def run_task(self): # {{{ """ Plots time-series output of properties in an ocean region. """ # Authors # ------- # Xylar Asay-Davis self.logger.info("\nPlotting TS diagram for {}" "...".format(self.regionName)) register_custom_colormaps() config = self.config sectionName = self.sectionName startYear = self.mpasClimatologyTask.startYear endYear = self.mpasClimatologyTask.endYear regionMaskSuffix = config.getExpression(sectionName, 'regionMaskSuffix') regionMaskFile = get_region_mask(config, '{}.geojson'.format(regionMaskSuffix)) fcAll = read_feature_collection(regionMaskFile) fc = FeatureCollection() for feature in fcAll.features: if feature['properties']['name'] == self.regionName: fc.add_feature(feature) break self.logger.info(' Make plots...') groupLink = 'tsDiag' + self.regionGroup[0].lower() + \ self.regionGroup[1:].replace(' ', '') nSubplots = 1 + len(self.obsDicts) if self.controlConfig is not None: nSubplots += 1 if nSubplots == 4: nCols = 2 nRows = 2 else: nCols = min(nSubplots, 3) nRows = (nSubplots - 1) // 3 + 1 axisIndices = numpy.reshape(numpy.arange(nRows * nCols), (nRows, nCols))[::-1, :].ravel() titleFontSize = config.get('plot', 'titleFontSize') axis_font = {'size': config.get('plot', 'axisFontSize')} title_font = { 'size': titleFontSize, 'color': config.get('plot', 'titleFontColor'), 'weight': config.get('plot', 'titleFontWeight') } width = 3 + 4.5 * nCols height = 2 + 4 * nRows # noinspection PyTypeChecker fig, axarray = plt.subplots(nrows=nRows, ncols=nCols, sharey=True, figsize=(width, height)) if nSubplots == 1: axarray = numpy.array(axarray) if nRows == 1: axarray = axarray.reshape((nRows, nCols)) T, S, zMid, volume, zmin, zmax = self._get_mpas_t_s(self.config) mainRunName = config.get('runs', 'mainRunName') plotFields = [{ 'S': S, 'T': T, 'z': zMid, 'vol': volume, 'title': mainRunName }] if self.controlConfig is not None: T, S, zMid, volume, _, _ = self._get_mpas_t_s(self.controlConfig) controlRunName = self.controlConfig.get('runs', 'mainRunName') plotFields.append({ 'S': S, 'T': T, 'z': zMid, 'vol': volume, 'title': 'Control: {}'.format(controlRunName) }) for obsName in self.obsDicts: obsT, obsS, obsZ, obsVol = self._get_obs_t_s( self.obsDicts[obsName]) plotFields.append({ 'S': obsS, 'T': obsT, 'z': obsZ, 'vol': obsVol, 'title': obsName }) Tbins = config.getExpression(sectionName, 'Tbins', usenumpyfunc=True) Sbins = config.getExpression(sectionName, 'Sbins', usenumpyfunc=True) normType = config.get(sectionName, 'normType') PT, SP = numpy.meshgrid(Tbins, Sbins) SA = gsw.SA_from_SP(SP, p=0., lon=0., lat=-75.) CT = gsw.CT_from_t(SA, PT, p=0.) neutralDensity = sigma0(SA, CT) rhoInterval = config.getfloat(sectionName, 'rhoInterval') contours = numpy.arange(24., 29. + rhoInterval, rhoInterval) diagramType = config.get(sectionName, 'diagramType') if diagramType not in ['volumetric', 'scatter']: raise ValueError('Unexpected diagramType {}'.format(diagramType)) lastPanel = None volMinMpas = None volMaxMpas = None for index in range(len(axisIndices)): panelIndex = axisIndices[index] row = nRows - 1 - index // nCols col = numpy.mod(index, nCols) if panelIndex >= nSubplots: plt.delaxes(axarray[row, col]) continue plt.sca(axarray[row, col]) T = plotFields[index]['T'] S = plotFields[index]['S'] z = plotFields[index]['z'] volume = plotFields[index]['vol'] title = plotFields[index]['title'] CS = plt.contour(SP, PT, neutralDensity, contours, linewidths=1., colors='k', zorder=2) plt.clabel(CS, fontsize=12, inline=1, fmt='%4.2f') if diagramType == 'volumetric': lastPanel, volMin, volMax = \ self._plot_volumetric_panel(T, S, volume) if index == 0: volMinMpas = volMin volMaxMpas = volMax if normType == 'linear': norm = colors.Normalize(vmin=0., vmax=volMaxMpas) elif normType == 'log': if volMinMpas is None or volMaxMpas is None: norm = None else: norm = colors.LogNorm(vmin=volMinMpas, vmax=volMaxMpas) else: raise ValueError( 'Unsupported normType {}'.format(normType)) if norm is not None: lastPanel.set_norm(norm) else: lastPanel = self._plot_scatter_panel(T, S, z, zmin, zmax) CTFreezing = freezing.CT_freezing(Sbins, 0, 1) PTFreezing = gsw.t_from_CT(gsw.SA_from_SP(Sbins, p=0., lon=0., lat=-75.), CTFreezing, p=0.) plt.plot(Sbins, PTFreezing, linestyle='--', linewidth=1., color='k') plt.ylim([Tbins[0], Tbins[-1]]) plt.xlim([Sbins[0], Sbins[-1]]) plt.xlabel('Salinity (PSU)', **axis_font) if col == 0: plt.ylabel(r'Potential temperature ($^\circ$C)', **axis_font) plt.title(title) # do this before the inset because otherwise it moves the inset # and cartopy doesn't play too well with tight_layout anyway plt.tight_layout() fig.subplots_adjust(right=0.91) if nRows == 1: fig.subplots_adjust(top=0.85) else: fig.subplots_adjust(top=0.88) suptitle = 'T-S diagram for {} ({}, {:04d}-{:04d})\n' \ ' {} m < z < {} m'.format(self.regionName, self.season, startYear, endYear, zmin, zmax) fig.text(0.5, 0.9, suptitle, horizontalalignment='center', **title_font) inset = add_inset(fig, fc, width=1.5, height=1.5) # move the color bar down a little ot avoid the inset pos0 = inset.get_position() pos1 = axarray[-1, -1].get_position() pad = 0.04 top = pos0.y0 - pad height = top - pos1.y0 cbar_ax = fig.add_axes([0.92, pos1.y0, 0.02, height]) cbar = fig.colorbar(lastPanel, cax=cbar_ax) if diagramType == 'volumetric': cbar.ax.get_yaxis().labelpad = 15 cbar.ax.set_ylabel(r'volume (m$^3$)', rotation=270) else: cbar.ax.set_ylabel('depth (m)', rotation=270) outFileName = '{}/TS_diagram_{}_{}.png'.format(self.plotsDirectory, self.prefix, self.season) savefig(outFileName, tight=False) caption = 'Regional mean of {}'.format(suptitle) write_image_xml(config=config, filePrefix='TS_diagram_{}_{}'.format( self.prefix, self.season), componentName='Ocean', componentSubdirectory='ocean', galleryGroup='T-S Diagrams', groupLink=groupLink, gallery=self.regionGroup, thumbnailDescription=self.regionName, imageDescription=caption, imageCaption=caption)
