+        else:
+            assert attr == value
+
+
+def test_init_hazard_forecast(haz_fc, member, lead_time, haz_kwargs):
+    assert isinstance(haz_fc, HazardForecast)
+    npt.assert_array_equal(haz_fc.lead_time, lead_time)
+    assert haz_fc.lead_time.dtype == lead_time.dtype
+    npt.assert_array_equal(haz_fc.member, member)
+    assert_hazard_kwargs(haz_fc, **haz_kwargs)
+
+
+def test_from_hazard(lead_time, member, hazard, haz_kwargs):
+    haz_fc_from_haz = HazardForecast.from_hazard(
+        hazard, lead_time=lead_time, member=member
+    )
+    assert isinstance(haz_fc_from_haz, HazardForecast)
+    npt.assert_array_equal(haz_fc_from_haz.lead_time, lead_time)
+    npt.assert_array_equal(haz_fc_from_haz.member, member)
+    assert_hazard_kwargs(haz_fc_from_haz, **haz_kwargs)