Source code for mpas_analysis.ocean.compute_anomaly_subtask

# -*- coding: utf-8 -*-
# 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

from mpas_analysis.shared import AnalysisTask

from mpas_analysis.shared.io import write_netcdf

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


[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) outFileName = self.outFileName if not os.path.isabs(outFileName): baseDirectory = build_config_full_path( config, 'output', 'timeSeriesSubdirectory') outFileName = '{}/{}'.format(baseDirectory, outFileName) write_netcdf(ds, outFileName) # }}}
# }}} # vim: foldmethod=marker ai ts=4 sts=4 et sw=4 ft=python