NASA Center for Climate Simulation

# Compute Average of Cloud Cover

import cwt, os

class TestWorkflow:

    plotter = cwt.initialize()
    host ="https://edas.nccs.nasa.gov/wps/cwt"
    wps = cwt.WPS( host,
log=True,log_file=os.path.expanduser("~/esgf_api.log"), verify=False )

    def spatial_ave( self ):

# Set the domain to be the continental United States, from 1980 to 2016

#        domain_data = { 'id': 'd0', 'lat': {'start':23.7,'end':49.2,'crs':'values'},
#                        'lon': {'start':-125, 'end':-70.3, 'crs':'values'},
#                        'time':{'start':'1980-01-01T00:00:00','end':'2016-12-31T23:00:00', 'crs':'timestamps'}}

# Set the domain to the ABoVE Study Domain, from 1980 to 2016

        domain_data = { 'id': 'd0', 'lat': {'start':-90, 'end':90,'crs':'values'}, 
                       'lon': {'start':0, 'end':360, 'crs':'values'},
                       'time':{'start':'1980-01-01T00:00:00', 'end':'2016-12-31T23:00:00', 'crs':'timestamps'}}

        d0 = cwt.Domain.from_dict(domain_data)

# Set the input data to be monthly MERRA data (variable clt)

        inputs = cwt.Variable("collection://cip_merra_mth", "clt",domain="d0" )

# Set the operation to be "average", operating over the time axis

        op_data = { 'name': "xarray.ave", 'axes': "t" }

        op = cwt.Process.from_dict( op_data )
        op.set_inputs( inputs )

        self.wps.execute( op, domains=[d0], async=True )
        dataPaths = self.wps.download_result(op)

# Plot average cloud cover over the spatial range plot

        for dataPath in dataPaths:
            self.plotter.mpl_spaceplot(dataPath)

executor = TestWorkflow()
executor.spatial_ave()