def run_task(self): # {{{ """ Plot a depth profile with variability """ # Authors # ------- # Xylar Asay-Davis config = self.config startYear = self.startYear endYear = self.endYear regionMaskFile = self.masksSubtask.geojsonFileName fcAll = read_feature_collection(regionMaskFile) fc = FeatureCollection() for feature in fcAll.features: if feature['properties']['name'] == self.regionName: fc.add_feature(feature) break inDirectory = build_config_full_path(config, 'output', 'profilesSubdirectory') timeSeriesName = self.timeSeriesName inFileName = '{}/{}_{}_{:04d}-{:04d}.nc'.format( inDirectory, timeSeriesName, self.season, self.startYear, self.endYear) regionGroup = self.masksSubtask.regionGroup regionGroupSection = 'profiles{}'.format( regionGroup.replace(' ', '')) ds = xr.open_dataset(inFileName) allRegionNames = decode_strings(ds.regionNames) regionIndex = allRegionNames.index(self.regionName) ds = ds.isel(nRegions=regionIndex) meanFieldName = '{}_mean'.format(self.field['prefix']) stdFieldName = '{}_std'.format(self.field['prefix']) mainRunName = config.get('runs', 'mainRunName') profileGalleryGroup = config.get(regionGroupSection, 'profileGalleryGroup') titleFieldName = self.field['titleName'] regionName = self.regionName.replace('_', ' ') xLabel = '{} ({})'.format(titleFieldName, self.field['units']) yLabel = 'depth (m)' outFileName = '{}/{}.png'.format(self.plotsDirectory, self.filePrefix) lineColors = ['k'] lineWidths = [1.6] zArrays = [ds.z.values] fieldArrays = [ds[meanFieldName].values] errArrays = [ds[stdFieldName].values] if self.controlConfig is None: title = '{} {}, years {:04d}-{:04d}\n{}'.format( regionName, self.season, startYear, endYear, mainRunName) legendText = [None] else: controlStartYear = self.controlConfig.getint('climatology', 'startYear') controlEndYear = self.controlConfig.getint('climatology', 'endYear') controlRunName = self.controlConfig.get('runs', 'mainRunName') if controlStartYear == startYear and controlEndYear == endYear: title = '{} {}, years {:04d}-{:04d}'.format( regionName, self.season, startYear, endYear) legendText = [mainRunName, controlRunName] elif mainRunName == controlRunName: title = '{} {}\n{}'.format( regionName, self.season, mainRunName) legendText = ['{:04d}-{:04d}'.format(startYear, endYear), '{:04d}-{:04d}'.format(controlStartYear, controlEndYear)] else: title = '{} {} '.format(regionName, self.season) legendText = ['{} {:04d}-{:04d}'.format(mainRunName, startYear, endYear), '{} {:04d}-{:04d}'.format(controlRunName, controlStartYear, controlEndYear)] controlDirectory = build_config_full_path( self.controlConfig, 'output', 'profilesSubdirectory') controlFileName = \ '{}/{}_{}_{:04d}-{:04d}.nc'.format( controlDirectory, timeSeriesName, self.season, controlStartYear, controlEndYear) dsControl = xr.open_dataset(controlFileName) allRegionNames = decode_strings(dsControl.regionNames) regionIndex = allRegionNames.index(self.regionName) dsControl = dsControl.isel(nRegions=regionIndex) lineColors.append('r') lineWidths.append(1.2) zArrays.append(dsControl.z.values) fieldArrays.append(dsControl[meanFieldName].values) errArrays.append(dsControl[stdFieldName].values) depthRange = config.getExpression(regionGroupSection, 'depthRange') if len(depthRange) == 0: depthRange = None fig = self.plot(zArrays, fieldArrays, errArrays, lineColors=lineColors, lineWidths=lineWidths, legendText=legendText, title=title, xLabel=xLabel, yLabel=yLabel, yLim=depthRange) # do this before the inset because otherwise it moves the inset # and cartopy doesn't play too well with tight_layout anyway plt.tight_layout() add_inset(fig, fc, width=1.0, height=1.0) savefig(outFileName, tight=False) caption = '{} {} vs depth'.format(regionName, titleFieldName) write_image_xml( config=config, filePrefix=self.filePrefix, componentName='Ocean', componentSubdirectory='ocean', galleryGroup=profileGalleryGroup, groupLink='ocnregprofs', imageDescription=caption, imageCaption=caption, gallery=titleFieldName, thumbnailDescription='{} {}'.format(regionName, self.season))
def run_task(self): # {{{ """ Plots time-series output of properties in an ocean region. """ # Authors # ------- # Xylar Asay-Davis self.logger.info("\nPlotting time series of ocean properties of {}" "...".format(self.regionName)) self.logger.info(' Load time series...') config = self.config calendar = self.calendar regionMaskSuffix = config.getExpression(self.sectionName, 'regionMaskSuffix') regionMaskFile = get_region_mask(config, '{}.geojson'.format(regionMaskSuffix)) fcAll = read_feature_collection(regionMaskFile) fc = FeatureCollection() for feature in fcAll.features: if feature['properties']['name'] == self.regionName: fc.add_feature(feature) break baseDirectory = build_config_full_path(config, 'output', 'timeSeriesSubdirectory') startYear = config.getint('timeSeries', 'startYear') endYear = config.getint('timeSeries', 'endYear') regionGroup = self.regionGroup timeSeriesName = regionGroup[0].lower() + \ regionGroup[1:].replace(' ', '') inFileName = '{}/{}/{}_{:04d}-{:04d}.nc'.format( baseDirectory, timeSeriesName, timeSeriesName, startYear, endYear) dsIn = xarray.open_dataset(inFileName).isel(nRegions=self.regionIndex) zbounds = dsIn.zbounds.values controlConfig = self.controlConfig plotControl = controlConfig is not None if plotControl: controlRunName = controlConfig.get('runs', 'mainRunName') baseDirectory = build_config_full_path(controlConfig, 'output', 'timeSeriesSubdirectory') startYear = controlConfig.getint('timeSeries', 'startYear') endYear = controlConfig.getint('timeSeries', 'endYear') inFileName = '{}/{}/{}_{:04d}-{:04d}.nc'.format( baseDirectory, timeSeriesName, timeSeriesName, startYear, endYear) dsRef = xarray.open_dataset(inFileName).isel( nRegions=self.regionIndex) zboundsRef = dsRef.zbounds.values mainRunName = config.get('runs', 'mainRunName') movingAverageMonths = 1 self.logger.info(' Make plots...') groupLink = self.regionGroup[0].lower() + \ self.regionGroup[1:].replace(' ', '') for var in self.variables: varName = var['name'] mainArray = dsIn[varName] is3d = mainArray.attrs['is3d'] == 'True' if is3d: title = 'Volume-Mean {} in {}'.format(var['title'], self.regionName) else: title = 'Area-Mean {} in {}'.format(var['title'], self.regionName) if plotControl: refArray = dsRef[varName] xLabel = 'Time (yr)' yLabel = '{} ({})'.format(var['title'], var['units']) filePrefix = '{}_{}'.format(self.prefix, varName) outFileName = '{}/{}.png'.format(self.plotsDirectory, filePrefix) fields = [mainArray] lineColors = ['k'] lineWidths = [2.5] legendText = [mainRunName] if plotControl: fields.append(refArray) lineColors.append('r') lineWidths.append(1.2) legendText.append(controlRunName) if is3d: if not plotControl or numpy.all(zbounds == zboundsRef): title = '{} ({} < z < {} m)'.format( title, zbounds[0], zbounds[1]) else: legendText[0] = '{} ({} < z < {} m)'.format( legendText[0], zbounds[0], zbounds[1]) legendText[1] = '{} ({} < z < {} m)'.format( legendText[1], zboundsRef[0], zboundsRef[1]) fig = timeseries_analysis_plot( config, fields, calendar=calendar, title=title, xlabel=xLabel, ylabel=yLabel, movingAveragePoints=movingAverageMonths, lineColors=lineColors, lineWidths=lineWidths, legendText=legendText) # do this before the inset because otherwise it moves the inset # and cartopy doesn't play too well with tight_layout anyway plt.tight_layout() add_inset(fig, fc, width=2.0, height=2.0) savefig(outFileName, tight=False) caption = 'Regional mean of {}'.format(title) write_image_xml(config=config, filePrefix=filePrefix, componentName='Ocean', componentSubdirectory='ocean', galleryGroup='{} Time Series'.format( self.regionGroup), groupLink=groupLink, gallery=var['title'], thumbnailDescription=self.regionName, imageDescription=caption, imageCaption=caption)
