Fix hardcoded text elements in the plots of the forecast class (#769)

* fix hardcoded text elements
* format forecast module
* rework tests of forecast module
* Update CHANGELOG.md

---------

Co-authored-by: Thomas Roosli <[email protected]>
Co-authored-by: Lukas Riedel <[email protected]>

Author: 3 people

CHANGELOG.md

@@ -63,6 +63,7 @@ Removed:
 - `util.lines_polys_handler` solve polygon disaggregation issue in metre-based projection [#666](https://github.com/CLIMADA-project/climada_python/pull/666)
 - Problem with `pyproj.CRS` as `Impact` attribute, [#706](https://github.com/CLIMADA-project/climada_python/issues/706). Now CRS is always stored as `str` in WKT format.
 - Correctly handle assertion errors in `Centroids.values_from_vector_files` and fix the associated test [#768](https://github.com/CLIMADA-project/climada_python/pull/768/)
+- Text in `Forecast` class plots can now be adjusted [#769](https://github.com/CLIMADA-project/climada_python/issues/769)
 
 ### Deprecated
 

climada/engine/forecast.py

@@ -313,6 +313,7 @@ def calc(self, force_reassign=False):
     def plot_imp_map(
         self,
         run_datetime=None,
+        explain_str=None,
         save_fig=True,
         close_fig=False,
         polygon_file=None,
@@ -321,13 +322,17 @@ def plot_imp_map(
         figsize=(9, 13),
         adapt_fontsize=True,
     ):
-        """plot a map of the impacts
+        """ plot a map of the impacts
 
         Parameters
         ----------
         run_datetime : datetime.datetime, optional
             Select the used hazard by the run_datetime,
             default is first element of attribute run_datetime.
+        explain_str : str, optional
+            Short str which explains type of impact, explain_str is included
+            in the title of the figure.
+            default is 'mean building damage caused by wind'
         save_fig : bool, optional
             Figure is saved if True, folder is within your configurable
             save_dir and filename is derived from the method summary_str()
@@ -372,7 +377,7 @@ def plot_imp_map(
             "run_start": (
                 run_datetime.strftime("%d.%m.%Y %HUTC +") + lead_time_str + "d"
             ),
-            "explain_text": ("mean building damage caused by wind"),
+            "explain_text": "mean building damage caused by wind" if explain_str is None else explain_str,
             "model_text": "CLIMADA IMPACT",
         }
         fig, axes = self._plot_imp_map(
@@ -526,15 +531,24 @@ def _plot_imp_map(
         return fig, axis_sub
 
     def plot_hist(
-        self, run_datetime=None, save_fig=True, close_fig=False, figsize=(9, 8)
+        self,
+        run_datetime=None,
+        explain_str=None,
+        save_fig=True,
+        close_fig=False,
+        figsize=(9, 8),
     ):
-        """plot histogram of the forecasted impacts all ensemble members
+        """ plot histogram of the forecasted impacts all ensemble members
 
         Parameters
         ----------
         run_datetime : datetime.datetime, optional
             Select the used hazard by the run_datetime,
             default is first element of attribute run_datetime.
+        explain_str : str, optional
+            Short str which explains type of impact, explain_str is included
+            in the title of the figure.
+            default is 'total building damage'
         save_fig : bool, optional
             Figure is saved if True, folder is within your configurable
             save_dir and filename is derived from the method summary_str()
@@ -603,7 +617,7 @@ def plot_hist(
         axes.xaxis.set_ticks(x_ticks)
         axes.xaxis.set_ticklabels(x_ticklabels)
         plt.xticks(rotation=15, horizontalalignment="right")
-        plt.xlim([(10**-0.25) * bins[0], (10**0.25) * bins[-1]])
+        plt.xlim([(10 ** -0.25) * bins[0], (10 ** 0.25) * bins[-1]])
 
