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Merged
peanutfun merged 4 commits into from
Dec 8, 2025
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Merged

Impact forecast class #1168

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0901b4d
Merge branch 'forecast-class' into impact-forecast-class
peanutfun Dec 8, 2025
b7b6545
Add ImpactForecast
peanutfun Dec 8, 2025
3b077a0
Add fixture for impact kwargs
peanutfun Dec 8, 2025
44bf09a
Use kwarg assignment in ImpactForecast.__init__
peanutfun Dec 8, 2025
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1 change: 1 addition & 0 deletions climada/engine/__init__.py
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Expand Up @@ -22,3 +22,4 @@
from .cost_benefit import *
from .impact import *
from .impact_calc import *
from .impact_forecast import ImpactForecast
70 changes: 70 additions & 0 deletions climada/engine/impact_forecast.py
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Expand Up @@ -18,3 +18,73 @@

Define Forecast variant of Impact.
"""

import logging

import numpy as np

from ..util import log_level
from ..util.forecast import Forecast
from .impact import Impact

LOGGER = logging.getLogger(__name__)


class ImpactForecast(Forecast, Impact):
"""An impact object with forecast information"""

def __init__(
self,
*,
lead_time: np.ndarray | None,
member: np.ndarray | None,
**impact_kwargs,
):
"""Initialize the impact forecast.

Parameters
----------
lead_time : np.ndarray, optional
The lead time associated with each event entry
member : np.ndarray, optional
The ensemble member associated with each event entry
impact_kwargs
Keyword-arguments passed to ~:py:class`climada.engine.impact.Impact`.
"""
# TODO: Maybe assert array lengths?
super().__init__(lead_time=lead_time, member=member, **impact_kwargs)

@classmethod
def from_impact(
cls, impact: Impact, lead_time: np.ndarray | None, member: np.ndarray | None
):
"""Create an impact forecast from an impact object and forecast information.

Parameters
----------
impact : climada.engine.impact.Impact
The impact object whose data to use in the forecast object
lead_time : np.ndarray, optional
The lead time associated with each event entry
member : np.ndarray, optional
The ensemble member associated with each event entry
"""
with log_level("WARNING", "climada.engine.impact"):
return cls(
lead_time=lead_time,
member=member,
event_id=impact.event_id,
event_name=impact.event_name,
date=impact.date,
frequency=impact.frequency,
frequency_unit=impact.frequency_unit,
coord_exp=impact.coord_exp,
crs=impact.crs,
eai_exp=impact.eai_exp,
at_event=impact.at_event,
tot_value=impact.tot_value,
aai_agg=impact.aai_agg,
unit=impact.unit,
imp_mat=impact.imp_mat,
haz_type=impact.haz_type,
)
40 changes: 22 additions & 18 deletions climada/engine/test/test_impact.py
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Expand Up @@ -47,26 +47,30 @@
STR_DT = h5py.special_dtype(vlen=str)


def dummy_impact():
"""Return an impact object for testing"""
return Impact(
event_id=np.arange(6) + 10,
event_name=[0, 1, "two", "three", 30, 31],
date=np.arange(6),
coord_exp=np.array([[1, 2], [1.5, 2.5]]),
crs=DEF_CRS,
eai_exp=np.array([7.2, 7.2]),
at_event=np.array([0, 2, 4, 6, 60, 62]),
frequency=np.array([1 / 6, 1 / 6, 1, 1, 1 / 30, 1 / 30]),
tot_value=7,
aai_agg=14.4,
unit="USD",
frequency_unit="1/month",
imp_mat=sparse.csr_matrix(
def impact_kwargs():
return {
"event_id": np.arange(6) + 10,
"event_name": [0, 1, "two", "three", 30, 31],
"date": np.arange(6),
"coord_exp": np.array([[1, 2], [1.5, 2.5]]),
"crs": DEF_CRS,
"eai_exp": np.array([7.2, 7.2]),
"at_event": np.array([0, 2, 4, 6, 60, 62]),
"frequency": np.array([1 / 6, 1 / 6, 1, 1, 1 / 30, 1 / 30]),
"tot_value": 7,
"aai_agg": 14.4,
"unit": "USD",
"frequency_unit": "1/month",
"imp_mat": sparse.csr_matrix(
np.array([[0, 0], [1, 1], [2, 2], [3, 3], [30, 30], [31, 31]])
),
haz_type="TC",
)
"haz_type": "TC",
}


def dummy_impact():
"""Return an impact object for testing"""
return Impact(**impact_kwargs())


def dummy_impact_yearly():
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54 changes: 54 additions & 0 deletions climada/engine/test/test_impact_forecast.py
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Expand Up @@ -18,3 +18,57 @@

Tests for Impact Forecast.
"""

import numpy as np
import numpy.testing as npt
import pandas as pd
import pytest
from scipy.sparse import csr_matrix

from climada.engine import Impact, ImpactForecast

from .test_impact import impact_kwargs as imp_kwargs


@pytest.fixture
def impact_kwargs():
return imp_kwargs()


@pytest.fixture
def impact(impact_kwargs):
return Impact(**impact_kwargs)


def assert_impact_kwargs(impact: Impact, **kwargs):
for key, value in kwargs.items():
attr = getattr(impact, 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


class TestImpactForecastInit:
lead_time = pd.date_range("2000-01-01", "2000-01-02", periods=6).to_numpy()
member = np.arange(6)

def test_impact_forecast_init(self, impact_kwargs):
forecast1 = ImpactForecast(
lead_time=self.lead_time,
member=self.member,
**impact_kwargs,
)
npt.assert_array_equal(forecast1.lead_time, self.lead_time)
npt.assert_array_equal(forecast1.member, self.member)
assert_impact_kwargs(forecast1, **impact_kwargs)

def test_impact_forecast_from_impact(self, impact, impact_kwargs):
forecast = ImpactForecast.from_impact(
impact, lead_time=self.lead_time, member=self.member
)
npt.assert_array_equal(forecast.lead_time, self.lead_time)
npt.assert_array_equal(forecast.member, self.member)
assert_impact_kwargs(forecast, **impact_kwargs)
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