diff --git a/run.py b/run.py
index 854a0e452526abb0742e72c9a3c6162ff03a6287..79492b0f92f5c32eb75c4e3443778336a6192656 100644
--- a/run.py
+++ b/run.py
@@ -34,9 +34,7 @@ def main(parser_args):
create_new_bootstraps=False,
plot_list=["PlotMonthlySummary", "PlotStationMap", "PlotClimatologicalSkillScore",
"PlotCompetitiveSkillScore", "PlotBootstrapSkillScore", "PlotConditionalQuantiles",
- "PlotAvailability", "PlotAvailabilityHistogram"],
-
-
+ "PlotAvailabilityHistogram"],
)
PreProcessing()
diff --git a/src/plotting/postprocessing_plotting.py b/src/plotting/postprocessing_plotting.py
index a4e7173bebc7d25bc276251b66cd8beae2d6d2bd..5c72b47585ccde6017f2f7769f9e09258d3943ca 100644
--- a/src/plotting/postprocessing_plotting.py
+++ b/src/plotting/postprocessing_plotting.py
@@ -27,10 +27,22 @@ logging.getLogger('matplotlib').setLevel(logging.WARNING)
class AbstractPlotClass:
- def __init__(self, plot_folder, plot_name, resolution=500):
+ def __init__(self, plot_folder, plot_name, resolution=500, rc_params=None):
+ if rc_params is None:
+ rc_params = {'axes.labelsize': 'large',
+ 'xtick.labelsize': 'large',
+ 'ytick.labelsize': 'large',
+ 'legend.fontsize': 'large',
+ 'axes.titlesize': 'large',
+ }
self.plot_folder = plot_folder
self.plot_name = plot_name
self.resolution = resolution
+ self.rc_params = rc_params
+ self._update_rc_params()
+
+ def _update_rc_params(self):
+ plt.rcParams.update(self.rc_params)
def _plot(self, *args):
raise NotImplementedError
@@ -67,7 +79,7 @@ class PlotMonthlySummary(AbstractPlotClass):
in data_path with name monthly_summary_box_plot.pdf and 500dpi resolution.
"""
def __init__(self, stations: List, data_path: str, name: str, target_var: str, window_lead_time: int = None,
- plot_folder: str = "."):
+ plot_folder: str = ".", target_var_unit: str = 'ppb'):
"""
Sets attributes and create plot
:param stations: all stations to plot
@@ -77,13 +89,14 @@ class PlotMonthlySummary(AbstractPlotClass):
:param window_lead_time: lead time to plot, if window_lead_time is higher than the available lead time or not given
the maximum lead time from data is used. (default None -> use maximum lead time from data).
:param plot_folder: path to save the plot (default: current directory)
+ :param target_var_unit: unit of target var for plot legend
"""
super().__init__(plot_folder, "monthly_summary_box_plot")
self._data_path = data_path
self._data_name = name
self._data = self._prepare_data(stations)
self._window_lead_time = self._get_window_lead_time(window_lead_time)
- self._plot(target_var)
+ self._plot(target_var, target_var_unit)
self._save()
def _prepare_data(self, stations: List) -> xr.DataArray:
@@ -128,7 +141,7 @@ class PlotMonthlySummary(AbstractPlotClass):
window_lead_time = ahead_steps
return min(ahead_steps, window_lead_time)
- def _plot(self, target_var: str):
+ def _plot(self, target_var: str, target_var_unit: str):
"""
Main plot function that creates a monthly grouped box plot over all stations but with separate boxes for each
lead time step.
@@ -140,9 +153,15 @@ class PlotMonthlySummary(AbstractPlotClass):
ax = sns.boxplot(x='index', y='values', hue='ahead', data=data.compute(), whis=1., palette=color_palette,
flierprops={'marker': '.', 'markersize': 1}, showmeans=True,
meanprops={'markersize': 1, 'markeredgecolor': 'k'})
- ax.set(xlabel='month', ylabel=f'{target_var}')
+ ylabel = self._spell_out_chemical_concentrations(target_var)
+ ax.set(xlabel='month', ylabel=f'{ylabel} (in {target_var_unit})')
plt.tight_layout()
+ @staticmethod
+ def _spell_out_chemical_concentrations(short_name: str):
+ short2long = {'o3': 'ozone', 'no': 'nitrogen oxide', 'no2': 'nitrogen dioxide', 'nox': 'nitrogen dioxides'}
+ return f"{short2long[short_name]} concentration"
+
@TimeTrackingWrapper
class PlotStationMap(AbstractPlotClass):
@@ -477,7 +496,7 @@ class PlotClimatologicalSkillScore(AbstractPlotClass):
name skill_score_clim_{extra_name_tag}{model_setup}.pdf and resolution of 500dpi.
