def _write_mpas_t_s(self, config): # {{{
climatologyName = 'TS_{}_{}'.format(self.prefix, self.season)
outFileName = get_masked_mpas_climatology_file_name(config,
self.season,
self.componentName,
climatologyName,
op='avg')
if os.path.exists(outFileName):
ds = xarray.open_dataset(outFileName)
zmin, zmax = ds.zbounds.values
return zmin, zmax
with dask.config.set(schedular='threads',
pool=ThreadPool(self.daskThreads)):
self.logger.info(' Extracting T and S in the region...')
sectionName = self.sectionName
cellsChunk = 32768
chunk = {'nCells': cellsChunk}
try:
restartFileName = self.runStreams.readpath('restart')[0]
except ValueError:
raise IOError('No MPAS-O restart file found: need at least one'
' restart file to plot T-S diagrams')
dsRestart = xarray.open_dataset(restartFileName)
dsRestart = dsRestart.isel(Time=0).chunk(chunk)
regionMaskFileName = self.mpasMasksSubtask.maskFileName
dsRegionMask = xarray.open_dataset(regionMaskFileName)
maskRegionNames = decode_strings(dsRegionMask.regionNames)
regionIndex = maskRegionNames.index(self.regionName)
dsMask = dsRegionMask.isel(nRegions=regionIndex).chunk(chunk)
cellMask = dsMask.regionCellMasks == 1
if 'landIceMask' in dsRestart:
# only the region outside of ice-shelf cavities
cellMask = numpy.logical_and(cellMask,
dsRestart.landIceMask == 0)
if config.has_option(sectionName, 'zmin'):
zmin = config.getfloat(sectionName, 'zmin')
else:
if 'zminRegions' in dsMask:
zmin = dsMask.zminRegions.values
else:
# the old naming convention, used in some pre-generated
# mask files
zmin = dsMask.zmin.values
if config.has_option(sectionName, 'zmax'):
zmax = config.getfloat(sectionName, 'zmax')
else:
if 'zmaxRegions' in dsMask:
zmax = dsMask.zmaxRegions.values
else:
# the old naming convention, used in some pre-generated
# mask files
zmax = dsMask.zmax.values
inFileName = get_unmasked_mpas_climatology_file_name(
config, self.season, self.componentName, op='avg')
ds = xarray.open_dataset(inFileName)
variableList = [
'timeMonthly_avg_activeTracers_temperature',
'timeMonthly_avg_activeTracers_salinity',
'timeMonthly_avg_layerThickness'
]
ds = ds[variableList]
ds['zMid'] = compute_zmid(dsRestart.bottomDepth,
dsRestart.maxLevelCell,
dsRestart.layerThickness)
ds['volume'] = (dsRestart.areaCell *
ds['timeMonthly_avg_layerThickness'])
ds = ds.where(cellMask, drop=True)
self.logger.info("Don't worry about the following dask "
"warnings.")
depthMask = numpy.logical_and(ds.zMid >= zmin, ds.zMid <= zmax)
depthMask.compute()
self.logger.info("Dask warnings should be done.")
ds['depthMask'] = depthMask
for var in variableList:
ds[var] = ds[var].where(depthMask)
T = ds['timeMonthly_avg_activeTracers_temperature'].values.ravel()
mask = numpy.isfinite(T)
T = T[mask]
S = ds['timeMonthly_avg_activeTracers_salinity'].values.ravel()
S = S[mask]
zMid = ds['zMid'].values.ravel()[mask]
volume = ds['volume'].values.ravel()[mask]
dsOut = xarray.Dataset()
dsOut['T'] = ('nPoints', T)
dsOut['S'] = ('nPoints', S)
dsOut['z'] = ('nPoints', zMid)
dsOut['volume'] = ('nPoints', volume)
dsOut['zbounds'] = ('nBounds', [zmin, zmax])
write_netcdf(dsOut, outFileName)
return zmin, zmax # }}}
def _write_obs_t_s(self, obsDict, zmin, zmax): # {{{
obsSection = '{}Observations'.format(self.componentName)
climatologyDirectory = build_config_full_path(
config=self.config,
section='output',
relativePathOption='climatologySubdirectory',
relativePathSection=obsSection)
outFileName = '{}/TS_{}_{}_{}.nc'.format(climatologyDirectory,
obsDict['suffix'],
self.prefix, self.season)
if os.path.exists(outFileName):
return
with dask.config.set(schedular='threads',
pool=ThreadPool(self.daskThreads)):
chunk = {obsDict['latVar']: 400, obsDict['lonVar']: 400}
regionMaskFileName = obsDict['maskTask'].maskFileName
dsRegionMask = \
xarray.open_dataset(regionMaskFileName).chunk(chunk).stack(
nCells=(obsDict['latVar'], obsDict['lonVar']))
dsRegionMask = dsRegionMask.reset_index('nCells').drop_vars(
[obsDict['latVar'], obsDict['lonVar']])
maskRegionNames = decode_strings(dsRegionMask.regionNames)
regionIndex = maskRegionNames.index(self.regionName)
dsMask = dsRegionMask.isel(nRegions=regionIndex)
cellMask = dsMask.regionCellMasks == 1
TVarName = obsDict['TVar']
SVarName = obsDict['SVar']
zVarName = obsDict['zVar']
volVarName = obsDict['volVar']
obsFileName = obsDict['climatologyTask'][self.season].fileName
ds = xarray.open_dataset(obsFileName, chunks=chunk)
ds = ds.stack(nCells=(obsDict['latVar'], obsDict['lonVar']))
ds = ds.reset_index('nCells').drop_vars(
[obsDict['latVar'], obsDict['lonVar']])
ds = ds.where(cellMask, drop=True)
cellsChunk = 32768
chunk = {'nCells': cellsChunk}
ds = ds.chunk(chunk)
depthMask = numpy.logical_and(ds[zVarName] >= zmin,
ds[zVarName] <= zmax)
ds = ds.where(depthMask)
ds.compute()
T = ds[TVarName].values.ravel()
mask = numpy.isfinite(T)
T = T[mask]
S = ds[SVarName].values.ravel()[mask]
z = ds['zBroadcast'].values.ravel()[mask]
volume = ds[volVarName].values.ravel()[mask]
dsOut = xarray.Dataset()
dsOut['T'] = ('nPoints', T)
dsOut['S'] = ('nPoints', S)
dsOut['z'] = ('nPoints', z)
dsOut['volume'] = ('nPoints', volume)
dsOut['zbounds'] = ('nBounds', [zmin, zmax])
write_netcdf(dsOut, outFileName)
def _compute_time_series_with_ncrcat(self):
# {{{
'''
Uses ncrcat to extact time series from timeSeriesMonthlyOutput files
Raises
------
OSError
If ``ncrcat`` is not in the system path.
