Merge branch 'develop' into fix-haz-creation-with-attrs

Author: ChrisFairless

CHANGELOG.md

@@ -6,6 +6,29 @@ Release date: 2025-09-30
 
 ### Dependency Changes
 
+### Added
+
+- Better type hints and overloads signatures for ImpactFuncSet [#1250](https://github.com/CLIMADA-project/climada_python/pull/1250)
+
+### Changed
+- Updated Impact Calculation Tutorial (`doc.climada_engine_Impact.ipynb`) [#1095](https://github.com/CLIMADA-project/climada_python/pull/1095).
+
+### Fixed
+
+- Fixed asset count in impact logging message [#1195](https://github.com/CLIMADA-project/climada_python/pull/1195).
+
+### Deprecated
+
+### Removed
+- `climada.util.earth_engine.py` Google Earth Engine methods did not facilitate direct use of GEE data in CLIMADA. Code was relocated to [climada-snippets](https://github.com/CLIMADA-project/climada-snippets). [#1109](https://github.com/CLIMADA-project/climada_python/pull/1109)
+- `doc.climada_util_earth_engine.ipynb` Tutorial about GEE not relevant to CLIMADA Core. Tutorial notebook was relocated to [climada-snippets](https://github.com/CLIMADA-project/climada-snippets).[#1109](https://github.com/CLIMADA-project/climada_python/pull/1109)
+
+## 6.1.0
+
+Release date: 2025-09-30
+
+### Dependency Changes
+
 Added:
 
 - `bayesian-optimization` >=1.5,<2.0

climada/_version.py

@@ -1 +1 @@
-__version__ = "6.1.0"
+__version__ = "6.1.1-dev"

climada/engine/impact_calc.py

@@ -186,7 +186,7 @@ def impact(
             return self._return_empty(save_mat)
         LOGGER.info(
             "Calculating impact for %s assets (>0) and %s events.",
-            exp_gdf.size,
+            len(exp_gdf),
             self.n_events,
         )
         imp_mat_gen = self.imp_mat_gen(exp_gdf, impf_col)

climada/engine/test/test_impact_calc.py

@@ -441,7 +441,7 @@ def test_calc_insured_impact_no_insurance(self):
         self.assertEqual(
             logs.output,
             [
-                "INFO:climada.engine.impact_calc:Calculating impact for 150 assets (>0) and 14450 events."
+                "INFO:climada.engine.impact_calc:Calculating impact for 50 assets (>0) and 14450 events."
             ],
         )
         self.assertEqual(icalc.n_events, len(impact.at_event))

climada/engine/unsequa/input_var.py

@@ -246,9 +246,8 @@ def haz(haz_list, n_ev=None, bounds_int=None, bounds_frac=None, bounds_freq=None
             The frequency of all events is multiplied by a number
             sampled uniformly from a distribution with (min, max) = bounds_freq
         HL: sample uniformly from hazard list
-            From the provided list of hazard is elements are uniformly
-            sampled. For example, Hazards outputs from dynamical models
-            for different input factors.
+            For each sample, one element is drawn uniformly from the provided list of hazards.
+            For example, Hazards outputs from dynamical models for different input factors.
 
         If a bounds is None, this parameter is assumed to have no uncertainty.
 
@@ -310,8 +309,8 @@ def exp(exp_list, bounds_totval=None, bounds_noise=None):
             with (min, max) = bounds_noise. EN is the value of the seed
             for  the uniform random number generator.
         EL: sample uniformly from exposure list
-            From the provided list of exposure is elements are uniformly
-            sampled. For example, LitPop instances with different exponents.
+            For each sample, one element is drawn uniformly from the provided list of exposures.
+            For example, LitPop instances with different exponents.
 
         If a bounds is None, this parameter is assumed to have no uncertainty.
 
