adapted venv to cartopy

setup/modules_hpc.sh

@@ -6,6 +6,8 @@ ml Stages/2020
 # compilers etc.
 # ml GCCcore/.9.3.0
 ml GCCcore/.10.3.0
+ml GCC/10.3.0
+ml ParaStationMPI/5.4.10-1
 # ml GCC/9.3.0
 # ml ParaStationMPI/5.4.7-1
 
@@ -19,4 +21,4 @@ ml PyTorch/1.8.1-Python-3.8.5
 ml PyTorch-Geometric/2.0.2-Python-3.8.5-PyTorch-1.8.1
 # ml GDAL/3.1.2-Python-3.8.5
 # ml Shapely/1.7.1-Python-3.8.5
-# ml Cartopy/0.18.0-Python-3.8.5
+ml Cartopy/0.18.0-Python-3.8.5

setup/requirements_juwels.txt

+geopandas==0.11.0

source/visualizations/plots_for_paper.py

+# general
+import pdb
+
+# plotting
+import matplotlib.pyplot as plt
+
+# own package
+import settings
+
+
+def station_ids_on_map():
+    """
+    To analyze what the station ids mean
+    """
+    from cartopy.io import shapereader
+    import numpy as np
+    import geopandas
+    import matplotlib.pyplot as plt
+
+    import cartopy.crs as ccrs
+
+    # get natural earth data (http://www.naturalearthdata.com/)
+
+    # get country borders
+    resolution = '10m'
+    category = 'cultural'
+    name = 'admin_0_countries'
+
+    shpfilename = shapereader.natural_earth(resolution, category, name)
+
+    # read the shapefile using geopandas
+    df = geopandas.read_file(shpfilename)
+
+    # read the german borders
+    poly = df.loc[df['ADMIN'] == 'Germany']['geometry'].values[0]
+
+    ax = plt.axes(projection=ccrs.PlateCarree())
+
+    ax.add_geometries(poly, crs=ccrs.PlateCarree(), facecolor='none',
+                      edgecolor='0.5')
+
+    ax.set_extent([5, 16, 46.5, 56], crs=ccrs.PlateCarree())
+
+
+
+    plt.savefig(settings.output_dir + 'station_ids_on_map.png')
+
+
+if __name__ == '__main__':
+    station_ids_on_map_ = True
+
+    if station_ids_on_map_:
+        station_ids_on_map()