diff --git a/mlair/plotting/abstract_plot_class.py b/mlair/plotting/abstract_plot_class.py
index a26023bb6cb8772623479491ac8bcc731dd42223..377dc6b7abbbb693c9d18175983101e622063a70 100644
--- a/mlair/plotting/abstract_plot_class.py
+++ b/mlair/plotting/abstract_plot_class.py
@@ -92,11 +92,10 @@ class AbstractPlotClass: # pragma: no cover
plt.rcParams.update(self.rc_params)
@staticmethod
- def _get_sampling(sampling):
- if sampling == "daily":
- return "D"
- elif sampling == "hourly":
- return "h"
+ def _get_sampling(sampling, pos=1):
+ sampling = (sampling, sampling) if isinstance(sampling, str) else sampling
+ sampling_letter = {"hourly": "H", "daily": "d"}.get(sampling[pos], "")
+ return sampling, sampling_letter
@staticmethod
def get_dataset_colors():
diff --git a/mlair/plotting/data_insight_plotting.py b/mlair/plotting/data_insight_plotting.py
index db2b3340e06545f988c81503df2aa27b655095bb..d33f3abb2e2cd04399a2d98f34bf2ed06acfcb6b 100644
--- a/mlair/plotting/data_insight_plotting.py
+++ b/mlair/plotting/data_insight_plotting.py
@@ -188,7 +188,7 @@ class PlotAvailability(AbstractPlotClass): # pragma: no cover
super().__init__(plot_folder, "data_availability")
self.time_dim = time_dimension
self.window_dim = window_dimension
- self.sampling = self._get_sampling(sampling)
+ self.sampling = self._get_sampling(sampling)[1]
self.linewidth = None
if self.sampling == 'h':
self.linewidth = 0.001
@@ -321,7 +321,7 @@ class PlotAvailabilityHistogram(AbstractPlotClass): # pragma: no cover
def _set_dims_from_datahandler(self, data_handler):
self.temporal_dim = data_handler.id_class.time_dim
self.target_dim = data_handler.id_class.target_dim
- self.freq = self._get_sampling(data_handler.id_class.sampling)
+ self.freq = self._get_sampling(data_handler.id_class.sampling)[1]
@property
def allowed_plot_types(self):
diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index 62ab3de3ca3647a41c61a0e8ac5ff94abe2ace47..d1a68896edf0b794b644bc325014efb4c7fe785f 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -748,19 +748,13 @@ class PlotFeatureImportanceSkillScore(AbstractPlotClass): # pragma: no cover
data = data.assign_coords({self._x_name: new_boot_coords})
except NotImplementedError:
pass
- _, sampling_letter = self._get_target_sampling(sampling, 1)
+ _, sampling_letter = self._get_sampling(sampling, 1)
self._labels = [str(i) + sampling_letter for i in data.coords[self._ahead_dim].values]
if station_dim not in data.dims:
data = data.expand_dims(station_dim)
self._number_of_bootstraps = np.unique(data.coords[self._boot_dim].values).shape[0]
return data.to_dataframe("data").reset_index(level=np.arange(len(data.dims)).tolist()).dropna()
- @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 _return_vars_without_number_tag(self, values, split_by, keep, as_unique=False):
arr = np.array([v.split(split_by) for v in values])
num = arr[:, 0]
@@ -1462,12 +1456,6 @@ class PlotSeasonalMSEStack(AbstractPlotClass):
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):
- sampling = (sampling, sampling) if isinstance(sampling, str) else sampling
- sampling_letter = {"hourly": "H", "daily": "d"}.get(sampling[pos], "")
- return sampling, sampling_letter
-
@staticmethod
def _set_bar_label(ax):
opts = {}
@@ -1481,7 +1469,7 @@ class PlotSeasonalMSEStack(AbstractPlotClass):
ax.bar_label(c, labels=_l, label_type='center')
def _plot(self, dim, split_ahead=True, sampling="daily", orientation="vertical"):
- _, sampling_letter = self._get_target_sampling(sampling, 1)
+ _, sampling_letter = self._get_sampling(sampling, 1)
if split_ahead is False:
self.plot_name = self.plot_name_orig + "_total_" + orientation
data = self._data.mean(dim)
@@ -1534,26 +1522,34 @@ class PlotErrorsOnMap(AbstractPlotClass):
from mlair.plotting.data_insight_plotting import PlotStationMap