"""
def __init__(self, data: Dict, plot_folder: str = ".", score_only: bool = True, extra_name_tag: str = "",
- model_setup: str = ""):
+ model_setup: str = "", font_size_all_terms: int = 22):
"""
Sets attributes and create plot
:param data: dictionary with station names as keys and 2D xarrays as values, consist on axis ahead and terms.
@@ -485,8 +504,10 @@ class PlotClimatologicalSkillScore(AbstractPlotClass):
:param score_only: if true plot only scores of CASE I to IV, otherwise plot all single terms (default True)
:param extra_name_tag: additional tag that can be included in the plot name (default "")
:param model_setup: architecture type to specify plot name (default "CNN")
+ :param font_size_all_terms: font size for summary plot containing all terms and skill scores
"""
super().__init__(plot_folder, f"skill_score_clim_{extra_name_tag}{model_setup}")
+ self.font_size_all_terms = font_size_all_terms
self._labels = None
self._data = self._prepare_data(data, score_only)
self._plot(score_only)
@@ -522,12 +543,23 @@ class PlotClimatologicalSkillScore(AbstractPlotClass):
fig, ax = plt.subplots()
if not score_only:
fig.set_size_inches(11.7, 8.27)
+ ax.tick_params(labelsize=self.font_size_all_terms)
+ plt.xticks(fontsize=self.font_size_all_terms, rotation=45)
+
sns.boxplot(x="terms", y="data", hue="ahead", data=self._data, ax=ax, whis=1., palette="Blues_d",
showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"}, flierprops={"marker": "."})
ax.axhline(y=0, color="grey", linewidth=.5)
- ax.set(ylabel=f"{self._label_add(score_only)}skill score", xlabel="", title="summary of all stations")
handles, _ = ax.get_legend_handles_labels()
- ax.legend(handles, self._labels)
+ if not score_only:
+ ax.set_xlabel("")
+ ax.set_ylabel(f"{self._label_add(score_only)}skill score", fontsize=self.font_size_all_terms)
+ ax.set_title("summary of all stations", fontsize=self.font_size_all_terms)
+ ax.legend(handles, self._labels, fontsize=self.font_size_all_terms, loc='lower left')
+
+ else:
+ ax.legend(handles, self._labels)
+ ax.set(ylabel=f"{self._label_add(score_only)}skill score", xlabel="", title="summary of all stations")
+
plt.tight_layout()
@@ -643,6 +675,7 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
sns.boxplot(x=self._x_name, y="data", hue="ahead", data=self._data, ax=ax, whis=1., palette="Blues_d",
showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"}, flierprops={"marker": "."})
ax.axhline(y=0, color="grey", linewidth=.5)
+ plt.xticks(rotation=45)
ax.set(ylabel=f"skill score", xlabel="", title="summary of all stations")
handles, _ = ax.get_legend_handles_labels()
ax.legend(handles, self._labels)
@@ -969,7 +1002,8 @@ class PlotAvailabilityHistogram(AbstractPlotClass):
plot_dataset.plot.step(color=colors[subset], ax=axes, label=subset)
plt.fill_between(plot_dataset.coords[self.temporal_dim].values, plot_dataset.values, color=colors[subset])
- lgd = fig.legend(loc="upper right", ncol=len(self.dataset_time_interval))
+ lgd = fig.legend(loc="upper right", ncol=len(self.dataset_time_interval),
+ facecolor='white', framealpha=1, edgecolor='black')
for lgd_line in lgd.get_lines():
lgd_line.set_linewidth(4.0)
plt.gca().xaxis.set_major_locator(mdates.YearLocator())