def __init__(self):
inputs = [
LiteralInput('model',
'Model',
abstract='Choose a model like MPI-ESM-LR.',
data_type='string',
allowed_values=['MPI-ESM-LR', 'MPI-ESM-MR'],
default='MPI-ESM-LR'),
LiteralInput(
'experiment',
'Experiment',
abstract='Choose an experiment like historical.',
data_type='string',
allowed_values=['historical', 'rcp26', 'rcp45', 'rcp85'],
default='historical'),
LiteralInput('ensemble',
'Ensemble',
abstract='Choose an ensemble like r1i1p1.',
data_type='string',
allowed_values=['r1i1p1', 'r2i1p1', 'r3i1p1'],
default='r1i1p1'),
LiteralInput('start_year',
'Start year',
data_type='integer',
abstract='Start year of model data.',
default="2000"),
LiteralInput('end_year',
'End year',
data_type='integer',
abstract='End year of model data.',
default="2001"),
]
outputs = [
ComplexOutput('recipe',
'recipe',
abstract='ESMValTool recipe used for processing.',
as_reference=True,
supported_formats=[Format('text/plain')]),
ComplexOutput('log',
'Log File',
abstract='Log File of ESMValTool processing.',
as_reference=True,
supported_formats=[Format('text/plain')]),
ComplexOutput(
'output',
'Output plot',
abstract='Generated output plot of ESMValTool processing.',
as_reference=True,
supported_formats=[Format('application/pdf')]),
]
super(MyDiag, self).__init__(
self._handler,
identifier="mydiag",
title="Simple plot",
version=runner.VERSION,
abstract="Generates a plot for temperature using ESMValTool."
" It is a diagnostic used in the ESMValTool tutoriaal doc/toy-diagnostic-tutorial.pdf."
" The default run uses the following CMIP5 data:"
" project=CMIP5, experiment=historical, ensemble=r1i1p1, variable=ta, model=MPI-ESM-LR, time_frequency=mon", # noqa
metadata=[
Metadata('ESMValTool', 'http://www.esmvaltool.org/'),
Metadata(
'Documentation',
'https://copernicus-wps-demo.readthedocs.io/en/latest/processes.html#mydiag',
role=util.WPS_ROLE_DOC),
Metadata('Media',
util.diagdata_url() + '/mydiag/mydiag_thumbnail.png',
role=util.WPS_ROLE_MEDIA),
Metadata(
'ESGF Testdata',
'https://esgf1.dkrz.de/thredds/catalog/esgcet/7/cmip5.output1.MPI-M.MPI-ESM-LR.historical.mon.atmos.Amon.r1i1p1.v20120315.html?dataset=cmip5.output1.MPI-M.MPI-ESM-LR.historical.mon.atmos.Amon.r1i1p1.v20120315.ta_Amon_MPI-ESM-LR_historical_r1i1p1_199001-199912.nc'
), # noqa
],
inputs=inputs,
outputs=outputs,
status_supported=True,
store_supported=True)
def __init__(self):
inputs = [
LiteralInput('model',
'Model',
abstract='Choose a model like MPI-ESM-LR.',
data_type='string',
allowed_values=['MPI-ESM-LR', 'MPI-ESM-MR'],
default='MPI-ESM-LR'),
LiteralInput(
'experiment',
'Experiment',
abstract='Choose an experiment like historical.',
data_type='string',
allowed_values=['historical', 'rcp26', 'rcp45', 'rcp85'],
default='historical'),
LiteralInput('ensemble',
'Ensemble',
abstract='Choose an ensemble like r1i1p1.',
data_type='string',
allowed_values=['r1i1p1', 'r2i1p1', 'r3i1p1'],
default='r1i1p1'),
LiteralInput('start_year',
'Start year',
data_type='integer',
abstract='Start year of model data.',
default="2000"),
LiteralInput('end_year',
'End year',
data_type='integer',
abstract='End year of model data.',
default="2001"),
]
outputs = [
ComplexOutput('namelist',
'namelist',
abstract='ESMValTool namelist used for processing.',
as_reference=True,
supported_formats=[Format('text/plain')]),
ComplexOutput('log',
'Log File',
abstract='Log File of ESMValTool processing.',
as_reference=True,
supported_formats=[Format('text/plain')]),
ComplexOutput(
'output',
'Output plot',
abstract='Generated output plot of ESMValTool processing.',
as_reference=True,
supported_formats=[Format('application/pdf')]),
]
super(Perfmetrics, self).__init__(
self._handler,
identifier="perfmetrics",
title="Performance metrics",
version=runner.VERSION,
abstract=
"Creates a performance metrics report comparing models using ESMValTool. "
" The goal is to create a standard namelist for the calculation of performance metrics to quantify "
" the ability of the models to reproduce the climatological mean annual cycle for selected "
" Essential Climate Variables (ECVs) plus some additional corresponding diagnostics "
" and plots to better understand and interpret the results. "
" The namelist can be used to calculate performance metrics at different vertical "
" levels (e.g., 5, 30, 200, 850 hPa as in Gleckler et al., 2008) and in four "
" regions (global, tropics 20N-20S, northern extratropics 20-90N, southern extratropics 20-90S). "
" As an additional reference, we consider the Righi et al. (2015) paper.",
metadata=[
Metadata('ESMValTool', 'http://www.esmvaltool.org/'),
Metadata(
'Documentation',
'https://copernicus-wps-demo.readthedocs.io/en/latest/processes.html#perfmetrics',
role=util.WPS_ROLE_DOC),
Metadata('Media',
util.diagdata_url() + '/perfmetrics/Portait.png',
role=util.WPS_ROLE_MEDIA),
Metadata('Diagnostic Description',
util.diagdata_url() + '/perfmetrics/description.md',
role=util.MAGIC_ROLE_DOC),
