import logging
import os
from pywps import FORMATS, ComplexInput, ComplexOutput, Format, LiteralInput, LiteralOutput, Process
from pywps.app.Common import Metadata
from .. import runner, util
from .utils import (default_outputs, model_experiment_ensemble, outputs_from_plot_names, year_ranges, check_constraints)
LOGGER = logging.getLogger("PYWPS")
[docs]class ShapeSelect(Process):
def __init__(self):
self.variables = ['tas', 'pr']
self.frequency = 'mon'
inputs = [
*model_experiment_ensemble(model='EC-EARTH',
experiment='historical',
ensemble='r1i1p1',
max_occurs=1,
required_variables=self.variables,
required_frequency=self.frequency),
*year_ranges((1990, 1999)),
LiteralInput('shape',
'Shape',
abstract='Shape of the area',
data_type='string',
allowed_values=['MotalaStrom', 'Elbe', 'multicatchment', 'testfile', 'Thames'],
default='MotalaStrom'),
LiteralInput(
'weighting_method',
'Weighting method',
abstract="""The preferred weighting method: mean_inside - mean of all
grid points inside polygon or representative - one point inside or close
to the polygon is used to represent the complete area.""",
data_type='string',
allowed_values=['mean_inside', 'representative'],
default='mean_inside'),
]
outputs = [
ComplexOutput('data',
'Data',
abstract='Generated NetCDF file with precipitation for the selected area.',
as_reference=True,
supported_formats=[FORMATS.NETCDF]),
ComplexOutput('xlsx_data',
'XLSX Data',
abstract='Generated excel file with precipitation for the selected area',
as_reference=True,
supported_formats=[Format('application/vnd.ms-excel')]),
ComplexOutput('archive',
'Archive',
abstract='The complete output of the ESMValTool processing as an zip archive.',
as_reference=True,
supported_formats=[Format('application/zip')]),
*default_outputs(),
]
super(ShapeSelect, self).__init__(
self._handler,
identifier="shapefile_selection",
title="Shapefile selection",
version=runner.VERSION,
abstract="""Metric showing selected gridded data within a user defined polygon shapefile and outputting
as NetCDF or csv file.
The estimated calculation time of this process is 10 seconds for the default values supplied.
""",
metadata=[
Metadata('ESMValTool', 'http://www.esmvaltool.org/'),
Metadata('Documentation',
'https://esmvaltool.readthedocs.io/en/v2.0a2/recipes/recipe_shapeselect.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,
cmor_table='Amon',
experiment=request.inputs['experiment'][0].data,
ensemble=request.inputs['ensemble'][0].data,
)
options = dict(
shape=request.inputs['shape'][0].data,
weighting_method=request.inputs['weighting_method'][0].data,
)
# generate recipe
response.update_status("generate recipe ...", 10)
recipe_file, config_file = runner.generate_recipe(
workdir=self.workdir,
diag='shapeselect',
constraints=constraints,
start_year=request.inputs['start_year'][0].data,
end_year=request.inputs['end_year'][0].data,
output_format='png',
options=options,
)
# 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(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, 'shapeselect_result.zip'))
response.update_status("done.", 100)
return response
[docs] def get_outputs(self, result, response):
# result plot
response.update_status("collecting output ...", 80)
response.outputs['data'].output_format = Format('application/png')
response.outputs['data'].file = runner.get_output(result['work_dir'],
path_filter=os.path.join('diagnostic1', 'script1'),
name_filter="CMIP5*",
output_format="nc")
response.outputs['xlsx_data'].output_format = Format('application/vnd.ms-excel')
response.outputs['xlsx_data'].file = runner.get_output(result['work_dir'],
path_filter=os.path.join('diagnostic1', 'script1'),
name_filter="CMIP5*",
output_format="xlsx")