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# coding=utf-8
import iris
import iris.analysis
import iris.exceptions
from earthdiagnostics.box import Box
from earthdiagnostics.diagnostic import Diagnostic, DiagnosticFloatOption, DiagnosticDomainOption, \
DiagnosticVariableOption
from earthdiagnostics.utils import TempFile
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from earthdiagnostics.modelingrealm import ModelingRealms
class VerticalMeanMetersIris(Diagnostic):
"""
Averages vertically any given variable
:original author: Virginie Guemas <virginie.guemas@bsc.es>
:contributor: Javier Vegas-Regidor<javier.vegas@bsc.es>
:created: February 2012
:last modified: June 2016
:param data_manager: data management object
:type data_manager: DataManager
:param startdate: startdate
:type startdate: str
:param member: member number
:type member: int
:param chunk: chunk's number
:type chunk: int
:param variable: variable to average
:type variable: str
:param box: box used to restrict the vertical mean
:type box: Box
"""
alias = 'vmean'
"Diagnostic alias for the configuration file"
def __init__(self, data_manager, startdate, member, chunk, domain, variable, box, grid_point):
Diagnostic.__init__(self, data_manager)
self.startdate = startdate
self.member = member
self.chunk = chunk
self.domain = domain
self.variable = variable
self.box = box
self.required_vars = [variable]
self.generated_vars = [variable + 'vmean']
self.grid_point = grid_point
def __eq__(self, other):
return self.startdate == other.startdate and self.member == other.member and self.chunk == other.chunk and \
self.box == other.box and self.variable == other.variable
def __str__(self):
return 'Vertical mean meters Startdate: {0} Member: {1} Chunk: {2} Variable: {3}:{4} ' \
'Box: {5}'.format(self.startdate, self.member, self.chunk, self.domain, self.variable, self.box)
@classmethod
def generate_jobs(cls, diags, options):
"""
Creates a job for each chunk to compute the diagnostic
:param diags: Diagnostics manager class
:type diags: Diags
:param options: variable, minimum depth (meters), maximum depth (meters)
:type options: list[str]
:return:
"""
options_available = (DiagnosticVariableOption(),
DiagnosticFloatOption('min_depth', -1),
DiagnosticFloatOption('max_depth', -1),
DiagnosticDomainOption(default_value=ModelingRealms.ocean))
options = cls.process_options(options, options_available)
box = Box(True)
if options['min_depth'] >= 0:
box.min_depth = options['min_depth']
if options['max_depth'] >= 0:
box.max_depth = options['max_depth']
job_list = list()
for startdate, member, chunk in diags.config.experiment.get_chunk_list():
job_list.append(VerticalMeanMetersIris(diags.data_manager, startdate, member, chunk,
options['domain'], options['variable'], box, options['grid_point']))
return job_list
def request_data(self):
self.variable_file = self.request_chunk(ModelingRealms.ocean, self.variable, self.startdate, self.member,
self.chunk)
def declare_data_generated(self):
self.results = self.declare_chunk(self.domain, self.variable + 'vmean', self.startdate, self.member,
self.chunk, box=self.box)
def compute(self):
"""
Runs the diagnostic
"""
iris.FUTURE.netcdf_no_unlimited = True
iris.FUTURE.netcdf_promote = True
var_cube = iris.load_cube(self.variable_file.local_file)
lev_names = ('lev', 'depth')
coord = None
for coord_name in lev_names:
try:
except iris.exceptions.CoordinateNotFoundError:
if self.box.min_depth is None:
lev_min = coord.points[0]
else:
lev_min = self.box.min_depth
if self.box.max_depth is None:
lev_max = coord.points[-1]
else:
lev_max = self.box.max_depth
var_cube = var_cube.extract(iris.Constraint(coord_values=
{coord.var_name: lambda cell: lev_min <= cell <= lev_max}))
var_cube = var_cube.collapsed(coord, iris.analysis.MEAN)
temp = TempFile.get()
iris.save(var_cube, temp, zlib=True)
self.results.set_local_file(temp, rename_var=var_cube.var_name)