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('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)
def run_task(self): # {{{ """ Plots time-series output of Antarctic sub-ice-shelf melt rates. """ # Authors # ------- # Xylar Asay-Davis, Stephen Price self.logger.info("\nPlotting Antarctic melt rate time series for " "{}...".format(self.iceShelf)) self.logger.info(' Load melt rate data...') config = self.config calendar = self.calendar iceShelfMasksFile = self.iceShelfMasksFile fcAll = read_feature_collection(iceShelfMasksFile) fc = FeatureCollection() for feature in fcAll.features: if feature['properties']['name'] == self.iceShelf: fc.add_feature(feature) break totalMeltFlux, meltRates = self._load_ice_shelf_fluxes(config) plotControl = self.controlConfig is not None if plotControl: controlRunName = self.controlConfig.get('runs', 'mainRunName') refTotalMeltFlux, refMeltRates = \ self._load_ice_shelf_fluxes(self.controlConfig) # Load observations from multiple files and put in dictionary based # on shelf keyname observationsDirectory = build_obs_path(config, 'ocean', 'meltSubdirectory') obsFileNameDict = {'Rignot et al. (2013)': 'Rignot_2013_melt_rates_20200623.csv', 'Rignot et al. (2013) SS': 'Rignot_2013_melt_rates_SS_20200623.csv'} obsDict = {} # dict for storing dict of obs data for obsName in obsFileNameDict: obsFileName = '{}/{}'.format(observationsDirectory, obsFileNameDict[obsName]) obsDict[obsName] = {} obsFile = csv.reader(open(obsFileName, 'rU')) next(obsFile, None) # skip the header line for line in obsFile: # some later useful values commented out shelfName = line[0] if shelfName != self.iceShelf: continue # surveyArea = line[1] meltFlux = float(line[2]) meltFluxUncertainty = float(line[3]) meltRate = float(line[4]) meltRateUncertainty = float(line[5]) # actualArea = float( line[6] ) # actual area here is in sq km # build dict of obs. keyed to filename description # (which will be used for plotting) obsDict[obsName] = { 'meltFlux': meltFlux, 'meltFluxUncertainty': meltFluxUncertainty, 'meltRate': meltRate, 'meltRateUncertainty': meltRateUncertainty} break # If areas from obs file used need to be converted from sq km to sq m mainRunName = config.get('runs', 'mainRunName') movingAverageMonths = config.getint('timeSeriesAntarcticMelt', 'movingAverageMonths') outputDirectory = build_config_full_path(config, 'output', 'timeseriesSubdirectory') make_directories(outputDirectory) self.logger.info(' Make plots...') # get obs melt flux and unc. for shelf (similar for rates) obsMeltFlux = [] obsMeltFluxUnc = [] obsMeltRate = [] obsMeltRateUnc = [] for obsName in obsDict: if len(obsDict[obsName]) > 0: obsMeltFlux.append( obsDict[obsName]['meltFlux']) obsMeltFluxUnc.append( obsDict[obsName]['meltFluxUncertainty']) obsMeltRate.append( obsDict[obsName]['meltRate']) obsMeltRateUnc.append( obsDict[obsName]['meltRateUncertainty']) else: # append NaN so this particular obs won't plot self.logger.warning('{} observations not available for ' '{}'.format(obsName, self.iceShelf)) obsMeltFlux.append(None) obsMeltFluxUnc.append(None) obsMeltRate.append(None) obsMeltRateUnc.append(None) title = self.iceShelf.replace('_', ' ') xLabel = 'Time (yr)' yLabel = 'Melt Flux (GT/yr)' timeSeries = totalMeltFlux.isel(nRegions=self.regionIndex) filePrefix = 'melt_flux_{}'.format(self.iceShelf.replace(' ', '_')) outFileName = '{}/{}.png'.format(self.plotsDirectory, filePrefix) fields = [timeSeries] lineColors = ['k'] lineWidths = [2.5] legendText = [mainRunName] if plotControl: fields.append(refTotalMeltFlux.isel(nRegions=self.regionIndex)) lineColors.append('r') lineWidths.append(1.2) legendText.append(controlRunName) fig = timeseries_analysis_plot(config, fields, calendar=calendar, title=title, xlabel=xLabel, ylabel=yLabel, movingAveragePoints=movingAverageMonths, lineColors=lineColors, lineWidths=lineWidths, legendText=legendText, obsMean=obsMeltFlux, obsUncertainty=obsMeltFluxUnc, obsLegend=list(obsDict.keys())) # do this before the inset because otherwise it moves the inset # and cartopy doesn't play too well with tight_layout anyway plt.tight_layout() add_inset(fig, fc, width=2.0, height=2.0) savefig(outFileName) caption = 'Running Mean of Total Melt Flux under Ice ' \ 'Shelves in the {} Region'.format(title) write_image_xml( config=config, filePrefix=filePrefix, componentName='Ocean', componentSubdirectory='ocean', galleryGroup='Antarctic Melt Time Series', groupLink='antmelttime', gallery='Total Melt Flux', thumbnailDescription=title, imageDescription=caption, imageCaption=caption) xLabel = 'Time (yr)' yLabel = 'Melt Rate (m/yr)' timeSeries = meltRates.isel(nRegions=self.regionIndex) filePrefix = 'melt_rate_{}'.format(self.iceShelf.replace(' ', '_')) outFileName = '{}/{}.png'.format(self.plotsDirectory, filePrefix) fields = [timeSeries] lineColors = ['k'] lineWidths = [2.5] legendText = [mainRunName] if plotControl: fields.append(refMeltRates.isel(nRegions=self.regionIndex)) lineColors.append('r') lineWidths.append(1.2) legendText.append(controlRunName) 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 fig = timeseries_analysis_plot(config, fields, calendar=calendar, title=title, xlabel=xLabel, ylabel=yLabel, movingAveragePoints=movingAverageMonths, lineColors=lineColors, lineWidths=lineWidths, legendText=legendText, firstYearXTicks=firstYearXTicks, yearStrideXTicks=yearStrideXTicks, obsMean=obsMeltRate, obsUncertainty=obsMeltRateUnc, obsLegend=list(obsDict.keys())) # do this before the inset because otherwise it moves the inset # and cartopy doesn't play too well with tight_layout anyway plt.tight_layout() add_inset(fig, fc, width=2.0, height=2.0) savefig(outFileName) caption = 'Running Mean of Area-averaged Melt Rate under Ice ' \ 'Shelves in the {} Region'.format(title) write_image_xml( config=config, filePrefix=filePrefix, componentName='Ocean', componentSubdirectory='ocean', galleryGroup='Antarctic Melt Time Series', groupLink='antmelttime', gallery='Area-averaged Melt Rate', thumbnailDescription=title, imageDescription=caption, imageCaption=caption)
