def handle(infiles, tables, user_input_path, **kwargs):
"""
Transform MPASO timeMonthly_avg_dThreshMLD into CMIP.mlotst
Parameters
----------
infiles : dict
a dictionary with namelist, mesh and time series file names
tables : str
path to CMOR tables
user_input_path : str
path to user input json file
Returns
-------
varname : str
the name of the processed variable after processing is complete
"""
msg = 'Starting {name}'.format(name=__name__)
logging.info(msg)
meshFileName = infiles['MPAS_mesh']
mappingFileName = infiles['MPAS_map']
timeSeriesFiles = infiles['MPASO']
dsMesh = xarray.open_dataset(meshFileName, mask_and_scale=False)
cellMask2D, _ = mpas.get_cell_masks(dsMesh)
variableList = [
'timeMonthly_avg_dThreshMLD', 'xtime_startMonthly', 'xtime_endMonthly'
]
ds = xarray.Dataset()
with mpas.open_mfdataset(timeSeriesFiles, variableList) as dsIn:
ds[VAR_NAME] = dsIn.timeMonthly_avg_dThreshMLD.where(cellMask2D)
ds = mpas.add_time(ds, dsIn)
ds.compute()
ds = mpas.add_mask(ds, cellMask2D)
ds = mpas.remap(ds, mappingFileName)
mpas.setup_cmor(VAR_NAME, tables, user_input_path, component='ocean')
# create axes
axes = [{
'table_entry': 'time',
'units': ds.time.units
}, {
'table_entry': 'latitude',
'units': 'degrees_north',
'coord_vals': ds.lat.values,
'cell_bounds': ds.lat_bnds.values
}, {
'table_entry': 'longitude',
'units': 'degrees_east',
'coord_vals': ds.lon.values,
'cell_bounds': ds.lon_bnds.values
}]
try:
mpas.write_cmor(axes, ds, VAR_NAME, VAR_UNITS)
except Exception:
return ""
return VAR_NAME
def handle(infiles, tables, user_input_path, **kwargs):
"""
Transform MPASO timeMonthly_avg_layerThickness into CMIP.zhalfo
Parameters
----------
infiles : dict
a dictionary with namelist, mesh and time series file names
tables : str
path to CMOR tables
user_input_path : str
path to user input json file
Returns
-------
varname : str
the name of the processed variable after processing is complete
"""
msg = 'Starting {name}'.format(name=__name__)
logging.info(msg)
meshFileName = infiles['MPAS_mesh']
mappingFileName = infiles['MPAS_map']
timeSeriesFiles = infiles['MPASO']
dsMesh = xarray.open_dataset(meshFileName, mask_and_scale=False)
_, cellMask3D = mpas.get_cell_masks(dsMesh)
variableList = [
'timeMonthly_avg_layerThickness', 'xtime_startMonthly',
'xtime_endMonthly'
]
nVertLevels = dsMesh.sizes['nVertLevels']
ds = xarray.Dataset()
with mpas.open_mfdataset(timeSeriesFiles, variableList) as dsIn:
layerThickness = dsIn.timeMonthly_avg_layerThickness
layerThickness = layerThickness.where(cellMask3D)
thicknessSum = layerThickness.sum(dim='nVertLevels')
mask = cellMask3D.isel(nVertLevels=0)
zSurface = (-dsMesh.bottomDepth + thicknessSum).where(mask)
zSurface.compute()
# print('done zSurface')
slices = [zSurface]
maskSlices = [mask]
zLayerBot = zSurface
for zIndex in range(nVertLevels):
mask = cellMask3D.isel(nVertLevels=zIndex)
zLayerBot = (zLayerBot -
layerThickness.isel(nVertLevels=zIndex)).where(mask)
zLayerBot.compute()
# print('done zLayerBot {}/{}'.format(zIndex+1, nVertLevels))
slices.append(zLayerBot)
maskSlices.append(mask)
ds[VAR_NAME] = xarray.concat(slices, dim='olevhalf')
mask = xarray.concat(maskSlices, dim='olevhalf')
ds = mpas.add_mask(ds, mask)
ds = ds.transpose('Time', 'olevhalf', 'nCells')
ds = mpas.add_time(ds, dsIn)
ds.compute()
ds = mpas.remap(ds, mappingFileName)
depth_coord_half = numpy.zeros(nVertLevels + 1)
depth_coord_half[1:] = dsMesh.refBottomDepth.values
mpas.setup_cmor(VAR_NAME, tables, user_input_path, component='ocean')
# create axes
axes = [{
'table_entry': 'time',
'units': ds.time.units
}, {
'table_entry': 'depth_coord_half',
'units': 'm',
'coord_vals': depth_coord_half
}, {
'table_entry': 'latitude',
'units': 'degrees_north',
'coord_vals': ds.lat.values,
'cell_bounds': ds.lat_bnds.values
}, {
'table_entry': 'longitude',
'units': 'degrees_east',
'coord_vals': ds.lon.values,
'cell_bounds': ds.lon_bnds.values
}]
try:
mpas.write_cmor(axes, ds, VAR_NAME, VAR_UNITS)
except Exception:
return ""
return VAR_NAME
def handle(infiles, tables, user_input_path, **kwargs):
"""
Transform MPASO timeMonthly_avg_frazilLayerThicknessTendency into
CMIP.hfsifrazil
Parameters
----------
infiles : dict
a dictionary with namelist, mesh and time series file names
tables : str
path to CMOR tables
user_input_path : str
path to user input json file
Returns
-------
varname : str
the name of the processed variable after processing is complete
"""
msg = 'Starting {name}'.format(name=__name__)
logging.info(msg)
timeSeriesFiles = infiles['MPASO']
mappingFileName = infiles['MPAS_map']
meshFileName = infiles['MPAS_mesh']
namelistFileName = infiles['MPASO_namelist']
namelist = mpas.convert_namelist_to_dict(namelistFileName)
config_density0 = float(namelist['config_density0'])
config_frazil_heat_of_fusion = \
float(namelist['config_frazil_heat_of_fusion'])
dsMesh = xarray.open_dataset(meshFileName, mask_and_scale=False)
_, cellMask3D = mpas.get_cell_masks(dsMesh)
variableList = [
'timeMonthly_avg_frazilLayerThicknessTendency', 'xtime_startMonthly',
'xtime_endMonthly'
]
ds = xarray.Dataset()
with mpas.open_mfdataset(timeSeriesFiles, variableList) as dsIn:
ds[VAR_NAME] = -config_density0 * config_frazil_heat_of_fusion * \
dsIn.timeMonthly_avg_frazilLayerThicknessTendency
ds = mpas.add_time(ds, dsIn)
ds.compute()
ds = mpas.add_mask(ds, cellMask3D)
ds = mpas.add_depth(ds, dsMesh)
ds.compute()
ds = mpas.remap(ds, mappingFileName)
mpas.setup_cmor(VAR_NAME, tables, user_input_path, component='ocean')
# create axes
axes = [{
'table_entry': 'time',
'units': ds.time.units
}, {
'table_entry': 'depth_coord',
'units': 'm',
'coord_vals': ds.depth.values,
'cell_bounds': ds.depth_bnds.values
}, {
'table_entry': 'latitude',
'units': 'degrees_north',
'coord_vals': ds.lat.values,
'cell_bounds': ds.lat_bnds.values
}, {
'table_entry': 'longitude',
'units': 'degrees_east',
'coord_vals': ds.lon.values,
'cell_bounds': ds.lon_bnds.values
}]
try:
mpas.write_cmor(axes, ds, VAR_NAME, VAR_UNITS, positive='down')
except Exception:
return ""
return VAR_NAME
def handle(infiles, tables, user_input_path, **kwargs):
"""
Transform MPASO timeMonthly_avg_activeTracers_salinity into CMIP.sob
Parameters
----------
infiles : dict
a dictionary with namelist, mesh and time series file names
tables : str
path to CMOR tables
user_input_path : str
path to user input json file
Returns
-------
varname : str
the name of the processed variable after processing is complete
"""
if kwargs.get('simple'):
msg = f"{VAR_NAME} is not supported for simple conversion"
print_message(msg)
return
msg = 'Starting {name}'.format(name=__name__)
logging.info(msg)
meshFileName = infiles['MPAS_mesh']
mappingFileName = infiles['MPAS_map']
timeSeriesFiles = infiles['MPASO']
dsMesh = xarray.open_dataset(meshFileName, mask_and_scale=False)
cellMask2D, _ = mpas.get_cell_masks(dsMesh)
variableList = [
'timeMonthly_avg_activeTracers_salinity', 'xtime_startMonthly',
'xtime_endMonthly'
]
ds = xarray.Dataset()
with mpas.open_mfdataset(timeSeriesFiles, variableList) as dsIn:
ds[VAR_NAME] = dsIn.timeMonthly_avg_activeTracers_salinity
ds = mpas.get_sea_floor_values(ds, dsMesh)
ds = mpas.add_time(ds, dsIn)
ds.compute()
ds = mpas.add_mask(ds, cellMask2D)
ds.compute()
ds = mpas.remap(ds, mappingFileName)
mpas.setup_cmor(VAR_NAME, tables, user_input_path, component='ocean')
# create axes
axes = [{
'table_entry': 'time',
'units': ds.time.units
}, {
'table_entry': 'latitude',
'units': 'degrees_north',
'coord_vals': ds.lat.values,
'cell_bounds': ds.lat_bnds.values
}, {
'table_entry': 'longitude',
'units': 'degrees_east',
'coord_vals': ds.lon.values,
'cell_bounds': ds.lon_bnds.values
}]
try:
mpas.write_cmor(axes,
ds,
VAR_NAME,
VAR_UNITS,
comment='Model prognostic salinity at bottom-most '
'model grid cell on the Practical Salinity '
'Scale of 1978.')
except Exception:
return ""
return VAR_NAME
