diff --git a/src/plotting/postprocessing_plotting.py b/src/plotting/postprocessing_plotting.py
index f469131343bc1ba4bcf5e86c1d98b95752afb275..44752d67dcacb769cf63fdbedac4b57757432abe 100644
--- a/src/plotting/postprocessing_plotting.py
+++ b/src/plotting/postprocessing_plotting.py
@@ -125,8 +125,6 @@ class PlotStationMap(RunEnvironment):
background, but this can be adjusted by loading locally stored topography data (not implemented yet). The plot is
saved under plot_path with the name station_map.pdf
"""
- import cartopy.crs as ccrs
- import cartopy.feature as cfeature
def __init__(self, generators: Dict, plot_folder: str = "."):
"""
@@ -137,17 +135,22 @@ class PlotStationMap(RunEnvironment):
"""
super().__init__()
self._ax = None
- self._plot(generators, plot_folder)
+ if self.data_store.get("hostname")[:2] in self.data_store.get("hpc_hosts"):
+ logging.info(f"Running on a hpc system {self.data_store.get('hostname')}. Skip {self.__class__.__name__}...")
+ else:
+ self._plot(generators, plot_folder)
def _draw_background(self):
"""
Draw coastline, lakes, ocean, rivers and country borders as background on the map.
"""
- self._ax.add_feature(self.cfeature.COASTLINE.with_scale("50m"), edgecolor='black')
- self._ax.add_feature(self.cfeature.LAKES.with_scale("50m"))
- self._ax.add_feature(self.cfeature.OCEAN.with_scale("50m"))
- self._ax.add_feature(self.cfeature.RIVERS.with_scale("50m"))
- self._ax.add_feature(self.cfeature.BORDERS.with_scale("50m"), facecolor='none', edgecolor='black')
+
+ import cartopy.feature as cfeature
+ self._ax.add_feature(cfeature.COASTLINE.with_scale("50m"), edgecolor='black')
+ self._ax.add_feature(cfeature.LAKES.with_scale("50m"))
+ self._ax.add_feature(cfeature.OCEAN.with_scale("50m"))
+ self._ax.add_feature(cfeature.RIVERS.with_scale("50m"))
+ self._ax.add_feature(cfeature.BORDERS.with_scale("50m"), facecolor='none', edgecolor='black')
def _plot_stations(self, generators):
"""
@@ -156,6 +159,8 @@ class PlotStationMap(RunEnvironment):
:param generators: dictionary with the plot color of each data set as key and the generator containing all
stations as value.
"""
+
+ import cartopy.crs as ccrs
if generators is not None:
for color, gen in generators.items():
for k, v in enumerate(gen):
@@ -163,7 +168,7 @@ class PlotStationMap(RunEnvironment):
# station_names = gen.get_data_generator(k).meta.loc[['station_id']]
IDx, IDy = float(station_coords.loc['station_lon'].values), float(
station_coords.loc['station_lat'].values)
- self._ax.plot(IDx, IDy, mfc=color, mec='k', marker='s', markersize=6, transform=self.ccrs.PlateCarree())
+ self._ax.plot(IDx, IDy, mfc=color, mec='k', marker='s', markersize=6, transform=ccrs.PlateCarree())
def _plot(self, generators: Dict, plot_folder: str):
"""
@@ -172,9 +177,11 @@ class PlotStationMap(RunEnvironment):
stations as value.
:param plot_folder: path to save the plot
"""
+
+ import cartopy.crs as ccrs
fig = plt.figure(figsize=(10, 5))
- self._ax = fig.add_subplot(1, 1, 1, projection=self.ccrs.PlateCarree())
- self._ax.set_extent([0, 20, 42, 58], crs=self.ccrs.PlateCarree())
+ self._ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
+ self._ax.set_extent([0, 20, 42, 58], crs=ccrs.PlateCarree())
self._draw_background()
self._plot_stations(generators)
self._save(plot_folder)
diff --git a/src/run_modules/experiment_setup.py b/src/run_modules/experiment_setup.py
index 47d3adb84976a30a03d035120890f062087b3d3c..71f21c7f62c26b9692b67909a693f9d645b93814 100644
--- a/src/run_modules/experiment_setup.py
+++ b/src/run_modules/experiment_setup.py
@@ -24,6 +24,7 @@ DEFAULT_VAR_ALL_DICT = {'o3': 'dma8eu', 'relhum': 'average_values', 'temp': 'max
DEFAULT_TRANSFORMATION = {"scope": "data", "method": "standardise", "mean": "estimate"}
DEFAULT_PLOT_LIST = ["PlotMonthlySummary", "PlotStationMap", "PlotClimatologicalSkillScore", "PlotTimeSeries",
"PlotCompetitiveSkillScore", "PlotBootstrapSkillScore", "plot_conditional_quantiles"]
+DEFAULT_HPC_HOST_LIST = ["jw", "jr"] #first part of node names for Juwels (jw) and Jureca(jr).
class ExperimentSetup(RunEnvironment):
@@ -48,8 +49,9 @@ class ExperimentSetup(RunEnvironment):
# experiment setup
self._set_param("data_path", helpers.prepare_host(sampling=sampling))
- # self._set_param("hostname", helpers.get_host())
- self._set_param("hostname", "jwc0123")
+ self._set_param("hostname", helpers.get_host())
+ # self._set_param("hostname", "jwc0123")
+ self._set_param("hpc_hosts", DEFAULT_HPC_HOST_LIST)
self._set_param("create_new_model", create_new_model, default=True)
if self.data_store.get("create_new_model"):
trainable = True
diff --git a/src/run_modules/post_processing.py b/src/run_modules/post_processing.py
index b32d030eff02948954ee980710b930fe36718ab9..f6f2dece85ac62f8e1739cc5068b3f0041dd4c03 100644
--- a/src/run_modules/post_processing.py
+++ b/src/run_modules/post_processing.py
@@ -199,7 +199,7 @@ class PostProcessing(RunEnvironment):
forecast_path=path, plot_name_affix="cali-ref", plot_folder=self.plot_path)
plot_conditional_quantiles(self.test_data.stations, pred_name="obs", ref_name="CNN",
forecast_path=path, plot_name_affix="like-bas", plot_folder=self.plot_path)
- if ("PlotStationMap" in plot_list) and (not self.data_store.get("hostname")[:2] == "jw"):
+ if ("PlotStationMap" in plot_list):# and (not self.data_store.get("hostname")[:2] == "jw"):
PlotStationMap(generators={'b': self.test_data}, plot_folder=self.plot_path)
if "PlotMonthlySummary" in plot_list:
PlotMonthlySummary(self.test_data.stations, path, r"forecasts_%s_test.nc", self.target_var,