@@ -376,9 +375,8 @@ def impfset(
             sampled uniformly from a distribution with
             (min, max) = bounds_int
         IL: sample uniformly from impact function set list
-            From the provided list of impact function sets elements are uniformly
-            sampled. For example, impact functions obtained from different
-            calibration methods.
+            For each sample, one element is drawn uniformly from the provided list of impact function sets.
+            For example, impact functions obtained from different calibration methods.
 
 
         If a bounds is None, this parameter is assumed to have no uncertainty.
@@ -468,8 +466,8 @@ def ent(
             with (min, max) = bounds_noise. EN is the value of the seed
             for  the uniform random number generator.
         EL: sample uniformly from exposure list
-            From the provided list of exposure is elements are uniformly
-            sampled. For example, LitPop instances with different exponents.
+            For each sample, one element is drawn uniformly from the provided list of exposures.
+            For example, LitPop instances with different exponents.
         MDD: scale the mdd (homogeneously)
             The value of mdd at each intensity is multiplied by a number
             sampled uniformly from a distribution with
@@ -483,9 +481,8 @@ def ent(
             sampled uniformly from a distribution with
             (min, max) = bounds_int
         IL: sample uniformly from impact function set list
-            From the provided list of impact function sets elements are uniformly
-            sampled. For example, impact functions obtained from different
-            calibration methods.
+            For each sample, one element is drawn uniformly from the provided list of impact function sets.
+            For example, impact functions obtained from different calibration methods.
 
         If a bounds is None, this parameter is assumed to have no uncertainty.
 
@@ -566,7 +563,7 @@ def ent(
                 bounds_noise=bounds_noise,
                 exp_list=exp_list,
                 meas_set=meas_set,
-                **kwargs
+                **kwargs,
             ),
             _ent_unc_dict(
                 bounds_totval=bounds_totval,
@@ -616,8 +613,8 @@ def entfut(
             with (min, max) = bounds_noise. EN is the value of the seed
             for  the uniform random number generator.
         EL: sample uniformly from exposure list
-            From the provided list of exposure is elements are uniformly
-            sampled. For example, LitPop instances with different exponents.
+            For each sample, one element is drawn uniformly from the provided list of exposures.
+            For example, LitPop instances with different exponents.
         MDD: scale the mdd (homogeneously)
             The value of mdd at each intensity is multiplied by a number
             sampled uniformly from a distribution with
@@ -631,9 +628,8 @@ def entfut(
             sampled uniformly from a distribution with
             (min, max) = bounds_impfi
         IL: sample uniformly from impact function set list
-            From the provided list of impact function sets elements are uniformly
-            sampled. For example, impact functions obtained from different
-            calibration methods.
+            For each sample, one element is drawn uniformly from the provided list of impact function sets.
+            For example, impact functions obtained from different calibration methods.
 
         If a bounds is None, this parameter is assumed to have no uncertainty.
 
@@ -706,7 +702,7 @@ def entfut(
                 impf_set_list=impf_set_list,
                 exp_list=exp_list,
                 meas_set=meas_set,
-                **kwargs
+                **kwargs,
             ),
             _entfut_unc_dict(
                 bounds_eg=bounds_eg,

climada/entity/impact_funcs/base.py

@@ -189,7 +189,7 @@ def from_step_impf(
         haz_type: str,
         mdd: tuple[float, float] = (0, 1),
         paa: tuple[float, float] = (1, 1),
-        impf_id: int = 1,
+        impf_id: int | str = 1,
         **kwargs,
     ):
         """Step function type impact function.
@@ -207,7 +207,7 @@ def from_step_impf(
             (min, max) mdd values. The default is (0, 1)
         paa: tuple(float, float)
             (min, max) paa values. The default is (1, 1)
-        impf_id : int, optional, default=1
+        impf_id : int|str, optional, default=1
             impact function id
         kwargs :
             keyword arguments passed to ImpactFunc()
@@ -250,7 +250,7 @@ def from_sigmoid_impf(
         k: float,
         x0: float,
         haz_type: str,
-        impf_id: int = 1,
+        impf_id: int | str = 1,
         **kwargs,
     ):
         r"""Sigmoid type impact function hinging on three parameter.
@@ -320,7 +320,7 @@ def from_poly_s_shape(
         scale: float,
         exponent: float,
         haz_type: str,
-        impf_id: int = 1,
+        impf_id: int | str = 1,
         **kwargs,
     ):
         r"""S-shape polynomial impact function hinging on four parameter.

