# This software is open source software available under the BSD-3 license.
#
# Copyright (c) 2020 Triad National Security, LLC. All rights reserved.
# Copyright (c) 2020 Lawrence Livermore National Security, LLC. All rights
# reserved.
# Copyright (c) 2020 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/master/LICENSE
from __future__ import absolute_import, division, print_function, \
unicode_literals
import os
import subprocess
from distutils.spawn import find_executable
import xarray as xr
import numpy
from mpas_analysis.shared.analysis_task import AnalysisTask
from mpas_analysis.shared.io.utility import build_config_full_path, \
make_directories, get_files_year_month, decode_strings
from mpas_analysis.shared.timekeeping.utility import get_simulation_start_time
[docs]class MpasTimeSeriesTask(AnalysisTask): # {{{
'''
An analysis tasks for computing time series from output from the
``timeSeriesStatsMonthly`` analysis member.
Attributes
----------
variableList : list of str
A list of variable names in ``timeSeriesStatsMonthly`` to be
included in the time series
allVariables : list of str
A list of all available variable names in ``timeSeriesStatsMonthly``
used to raise an exception when an unavailable variable is requested
inputFiles : list of str
A list of input files from which to extract the time series.
startDate, endDate : str
The start and end dates of the time series as strings
startYear, endYear : int
The start and end years of the time series
'''
# Authors
# -------
# Xylar Asay-Davis
[docs] def __init__(self, config, componentName, taskName=None,
subtaskName=None, section='timeSeries'): # {{{
'''
Construct the analysis task for extracting time series.
Parameters
----------
config : ``MpasAnalysisConfigParser``
Contains configuration options
componentName : {'ocean', 'seaIce'}
The name of the component (same as the folder where the task
resides)
taskName : str, optional
The name of the task, 'mpasTimeSeriesOcean' or
'mpasTimeSeriesSeaIce' by default (depending on ``componentName``)
subtaskName : str, optional
The name of the subtask (if any)
section : str, optional
The section of the config file from which to read the start and
end times for the time series, also added as a tag
'''
# Authors
# -------
# Xylar Asay-Davis
self.variableList = []
self.section = section
tags = [section]
self.allVariables = None
if taskName is None:
suffix = section[0].upper() + section[1:] + \
componentName[0].upper() + componentName[1:]
taskName = 'mpas{}'.format(suffix)
# call the constructor from the base class (AnalysisTask)
super(MpasTimeSeriesTask, self).__init__(
config=config,
taskName=taskName,
subtaskName=subtaskName,
componentName=componentName,
tags=tags)
# }}}
def add_variables(self, variableList): # {{{
'''
Add one or more variables to extract as a time series.
Parameters
----------
variableList : list of str
A list of variable names in ``timeSeriesStatsMonthly`` to be
included in the time series
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
``timeSeriesStatsMonthly`` output.
'''
# Authors
# -------
# Xylar Asay-Davis
if self.allVariables is None:
raise ValueError('add_variables() can only be called after '
'setup_and_check() in MpasTimeSeriesTask.\n'
'Presumably tasks were added in the wrong order '
'or add_variables() is being called in the wrong '
'place.')
for variable in variableList:
if variable not in self.allVariables:
raise ValueError(
'{} is not available in timeSeriesStatsMonthly '
'output:\n{}'.format(variable, self.allVariables))
if variable not in self.variableList:
self.variableList.append(variable)
# }}}
def setup_and_check(self): # {{{
'''
Perform steps to set up the analysis and check for errors in the setup.
'''
# 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(MpasTimeSeriesTask, self).setup_and_check()
config = self.config
baseDirectory = build_config_full_path(
config, 'output', 'timeSeriesSubdirectory')
make_directories(baseDirectory)
self.outputFile = '{}/{}.nc'.format(baseDirectory,
self.fullTaskName)
self.check_analysis_enabled(
analysisOptionName='config_am_timeseriesstatsmonthly_enable',
raiseException=True)
# get a list of timeSeriesStats output files from the streams file,
# reading only those that are between the start and end dates
startDate = config.get(self.section, 'startDate')
endDate = config.get(self.section, 'endDate')
streamName = 'timeSeriesStatsMonthlyOutput'
self.inputFiles = self.historyStreams.readpath(
streamName, startDate=startDate, endDate=endDate,
calendar=self.calendar)
if len(self.inputFiles) == 0:
raise IOError('No files were found in stream {} between {} and '
'{}.'.format(streamName, startDate, endDate))
self.runMessage = '\nComputing MPAS time series from first year ' \
'plus files:\n' \
' {} through\n {}'.format(
os.path.basename(self.inputFiles[0]),
os.path.basename(self.inputFiles[-1]))
# Make sure first year of data is included for computing anomalies
if config.has_option('timeSeries', 'anomalyRefYear'):
anomalyYear = config.getint('timeSeries', 'anomalyRefYear')
anomalyStartDate = '{:04d}-01-01_00:00:00'.format(anomalyYear)
else:
anomalyStartDate = get_simulation_start_time(self.runStreams)
anomalyYear = int(anomalyStartDate[0:4])
anomalyEndDate = '{:04d}-12-31_23:59:59'.format(anomalyYear)
firstYearInputFiles = self.historyStreams.readpath(
streamName, startDate=anomalyStartDate,
endDate=anomalyEndDate,
calendar=self.calendar)
for fileName in firstYearInputFiles:
if fileName not in self.inputFiles:
self.inputFiles.append(fileName)
self.inputFiles = sorted(self.inputFiles)
with xr.open_dataset(self.inputFiles[0]) as ds:
self.allVariables = list(ds.data_vars.keys())
# }}}
def run_task(self): # {{{
'''
Compute the requested time series
'''
# Authors
# -------
# Xylar Asay-Davis
if len(self.variableList) == 0:
# nothing to do
return
self.logger.info(self.runMessage)
self._compute_time_series_with_ncrcat()
# }}}
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))
# }}}
# }}}
# vim: foldmethod=marker ai ts=4 sts=4 et sw=4 ft=python