Newer
Older
from bscearth.utils.log import Log
import iris
from iris.coords import DimCoord, AuxCoord
from iris.cube import CubeList
import iris.analysis
from earthdiagnostics.constants import Basins
from earthdiagnostics.diagnostic import Diagnostic, DiagnosticBasinListOption
from earthdiagnostics.modelingrealm import ModelingRealms
from earthdiagnostics.utils import Utils, TempFile
import diagonals.moc as moc
from diagonals.mesh_helpers.nemo import Nemo
class Moc(Diagnostic):
"""
Compute the MOC for oceanic basins
: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
alias = 'moc'
"Diagnostic alias for the configuration file"
def __init__(self, data_manager, startdate, member, chunk, basins):
Diagnostic.__init__(self, data_manager)
self.startdate = startdate
self.member = member
self.chunk = chunk
basins = []
return 'MOC Startdate: {0.startdate} Member: {0.member} ' \
'Chunk: {0.chunk} Basins: {1}'.format(self, basins)
def __hash__(self):
return hash(str(self))
if self._different_type(other):
return False
return self.startdate == other.startdate and self.member == other.member and self.chunk == other.chunk
@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: None
basins = Basins()
options_available = (
DiagnosticBasinListOption(
'basins',
'glob'
),
)
options = cls.process_options(options, options_available)
basins = options['basins']
if not basins:
Log.error('Basins not recognized')
return ()
for startdate, member, chunk in diags.config.experiment.get_chunk_list():
job_list.append(Moc(diags.data_manager, startdate, member, chunk,
basins))
self.variable_file = self.request_chunk(ModelingRealms.ocean, 'vo',
self.startdate, self.member,
self.chunk)
def declare_data_generated(self):
self.results = self.declare_chunk(ModelingRealms.ocean, Moc.vsftmyz,
self.startdate, self.member,
self.chunk)
vo_cube = iris.load_cube(self.variable_file.local_file)
vo = np.ma.filled(vo_cube.data, 0.0).astype(np.float32)
mesh = Nemo('mesh_hgr.nc', 'mask_regions.nc')
e1v = mesh.get_i_length(cell_point='V')
e3v = mesh.get_k_length(cell_point='V')
masks = {}
self.basins.sort()
for basin in self.basins:
if basin is 'Global':
global_mask = mesh.get_landsea_mask(cell_point='V')
global_mask[..., 0] = 0.0
global_mask[..., -1] = 0.0
masks[basin] = global_mask
else:
masks[basin] = Utils.get_mask(basin)
moc_results = moc.compute(masks, e1v, e3v, vo)
del vo, e1v, e3v
self._save_result(moc_results, mesh)
def _save_result(self, result, mesh):
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
handler_source = Utils.open_cdf(self.variable_file.local_file)
handler_temp = Utils.open_cdf(temp, 'w')
gphiv = np.squeeze(mesh.get_grid_latitude(cell_point='V'))
max_gphiv = np.unravel_index(np.argmax(gphiv), gphiv.shape)[1]
Utils.copy_variable(handler_source, handler_temp, 'time', True, True)
Utils.copy_variable(handler_source, handler_temp, 'lev', True, True)
handler_temp.createDimension('i', 1)
handler_temp.createDimension('j', gphiv.shape[0])
handler_temp.createDimension('region', len(result))
handler_temp.createDimension('region_length', 50)
var_region = handler_temp.createVariable('region', 'S1',
('region', 'region_length'))
lat = handler_temp.createVariable('lat', float, ('j', 'i'))
lat[...] = gphiv[:, max_gphiv]
lat.units = 'degrees_north'
lat.long_name = "Latitude"
lon = handler_temp.createVariable('lon', float, ('j', 'i'))
lon[...] = 0
lon.units = 'degrees_east'
lon.long_name = "Longitude"
var = handler_temp.createVariable('vsftmyz', float, ('time', 'lev',
'i', 'j',
'region'))
var.units = 'Sverdrup'
var.coordinates = 'lev time'
var.long_name = 'Ocean meridional overturning volume streamfunction'
var.missing_value = 1e20
var.fill_value = 1e20
for i, basin in enumerate(result):
var_region[i, ...] = netCDF4.stringtoarr(str(basin), 50)
var[..., i] = result[basin]
handler_temp.close()
self.results.set_local_file(temp, diagnostic=self)