Author
------
Xylar Asay-Davis
'''
if find_executable('ncrcat') is None:
raise OSError('ncrcat not found. Make sure the latest nco '
'package is installed: \n'
'conda install nco\n'
'Note: this presumes use of the conda-forge '
'channel.')
inputFiles = self.inputFiles
append = False
if os.path.exists(self.outputFile):
# make sure all the necessary variables are also present
with xr.open_dataset(self.outputFile) as ds:
if ds.sizes['Time'] == 0:
updateSubset = False
else:
updateSubset = True
for variableName in self.variableList:
if variableName not in ds.variables:
updateSubset = False
break
if updateSubset:
# add only input files wiht times that aren't already in
# the output file
append = True
fileNames = sorted(self.inputFiles)
inYears, inMonths = get_files_year_month(
fileNames, self.historyStreams,
'timeSeriesStatsMonthlyOutput')
inYears = numpy.array(inYears)
inMonths = numpy.array(inMonths)
totalMonths = 12 * inYears + inMonths
dates = decode_strings(ds.xtime_startMonthly)
lastDate = dates[-1]
lastYear = int(lastDate[0:4])
lastMonth = int(lastDate[5:7])
lastTotalMonths = 12 * lastYear + lastMonth
inputFiles = []
for index, inputFile in enumerate(fileNames):
if totalMonths[index] > lastTotalMonths:
inputFiles.append(inputFile)
if len(inputFiles) == 0:
# nothing to do
return
else:
# there is an output file but it has the wrong variables
# so we need ot delete it.
self.logger.warning('Warning: deleting file {} because '
'it is empty or some variables were '
'missing'.format(self.outputFile))
os.remove(self.outputFile)
variableList = self.variableList + ['xtime_startMonthly',
'xtime_endMonthly']
args = ['ncrcat', '-4', '--no_tmp_fl',
'-v', ','.join(variableList)]
if append:
args.append('--record_append')
printCommand = '{} {} ... {} {}'.format(' '.join(args), inputFiles[0],
inputFiles[-1],
self.outputFile)
args.extend(inputFiles)
args.append(self.outputFile)
self.logger.info('running: {}'.format(printCommand))
for handler in self.logger.handlers:
handler.flush()
process = subprocess.Popen(args, stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
stdout, stderr = process.communicate()
if stdout:
stdout = stdout.decode('utf-8')
for line in stdout.split('\n'):
self.logger.info(line)
if stderr:
stderr = stderr.decode('utf-8')
for line in stderr.split('\n'):
self.logger.error(line)
if process.returncode != 0:
raise subprocess.CalledProcessError(process.returncode,
' '.join(args))
def run_task(self): # {{{
"""
Compute the regional-mean time series
"""
# Authors
# -------
# Xylar Asay-Davis
config = self.config
self.logger.info("\nCompute depth mask for regional means...")
regionGroup = self.regionGroup
sectionSuffix = regionGroup[0].upper() + \
regionGroup[1:].replace(' ', '')
timeSeriesName = sectionSuffix[0].lower() + sectionSuffix[1:]
sectionName = 'timeSeries{}'.format(sectionSuffix)
outputDirectory = '{}/{}/'.format(
build_config_full_path(config, 'output', 'timeseriesSubdirectory'),
timeSeriesName)
try:
os.makedirs(outputDirectory)
except OSError:
pass
outFileName = '{}/depthMasks{}.nc'.format(outputDirectory,
timeSeriesName)
if os.path.exists(outFileName):
self.logger.info(' Mask file exists -- Done.')
return
# Load mesh related variables
try:
restartFileName = self.runStreams.readpath('restart')[0]
except ValueError:
raise IOError('No MPAS-O restart file found: need at least one '
'restart file for ocean region time series')
if config.has_option(sectionName, 'zmin'):
config_zmin = config.getfloat(sectionName, 'zmin')
else:
config_zmin = None
if config.has_option(sectionName, 'zmax'):
config_zmax = config.getfloat(sectionName, 'zmax')
else:
config_zmax = None
dsRestart = xarray.open_dataset(restartFileName).isel(Time=0)
zMid = compute_zmid(dsRestart.bottomDepth, dsRestart.maxLevelCell,
dsRestart.layerThickness)
areaCell = dsRestart.areaCell
if 'landIceMask' in dsRestart:
# only the region outside of ice-shelf cavities
openOceanMask = dsRestart.landIceMask == 0
else:
openOceanMask = None
regionMaskFileName = self.masksSubtask.maskFileName
dsRegionMask = xarray.open_dataset(regionMaskFileName)
maskRegionNames = decode_strings(dsRegionMask.regionNames)
regionIndices = []
for regionName in self.regionNames:
for index, otherName in enumerate(maskRegionNames):
if regionName == otherName:
regionIndices.append(index)
break
# select only those regions we want to plot
dsRegionMask = dsRegionMask.isel(nRegions=regionIndices)
nRegions = dsRegionMask.sizes['nRegions']
datasets = []
for regionIndex in range(nRegions):
self.logger.info(' region: {}'.format(
self.regionNames[regionIndex]))
dsRregion = dsRegionMask.isel(nRegions=regionIndex)
cellMask = dsRregion.regionCellMasks == 1
if openOceanMask is not None:
cellMask = numpy.logical_and(cellMask, openOceanMask)
totalArea = areaCell.where(cellMask).sum()
self.logger.info(' totalArea: {} mil. km^2'.format(
1e-12 * totalArea.values))
if config_zmin is None:
if 'zminRegions' in dsRregion:
zmin = dsRregion.zminRegions
else:
# the old naming convention, used in some pre-generated
# mask files
zmin = dsRregion.zmin
else:
zmin = (('nRegions', ), config_zmin)
if config_zmax is None:
if 'zmaxRegions' in dsRregion:
zmax = dsRregion.zmaxRegions
else:
# the old naming convention, used in some pre-generated
# mask files
zmax = dsRregion.zmax
else:
zmax = (('nRegions', ), config_zmax)
depthMask = numpy.logical_and(zMid >= zmin, zMid <= zmax)
dsOut = xarray.Dataset()
dsOut['zmin'] = zmin
dsOut['zmax'] = zmax
dsOut['totalArea'] = totalArea
dsOut['cellMask'] = cellMask
dsOut['depthMask'] = depthMask
datasets.append(dsOut)
dsOut = xarray.concat(objs=datasets, dim='nRegions')
zbounds = numpy.zeros((nRegions, 2))
zbounds[:, 0] = dsOut.zmin.values
zbounds[:, 1] = dsOut.zmax.values
dsOut['zbounds'] = (('nRegions', 'nbounds'), zbounds)
dsOut['areaCell'] = areaCell
dsOut['regionNames'] = dsRegionMask.regionNames
write_netcdf(dsOut, outFileName)
def run_task(self): # {{{
"""
Compute time series of regional profiles
"""
# Authors
# -------
# Milena Veneziani, Mark Petersen, Phillip J. Wolfram, Xylar Asay-Davis
self.logger.info("\nCompute time series of regional profiles...")
startDate = '{:04d}-01-01_00:00:00'.format(self.startYear)
endDate = '{:04d}-12-31_23:59:59'.format(self.endYear)
timeSeriesName = self.masksSubtask.regionGroup.replace(' ', '')
outputDirectory = '{}/{}/'.format(
build_config_full_path(self.config, 'output',
'timeseriesSubdirectory'),
timeSeriesName)
try:
os.makedirs(outputDirectory)
except OSError:
pass
outputFileName = '{}/regionalProfiles_{}_{:04d}-{:04d}.nc'.format(
outputDirectory, timeSeriesName, self.startYear, self.endYear)
inputFiles = sorted(self.historyStreams.readpath(
'timeSeriesStatsMonthlyOutput', startDate=startDate,
endDate=endDate, calendar=self.calendar))
years, months = get_files_year_month(inputFiles,
self.historyStreams,
'timeSeriesStatsMonthlyOutput')
variableList = [field['mpas'] for field in self.fields]
outputExists = os.path.exists(outputFileName)
outputValid = outputExists
if outputExists:
with open_mpas_dataset(fileName=outputFileName,
calendar=self.calendar,
timeVariableNames=None,
variableList=None,
startDate=startDate,
endDate=endDate) as dsIn:
for inIndex in range(dsIn.dims['Time']):
mask = np.logical_and(
dsIn.year[inIndex].values == years,
dsIn.month[inIndex].values == months)
if np.count_nonzero(mask) == 0:
outputValid = False
break
if outputValid:
self.logger.info(' Time series exists -- Done.')
