Risk Trajectory Split 5: Interpolated Risk Trajectories

climada/conf/climada.conf

@@ -69,5 +69,6 @@
         "cache_dir": "{local_data.system}/.apicache",
         "supported_hazard_types": ["river_flood", "tropical_cyclone", "storm_europe", "relative_cropyield", "wildfire", "earthquake", "flood", "hail", "aqueduct_coastal_flood"],
         "supported_exposures_types": ["litpop", "crop_production", "ssp_population", "crops"]
-    }
+    },
+    "trajectory_caching": true
 }

climada/test/common_test_fixtures.py

@@ -0,0 +1,205 @@
+"""
+This file is part of CLIMADA.
+
+Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS.
+
+CLIMADA is free software: you can redistribute it and/or modify it under the
+terms of the GNU General Public License as published by the Free
+Software Foundation, version 3.
+
+CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY
+WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+PARTICULAR PURPOSE.  See the GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License along
+with CLIMADA. If not, see <https://www.gnu.org/licenses/>.
+---
+
+A set of reusable objects for testing purpose.
+
+The objective of this file is to provide minimalistic, understandable and consistent
+default objects for unit and integration testing.
+
+"""
+
+import geopandas as gpd
+import numpy as np
+from scipy.sparse import csr_matrix
+from shapely.geometry import Point
+
+from climada.entity import Exposures, ImpactFunc, ImpactFuncSet
+from climada.hazard import Centroids, Hazard
+from climada.trajectories.snapshot import Snapshot
+
+# ---------------------------------------------------------------------------
+# Coordinate system and metadata
+# ---------------------------------------------------------------------------
+CRS_WGS84 = "EPSG:4326"
+
+# ---------------------------------------------------------------------------
+# Exposure attributes
+# ---------------------------------------------------------------------------
+EXP_DESC = "Test exposure dataset"
+EXP_DESC_LATLON = "Test exposure dataset (lat/lon)"
+EXPOSURE_REF_YEAR = 2020
+EXPOSURE_VALUE_UNIT = "USD"
+VALUES = np.array([0, 1000, 2000, 3000])
+REGIONS = np.array(["A", "A", "B", "B"])
+CATEGORIES = np.array([1, 1, 2, 1])
+
+# Exposure coordinates
+EXP_LONS = np.array([4, 4.5, 4, 4.5])
+EXP_LATS = np.array([45, 45, 45.5, 45.5])
+
+# ---------------------------------------------------------------------------
+# Hazard definition
+# ---------------------------------------------------------------------------
+HAZARD_TYPE = "TEST_HAZARD_TYPE"
+HAZARD_UNIT = "TEST_HAZARD_UNIT"
+
+# Hazard centroid positions
+HAZ_JITTER = 0.1  # To test centroid matching
+HAZ_LONS = EXP_LONS + HAZ_JITTER
+HAZ_LATS = EXP_LATS + HAZ_JITTER
+
+# Hazard events
+EVENT_IDS = np.array([1, 2, 3, 4])
+EVENT_NAMES = ["ev1", "ev2", "ev3", "ev4"]
+DATES = np.array([1, 2, 3, 4])
+
+# Frequency are choosen so that they cumulate nicely
+# to correspond to 100, 50, and 20y return periods (for impacts)
+FREQUENCY = np.array([0.1, 0.03, 0.01, 0.01])
+FREQUENCY_UNIT = "1/year"
+
+# Hazard maximum intensity
+# 100 to match 0 to 100% idea
+# also in line with linear 1:1 impact function
+# for easy mental calculus
+HAZARD_MAX_INTENSITY = 100
+
+# ---------------------------------------------------------------------------
+# Impact function
+# ---------------------------------------------------------------------------
+IMPF_ID = 1
+IMPF_NAME = "IMPF_1"
+
+# ---------------------------------------------------------------------------
