fix indentation

Author: emanuel-schmid

climada/engine/uncertainty/base.py

@@ -608,110 +608,110 @@ def calc_sensitivity(self, salib_method='sobol', method_kwargs=None):
 
 
     def plot_distribution(self, metric_list=None, figsize=None, log=False):
-     """
-     Plot the distribution of values of the risk metrics over the sampled
-     input parameters (i.e. plot the "uncertainty" distributions).
-
-     Parameters
-     ----------
-     metric_list: list, optional
-         List of metrics to plot the distribution.
-         The default is None.
-     figsize: tuple(int or float, int or float), optional
-         The figsize argument of matplotlib.pyplot.subplots()
-         The default is derived from the total number of plots (nplots) as:
-             nrows, ncols = int(np.ceil(nplots / 3)), min(nplots, 3)
-             figsize = (ncols * FIG_W, nrows * FIG_H)
-     log: boolean
-         Use log10 scale for x axis. Default is False
-
-     Raises
-     ------
-     ValueError
-         If no metric distribution was computed the plot cannot be made.
-
-     Returns
-     -------
-     axes: matplotlib.pyplot.axes
-         The axes handle of the plot.
-
-     """
-     fontsize = 18 #default label fontsize
-
-     if not self.metrics:
-         raise ValueError("No uncertainty data present for these metrics. "+
-                 "Please run an uncertainty analysis first.")
-
-     if metric_list is None:
-         metric_list = self.metric_names
-
-     df_values = pd.DataFrame()
-     for metric in metric_list:
-         if metric not in METRICS_2D:
-             df_values = df_values.append(self.metrics[metric])
-
-     if log:
-         df_values_plt = df_values.apply(np.log10).copy()
-         df_values_plt = df_values_plt.replace([np.inf, -np.inf], np.nan)
-     else:
-         df_values_plt = df_values.copy()
-
-     cols = df_values_plt.columns
-     nplots = len(cols)
-     nrows, ncols = int(np.ceil(nplots / 2)), min(nplots, 2)
-     if not figsize:
-         figsize = (ncols * FIG_W, nrows * FIG_H)
-     _fig, axes = plt.subplots(nrows = nrows,
-                              ncols = ncols,
-                              figsize = figsize)
-     if nplots > 1:
-         flat_axes = axes.flatten()
-     else:
-         flat_axes = np.array([axes])
-
-     for ax, col in zip_longest(flat_axes, cols, fillvalue=None):
-         if col is None:
-             ax.remove()
-             continue
-         data = df_values_plt[col]
-         if data.empty:
-             ax.remove()
-             continue
-         data.hist(ax=ax, bins=30, density=True, histtype='bar',
-                   color='lightsteelblue', edgecolor='black')
-         try:
-             data.plot.kde(ax=ax, color='darkblue', linewidth=4, label='')
-         except np.linalg.LinAlgError:
-             pass
-         avg, std = df_values[col].mean(), df_values[col].std()
-         _, ymax = ax.get_ylim()
-         if log:
-             avg_plot = np.log10(avg)
-         else:
-             avg_plot = avg
-         ax.axvline(avg_plot, color='darkorange', linestyle='dashed', linewidth=2,
-                 label="avg=%.2f%s" %u_vtm(avg))
-         if log:
-             std_m, std_p = np.log10(avg - std), np.log10(avg + std)
-         else:
-             std_m, std_p = avg - std, avg + std
-         ax.plot([std_m, std_p],
-                 [0.3 * ymax, 0.3 * ymax], color='black',
-                 label="std=%.2f%s" %u_vtm(std))
-         ax.set_title(col)
-         if log:
-             ax.set_xlabel('value [log10]')
-         else:
-             ax.set_xlabel('value')
-         ax.set_ylabel('density of events')
-         ax.legend(fontsize=fontsize-2)
-
-         ax.tick_params(labelsize=fontsize)
-         for item in [ax.title, ax.xaxis.label, ax.yaxis.label]:
-             item.set_fontsize(fontsize)
-     plt.tight_layout()
-
-     return axes
+        """