def run_task(self): # {{{ """ Performs analysis of time series of sea-ice properties. """ # Authors # ------- # Xylar Asay-Davis, Milena Veneziani self.logger.info("\nPlotting sea-ice area and volume time series...") config = self.config calendar = self.calendar sectionName = self.taskName plotTitles = {'iceArea': 'Sea-ice area', 'iceVolume': 'Sea-ice volume', 'iceThickness': 'Sea-ice mean thickness'} units = {'iceArea': '[km$^2$]', 'iceVolume': '[10$^3$ km$^3$]', 'iceThickness': '[m]'} obsFileNames = { 'iceArea': {'NH': build_obs_path( config, 'seaIce', relativePathOption='areaNH', relativePathSection=sectionName), 'SH': build_obs_path( config, 'seaIce', relativePathOption='areaSH', relativePathSection=sectionName)}, 'iceVolume': {'NH': build_obs_path( config, 'seaIce', relativePathOption='volNH', relativePathSection=sectionName), 'SH': build_obs_path( config, 'seaIce', relativePathOption='volSH', relativePathSection=sectionName)}} # Some plotting rules titleFontSize = config.get('timeSeriesSeaIceAreaVol', 'titleFontSize') mainRunName = config.get('runs', 'mainRunName') preprocessedReferenceRunName = \ config.get('runs', 'preprocessedReferenceRunName') preprocessedReferenceDirectory = \ config.get('seaIcePreprocessedReference', 'baseDirectory') compareWithObservations = config.getboolean('timeSeriesSeaIceAreaVol', 'compareWithObservations') movingAveragePoints = config.getint('timeSeriesSeaIceAreaVol', 'movingAveragePoints') polarPlot = config.getboolean('timeSeriesSeaIceAreaVol', 'polarPlot') outputDirectory = build_config_full_path(config, 'output', 'timeseriesSubdirectory') make_directories(outputDirectory) self.logger.info(' Load sea-ice data...') # Load mesh dsTimeSeries = self._compute_area_vol() yearStart = days_to_datetime(dsTimeSeries['NH'].Time.min(), calendar=calendar).year yearEnd = days_to_datetime(dsTimeSeries['NH'].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) if preprocessedReferenceRunName != 'None': # determine if we're beyond the end of the preprocessed data # (and go ahead and cache the data set while we're checking) outFolder = '{}/preprocessed'.format(outputDirectory) make_directories(outFolder) inFilesPreprocessed = '{}/icevol.{}.year*.nc'.format( preprocessedReferenceDirectory, preprocessedReferenceRunName) outFileName = '{}/iceVolume.nc'.format(outFolder) combine_time_series_with_ncrcat(inFilesPreprocessed, outFileName, logger=self.logger) dsPreprocessed = open_mpas_dataset(fileName=outFileName, calendar=calendar, timeVariableNames='xtime') preprocessedYearEnd = days_to_datetime(dsPreprocessed.Time.max(), calendar=calendar).year if yearStart <= preprocessedYearEnd: dsPreprocessedTimeSlice = \ dsPreprocessed.sel(Time=slice(timeStart, timeEnd)) else: self.logger.warning('Preprocessed time series ends before the ' 'timeSeries startYear and will not be ' 'plotted.') preprocessedReferenceRunName = 'None' if self.controlConfig is not None: dsTimeSeriesRef = {} baseDirectory = build_config_full_path( self.controlConfig, 'output', 'timeSeriesSubdirectory') controlRunName = self.controlConfig.get('runs', 'mainRunName') for hemisphere in ['NH', 'SH']: inFileName = '{}/seaIceAreaVol{}.nc'.format(baseDirectory, hemisphere) dsTimeSeriesRef[hemisphere] = xr.open_dataset(inFileName) norm = {'iceArea': 1e-6, # m^2 to km^2 'iceVolume': 1e-12, # m^3 to 10^3 km^3 'iceThickness': 1.} xLabel = 'Time [years]' galleryGroup = 'Time Series' groupLink = 'timeseries' obs = {} preprocessed = {} figureNameStd = {} figureNamePolar = {} title = {} plotVars = {} obsLegend = {} plotVarsRef = {} for hemisphere in ['NH', 'SH']: self.logger.info(' Make {} plots...'.format(hemisphere)) for variableName in ['iceArea', 'iceVolume']: key = (hemisphere, variableName) # apply the norm to each variable plotVars[key] = (norm[variableName] * dsTimeSeries[hemisphere][variableName]) if self.controlConfig is not None: plotVarsRef[key] = norm[variableName] * \ dsTimeSeriesRef[hemisphere][variableName] prefix = '{}/{}{}_{}'.format(self.plotsDirectory, variableName, hemisphere, mainRunName) figureNameStd[key] = '{}.png'.format(prefix) figureNamePolar[key] = '{}_polar.png'.format(prefix) title[key] = '{} ({})'.format(plotTitles[variableName], hemisphere) if compareWithObservations: key = (hemisphere, 'iceArea') obsLegend[key] = 'SSM/I observations, annual cycle ' if hemisphere == 'NH': key = (hemisphere, 'iceVolume') obsLegend[key] = 'PIOMAS, annual cycle (blue)' if preprocessedReferenceRunName != 'None': for variableName in ['iceArea', 'iceVolume']: key = (hemisphere, variableName) if compareWithObservations: outFolder = '{}/obs'.format(outputDirectory) make_directories(outFolder) outFileName = '{}/iceArea{}.nc'.format(outFolder, hemisphere) combine_time_series_with_ncrcat( obsFileNames['iceArea'][hemisphere], outFileName, logger=self.logger) dsObs = open_mpas_dataset(fileName=outFileName, calendar=calendar, timeVariableNames='xtime') key = (hemisphere, 'iceArea') obs[key] = self._replicate_cycle(plotVars[key], dsObs.IceArea, calendar) key = (hemisphere, 'iceVolume') if hemisphere == 'NH': outFileName = '{}/iceVolume{}.nc'.format(outFolder, hemisphere) combine_time_series_with_ncrcat( obsFileNames['iceVolume'][hemisphere], outFileName, logger=self.logger) dsObs = open_mpas_dataset(fileName=outFileName, calendar=calendar, timeVariableNames='xtime') obs[key] = self._replicate_cycle(plotVars[key], dsObs.IceVol, calendar) else: obs[key] = None if preprocessedReferenceRunName != 'None': outFolder = '{}/preprocessed'.format(outputDirectory) inFilesPreprocessed = '{}/icearea.{}.year*.nc'.format( preprocessedReferenceDirectory, preprocessedReferenceRunName) outFileName = '{}/iceArea.nc'.format(outFolder) combine_time_series_with_ncrcat(inFilesPreprocessed, outFileName, logger=self.logger) dsPreprocessed = open_mpas_dataset(fileName=outFileName, calendar=calendar, timeVariableNames='xtime') dsPreprocessedTimeSlice = dsPreprocessed.sel( Time=slice(timeStart, timeEnd)) key = (hemisphere, 'iceArea') preprocessed[key] = dsPreprocessedTimeSlice[ 'icearea_{}'.format(hemisphere.lower())] inFilesPreprocessed = '{}/icevol.