adapted docstrings

Author: ValentinGebhart

climada/engine/impact.py

@@ -556,7 +556,7 @@ def local_exceedance_impact(
         Notes
         -------
         Contrary to Impact.calc_freq_curve(), impacts are binned according to their n_sig_dig
-        significant digits. This results in a coarser (and smoother) interpolation, and a
+        significant digits. This results in a smoother (and coarser) interpolation, and a
         more stable extrapolation. To not bin the values, please use a large value for n_sig_dig,
         e.g., n_sig_dig=7. For more information about the binning, see
         climada.util.interpolation.preprocess_and_interpolate_ev().
@@ -709,7 +709,7 @@ def local_return_period(
         Notes
         -------
         Contrary to Impact.calc_freq_curve(), impacts are binned according to their n_sig_dig
-        significant digits. This results in a coarser (and smoother) interpolation, and a
+        significant digits. This results in a smoother (and coarser) interpolation, and a
         more stable extrapolation. To not bin the values, please use a large value for n_sig_dig,
         e.g., n_sig_dig=7. For more information about the binning, see
         climada.util.interpolation.preprocess_and_interpolate_ev().

climada/hazard/base.py

@@ -550,7 +550,7 @@ def local_exceedance_intensity(
         Notes
         -------
         Contrary to Impact.calc_freq_curve(), intensities are binned according to their n_sig_dig
-        significant digits. This results in a coarser (and smoother) interpolation, and a
+        significant digits. This results in a smoother (and coarser) interpolation, and a
         more stable extrapolation. To not bin the values, please use a large value for n_sig_dig,
         e.g., n_sig_dig=7. For more information about the binning, see
         util.interpolation.preprocess_and_interpolate_ev().
@@ -702,7 +702,7 @@ def local_return_period(
         Notes
         -------
         Contrary to Impact.calc_freq_curve(), intensities are binned according to their n_sig_dig
-        significant digits. This results in a coarser (and smoother) interpolation, and a
+        significant digits. This results in a smoother (and coarser) interpolation, and a
         more stable extrapolation. To not bin the values, please use a large value for n_sig_dig,
         e.g., n_sig_dig=7. For more information about the binning, see
         climada.util.interpolation.preprocess_and_interpolate_ev().

climada/util/test/test_interpolation.py

@@ -35,17 +35,17 @@ def test_interpolate_ev_linear_interp(self):
         y_train = np.array([8.0, 4.0, 2.0])
         x_test = np.array([0.0, 3.0, 4.0, 6.0])
         np.testing.assert_allclose(
-            u_interp.interpolate_ev(x_test, x_train, y_train),
+            u_interp._interpolate_ev(x_test, x_train, y_train),
             np.array([np.nan, 4.0, 3.0, np.nan]),
         )
         np.testing.assert_allclose(
-            u_interp.interpolate_ev(
+            u_interp._interpolate_ev(
                 x_test, x_train, y_train, extrapolation="extrapolate_constant"
             ),
             np.array([8.0, 4.0, 3.0, np.nan]),
         )
         np.testing.assert_allclose(
-            u_interp.interpolate_ev(
+            u_interp._interpolate_ev(
                 x_test,
                 x_train,
                 y_train,
@@ -61,13 +61,13 @@ def test_interpolate_ev_threshold_parameters(self):
         y_train = np.array([4.0, 1.0, 4.0])
         x_test = np.array([-1.0, 3.0, 4.0])
         np.testing.assert_allclose(
-            u_interp.interpolate_ev(
+            u_interp._interpolate_ev(
                 x_test, x_train, y_train, extrapolation="extrapolate_constant"
             ),
             np.array([4.0, 1.0, 2.0]),
         )
         np.testing.assert_allclose(
-            u_interp.interpolate_ev(
+            u_interp._interpolate_ev(
                 x_test,
                 x_train,
                 y_train,
@@ -77,7 +77,7 @@ def test_interpolate_ev_threshold_parameters(self):
             np.array([1.0, 1.0, 2.0]),
         )
         np.testing.assert_allclose(
-            u_interp.interpolate_ev(
+            u_interp._interpolate_ev(
                 x_test,
                 x_train,
                 y_train,
@@ -93,13 +93,13 @@ def test_interpolate_ev_scale_parameters(self):
         y_train = np.array([1.0, 3.0])
         x_test = np.array([1e0, 1e2])
         np.testing.assert_allclose(
-            u_interp.interpolate_ev(
+            u_interp._interpolate_ev(
                 x_test, x_train, y_train, logx=True, extrapolation="extrapolate"
             ),
             np.array([0.0, 2.0]),
         )
         np.testing.assert_allclose(
-            u_interp.interpolate_ev(
+            u_interp._interpolate_ev(
                 x_test,
                 x_train,
                 y_train,
@@ -112,7 +112,7 @@ def test_interpolate_ev_scale_parameters(self):
         y_train = np.array([1e1, 1e3])
         x_test = np.array([0.0, 2.0])
         np.testing.assert_allclose(
-            u_interp.interpolate_ev(
+            u_interp._interpolate_ev(
                 x_test, x_train, y_train, logy=True, extrapolation="extrapolate"
             ),
             np.array([1e0, 1e2]),
@@ -121,7 +121,7 @@ def test_interpolate_ev_scale_parameters(self):
         y_train = np.array([1e1, 1e5])
         x_test = np.array([1e0, 1e2])
         np.testing.assert_allclose(
-            u_interp.interpolate_ev(
+            u_interp._interpolate_ev(
                 x_test,
                 x_train,
                 y_train,
@@ -138,7 +138,7 @@ def test_interpolate_ev_degenerate_input(self):
         x_test = np.array([0.0, 2.0, 4.0])
         y_train = np.zeros(3)
         np.testing.assert_allclose(
-            u_interp.interpolate_ev(x_test, x_train, y_train),
+            u_interp._interpolate_ev(x_test, x_train, y_train),
             np.array([np.nan, 0.0, 0.0]),
         )
 