+        Plot the distribution of values of the risk metrics over the sampled
+        input parameters (i.e. plot the "uncertainty" distributions).
+
+        Parameters
+        ----------
+        metric_list: list, optional
+            List of metrics to plot the distribution.
+            The default is None.
+        figsize: tuple(int or float, int or float), optional
+            The figsize argument of matplotlib.pyplot.subplots()
+            The default is derived from the total number of plots (nplots) as:
+                nrows, ncols = int(np.ceil(nplots / 3)), min(nplots, 3)
+                figsize = (ncols * FIG_W, nrows * FIG_H)
+        log: boolean
+            Use log10 scale for x axis. Default is False
+
+        Raises
+        ------
+        ValueError
+            If no metric distribution was computed the plot cannot be made.
+
+        Returns
+        -------
+        axes: matplotlib.pyplot.axes
+            The axes handle of the plot.
+
+        """
+        fontsize = 18 #default label fontsize
+
+        if not self.metrics:
+            raise ValueError("No uncertainty data present for these metrics. "+
+                    "Please run an uncertainty analysis first.")
+
+        if metric_list is None:
+            metric_list = self.metric_names
+
+        df_values = pd.DataFrame()
+        for metric in metric_list:
+            if metric not in METRICS_2D:
+                df_values = df_values.append(self.metrics[metric])
+
+        if log:
+            df_values_plt = df_values.apply(np.log10).copy()
+            df_values_plt = df_values_plt.replace([np.inf, -np.inf], np.nan)
+        else:
+            df_values_plt = df_values.copy()
+
+        cols = df_values_plt.columns
+        nplots = len(cols)
+        nrows, ncols = int(np.ceil(nplots / 2)), min(nplots, 2)
+        if not figsize:
+            figsize = (ncols * FIG_W, nrows * FIG_H)
+        _fig, axes = plt.subplots(nrows = nrows,
+                                ncols = ncols,
+                                figsize = figsize)
+        if nplots > 1:
+            flat_axes = axes.flatten()
+        else:
+            flat_axes = np.array([axes])
+
+        for ax, col in zip_longest(flat_axes, cols, fillvalue=None):
+            if col is None:
+                ax.remove()
+                continue
+            data = df_values_plt[col]
+            if data.empty:
+                ax.remove()
+                continue
+            data.hist(ax=ax, bins=30, density=True, histtype='bar',
+                    color='lightsteelblue', edgecolor='black')
+            try:
+                data.plot.kde(ax=ax, color='darkblue', linewidth=4, label='')
+            except np.linalg.LinAlgError:
+                pass
+            avg, std = df_values[col].mean(), df_values[col].std()
+            _, ymax = ax.get_ylim()
+            if log:
+                avg_plot = np.log10(avg)
+            else:
+                avg_plot = avg
+            ax.axvline(avg_plot, color='darkorange', linestyle='dashed', linewidth=2,
+                    label="avg=%.2f%s" %u_vtm(avg))
+            if log:
+                std_m, std_p = np.log10(avg - std), np.log10(avg + std)
+            else:
+                std_m, std_p = avg - std, avg + std
+            ax.plot([std_m, std_p],
+                    [0.3 * ymax, 0.3 * ymax], color='black',
+                    label="std=%.2f%s" %u_vtm(std))
+            ax.set_title(col)
+            if log:
+                ax.set_xlabel('value [log10]')
+            else:
+                ax.set_xlabel('value')
+            ax.set_ylabel('density of events')
+            ax.legend(fontsize=fontsize-2)
+
+            ax.tick_params(labelsize=fontsize)
+            for item in [ax.title, ax.xaxis.label, ax.yaxis.label]:
+                item.set_fontsize(fontsize)
+        plt.tight_layout()
+
+        return axes
 
 
     def plot_sample(self, figsize=None):