Merge branch 'develop' into feature/pyproject-definition

Author: emanuel-schmid

CHANGELOG.md

@@ -13,6 +13,8 @@ Code freeze date: YYYY-MM-DD
 Removed:
 
 - `pandas-datareader`
+- `intensity_thres` of `Hazard`, `StormEurope`, and `TropCyclones` object [#1065](https://github.com/CLIMADA-project/climada_python/pull/1065)
+- Deprecated method `climada.hazard.trop_cyclone.trop_cyclone.TropCyclone.set_from_tracks` [#1065](https://github.com/CLIMADA-project/climada_python/pull/1065)
 
 ### Added
 - Added optional parameter to `geo_im_from_array`, `plot_from_gdf`, `plot_rp_imp`, `plot_rp_intensity`,
@@ -22,7 +24,8 @@ Removed:
 - `ImpactFunc` and `ImpactFuncSet` now support equality comparisons via `==` [#1027](https://github.com/CLIMADA-project/climada_python/pull/1027)
 
 ### Changed
-
+- Changed the default mask_distance in `util.plot.geo_im_from_array` to 0.03 to avoid white gaps in gridded hazard data with comparably low resolution (>80 centroids per axis) [#1073](https://github.com/CLIMADA-project/climada_python/pull/1073)
+- Increased speed of `util.plot.add_shapes` by avoiding for loops, substantially speeding up `Hazard.plot_intensity` and other functions. [#1073](https://github.com/CLIMADA-project/climada_python/pull/1073)
 - `Hazard.local_exceedance_intensity`, `Hazard.local_return_period` and `Impact.local_exceedance_impact`, `Impact.local_return_period`, using the `climada.util.interpolation` module: New default (no binning), binning on decimals, and faster implementation [#1012](https://github.com/CLIMADA-project/climada_python/pull/1012)
 - World Bank indicator data is now downloaded directly from their API via the function `download_world_bank_indicator`, instead of relying on the `pandas-datareader` package [#1033](https://github.com/CLIMADA-project/climada_python/pull/1033)
 - `Exposures.write_hdf5` pickles geometry data in WKB format, which is faster and more sustainable. [#1051](https://github.com/CLIMADA-project/climada_python/pull/1051)

climada/engine/cost_benefit.py

@@ -1079,9 +1079,7 @@ def _calc_impact_measures(
         # compute impact for each measure
         for measure in meas_set.get_measure(hazard.haz_type):
             LOGGER.debug("%s impact of measure %s.", when, measure.name)
-            imp_tmp, risk_transf = measure.calc_impact(
-                exposures, imp_fun_set, hazard, assign_centroids=False
-            )
+            imp_tmp, risk_transf = measure.calc_impact(exposures, imp_fun_set, hazard)
             impact_meas[measure.name] = dict()
             impact_meas[measure.name]["cost"] = (
                 measure.cost,

climada/engine/impact.py

@@ -1178,7 +1178,7 @@ def plot_rp_imp(
         return_periods=(25, 50, 100, 250),
         log10_scale=True,
         axis=None,
-        mask_distance=0.01,
+        mask_distance=0.03,
         kwargs_local_exceedance_impact=None,
         **kwargs,
     ):
@@ -1198,8 +1198,8 @@ def plot_rp_imp(
         mask_distance: float, optional
             Only regions are plotted that are closer to any of the data points than this distance,
             relative to overall plot size. For instance, to only plot values
-            at the centroids, use mask_distance=0.01. If None, the plot is not masked.
-            Default is 0.01.
+            at the centroids, use mask_distance=0.03. If None, the plot is not masked.
+            Default is 0.03.
         kwargs_local_exceedance_impact: dict
             Dictionary of keyword arguments for the method impact.local_exceedance_impact.
         kwargs : dict, optional

climada/engine/test/test_cost_benefit.py

@@ -35,6 +35,8 @@
 )
 from climada.entity.disc_rates import DiscRates
 from climada.entity.entity_def import Entity
+from climada.entity.measures import Measure
+from climada.entity.measures.base import LOGGER as ILOG
 from climada.hazard.base import Hazard
 from climada.test import get_test_file
 from climada.util.api_client import Client
@@ -1119,6 +1121,66 @@ def test_remove_measure(self):
         self.assertEqual(len(cost_ben.cost_ben_ratio), 3)
         self.assertEqual(len(cost_ben.benefit), 3)
 