return
# get areaCell
restartFileName = \
self.runStreams.readpath('restart')[0]
dsRestart = xr.open_dataset(restartFileName)
dsRestart = dsRestart.isel(Time=0)
areaCell = dsRestart.areaCell
nVertLevels = dsRestart.sizes['nVertLevels']
vertIndex = \
xr.DataArray.from_dict({'dims': ('nVertLevels',),
'data': np.arange(nVertLevels)})
vertMask = vertIndex < dsRestart.maxLevelCell
# get region masks
regionMaskFileName = self.masksSubtask.maskFileName
dsRegionMask = xr.open_dataset(regionMaskFileName)
# figure out the indices of the regions to plot
regionNames = decode_strings(dsRegionMask.regionNames)
regionIndices = []
for regionToPlot in self.regionNames:
for index, regionName in enumerate(regionNames):
if regionToPlot == regionName:
regionIndices.append(index)
break
# select only those regions we want to plot
dsRegionMask = dsRegionMask.isel(nRegions=regionIndices)
cellMasks = dsRegionMask.regionCellMasks
regionNamesVar = dsRegionMask.regionNames
totalArea = (cellMasks * areaCell * vertMask).sum('nCells')
datasets = []
for timeIndex, fileName in enumerate(inputFiles):
dsLocal = open_mpas_dataset(
fileName=fileName,
calendar=self.calendar,
variableList=variableList,
startDate=startDate,
endDate=endDate)
dsLocal = dsLocal.isel(Time=0)
time = dsLocal.Time.values
date = days_to_datetime(time, calendar=self.calendar)
self.logger.info(' date: {:04d}-{:02d}'.format(date.year,
date.month))
# for each region and variable, compute area-weighted sum and
# squared sum
for field in self.fields:
variableName = field['mpas']
prefix = field['prefix']
self.logger.info(' {}'.format(field['titleName']))
var = dsLocal[variableName].where(vertMask)
meanName = '{}_mean'.format(prefix)
dsLocal[meanName] = \
(cellMasks * areaCell * var).sum('nCells') / totalArea
meanSquaredName = '{}_meanSquared'.format(prefix)
dsLocal[meanSquaredName] = \
(cellMasks * areaCell * var**2).sum('nCells') / totalArea
# drop the original variables
dsLocal = dsLocal.drop_vars(variableList)
datasets.append(dsLocal)
# combine data sets into a single data set
dsOut = xr.concat(datasets, 'Time')
dsOut.coords['regionNames'] = regionNamesVar
dsOut['totalArea'] = totalArea
dsOut.coords['year'] = (('Time',), years)
dsOut['year'].attrs['units'] = 'years'
dsOut.coords['month'] = (('Time',), months)
dsOut['month'].attrs['units'] = 'months'
# Note: restart file, not a mesh file because we need refBottomDepth,
# not in a mesh file
try:
restartFile = self.runStreams.readpath('restart')[0]
except ValueError:
raise IOError('No MPAS-O restart file found: need at least one '
'restart file for plotting time series vs. depth')
with xr.open_dataset(restartFile) as dsRestart:
depths = dsRestart.refBottomDepth.values
z = np.zeros(depths.shape)
z[0] = -0.5 * depths[0]
z[1:] = -0.5 * (depths[0:-1] + depths[1:])
dsOut.coords['z'] = (('nVertLevels',), z)
dsOut['z'].attrs['units'] = 'meters'
write_netcdf(dsOut, outputFileName)
def run_task(self): # {{{
"""
Plot a depth profile with variability
"""
# Authors
# -------
# Xylar Asay-Davis
config = self.config
startYear = self.startYear
endYear = self.endYear
regionMaskFile = self.masksSubtask.geojsonFileName
fcAll = read_feature_collection(regionMaskFile)
fc = FeatureCollection()
for feature in fcAll.features:
if feature['properties']['name'] == self.regionName:
fc.add_feature(feature)
break
inDirectory = build_config_full_path(config, 'output',
'profilesSubdirectory')
timeSeriesName = self.timeSeriesName
inFileName = '{}/{}_{}_{:04d}-{:04d}.nc'.format(
inDirectory, timeSeriesName, self.season,
self.startYear, self.endYear)
regionGroup = self.masksSubtask.regionGroup
regionGroupSection = 'profiles{}'.format(
regionGroup.replace(' ', ''))
ds = xr.open_dataset(inFileName)
allRegionNames = decode_strings(ds.regionNames)
regionIndex = allRegionNames.index(self.regionName)
ds = ds.isel(nRegions=regionIndex)
meanFieldName = '{}_mean'.format(self.field['prefix'])
stdFieldName = '{}_std'.format(self.field['prefix'])
mainRunName = config.get('runs', 'mainRunName')
profileGalleryGroup = config.get(regionGroupSection,
'profileGalleryGroup')
titleFieldName = self.field['titleName']
regionName = self.regionName.replace('_', ' ')
xLabel = '{} ({})'.format(titleFieldName, self.field['units'])
yLabel = 'depth (m)'
outFileName = '{}/{}.png'.format(self.plotsDirectory, self.filePrefix)
lineColors = ['k']
lineWidths = [1.6]
zArrays = [ds.z.values]
fieldArrays = [ds[meanFieldName].values]
errArrays = [ds[stdFieldName].values]
if self.controlConfig is None:
title = '{} {}, years {:04d}-{:04d}\n{}'.format(
regionName, self.season, startYear, endYear, mainRunName)
legendText = [None]
else:
controlStartYear = self.controlConfig.getint('climatology',
'startYear')
controlEndYear = self.controlConfig.getint('climatology',
'endYear')
controlRunName = self.controlConfig.get('runs', 'mainRunName')
if controlStartYear == startYear and controlEndYear == endYear:
title = '{} {}, years {:04d}-{:04d}'.format(
regionName, self.season, startYear, endYear)
legendText = [mainRunName, controlRunName]
elif mainRunName == controlRunName:
title = '{} {}\n{}'.format(
regionName, self.season, mainRunName)
legendText = ['{:04d}-{:04d}'.format(startYear, endYear),
'{:04d}-{:04d}'.format(controlStartYear,
controlEndYear)]
else:
title = '{} {} '.format(regionName, self.season)
legendText = ['{} {:04d}-{:04d}'.format(mainRunName, startYear,
endYear),
'{} {:04d}-{:04d}'.format(controlRunName,
controlStartYear,
controlEndYear)]
controlDirectory = build_config_full_path(
self.controlConfig, 'output',
'profilesSubdirectory')
controlFileName = \
'{}/{}_{}_{:04d}-{:04d}.nc'.format(
controlDirectory, timeSeriesName, self.season,
controlStartYear, controlEndYear)
dsControl = xr.open_dataset(controlFileName)
allRegionNames = decode_strings(dsControl.regionNames)
regionIndex = allRegionNames.index(self.regionName)
dsControl = dsControl.isel(nRegions=regionIndex)
lineColors.append('r')
lineWidths.append(1.2)
zArrays.append(dsControl.z.values)
fieldArrays.append(dsControl[meanFieldName].values)
errArrays.append(dsControl[stdFieldName].values)
depthRange = config.getExpression(regionGroupSection, 'depthRange')
if len(depthRange) == 0:
depthRange = None
fig = self.plot(zArrays, fieldArrays, errArrays,
lineColors=lineColors, lineWidths=lineWidths,
legendText=legendText, title=title, xLabel=xLabel,
yLabel=yLabel, yLim=depthRange)
# do this before the inset because otherwise it moves the inset
# and cartopy doesn't play too well with tight_layout anyway
plt.tight_layout()
add_inset(fig, fc, width=1.0, height=1.0)
savefig(outFileName, tight=False)
caption = '{} {} vs depth'.format(regionName, titleFieldName)
write_image_xml(
config=config,
filePrefix=self.filePrefix,
componentName='Ocean',
componentSubdirectory='ocean',
galleryGroup=profileGalleryGroup,
groupLink='ocnregprofs',
imageDescription=caption,
imageCaption=caption,
gallery=titleFieldName,
thumbnailDescription='{} {}'.format(regionName, self.season))
def run_task(self): # {{{
"""
Computes time-series of Antarctic sub-ice-shelf melt rates.
