From b2003ea31db70cc2f74a39d7edea8bd8503fd05c Mon Sep 17 00:00:00 2001
From: leufen1 <l.leufen@fz-juelich.de>
Date: Tue, 30 Aug 2022 14:24:44 +0200
Subject: [PATCH] new plot avail PlotSeasonalMSEStack, not in default plot
list. \close #422
---
mlair/plotting/postprocessing_plotting.py | 87 +++++++++++++++++++++++
mlair/run_modules/post_processing.py | 12 +++-
2 files changed, 98 insertions(+), 1 deletion(-)
diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index c7647ef5..d25736f1 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -1319,6 +1319,93 @@ class PlotTimeEvolutionMetric(AbstractPlotClass):
self._save()
+@TimeTrackingWrapper
+class PlotSeasonalMSEStack(AbstractPlotClass):
+
+ def __init__(self, data_path: str, plot_folder: str = ".", boot_dim="boots", ahead_dim="ahead",
+ sampling: str = "daily", error_measure: str = "MSE", error_unit: str = "ppb$^2$"):
+ """Set attributes and create plot."""
+ super().__init__(plot_folder, "seasonal_mse_stack_plot")
+ self.plot_name_orig = "seasonal_mse_stack_plot"
+ self._data_path = data_path
+ self.season_dim = "season"
+ self.error_unit = error_unit
+ self.error_measure = error_measure
+ self._data = self._prepare_data(boot_dim, data_path)
+ for orientation in ["horizontal", "vertical"]:
+ for split_ahead in [True, False]:
+ self._plot(ahead_dim, split_ahead, sampling, orientation)
+ self._save(bbox_inches="tight")
+
+ def _prepare_data(self, boot_dim, data_path):
+ season_dim = self.season_dim
+ data = {}
+ for season in ["total", "DJF", "MAM", "JJA", "SON"]:
+ if season == "total":
+ file_name = "uncertainty_estimate_raw_results.nc"
+ else:
+ file_name = f"uncertainty_estimate_raw_results_{season}.nc"
+ with xr.open_dataarray(os.path.join(data_path, file_name)) as d:
+ data[season] = d
+ mean = {}
+ for season in data.keys():
+ mean[season] = data[season].mean(boot_dim)
+ xr_data = xr.Dataset(mean).to_array(season_dim)
+ factor = xr_data.sel({season_dim: "total"}) / xr_data.sel({season_dim: ["DJF", "MAM", "JJA", "SON"]}).sum(
+ season_dim)
+ return xr_data.sel({season_dim: ["DJF", "MAM", "JJA", "SON"]}) * factor
+
+ @staticmethod
+ def _get_target_sampling(sampling, pos):
+ sampling = (sampling, sampling) if isinstance(sampling, str) else sampling
+ sampling_letter = {"hourly": "H", "daily": "d"}.get(sampling[pos], "")
+ return sampling, sampling_letter
+
+ def _plot(self, dim, split_ahead=True, sampling="daily", orientation="vertical"):
+ _, sampling_letter = self._get_target_sampling(sampling, 1)
+ if split_ahead is True:
+ self.plot_name = self.plot_name_orig + "_total_" + orientation
+ data = self._data.mean(dim)
+ if orientation == "vertical":
+ fig, ax = plt.subplots(1, 1)
+ data.to_pandas().T.plot.bar(ax=ax, stacked=True, cmap="Dark2", legend=False)
+ ax.xaxis.label.set_visible(False)
+ ax.set_ylabel(f"{self.error_measure} (in {self.error_unit})")
+ else:
+ m = data.to_pandas().T.shape[0]
+ fig, ax = plt.subplots(1, 1, figsize=(6, m))
+ data.to_pandas().T.plot.barh(ax=ax, stacked=True, cmap="Dark2", legend=False)
+ ax.yaxis.label.set_visible(False)
+ ax.set_xlabel(f"{self.error_measure} (in {self.error_unit})")
+ fig.legend(*ax.get_legend_handles_labels(), loc="upper center", ncol=4)
