Merge branch 'develop' into update-readme

Author: emanuel-schmid

CHANGELOG.md

@@ -12,13 +12,20 @@ Code freeze date: YYYY-MM-DD
 
 ### 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
 
@@ -151,9 +158,9 @@ Removed:
 - `climada.entity.impact_funcs.trop_cyclone.ImpfSetTropCyclone.get_impf_id_regions_per_countries` function [#1034](https://github.com/CLIMADA-project/climada_python/pull/1034)
 - `climada.hazard.tc_tracks.BasinBoundsStorm` Enum class `climada.hazard.tc_tracks.subset_by_basin` function [#1031](https://github.com/CLIMADA-project/climada_python/pull/1031)
 - `climada.hazard.tc_tracks.TCTracks.subset_years` function [#1023](https://github.com/CLIMADA-project/climada_python/pull/1023)
--`climada.hazard.tc_tracks.compute_track_density` function, `climada.hazard.tc_tracks.compute_genesis_density` function, `climada.hazard.plot.plot_track_density` function
+- `climada.hazard.tc_tracks.compute_track_density` function, `climada.hazard.tc_tracks.compute_genesis_density` function, `climada.hazard.plot.plot_track_density` function
  [#1003](https://github.com/CLIMADA-project/climada_python/pull/1003)
--`climada.hazard.tc_tracks.TCTracks.from_FAST` function, add Australia basin (AU) [#993](https://github.com/CLIMADA-project/climada_python/pull/993)
+- `climada.hazard.tc_tracks.TCTracks.from_FAST` function, add Australia basin (AU) [#993](https://github.com/CLIMADA-project/climada_python/pull/993)
 - Add `osm-flex` package to CLIMADA core [#981](https://github.com/CLIMADA-project/climada_python/pull/981)
 - `doc.tutorial.climada_entity_Exposures_osm.ipynb` tutorial explaining how to use `osm-flex` with CLIMADA
 - `climada.util.coordinates.bounding_box_global` function [#980](https://github.com/CLIMADA-project/climada_python/pull/980)

climada/data/system/entity_template.xlsx

@@ -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/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/test/test_impact_calc.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/engine/unsequa/input_var.py

@@ -41,22 +41,18 @@
 from climada.entity import Exposures, ImpactFunc, ImpactFuncSet
 from climada.entity.entity_def import Entity
 from climada.hazard import Hazard
-from climada.util.api_client import Client
+from climada.test import get_test_file
 from climada.util.constants import (
     ENT_DEMO_FUTURE,
     ENT_DEMO_TODAY,
-    EXP_DEMO_H5,
     HAZ_DEMO_H5,
     TEST_UNC_OUTPUT_COSTBEN,
     TEST_UNC_OUTPUT_IMPACT,
 )
 
-test_unc_output_impact = Client().get_dataset_file(
-    name=TEST_UNC_OUTPUT_IMPACT, status="test_dataset"
-)
-test_unc_output_costben = Client().get_dataset_file(
-    name=TEST_UNC_OUTPUT_COSTBEN, status="test_dataset"
-)
+EXP_DEMO_H5 = get_test_file("exp_demo_today", file_format="hdf5")
+test_unc_output_impact = get_test_file(TEST_UNC_OUTPUT_IMPACT)
+test_unc_output_costben = get_test_file(TEST_UNC_OUTPUT_COSTBEN)
 
 
 def impf_dem(x_paa=1, x_mdd=1):
@@ -578,7 +574,7 @@ def test_calc_sensitivity_all_pass(self):
                 "sensitivity_kwargs": {"S": 10, "seed": 12345},
                 "test_param_name": ["x_exp", 0],
                 "test_si_name": ["CV", 16],
-                "test_si_value": [0.25000, 2],
+                "test_si_value": [0.250000, 2],
             },
             "hdmr": {
                 "sampling_method": "saltelli",
@@ -587,7 +583,7 @@ def test_calc_sensitivity_all_pass(self):
                 "sensitivity_kwargs": {},
                 "test_param_name": ["x_exp", 2],
                 "test_si_name": ["Sa", 4],
-                "test_si_value": [0.004658, 3],
+                "test_si_value": [0.004649, 3],
             },
             "ff": {
                 "sampling_method": "ff",
@@ -618,7 +614,7 @@ def test_calc_sensitivity_all_pass(self):
                 },
                 "test_param_name": ["x_exp", 0],
                 "test_si_name": ["dgsm", 8],
-                "test_si_value": [1.697516e-01, 9],
+                "test_si_value": [0.1697516, 9],
             },
             "fast": {
                 "sampling_method": "fast_sampler",
@@ -627,7 +623,7 @@ def test_calc_sensitivity_all_pass(self):
                 "sensitivity_kwargs": {"M": 4, "seed": 12345},
                 "test_param_name": ["x_exp", 0],
                 "test_si_name": ["S1_conf", 8],
-                "test_si_value": [0.671396, 1],
+                "test_si_value": [0.671546, 1],
             },
             "rbd_fast": {
                 "sampling_method": "saltelli",
@@ -636,7 +632,7 @@ def test_calc_sensitivity_all_pass(self):
                 "sensitivity_kwargs": {"M": 4, "seed": 12345},
                 "test_param_name": ["x_exp", 0],
                 "test_si_name": ["S1_conf", 4],
-                "test_si_value": [0.152609, 4],
+                "test_si_value": [0.129919, 4],
             },
             "morris": {
                 "sampling_method": "morris",
@@ -645,7 +641,7 @@ def test_calc_sensitivity_all_pass(self):
                 "sensitivity_kwargs": {},
                 "test_param_name": ["x_exp", 0],
                 "test_si_name": ["mu", 1],
-                "test_si_value": [5066460029.63911, 8],
+                "test_si_value": [7935400297.813827, 8],
             },
         }
 