"""
# Authors
# -------
# Xylar Asay-Davis, Stephen Price
self.logger.info("Computing Antarctic melt rate time series...")
mpasTimeSeriesTask = self.mpasTimeSeriesTask
config = self.config
baseDirectory = build_config_full_path(
config, 'output', 'timeSeriesSubdirectory')
outFileName = '{}/iceShelfAggregatedFluxes.nc'.format(baseDirectory)
# Load data:
inputFile = mpasTimeSeriesTask.outputFile
dsIn = open_mpas_dataset(fileName=inputFile,
calendar=self.calendar,
variableList=self.variableList,
startDate=self.startDate,
endDate=self.endDate)
try:
if os.path.exists(outFileName):
# The file already exists so load it
dsOut = xarray.open_dataset(outFileName)
if numpy.all(dsOut.Time.values == dsIn.Time.values):
return
else:
self.logger.warning('File {} is incomplete. Deleting '
'it.'.format(outFileName))
os.remove(outFileName)
except OSError:
# something is potentailly wrong with the file, so let's delete
# it and try again
self.logger.warning('Problems reading file {}. Deleting '
'it.'.format(outFileName))
os.remove(outFileName)
restartFileName = \
mpasTimeSeriesTask.runStreams.readpath('restart')[0]
dsRestart = xarray.open_dataset(restartFileName)
areaCell = \
dsRestart.landIceFraction.isel(Time=0) * dsRestart.areaCell
regionMaskFileName = self.masksSubtask.maskFileName
dsRegionMask = xarray.open_dataset(regionMaskFileName)
# figure out the indices of the regions to plot
regionNames = decode_strings(dsRegionMask.regionNames)
regionIndices = []
for iceShelf in self.iceShelvesToPlot:
for index, regionName in enumerate(regionNames):
if iceShelf == regionName:
regionIndices.append(index)
break
# select only those regions we want to plot
dsRegionMask = dsRegionMask.isel(nRegions=regionIndices)
datasets = []
nTime = dsIn.sizes['Time']
for tIndex in range(nTime):
self.logger.info(' {}/{}'.format(tIndex+1, nTime))
freshwaterFlux = \
dsIn.timeMonthly_avg_landIceFreshwaterFlux.isel(Time=tIndex)
nRegions = dsRegionMask.sizes['nRegions']
meltRates = numpy.zeros((nRegions,))
totalMeltFluxes = numpy.zeros((nRegions,))
for regionIndex in range(nRegions):
cellMask = \
dsRegionMask.regionCellMasks.isel(nRegions=regionIndex)
# convert from kg/s to kg/yr
totalMeltFlux = constants.sec_per_year * \
(cellMask * areaCell * freshwaterFlux).sum(dim='nCells')
totalArea = (cellMask * areaCell).sum(dim='nCells')
# from kg/m^2/yr to m/yr
meltRates[regionIndex] = ((1. / constants.rho_fw) *
(totalMeltFlux / totalArea))
# convert from kg/yr to GT/yr
totalMeltFlux /= constants.kg_per_GT
totalMeltFluxes[regionIndex] = totalMeltFlux
dsOut = xarray.Dataset()
dsOut.coords['Time'] = dsIn.Time.isel(Time=tIndex)
dsOut['totalMeltFlux'] = (('nRegions'), totalMeltFluxes)
dsOut['meltRates'] = (('nRegions'), meltRates)
datasets.append(dsOut)
dsOut = xarray.concat(objs=datasets, dim='Time')
dsOut['regionNames'] = dsRegionMask.regionNames
dsOut.totalMeltFlux.attrs['units'] = 'GT a$^{-1}$'
dsOut.totalMeltFlux.attrs['description'] = \
'Total melt flux summed over each ice shelf or region'
dsOut.meltRates.attrs['units'] = 'm a$^{-1}$'
dsOut.meltRates.attrs['description'] = \
'Melt rate averaged over each ice shelf or region'
write_netcdf(dsOut, outFileName)
def run_task(self): # {{{
"""
Compute time-series output of properties in an ocean region.
"""
# Authors
# -------
# Xylar Asay-Davis
self.logger.info("\nAveraging T and S for {}...".format(
self.regionName))
obsDict = self.obsDict
config = self.config
regionGroup = self.regionGroup
timeSeriesName = regionGroup[0].lower() + \
regionGroup[1:].replace(' ', '')
sectionSuffix = regionGroup[0].upper() + \
regionGroup[1:].replace(' ', '')
sectionName = 'timeSeries{}'.format(sectionSuffix)
outputDirectory = '{}/{}/'.format(
build_config_full_path(self.config, 'output',
'timeseriesSubdirectory'), timeSeriesName)
try:
os.makedirs(outputDirectory)
except OSError:
pass
outFileName = '{}/TS_{}_{}.nc'.format(outputDirectory,
obsDict['suffix'], self.prefix)
if os.path.exists(outFileName):
return
regionMaskFileName = obsDict['maskTask'].maskFileName
print(regionMaskFileName)
print(xarray.open_dataset(regionMaskFileName))
dsRegionMask = \
xarray.open_dataset(regionMaskFileName).stack(
nCells=(obsDict['latVar'], obsDict['lonVar']))
dsRegionMask = dsRegionMask.reset_index('nCells').drop_vars(
[obsDict['latVar'], obsDict['lonVar']])
maskRegionNames = decode_strings(dsRegionMask.regionNames)
regionIndex = maskRegionNames.index(self.regionName)
dsMask = dsRegionMask.isel(nRegions=regionIndex)
cellMask = dsMask.regionCellMasks == 1
if config.has_option(sectionName, 'zmin'):
zmin = config.getfloat(sectionName, 'zmin')
else:
zmin = dsMask.zminRegions.values
if config.has_option(sectionName, 'zmax'):
zmax = config.getfloat(sectionName, 'zmax')
else:
zmax = dsMask.zmaxRegions.values
TVarName = obsDict['TVar']
SVarName = obsDict['SVar']
zVarName = obsDict['zVar']
lonVarName = obsDict['lonVar']
latVarName = obsDict['latVar']
volVarName = obsDict['volVar']
tDim = obsDict['tDim']
obsFileName = build_obs_path(config,
component=self.componentName,
relativePath=obsDict['TFileName'])
self.logger.info(' Reading from {}...'.format(obsFileName))
ds = xarray.open_dataset(obsFileName)
if obsDict['SFileName'] != obsDict['TFileName']:
obsFileName = build_obs_path(config,
component=self.componentName,
relativePath=obsDict['SFileName'])
self.logger.info(' Reading from {}...'.format(obsFileName))
dsS = xarray.open_dataset(obsFileName)
ds[SVarName] = dsS[SVarName]
if obsDict['volFileName'] is None:
# compute volume from lat, lon, depth bounds
self.logger.info(' Computing volume...'.format(obsFileName))
latBndsName = ds[latVarName].attrs['bounds']
lonBndsName = ds[lonVarName].attrs['bounds']
zBndsName = ds[zVarName].attrs['bounds']
latBnds = ds[latBndsName]
lonBnds = ds[lonBndsName]
zBnds = ds[zBndsName]
dLat = numpy.deg2rad(latBnds[:, 1] - latBnds[:, 0])
dLon = numpy.deg2rad(lonBnds[:, 1] - lonBnds[:, 0])
lat = numpy.deg2rad(ds[latVarName])
dz = zBnds[:, 1] - zBnds[:, 0]
radius = 6378137.0
area = radius**2 * numpy.cos(lat) * dLat * dLon
volume = dz * area
ds[volVarName] = volume
elif obsDict['volFileName'] != obsDict['TFileName']:
obsFileName = build_obs_path(config,
component=self.componentName,
relativePath=obsDict['volFileName'])
self.logger.info(' Reading from {}...'.format(obsFileName))
dsVol = xarray.open_dataset(obsFileName)
ds[volVarName] = dsVol[volVarName]
if 'positive' in ds[zVarName].attrs and \
ds[zVarName].attrs['positive'] == 'down':
attrs = ds[zVarName].attrs
ds[zVarName] = -ds[zVarName]
ds[zVarName].attrs = attrs
ds[zVarName].attrs['positive'] = 'up'
TMean = numpy.zeros(ds.sizes[tDim])
SMean = numpy.zeros(ds.sizes[tDim])
depthMask = numpy.logical_and(ds[zVarName] >= zmin,
ds[zVarName] <= zmax)
for tIndex in range(ds.sizes[tDim]):
dsMonth = ds.isel({tDim: tIndex})
dsMonth = dsMonth.stack(nCells=(obsDict['latVar'],
obsDict['lonVar']))
dsMonth = dsMonth.reset_index('nCells').drop_vars(
[obsDict['latVar'], obsDict['lonVar']])
dsMonth = dsMonth.where(cellMask, drop=True)
dsMonth = dsMonth.where(depthMask)
mask = dsMonth[TVarName].notnull()
TSum = (dsMonth[TVarName] *
dsMonth[volVarName]).sum(dim=('nCells', zVarName))
volSum = (mask * dsMonth[volVarName]).sum(dim=('nCells', zVarName))
TMean[tIndex] = TSum / volSum
mask = dsMonth[SVarName].notnull()
SSum = (dsMonth[SVarName] *
dsMonth[volVarName]).sum(dim=('nCells', zVarName))
volSum = (mask * dsMonth[volVarName]).sum(dim=('nCells', zVarName))
SMean[tIndex] = SSum / volSum
dsOut = xarray.Dataset()
dsOut['temperature'] = ('Time', TMean)
dsOut['salinity'] = ('Time', SMean)
dsOut['zbounds'] = ('nBounds', [zmin, zmax])
dsOut['month'] = ('Time', numpy.array(ds.month.values, dtype=float))
dsOut['year'] = ('Time', numpy.ones(ds.sizes[tDim]))
write_netcdf(dsOut, outFileName)
def run_task(self): # {{{
"""
Make the Hovmoller plot from the time series.
