Source code for mpas_analysis.ocean.plot_depth_integrated_time_series_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
import xarray

from mpas_analysis.shared import AnalysisTask

from mpas_analysis.shared.plot import timeseries_analysis_plot, savefig

from mpas_analysis.shared.io import open_mpas_dataset, write_netcdf

from mpas_analysis.shared.timekeeping.utility import date_to_days, \
    days_to_datetime

from mpas_analysis.shared.io.utility import build_config_full_path, \
    make_directories

from mpas_analysis.shared.html import write_image_xml

from mpas_analysis.shared.time_series import compute_moving_avg, \
    combine_time_series_with_ncrcat


[docs]class PlotDepthIntegratedTimeSeriesSubtask(AnalysisTask): """ Plots a time series, summed or averaged over various depth ranges Attributes ---------- regionName : str The name of the region to plot inFileName : str The file containing the time-depth data set to plot outFileLabel : str The prefix on each plot and associated XML file fieldNameInTitle : str The name of the field being plotted, as used in the plot title mpasFieldName : str The name of the variable in the MPAS timeSeriesStatsMonthly output yAxisLabel : str the y-axis label of the plotted field (including units) sectionName : str A section in the config file where the colormap and contour values are defined thumbnailSuffix : str The text to be displayed under the thumbnail image, to which the region name will be prepended imageCaption : str The caption when mousing over the plot or displaying it full screen galleryGroup : str The name of the group of galleries in which this plot belongs groupSubtitle : str The subtitle of the group in which this plot belongs (or blank if none) groupLink : str A short name (with no spaces) for the link to the gallery group galleryName : str The name of the gallery in which this plot belongs controlConfig : ``MpasAnalysisConfigParser`` The configuration options for the control run (if any) """ # Authors # ------- # Xylar Asay-Davis, Milena Veneziani, Greg Streletz
[docs] def __init__(self, parentTask, regionName, inFileName, outFileLabel, fieldNameInTitle, mpasFieldName, yAxisLabel, sectionName, thumbnailSuffix, imageCaption, galleryGroup, groupSubtitle, groupLink, galleryName, subtaskName=None, controlConfig=None): # {{{ """ Construct the analysis task. Parameters ---------- parentTask : ``AnalysisTask`` The parent task of which this is a subtask regionName : str The name of the region to plot inFileName : str The file containing the time-depth data set to plot outFileLabel : str The prefix on each plot and associated XML file fieldNameInTitle : str The name of the field being plotted, as used in the plot title mpasFieldName : str The name of the variable in the MPAS timeSeriesStatsMonthly output yAxisLabel : str the y-axis label of the plotted field sectionName : str a section in the config file where the colormap and contour values are defined thumbnailSuffix : str The text to be displayed under the thumbnail image, to which the region name will be prepended imageCaption : str the caption when mousing over the plot or displaying it full screen galleryGroup : str the name of the group of galleries in which this plot belongs groupSubtitle : str the subtitle of the group in which this plot belongs (or blank if none) groupLink : str a short name (with no spaces) for the link to the gallery group galleryName : str the name of the gallery in which this plot belongs subtaskName : str, optional The name of the subtask (``plotTimeSeries<RegionName>`` by default) controlConfig : ``MpasAnalysisConfigParser``, optional The configuration options for the control run (if any) """ # Authors # ------- # Xylar Asay-Davis if subtaskName is None: suffix = regionName[0].upper() + regionName[1:] subtaskName = 'plotDepthIntegratedTimeSeries{}'.format(suffix) # first, call the constructor from the base class (AnalysisTask) super(PlotDepthIntegratedTimeSeriesSubtask, self).__init__( config=parentTask.config, taskName=parentTask.taskName, componentName='ocean', tags=parentTask.tags, subtaskName=subtaskName) self.regionName = regionName self.inFileName = inFileName self.outFileLabel = outFileLabel self.fieldNameInTitle = fieldNameInTitle self.mpasFieldName = mpasFieldName self.yAxisLabel = yAxisLabel self.sectionName = sectionName self.controlConfig = controlConfig # xml/html related variables self.thumbnailSuffix = thumbnailSuffix self.imageCaption = imageCaption self.galleryGroup = galleryGroup self.groupSubtitle = groupSubtitle self.groupLink = groupLink self.galleryName = galleryName