{}.year*.nc'.format( preprocessedReferenceDirectory, preprocessedReferenceRunName) outFileName = '{}/iceVolume.nc'.format(outFolder) combine_time_series_with_ncrcat(inFilesPreprocessed, outFileName, logger=self.logger) dsPreprocessed = open_mpas_dataset(fileName=outFileName, calendar=calendar, timeVariableNames='xtime') dsPreprocessedTimeSlice = dsPreprocessed.sel( Time=slice(timeStart, timeEnd)) key = (hemisphere, 'iceVolume') preprocessed[key] = dsPreprocessedTimeSlice[ 'icevolume_{}'.format(hemisphere.lower())] for variableName in ['iceArea', 'iceVolume']: key = (hemisphere, variableName) dsvalues = [plotVars[key]] legendText = [mainRunName] lineColors = ['k'] lineWidths = [3] if compareWithObservations and key in obsLegend.keys(): dsvalues.append(obs[key]) legendText.append(obsLegend[key]) lineColors.append('b') lineWidths.append(1.2) if preprocessedReferenceRunName != 'None': dsvalues.append(preprocessed[key]) legendText.append(preprocessedReferenceRunName) lineColors.append('purple') lineWidths.append(1.2) if self.controlConfig is not None: dsvalues.append(plotVarsRef[key]) legendText.append(controlRunName) lineColors.append('r') lineWidths.append(1.2) if config.has_option(sectionName, 'firstYearXTicks'): firstYearXTicks = config.getint(sectionName, 'firstYearXTicks') else: firstYearXTicks = None if config.has_option(sectionName, 'yearStrideXTicks'): yearStrideXTicks = config.getint(sectionName, 'yearStrideXTicks') else: yearStrideXTicks = None # separate plots for nothern and southern hemispheres timeseries_analysis_plot(config, dsvalues, movingAveragePoints, title[key], xLabel, units[variableName], calendar=calendar, lineColors=lineColors, lineWidths=lineWidths, legendText=legendText, titleFontSize=titleFontSize, firstYearXTicks=firstYearXTicks, yearStrideXTicks=yearStrideXTicks) savefig(figureNameStd[key]) filePrefix = '{}{}_{}'.format(variableName, hemisphere, mainRunName) thumbnailDescription = '{} {}'.format( hemisphere, plotTitles[variableName]) caption = 'Running mean of {}'.format( thumbnailDescription) write_image_xml( config, filePrefix, componentName='Sea Ice', componentSubdirectory='sea_ice', galleryGroup=galleryGroup, groupLink=groupLink, thumbnailDescription=thumbnailDescription, imageDescription=caption, imageCaption=caption) if (polarPlot): timeseries_analysis_plot_polar( config, dsvalues, movingAveragePoints, title[key], lineColors=lineColors, lineWidths=lineWidths, legendText=legendText, titleFontSize=titleFontSize) savefig(figureNamePolar[key]) filePrefix = '{}{}_{}_polar'.format(variableName, hemisphere, mainRunName) write_image_xml( config, filePrefix, componentName='Sea Ice', componentSubdirectory='sea_ice', galleryGroup=galleryGroup, groupLink=groupLink, thumbnailDescription=thumbnailDescription, imageDescription=caption, imageCaption=caption)
def run_task(self): # {{{ """ Make the Hovmoller plot from the time series. """ # Authors # ------- # Xylar Asay-Davis, Milena Veneziani, Greg Streletz self.logger.info("\nPlotting {} time series vs. depth...".format( self.fieldNameInTitle)) config = self.config mainRunName = config.get('runs', 'mainRunName') self.logger.info(' Load ocean data...') ds = xr.open_dataset(self.inFileName) if 'regionNames' in ds.coords: allRegionNames = decode_strings(ds.regionNames) regionIndex = allRegionNames.index(self.regionName) regionNameInTitle = self.regionName.replace('_', ' ') regionDim = ds.regionNames.dims[0] else: plotTitles = config.getExpression('regions', 'plotTitles') allRegionNames = config.getExpression('regions', 'regions') regionIndex = allRegionNames.index(self.regionName) regionNameInTitle = plotTitles[regionIndex] regionDim = 'nOceanRegionsTmp' ds = ds.isel(**{regionDim: regionIndex}) # Note: restart file, not a mesh file because we need refBottomDepth, # not in a mesh file try: restartFile = self.runStreams.readpath('restart')[0] except ValueError: raise IOError('No MPAS-O restart file found: need at least one ' 'restart file for plotting time series vs. depth') # Define/read in general variables self.logger.info(' Read in depth...') with xr.open_dataset(restartFile) as dsRestart: # reference depth [m] depths = dsRestart.refBottomDepth.values z = np.zeros(depths.shape) z[0] = -0.5 * depths[0] z[1:] = -0.5 * (depths[0:-1] + depths[1:]) Time = ds.Time.values field = ds[self.mpasFieldName].values.transpose() xLabel = 'Time (years)' yLabel = 'Depth (m)' title = '{}, {}'.format(self.fieldNameInTitle, regionNameInTitle) outFileName = '{}/{}.png'.format(self.plotsDirectory, self.filePrefix) if config.has_option(self.sectionName, 'firstYearXTicks'): firstYearXTicks = config.getint(self.sectionName, 'firstYearXTicks') else: firstYearXTicks = None if config.has_option(self.sectionName, 'yearStrideXTicks'): yearStrideXTicks = config.getint(self.sectionName, 'yearStrideXTicks') else: yearStrideXTicks = None movingAverageMonths = config.getWithDefault(self.sectionName, 'movingAverageMonths', 1) if config.has_option(self.sectionName, 'yLim'): yLim = config.getExpression(self.sectionName, 'yLim') else: yLim = None if self.controlConfig is None: refField = None diff = None refTitle = None diffTitle = None else: controlConfig = self.controlConfig dsRef = xr.open_dataset(self.controlFileName) if 'regionNames' in dsRef.coords: allRegionNames = decode_strings(dsRef.regionNames) regionIndex = allRegionNames.index(self.regionName) regionNameInTitle = self.regionName.replace('_', ' ') regionDim = dsRef.regionNames.dims[0] else: plotTitles = controlConfig.getExpression( 'regions', 'plotTitles') allRegionNames = controlConfig.getExpression( 'regions', 'regions') regionIndex = allRegionNames.index(self.regionName) regionNameInTitle = plotTitles[regionIndex] regionDim = 'nOceanRegionsTmp' dsRef = dsRef.isel(**{regionDim: regionIndex}) refField = dsRef[self.mpasFieldName].values.transpose() assert (field.shape == refField.shape) diff = field - refField refTitle = self.controlConfig.get('runs', 'mainRunName') diffTitle = 'Main - Control' fig, _, suptitle = plot_vertical_section_comparison( config, Time, z, field, refField, diff, self.sectionName, colorbarLabel=self.unitsLabel, title=title, modelTitle=mainRunName, refTitle=refTitle, diffTitle=diffTitle, xlabel=xLabel, ylabel=yLabel, lineWidth=1, xArrayIsTime=True, movingAveragePoints=movingAverageMonths, calendar=self.calendar, firstYearXTicks=firstYearXTicks, yearStrideXTicks=yearStrideXTicks, yLim=yLim, invertYAxis=False) if self.regionMaskFile is not None: # shift the super-title a little to the left to make room for the # inset pos = suptitle.get_position() suptitle.set_position((pos[0] - 0.05, pos[1])) fcAll = read_feature_collection(self.regionMaskFile) fc = FeatureCollection() for feature in fcAll.features: if feature['properties']['name'] == self.regionName: fc.add_feature(feature) break add_inset(fig, fc, width=1.0, height=1.0, xbuffer=0.1, ybuffer=0.1) savefig(outFileName, tight=False) else: savefig(outFileName) write_image_xml(config=config, filePrefix=self.filePrefix, componentName='Ocean', componentSubdirectory='ocean', galleryGroup=self.galleryGroup, groupSubtitle=self.groupSubtitle, groupLink=self.groupLink, gallery=self.galleryName, thumbnailDescription='{} {}'.format( regionNameInTitle, self.thumbnailSuffix), imageDescription=self.imageCaption, imageCaption=self.imageCaption)