"""
# Authors
# -------
# Xylar Asay-Davis, Milena Veneziani, Greg Streletz
self.logger.info("\nPlotting {} time series vs. depth...".format(
self.fieldNameInTitle))
config = self.config
mainRunName = config.get('runs', 'mainRunName')
self.logger.info(' Load ocean data...')
ds = xr.open_dataset(self.inFileName)
if 'regionNames' in ds.coords:
allRegionNames = decode_strings(ds.regionNames)
regionIndex = allRegionNames.index(self.regionName)
regionNameInTitle = self.regionName.replace('_', ' ')
regionDim = ds.regionNames.dims[0]
else:
plotTitles = config.getExpression('regions', 'plotTitles')
allRegionNames = config.getExpression('regions', 'regions')
regionIndex = allRegionNames.index(self.regionName)
regionNameInTitle = plotTitles[regionIndex]
regionDim = 'nOceanRegionsTmp'
ds = ds.isel(**{regionDim: regionIndex})
# Note: restart file, not a mesh file because we need refBottomDepth,
# not in a mesh file
try:
restartFile = self.runStreams.readpath('restart')[0]
except ValueError:
raise IOError('No MPAS-O restart file found: need at least one '
'restart file for plotting time series vs. depth')
# Define/read in general variables
self.logger.info(' Read in depth...')
with xr.open_dataset(restartFile) as dsRestart:
# reference depth [m]
depths = dsRestart.refBottomDepth.values
z = np.zeros(depths.shape)
z[0] = -0.5 * depths[0]
z[1:] = -0.5 * (depths[0:-1] + depths[1:])
Time = ds.Time.values
field = ds[self.mpasFieldName].values.transpose()
xLabel = 'Time (years)'
yLabel = 'Depth (m)'
title = '{}, {}'.format(self.fieldNameInTitle, regionNameInTitle)
outFileName = '{}/{}.png'.format(self.plotsDirectory, self.filePrefix)
if config.has_option(self.sectionName, 'firstYearXTicks'):
firstYearXTicks = config.getint(self.sectionName,
'firstYearXTicks')
else:
firstYearXTicks = None
if config.has_option(self.sectionName, 'yearStrideXTicks'):
yearStrideXTicks = config.getint(self.sectionName,
'yearStrideXTicks')
else:
yearStrideXTicks = None
movingAverageMonths = config.getWithDefault(self.sectionName,
'movingAverageMonths', 1)
if config.has_option(self.sectionName, 'yLim'):
yLim = config.getExpression(self.sectionName, 'yLim')
else:
yLim = None
if self.controlConfig is None:
refField = None
diff = None
refTitle = None
diffTitle = None
else:
controlConfig = self.controlConfig
dsRef = xr.open_dataset(self.controlFileName)
if 'regionNames' in dsRef.coords:
allRegionNames = decode_strings(dsRef.regionNames)
regionIndex = allRegionNames.index(self.regionName)
regionNameInTitle = self.regionName.replace('_', ' ')
regionDim = dsRef.regionNames.dims[0]
else:
plotTitles = controlConfig.getExpression(
'regions', 'plotTitles')
allRegionNames = controlConfig.getExpression(
'regions', 'regions')
regionIndex = allRegionNames.index(self.regionName)
regionNameInTitle = plotTitles[regionIndex]
regionDim = 'nOceanRegionsTmp'
dsRef = dsRef.isel(**{regionDim: regionIndex})
refField = dsRef[self.mpasFieldName].values.transpose()
assert (field.shape == refField.shape)
diff = field - refField
refTitle = self.controlConfig.get('runs', 'mainRunName')
diffTitle = 'Main - Control'
fig, _, suptitle = plot_vertical_section_comparison(
config,
Time,
z,
field,
refField,
diff,
self.sectionName,
colorbarLabel=self.unitsLabel,
title=title,
modelTitle=mainRunName,
refTitle=refTitle,
diffTitle=diffTitle,
xlabel=xLabel,
ylabel=yLabel,
lineWidth=1,
xArrayIsTime=True,
movingAveragePoints=movingAverageMonths,
calendar=self.calendar,
firstYearXTicks=firstYearXTicks,
yearStrideXTicks=yearStrideXTicks,
yLim=yLim,
invertYAxis=False)
if self.regionMaskFile is not None:
# shift the super-title a little to the left to make room for the
# inset
pos = suptitle.get_position()
suptitle.set_position((pos[0] - 0.05, pos[1]))
fcAll = read_feature_collection(self.regionMaskFile)
fc = FeatureCollection()
for feature in fcAll.features:
if feature['properties']['name'] == self.regionName:
fc.add_feature(feature)
break
add_inset(fig, fc, width=1.0, height=1.0, xbuffer=0.1, ybuffer=0.1)
savefig(outFileName, tight=False)
else:
savefig(outFileName)
write_image_xml(config=config,
filePrefix=self.filePrefix,
componentName='Ocean',
componentSubdirectory='ocean',
galleryGroup=self.galleryGroup,
groupSubtitle=self.groupSubtitle,
groupLink=self.groupLink,
gallery=self.galleryName,
thumbnailDescription='{} {}'.format(
regionNameInTitle, self.thumbnailSuffix),
imageDescription=self.imageCaption,
imageCaption=self.imageCaption)
climoYear1 = 148
climoYear2 = 177
#
#meshfile = '/global/project/projectdirs/e3sm/inputdata/ocn/mpas-o/oEC60to30v3/oEC60to30v3_60layer.170506.nc'
#maskfile = '/global/project/projectdirs/e3sm/milena/mpas-region_masks/oEC60to30v3_oceanSubBasins20210315.nc'
#casename = 'E3SM-LR-OSI' # no spaces
#modeldir = '/global/project/projectdirs/m1199/milena/analysis/mpas/E3SM60to30/clim/mpas/avg/unmasked_oEC60to30v3'
#climoYear1 = 148
#climoYear2 = 177
#seasons = ['ANN', 'JFM', 'JAS']
seasons = ['ANN']
if os.path.exists(maskfile):
dsRegionMask = xr.open_dataset(maskfile)
regionNames = decode_strings(dsRegionMask.regionNames)
regionsToPlot = [
'Southern Ocean Atlantic Sector', 'South Atlantic Basin',
'North Atlantic Basin', 'Arctic Ocean Basin'
]
else:
raise IOError('No regional mask file found')
figdir = './verticalSections/{}'.format(casename)
if not os.path.isdir(figdir):
os.makedirs(figdir)
figsize = (32, 5)
figdpi = 300
colorIndices0 = [
0, 10, 28, 57, 85, 113, 125, 142, 155, 170, 198, 227, 242, 255
]
def run_task(self): # {{{
"""
Computes and plots table of Antarctic sub-ice-shelf melt rates.
