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/conftest.py

@@ -0,0 +1,307 @@
+"""
+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 fixtures for testing purpose.
+
+The objective of this file is to provide minimalistic, understandable and consistent
+default objects for unit and integration testing.
+
+Values are chosen such that:
+    - Exposure value of the first points is 0. (First location should always have 0 impacts)
+    - Category / Group id of all points is 1, except for third point, valued at 2000 (Impacts on that category are always a share of 2000)
+    - Hazard centroids are the exposure centroids shifted by `HAZARD_JITTER` on both lon and lat.
+    - There are 4 events, with frequencies == 0.03, 0.01, 0.006, 0.004, 0,
+      such that impacts for RP250, 100 and 50 and 20 are at_event,
+      (freq sorted cumulate to 1/250, 1/100, 1/50 and 1/20).
+    - Hazard intensity is:
+        * Event 1: zero everywhere (always no impact)
+        * Event 2: max intensity at first centroid (also always no impact (first centroid is 0))
+        * Event 3: half max intensity at second centroid (impact == half second centroid)
+        * Event 4: quarter max intensity everywhere (impact == 1/4 total value)
+        * Event 5: max intensity everywhere (but zero frequency)
+      With max intensity set at 100
+    - Impact function is the "identity function", x intensity is x% damages
+    - Impact values should be round.
+
+"""
+
+import geopandas as gpd
+import numpy as np
+import pytest
+from scipy.sparse import csr_matrix
+from shapely.geometry import Point
+
+from climada.entity import Exposures, ImpactFunc, ImpactFuncSet
+from climada.hazard import Centroids, Hazard
+
+# ---------------------------------------------------------------------------
+# Coordinate system and metadata
+# ---------------------------------------------------------------------------
+CRS_WGS84 = "EPSG:4326"
+
+# ---------------------------------------------------------------------------
+# Exposure attributes
+# ---------------------------------------------------------------------------
+EXP_DESC = "Test exposure dataset"
+EXPOSURE_REF_YEAR = 2020
+EXPOSURE_VALUE_UNIT = "USD"
+VALUES = np.array([0, 1000, 2000, 3000, 4000, 5000])
+CATEGORIES = np.array([1, 1, 2, 1, 1, 3])
+
+# Exposure coordinates
+EXP_LONS = np.array([4, 4.25, 4.5, 4, 4.25, 4.5])
+EXP_LATS = np.array([45, 45, 45, 45.25, 45.25, 45.25])
+
+# ---------------------------------------------------------------------------
+# 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, 5])
+EVENT_NAMES = ["ev1", "ev2", "ev3", "ev4", "ev5"]
+DATES = np.array([1, 2, 3, 4, 5])
+
+# Frequency are choosen so that they cumulate nicely
+# to correspond to 250, 100, 50, and 20y return periods (for impacts)
+FREQUENCY = np.array([0.03, 0.01, 0.006, 0.004, 0.0])
+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"
+
+# Sanity checks
+for const in [VALUES, CATEGORIES, EXP_LONS, EXP_LATS]:
+    assert len(const) == len(
+        VALUES
+    ), "VALUES, REGIONS, CATEGORIES, EXP_LONS, EXP_LATS should all have the same lengths."
+
+for const in [EVENT_IDS, EVENT_NAMES, DATES, FREQUENCY]:
+    assert len(const) == len(
+        EVENT_IDS
+    ), "EVENT_IDS, EVENT_NAMES, DATES, FREQUENCY should all have the same lengths."
