def main():
plt.close('all')
shp_file = '/Users/mpim/Desktop/ben/TP/TibeatanPlateau' # specify name of shapefile; note that it should be done WITHOUT the file extension
# set a array as masked array: x.data = np.ma.array(arr, mask=arr!=arr)
#set the region for masking
r = RegionBboxLatLon(777, 70., 105., 25., 40., label='testregion')
r.mask = None
#Read files
filename_Landevl = '/Users/mpim/Desktop/ben/chen_sebs_wgs84_n_0.13x0.13.nc' #'/data/share/mpiles/TRS/m300157/land_eval/LandFluxEVAL.merged.89-05.monthly.diagnostic.nc'
Landevl = Data(filename_Landevl, 'ETmon', read=True) #'lat','lon', ET_mean
#get aoi
Landevl.get_aoi_lat_lon(r)
Landevl.cut_bounding_box()
# read regions from shapefile
# This gives an object which contains all regions stored in the shapefile
RS = RegionShape(shp_file)
# just print the region keys for illustration
for k in RS.regions.keys():
print k
# if you now want to generate a particular mask we can do that
# in the following example we mask the airt temperature for the
# Tibetean plateau
# and then mask it
r_tibet = RS.regions[1] # gives a Region object
# mask with region
Landevl.mask_region(r_tibet)
Landevl.save('/Users/mpim/Desktop/ben/chen_sebs_recut2.nc')
plt.show()
# read some data as Data object filename = download.get_sample_file(name='air', return_object=False) air = Data(filename, 'air', read=True) # generate some plot BEFORE the masking map_plot(air, title='before', use_basemap=True) # now mask the data ... air.get_aoi_lat_lon(r) # generate some plot AFTER the masking map_plot(air, title='after', use_basemap=True) #... o.k., so far so good, but the dataset "air" still contains data for the entire domain. # even if it is masked it will eat some of your memory. You can see this by plotting the size of the matrix print(air.shape) # wouldn't it be nice to just remove everything which is not needed? # Here you go ... air.cut_bounding_box() map_plot(air, title='after cutting', use_basemap=True) # still looks the same, isn't it? No it isn't, look at the size! print(air.shape) # To illustrate what happens, you can plot WITHOUT using a map projection map_plot(air, title='after cutting (no projection)') plt.show()
# read some data as Data object filename = download.get_sample_file(name="air", return_object=False) air = Data(filename, "air", read=True) # generate some plot BEFORE the masking map_plot(air, title="before", use_basemap=True) # now mask the data ... air.get_aoi_lat_lon(r) # generate some plot AFTER the masking map_plot(air, title="after", use_basemap=True) # ... o.k., so far so good, but the dataset "air" still contains data for the entire domain. # even if it is masked it will eat some of your memory. You can see this by plotting the size of the matrix print(air.shape) # wouldn't it be nice to just remove everything which is not needed? # Here you go ... air.cut_bounding_box() map_plot(air, title="after cutting", use_basemap=True) # still looks the same, isn't it? No it isn't, look at the size! print(air.shape) # To illustrate what happens, you can plot WITHOUT using a map projection map_plot(air, title="after cutting (no projection)") plt.show()