def run_task(self): # {{{ """ Process MHT analysis member data if available. Plots MHT as: 1D function of latitude 2D function of latitude and depth """ # Authors # ------- # Mark Petersen, Milena Veneziani, Xylar Asay-Davis self.logger.info("\nPlotting meridional heat transport (MHT)...") config = self.config mainRunName = config.get('runs', 'mainRunName') depthLimGlobal = config.getExpression(self.sectionName, 'depthLimGlobal') xLimGlobal = config.getExpression(self.sectionName, 'xLimGlobal') movingAveragePoints = config.getint('meridionalHeatTransport', 'movingAveragePoints') outputDirectory = get_climatology_op_directory(config) make_directories(outputDirectory) outFileName = \ '{}/meridionalHeatTransport_years{:04d}-{:04d}.nc'.format( outputDirectory, self.startYear, self.endYear) if os.path.exists(outFileName): self.logger.info(' Reading results from previous analysis run...') annualClimatology = xr.open_dataset(outFileName) refZMid = annualClimatology.refZMid.values binBoundaryMerHeatTrans = \ annualClimatology.binBoundaryMerHeatTrans.values else: # Read in depth and MHT latitude points # Latitude is from binBoundaryMerHeatTrans try: restartFileName = self.runStreams.readpath('restart')[0] except ValueError: raise IOError('No MPAS-O restart file found: need at least ' 'one for MHT calcuation') with xr.open_dataset(restartFileName) as dsRestart: refBottomDepth = dsRestart.refBottomDepth.values nVertLevels = len(refBottomDepth) refLayerThickness = np.zeros(nVertLevels) refLayerThickness[0] = refBottomDepth[0] refLayerThickness[1:nVertLevels] = \ refBottomDepth[1:nVertLevels] - \ refBottomDepth[0:nVertLevels - 1] refZMid = -refBottomDepth + 0.5 * refLayerThickness binBoundaryMerHeatTrans = None # first try timeSeriesStatsMonthly for bin boundaries, then try # meridionalHeatTransport stream as a backup option for streamName in [ 'timeSeriesStatsMonthlyOutput', 'meridionalHeatTransportOutput' ]: try: inputFile = self.historyStreams.readpath(streamName)[0] except ValueError: raise IOError('At least one file from stream {} is needed ' 'to compute MHT'.format(streamName)) with xr.open_dataset(inputFile) as ds: if 'binBoundaryMerHeatTrans' in ds.data_vars: binBoundaryMerHeatTrans = \ ds.binBoundaryMerHeatTrans.values break if binBoundaryMerHeatTrans is None: raise ValueError('Could not find binBoundaryMerHeatTrans in ' 'either timeSeriesStatsMonthlyOutput or ' 'meridionalHeatTransportOutput streams') binBoundaryMerHeatTrans = np.rad2deg(binBoundaryMerHeatTrans) ################################################################### # Mark P Note: Currently only supports global MHT. # Need to add variables merHeatTransLatRegion and # merHeatTransLatZRegion # These are not computed by default in ACME right now. # Then we will need to add another section for regions with a loop # over number of regions. ################################################################### self.logger.info('\n Plotting global meridional heat transport') self.logger.info(' Load data...') climatologyFileName = self.mpasClimatologyTask.get_file_name( season='ANN') variableList = [ 'timeMonthly_avg_meridionalHeatTransportLat', 'timeMonthly_avg_meridionalHeatTransportLatZ' ] annualClimatology = xr.open_dataset(climatologyFileName) annualClimatology = annualClimatology[variableList] if 'Time' in annualClimatology.dims: annualClimatology = annualClimatology.isel(Time=0) annualClimatology.coords['refZMid'] = (('nVertLevels', ), refZMid) annualClimatology.coords['binBoundaryMerHeatTrans'] = \ (('nMerHeatTransBinsP1',), binBoundaryMerHeatTrans) write_netcdf(annualClimatology, outFileName) # **** Plot MHT **** maxTitleLength = 70 self.logger.info(' Plot global MHT...') # Plot 1D MHT (zonally averaged, depth integrated) x = binBoundaryMerHeatTrans y = annualClimatology.timeMonthly_avg_meridionalHeatTransportLat xLabel = 'latitude [deg]' yLabel = 'meridional heat transport [PW]' title = 'Global MHT (ANN, years {:04d}-{:04d})\n {}'.format( self.startYear, self.endYear, mainRunName) filePrefix = self.filePrefixes['mht'] figureName = '{}/{}.png'.format(self.plotsDirectory, filePrefix) lineColors = ['k'] lineWidths = [1.6] legendText = [mainRunName] xArrays = [x] fieldArrays = [y] errArrays = [None] if self.observationsFile is not None: # Load in observations dsObs = xr.open_dataset(self.observationsFile) xObs = dsObs.LATITUDE ncepGlobal = dsObs.GLOBALNCEP_ADJUSTED ncepErrGlobal = dsObs.GLOBALNCEP_ERR ecmwfGlobal = dsObs.GLOBALECMWF_ADJUSTED ecmwfErrGlobal = dsObs.GLOBALECMWF_ERR lineColors.extend(['b', 'g']) lineWidths.extend([1.2, 1.2]) legendText.extend( ['Trenberth and Caron - NCEP', 'Trenberth and Caron - ECMWF']) xArrays.extend([xObs, xObs]) fieldArrays.extend([ncepGlobal, ecmwfGlobal]) errArrays.extend([ncepErrGlobal, ecmwfErrGlobal]) if self.controlConfig is not None: controlStartYear = self.controlConfig.getint( 'climatology', 'startYear') controlEndYear = self.controlConfig.getint('climatology', 'endYear') controlDirectory = get_climatology_op_directory(self.controlConfig) controlFileName = \ '{}/meridionalHeatTransport_years{:04d}-{:04d}.nc'.format( controlDirectory, controlStartYear, controlEndYear) dsControl = xr.open_dataset(controlFileName) controlRunName = self.controlConfig.get('runs', 'mainRunName') lineColors.append('r') lineWidths.append(1.2) legendText.append(controlRunName) xArrays.append(dsControl.binBoundaryMerHeatTrans) fieldArrays.append( dsControl.timeMonthly_avg_meridionalHeatTransportLat) errArrays.append(None) if len(legendText) == 1: # no need for a legend legendText = [None] plot_1D(config, xArrays, fieldArrays, errArrays, lineColors=lineColors, lineWidths=lineWidths, legendText=legendText, title=title, xlabel=xLabel, ylabel=yLabel, fileout=figureName, xLim=xLimGlobal, maxTitleLength=maxTitleLength) self._write_xml(filePrefix) if config.getboolean(self.sectionName, 'plotVerticalSection'): # Plot 2D MHT (zonally integrated) # normalize 2D MHT by layer thickness MHTLatZVar = \ annualClimatology.timeMonthly_avg_meridionalHeatTransportLatZ MHTLatZ = MHTLatZVar.values.T[:, :] for k in range(nVertLevels): MHTLatZ[k, :] = MHTLatZ[k, :] / refLayerThickness[k] x = binBoundaryMerHeatTrans y = refZMid z = MHTLatZ xLabel = 'latitude [deg]' yLabel = 'depth [m]' title = 'Global MHT (ANN, years {:04d}-{:04d})\n {}'.format( self.startYear, self.endYear, mainRunName) filePrefix = self.filePrefixes['mhtZ'] outFileName = '{}/{}.png'.format(self.plotsDirectory, filePrefix) colorbarLabel = '[PW/m]' plot_vertical_section(config, x, y, z, self.sectionName, suffix='', colorbarLabel=colorbarLabel, title=title, xlabel=xLabel, ylabel=yLabel, xLim=xLimGlobal, yLim=depthLimGlobal, invertYAxis=False, movingAveragePoints=movingAveragePoints, maxTitleLength=maxTitleLength) savefig(outFileName) self._write_xml(filePrefix)