"""
# Authors
# -------
# Xylar Asay-Davis
self.logger.info("Computing Antarctic melt rate table...")
config = self.config
sectionName = self.taskName
iceShelvesInTable = config.getExpression(sectionName,
'iceShelvesInTable')
if len(iceShelvesInTable) == 0:
return
iceShelvesInTable = self.masksSubtask.expand_region_names(
iceShelvesInTable)
meltRateFileName = get_masked_mpas_climatology_file_name(
config,
self.season,
self.componentName,
climatologyName='antarcticMeltTable')
if not os.path.exists(meltRateFileName):
with dask.config.set(schedular='threads', pool=ThreadPool(1)):
# Load data:
inFileName = self.mpasClimatologyTask.get_file_name(
self.season)
mpasFieldName = 'timeMonthly_avg_landIceFreshwaterFlux'
dsIn = xr.open_dataset(inFileName)
freshwaterFlux = dsIn[mpasFieldName]
if 'Time' in freshwaterFlux.dims:
freshwaterFlux = freshwaterFlux.isel(Time=0)
regionMaskFileName = self.masksSubtask.maskFileName
dsRegionMask = xr.open_dataset(regionMaskFileName)
# figure out the indices of the regions to plot
regionNames = decode_strings(dsRegionMask.regionNames)
regionIndices = []
for iceShelf in iceShelvesInTable:
for index, regionName in enumerate(regionNames):
if iceShelf == regionName:
regionIndices.append(index)
break
# select only those regions we want to plot
dsRegionMask = dsRegionMask.isel(nRegions=regionIndices)
cellMasks = dsRegionMask.regionCellMasks.chunk(
{'nRegions': 10})
restartFileName = \
self.runStreams.readpath('restart')[0]
dsRestart = xr.open_dataset(restartFileName)
areaCell = \
dsRestart.landIceFraction.isel(Time=0) * dsRestart.areaCell
# convert from kg/s to kg/yr
totalMeltFlux = constants.sec_per_year * \
(cellMasks * areaCell * freshwaterFlux).sum(dim='nCells')
totalMeltFlux.compute()
totalArea = (cellMasks * areaCell).sum(dim='nCells')
# from kg/m^2/yr to m/yr
meltRates = ((1. / constants.rho_fw) *
(totalMeltFlux / totalArea))
meltRates.compute()
# convert from kg/yr to GT/yr
totalMeltFlux /= constants.kg_per_GT
ds = xr.Dataset()
ds['totalMeltFlux'] = totalMeltFlux
ds.totalMeltFlux.attrs['units'] = 'GT a$^{-1}$'
ds.totalMeltFlux.attrs['description'] = \
'Total melt flux summed over each ice shelf or region'
ds['meltRates'] = meltRates
ds.meltRates.attrs['units'] = 'm a$^{-1}$'
ds.meltRates.attrs['description'] = \
'Melt rate averaged over each ice shelf or region'
ds['area'] = 1e-6 * totalArea
ds.meltRates.attrs['units'] = 'km$^2$'
ds.meltRates.attrs['description'] = \
'Region or ice shelf area'
ds['regionNames'] = dsRegionMask.regionNames
write_netcdf(ds, meltRateFileName)
else:
ds = xr.open_dataset(meltRateFileName)
mainRunName = config.get('runs', 'mainRunName')
fieldNames = ['Region', 'Area', mainRunName]
controlConfig = self.controlConfig
if controlConfig is not None:
controlFileName = get_masked_mpas_climatology_file_name(
controlConfig,
self.season,
self.componentName,
climatologyName='antarcticMeltTable')
dsControl = xr.open_dataset(controlFileName)
controlRunName = controlConfig.get('runs', 'mainRunName')
fieldNames.append(controlRunName)
else:
dsControl = None
controlRunName = None
regionNames = decode_strings(ds.regionNames)
outDirectory = '{}/antarcticMelt/'.format(
build_config_full_path(config, 'output', 'tablesSubdirectory'))
try:
os.makedirs(outDirectory)
except OSError:
pass
tableFileName = '{}/antarcticMeltRateTable_{}.csv'.format(
outDirectory, self.season)
with open(tableFileName, 'w', newline='') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=fieldNames)
writer.writeheader()
for index, regionName in enumerate(regionNames):
row = {
'Region': regionName,
'Area': '{}'.format(ds.area[index].values),
mainRunName: '{}'.format(ds.meltRates[index].values)
}
if dsControl is not None:
row[controlRunName] = \
'{}'.format(dsControl.meltRates[index].values)
writer.writerow(row)
tableFileName = '{}/antarcticMeltFluxTable_{}.csv'.format(
outDirectory, self.season)
with open(tableFileName, 'w', newline='') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=fieldNames)
writer.writeheader()
for index, regionName in enumerate(regionNames):
row = {
'Region': regionName,
'Area': '{}'.format(ds.area[index].values),
mainRunName: '{}'.format(ds.totalMeltFlux[index].values)
}
if dsControl is not None:
row[controlRunName] = \
'{}'.format(dsControl.totalMeltFlux[index].values)
writer.writerow(row)
def run_task(self): # {{{
"""
Computes time-series of transport through transects.
"""
# Authors
# -------
# Xylar Asay-Davis, Stephen Price
self.logger.info("Computing time series of transport through "
"transects...")
config = self.config
startDate = '{:04d}-01-01_00:00:00'.format(self.startYear)
endDate = '{:04d}-12-31_23:59:59'.format(self.endYear)
outputDirectory = '{}/transport/'.format(
build_config_full_path(config, 'output', 'timeseriesSubdirectory'))
try:
os.makedirs(outputDirectory)
except OSError:
pass
outFileName = '{}/transport_{:04d}-{:04d}.nc'.format(
outputDirectory, self.startYear, self.endYear)
inputFiles = sorted(
self.historyStreams.readpath('timeSeriesStatsMonthlyOutput',
startDate=startDate,
endDate=endDate,
calendar=self.calendar))
years, months = get_files_year_month(inputFiles, self.historyStreams,
'timeSeriesStatsMonthlyOutput')
variableList = ['timeMonthly_avg_layerThickness']
with open_mpas_dataset(fileName=inputFiles[0],
calendar=self.calendar,
startDate=startDate,
endDate=endDate) as dsIn:
if 'timeMonthly_avg_normalTransportVelocity' in dsIn:
variableList.append('timeMonthly_avg_normalTransportVelocity')
elif 'timeMonthly_avg_normalGMBolusVelocity' in dsIn:
variableList = variableList + \
['timeMonthly_avg_normalVelocity',
'timeMonthly_avg_normalGMBolusVelocity']
else:
self.logger.warning('Cannot compute transport velocity. '
'Using advection velocity.')
