# This software is open source software available under the BSD-3 license.
#
# Copyright (c) 2022 Triad National Security, LLC. All rights reserved.
# Copyright (c) 2022 Lawrence Livermore National Security, LLC. All rights
# reserved.
# Copyright (c) 2022 UT-Battelle, LLC. All rights reserved.
#
# Additional copyright and license information can be found in the LICENSE file
# distributed with this code, or at
# https://raw.githubusercontent.com/MPAS-Dev/MPAS-Analysis/main/LICENSE
import xarray
import os
import subprocess
from distutils.spawn import find_executable
import dask
import multiprocessing
from multiprocessing.pool import ThreadPool
import glob
from mpas_tools.io import write_netcdf
from mpas_analysis.shared.analysis_task import AnalysisTask
from mpas_analysis.shared.climatology.climatology import \
get_unmasked_mpas_climatology_directory, \
get_unmasked_mpas_climatology_file_name, \
get_climatology_op_directory
from mpas_analysis.shared.io.utility import make_directories, \
get_files_year_month
from mpas_analysis.shared.constants import constants
[docs]
class MpasClimatologyTask(AnalysisTask):
"""
An analysis tasks for computing climatologies from output from the
``timeSeriesStatsMonthly*`` analysis members.
Attributes
----------
variableList : dict of lists
A dictionary with seasons as keys and a list of variable names in
the stream to be included in the climatologies for each season in the
values.
allVariables : list of str
A list of all available variable names in the stream used to raise an
exception when an unavailable variable is requested
inputFiles : list of str
A list of input files used to compute the climatologies.
ncclimoModel : {'mpaso', 'mpascice'}
The name of the component expected by ``ncclimo``
startDate, endDate : str
The start and end dates of the climatology as strings
startYear, endYear : int
The start and end years of the climatology
seasonSubtasks : dict
If using xarray to compute climatologies, a dictionary of subtasks, one
for each possible season
op : {'avg', 'min', 'max'}
operator for monthly stats
streamName : str
The name of the stream to read from, one of
``timeSeriesStatsMonthlyOutput``,
``timeSeriesStatsMonthlyMinOutput``,
``timeSeriesStatsMonthlyMaxOutput``
"""
# Authors
# -------
# Xylar Asay-Davis
[docs]
def __init__(self, config, componentName, taskName=None, op='avg'):
"""
Construct the analysis task.
Parameters
----------
config : mpas_tools.config.MpasConfigParser
Contains configuration options
componentName : {'ocean', 'seaIce'}
The name of the component (same as the folder where the task
resides)
op : {'avg', 'min', 'max'}, optioinal
operator for monthly stats
taskName : str, optional
the name of the task, defaults to
mpasClimatology<ComponentName><Op>
"""
# Authors
# -------
# Xylar Asay-Davis
self.variableList = {}
self.op = op
if op == 'avg':
self.streamName = 'timeSeriesStatsMonthlyOutput'
elif op == 'min':
self.streamName = 'timeSeriesStatsMonthlyMinOutput'
elif op == 'max':
self.streamName = 'timeSeriesStatsMonthlyMaxOutput'
else:
raise ValueError('Unexpected monthly stats operator {}'.format(op))
tags = ['climatology', op]
if componentName == 'ocean':
self.ncclimoModel = 'mpaso'
elif componentName == 'seaIce':
self.ncclimoModel = 'mpascice'
else:
raise ValueError('component {} is not supported by ncclimo.\n'
'Check with Charlie Zender and Xylar Asay-Davis\n'
'about getting it added'.format(componentName))
if taskName is None:
suffix = componentName[0].upper() + componentName[1:] + \
op[0].upper() + op[1:]
taskName = 'mpasClimatology{}'.format(suffix)
self.allVariables = None
self.useNcclimo = config.getboolean('climatology', 'useNcclimo')
# call the constructor from the base class (AnalysisTask)
super(MpasClimatologyTask, self).__init__(
config=config,
taskName=taskName,
componentName=componentName,
tags=tags)
ncclimoParallelMode = config.get('execute', 'ncclimoParallelMode')
if self.useNcclimo:
if ncclimoParallelMode in ['bck', 'mpi']:
ncclimoThreads = config.getint('execute', 'ncclimoThreads')
self.subprocessCount = ncclimoThreads
else:
# running in serial
self.subprocessCount = 1
self.seasonSubtasks = {}
if not self.useNcclimo:
# this process doesn't do anything on its own, so no need to
# block other tasks
self.subprocessCount = 1
# setup one subtask for each possible season that could be added
for season in constants.monthDictionary:
self.seasonSubtasks[season] = MpasClimatologySeasonSubtask(
self, season)
self.add_subtask(self.seasonSubtasks[season])
# make sure each season runs after the months that make up that
# season
for season in constants.monthDictionary:
if season in constants.abrevMonthNames:
continue
monthValues = constants.monthDictionary[season]
monthNames = [constants.abrevMonthNames[month - 1] for month in
monthValues]
for monthName in monthNames:
self.seasonSubtasks[season].run_after(
self.seasonSubtasks[monthName])
[docs]
def add_variables(self, variableList, seasons=None):
"""
Add one or more variables and optionally one or more seasons for which
to compute climatologies.
Parameters
----------
variableList : list of str
A list of variable names in the stream to be included in the
climatologies
seasons : list of str, optional
A list of seasons (keys in ``shared.constants.monthDictionary``)
to be computed or ``None`` if only monthly
climatologies are needed.
Raises
------
ValueError
if this funciton is called before this task has been set up (so
the list of available variables has not yet been set) or if one
or more of the requested variables is not available in the stream.
"""
# Authors
# -------
# Xylar Asay-Davis
if self.allVariables is None:
raise ValueError('add_variables() can only be called after '
'setup_and_check() in MpasClimatologyTask.\n'
'Presumably tasks were added in the wrong order '
'or add_variables() is being called in the wrong '
'place.')
if seasons is None:
seasons = list(constants.abrevMonthNames)
for variable in variableList:
if variable not in self.allVariables:
raise ValueError(
'{} is not available in {} output:\n{}'.format(
variable, self.streamName, self.allVariables))
for season in seasons:
if season not in self.variableList:
self.variableList[season] = []
if variable not in self.variableList[season]:
self.variableList[season].append(variable)
# add variables to individual months as well, since those will
# be computed first
for season in seasons:
if season not in constants.abrevMonthNames:
monthValues = constants.monthDictionary[season]
monthNames = [constants.abrevMonthNames[month - 1] for month in
monthValues]
self.add_variables(variableList, seasons=monthNames)
def setup_and_check(self):
"""
Perform steps to set up the analysis and check for errors in the setup.
Raises
------
IOError :
If a restart file is not available from which to read mesh
information or if no history files are available from which to
compute the climatology in the desired time range.
