minor edits

climada/util/calibrate/base.py

@@ -166,17 +166,16 @@ def plot_impf_variability(
         plot_haz: bool = True,
         **plot_kwargs
     ):
-        """Plot impact function variability with parameter combinations of almost equal
-        cost function values
+
+        """Plot impact function variability with parameter combinations of
+        almost equal cost function values
 
         Args:
-            cost_func_diff (float, optional): Max deviation from optimal cost function value
-                (as fraction). Defaults to 0.1 (i.e. 10%).
+            cost_func_diff (float, optional): Max deviation from optimal cost
+            function value (as fraction). Defaults to 0.1 (i.e. 10%).
             p_space_df (pd.DataFrame, optional): parameter space. Defaults to None.
-            plot_haz (bool, optional): Whether or not to plot hazard intensity distibution.
-                Defaults to False.
-            haz_vals (np.array, optional): Hazard values at exposure points (if
-                pre-calculated). Defaults to None.
+            plot_haz (bool, optional): Whether or not to plot hazard intensity
+            distibution. Defaults to False.
         """
 
         haz_type = self.input.hazard.haz_type
@@ -194,16 +193,16 @@ def plot_impf_variability(
         # Retrieve parameters of impact functions with cost function values
         # within 'cost_func_diff' % of the best estimate
         max_cost_func_val = p_space_df['Cost Function'].min()*(1+cost_func_diff)
-        params_within_range = p_space_df.loc[p_space_df['Cost Function']<=max_cost_func_val,
-                                             params]
+        params_within_range = p_space_df.loc[p_space_df['Cost Function'] <=
+                                             max_cost_func_val,params]
 
         # Initialize figure
         fig,ax = plt.subplots()
 
         #Plot best-fit impact function
         best_impf = self.impf_set.get_func(haz_type=haz_type)[0]
-        ax.plot(best_impf.intensity,best_impf.mdd*best_impf.paa*100,color='tab:blue',
-                lw=2,zorder=3,label='best fit')
+        ax.plot(best_impf.intensity,best_impf.mdd*best_impf.paa*100,
+                color='tab:blue',lw=2,zorder=3,label='best fit',**plot_kwargs)
 
         #Plot all impact functions within 'cost_func_diff' % of best estimate
         for row in range(params_within_range.shape[0]):
@@ -213,8 +212,8 @@ def plot_impf_variability(
             temp_impf_set = self.input.impact_func_creator(**sel_params)
             temp_impf = temp_impf_set.get_func(haz_type=haz_type)[0]
 
-            ax.plot(temp_impf.intensity,temp_impf.mdd*temp_impf.paa*100,color='grey',
-                    alpha=0.4,label=label)
+            ax.plot(temp_impf.intensity,temp_impf.mdd*temp_impf.paa*100,
+                    color='grey',alpha=0.4,label=label)
 
         # Plot hazard intensity value distributions
         if plot_haz:
@@ -224,7 +223,8 @@ def plot_impf_variability(
             ax2.hist(haz_vals.data,bins=40,color='tab:orange',
                      alpha=0.3,label='Hazard intensity\noccurence')
             ax2.set(ylabel='Hazard intensity occurence (#Exposure points)')
-            ax.axvline(x=haz_vals.max(),label='Maximum hazard value',color='tab:orange')
+            ax.axvline(x=haz_vals.max(),label='Maximum hazard value',
+                       color='tab:orange')
             ax2.legend(loc='lower right')
 
         ax.set(xlabel=f"Intensity ({self.input.hazard.units})",

doc/tutorial/climada_util_calibrate.ipynb

@@ -2503,7 +2503,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -2512,7 +2512,7 @@
        "<Axes: xlabel='Intensity (m/s)', ylabel='Mean Damage Ratio (MDR) in %'>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     },