From 527ddd38953581e49092975e68cde1faf18abcbd Mon Sep 17 00:00:00 2001
From: "v.gramlich1" <v.gramlichfz-juelich.de>
Date: Wed, 4 Aug 2021 09:01:50 +0200
Subject: [PATCH] Made PlotOversamplingContingency get min and max_threshold
---
mlair/plotting/postprocessing_plotting.py | 30 ++++++++++++++---------
1 file changed, 19 insertions(+), 11 deletions(-)
diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index a0d54c18..ed114830 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -48,11 +48,10 @@ class PlotOversamplingContingency(AbstractPlotClass):
self._all_names = [self._model_name]
self._all_names.extend(self._comp_names)
self._plot_names = plot_names
- contingency_array, borders = self._calculate_contingencies()
+ self._min_threshold, self._max_threshold = self._min_max_threshold()
+ contingency_array = self._calculate_contingencies()
self._scores = ["ts", "h", "f"]
score_array = self._calculate_all_scores(contingency_array)
- self._min_label = borders[0]
- self._max_label = borders[1]
self._plot_counter = 0
self._plot(score_array, "ts")
@@ -67,10 +66,10 @@ class PlotOversamplingContingency(AbstractPlotClass):
def _plot(self, data, score):
if score == "all_scores":
for score_name in data.scores.values.tolist():
- plt.plot(range(self._min_label, self._max_label), data.loc[dict(type="nn", scores=score_name)], label=score_name)
+ plt.plot(range(self._min_threshold, self._max_threshold), data.loc[dict(type="nn", scores=score_name)], label=score_name)
else:
for type in data.type.values.tolist():
- plt.plot(range(self._min_label, self._max_label), data.loc[dict(type=type, scores=score)], label=type)
+ plt.plot(range(self._min_threshold, self._max_threshold), data.loc[dict(type=type, scores=score)], label=type)
plt.legend()
self.plot_name = self._plot_names[self._plot_counter]
self._plot_counter = self._plot_counter + 1
@@ -116,11 +115,20 @@ class PlotOversamplingContingency(AbstractPlotClass):
continue
return xr.concat(competing_predictions, "type") if len(competing_predictions) > 0 else None
+ def _min_max_threshold(self):
+ min_threshold = 0
+ max_threshold = 0
+ for station in self._stations:
+ file = os.path.join(self._file_path, self._file_name % station)
+ forecast_file = xr.open_dataarray(file)
+ obs = forecast_file.sel(type=self._obs_name)
+ obs = obs.fillna(0)
+ min_threshold = np.minimum(min_threshold, int(np.min(obs.values.flatten())))
+ max_threshold = np.maximum(max_threshold, int(np.max(obs.values.flatten())))
+ return min_threshold, max_threshold
+
def _calculate_contingencies(self):
- min_label = 0
- max_label = 100
- borders = [min_label, max_label]
- thresholds = np.arange(min_label, max_label)
+ thresholds = np.arange(self._min_threshold, self._max_threshold)
contingency_cell = ["ta", "fa", "fb", "tb"]
contingency_array = xr.DataArray(dims=["thresholds", "contingency_cell", "type"],
coords=[thresholds, contingency_cell, self._all_names])
@@ -132,14 +140,14 @@ class PlotOversamplingContingency(AbstractPlotClass):
predictions = [forecast_file.sel(type=self._model_name)]
competitors = [self._load_competitors(station, [comp]).sel(type=comp) for comp in self._comp_names]
predictions.extend(competitors)
- for threshold in range(min_label, max_label):
+ for threshold in range(self._min_threshold, self._max_threshold):
for pred in predictions:
ta, fa, fb, tb = self._single_contingency(obs, pred, threshold)
contingency_array.loc[dict(thresholds=threshold, contingency_cell="ta", type=pred.type.values)] = ta
contingency_array.loc[dict(thresholds=threshold, contingency_cell="fa", type=pred.type.values)] = fa
contingency_array.loc[dict(thresholds=threshold, contingency_cell="fb", type=pred.type.values)] = fb
contingency_array.loc[dict(thresholds=threshold, contingency_cell="tb", type=pred.type.values)] = tb
- return contingency_array, borders
+ return contingency_array
def _single_contingency(self, obs, pred, threshold):
ta = 0
--
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