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import netCDF4
import os
from earthdiagnostics.basins import Basins
from earthdiagnostics.diagnostic import Diagnostic
from earthdiagnostics.utils import Utils, TempFile
import cdftoolspython
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class Siasiesiv(Diagnostic):
Compute the sea ice extent , area and volume in both hemispheres or a specified region.
:original author: Virginie Guemas <virginie.guemas@bsc.es>
:contributor: Neven Fuckar <neven.fuckar@bsc.es>
:contributor: Ruben Cruz <ruben.cruzgarcia@bsc.es>
:contributor: Javier Vegas-Regidor <javier.vegas@bsc.es>
:created: April 2012
:last modified: June 2016
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def __init__(self, data_manager, basin, startdate, member, chunk):
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Diagnostic.__init__(self, data_manager)
self.basin = basin
if basin == Basins.Global:
self._region = None
else:
self._region = basin.shortname
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self.startdate = startdate
self.member = member
self.chunk = chunk
self.required_vars = ['sit', 'sic']
self.generated_vars = ['siextents', 'sivols', 'siareas', 'siextentn', 'sivoln', 'siarean']
def __str__(self):
return 'Siasiesiv Startdate: {0} Member: {1} Chunk: {2} Basin: {3}'.format(self.startdate, self.member,
self.chunk, self.basin.fullname)
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: basin
:return:
"""
if len(options) != 2:
raise Exception('You must specify the basin for the siasiesiv diagnostic (and nothing else)')
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basin = Basins.parse(options[1])
job_list = list()
for startdate in diags.startdates:
for member in diags.members:
for chunk in range(1, diags.chunks + 1):
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job_list.append(Siasiesiv(diags.data_manager, basin, startdate, member, chunk))
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return job_list
def compute(self):
nco = Utils.nco
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mesh_handler = Utils.openCdf('mesh_hgr.nc')
e1t = mesh_handler.variables['e1t'][0, :]
e2t = mesh_handler.variables['e2t'][0, :]
gphit = mesh_handler.variables['gphit'][0, :]
mesh_handler.close()
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if self.basin != Basins.Global:
mask_handler = Utils.openCdf('mask_regions.nc')
mask = mask_handler.variables[self.basin.fullname][:, 0, :]
mask_handler.close()
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else:
mask_handler = Utils.openCdf('mask.nc')
mask = mask_handler.variables['tmask'][0, 0, :]
mask_handler.close()
sit_file = self.data_manager.get_file('seaIce', 'sit', self.startdate, self.member, self.chunk)
sic_file = self.data_manager.get_file('seaIce', 'sic', self.startdate, self.member, self.chunk)
sit_handler = Utils.openCdf(sit_file)
sic_handler = Utils.openCdf(sic_file)
for t in range(0, sit_handler.dimensions['time'].size):
try:
sit = sit_handler.variables['sit'][t, :]
sic = sic_handler.variables['sic'][t, :]
results.append(cdftoolspython.icediag.icediags(e1t, e2t, mask, gphit, sit, sic))
except Exception as ex:
print ex
sit_handler.close()
sic_handler.close()
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self.data_manager.send_file(self._extract_variable_and_rename(sit_file, results[0], 'sivols',
'seaIce', 'sivols', self.startdate, self.member, self.chunk,
region=self.basin.fullname)
self.data_manager.send_file(self._extract_variable_and_rename(sit_file, results[0], 'siareas',
'seaIce', 'siareas', self.startdate, self.member, self.chunk,
region=self.basin.fullname)
self.data_manager.send_file(self._extract_variable_and_rename(sit_file, results[0], 'siextents',
'seaIce', 'siextents', self.startdate, self.member, self.chunk,
region=self.basin.fullname)
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self.data_manager.send_file(self._extract_variable_and_rename(sit_file, results[0], 'sivoln',
'seaIce', 'sivoln', self.startdate, self.member, self.chunk,
region=self.basin.fullname)
self.data_manager.send_file(self._extract_variable_and_rename(sit_file, results[0], 'siarean', "10^6 km2"),
'seaIce', 'siarean', self.startdate, self.member, self.chunk,
region=self.basin.fullname)
self.data_manager.send_file(self._extract_variable_and_rename(sit_file, results[0], 'siextentn',
'seaIce', 'siextentn', self.startdate, self.member, self.chunk,
region=self.basin.fullname)
os.remove(temp)
def _extract_variable_and_rename(self, reference_file, values, cmor_name, output_units):
temp = TempFile.get()
os.remove(temp)
handler = netCDF4.Dataset(temp, 'w')
# Create dimensions
handler.createDimension('bnds', 2)
# Copy time variable
Utils.copy_variable(reference_handler, handler, 'time')
Utils.copy_variable(reference_handler, handler, 'time_bnds')
Utils.copy_variable(reference_handler, handler, 'leadtime')
reference_handler.close()
new_var = handler.createVariable(cmor_name, float, 'time', fill_value=0.0)
factor = self._get_conversion_factor('10^9 m3', output_units)
values *= factor
new_var[:] = values
new_var.units = output_units
new_var.short_name = cmor_name
new_var.valid_min = 0.0
new_var.valid_max = np.max(values)
handler.close()
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return temp
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def _get_conversion_factor(self, input_units, output_units):
units = input_units.split()
if len(units) == 1:
scale_unit = 1
unit = units[0]
else:
if '^' in units[0]:
values = units[0].split('^')
scale_unit = pow(int(values[0]), int(values[1]))
else:
scale_unit = float(units[0])
unit = units[1]
units = output_units.split()
if len(units) == 1:
scale_new_unit = 1
new_unit = units[0]
else:
if '^' in units[0]:
values = units[0].split('^')
scale_new_unit = pow(int(values[0]), int(values[1]))
else:
scale_new_unit = float(units[0])
new_unit = units[1]
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factor = self._get_factor(new_unit, unit)
invert = False
if factor is None:
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factor = self._get_factor(unit, new_unit)
invert = True
if factor is None:
raise Exception("Conversion from {0} to {1} not supported".format(units, output_units))
if invert:
factor = scale_unit / float(scale_new_unit * factor)
else:
factor = (factor * scale_unit) / float(scale_new_unit)
return factor
@staticmethod
def _get_factor(new_unit, unit):
# Add only the conversions with a factor greater than 1
if unit == new_unit:
return 1
if unit == 'km3':
if new_unit == 'm3':
return pow(1000, 3)
elif unit == 'km2':
if new_unit == 'm2':
return pow(1000, 2)
return None