import os.path
from pywps import Process, LiteralInput, ComplexOutput
from pywps import Format
from pywps.app.Common import Metadata
from climafwps import util
ALLOWED_VALUES = {
'model':
'ACCESS1-0 ACCESS1-3 bcc-csm1-1 bcc-csm1-1-m BNU-ESM CanCM4 '
'CanESM2 CCSM4 CESM1-BGC CESM1-CAM5 CESM1-WACCM CMCC-CM CMCC-CMS '
'CNRM-CM5 CSIRO-Mk3-6-0 EC-EARTH FGOALS-g2 FIO-ESM GFDL-CM2p1 '
'GFDL-CM3 GFDL-ESM2G GFDL-ESM2M GISS-E2-H GISS-E2-H-CC GISS-E2-R '
'GISS-E2-R-CC HadCM3 HadGEM2-AO HadGEM2-CC HadGEM2-ES inmcm4 '
'IPSL-CM5A-LR IPSL-CM5A-MR IPSL-CM5B-LR MIROC4h MIROC5 MIROC-ESM '
'MIROC-ESM-CHEM MPI-ESM-LR MPI-ESM-MR MRI-CGCM3 NorESM1-M NorESM1-ME'.split(),
'experiment':
'rcp45 rcp60 rcp8 historical'.split(),
'variable':
'cl clt evspsbl hur huss prc ps rldscs rlutcs rsdt '
'rsut sci tas tauu ua vas cli clw hfls hurs '
'mc prsn psl rlus rsds rsus rsutcs sfcWind tasmax '
'tauv uas wap clivi clwvi hfss hus pr prw rlds rlut '
'rsdscs rsuscs sbl ta tasmin ts va zg'.split()
}
[docs]class TimeSeriesPlot(Process):
def __init__(self):
inputs = [
LiteralInput('model', 'Model',
abstract="Climate model ID",
default='MPI-ESM-LR', data_type='string',
allowed_values=ALLOWED_VALUES['model']),
LiteralInput('experiment', 'Experiment',
abstract="Experiment name",
default='rcp45', data_type='string',
allowed_values=ALLOWED_VALUES['experiment']),
LiteralInput('variable', 'Variable',
abstract="Variable ID",
default='tas', data_type='string',
allowed_values=ALLOWED_VALUES['variable']),
LiteralInput('start_year', 'Start Year',
abstract="4-digit start year",
default='2010', data_type='integer'),
LiteralInput('end_year', 'End Year',
abstract="4-digit end year",
default='2020', data_type='integer'),
]
outputs = [
ComplexOutput('output', 'Output plot',
abstract='Generated timeseries plot.',
as_reference=True,
supported_formats=[Format('image/png')])
]
super(TimeSeriesPlot, self).__init__(
self._handler,
identifier='tsplot',
version='1.1.1',
title='CMIP5 Global Mean Time Series',
abstract='Uses the CliMAF tool to calculate a time series of global mean values'
' for a variable, model, experiment and ensemble member from the CMIP5 archive.'
' The time series is plotted as a line graph showing change '
' in the global mean value against time.',
profile='',
metadata=[
Metadata('CliMAF', 'http://climaf.readthedocs.io/en/latest/'),
Metadata('Documentation',
'https://climaf-wps-demo.readthedocs.io/en/latest/processes.html#tsplot',
role=util.WPS_ROLE_DOC),
Metadata('Media',
'https://climaf-wps-demo.readthedocs.io/en/latest/_static/media/tsplot_thumbnail.png',
role=util.WPS_ROLE_MEDIA),
],
inputs=inputs,
outputs=outputs,
store_supported=True,
status_supported=True
)
def _handler(self, request, response):
os.environ['CLIMAF_LOG_DIR'] = self.workdir
from climafwps import tsplot
response.update_status('Plotting ...', 0)
# output in workdir
output_filename = os.path.join(self.workdir, 'output.png')
# start tsplot
tsplot.create_global_mean_ts_plot(
model=request.inputs['model'][0].data,
experiment=request.inputs['experiment'][0].data,
start_year=request.inputs['start_year'][0].data,
end_year=request.inputs['end_year'][0].data,
variable=request.inputs['variable'][0].data,
output=output_filename)
# store result
response.outputs['output'].file = output_filename
# done
response.update_status('Plotting done', 100)
return response