         lead_time_str = "{:.0f}".format(
             self.lead_time(run_datetime).days
@@ -614,7 +628,7 @@ def plot_hist(
             "run_start": (
                 run_datetime.strftime("%d.%m.%Y %HUTC +") + lead_time_str + "d"
             ),
-            "explain_text": ("total building damage"),
+            "explain_text": ("total building damage") if explain_str is None else explain_str,
             "model_text": "CLIMADA IMPACT",
         }
         title_position = {
@@ -656,8 +670,9 @@ def plot_hist(
         plt.text(
             0.75,
             0.85,
-            "mean damage:\nCHF "
-            + self._number_to_str(self._impact[haz_ind].at_event.mean()),
+            "mean impact:\n "
+            + self._number_to_str(self._impact[haz_ind].at_event.mean())
+            + ' ' + self._impact[haz_ind].unit,
             horizontalalignment="center",
             verticalalignment="center",
             transform=axes.transAxes,
@@ -740,7 +755,7 @@ def plot_exceedence_prob(
             The default is (9, 13)
         adapt_fontsize : bool, optional
             If set to true, the size of the fonts will be adapted to the size of the figure.
-            Otherwise the default matplotlib font size is used. Default is True.
+            Otherwise, the default matplotlib font size is used. Default is True.
 
         Returns
         -------
@@ -750,10 +765,10 @@ def plot_exceedence_prob(
         if run_datetime is None:
             run_datetime = self.run_datetime[0]
         haz_ind = np.argwhere(np.isin(self.run_datetime, run_datetime))[0][0]
-        wind_map_file_name = (
+        exceedence_map_file_name = (
             self.summary_str(run_datetime) + "_exceed_" + str(threshold) + "_map.jpeg"
         )
-        wind_map_file_name_full = FORECAST_PLOT_DIR / wind_map_file_name
+        exceedence_map_file_name_full = FORECAST_PLOT_DIR / exceedence_map_file_name
         lead_time_str = "{:.0f}".format(
             self.lead_time(run_datetime).days
             + self.lead_time(run_datetime).seconds / 60 / 60 / 24
@@ -783,7 +798,7 @@ def plot_exceedence_prob(
             adapt_fontsize=adapt_fontsize,
         )
         if save_fig:
-            plt.savefig(wind_map_file_name_full)
+            plt.savefig(exceedence_map_file_name_full)
         if close_fig:
             plt.clf()
             plt.close(fig)
@@ -974,7 +989,7 @@ def plot_warn_map(
             Figure is not drawn if True. The default is False.
         adapt_fontsize : bool, optional
             If set to true, the size of the fonts will be adapted to the size of the figure.
-            Otherwise the default matplotlib font size is used. Default is True.
+            Otherwise, the default matplotlib font size is used. Default is True.
 
         Returns
         -------
@@ -1086,7 +1101,7 @@ def _plot_warn(
                 decision_dict_functions[aggregation] = np.mean
             else:
                 raise ValueError(
-                    "Parameter area_aggregation of "
+                    "Parameter " + aggregation + " of "
                     + "Forecast.plot_warn_map() must eiter be "
                     + "a float between [0..1], which "
                     + "specifys a quantile. or 'sum' or 'mean'."

climada/engine/test/test_forecast.py

@@ -104,7 +104,8 @@ class TestPlot(unittest.TestCase):
 
