pyCMBS gallery¶

In the following, examples will be given that introduce different features of pyCMBS.

Basic plotting¶

For plotting you have a rich suite of keyword parameters. Please use the python help() system to get full documentation from docstrings. Some more illustrations of options are provided:

map_plot(air,show_timeseries=True, use_basemap=True,title='show_timeseries=True')
map_plot(air,show_zonal=True, use_basemap=True,title='show_zonal=True')
map_plot(air,show_histogram=True, use_basemap=True,title='show_histogram=True')

And a few more details on customizing your map ...:

map_plot(air, use_basemap=True, title='vmin=-30.,vmax=30.,cmap_data=RdBu_r', vmin=-30., vmax=30., cmap_data='RdBu_r', ax=ax1)
map_plot(air, contours=True, use_basemap=True, levels=np.arange(-50.,50.,10.), title='contours=True', ax=ax2)
map_plot(air, show_stat=True, use_basemap=True,title='show_stat=True',ax=ax3)
map_plot(air, show_stat=True, stat_type='median', use_basemap=True, title='show_stat=True,stat_type="median"', ax=ax4)

Basic data analysis¶

Temporal mean¶

Calculating the temporal mean field of a variable is as simple as:

air.timmean()

Spatial mean¶

The (area weighted) spatial mean is obtained as:

air.fldmean()

Masking an area¶

You probably want to work only on particular regions. The following script shows you how to easily to this.

Temporal slicing¶

If you want to perform a temporal subsetting of the data, this can be done as follows:

# temporal subsetting using existing start/stop dates
import datetime.datetime

start_date = datetime(2001,05,01)
stop_date = datetime(2010,04,15)
air.apply_temporal_subsetting(start_date, stop_date):