def __init__(self, data_gen, errors, error_metric, plot_folder: str = ".", iter_dim: str = "station",
- model_type_dim: str = "type", ahead_dim: str = "ahead"):
+ model_type_dim: str = "type", ahead_dim: str = "ahead", sampling: str = "daily"):
super().__init__(plot_folder, f"map_plot_{error_metric}")
-
plot_path = os.path.join(self.plot_folder, f"{self.plot_name}.pdf")
pdf_pages = matplotlib.backends.backend_pdf.PdfPages(plot_path)
error_metric_units = helpers.statistics.get_error_metrics_units("ppb")[error_metric]
error_metric_name = helpers.statistics.get_error_metrics_long_name()[error_metric]
+ self.sampling = self._get_sampling(sampling, 1)[1]
coords = self._extract_coords(data_gen)
- error_data = {}
- for model_type in errors.coords[model_type_dim].values:
- error_data[model_type] = self._prepare_data(errors, model_type_dim, model_type, ahead_dim, error_metric)
-
- limits = self._calculate_limits(error_data)
-
- for model_type, error in error_data.items():
- plot_data = pd.concat([coords, error], axis=1)
- self.plot(plot_data, error_metric, error_metric_name, error_metric_units, model_type, limits)
- pdf_pages.savefig()
+ for split_ahead in [False, True]:
+ error_data = {}
+ for model_type in errors.coords[model_type_dim].values:
+ error_data[model_type] = self._prepare_data(errors, model_type_dim, model_type, ahead_dim, error_metric,
+ split_ahead=split_ahead)
+ limits = self._calculate_limits(error_data)
+ for model_type, error in error_data.items():
+ if split_ahead is True:
+ for ahead in error.index.unique(1).to_list():
+ error_ahead = error.query(f"{ahead_dim} == {ahead}").droplevel(1)
+ plot_data = pd.concat([coords, error_ahead], axis=1)
+ self.plot(plot_data, error_metric, error_metric_name, error_metric_units, model_type, limits,
+ ahead=ahead)
+ pdf_pages.savefig()
+ else:
+ plot_data = pd.concat([coords, error], axis=1)
+ self.plot(plot_data, error_metric, error_metric_name, error_metric_units, model_type, limits)
+ pdf_pages.savefig()
pdf_pages.close()
plt.close('all')
@@ -1571,7 +1567,7 @@ class PlotErrorsOnMap(AbstractPlotClass):
vmin = relative_round(bound_lims[0], 2, floor=True)
vmax = relative_round(bound_lims[1], 2, ceil=True)
interval = relative_round((vmax - vmin) / ncolors, 1, ceil=True)
- bounds = np.arange(vmin, vmax, interval)
+ bounds = np.sort(np.arange(vmax, vmin, -interval))
return bounds
@staticmethod
@@ -1588,7 +1584,7 @@ class PlotErrorsOnMap(AbstractPlotClass):
cmap = sns.color_palette("magma_r", as_cmap=True)
return cmap
- def plot(self, plot_data, error_metric, error_long_name, error_units, model_type, limits):
+ def plot(self, plot_data, error_metric, error_long_name, error_units, model_type, limits, ahead=None):
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
fig = plt.figure(figsize=(10, 5))
@@ -1606,7 +1602,8 @@ class PlotErrorsOnMap(AbstractPlotClass):
cbar_label = f"{error_long_name} (in {error_units})" if error_units is not None else error_long_name
plt.colorbar(cb, label=cbar_label)
self._adjust_extent(ax)
- plt.title(model_type)
+ title = model_type if ahead is None else f"{model_type} ({ahead}{self.sampling})"
+ plt.title(title)
plt.tight_layout()
@staticmethod
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index 0ff09e92d90a31dde6ac5fe01d75776bce6ac4e5..0fb14f55cf0d6270a8c26937b955e09758567101 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -735,7 +735,7 @@ class PostProcessing(RunEnvironment):
for error_metric in self.errors.keys():
try:
PlotErrorsOnMap(self.test_data, self.errors[error_metric], error_metric,
- plot_folder=self.plot_path)
+ plot_folder=self.plot_path, sampling=self._sampling)
except Exception as e:
logging.error(f"Could not create plot PlotErrorsOnMap for {error_metric} due to the following "
f"error: {e}\n{sys.exc_info()[0]}\n{sys.exc_info()[1]}\n{sys.exc_info()[2]}")