# coding=utf-8
"""Compute an Atlantic MOC index from the average"""
import os
import numpy as np
from earthdiagnostics.box import Box
from earthdiagnostics.constants import Basins
from earthdiagnostics.diagnostic import Diagnostic, DiagnosticIntOption, DiagnosticBasinOption
from earthdiagnostics.modelingrealm import ModelingRealms
from earthdiagnostics.utils import Utils, TempFile
[docs]class AreaMoc(Diagnostic):
"""
Compute an Atlantic MOC index
Averages the meridional overturning
in a latitude band between 1km and 2km
or any other index averaging the meridional overturning in
a given basin and a given domain
:original author: Virginie Guemas <virginie.guemas@bsc.es>
:contributor: Javier Vegas-Regidor<javier.vegas@bsc.es>
:created: March 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 basin: basin to compute
:type basin: Basin
:param box: box to compute
:type box: Box
"""
alias = 'mocarea'
"Diagnostic alias for the configuration file"
vsftmyz = 'vsftmyz'
def __init__(self, data_manager, startdate, member, chunk, basin, box):
Diagnostic.__init__(self, data_manager)
self.basin = basin
self.startdate = startdate
self.member = member
self.chunk = chunk
self.required_vars = ['vo']
self.generated_vars = ['vsftmyz']
self.box = box
def __eq__(self, other):
if self._different_type(other):
return False
return self.startdate == other.startdate and self.member == other.member and self.chunk == other.chunk and \
self.basin == other.basin and self.box == other.box
def __str__(self):
return 'Area MOC Startdate: {0} Member: {1} Chunk: {2} Box: {3} Basin: {4}'.format(self.startdate, self.member,
self.chunk, self.box,
self.basin)
[docs] @classmethod
def generate_jobs(cls, diags, options):
"""
Create a job for each chunk to compute the diagnostic
:param diags: Diagnostics manager class
:type diags: Diags
:param options: minimum latitude, maximum latitude, minimum depth, maximum depth, basin=Global
:type options: list[str]
:return:
"""
options_available = (DiagnosticIntOption('min_lat'),
DiagnosticIntOption('max_lat'),
DiagnosticIntOption('min_depth'),
DiagnosticIntOption('max_depth'),
DiagnosticBasinOption('basin', Basins().Global))
options = cls.process_options(options, options_available)
box = Box()
box.min_lat = options['min_lat']
box.max_lat = options['max_lat']
box.min_depth = options['min_depth']
box.max_depth = options['max_depth']
job_list = list()
for startdate, member, chunk in diags.config.experiment.get_chunk_list():
job_list.append(AreaMoc(diags.data_manager, startdate, member, chunk, options['basin'], box))
return job_list
[docs] def request_data(self):
"""Request data required by the diagnostic"""
self.variable_file = self.request_chunk(ModelingRealms.ocean, AreaMoc.vsftmyz,
self.startdate, self.member, self.chunk)
[docs] def declare_data_generated(self):
"""Declare data to be generated by the diagnostic"""
self.results = self.declare_chunk(ModelingRealms.ocean, AreaMoc.vsftmyz,
self.startdate, self.member, self.chunk,
box=self.box)
[docs] def compute(self):
"""Run the diagnostic"""
nco = Utils.nco
cdo = Utils.cdo
temp = TempFile.get()
temp2 = TempFile.get()
Utils.copy_file(self.variable_file.local_file, temp)
handler = Utils.open_cdf(temp)
if 'i' in handler.dimensions:
handler.close()
nco.ncwa(input=temp, output=temp, options=('-O -a i',))
handler = Utils.open_cdf(temp)
basin_index = np.where(handler.variables['basin'][:] == self.basin.name)
if 'lat' in handler.variables:
lat_name = 'lat'
else:
lat_name = 'latitude'
var_lat = handler.variables[lat_name]
lat_values = var_lat[:]
lat_type = var_lat.dtype
lat_units = var_lat.units
lat_long_name = var_lat.long_name
handler.close()
if len(basin_index) == 0:
raise Exception('Basin {0} not defined in file')
basin_index = basin_index[0][0]
# To select basin and remove dimension
nco.ncwa(input=temp, output=temp, options=('-O -d basin,{0} -a basin'.format(basin_index),))
source = Utils.open_cdf(temp)
destiny = Utils.open_cdf(temp2, 'w')
Utils.copy_dimension(source, destiny, 'time')
Utils.copy_dimension(source, destiny, 'lev')
Utils.copy_dimension(source, destiny, 'j', new_names={'j': lat_name})
lat_variable = destiny.createVariable(lat_name, lat_type, lat_name)
lat_variable[:] = lat_values[:]
lat_variable.units = lat_units
lat_variable.long_name = lat_long_name
Utils.copy_variable(source, destiny, 'lev')
Utils.copy_variable(source, destiny, 'time')
Utils.copy_variable(source, destiny, 'vsftmyz', new_names={'j': lat_name})
source.close()
destiny.close()
nco.ncks(input=temp2, output=temp,
options=('-d lev,{0:.1f},{1:.1f} -d {4},{2:.1f},{3:.1f}'.format(self.box.min_depth,
self.box.max_depth,
self.box.min_lat,
self.box.max_lat,
lat_name),))
cdo.vertmean(input=temp, output=temp2)
os.remove(temp)
nco.ncap2(input=temp2, output=temp2,
options=('-s "coslat[{0}]=cos({0}[{0}]*3.141592657/180.0)"'.format(lat_name),))
nco.ncwa(input=temp2, output=temp2, options=('-w coslat -a {0}'.format(lat_name),))
nco.ncks(input=temp2, output=temp2, options=('-v vsftmyz,time',))
self.results.set_local_file(temp2)