+    def test_measure_exposure_no_centroids(self):
+        """Test centroids assigned if no centroids were assigned in Measure Exposures object"""
+        hazard = Hazard.from_hdf5(HAZ_TEST_TC)
+        entity = Entity.from_excel(ENT_DEMO_TODAY)
+        entity.check()
+        entity.exposures.ref_year = 2018
+
+        # test that warning is not raised when centroid are assigned
+        try:
+            with self.assertLogs(ILOG, level="WARNING") as logs:
+                cost_ben = CostBenefit()
+                cost_ben.calc(
+                    hazard,
+                    entity,
+                    future_year=2040,
+                    risk_func=risk_aai_agg,
+                    imp_time_depen=None,
+                    save_imp=True,
+                )
+                for log in logs.output:
+                    self.assertNotIn(
+                        "No assigned hazard centroids in exposure object after",
+                        log,
+                        "Centroids are already assinged in Measure Exposure object"
+                        "and should not be reassigned.",
+                    )
+        except AssertionError as e:
+            if "no logs" in str(e).lower():
+                pass
+            else:
+                raise
+
+        # add measure with exposure without assigned centroids
+        exp_no_assigned_centroids = entity.exposures.copy()
+        exp_no_assigned_centroids.data = entity.exposures.data.drop(
+            columns=["centr_TC"]
+        )
+        entity.measures.append(
+            Measure(
+                name="no centroids",
+                haz_type="TC",
+                exposures_set=exp_no_assigned_centroids,
+            )
+        )
+
+        # test if warning is raised
+        with self.assertLogs(ILOG, level="WARNING") as logs:
+            cost_ben = CostBenefit()
+            cost_ben.calc(
+                hazard,
+                entity,
+                future_year=2040,
+                risk_func=risk_aai_agg,
+                imp_time_depen=None,
+                save_imp=True,
+            )
+        self.assertIn(
+            "No assigned hazard centroids in exposure object after", logs.output[0]
+        )
+
 
 class TestCalc(unittest.TestCase):
     """Test calc"""

climada/engine/unsequa/test/test_unsequa.py

@@ -650,7 +650,7 @@ def test_calc_sensitivity_all_pass(self):
         }
 
         def test_sensitivity_method(
-            exp_unc, impf_unc, haz_unc, sensitivity_method, param_dict
+            exp_unc, impf_unc, haz_unc, sensitivity_method, param_dict, places
         ):
             """Function to test each seaprate sensitivity method"""
             unc_calc = CalcImpact(exp_unc, impf_unc, haz_unc)
@@ -681,7 +681,7 @@ def test_sensitivity_method(
             self.assertAlmostEqual(
                 param_dict["test_si_value"][0],
                 unc_data.aai_agg_sens_df["aai_agg"][param_dict["test_si_value"][1]],
-                places=5,
+                places=places,
             )
 
             self.assertEqual(unc_data.aai_agg_unc_df.size, unc_data.n_samples)
@@ -695,7 +695,12 @@ def test_sensitivity_method(
         # loop over each method and do test
         for sensitivity_method, method_params in test_dict.items():
             test_sensitivity_method(
-                exp_unc, impf_unc, haz_unc, sensitivity_method, method_params
+                exp_unc,
+                impf_unc,
+                haz_unc,
+                sensitivity_method,
+                method_params,
+                places=2 if sensitivity_method == "rbd_fast" else 5,
             )
 
 

climada/entity/measures/base.py

@@ -179,7 +179,7 @@ def check(self):
         u_check.size(2, self.mdd_impact, "Measure.mdd_impact")
         u_check.size(2, self.paa_impact, "Measure.paa_impact")
 