@@ -148,29 +148,29 @@ def test_interpolate_ev_small_input(self):
         y_train = np.array([2.0])
         x_test = np.array([0.0, 1.0, 2.0])
         np.testing.assert_allclose(
-            u_interp.interpolate_ev(
+            u_interp._interpolate_ev(
                 x_test, x_train, y_train, extrapolation="extrapolate"
             ),
             np.array([2.0, 2.0, np.nan]),
         )
         np.testing.assert_allclose(
-            u_interp.interpolate_ev(
+            u_interp._interpolate_ev(
                 x_test, x_train, y_train, extrapolation="extrapolate", y_asymptotic=0
             ),
             np.array([2.0, 2.0, 0.0]),
         )
         np.testing.assert_allclose(
-            u_interp.interpolate_ev(x_test, x_train, y_train), np.full(3, np.nan)
+            u_interp._interpolate_ev(x_test, x_train, y_train), np.full(3, np.nan)
         )
 
         x_train = np.array([])
         y_train = np.array([])
         x_test = np.array([0.0, 1.0, 2.0])
         np.testing.assert_allclose(
-            u_interp.interpolate_ev(x_test, x_train, y_train), np.full(3, np.nan)
+            u_interp._interpolate_ev(x_test, x_train, y_train), np.full(3, np.nan)
         )
         np.testing.assert_allclose(
-            u_interp.interpolate_ev(
+            u_interp._interpolate_ev(
                 x_test,
                 x_train,
                 y_train,
@@ -186,11 +186,11 @@ def test_stepfunction_ev(self):
         y_train = np.array([8.0, 4.0, 2.0])
         x_test = np.array([0.0, 3.0, 4.0, 6.0])
         np.testing.assert_allclose(
-            u_interp.stepfunction_ev(x_test, x_train, y_train),
+            u_interp._stepfunction_ev(x_test, x_train, y_train),
             np.array([8.0, 4.0, 2.0, np.nan]),
         )
         np.testing.assert_allclose(
-            u_interp.stepfunction_ev(x_test, x_train, y_train, y_asymptotic=0.0),
+            u_interp._stepfunction_ev(x_test, x_train, y_train, y_asymptotic=0.0),
             np.array([8.0, 4.0, 2.0, 0.0]),
         )
 
@@ -200,21 +200,21 @@ def test_stepfunction_ev_small_input(self):
         y_train = np.array([2.0])
         x_test = np.array([0.0, 1.0, 2.0])
         np.testing.assert_allclose(
-            u_interp.stepfunction_ev(x_test, x_train, y_train),
+            u_interp._stepfunction_ev(x_test, x_train, y_train),
             np.array([2.0, 2.0, np.nan]),
         )
         np.testing.assert_allclose(
-            u_interp.stepfunction_ev(x_test, x_train, y_train, y_asymptotic=0),
+            u_interp._stepfunction_ev(x_test, x_train, y_train, y_asymptotic=0),
             np.array([2.0, 2.0, 0.0]),
         )
         x_train = np.array([])
         y_train = np.array([])
         x_test = np.array([0.0, 1.0, 2.0])
         np.testing.assert_allclose(
-            u_interp.stepfunction_ev(x_test, x_train, y_train), np.full(3, np.nan)
+            u_interp._stepfunction_ev(x_test, x_train, y_train), np.full(3, np.nan)
         )
         np.testing.assert_allclose(
-            u_interp.stepfunction_ev(x_test, x_train, y_train, y_asymptotic=0),
+            u_interp._stepfunction_ev(x_test, x_train, y_train, y_asymptotic=0),
             np.zeros(3),
         )
 
@@ -223,14 +223,12 @@ def test_frequency_group(self):
         frequency = np.ones(6)
         intensity = np.array([1.00001, 0.9998, 1.0, 2.0, 3.0, 3])
         np.testing.assert_allclose(
-            u_interp.group_frequency(frequency, intensity, n_sig_dig=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), ([], [])
+            u_interp._group_frequency([], [], n_sig_dig=3), ([], [])
         )
-        with self.assertRaises(ValueError):
-            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]