"""
# Authors
# -------
# Xylar Asay-Davis
# first, call setup_and_check from the base class (AnalysisTask),
# which will perform some common setup, including storing:
# self.runDirectory , self.historyDirectory, self.plotsDirectory,
# self.namelist, self.runStreams, self.historyStreams,
# self.calendar
super(MpasClimatologyTask, self).setup_and_check()
if self.op == 'avg':
self.check_analysis_enabled(
analysisOptionName='config_am_timeseriesstatsmonthly_enable',
raiseException=True)
elif self.op == 'min':
self.check_analysis_enabled(
analysisOptionName='config_AM_timeSeriesStatsMonthlyMin_enable',
raiseException=True)
elif self.op == 'max':
self.check_analysis_enabled(
analysisOptionName='config_AM_timeSeriesStatsMonthlyMax_enable',
raiseException=True)
self.startYear, self.endYear = self.get_start_and_end()
self.startDate = '{:04d}-01-01_00:00:00'.format(self.startYear)
self.endDate = '{:04d}-12-31_23:59:59'.format(self.endYear)
# get a list of timeSeriesSta output files from the streams file,
# reading only those that are between the start and end dates
self.inputFiles = self.historyStreams.readpath(
self.streamName, startDate=self.startDate, endDate=self.endDate,
calendar=self.calendar)
if len(self.inputFiles) == 0:
raise IOError('No files were found in stream {} between {} and '
'{}.'.format(self.streamName, self.startDate,
self.endDate))
self.symlinkDirectory = self._create_symlinks()
with xarray.open_dataset(self.inputFiles[0]) as ds:
self.allVariables = list(ds.data_vars.keys())
def run_task(self):
"""
Compute the requested climatologies
"""
# Authors
# -------
# Xylar Asay-Davis
if len(self.variableList.keys()) == 0:
# nothing to do
return
if not self.useNcclimo:
# subtasks will take care of it, so nothing to do
return
self.logger.info(
f'\nComputing MPAS climatologies from files:\n'
f' {os.path.basename(self.inputFiles[0])} through\n'
f' {os.path.basename(self.inputFiles[-1])}')
seasonsToCheck = list(constants.abrevMonthNames)
for season in self.variableList:
if season not in seasonsToCheck:
seasonsToCheck.append(season)
allExist = True
for season in seasonsToCheck:
climatologyFileName = self.get_file_name(season)
climatologyDirectory = get_unmasked_mpas_climatology_directory(
self.config, self.op)
if not os.path.exists(climatologyFileName):
allExist = False
break
if allExist:
for season in seasonsToCheck:
if season not in self.variableList:
continue
# make sure all the necessary variables are also present
with xarray.open_dataset(self.get_file_name(season)) as ds:
for variableName in self.variableList[season]:
if variableName not in ds.variables:
allExist = False
break
if not allExist:
self._compute_climatologies_with_ncclimo(
inDirectory=self.symlinkDirectory,
outDirectory=climatologyDirectory)
def get_start_and_end(self):
"""
Get the start and end years and dates for the climatology. This
function is provided to allow a custom method for setting the start
and end years of the climatology. By default, they are read from the
climatology section of the config file
Returns
-------
startYear, endYear : int
The start and end years of the climatology
"""
# Authors
# -------
# Xylar Asay-Davis
startYear = self.config.getint('climatology', 'startYear')
endYear = self.config.getint('climatology', 'endYear')
return startYear, endYear
[docs]
def get_file_name(self, season):
"""
Returns the full path for MPAS climatology file produced by ncclimo.
Parameters
----------
season : str
One of the seasons in ``constants.monthDictionary``
Returns
-------
fileName : str
The path to the climatology file for the specified season.
"""
# Authors
# -------
# Xylar Asay-Davis
return get_unmasked_mpas_climatology_file_name(self.config, season,
self.componentName,
self.op)
def _create_symlinks(self):
"""
Create symlinks to monthly mean files so they have the expected file
naming convention for ncclimo.
Returns
-------
symlinkDirectory : str
The path to the symlinks created for each timeSeriesStatsMonthly
input file
"""
# Authors
# -------
# Xylar Asay-Davis
config = self.config
fileNames = sorted(self.inputFiles)
years, months = get_files_year_month(fileNames,
self.historyStreams,
self.streamName)
climatologyOpDirectory = get_climatology_op_directory(config, self.op)
symlinkDirectory = '{}/source_symlinks'.format(
climatologyOpDirectory)
make_directories(symlinkDirectory)
for inFileName, year, month in zip(fileNames, years, months):
outFileName = \
f'{symlinkDirectory}/{self.ncclimoModel}.hist.am.' \
f'timeSeriesStatsMonthly.{year:04d}-{month:02d}-01.nc'
try:
os.symlink(inFileName, outFileName)
except OSError:
pass
return symlinkDirectory
def _compute_climatologies_with_ncclimo(self, inDirectory, outDirectory,
remapper=None,
remappedDirectory=None):
"""
Uses ncclimo to compute monthly, seasonal and/or annual climatologies.