+ fig.tight_layout(rect=[0, 0, 1, 0.9])
+ else:
+ self.plot_name = self.plot_name_orig + "_" + orientation
+ data = self._data
+ n = len(data.coords[dim])
+ if orientation == "vertical":
+ fig, ax = plt.subplots(1, n, sharey=True)
+ for i, sel in enumerate(data.coords[dim].values):
+ data.sel({dim: sel}).to_pandas().T.plot.bar(ax=ax[i], stacked=True, cmap="Dark2", legend=False)
+ label = str(sel) + sampling_letter
+ ax[i].set_title(label)
+ ax[i].xaxis.label.set_visible(False)
+ ax[0].set_ylabel(f"{self.error_measure} (in {self.error_unit})")
+ fig.legend(*ax[0].get_legend_handles_labels(), loc="upper center", ncol=4)
+ fig.tight_layout(rect=[0, 0, 1, 0.9])
+ else:
+ m = data.max(self.season_dim).shape
+ fig, ax = plt.subplots(n, 1, sharex=True, figsize=(6, np.prod(m) * 0.6))
+ for i, sel in enumerate(data.coords[dim].values):
+ data.sel({dim: sel}).to_pandas().T.plot.barh(ax=ax[i], stacked=True, cmap="Dark2", legend=False)
+ label = str(sel) + sampling_letter
+ ax[i].set_title(label)
+ ax[i].yaxis.label.set_visible(False)
+ ax[-1].set_xlabel(f"{self.error_measure} (in {self.error_unit})")
+ fig.legend(*ax[0].get_legend_handles_labels(), loc="upper center", ncol=4)
+ fig.tight_layout(rect=[0, 0, 1, 0.95])
+
+
if __name__ == "__main__":
stations = ['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087']
path = "../../testrun_network/forecasts"
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index a48a82b2..d65a2001 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -24,7 +24,7 @@ from mlair.model_modules.linear_model import OrdinaryLeastSquaredModel
from mlair.model_modules import AbstractModelClass
from mlair.plotting.postprocessing_plotting import PlotMonthlySummary, PlotClimatologicalSkillScore, \
PlotCompetitiveSkillScore, PlotTimeSeries, PlotFeatureImportanceSkillScore, PlotConditionalQuantiles, \
- PlotSeparationOfScales, PlotSampleUncertaintyFromBootstrap, PlotTimeEvolutionMetric
+ PlotSeparationOfScales, PlotSampleUncertaintyFromBootstrap, PlotTimeEvolutionMetric, PlotSeasonalMSEStack
from mlair.plotting.data_insight_plotting import PlotStationMap, PlotAvailability, PlotAvailabilityHistogram, \
PlotPeriodogram, PlotDataHistogram
from mlair.run_modules.run_environment import RunEnvironment
@@ -681,6 +681,16 @@ class PostProcessing(RunEnvironment):
logging.error(f"Could not create plot PlotTimeEvolutionMetric due to the following error: {e}"
f"\n{sys.exc_info()[0]}\n{sys.exc_info()[1]}\n{sys.exc_info()[2]}")
+ try:
+ if "PlotSeasonalMSEStack" in plot_list:
+ report_path = os.path.join(self.data_store.get("experiment_path"), "latex_report")
+ PlotSeasonalMSEStack(data_path=report_path, plot_folder=self.plot_path,
+ boot_dim=self.uncertainty_estimate_boot_dim, ahead_dim=self.ahead_dim,
+ sampling=self._sampling, error_measure="Mean Squared Error", error_unit=r"ppb$^2$")
+ except Exception as e:
+ logging.error(f"Could not create plot PlotSeasonalMSEStack due to the following error: {e}"
+ f"\n{sys.exc_info()[0]}\n{sys.exc_info()[1]}\n{sys.exc_info()[2]}")
+
@TimeTrackingWrapper
def calculate_test_score(self):
"""Evaluate test score of model and save locally."""
--
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