import logging
import os
from pywps import FORMATS, ComplexInput, ComplexOutput, Format, LiteralInput, LiteralOutput, Process
from pywps.app.Common import Metadata
from pywps.response.status import WPS_STATUS
from pywps.inout.literaltypes import AllowedValue
from pywps.validator.allowed_value import ALLOWEDVALUETYPE
from .. import runner, util
from .utils import (default_outputs, model_experiment_ensemble, outputs_from_plot_names, year_ranges,
reference_year_ranges, check_constraints)
LOGGER = logging.getLogger("PYWPS")
[docs]class QuantileBias(Process):
def __init__(self):
self.variables = ['pr']
self.frequency = 'mon'
inputs = [
*model_experiment_ensemble(model='MPI-ESM-P',
experiment='historical',
ensemble='r1i1p1',
max_occurs=1,
required_variables=self.variables,
required_frequency=self.frequency),
*year_ranges((1997, 1997), start_year=1979, end_year=2018),
LiteralInput('ref_dataset',
'Reference Dataset',
abstract='Choose a reference dataset like GPCP-SG.',
data_type='string',
allowed_values=['GPCP-SG'],
default='GPCP-SG',
min_occurs=1,
max_occurs=1),
LiteralInput('perc_lev',
'Quantile',
abstract='Quantile in percentage (%).',
data_type='integer',
allowed_values=AllowedValue(allowed_type=ALLOWEDVALUETYPE.RANGE, minval=0, maxval=100),
default=75),
]
outputs = [
ComplexOutput('model',
'Model Quantile Data',
abstract='Generated output data of ESMValTool processing.',
as_reference=True,
supported_formats=[FORMATS.NETCDF]),
ComplexOutput('archive',
'Archive',
abstract='The complete output of the ESMValTool processing as a zip archive.',
as_reference=True,
supported_formats=[Format('application/zip')]),
*default_outputs(),
]
super(QuantileBias, self).__init__(
self._handler,
identifier="quantile_bias",
title="Quantile Bias",
version=runner.VERSION,
abstract="""Diagnostic showing the quantile bias between models and a reference dataset.
The estimated calculation time of this process is 1 minute for the default values supplied.
""",
metadata=[
Metadata('ESMValTool', 'http://www.esmvaltool.org/'),
Metadata(
'Documentation',
'https://esmvaltool.readthedocs.io/en/v2.0a2/recipes/recipe_quantilebias.html',
role=util.WPS_ROLE_DOC,
),
],
inputs=inputs,
outputs=outputs,
status_supported=True,
store_supported=True)
def _handler(self, request, response):
response.update_status("starting ...", 0)
# build esgf search constraints
constraints = dict(
model=request.inputs['model'][0].data,
experiment=request.inputs['experiment'][0].data,
ensemble=request.inputs['ensemble'][0].data,
reference=request.inputs['ref_dataset'][0].data,
)
# automatically determine OBS tier
if constraints['reference'] == 'ERA-Interim':
constraints['ref_tier'] = '3'
else:
constraints['ref_tier'] = '2'
options = dict(perc_lev=request.inputs['perc_lev'][0].data)
# generate recipe
response.update_status("generate recipe ...", 10)
start_year = request.inputs['start_year'][0].data
end_year = request.inputs['end_year'][0].data
recipe_file, config_file = runner.generate_recipe(
workdir=self.workdir,
diag='quantilebias',
constraints=constraints,
options=options,
start_year=start_year,
end_year=end_year,
output_format='svg',
)
# recipe output
response.outputs['recipe'].output_format = FORMATS.TEXT
response.outputs['recipe'].file = recipe_file
# run diag
response.update_status("running diagnostic (this could take a while)...", 20)
result = runner.run(recipe_file, config_file)
response.outputs['success'].data = result['success']
# log output
response.outputs['log'].output_format = FORMATS.TEXT
response.outputs['log'].file = result['logfile']
# debug log output
response.outputs['debug_log'].output_format = FORMATS.TEXT
response.outputs['debug_log'].file = result['debug_logfile']
if result['success']:
try:
self.get_outputs(constraints['model'], result, response)
except Exception as e:
response.update_status("exception occured: " + str(e), 85)
else:
LOGGER.exception('esmvaltool failed!')
response.update_status("exception occured: " + result['exception'], 85)
response.update_status("creating archive of diagnostic result ...", 90)
response.outputs['archive'].output_format = Format('application/zip')
response.outputs['archive'].file = runner.compress_output(
os.path.join(self.workdir, 'output'),
os.path.join(self.workdir, 'quantilebias_result.zip'))
response.update_status("done.", 100)
return response
[docs] def get_outputs(self, model, result, response):
# result plot
response.update_status("collecting output ...", 80)
response.outputs['model'].output_format = FORMATS.NETCDF
response.outputs['model'].file = runner.get_output(result['work_dir'],
path_filter=os.path.join('quantilebias', 'main'),
name_filter="{}*".format(model),
output_format="nc")