+# Future years
+# ---------------------------------------------------------------------------
+EXPOSURE_FUTURE_YEAR = 2040
+
+
+def reusable_minimal_exposures(
+    values=VALUES,
+    regions=REGIONS,
+    group_id=None,
+    lon=EXP_LONS,
+    lat=EXP_LATS,
+    crs=CRS_WGS84,
+    desc=EXP_DESC,
+    ref_year=EXPOSURE_REF_YEAR,
+    value_unit=EXPOSURE_VALUE_UNIT,
+    assign_impf=IMPF_ID,
+    increase_value_factor=1,
+) -> Exposures:
+    data = gpd.GeoDataFrame(
+        {
+            "value": values * increase_value_factor,
+            "region_id": regions,
+            f"impf_{HAZARD_TYPE}": assign_impf,
+            "geometry": [Point(lon, lat) for lon, lat in zip(lon, lat)],
+        },
+        crs=crs,
+    )
+    if group_id is not None:
+        data["group_id"] = group_id
+    return Exposures(
+        data=data,
+        description=desc,
+        ref_year=ref_year,
+        value_unit=value_unit,
+    )
+
+
+def reusable_intensity_mat(max_intensity=HAZARD_MAX_INTENSITY):
+    # Choosen such that:
+    # - 1st event has 0 intensity
+    # - 2nd event has max intensity in first exposure point (defaulting to 0 value)
+    # - 3rd event has 1/2* of max intensity in second centroid
+    # - 4th event has 1/4* of max intensity everywhere
+    # *: So that you can double intensity of the hazard and expect double impacts
+    return csr_matrix(
+        [
+            [0, 0, 0, 0],
+            [max_intensity, 0, 0, 0],
+            [0, max_intensity / 2, 0, 0],
+            [
+                max_intensity / 4,
+                max_intensity / 4,
+                max_intensity / 4,
+                max_intensity / 4,
+            ],
+        ]
+    )
+
+
+def reusable_minimal_hazard(
+    haz_type=HAZARD_TYPE,
+    units=HAZARD_UNIT,
+    lat=HAZ_LATS,
+    lon=HAZ_LONS,
+    crs=CRS_WGS84,
+    event_id=EVENT_IDS,
+    event_name=EVENT_NAMES,
+    date=DATES,
+    frequency=FREQUENCY,
+    frequency_unit=FREQUENCY_UNIT,
+    intensity=None,
+    intensity_factor=1,
+) -> Hazard:
+    intensity = reusable_intensity_mat() if intensity is None else intensity
+    intensity *= intensity_factor
+    return Hazard(
+        haz_type=haz_type,
+        units=units,
+        centroids=Centroids(lat=lat, lon=lon, crs=crs),
+        event_id=event_id,
+        event_name=event_name,
+        date=date,
+        frequency=frequency,
+        frequency_unit=frequency_unit,
+        intensity=intensity,
+    )
+
+
+def reusable_minimal_impfset(
+    hazard=None, name=IMPF_NAME, impf_id=IMPF_ID, max_intensity=HAZARD_MAX_INTENSITY
+):
+    hazard = reusable_minimal_hazard() if hazard is None else hazard
+    return ImpactFuncSet(
+        [
+            ImpactFunc(
+                haz_type=hazard.haz_type,
+                intensity_unit=hazard.units,
+                name=name,
+                intensity=np.array([0, max_intensity / 2, max_intensity]),
+                mdd=np.array([0, 0.5, 1]),
+                paa=np.array([1, 1, 1]),
+                id=impf_id,
+            )
+        ]
+    )
+
+
+def reusable_snapshot(
+    hazard_intensity_increase_factor=1,
+    exposure_value_increase_factor=1,
+    date=EXPOSURE_REF_YEAR,
+):
+    exposures = reusable_minimal_exposures(
+        increase_value_factor=exposure_value_increase_factor
+    )
+    hazard = reusable_minimal_hazard(intensity_factor=hazard_intensity_increase_factor)
+    impfset = reusable_minimal_impfset()
+    return Snapshot(exposure=exposures, hazard=hazard, impfset=impfset, date=date)