+
+
+@pytest.fixture(scope="session")
+def exposure_values():
+    return VALUES.copy()
+
+
+@pytest.fixture(scope="session")
+def categories():
+    return CATEGORIES.copy()
+
+
+@pytest.fixture(scope="session")
+def exposure_geometry():
+    return [Point(lon, lat) for lon, lat in zip(EXP_LONS, EXP_LATS)]
+
+
+@pytest.fixture(scope="session")
+def exposures_factory(
+    exposure_values,
+    exposure_geometry,
+):
+    def _make_exposures(
+        value_factor=1.0,
+        ref_year=EXPOSURE_REF_YEAR,
+        hazard_type=HAZARD_TYPE,
+        group_id=None,
+    ):
+        gdf = gpd.GeoDataFrame(
+            {
+                "value": exposure_values * value_factor,
+                f"impf_{hazard_type}": IMPF_ID,
+                "geometry": exposure_geometry,
+            },
+            crs=CRS_WGS84,
+        )
+        if group_id is not None:
+            gdf["group_id"] = group_id
+
+        return Exposures(
+            data=gdf,
+            description=EXP_DESC,
+            ref_year=ref_year,
+            value_unit=EXPOSURE_VALUE_UNIT,
+        )
+
+    return _make_exposures
+
+
+@pytest.fixture(scope="session")
+def exposures(exposures_factory):
+    return exposures_factory()
+
+
+@pytest.fixture(scope="session")
+def hazard_frequency_factory():
+    base = FREQUENCY
+
+    def _make_frequency(scale=1.0):
+        return base * scale
+
+    return _make_frequency
+
+
+@pytest.fixture(scope="session")
+def hazard_frequency():
+    return hazard_frequency_factory()
+
+
+@pytest.fixture(scope="session")
+def hazard_intensity_factory():
+    """
+    Intensity matrix designed for analytical expectations:
+    - Event 1: zero
+    - Event 2: max intensity at first centroid
+    - Event 3: half max intensity at second centroid
+    - Event 4: quarter max intensity everywhere
+    """
+    base = csr_matrix(
+        [
+            [0, 0, 0, 0, 0, 0],
+            [HAZARD_MAX_INTENSITY, 0, 0, 0, 0, 0],
+            [0, HAZARD_MAX_INTENSITY / 2, 0, 0, 0, 0],
+            [
+                HAZARD_MAX_INTENSITY / 4,
+                HAZARD_MAX_INTENSITY / 4,
+                HAZARD_MAX_INTENSITY / 4,
+                HAZARD_MAX_INTENSITY / 4,
+                HAZARD_MAX_INTENSITY / 4,
+                HAZARD_MAX_INTENSITY / 4,
+            ],
+            [
+                HAZARD_MAX_INTENSITY,
+                HAZARD_MAX_INTENSITY,
+                HAZARD_MAX_INTENSITY,
+                HAZARD_MAX_INTENSITY,
+                HAZARD_MAX_INTENSITY,
+                HAZARD_MAX_INTENSITY,
+            ],
+        ]
+    )
+
+    def _make_intensity(scale=1.0):
+        return base * scale
+
+    return _make_intensity
+
+
+@pytest.fixture(scope="session")
+def hazard_intensity_matrix(hazard_intensity_factory):
+    return hazard_intensity_factory()
+
+
+@pytest.fixture(scope="session")
+def centroids():
+    return Centroids(lat=HAZ_LATS, lon=HAZ_LONS, crs=CRS_WGS84)
+
+
+@pytest.fixture(scope="session")
+def hazard_factory(
+    hazard_intensity_factory,
+    hazard_frequency_factory,
+    centroids,
+):
+    def _make_hazard(
+        intensity_scale=1.0,
+        frequency_scale=1.0,
+        hazard_type=HAZARD_TYPE,
+        hazard_unit=HAZARD_UNIT,
+    ):
+        return Hazard(
+            haz_type=hazard_type,
+            units=hazard_unit,
+            centroids=centroids,
+            event_id=EVENT_IDS,
+            event_name=EVENT_NAMES,
+            date=DATES,
+            frequency=hazard_frequency_factory(scale=frequency_scale),
+            frequency_unit=FREQUENCY_UNIT,
+            intensity=hazard_intensity_factory(scale=intensity_scale),
+        )
+
+    return _make_hazard
+
+
+@pytest.fixture(scope="session")
+def hazard(hazard_factory):
+    return hazard_factory()
+
+
+@pytest.fixture(scope="session")
+def impf_factory():
+    def _make_impf(
+        paa_scale=1.0,
+        max_intensity=HAZARD_MAX_INTENSITY,
+        hazard_type=HAZARD_TYPE,
+        hazard_unit=HAZARD_UNIT,
+        impf_id=IMPF_ID,
+    ):
+        return ImpactFunc(
+            haz_type=hazard_type,
+            intensity_unit=hazard_unit,
+            name=IMPF_NAME,
+            intensity=np.array([0, max_intensity / 2, max_intensity]),
+            mdd=np.array([0, 0.5, 1]),
+            paa=np.array([1, 1, 1]) * paa_scale,
+            id=impf_id,
+        )
+
+    return _make_impf
+
+
+@pytest.fixture(scope="session")
+def linear_impact_function(impf_factory):
+    return impf_factory()
+
+
+@pytest.fixture(scope="session")
+def impfset_factory(impf_factory):
+    def _make_impfset(
+        paa_scale=1.0,
+        max_intensity=HAZARD_MAX_INTENSITY,
+        hazard_type=HAZARD_TYPE,
+        hazard_unit=HAZARD_UNIT,
+        impf_id=IMPF_ID,
+    ):
+        return ImpactFuncSet(
+            [impf_factory(paa_scale, max_intensity, hazard_type, hazard_unit, impf_id)]
+        )
+
+    return _make_impfset
+
+
+@pytest.fixture(scope="session")
+def impfset(impfset_factory):
+    return impfset_factory()