Parameters
----------
inDirectory : str
The run directory containing timeSeriesStatsMonthly output
outDirectory : str
The output directory where climatologies will be written
remapper : ``pyremap.Remapper`` object, optional
If present, a remapper that defines the source and desitnation
grids for remapping the climatologies.
remappedDirectory : str, optional
If present, the path where remapped climatologies should be
written. By default, remapped files are stored in the same
directory as the climatologies on the source grid. Has no effect
if ``remapper`` is ``None``.
Raises
------
OSError
If ``ncclimo`` is not in the system path.
"""
# Authors
# -------
# Xylar Asay-Davis
if find_executable('ncclimo') is None:
raise OSError('ncclimo not found. Make sure the latest nco '
'package is installed: \n'
'conda install nco\n'
'Note: this presumes use of the conda-forge '
'channel.')
parallelMode = self.config.get('execute', 'ncclimoParallelMode')
seasons = [season for season in self.variableList
if season not in constants.abrevMonthNames]
variableList = []
for season in self.variableList:
variableList.extend(self.variableList[season])
# include each variable only once
variableList = sorted(list(set(variableList)))
if len(seasons) == 0:
seasons = ['none']
workDir = os.getcwd()
os.chdir(inDirectory)
inFiles = sorted(glob.glob(f'{self.ncclimoModel}*'))
args = ['ncclimo',
'--no_stdin',
'-4',
'--clm_md=mth',
'-a', 'sdd',
'-P', self.ncclimoModel,
'-p', parallelMode,
'-j', '{}'.format(self.subprocessCount),
'-v', ','.join(variableList),
'--seasons={}'.format(','.join(seasons)),
'-s', '{:04d}'.format(self.startYear),
'-e', '{:04d}'.format(self.endYear),
'-o', outDirectory] + inFiles
if remapper is not None:
args.extend(['-r', remapper.mappingFileName])
if remappedDirectory is not None:
args.extend(['-O', remappedDirectory])
self.logger.info('running: {}'.format(' '.join(args)))
for handler in self.logger.handlers:
handler.flush()
# set an environment variable to make sure we're not using czender's
# local version of NCO instead of one we have intentionally loaded
env = os.environ.copy()
env['NCO_PATH_OVERRIDE'] = 'No'
process = subprocess.Popen(args, stdout=subprocess.PIPE,
stderr=subprocess.PIPE, env=env)
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))
os.chdir(workDir)
class MpasClimatologySeasonSubtask(AnalysisTask):
"""
An analysis subtasks for computing climatologies from output from the
``timeSeriesStatsMonthly`` analysis member for a single month or season.
Attributes
----------
season : str
The season of the climatology
parentTask : ``MpasClimatologyTask``
The task that this subtask belongs to.
"""
# Authors
# -------
# Xylar Asay-Davis
def __init__(self, parentTask, season, subtaskName=None):
"""
Construct the analysis task and adds it as a subtask of the
``parentTask``.
Parameters
----------
parentTask : ``MpasClimatologyTask``
The task that this subtask belongs to.