variableList.append('timeMonthly_avg_normalVelocity')
outputExists = os.path.exists(outFileName)
outputValid = outputExists
if outputExists:
with open_mpas_dataset(fileName=outFileName,
calendar=self.calendar,
timeVariableNames=None,
variableList=None,
startDate=startDate,
endDate=endDate) as dsOut:
for inIndex in range(dsOut.dims['Time']):
mask = numpy.logical_and(
dsOut.year[inIndex].values == years,
dsOut.month[inIndex].values == months)
if numpy.count_nonzero(mask) == 0:
outputValid = False
break
if outputValid:
self.logger.info(' Time series exists -- Done.')
return
transectMaskFileName = self.masksSubtask.maskFileName
dsTransectMask = xarray.open_dataset(transectMaskFileName)
# figure out the indices of the transects to plot
maskTransectNames = decode_strings(dsTransectMask.transectNames)
dsMesh = xarray.open_dataset(self.restartFileName)
dvEdge = dsMesh.dvEdge
cellsOnEdge = dsMesh.cellsOnEdge - 1
timeDatasets = []
self.logger.info(' Computing transport...')
for fileName in inputFiles:
self.logger.info(' input file: {}'.format(fileName))
dsTimeSlice = open_mpas_dataset(fileName=fileName,
calendar=self.calendar,
variableList=variableList,
startDate=startDate,
endDate=endDate)
transectDatasets = []
transectIndices = []
for transect in self.transectsToPlot:
self.logger.info(' transect: {}'.format(transect))
try:
transectIndex = maskTransectNames.index(transect)
except ValueError:
self.logger.warning(' Not found in masks. '
'Skipping.')
continue
transectIndices.append(transectIndex)
# select the current transect
dsMask = dsTransectMask.isel(nTransects=[transectIndex])
edgeIndices = dsMask.transectEdgeGlobalIDs - 1
edgeIndices = edgeIndices.where(edgeIndices >= 0,
drop=True).astype(int)
edgeSign = dsMask.transectEdgeMaskSigns.isel(
nEdges=edgeIndices)
dsIn = dsTimeSlice.isel(nEdges=edgeIndices)
dv = dvEdge.isel(nEdges=edgeIndices)
coe = cellsOnEdge.isel(nEdges=edgeIndices)
# work on data from simulations
if 'timeMonthly_avg_normalTransportVelocity' in dsIn:
vel = dsIn.timeMonthly_avg_normalTransportVelocity
elif 'timeMonthly_avg_normalGMBolusVelocity' in dsIn:
vel = (dsIn.timeMonthly_avg_normalVelocity +
dsIn.timeMonthly_avg_normalGMBolusVelocity)
else:
vel = dsIn.timeMonthly_avg_normalVelocity
# get layer thickness on edges by averaging adjacent cells
h = 0.5 * dsIn.timeMonthly_avg_layerThickness.isel(
nCells=coe).sum(dim='TWO')
edgeTransport = edgeSign * vel * h * dv
# convert from m^3/s to Sv
transport = (constants.m3ps_to_Sv * edgeTransport.sum(
dim=['maxEdgesInTransect', 'nVertLevels']))
dsOut = xarray.Dataset()
dsOut['transport'] = transport
dsOut.transport.attrs['units'] = 'Sv'
dsOut.transport.attrs['description'] = \
'Transport through transects'
transectDatasets.append(dsOut)
dsOut = xarray.concat(transectDatasets, 'nTransects')
timeDatasets.append(dsOut)
# combine data sets into a single data set
dsOut = xarray.concat(timeDatasets, 'Time')
dsOut.coords['transectNames'] = dsTransectMask.transectNames.isel(
nTransects=transectIndices)
dsOut.coords['year'] = (('Time'), years)
dsOut['year'].attrs['units'] = 'years'
dsOut.coords['month'] = (('Time'), months)
dsOut['month'].attrs['units'] = 'months'
write_netcdf(dsOut, outFileName)
def run_task(self): # {{{
"""
Make the Hovmoller plot from the time series.
"""
# Authors
# -------
# Xylar Asay-Davis, Milena Veneziani, Greg Streletz
self.logger.info("\nPlotting {} time series vs. depth...".format(
self.fieldNameInTitle))
config = self.config
mainRunName = config.get('runs', 'mainRunName')
self.logger.info(' Load ocean data...')
ds = xr.open_dataset(self.inFileName)
if 'regionNames' in ds.coords:
allRegionNames = decode_strings(ds.regionNames)
regionIndex = allRegionNames.index(self.regionName)
regionNameInTitle = self.regionName.replace('_', ' ')
regionDim = ds.regionNames.dims[0]
else:
plotTitles = config.getExpression('regions', 'plotTitles')
allRegionNames = config.getExpression('regions', 'regions')
regionIndex = allRegionNames.index(self.regionName)
regionNameInTitle = plotTitles[regionIndex]
regionDim = 'nOceanRegionsTmp'
ds = ds.isel(**{regionDim: regionIndex})
# Note: restart file, not a mesh file because we need refBottomDepth,
# not in a mesh file
try:
restartFile = self.runStreams.readpath('restart')[0]
except ValueError:
raise IOError('No MPAS-O restart file found: need at least one '
'restart file for plotting time series vs. depth')
# Define/read in general variables
self.logger.info(' Read in depth...')
with xr.open_dataset(restartFile) as dsRestart:
# reference depth [m]
depths = dsRestart.refBottomDepth.values
z = np.zeros(depths.shape)
z[0] = -0.5 * depths[0]
z[1:] = -0.5 * (depths[0:-1] + depths[1:])
Time = ds.Time.values
field = ds[self.mpasFieldName].values.transpose()
xLabel = 'Time (years)'
yLabel = 'Depth (m)'
title = '{}, {} \n {}'.format(self.fieldNameInTitle, regionNameInTitle,
mainRunName)
outFileName = '{}/{}.png'.format(self.plotsDirectory, self.filePrefix)
if config.has_option(self.sectionName, 'firstYearXTicks'):
firstYearXTicks = config.getint(self.sectionName,
'firstYearXTicks')
else:
firstYearXTicks = None
if config.has_option(self.sectionName, 'yearStrideXTicks'):
yearStrideXTicks = config.getint(self.sectionName,
'yearStrideXTicks')
else:
yearStrideXTicks = None
if config.has_option(self.sectionName, 'yLim'):
yLim = config.getExpression(self.sectionName, 'yLim')
else:
yLim = None
plot_vertical_section(config,
Time,
z,
field,
self.sectionName,
suffix='',
colorbarLabel=self.unitsLabel,
title=title,
xlabel=xLabel,
ylabel=yLabel,
lineWidth=1,
xArrayIsTime=True,
calendar=self.calendar,
firstYearXTicks=firstYearXTicks,
yearStrideXTicks=yearStrideXTicks,
yLim=yLim,
invertYAxis=False)
savefig(outFileName)
write_image_xml(config=config,
filePrefix=self.filePrefix,
componentName='Ocean',
componentSubdirectory='ocean',
galleryGroup=self.galleryGroup,
groupSubtitle=self.groupSubtitle,
groupLink=self.groupLink,
gallery=self.galleryName,
thumbnailDescription='{} {}'.format(
regionNameInTitle, self.thumbnailSuffix),
imageDescription=self.imageCaption,
imageCaption=self.imageCaption)
def run_task(self): # {{{
'''
Compute the regional-mean time series
'''
# Authors
# -------
# Xylar Asay-Davis
config = self.config
self.logger.info("\nCompute time series of regional means...")
