change error handling

Author: ValentinGebhart

climada/engine/impact.py

@@ -548,12 +548,17 @@ def local_exceedance_impact(
         column_label : function
             Column-label-generating function, for reporting and plotting
 
+        See Also
+        --------
+        util.interpolation.preprocess_and_interpolate_ev :
+            inter- and extrapolation method
+
         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
-        more stable extrapolation. To not bin the values, please use, e.g., n_sig_dig=7. For more
-        information about the binning, see docstring of
+        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().
         """
         LOGGER.info(
@@ -696,12 +701,17 @@ def local_return_period(
         column_label : function
             Column-label-generating function, for reporting and plotting
 
+        See Also
+        --------
+        util.interpolation.preprocess_and_interpolate_ev :
+            inter- and extrapolation method
+
         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
-        more stable extrapolation. To not bin the values, please use, e.g., n_sig_dig=7. For more
-        information about the binning, see docstring of
+        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

@@ -542,13 +542,19 @@ def local_exceedance_intensity(
         column_label : function
             Column-label-generating function, for reporting and plotting
 
+        See Also
+        --------
+        util.interpolation.preprocess_and_interpolate_ev :
+            inter- and extrapolation method
+
         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
-        more stable extrapolation. To not bin the values, please use, e.g., n_sig_dig=7. For more
-        information about the binning, see docstring of
-        climada.util.interpolation.preprocess_and_interpolate_ev().
+        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().
+
         """
         if not min_intensity and min_intensity != 0:
             min_intensity = self.intensity_thres
@@ -688,12 +694,17 @@ def local_return_period(
         column_label : function
             Column-label-generating function, for reporting and plotting
 
+        See Also
+        --------
+        util.interpolation.preprocess_and_interpolate_ev :
+            inter- and extrapolation method
+
         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
-        more stable extrapolation. To not bin the values, please use, e.g., n_sig_dig=7. For more
-        information about the binning, see docstring of
+        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().
         """
         if not min_intensity and min_intensity != 0:

climada/util/interpolation.py

@@ -89,20 +89,20 @@ def preprocess_and_interpolate_ev(
         If both test frequencies and test values are given or none of them.
 
     Notes
-    -------
+    -----
     Before inter- and extrapolation, the values are binned according to their n_sig_dig
     significant digits, and their corresponding frequencies are summed. For instance, if
     n_sig_dig=3, the two values 12.01 and 11.97 with corresponding frequencies 0.1 and 0.2 are
-    combined to a value 12.0 with frequency 0.3. This binning leads to a coarser (and smoother)
+    combined to a value 12.0 with frequency 0.3. This binning leads to a smoother (and coarser)
     interpolation, and a more stable extrapolation. To not bin the values, you can use a large
     n_sig_dig, e.g., n_sig_dig=7.
     """
 
     # check that only test frequencies or only test values are given
     if test_frequency is not None and test_values is not None:
         raise ValueError(
-            "Both test frequencies and test values are given. "
-            "To use this method, please only use one of them."
+            "Both test frequencies and test values are given. This method only handles one of "
+            "the two. To use this method, please only use one of them."
         )
     elif test_frequency is None and test_values is None:
         raise ValueError("No test values or test frequencies are given.")
@@ -113,15 +113,15 @@ def preprocess_and_interpolate_ev(
     frequency = frequency[sorted_idxs]
 
     # group similar values together
-    frequency, values = group_frequency(frequency, values, n_sig_dig)
+    frequency, values = _group_frequency(frequency, values, n_sig_dig)
 
