diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index 22e55220c6aa54f8352435cc5f5ddaf4f072f0b7..da671d98ef07869ccf1e584a349f78129f2e5784 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -1397,23 +1397,45 @@ class PlotTimeEvolutionMetric(AbstractPlotClass):
@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$^2$",
- model_name: str = "NN", model_indicator: str = "nn", model_type_dim: str = "type"):
+ def __init__(self, data, data_path: str, plot_folder: str = ".", boot_dim="boots", ahead_dim="ahead",
+ sampling: str = "daily", error_measure: str = "MSE", error_unit: str = "ppb$^2$", time_dim="index",
+ model_type_dim: str = "type", model_name: str = "NN", model_indicator: str = "nn",):
"""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.time_dim = time_dim
+ self.ahead_dim = ahead_dim
self.error_unit = error_unit
self.error_measure = error_measure
- self._data = self._prepare_data(boot_dim, data_path, model_type_dim, model_indicator, model_name)
+ self.dim_order = [self.season_dim, ahead_dim, model_type_dim]
+
+ # mse from monthly blocks
+ self.plot_name_orig = "seasonal_mse_stack_plot"
+ self._data = self._prepare_data(data)
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, model_type_dim, model_indicator, model_name):
+ # mes from resampling
+ self.plot_name_orig = "seasonal_mse_from_uncertainty_stack_plot"
+ self._data = self._prepare_data_from_uncertainty(boot_dim, data_path, model_type_dim, model_indicator,
+ model_name)
+ 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, data):
+ season_mean = data.groupby(f"{self.time_dim}.{self.season_dim}").mean()
+ total_mean = data.mean(self.time_dim)
+ factor = season_mean / season_mean.sum(self.season_dim)
+ season_share = (total_mean * factor).reindex({self.season_dim: ["DJF", "MAM", "JJA", "SON"]})
+ season_share = season_share.mean(set(season_share.dims).difference(self.dim_order))
+ return season_share.sortby(season_share.sum([self.season_dim, self.ahead_dim])).transpose(*self.dim_order)
+
+ def _prepare_data_from_uncertainty(self, boot_dim, data_path, model_type_dim, model_indicator, model_name):
season_dim = self.season_dim
data = {}
for season in ["total", "DJF", "MAM", "JJA", "SON"]:
@@ -1427,11 +1449,11 @@ class PlotSeasonalMSEStack(AbstractPlotClass):
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)
- xr_data = xr_data.sel({season_dim: ["DJF", "MAM", "JJA", "SON"]}) * factor
xr_data[model_type_dim] = [v if v != model_indicator else model_name for v in xr_data[model_type_dim].values]
- return xr_data
+ xr_season = xr_data.sel({season_dim: ["DJF", "MAM", "JJA", "SON"]})
+ factor = xr_season / xr_season.sum(season_dim)
+ season_share = xr_data.sel({season_dim: "total"}) * factor
+ return season_share.sortby(season_share.sum([self.season_dim, self.ahead_dim])).transpose(*self.dim_order)
@staticmethod
def _get_target_sampling(sampling, pos):
@@ -1453,7 +1475,7 @@ class PlotSeasonalMSEStack(AbstractPlotClass):
def _plot(self, dim, split_ahead=True, sampling="daily", orientation="vertical"):
_, sampling_letter = self._get_target_sampling(sampling, 1)
- if split_ahead is True:
+ if split_ahead is False:
self.plot_name = self.plot_name_orig + "_total_" + orientation
data = self._data.mean(dim)
if orientation == "vertical":
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index 459a1928958a96238af89caa4241911554df416f..a6f1423fbc6eb3869e4d628f7a8f787dbd1d2cc4 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -91,6 +91,7 @@ class PostProcessing(RunEnvironment):
self.uncertainty_estimate = None
self.uncertainty_estimate_seasons = {}
self.block_mse_per_station = None
+ self.block_mse = None
self.competitor_path = self.data_store.get("competitor_path")
self.competitors = to_list(self.data_store.get_default("competitors", default=[]))
self.forecast_indicator = "nn"
@@ -117,6 +118,10 @@ class PostProcessing(RunEnvironment):
# calculate error metrics on test data
self.calculate_test_score()
+ # calculate monthly block mse
+ self.block_mse, self.block_mse_per_station = self.calculate_block_mse(evaluate_competitors=True,
+ separate_ahead=True, block_length="1m")
+
# sample uncertainty
if self.data_store.get("do_uncertainty_estimate", "postprocessing"):
self.estimate_sample_uncertainty(separate_ahead=True)
@@ -156,10 +161,12 @@ class PostProcessing(RunEnvironment):
block_length = self.data_store.get_default("block_length", default="1m", scope="uncertainty_estimate")
evaluate_competitors = self.data_store.get_default("evaluate_competitors", default=True,
scope="uncertainty_estimate")
- block_mse, block_mse_per_station = self.calculate_block_mse(evaluate_competitors=evaluate_competitors,
- separate_ahead=separate_ahead,
- block_length=block_length)
- self.block_mse_per_station = block_mse_per_station
+ if evaluate_competitors is True and separate_ahead is True and block_length == "1m":
+ block_mse, block_mse_per_station = self.block_mse, self.block_mse_per_station
+ else:
+ block_mse, block_mse_per_station = self.calculate_block_mse(evaluate_competitors=evaluate_competitors,
+ separate_ahead=separate_ahead,
+ block_length=block_length)
estimate = statistics.create_n_bootstrap_realizations(
block_mse, dim_name_time=self.index_dim, dim_name_model=self.model_type_dim,
dim_name_boots=self.uncertainty_estimate_boot_dim, n_boots=n_boots, seasons=["DJF", "MAM", "JJA", "SON"])
@@ -692,7 +699,7 @@ class PostProcessing(RunEnvironment):
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,
+ PlotSeasonalMSEStack(data=self.block_mse_per_station, 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$^2$",
model_indicator=self.forecast_indicator, model_name=self.model_display_name,