season : str
A keys in ``shared.constants.monthDictionary``
subtaskName : str, optional
the name of the subtask, defaults to season
"""
# Authors
# -------
# Xylar Asay-Davis
self.season = season
if subtaskName is None:
subtaskName = season
# call the constructor from the base class (AnalysisTask)
super(MpasClimatologySeasonSubtask, self).__init__(
config=parentTask.config,
taskName=parentTask.taskName,
componentName=parentTask.componentName,
tags=parentTask.tags,
subtaskName=subtaskName)
self.parentTask = parentTask
parallelTaskCount = self.config.getint('execute', 'parallelTaskCount')
self.subprocessCount = min(parallelTaskCount,
self.config.getint('climatology',
'subprocessCount'))
self.daskThreads = min(
multiprocessing.cpu_count(),
self.config.getint('climatology', 'daskThreads'))
def run_task(self):
"""
Compute the requested climatologies
"""
# Authors
# -------
# Xylar Asay-Davis
season = self.season
parentTask = self.parentTask
if season not in parentTask.variableList:
# nothing to do
return
variableList = parentTask.variableList[season]
if len(variableList) == 0:
# nothing to do
return
self.logger.info(
f'\nComputing MPAS climatologies from files:\n'
f' {os.path.basename(parentTask.inputFiles[0])} through\n'
f' {os.path.basename(parentTask.inputFiles[-1])}')
climatologyFileName = parentTask.get_file_name(season)
climatologyDirectory = get_unmasked_mpas_climatology_directory(
self.config)
allExist = False
if os.path.exists(climatologyFileName):
allExist = True
# make sure all the necessary variables are also present
with xarray.open_dataset(climatologyFileName) as ds:
for variableName in variableList:
if variableName not in ds.variables:
allExist = False
break
if not allExist:
with dask.config.set(schedular='threads',
pool=ThreadPool(self.daskThreads)):
self._compute_climatologies_with_xarray(
inDirectory=parentTask.symlinkDirectory,
outDirectory=climatologyDirectory)
def _compute_climatologies_with_xarray(self, inDirectory, outDirectory):
"""
Uses xarray to compute seasonal and/or annual climatologies.
Parameters
----------
inDirectory : str
The run directory containing timeSeriesStatsMonthly output
outDirectory : str
The output directory where climatologies will be written
"""
# Authors
# -------
# Xylar Asay-Davis
def _preprocess(ds):
# drop unused variables during preprocessing because only the
# variables we want are guaranteed to be in all the files
return ds[variableList]
season = self.season
parentTask = self.parentTask
variableList = parentTask.variableList[season]
chunkSize = self.config.getint('input', 'maxChunkSize')
if season in constants.abrevMonthNames:
# this is an individual month, so create a climatology from
# timeSeriesStatsMonthlyOutput
fileNames = sorted(parentTask.inputFiles)
years, months = get_files_year_month(
fileNames, self.historyStreams,
parentTask.streamName)
with xarray.open_mfdataset(parentTask.inputFiles,
combine='nested',
concat_dim='Time',
chunks={'nCells': chunkSize},
decode_times=False,
preprocess=_preprocess) as ds:
ds.coords['year'] = ('Time', years)
ds.coords['month'] = ('Time', months)
month = constants.abrevMonthNames.index(season) + 1
climatologyFileName = parentTask.get_file_name(season)
self.logger.info('computing climatology {}'.format(
os.path.basename(climatologyFileName)))
ds = ds.where(ds.month == month, drop=True)
ds = ds.mean(dim='Time', keep_attrs=True)
ds.compute(num_workers=self.subprocessCount)
write_netcdf(ds, climatologyFileName)
else:
outFileName = parentTask.get_file_name(season=season)
self.logger.info('computing climatology {}'.format(
os.path.basename(outFileName)))
fileNames = []
weights = []
for month in constants.monthDictionary[season]:
monthName = constants.abrevMonthNames[month - 1]
fileNames.append(parentTask.get_file_name(season=monthName))
weights.append(constants.daysInMonth[month - 1])
with xarray.open_mfdataset(fileNames, concat_dim='weight',
combine='nested',
chunks={'nCells': chunkSize},
decode_times=False,
preprocess=_preprocess) as ds:
ds.coords['weight'] = ('weight', weights)
dsNew = ((ds.weight * ds).sum(dim='weight') /
ds.weight.sum(dim='weight'))
for varName in ds.data_vars:
attrs = ds[varName].attrs
# _FillValue causes trouble
attrs.pop('_FillValue', None)
dsNew[varName].attrs = attrs
dsNew.compute(num_workers=self.subprocessCount)
write_netcdf(dsNew, outFileName)