Metadata('Diagnostic Metadata',
util.diagdata_url() + '/perfmetrics/perfmetrics.yml',
role=util.MAGIC_ROLE_METADATA),
],
inputs=inputs,
outputs=outputs,
status_supported=True,
store_supported=True)
def __init__(self):
inputs = [
LiteralInput('model',
'Model',
abstract='Choose a model like MPI-ESM-LR.',
data_type='string',
allowed_values=[
'ACCESS1-0',
],
default='ACCESS1-0'),
LiteralInput('experiment',
'Experiment',
abstract='Choose an experiment like historical.',
data_type='string',
allowed_values=[
'historical',
],
default='historical'),
LiteralInput('start_year',
'Start year',
data_type='integer',
abstract='Start year of model data.',
default="1997"),
LiteralInput('end_year',
'End year',
data_type='integer',
abstract='End year of model data.',
default="1997"),
LiteralInput(
'subset',
'Geographical subset',
abstract=
'Choose a geographical subset with a Bounding Box: 4,13,44,53',
data_type='string',
default='4,13,44,53'),
LiteralInput('regridding',
'Regridding',
abstract='Flag for regridding.',
data_type='boolean',
default='0'),
LiteralInput('slope',
'Slope',
abstract='Flag for slope.',
data_type='boolean',
default='0'),
LiteralInput('num_ens_members',
'Number of ensemble members',
abstract='Choose a number of ensemble members.',
data_type='integer',
default='2'),
LiteralInput('num_subdivs',
'Number of subdivisions',
abstract='Choose a number of subdivisions.',
data_type='integer',
default='8'),
]
outputs = [
ComplexOutput(
'output',
'Output plot',
abstract='Generated output plot of ESMValTool processing.',
as_reference=True,
supported_formats=[Format('image/png')]),
]
super(RainFarm, self).__init__(
self._handler,
identifier="rainfarm",
title="RainFARM stochastic downscaling",
version=runner.VERSION,
abstract=
"Tool to perform stochastic precipitation downscaling, generating an ensemble of fine-scale "
" precipitation fields from information simulated by climate models at regional scale.",
metadata=[
Metadata('ESMValTool', 'http://www.esmvaltool.org/'),
Metadata(
'Documentation',
'https://copernicus-wps-demo.readthedocs.io/en/latest/processes.html#rainfarm',
role=util.WPS_ROLE_DOC),
Metadata('Media',
util.diagdata_url() +
'/rainfarm/rainfarm_thumbnail.png',
role=util.WPS_ROLE_MEDIA),
Metadata('Diagnostic Description',
util.diagdata_url() + '/rainfarm/description.md',
role=util.MAGIC_ROLE_DOC),
Metadata('Diagnostic Metadata',
util.diagdata_url() + '/rainfarm/rainfarm.yml',
role=util.MAGIC_ROLE_METADATA),
],
inputs=inputs,
outputs=outputs,
status_supported=True,
store_supported=True)
def test_diagdata_url():
assert util.diagdata_url(
) == 'http://localhost:5000/static/diagnosticsdata'
def __init__(self):
inputs = [
LiteralInput('region', 'Region',
abstract='Choose a region like Arctic.',
data_type='string',
allowed_values=['Arctic', ],
default='Arctic'),
LiteralInput('model', 'Model',
abstract='Choose a model like NASA.',
data_type='string',
allowed_values=['NASA', ],
default='NASA'),
LiteralInput('variable', 'Variable',
abstract='Choose a variable like sic.',
data_type='string',
allowed_values=['sic', ],
default='sic'),
LiteralInput('ncenters', 'Number of Centers',
abstract='Choose a number of centers.',
data_type='integer',
default='4'),
LiteralInput('cluster_method', 'Cluster Method',
abstract='Choose a cluster method.',
data_type='string',
allowed_values=['kmeans', ],
default='kmeans'),
LiteralInput('eofs', 'EOFS',
abstract='Choose EOFS.',
data_type='boolean',
default='0'),
LiteralInput('detrend', 'detrend',
abstract='Choose a detrend.',
data_type='integer',
default='2'),
LiteralInput('experiment', 'Experiment',
abstract='Choose an experiment.',
data_type='string',
allowed_values=['historical', 'rcp26', 'rcp85'],
default='historical'),
]
outputs = [
ComplexOutput('output', 'Output plot',
abstract='Generated output plot of ESMValTool processing.',
as_reference=True,
supported_formats=[Format('image/png')]),
]
super(RMSE, self).__init__(
self._handler,
identifier="rmse",
title="Modes of variability",
version=runner.VERSION,
abstract="Tool to compute the RMSE between the observed and modelled patterns of variability "
" obtained through classification and their relative relative bias (percentage) "
" in the frequency of occurrence and the persistence of each mode.",
metadata=[
Metadata('ESMValTool', 'http://www.esmvaltool.org/'),
Metadata('Documentation',
'https://copernicus-wps-demo.readthedocs.io/en/latest/processes.html#rmse',
role=util.WPS_ROLE_DOC),
Metadata('Media',
util.diagdata_url() + '/modes_of_variability/era_interim_1990-01-2010-01_clusters.png',
role=util.WPS_ROLE_MEDIA),
Metadata('Diagnostic Description',
util.diagdata_url() + '/modes_of_variability/description.md',
role=util.MAGIC_ROLE_DOC),
Metadata('Diagnostic Metadata',
util.diagdata_url() + '/modes_of_variability/modes_of_variability.yml',
role=util.MAGIC_ROLE_METADATA),
],
inputs=inputs,
outputs=outputs,
status_supported=True,
store_supported=True)