climada/entity/impact_funcs/impact_func_set.py

@@ -24,7 +24,7 @@
 import copy
 import logging
 from itertools import repeat
-from typing import Iterable, Optional
+from typing import Iterable, Optional, Union, overload
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -119,7 +119,7 @@ def clear(self):
         """Reinitialize attributes."""
         self._data = dict()  # {hazard_type : {id:ImpactFunc}}
 
-    def append(self, func):
+    def append(self, func: ImpactFunc):
         """Append a ImpactFunc. Overwrite existing if same id and haz_type.
 
         Parameters
@@ -141,7 +141,9 @@ def append(self, func):
             self._data[func.haz_type] = dict()
         self._data[func.haz_type][func.id] = func
 
-    def remove_func(self, haz_type=None, fun_id=None):
+    def remove_func(
+        self, haz_type: Optional[str] = None, fun_id: Optional[str | int] = None
+    ):
         """Remove impact function(s) with provided hazard type and/or id.
         If no input provided, all impact functions are removed.
 
@@ -173,7 +175,29 @@ def remove_func(self, haz_type=None, fun_id=None):
         else:
             self._data = dict()
 
-    def get_func(self, haz_type=None, fun_id=None):
+    @overload
+    def get_func(
+        self, haz_type: None = None, fun_id: None = None
+    ) -> dict[str, dict[Union[int, str], ImpactFunc]]: ...
+
+    @overload
+    def get_func(
+        self, haz_type: None = ..., fun_id: int | str = ...
+    ) -> list[ImpactFunc]: ...
+
+    @overload
+    def get_func(
+        self, haz_type: str = ..., fun_id: None = None
+    ) -> list[ImpactFunc]: ...
+
+    @overload
+    def get_func(self, haz_type: str = ..., fun_id: int | str = ...) -> ImpactFunc: ...
+
+    def get_func(
+        self, haz_type: Optional[str] = None, fun_id: Optional[int | str] = None
+    ) -> Union[
+        ImpactFunc, list[ImpactFunc], dict[str, dict[Union[int, str], ImpactFunc]]
+    ]:
         """Get ImpactFunc(s) of input hazard type and/or id.
         If no input provided, all impact functions are returned.
 
@@ -209,7 +233,7 @@ def get_func(self, haz_type=None, fun_id=None):
         else:
             return self._data
 
-    def get_hazard_types(self, fun_id=None):
+    def get_hazard_types(self, fun_id: Optional[str | int] = None) -> list[str]:
         """Get impact functions hazard types contained for the id provided.
         Return all hazard types if no input id.
 
@@ -231,7 +255,15 @@ def get_hazard_types(self, fun_id=None):
                 haz_types.append(vul_haz)
         return haz_types
 
-    def get_ids(self, haz_type=None):
+    @overload
+    def get_ids(self, haz_type: None = None) -> dict[str, list[str | int]]: ...
+
+    @overload
+    def get_ids(self, haz_type: str) -> list[int | str]: ...
+
+    def get_ids(
+        self, haz_type: Optional[str] = None
+    ) -> dict[str, list[str | int]] | list[int | str]:
         """Get impact functions ids contained for the hazard type provided.
         Return all ids for each hazard type if no input hazard type.
 
@@ -256,7 +288,9 @@ def get_ids(self, haz_type=None):
         except KeyError:
             return list()
 
-    def size(self, haz_type=None, fun_id=None):
+    def size(
+        self, haz_type: Optional[str] = None, fun_id: Optional[str | int] = None
+    ) -> int:
         """Get number of impact functions contained with input hazard type and
         /or id. If no input provided, get total number of impact functions.
 
