Resolve "release v1.2.0"
Compare changes
- #211):Felix Kleinert authored
- separated BootPlotSkills - PlotAvailabilityHistogram (hist and cum) - AbstactPlotClass: get_sampling, get_dataset_color, _update_rc_parameters
+ 321
− 14
@@ -8,10 +8,10 @@ import os
@@ -8,10 +8,10 @@ import os
@@ -75,7 +75,7 @@ class AbstractPlotClass:
@@ -75,7 +75,7 @@ class AbstractPlotClass:
@@ -83,6 +83,15 @@ class AbstractPlotClass:
@@ -83,6 +83,15 @@ class AbstractPlotClass:
@@ -95,6 +104,24 @@ class AbstractPlotClass:
@@ -95,6 +104,24 @@ class AbstractPlotClass:
@@ -113,18 +140,19 @@ class PlotMonthlySummary(AbstractPlotClass):
@@ -113,18 +140,19 @@ 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
def __init__(self, stations: List, data_path: str, name: str, target_var: str, window_lead_time: int = None,
@@ -176,7 +204,12 @@ class PlotMonthlySummary(AbstractPlotClass):
@@ -176,7 +204,12 @@ class PlotMonthlySummary(AbstractPlotClass):
Create a monthly grouped box plot over all stations but with separate boxes for each lead time step.
@@ -189,7 +222,8 @@ class PlotMonthlySummary(AbstractPlotClass):
@@ -189,7 +222,8 @@ class PlotMonthlySummary(AbstractPlotClass):
ax = sns.boxplot(x='index', y='values', hue='ahead', data=data.compute(), whis=1., palette=color_palette,
@@ -453,7 +487,8 @@ class PlotConditionalQuantiles(AbstractPlotClass):
@@ -453,7 +487,8 @@ class PlotConditionalQuantiles(AbstractPlotClass):
logging.info(f"start plotting {self.__class__.__name__}, scheduled number of plots: {(len(self._seasons) + 1) * 2}")
@@ -504,7 +539,7 @@ class PlotConditionalQuantiles(AbstractPlotClass):
@@ -504,7 +539,7 @@ class PlotConditionalQuantiles(AbstractPlotClass):
segmented_data.loc[x_model, d, :].to_pandas().hist(bins=self._bins, ax=ax2, color='k', alpha=.3, grid=False,
@@ -693,20 +728,26 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
@@ -693,20 +728,26 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
:param data: dictionary with station names as keys and 2D xarrays as values, consist on axis ahead and terms.
@@ -719,11 +760,30 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
@@ -719,11 +760,30 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
@@ -733,12 +793,111 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
@@ -733,12 +793,111 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
sns.boxplot(x=self._x_name, y="data", hue="ahead", data=self._data, ax=ax, whis=1., palette="Blues_d",
@@ -859,6 +1018,7 @@ class PlotTimeSeries:
@@ -859,6 +1018,7 @@ class PlotTimeSeries:
@@ -978,9 +1138,7 @@ class PlotAvailability(AbstractPlotClass):
@@ -978,9 +1138,7 @@ class PlotAvailability(AbstractPlotClass):
# colors = {"train": (230, 159, 0), "val": (0, 158, 115), "test": (86, 180, 233)} # in rgb but as abs values
@@ -1025,6 +1183,155 @@ class PlotSeparationOfScales(AbstractPlotClass):
@@ -1025,6 +1183,155 @@ class PlotSeparationOfScales(AbstractPlotClass):