startDate = '{:04d}-01-01_00:00:00'.format(self.startYear)
endDate = '{:04d}-12-31_23:59:59'.format(self.endYear)
regionGroup = self.regionGroup
sectionSuffix = regionGroup[0].upper() + \
regionGroup[1:].replace(' ', '')
timeSeriesName = sectionSuffix[0].lower() + sectionSuffix[1:]
sectionName = 'timeSeries{}'.format(sectionSuffix)
outputDirectory = '{}/{}/'.format(
build_config_full_path(config, 'output', 'timeseriesSubdirectory'),
timeSeriesName)
try:
os.makedirs(outputDirectory)
except OSError:
pass
outFileName = '{}/{}_{:04d}-{:04d}.nc'.format(outputDirectory,
timeSeriesName,
self.startYear,
self.endYear)
inputFiles = sorted(
self.historyStreams.readpath('timeSeriesStatsMonthlyOutput',
startDate=startDate,
endDate=endDate,
calendar=self.calendar))
years, months = get_files_year_month(inputFiles, self.historyStreams,
'timeSeriesStatsMonthlyOutput')
variables = config.getExpression(sectionName, 'variables')
variableList = [var['mpas'] for var in variables] + \
['timeMonthly_avg_layerThickness']
outputExists = os.path.exists(outFileName)
outputValid = outputExists
if outputExists:
with open_mpas_dataset(fileName=outFileName,
calendar=self.calendar,
timeVariableNames=None,
variableList=None,
startDate=startDate,
endDate=endDate) as dsOut:
for inIndex in range(dsOut.dims['Time']):
mask = numpy.logical_and(
dsOut.year[inIndex].values == years,
dsOut.month[inIndex].values == months)
if numpy.count_nonzero(mask) == 0:
outputValid = False
break
if outputValid:
self.logger.info(' Time series exists -- Done.')
return
# Load mesh related variables
try:
restartFileName = self.runStreams.readpath('restart')[0]
except ValueError:
raise IOError('No MPAS-O restart file found: need at least one '
'restart file for ocean region time series')
cellsChunk = 32768
timeChunk = 1
datasets = []
for timeIndex, fileName in enumerate(inputFiles):
dsTimeSlice = open_mpas_dataset(fileName=fileName,
calendar=self.calendar,
variableList=variableList,
startDate=startDate,
endDate=endDate)
datasets.append(dsTimeSlice)
chunk = {'Time': timeChunk, 'nCells': cellsChunk}
if config.has_option(sectionName, 'zmin'):
config_zmin = config.getfloat(sectionName, 'zmin')
else:
config_zmin = None
if config.has_option(sectionName, 'zmax'):
config_zmax = config.getfloat(sectionName, 'zmax')
else:
config_zmax = None
with dask.config.set(schedular='threads',
pool=ThreadPool(self.daskThreads)):
# combine data sets into a single data set
dsIn = xarray.concat(datasets, 'Time').chunk(chunk)
chunk = {'nCells': cellsChunk}
dsRestart = xarray.open_dataset(restartFileName)
dsRestart = dsRestart.isel(Time=0).chunk(chunk)
dsIn['areaCell'] = dsRestart.areaCell
if 'landIceMask' in dsRestart:
# only the region outside of ice-shelf cavities
dsIn['openOceanMask'] = dsRestart.landIceMask == 0
dsIn['zMid'] = compute_zmid(dsRestart.bottomDepth,
dsRestart.maxLevelCell,
dsRestart.layerThickness)
regionMaskFileName = self.masksSubtask.maskFileName
dsRegionMask = xarray.open_dataset(regionMaskFileName)
maskRegionNames = decode_strings(dsRegionMask.regionNames)
datasets = []
regionIndices = []
for regionName in self.regionNames:
self.logger.info(' region: {}'.format(regionName))
regionIndex = maskRegionNames.index(regionName)
regionIndices.append(regionIndex)
chunk = {'nCells': cellsChunk}
dsMask = dsRegionMask.isel(nRegions=regionIndex).chunk(chunk)
cellMask = dsMask.regionCellMasks == 1
if 'openOceanMask' in dsIn:
cellMask = numpy.logical_and(cellMask, dsIn.openOceanMask)
dsRegion = dsIn.where(cellMask, drop=True)
totalArea = dsRegion['areaCell'].sum()
self.logger.info(' totalArea: {} mil. km^2'.format(
1e-12 * totalArea.values))
self.logger.info("Don't worry about the following dask "
"warnings.")
if config_zmin is None:
zmin = dsMask.zmin
else:
zmin = config_zmin
if config_zmax is None:
zmax = dsMask.zmax
else:
zmax = config_zmax
depthMask = numpy.logical_and(dsRegion.zMid >= zmin,
dsRegion.zMid <= zmax)
depthMask.compute()
self.logger.info("Dask warnings should be done.")
dsRegion['depthMask'] = depthMask
layerThickness = dsRegion.timeMonthly_avg_layerThickness
dsRegion['volCell'] = (dsRegion.areaCell *
layerThickness).where(depthMask)
totalVol = dsRegion.volCell.sum(dim='nVertLevels').sum(
dim='nCells')
totalVol.compute()
self.logger.info(' totalVol (mil. km^3): {}'.format(
1e-15 * totalVol.values))
dsRegion = dsRegion.transpose('Time', 'nCells', 'nVertLevels')
dsOut = xarray.Dataset()
dsOut['totalVol'] = totalVol
dsOut.totalVol.attrs['units'] = 'm^3'
dsOut['totalArea'] = totalArea
dsOut.totalArea.attrs['units'] = 'm^2'
dsOut['zbounds'] = ('nbounds', [zmin, zmax])
dsOut.zbounds.attrs['units'] = 'm'
for var in variables:
outName = var['name']
self.logger.info(' {}'.format(outName))
mpasVarName = var['mpas']
timeSeries = dsRegion[mpasVarName]
units = timeSeries.units
description = timeSeries.long_name
is3d = 'nVertLevels' in timeSeries.dims
if is3d:
timeSeries = \
(dsRegion.volCell*timeSeries.where(depthMask)).sum(
dim='nVertLevels').sum(dim='nCells') / totalVol
else:
timeSeries = \
(dsRegion.areaCell*timeSeries).sum(
dim='nCells') / totalArea
timeSeries.compute()
dsOut[outName] = timeSeries
dsOut[outName].attrs['units'] = units
dsOut[outName].attrs['description'] = description
dsOut[outName].attrs['is3d'] = str(is3d)
datasets.append(dsOut)
# combine data sets into a single data set
dsOut = xarray.concat(datasets, 'nRegions')
dsOut.coords['regionNames'] = dsRegionMask.regionNames.isel(
nRegions=regionIndices)
dsOut.coords['year'] = (('Time'), years)
dsOut['year'].attrs['units'] = 'years'
dsOut.coords['month'] = (('Time'), months)
dsOut['month'].attrs['units'] = 'months'
write_netcdf(dsOut, outFileName)
#
cnormT = mpl.colors.BoundaryNorm(clevelsT, colormapT.N)
cnormS = mpl.colors.BoundaryNorm(clevelsS, colormapS.N)
cnormV = mpl.colors.BoundaryNorm(clevelsV, colormapV.N)
#sigma2contours = [35, 36, 36.5, 36.8, 37, 37.1, 37.2, 37.25, 37.44, 37.52, 37.6]
sigma2contours = None
#sigma0contours = np.arange(26.0, 28.0, 0.2) # Good for OSNAP, but not for all arcticSections
sigma0contours = [24.0, 25.0, 26.0, 27.0, 27.2, 27.4, 27.6, 27.8, 28.0]
#sigma0contours = None
# Load in MPAS mesh and transect mask file
mesh = xr.open_dataset(meshfile)
mask = xr.open_dataset(maskfile)
allTransects = decode_strings(mask.transectNames)
if transectNames[0]=='all' or transectNames[0]=='StandardTransportSectionsRegionsGroup':
transectNames = allTransects
# Get depth
z = mesh.refBottomDepth.values
nlevels = len(z)
nTransects = len(transectNames)
maxEdges = mask.dims['maxEdgesInTransect']
for n in range(nTransects):
# Identify transect
transectName = transectNames[n]
transectIndex = allTransects.index(transectName)
print('Plotting sections for transect: ', transectName)
