adapt number of significant digits to 3 for rounding

Author: ValentinGebhart

climada/engine/impact.py

@@ -586,6 +586,7 @@ def local_exceedance_impact(
                         value_threshold=min_impact,
                         method=method,
                         y_asymptotic=0.0,
+                        n_sig_dig=3,
                     )
                     for i_centroid in nonzero_centroids
                 ]
@@ -711,6 +712,7 @@ def local_return_period(
                         value_threshold=min_impact,
                         method=method,
                         y_asymptotic=np.nan,
+                        n_sig_dig=3,
                     )
                     for i_centroid in nonzero_centroids
                 ]

climada/hazard/base.py

@@ -491,7 +491,6 @@ def local_exceedance_intensity(
         min_intensity=None,
         log_frequency=True,
         log_intensity=True,
-        n_sig_dig=2,
     ):
         """Compute local exceedance intensity for given return periods. The default method
         is fitting the ordered intensitites per centroid to the corresponding cummulated
@@ -574,7 +573,7 @@ def local_exceedance_intensity(
                         value_threshold=min_intensity,
                         method=method,
                         y_asymptotic=0.0,
-                        n_sig_dig=n_sig_dig,
+                        n_sig_dig=3,
                     )
                     for i_centroid in nonzero_centroids
                 ]
@@ -622,7 +621,6 @@ def local_return_period(
         min_intensity=None,
         log_frequency=True,
         log_intensity=True,
-        n_sig_dig=2,
     ):
         """Compute local return periods for given hazard intensities. The default method
         is fitting the ordered intensitites per centroid to the corresponding cummulated
@@ -702,7 +700,7 @@ def local_return_period(
                         value_threshold=min_intensity,
                         method=method,
                         y_asymptotic=np.nan,
-                        n_sig_dig=n_sig_dig,
+                        n_sig_dig=3,
                     )
                     for i_centroid in nonzero_centroids
                 ]

climada/util/interpolation.py

@@ -71,6 +71,8 @@ def preprocess_and_interpolate_ev(
         Has no effect if method is "interpolate". Else, provides return value and if
         for test x values larger than given x values, if size < 2 or if method is set
         to "extrapolate_constant" or "stepfunction". Defaults to np.nan.
+    n_sig_dig : int, optional
+        number of significant digits to group the values (in order to avoid bad extrapolation behaviour). Defaults to 3.
 
     Returns
     -------
@@ -96,7 +98,7 @@ def preprocess_and_interpolate_ev(
     frequency = frequency[sorted_idxs]
 
     # group similar values together
-    frequency, values = group_frequency(frequency, values, n_sig_dig=n_sig_dig)
+    frequency, values = group_frequency(frequency, values, n_sig_dig)
 
     # transform frequencies to cummulative frequencies
     frequency = np.cumsum(frequency[::-1])[::-1]
@@ -368,6 +370,8 @@ def group_frequency(frequency, value, n_sig_dig):
         start_indices = np.insert(start_indices, value_unique.size, frequency.size)
         frequency = np.add.reduceat(frequency, start_indices[:-1])
         return frequency, value_unique
+    elif not all(sorted(value) == value):
+        raise ValueError("Value array must be sorted in ascending order!")
 
     return frequency, value
 

climada/util/test/test_interpolation.py

@@ -221,13 +221,16 @@ def test_stepfunction_ev_small_input(self):
     def test_frequency_group(self):
         """Test frequency grouping method"""
         frequency = np.ones(6)
-        intensity = np.array([1.00001, 0.999, 1.0, 2.0, 3.0, 3])
+        intensity = np.array([1.00001, 0.9998, 1.0, 2.0, 3.0, 3])
         np.testing.assert_allclose(
-            u_interp.group_frequency(frequency, intensity), ([3, 1, 2], [1, 2, 3])
+            u_interp.group_frequency(frequency, intensity, n_sig_dig=3),
+            ([3, 1, 2], [1, 2, 3]),
+        )
+        np.testing.assert_allclose(
+            u_interp.group_frequency([], [], n_sig_dig=3), ([], [])
         )
-        np.testing.assert_allclose(u_interp.group_frequency([], []), ([], []))
         with self.assertRaises(ValueError):
-            u_interp.group_frequency(frequency, intensity[::-1])
+            u_interp.group_frequency(frequency, intensity[::-1], n_sig_dig=3)
 
     def test_round_to_sig_digits(self):
         array = [0.00111, 999.0, 55.5, 0.0, -1.001, -1.08]