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# coding=utf-8
from earthdiagnostics.box import Box
from earthdiagnostics.diagnostic import Diagnostic, DiagnosticIntOption, DiagnosticVariableOption
from earthdiagnostics.utils import Utils, TempFile
from earthdiagnostics.modelingrealm import ModelingRealms
class VerticalGradient(Diagnostic):
"""
Chooses vertical level in ocean, or vertically averages between
2 or more ocean levels
:original author: Virginie Guemas <virginie.guemas@bsc.es>
:contributor: Eleftheria Exarchou <eleftheria.exarchou@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 = 'vgrad'
"Diagnostic alias for the configuration file"
def __init__(self, data_manager, startdate, member, chunk, variable, box):
Diagnostic.__init__(self, data_manager)
self.startdate = startdate
self.member = member
self.chunk = chunk
self.variable = variable
self.box = box
self.required_vars = [variable]
self.generated_vars = [variable + 'vmean']
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 gradient Startdate: {0} Member: {1} Chunk: {2} Variable: {3} ' \
'Box: {4}'.format(self.startdate, self.member, self.chunk, 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 (level), maximum depth (level)
:type options: list[str]
:return:
"""
options_available = (DiagnosticVariableOption('variable'),
DiagnosticIntOption('upper_level', 1),
DiagnosticIntOption('low_level', 2))
options = cls.process_options(options, options_available)
box = Box(False)
if options['upper_level'] >= 0:
box.min_depth = options['upper_level']
if options['low_level'] >= 0:
box.max_depth = options['low_level']
job_list = list()
for startdate, member, chunk in diags.config.experiment.get_chunk_list():
job_list.append(VerticalGradient(diags.data_manager, startdate, member, chunk,
options['variable'], box))
return job_list
def compute(self):
"""
Runs the diagnostic
"""
variable_file = self.data_manager.get_file(ModelingRealms.ocean, self.variable, self.startdate, self.member,
self.chunk)
handler = Utils.openCdf(variable_file)
if 'lev' not in handler.dimensions:
raise Exception('Variable {0} does not have a level dimension')
var_handler = handler.variables[self.variable]
upper_level = var_handler[:, self.box.min_depth-1, ...]
lower_level = var_handler[:, self.box.max_depth-1, ...]
gradient = upper_level - lower_level
temp = TempFile.get()
new_file = Utils.openCdf(temp, 'w')
for var in handler.variables.keys():
if var in (self.variable, 'lev', 'lev_bnds'):
continue
Utils.copy_variable(handler, new_file, var, add_dimensions=True)
new_var = new_file.createVariable(self.variable + 'vgrad', var_handler.dtype,
dimensions=('time', 'j', 'i'), zlib=True)
Utils.copy_attributes(new_var, var_handler)
new_var[...] = gradient[...]
new_var.long_name += ' Vertical gradient'
new_var.standard_name += '_vertical_gradient'
self.send_file(temp, ModelingRealms.ocean, self.variable + 'vgrad', self.startdate, self.member, self.chunk,
box=self.box)