def run_task(self): # {{{ """ Plots time-series output of transport through transects. """ # Authors # ------- # Xylar Asay-Davis, Stephen Price self.logger.info("\nPlotting time series of transport through " "{}...".format(self.transect)) self.logger.info(' Load transport data...') obsDict = { 'Drake Passage': [120, 175], 'Tasmania-Ant': [147, 167], 'Africa-Ant': None, 'Antilles Inflow': [-23.1, -13.7], 'Mona Passage': [-3.8, -1.4], 'Windward Passage': [-7.2, -6.8], 'Florida-Cuba': [30, 33], 'Florida-Bahamas': [30, 33], 'Indonesian Throughflow': [-21, -11], 'Agulhas': [-90, -50], 'Mozambique Channel': [-20, -8], 'Bering Strait': [0.6, 1.0], 'Lancaster Sound': [-1.0, -0.5], 'Fram Strait': [-4.7, 0.7], 'Davis Strait': [-1.6, -3.6], 'Barents Sea Opening': [1.4, 2.6], 'Nares Strait': [-1.8, 0.2] } config = self.config calendar = self.calendar fcAll = read_feature_collection(self.transportTransectFileName) fc = FeatureCollection() for feature in fcAll.features: if feature['properties']['name'] == self.transect: fc.add_feature(feature) break transport, trans_mean, trans_std = self._load_transport(config) if self.transect in obsDict: bounds = obsDict[self.transect] else: bounds = None plotControl = self.controlConfig is not None mainRunName = config.get('runs', 'mainRunName') movingAverageMonths = config.getint('timeSeriesTransport', 'movingAverageMonths') self.logger.info(' Plotting...') transectName = self.transect.replace('_', ' ') title = transectName thumbnailDescription = transectName xLabel = 'Time (yr)' yLabel = 'Transport (Sv)' filePrefix = 'transport_{}'.format(self.transect.replace(' ', '_')) outFileName = '{}/{}.png'.format(self.plotsDirectory, filePrefix) fields = [transport] lineColors = ['k'] lineWidths = [2.5] meanString = 'mean={:.2f} $\pm$ {:.2f}'.format(trans_mean, trans_std) if plotControl: controlRunName = self.controlConfig.get('runs', 'mainRunName') ref_transport, ref_mean, ref_std = \ self._load_transport(self.controlConfig) refMeanString = 'mean={:.2f} $\pm$ {:.2f}'.format( ref_mean, ref_std) fields.append(ref_transport) lineColors.append('r') lineWidths.append(1.2) legendText = [ '{} ({})'.format(mainRunName, meanString), '{} ({})'.format(controlRunName, refMeanString) ] else: legendText = [mainRunName] title = '{} ({})'.format(title, meanString) 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 fig = timeseries_analysis_plot(config, fields, calendar=calendar, title=title, xlabel=xLabel, ylabel=yLabel, movingAveragePoints=movingAverageMonths, lineColors=lineColors, lineWidths=lineWidths, legendText=legendText, firstYearXTicks=firstYearXTicks, yearStrideXTicks=yearStrideXTicks) if bounds is not None: t = transport.Time.values plt.gca().fill_between(t, bounds[0] * numpy.ones_like(t), bounds[1] * numpy.ones_like(t), alpha=0.3, label='observations') plt.legend(loc='lower left') # do this before the inset because otherwise it moves the inset # and cartopy doesn't play too well with tight_layout anyway plt.tight_layout() add_inset(fig, fc, width=2.0, height=2.0) savefig(outFileName) caption = 'Transport through the {} Transect'.format(transectName) write_image_xml(config=config, filePrefix=filePrefix, componentName='Ocean', componentSubdirectory='ocean', galleryGroup='Transport Time Series', groupLink='transporttime', thumbnailDescription=thumbnailDescription, imageDescription=caption, imageCaption=caption)
def _plot_transect(self, remappedModelClimatology, remappedRefClimatology): # {{{ """ plotting the transect """ season = self.season config = self.config configSectionName = self.configSectionName mainRunName = config.get('runs', 'mainRunName') # broadcast x and z to have the same dimensions x, z = xr.broadcast(remappedModelClimatology.x, remappedModelClimatology.z) # set lat and lon in case we want to plot versus these quantities lat = remappedModelClimatology.lat lon = remappedModelClimatology.lon # convert x, z, lat, and lon to numpy arrays; make a copy because # they are sometimes read-only (not sure why) x = x.values.copy().transpose() z = z.values.copy().transpose() lat = lat.values.copy().transpose() lon = lon.values.copy().transpose() self.lat = lat self.lon = lon # This will do strange things at the antemeridian but there's little # we can do about that. lon_pm180 = numpy.mod(lon + 180., 360.) - 180. if self.horizontalBounds is not None: mask = numpy.logical_and( remappedModelClimatology.x.values >= self.horizontalBounds[0], remappedModelClimatology.x.values <= self.horizontalBounds[1]) inset_lon = lon_pm180[mask] inset_lat = lat[mask] else: inset_lon = lon_pm180 inset_lat = lat fc = FeatureCollection() fc.add_feature({ "type": "Feature", "properties": { "name": self.transectName, "author": 'Xylar Asay-Davis', "object": 'transect', "component": 'ocean', "tags": '' }, "geometry": { "type": "LineString", "coordinates": list(map(list, zip(inset_lon, inset_lat))) } }) # z is masked out with NaNs in some locations (where there is land) but # this makes pcolormesh unhappy so we'll zero out those locations z[numpy.isnan(z)] = 0. modelOutput = nans_to_numpy_mask( remappedModelClimatology[self.mpasFieldName].values) modelOutput = modelOutput.transpose() if remappedRefClimatology is None: refOutput = None bias = None else: refOutput = remappedRefClimatology[self.refFieldName] dims = refOutput.dims refOutput = nans_to_numpy_mask(refOutput.values) if dims[1] != 'nPoints': assert (dims[0] == 'nPoints') refOutput = refOutput.transpose() bias = modelOutput - refOutput filePrefix = self.filePrefix outFileName = '{}/{}.png'.format(self.plotsDirectory, filePrefix) title = '{}\n({}, years {:04d}-{:04d})'.format(self.fieldNameInTitle, season, self.startYear, self.endYear) xLabel = 'Distance [km]' yLabel = 'Depth [m]' # define the axis labels and the data to use for the upper # x axis or axes, if such additional axes have been