In [38]:

    

from owslib.wps import WebProcessingService, monitorExecution
wps = WebProcessingService(url="http://localhost:8093/wps", verbose=False, skip_caps=False)


    

In [39]:

    

wps.identification.title


    

    
Out[39]:





'Flyingpigeon'

In [40]:

    

for process in wps.processes:
    process.identifier, process.title


    

In [44]:

    

for process in wps.processes:
    print '%s \t : %s '% (process.identifier, process.abstract)


    

    




visualisation 	 : Just testing a nice script to visualise some variables 
extractpoints 	 : Extract Timeseries for specified coordinates from grid data 
segetalflora 	 : Species biodiversity of segetal flora. Imput files: variable:tas , domain: EUR-11 or EUR-44 
indices_single 	 : This process calculates climate indices based on one single variable. 
subset_countries 	 : This process returns only the given polygon from input netCDF files. 
eobs_to_cordex 	 : downloades EOBS data in adaped CORDEX format 
modelUncertainty 	 : Calculates the ensemble mean and uncertainty mask 

In [48]:

    

execute = wps.execute(
    identifier="indices_single",
    inputs=[
    ('indices','GD4'),
    ('groupings','yr'),
    ('resource','file:///homel/nhempel/data/cache/pywps/tas_EUR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_day_19860101-19901231.nc'), 
    ('polygons','FRA'),
    ('polygons','DEU')],
    #outputs = [('logout', 'True'),('tarout', 'True') ,('anomaliesout', 'True') ]
    )
# check process if completed ...
monitorExecution(execute, sleepSecs=5)

o = execute.processOutputs[0]
print o.reference


    

    




http://localhost:8090/wpsoutputs/flyingpigeon/output-2c8a7bec-7bf6-11e5-927d-1cc1de57e207.tar

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