-    def calc_impact(self, exposures, imp_fun_set, hazard, assign_centroids=True):
+    def calc_impact(self, exposures, imp_fun_set, hazard):
         """
         Apply measure and compute impact and risk transfer of measure
         implemented over inputs.
@@ -192,12 +192,6 @@ def calc_impact(self, exposures, imp_fun_set, hazard, assign_centroids=True):
             impact function set instance
         hazard : climada.hazard.Hazard
             hazard instance
-        assign_centroids : bool, optional
-            indicates whether centroids are assigned to the self.exposures object.
-            Centroids assignment is an expensive operation; set this to ``False`` to save
-            computation time if the hazards' centroids are already assigned to the exposures
-            object.
-            Default: True
 
         Returns
         -------
@@ -206,7 +200,17 @@ def calc_impact(self, exposures, imp_fun_set, hazard, assign_centroids=True):
         """
 
         new_exp, new_impfs, new_haz = self.apply(exposures, imp_fun_set, hazard)
-        return self._calc_impact(new_exp, new_impfs, new_haz, assign_centroids)
+        # assign centroids if missing
+        if new_haz.centr_exp_col not in new_exp.gdf.columns:
+            LOGGER.warning(
+                "No assigned hazard centroids in exposure object after the "
+                "application of the measure. The centroids will be assigned during impact "
+                "calculation. This is potentiall costly. To silence this warning, make sure "
+                "that centroids are assigned to all exposures."
+            )
+            new_exp.assign_centroids(new_haz)
+
+        return self._calc_impact(new_exp, new_impfs, new_haz)
 
     def apply(self, exposures, imp_fun_set, hazard):
         """
@@ -246,7 +250,7 @@ def apply(self, exposures, imp_fun_set, hazard):
 
         return new_exp, new_impfs, new_haz
 
-    def _calc_impact(self, new_exp, new_impfs, new_haz, assign_centroids):
+    def _calc_impact(self, new_exp, new_impfs, new_haz):
         """Compute impact and risk transfer of measure implemented over inputs.
 
         Parameters
@@ -267,7 +271,7 @@ def _calc_impact(self, new_exp, new_impfs, new_haz, assign_centroids):
         )
 
         imp = ImpactCalc(new_exp, new_impfs, new_haz).impact(
-            save_mat=False, assign_centroids=assign_centroids
+            save_mat=False, assign_centroids=False
         )
         return imp.calc_risk_transfer(self.risk_transf_attach, self.risk_transf_cover)
 

climada/hazard/base.py

@@ -101,10 +101,6 @@ class Hazard(HazardIO, HazardPlot):
         (i.e., is equivalent to fraction is 1 everywhere).
     """
 
-    intensity_thres = 10
-    """Intensity threshold per hazard used to filter lower intensities. To be
-    set for every hazard type"""
-
     vars_oblig = {
         "units",
         "centroids",
@@ -485,7 +481,7 @@ def local_exceedance_intensity(
         self,
         return_periods=(25, 50, 100, 250),
         method="interpolate",
-        min_intensity=None,
+        min_intensity=0,
         log_frequency=True,
         log_intensity=True,
         bin_decimals=None,
@@ -512,8 +508,7 @@ def local_exceedance_intensity(
             periods and extends the interpolation between these points to the given return period
             (similar for small return periods). Defauls to "interpolate".
         min_intensity : float, optional
-            Minimum threshold to filter the hazard intensity. If set to None, self.intensity_thres
-            will be used. Defaults to None.
+            Minimum threshold to filter the hazard intensity. Defaults to 0.
         log_frequency : bool, optional
             If set to True, (cummulative) frequency values are converted to log scale before
             inter- and extrapolation. Defaults to True.
@@ -554,8 +549,6 @@ def local_exceedance_intensity(
         intensities range from 1e6 to 1e9, you could use bin_decimals=-5, if your intensities
         range from 0.0001 to .01, you could use bin_decimals=5.
         """
-        if not min_intensity and min_intensity != 0:
-            min_intensity = self.intensity_thres
         # check frequency unit
         return_period_unit = u_dt.convert_frequency_unit_to_time_unit(
             self.frequency_unit
@@ -639,7 +632,7 @@ def local_return_period(
         self,
         threshold_intensities=(10.0, 20.0),
         method="interpolate",
-        min_intensity=None,
+        min_intensity=0,
         log_frequency=True,
         log_intensity=True,
         bin_decimals=None,
@@ -667,8 +660,7 @@ def local_return_period(
             points to the given threshold intensity (similar for small threshold intensites).
             Defaults to "interpolate".
         min_intensity : float, optional
-            Minimum threshold to filter the hazard intensity. If set to None, self.intensity_thres
-            will be used. Defaults to None.
+            Minimum threshold to filter the hazard intensity. Defaults to 0.
         log_frequency : bool, optional
             If set to True, (cummulative) frequency values are converted to log scale before
             inter- and extrapolation. Defaults to True.
@@ -710,8 +702,6 @@ def local_return_period(
         intensities range from 1e6 to 1e9, you could use bin_decimals=-5, if your intensities
         range from 0.0001 to .01, you could use bin_decimals=5.
         """
-        if not min_intensity and min_intensity != 0:
-            min_intensity = self.intensity_thres
         # check frequency unit
         return_period_unit = u_dt.convert_frequency_unit_to_time_unit(
             self.frequency_unit

