# -*- coding: utf-8 -*-
# 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 os
from mpas_analysis.shared import AnalysisTask
from mpas_analysis.shared.io import write_netcdf_with_fill
from mpas_analysis.shared.timekeeping.utility import \
get_simulation_start_time, string_to_datetime
from mpas_analysis.shared.timekeeping.MpasRelativeDelta import \
MpasRelativeDelta
from mpas_analysis.shared.io.utility import build_config_full_path
from mpas_analysis.shared.time_series import \
compute_moving_avg_anomaly_from_start
from mpas_analysis.ocean.utility import add_standard_regions_and_subset
[docs]
class ComputeAnomalySubtask(AnalysisTask):
"""
A subtask for computing anomalies of moving averages and writing them out.
Attributes
----------
mpasTimeSeriesTask : ``MpasTimeSeriesTask``
The task that extracts the time series from MPAS monthly output
outFileName : str
The file name (usually without full path) where the resulting
data set should be written
variableList : list of str
Variables to be included in the data set
movingAveragePoints : int
The number of points (months) used in the moving average used to
smooth the data set
alter_dataset : function
A function that takes an ``xarray.Dataset`` and returns an
``xarray.Dataset`` for manipulating the data set (e.g. adding a new
variable computed from others). This operation is performed before
computing moving averages and anomalies, so that these operations are
also performed on any new variables added to the data set.
"""
# Authors
# -------
# Xylar Asay-Davis
[docs]
def __init__(self, parentTask, mpasTimeSeriesTask, outFileName,
variableList, movingAveragePoints,
subtaskName='computeAnomaly', alter_dataset=None):
"""
Construct the analysis task.
Parameters
----------
parentTask : ``AnalysisTask``
The parent task of which this is a subtask
mpasTimeSeriesTask : ``MpasTimeSeriesTask``
The task that extracts the time series from MPAS monthly output
outFileName : str
The file name (usually without full path) where the resulting
data set should be written
variableList : list of str
Variables to be included in the data set
movingAveragePoints : int
The number of points (months) used in the moving average used to
smooth the data set
subtaskName : str, optional
The name of the subtask
alter_dataset : function
A function that takes an ``xarray.Dataset`` and returns an
``xarray.Dataset`` for manipulating the data set (e.g. adding a new
variable computed from others). This operation is performed before
computing moving averages and anomalies, so that these operations
are also performed on any new variables added to the data set.
"""
# Authors
# -------
# Xylar Asay-Davis
# first, call the constructor from the base class (AnalysisTask)
super(ComputeAnomalySubtask, self).__init__(
config=parentTask.config,
taskName=parentTask.taskName,
componentName='ocean',
tags=parentTask.tags,
subtaskName=subtaskName)
self.mpasTimeSeriesTask = mpasTimeSeriesTask
self.run_after(mpasTimeSeriesTask)
self.outFileName = outFileName
self.variableList = variableList
self.movingAveragePoints = movingAveragePoints
self.alter_dataset = alter_dataset
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(ComputeAnomalySubtask, self).setup_and_check()
startDate = self.config.get('timeSeries', 'startDate')
endDate = self.config.get('timeSeries', 'endDate')
delta = MpasRelativeDelta(string_to_datetime(endDate),
string_to_datetime(startDate),
calendar=self.calendar)
months = delta.months + 12*delta.years
if months <= self.movingAveragePoints:
raise ValueError('Cannot meaninfully perform a rolling mean '
'because the time series is too short.')
self.mpasTimeSeriesTask.add_variables(variableList=self.variableList)
self.inputFile = self.mpasTimeSeriesTask.outputFile
def run_task(self):
"""
Performs analysis of ocean heat content (OHC) from time-series output.
"""
# Authors
# -------
# Xylar Asay-Davis, Milena Veneziani, Greg Streletz
self.logger.info("\nComputing anomalies...")
config = self.config
startDate = config.get('timeSeries', 'startDate')
endDate = config.get('timeSeries', 'endDate')
if config.has_option('timeSeries', 'anomalyRefYear'):
anomalyYear = config.getint('timeSeries', 'anomalyRefYear')
anomalyRefDate = '{:04d}-01-01_00:00:00'.format(anomalyYear)
anomalyEndDate = '{:04d}-12-31_23:59:59'.format(anomalyYear)
else:
anomalyRefDate = get_simulation_start_time(self.runStreams)
anomalyYear = int(anomalyRefDate[0:4])
anomalyEndDate = '{:04d}-12-31_23:59:59'.format(anomalyYear)
ds = compute_moving_avg_anomaly_from_start(
timeSeriesFileName=self.inputFile,
variableList=self.variableList,
anomalyStartTime=anomalyRefDate,
anomalyEndTime=anomalyEndDate,
startDate=startDate,
endDate=endDate,
calendar=self.calendar,
movingAveragePoints=self.movingAveragePoints,
alter_dataset=self.alter_dataset)
if 'nOceanRegions' in ds.dims or 'nOceanRegionsTmp' in ds.dims:
ds = add_standard_regions_and_subset(ds, config)
outFileName = self.outFileName
if not os.path.isabs(outFileName):
baseDirectory = build_config_full_path(
config, 'output', 'timeSeriesSubdirectory')
outFileName = '{}/{}'.format(baseDirectory,
outFileName)
write_netcdf_with_fill(ds, outFileName)