# }}} def setup_and_check(self): # {{{ """ Perform steps to set up the analysis and check for errors in the setup. """ # Authors # ------- # Xylar Asay-Davis, Greg Streletz # 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(PlotDepthIntegratedTimeSeriesSubtask, self).setup_and_check() config = self.config if self.controlConfig is not None: # we need to know what file to read from the control run so # an absolute path won't work assert(not os.path.isabs(self.inFileName)) baseDirectory = build_config_full_path( self.controlConfig, 'output', 'timeSeriesSubdirectory') self.refFileName = '{}/{}'.format(baseDirectory, self.inFileName) preprocessedReferenceRunName = config.get( 'runs', 'preprocessedReferenceRunName') if preprocessedReferenceRunName != 'None': assert(not os.path.isabs(self.inFileName)) baseDirectory = build_config_full_path( config, 'output', 'timeSeriesSubdirectory') make_directories('{}/preprocessed'.format(baseDirectory)) self.preprocessedIntermediateFileName = \ '{}/preprocessed/intermediate_{}'.format(baseDirectory, self.inFileName) self.preprocessedFileName = '{}/preprocessed/{}'.format( baseDirectory, self.inFileName) if not os.path.isabs(self.inFileName): baseDirectory = build_config_full_path( config, 'output', 'timeSeriesSubdirectory') self.inFileName = '{}/{}'.format(baseDirectory, self.inFileName) mainRunName = self.config.get('runs', 'mainRunName') self.filePrefix = '{}_{}_{}'.format(self.outFileLabel, self.regionName, mainRunName) self.xmlFileNames = ['{}/{}.xml'.format( self.plotsDirectory, self.filePrefix)] return # }}} def run_task(self): # {{{ """ Compute vertical agregates of the data and plot the time series """ # Authors # ------- # Xylar Asay-Davis, Milena Veneziani, Greg Streletz self.logger.info("\nPlotting depth-integrated time series of " "{}...".format(self.fieldNameInTitle)) config = self.config calendar = self.calendar mainRunName = config.get('runs', 'mainRunName') plotTitles = config.getExpression('regions', 'plotTitles') allRegionNames = config.getExpression('regions', 'regions') regionIndex = allRegionNames.index(self.regionName) regionNameInTitle = plotTitles[regionIndex] startDate = config.get('timeSeries', 'startDate') endDate = config.get('timeSeries', 'endDate') # Load data self.logger.info(' Load ocean data...') ds = open_mpas_dataset(fileName=self.inFileName, calendar=calendar, variableList=[self.mpasFieldName, 'depth'], timeVariableNames=None, startDate=startDate, endDate=endDate) ds = ds.isel(nOceanRegionsTmp=regionIndex) depths = ds.depth.values divisionDepths = config.getExpression(self.sectionName, 'depths') # for each depth interval to plot, determine the top and bottom depth topDepths = [0, 0] + divisionDepths bottomDepths = [depths[-1]] + divisionDepths + [depths[-1]] legends = [] for top, bottom in zip(topDepths, bottomDepths): if bottom == depths[-1]: legends.append('{}m-bottom'.format(top)) else: legends.append('{}m-{}m'.format(top, bottom)) # more possible symbols than we typically use lines = ['-', '-', '--', None, None, None, None] markers = [None, None, None, '+', 'o', '^', 'v'] widths = [5, 3, 3, 3, 3, 3, 3] points = [None, None, None, 300, 300, 300, 300] color = 'k' xLabel = 'Time [years]' yLabel = self.yAxisLabel title = '{}, {} \n {} (black)'.format(self.fieldNameInTitle, regionNameInTitle, mainRunName) outFileName = '{}/{}.png'.format(self.plotsDirectory, self.filePrefix) timeSeries = [] lineColors = [] lineStyles = [] lineMarkers = [] lineWidths = [] maxPoints = [] legendText = [] for rangeIndex in range(len(topDepths)): top = topDepths[rangeIndex] bottom = bottomDepths[rangeIndex] field = ds[self.mpasFieldName].where(ds.depth > top) field = field.where(ds.depth <= bottom) timeSeries.append(field.sum('nVertLevels')) lineColors.append(color) lineStyles.append(lines[rangeIndex]) lineMarkers.append(markers[rangeIndex]) lineWidths.append(widths[rangeIndex]) maxPoints.append(points[rangeIndex]) legendText.append(legends[rangeIndex]) preprocessedReferenceRunName = config.get( 'runs', 'preprocessedReferenceRunName') if preprocessedReferenceRunName != 'None': preprocessedInputDirectory = config.get( 'oceanPreprocessedReference', 'baseDirectory') self.logger.info(' Load in preprocessed reference data...') preprocessedFilePrefix = config.get(self.sectionName, 'preprocessedFilePrefix') inFilesPreprocessed = '{}/{}.