@@ -700,7 +696,7 @@ def test_sensitivity_method(
                 haz_unc,
                 sensitivity_method,
                 method_params,
-                places=2 if sensitivity_method == "rbd_fast" else 5,
+                places=5,
             )
 
 

climada/engine/unsequa/test/test_unsequa.py

@@ -403,6 +403,9 @@ def __init__(
 
         self.description = self._consolidate(meta, "description", description)
         self.ref_year = self._consolidate(meta, "ref_year", ref_year, DEF_REF_YEAR)
+
+        if geodata.shape[0] > 0:
+            value_unit = self._consolidate(geodata.iloc[0], "value_unit", value_unit)
         self.value_unit = self._consolidate(
             meta, "value_unit", value_unit, DEF_VALUE_UNIT
         )
@@ -644,15 +647,17 @@ def assign_centroids(
 
         Caution: nearest neighbourg matching can introduce serious artefacts
         such as:
-            - exposure and hazard centroids with shifted grids can lead
-            to systematically wrong assignements.
-            - hazard centroids covering larger areas than exposures may lead
-            to sub-optimal matching if the threshold is too large
-            - projected crs often diverge at the anti-meridian and close points
-            on either side will be at a large distance. For proper handling
-            of the anti-meridian please use degree coordinates in EPSG:4326.
-            This might be relevant for countries like the Fidji or the US that
-            cross the anti-meridian.
+
+        - exposure and hazard centroids with shifted grids can lead
+          to systematically wrong assignements.
+        - hazard centroids covering larger areas than exposures may lead
+          to sub-optimal matching if the threshold is too large
+        - projected crs often diverge at the anti-meridian and close points
+          on either side will be at a large distance. For proper handling
+          of the anti-meridian please use degree coordinates in EPSG:4326.
+          This might be relevant for countries like the Fidji or the US that
+          cross the anti-meridian.
+
 
         Users are free to implement their own matching alrogithm and save the
         matching centroid index in the appropriate column ``centr_[hazard.HAZ_TYPE]``.

climada/entity/exposures/base.py

@@ -182,7 +182,7 @@ def test__init__mda_in_kwargs(self):
     def test_read_raster_pass(self):
         """from_raster"""
         exp = Exposures.from_raster(
-            HAZ_DEMO_FL, window=Window(10, 20, 50, 60), attrs={"value_unit": "USD"}
+            HAZ_DEMO_FL, window=Window(10, 20, 50, 60), attrs={"value_unit": "PKR"}
         )
         exp.check()
         self.assertTrue(u_coord.equal_crs(exp.crs, DEF_CRS))
@@ -204,7 +204,7 @@ def test_read_raster_pass(self):
         self.assertAlmostEqual(
             exp.gdf["value"].values.reshape((60, 50))[25, 12], 0.056825936
         )
-        self.assertEqual(exp.value_unit, "USD")
+        self.assertEqual(exp.value_unit, "PKR")
 
     def test_assign_raster_pass(self):
         """Test assign_centroids with raster hazard"""
@@ -429,16 +429,23 @@ def test_read_template_pass(self):
         exp_df = Exposures(df)
         # set metadata
         exp_df.ref_year = 2020
-        exp_df.value_unit = "XSD"
+        exp_df.value_unit = "PAK"
         exp_df.check()
 
+    def test_handling_unit_conflicts_pass(self):
+        """Check that the value_unit is correctly set when there are conflicting value_unit definitions in the data frame and the meta attribute."""
+        df = pd.read_excel(ENT_TEMPLATE_XLS)
+        exp_df = Exposures(df, meta={"value_unit": "XSD"}, value_unit="XSD")
+        exp_df.check()
+        self.assertEqual(exp_df.value_unit, "XSD")
+        with self.assertRaises(ValueError) as cm:
+            exp_df = Exposures(df, meta={"value_unit": "XSD"}, value_unit="PAK")
+
     def test_io_hdf5_pass(self):
         """write and read hdf5"""
-        exp = Exposures(pd.read_excel(ENT_TEMPLATE_XLS), crs="epsg:32632")
-
-        # set metadata
-        exp.ref_year = 2020
-        exp.value_unit = "XSD"
+        exp = Exposures(
+            pd.read_excel(ENT_TEMPLATE_XLS), crs="epsg:32632", ref_year=2020
+        )
 
         # add another geometry column
         exp.data["geocol2"] = exp.data.geometry.copy(deep=True)

climada/entity/exposures/test/test_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.