@@ -279,6 +313,7 @@ def size(self, haz_type=None, fun_id=None):
             return 1
         if (haz_type is not None) or (fun_id is not None):
             return len(self.get_func(haz_type, fun_id))
+
         return sum(len(vul_list) for vul_list in self.get_ids().values())
 
     def check(self):
@@ -300,7 +335,7 @@ def check(self):
                     )
                 vul.check()
 
-    def extend(self, impact_funcs):
+    def extend(self, impact_funcs: "ImpactFuncSet"):
         """Append impact functions of input ImpactFuncSet to current
         ImpactFuncSet. Overwrite ImpactFunc if same id and haz_type.
 
@@ -323,7 +358,13 @@ def extend(self, impact_funcs):
             for _, vul in vul_dict.items():
                 self.append(vul)
 
-    def plot(self, haz_type=None, fun_id=None, axis=None, **kwargs):
+    def plot(
+        self,
+        haz_type: Optional[str] = None,
+        fun_id: Optional[str | int] = None,
+        axis=None,
+        **kwargs,
+    ):
         """Plot impact functions of selected hazard (all if not provided) and
         selected function id (all if not provided).
 

climada/hazard/test/test_xarray.py

@@ -28,7 +28,7 @@
 import numpy as np
 import xarray as xr
 from pyproj import CRS
-from scipy.sparse import csr_matrix
+from scipy.sparse import csr_array, csr_matrix
 
 from climada.hazard.base import Hazard
 from climada.util.constants import DEF_CRS
@@ -104,8 +104,8 @@ def _assert_default_types(self, hazard):
         self.assertIsInstance(hazard.event_id, np.ndarray)
         self.assertIsInstance(hazard.event_name, list)
         self.assertIsInstance(hazard.frequency, np.ndarray)
-        self.assertIsInstance(hazard.intensity, csr_matrix)
-        self.assertIsInstance(hazard.fraction, csr_matrix)
+        self.assertIsInstance(hazard.intensity, csr_matrix | csr_array)
+        self.assertIsInstance(hazard.fraction, csr_matrix | csr_array)
         self.assertIsInstance(hazard.date, np.ndarray)
 
     def test_load_path(self):
@@ -149,8 +149,11 @@ def _load_and_assert(**kwargs):
     def test_type_error(self):
         """Calling 'from_xarray_raster' with wrong data type should throw"""
         # Passing a DataArray
-        with xr.open_dataset(self.netcdf_path) as dset, self.assertRaisesRegex(
-            TypeError, "This method only supports passing xr.Dataset"
+        with (
+            xr.open_dataset(self.netcdf_path) as dset,
+            self.assertRaisesRegex(
+                TypeError, "This method only supports passing xr.Dataset"
+            ),
         ):
             Hazard.from_xarray_raster(dset["intensity"], "", "")
 

climada/hazard/xarray.py

@@ -58,7 +58,7 @@ def _to_csr_matrix(array: xr.DataArray) -> sparse.csr_matrix:
         output_dtypes=[array.dtype],
     )
     sparse_coo = array.compute().data  # Load into memory
-    return sparse_coo.tocsr()  # Convert sparse.COO to scipy.sparse.csr_matrix
+    return sparse_coo.tocsr()  # Convert sparse.COO to scipy.sparse.csr_array
 
 
 # Define accessors for xarray DataArrays

climada/trajectories/__init__.py

@@ -0,0 +1,31 @@
+"""
+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/>.
+
+---
+
+This module implements risk trajectory objects which enable computation and
+possibly interpolation of risk metric over multiple dates.
+
+"""
+
+from .interpolation import AllLinearStrategy, ExponentialExposureStrategy
+from .snapshot import Snapshot
+
+__all__ = [
+    "AllLinearStrategy",
+    "ExponentialExposureStrategy",
+    "Snapshot",
+]