     # transform frequencies to cummulative frequencies
     frequency = np.cumsum(frequency[::-1])[::-1]
 
     # if test frequencies are provided
     if test_frequency is not None:
         if method == "stepfunction":
-            return stepfunction_ev(
+            return _stepfunction_ev(
                 test_frequency,
                 frequency[::-1],
                 values[::-1],
@@ -130,7 +130,7 @@ def preprocess_and_interpolate_ev(
             )
         else:
             extrapolation = None if method == "interpolate" else method
-            return interpolate_ev(
+            return _interpolate_ev(
                 test_frequency,
                 frequency[::-1],
                 values[::-1],
@@ -144,7 +144,7 @@ def preprocess_and_interpolate_ev(
     # if test values are provided
     else:
         if method == "stepfunction":
-            return stepfunction_ev(
+            return _stepfunction_ev(
                 test_values,
                 values,
                 frequency,
@@ -153,7 +153,7 @@ def preprocess_and_interpolate_ev(
             )
         else:
             extrapolation = None if method == "interpolate" else method
-            return interpolate_ev(
+            return _interpolate_ev(
                 test_values,
                 values,
                 frequency,
@@ -164,7 +164,7 @@ def preprocess_and_interpolate_ev(
             )
 
 
-def interpolate_ev(
+def _interpolate_ev(
     x_test,
     x_train,
     y_train,
@@ -184,7 +184,7 @@ def interpolate_ev(
         x_test : array_like
             1-D array of x-values for which training data should be interpolated
         x_train : array_like
-            1-D array of x-values of training data
+            1-D array of x-values of training data sorted in ascending order
         y_train : array_like
             1-D array of y-values of training data
         logx : bool, optional
@@ -227,11 +227,6 @@ def interpolate_ev(
     if extrapolation == "extrapolate":
         fill_value = "extrapolate"
     elif extrapolation == "extrapolate_constant":
-        if not all(sorted(x_train) == x_train):
-            raise ValueError(
-                "x_train array must be sorted in ascending order. This might be due to floating "
-                "point errors in the rounding process of `group_frequency()`."
-            )
         fill_value = (y_train[0], np.log10(y_asymptotic) if logy else y_asymptotic)
     else:
         fill_value = np.nan
@@ -247,7 +242,7 @@ def interpolate_ev(
     return y_test
 
 
-def stepfunction_ev(
+def _stepfunction_ev(
     x_test, x_train, y_train, x_threshold=None, y_threshold=None, y_asymptotic=np.nan
 ):
     """
@@ -259,7 +254,7 @@ def stepfunction_ev(
         x_test : array_like
             1-D array of x-values for which training data should be interpolated
         x_train : array_like
-            1-D array of x-values of training data
+            1-D array of x-values of training data sorted in ascending order
         y_train : array_like
             1-D array of y-values of training data
         x_threshold : float, optional
@@ -285,11 +280,6 @@ def stepfunction_ev(
         return _interpolate_small_input(x_test, x_train, y_train, None, y_asymptotic)
 
     # find indices of x_test if sorted into x_train
-    if not all(sorted(x_train) == x_train):
-        raise ValueError(
-            "Input array x_train must be sorted in ascending order. This might be due to "
-            "floating point errors in the rounding process of `group_frequency()`."
-        )
     indx = np.searchsorted(x_train, x_test)
     y_test = y_train[indx.clip(max=len(x_train) - 1)]
     y_test[indx == len(x_train)] = y_asymptotic
@@ -357,7 +347,7 @@ def _interpolate_small_input(x_test, x_train, y_train, logy, y_asymptotic):
     return y_test
 
 
-def group_frequency(frequency, value, n_sig_dig):
+def _group_frequency(frequency, value, n_sig_dig):
     """
     Util function to aggregate (add) frequencies for equal values
 
@@ -383,22 +373,18 @@ def group_frequency(frequency, value, n_sig_dig):
     # round values and group them
     value = round_to_sig_digits(value, n_sig_dig)
     value_unique, start_indices = np.unique(value, return_index=True)
-
     if value_unique.size != frequency.size:
         if not all(sorted(start_indices) == start_indices):
-            raise ValueError(
-                "Value array must be sorted in ascending order. This might be due to floating "
-                "point errors in the rounding process of `round_to_sig_digits()`."
+            LOGGER.warning(
+                "After grouping values to significant digits, the value array is not sorted."
+                f"The values are not binned. Please choose a larger value of n_sig_dig={n_sig_dig}."
             )
+            return frequency, value
+
         # add frequency for equal value
         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. This might be due to floating point "
-            "errors in the rounding process of `round_to_sig_digits()`."
-        )
 
     return frequency, value