{}.year*.nc'.format( preprocessedInputDirectory, preprocessedFilePrefix, preprocessedReferenceRunName) combine_time_series_with_ncrcat( inFilesPreprocessed, self.preprocessedIntermediateFileName, logger=self.logger) dsPreprocessed = open_mpas_dataset( fileName=self.preprocessedIntermediateFileName, calendar=calendar, timeVariableNames='xtime') yearStart = days_to_datetime(ds.Time.min(), calendar=calendar).year yearEnd = days_to_datetime(ds.Time.max(), calendar=calendar).year timeStart = date_to_days(year=yearStart, month=1, day=1, calendar=calendar) timeEnd = date_to_days(year=yearEnd, month=12, day=31, calendar=calendar) yearEndPreprocessed = days_to_datetime(dsPreprocessed.Time.max(), calendar=calendar).year if yearStart <= yearEndPreprocessed: dsPreprocessed = dsPreprocessed.sel(Time=slice(timeStart, timeEnd)) else: self.logger.warning('Warning: Preprocessed time series ends ' 'before the timeSeries startYear and will ' 'not be plotted.') preprocessedReferenceRunName = 'None' # rolling mean seems to have trouble with dask data sets so we # write out the data set and read it back as a single-file data set # (without dask) dsPreprocessed = dsPreprocessed.drop_vars('xtime') write_netcdf(dsPreprocessed, self.preprocessedFileName) dsPreprocessed = xarray.open_dataset(self.preprocessedFileName) if preprocessedReferenceRunName != 'None': color = 'purple' title = '{} \n {} (purple)'.format(title, preprocessedReferenceRunName) preprocessedFieldPrefix = config.get(self.sectionName, 'preprocessedFieldPrefix') movingAveragePoints = config.getint(self.sectionName, 'movingAveragePoints') suffixes = ['tot'] + ['{}m'.format(depth) for depth in divisionDepths] + ['btm'] # these preprocessed data are already anomalies dsPreprocessed = compute_moving_avg(dsPreprocessed, movingAveragePoints) for rangeIndex in range(len(suffixes)): variableName = '{}_{}'.format(preprocessedFieldPrefix, suffixes[rangeIndex]) if variableName in list(dsPreprocessed.data_vars.keys()): timeSeries.append(dsPreprocessed[variableName]) else: self.logger.warning('Warning: Preprocessed variable {} ' 'not found. Skipping.'.format( variableName)) timeSeries.extend(None) lineColors.append(color) lineStyles.append(lines[rangeIndex]) lineMarkers.append(markers[rangeIndex]) lineWidths.append(widths[rangeIndex]) maxPoints.append(points[rangeIndex]) legendText.append(None) if self.controlConfig is not None: controlRunName = self.controlConfig.get('runs', 'mainRunName') title = '{} \n {} (red)'.format(title, controlRunName) self.logger.info(' Load ocean data from control run...') controlStartYear = self.controlConfig.getint('timeSeries', 'startYear') controlEndYear = self.controlConfig.getint('timeSeries', 'endYear') controlStartDate = '{:04d}-01-01_00:00:00'.format(controlStartYear) controlEndDate = '{:04d}-12-31_23:59:59'.format(controlEndYear) dsRef = open_mpas_dataset(fileName=self.refFileName, calendar=calendar, variableList=[self.mpasFieldName, 'depth'], timeVariableNames=None, startDate=controlStartDate, endDate=controlEndDate) dsRef = dsRef.isel(nOceanRegionsTmp=regionIndex) color = 'r' for rangeIndex in range(len(topDepths)): top = topDepths[rangeIndex] bottom = bottomDepths[rangeIndex] field = dsRef[self.mpasFieldName].where(dsRef.depth > top) field = field.where(dsRef.depth <= bottom) timeSeries.append(field.sum('nVertLevels')) lineColors.append(color) lineStyles.append(lines[rangeIndex]) lineMarkers.append(markers[rangeIndex]) lineWidths.append(widths[rangeIndex]) maxPoints.append(points[rangeIndex]) legendText.append(None) if config.has_option(self.taskName, 'firstYearXTicks'): firstYearXTicks = config.getint(self.taskName, 'firstYearXTicks') else: firstYearXTicks = None if config.has_option(self.taskName, 'yearStrideXTicks'): yearStrideXTicks = config.getint(self.taskName, 'yearStrideXTicks') else: yearStrideXTicks = None timeseries_analysis_plot(config=config, dsvalues=timeSeries, calendar=calendar, title=title, xlabel=xLabel, ylabel=yLabel, movingAveragePoints=None, lineColors=lineColors, lineStyles=lineStyles, markers=lineMarkers, lineWidths=lineWidths, legendText=legendText, maxPoints=maxPoints, firstYearXTicks=firstYearXTicks, yearStrideXTicks=yearStrideXTicks) savefig(outFileName) write_image_xml( config=config, filePrefix=self.filePrefix, componentName='Ocean', componentSubdirectory='ocean', galleryGroup=self.galleryGroup, groupLink=self.groupLink, gallery=self.galleryName, thumbnailDescription='{} {}'.format(self.regionName, self.thumbnailSuffix), imageDescription=self.imageCaption, imageCaption=self.imageCaption)
# }}} # }}} # vim: foldmethod=marker ai ts=4 sts=4 et sw=4 ft=python