     def test_Forecast_plot(self):
         """Test cplotting functions from the Forecast class"""
-                #hazard
+        ## given a forecast based on hazard exposure and vulnerability
+        #hazard
         haz1 = StormEurope.from_cosmoe_file(
             HAZ_DIR.joinpath('storm_europe_cosmoe_forecast_vmax_testfile.nc'),
             run_datetime=dt.datetime(2018,1,1),
@@ -149,21 +150,41 @@ def test_Forecast_plot(self):
                     for f in source:
                         if f['properties']['adm0_a3'] == 'CHE':
                             sink.write(f)
-        #test plotting functions
+        ## test plotting functions
+        # should save plot without failing
         forecast.plot_imp_map(run_datetime=dt.datetime(2017,12,31),
+                              explain_str='test text',
                               polygon_file=str(cantons_file),
                               save_fig=True, close_fig=True)
         map_file_name = (forecast.summary_str(dt.datetime(2017,12,31)) +
                          '_impact_map' +
                          '.jpeg')
         map_file_name_full = Path(FORECAST_PLOT_DIR) / map_file_name
         map_file_name_full.absolute().unlink(missing_ok=False)
-        forecast.plot_hist(run_datetime=dt.datetime(2017,12,31),
-                           save_fig=False, close_fig=True)
-        forecast.plot_exceedence_prob(run_datetime=dt.datetime(2017,12,31),
-                                      threshold=5000, save_fig=False, close_fig=True)
-
-
+        #should contain title strings
+        ax = forecast.plot_hist(run_datetime=dt.datetime(2017,12,31),
+                           explain_str='test text',
+                           save_fig=False, close_fig=False)
+        title_artists = ax.get_figure().get_children()
+        title_texts = [x.get_text() for x in title_artists if isinstance(x, plt.Text)]
+        self.assertIn('test text', title_texts)
+        self.assertIn('Wed 03 Jan 2018 00-24UTC', title_texts)
+        self.assertIn('31.12.2017 00UTC +3d', title_texts)
+        #should contain average impact in axes
+        artists = ax.get_children()
+        texts = [x.get_text() for x in artists if type(x) == plt.Text]
+        self.assertIn('mean impact:\n 26  USD', texts)
+        ax.get_figure().clf()
+        #should contain title strings
+        ax = forecast.plot_exceedence_prob(run_datetime=dt.datetime(2017,12,31),
+                                      threshold=5000, explain_str='test text exceedence',
+                                      save_fig=False, close_fig=False)[0][0]
+        title_artists = ax.get_figure().get_children()
+        title_texts = [x.get_text() for x in title_artists if isinstance(x, plt.Text)]
+        self.assertIn('test text exceedence', title_texts)
+        self.assertIn('Wed 03 Jan 2018 00-24UTC', title_texts)
+        self.assertIn('31.12.2017 00UTC +3d', title_texts)
+        ax.get_figure().clf()
         forecast.plot_warn_map(str(cantons_file),
                                decision_level = 'polygon',
                                thresholds=[100000,500000,
@@ -187,36 +208,48 @@ def test_Forecast_plot(self):
                                close_fig=True)
         forecast.plot_hexbin_ei_exposure()
         plt.close()
-        with self.assertRaises(ValueError):
+        # should fail because of invalid decision_level
+        with self.assertRaises(ValueError) as cm:
             forecast.plot_warn_map(str(cantons_file),
-                                   decision_level = 'test_fail',
+                                   decision_level='test_fail',
                                    probability_aggregation=0.2,
                                    area_aggregation=0.2,
                                    title="Building damage warning",
                                    explain_text="warn level based on aggregated damages",
                                    save_fig=False,
                                    close_fig=True)
         plt.close()
-        with self.assertRaises(ValueError):
+        self.assertIn(
+            "Parameter decision_level", str(cm.exception)
+        )
+        # should fail because of invalid probability_aggregation
+        with self.assertRaises(ValueError) as cm:
             forecast.plot_warn_map(str(cantons_file),
-                                   decision_level = 'exposure_point',
+                                   decision_level='exposure_point',
                                    probability_aggregation='test_fail',
                                    area_aggregation=0.2,
                                    title="Building damage warning",
                                    explain_text="warn level based on aggregated damages",
                                    save_fig=False,
                                    close_fig=True)
         plt.close()
-        with self.assertRaises(ValueError):
+        self.assertIn(
+            "Parameter probability_aggregation", str(cm.exception)
+        )
+        # should fail because of invalid area_aggregation
+        with self.assertRaises(ValueError) as cm:
             forecast.plot_warn_map(str(cantons_file),
-                                   decision_level = 'exposure_point',
+                                   decision_level='exposure_point',
                                    probability_aggregation=0.2,
                                    area_aggregation='test_fail',
                                    title="Building damage warning",
                                    explain_text="warn level based on aggregated damages",
                                    save_fig=False,
                                    close_fig=True)
         plt.close()
+        self.assertIn(
+            "Parameter area_aggregation", str(cm.exception)
+        )
 
 
 # Execute Tests