climada/hazard/plot.py

@@ -40,7 +40,7 @@ def plot_rp_intensity(
         self,
         return_periods=(25, 50, 100, 250),
         axis=None,
-        mask_distance=0.01,
+        mask_distance=0.03,
         kwargs_local_exceedance_intensity=None,
         **kwargs,
     ):
@@ -60,8 +60,8 @@ def plot_rp_intensity(
         mask_distance: float, optional
             Only regions are plotted that are closer to any of the data points than this distance,
             relative to overall plot size. For instance, to only plot values
-            at the centroids, use mask_distance=0.01. If None, the plot is not masked.
-            Default is 0.01.
+            at the centroids, use mask_distance=0.03. If None, the plot is not masked.
+            Default is 0.03.
         kwargs: optional
             arguments for pcolormesh matplotlib function used in event plots
 
@@ -111,7 +111,7 @@ def plot_intensity(
         smooth=True,
         axis=None,
         adapt_fontsize=True,
-        mask_distance=0.01,
+        mask_distance=0.03,
         **kwargs,
     ):
         """Plot intensity values for a selected event or centroid.
@@ -138,8 +138,8 @@ def plot_intensity(
         mask_distance: float, optional
             Only regions are plotted that are closer to any of the data points than this distance,
             relative to overall plot size. For instance, to only plot values
-            at the centroids, use mask_distance=0.01. If None, the plot is not masked.
-            Default is 0.01.
+            at the centroids, use mask_distance=0.03. If None, the plot is not masked.
+            Default is 0.03.
         kwargs: optional
             arguments for pcolormesh matplotlib function
             used in event plots or for plot function used in centroids plots
@@ -181,7 +181,7 @@ def plot_fraction(
         centr=None,
         smooth=True,
         axis=None,
-        mask_distance=0.01,
+        mask_distance=0.03,
         **kwargs,
     ):
         """Plot fraction values for a selected event or centroid.
@@ -208,8 +208,8 @@ def plot_fraction(
         mask_distance: float, optional
             Relative distance (with respect to maximal map extent in longitude or latitude) to data
             points above which plot should not display values. For instance, to only plot values
-            at the centroids, use mask_distance=0.01. If None, the plot is not masked.
-            Default is None.
+            at the centroids, use mask_distance=0.03. If None, the plot is not masked.
+            Default is 0.03.
         kwargs: optional
             arguments for pcolormesh matplotlib function
             used in event plots or for plot function used in centroids plots
@@ -252,7 +252,7 @@ def _event_plot(
         axis=None,
         figsize=(9, 13),
         adapt_fontsize=True,
-        mask_distance=0.01,
+        mask_distance=0.03,
         **kwargs,
     ):
         """Plot an event of the input matrix.
@@ -277,8 +277,8 @@ def _event_plot(
         mask_distance: float, optional
             Only regions are plotted that are closer to any of the data points than this distance,
             relative to overall plot size. For instance, to only plot values
-            at the centroids, use mask_distance=0.01. If None, the plot is not masked.
-            Default is None.
+            at the centroids, use mask_distance=0.03. If None, the plot is not masked.
+            Default is 0.03.
         kwargs: optional
             arguments for pcolormesh matplotlib function