From d8197b1973ce079660b7032bfdcbfaf6c1b125e8 Mon Sep 17 00:00:00 2001
From: Felix Kleinert <f.kleinert@fz-juelich.de>
Date: Thu, 10 Feb 2022 14:39:50 +0100
Subject: [PATCH] update sectorial skill score plots
---
mlair/plotting/postprocessing_plotting.py | 46 +++++++++++++----------
1 file changed, 27 insertions(+), 19 deletions(-)
diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index fa121fe8..6cf92c08 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -641,13 +641,12 @@ class PlotSectorialSkillScore(AbstractPlotClass): # pragma: no cover
self._reference_dim = reference_dim
self._labels = None
self._model_name_for_plots = model_name_for_plots
+ self._sns_settings = {"hue": "ahead", "whis": 1., "palette": "Blues_r",
+ "showmeans": False, "flierprops": {"marker": "."}}
self._data, self._reference_model = self._prepare_data(data)
- logging.info("PlotSectorialSkillScore: finished _prepare_data(data)")
self._plot()
- logging.info("PlotSectorialSkillScore: finished _plot()")
self.plot_name = self.plot_name + "_vertical"
self._plot_vertical()
- logging.info("PlotSectorialSkillScore: finished _plot_vertical()")
@TimeTrackingWrapper
def _prepare_data(self, data: xr.DataArray):
@@ -659,6 +658,7 @@ class PlotSectorialSkillScore(AbstractPlotClass): # pragma: no cover
name="data")
return data, reference_model
+ @TimeTrackingWrapper
def _plot(self):
size = max([len(np.unique(self._data.sector)), 6])
data = self._data
@@ -667,16 +667,17 @@ class PlotSectorialSkillScore(AbstractPlotClass): # pragma: no cover
for ref in self._reference_model:
ref_data = data[data[self._reference_dim]==ref]
fig, ax = plt.subplots(figsize=(size, size * 0.8))
- sns.boxplot(x="sector", y="data", hue="ahead", data=ref_data,
- whis=1, ax=ax, palette="Blues_r",
- showmeans=False, #meanprops={"markersize": 3, "markeredgecolor": "k"},
- flierprops={"marker": "."},
- )
+ # sns.boxplot(x="sector", y="data", hue="ahead", data=ref_data,
+ # whis=1, ax=ax, palette="Blues_r",
+ # showmeans=False, #meanprops={"markersize": 3, "markeredgecolor": "k"},
+ # flierprops={"marker": "."},
+ # )
+ sns.boxplot(x="sector", y="data", data=ref_data, ax=ax, **self._sns_settings)
ax.axhline(y=0, color="grey", linewidth=.5)
ax.set(ylabel=f"skill score ({self._model_setup} vs. {ref})", xlabel="sector",
title="summary of all stations", ylim=self._lim(ref_data))
handles, _ = ax.get_legend_handles_labels()
- plt.xticks(rotation=45, horizontalalignment="right")
+ # plt.xticks(rotation=45, horizontalalignment="right")
ax.legend(handles, self._labels)
plt.tight_layout()
pdf_pages.savefig()
@@ -684,19 +685,26 @@ class PlotSectorialSkillScore(AbstractPlotClass): # pragma: no cover
pdf_pages.close()
plt.close('all')
+ @TimeTrackingWrapper
def _plot_vertical(self):
"""Plot skill scores of the comparisons, but vertically aligned."""
- fig, ax = plt.subplots()
data = self._data
- sns.boxplot(y="sector", x="data", hue="ahead", data=data, whis=1.5, ax=ax, palette="Blues_r",
- showmeans=False, meanprops={"markersize": 3, "markeredgecolor": "k"}, flierprops={"marker": "."},
- )
- ax.axvline(x=0, color="grey", linewidth=.5)
- ax.set(xlabel=f"skill score ({self._model_setup} vs. {self._reference_model})", ylabel="sector",
- title="summary of all stations", xlim=self._lim(data))
- handles, _ = ax.get_legend_handles_labels()
- ax.legend(handles, self._labels)
- plt.tight_layout()
+ plot_path = os.path.join(os.path.abspath(self.plot_folder), f"{self.plot_name}.pdf")
+ pdf_pages = matplotlib.backends.backend_pdf.PdfPages(plot_path)
+ for ref in self._reference_model:
+ ref_data = data[data[self._reference_dim] == ref]
+ fig, ax = plt.subplots()
+ sns.boxplot(y="sector", x="data", data=ref_data, ax=ax, **self._sns_settings)
+ ax.axvline(x=0, color="grey", linewidth=.5)
+ ax.set(xlabel=f"skill score ({self._model_setup} vs. {ref})", ylabel="sector",
+ title="summary of all stations", xlim=self._lim(ref_data))
+ handles, _ = ax.get_legend_handles_labels()
+ ax.legend(handles, self._labels)
+ plt.tight_layout()
+ pdf_pages.savefig()
+ # close all open figures / plots
+ pdf_pages.close()
+ plt.close('all')
@staticmethod
def _lim(data) -> Tuple[float, float]:
--
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