requested upperXAxes = config.get('transects', 'upperXAxes') numUpperTicks = config.getint('transects', 'numUpperTicks') upperXAxisTickLabelPrecision = config.getint( 'transects', 'upperXAxisTickLabelPrecision') self._set_third_x_axis_to_none() if upperXAxes == 'neither': self._set_second_x_axis_to_none() elif upperXAxes == 'lat': self._set_second_x_axis_to_latitude() elif upperXAxes == 'lon': self._set_second_x_axis_to_longitude() elif upperXAxes == 'both': self._set_second_x_axis_to_longitude() self._set_third_x_axis_to_latitude() elif upperXAxes == 'greatestExtent': if self._greatest_extent(lat, lon): self._set_second_x_axis_to_latitude() else: self._set_second_x_axis_to_longitude() elif upperXAxes == 'strictlyMonotonic': if self._strictly_monotonic(lat, lon): self._set_second_x_axis_to_latitude() else: self._set_second_x_axis_to_longitude() elif upperXAxes == 'mostMonotonic': if self._most_monotonic(lat, lon): self._set_second_x_axis_to_latitude() else: self._set_second_x_axis_to_longitude() elif upperXAxes == 'mostStepsInSameDirection': if self._most_steps_in_same_direction(lat, lon): self._set_second_x_axis_to_latitude() else: self._set_second_x_axis_to_longitude() elif upperXAxes == 'fewestDirectionChanges': if self._fewest_direction_changes(lat, lon): self._set_second_x_axis_to_latitude() else: self._set_second_x_axis_to_longitude() else: raise ValueError('invalid option for upperXAxes') # get the parameters determining what type of plot to use, # what line styles and line colors to use, and whether and how # to label contours compareAsContours = config.getboolean('transects', 'compareAsContoursOnSinglePlot') contourLineStyle = config.get('transects', 'contourLineStyle') contourLineColor = config.get('transects', 'contourLineColor') comparisonContourLineStyle = config.get('transects', 'comparisonContourLineStyle') comparisonContourLineColor = config.get('transects', 'comparisonContourLineColor') if compareAsContours: labelContours = config.getboolean( 'transects', 'labelContoursOnContourComparisonPlots') else: labelContours = config.getboolean('transects', 'labelContoursOnHeatmaps') contourLabelPrecision = config.getint('transects', 'contourLabelPrecision') # construct a three-panel comparison plot for the transect, or a # single-panel contour comparison plot if compareAsContours is True fig, axes, suptitle = plot_vertical_section_comparison( config, x, z, modelOutput, refOutput, bias, configSectionName, cbarLabel=self.unitsLabel, xlabel=xLabel, ylabel=yLabel, title=title, modelTitle='{}'.format(mainRunName), refTitle=self.refTitleLabel, diffTitle=self.diffTitleLabel, secondXAxisData=self.secondXAxisData, secondXAxisLabel=self.secondXAxisLabel, thirdXAxisData=self.thirdXAxisData, thirdXAxisLabel=self.thirdXAxisLabel, numUpperTicks=numUpperTicks, upperXAxisTickLabelPrecision=upperXAxisTickLabelPrecision, invertYAxis=False, backgroundColor='#918167', xLim=self.horizontalBounds, compareAsContours=compareAsContours, lineStyle=contourLineStyle, lineColor=contourLineColor, comparisonContourLineStyle=comparisonContourLineStyle, comparisonContourLineColor=comparisonContourLineColor, labelContours=labelContours, contourLabelPrecision=contourLabelPrecision) # shift the super-title a little to the left to make room for the inset pos = suptitle.get_position() suptitle.set_position((pos[0] - 0.05, pos[1])) # make a red start axis and green end axis to correspond to the dots # in the inset for ax in axes: ax.spines['left'].set_color('red') ax.spines['right'].set_color('green') ax.spines['left'].set_linewidth(4) ax.spines['right'].set_linewidth(4) add_inset(fig, fc, width=1.5, height=1.5, xbuffer=0.1, ybuffer=0.1) savefig(outFileName, tight=False) caption = '{} {}'.format(season, self.imageCaption) write_image_xml(config, filePrefix, componentName='Ocean', componentSubdirectory='ocean', galleryGroup=self.galleryGroup, groupSubtitle=self.groupSubtitle, groupLink=self.groupLink, gallery=self.galleryName, thumbnailDescription=self.thumbnailDescription, imageDescription=caption, imageCaption=caption)
def run_task(self): # {{{ """ Make the Hovmoller plot from the time series. """ # Authors # ------- # Xylar Asay-Davis, Milena Veneziani, Greg Streletz self.logger.info("\nPlotting {} time series vs. depth...".format( self.fieldNameInTitle)) config = self.config mainRunName = config.get('runs', 'mainRunName') self.logger.info(' Load ocean data...') ds = xr.open_dataset(self.inFileName) if 'regionNames' in ds.coords: allRegionNames = decode_strings(ds.regionNames) regionIndex = allRegionNames.index(self.regionName) regionNameInTitle = self.regionName.replace('_', ' ') regionDim = ds.regionNames.dims[0] else: plotTitles = config.getExpression('regions', 'plotTitles') allRegionNames = config.getExpression('regions', 'regions') regionIndex = allRegionNames.index(self.regionName) regionNameInTitle = plotTitles[regionIndex] regionDim = 'nOceanRegionsTmp' ds = ds.isel(**{regionDim: regionIndex}) # Note: restart file, not a mesh file because we need refBottomDepth, # not in a mesh file try: restartFile = self.runStreams.readpath('restart')[0] except ValueError: raise IOError('No MPAS-O restart file found: need at least one ' 'restart file for plotting time series vs. depth') # Define/read in general variables self.logger.info(' Read in depth...') with xr.open_dataset(restartFile) as dsRestart: # reference depth [m] depths = dsRestart.refBottomDepth.values z = np.zeros(depths.shape) z[0] = -0.5 * depths[0] z[1:] = -0.5 * (depths[0:-1] + depths[1:]) Time = ds.Time.values field = ds[self.mpasFieldName].values.transpose() xLabel = 'Time (years)' yLabel = 'Depth (m)' title = '{}, {} \n {}'.format(self.fieldNameInTitle, regionNameInTitle, mainRunName) outFileName = '{}/{}.png'.format(self.plotsDirectory, self.filePrefix) if config.has_option(self.sectionName, 'firstYearXTicks'): firstYearXTicks = config.getint(self.sectionName, 'firstYearXTicks') else: firstYearXTicks = None if config.has_option(self.sectionName, 'yearStrideXTicks'): yearStrideXTicks = config.getint(self.sectionName, 'yearStrideXTicks') else: yearStrideXTicks = None if config.has_option(self.sectionName, 'yLim'): yLim = config.getExpression(self.sectionName, 'yLim') else: yLim = None plot_vertical_section(config, Time, z, field, self.sectionName, suffix='', colorbarLabel=self.unitsLabel, title=title, xlabel=xLabel, ylabel=yLabel, lineWidth=1, xArrayIsTime=True, calendar=self.calendar, firstYearXTicks=firstYearXTicks, yearStrideXTicks=yearStrideXTicks, yLim=yLim, invertYAxis=False) savefig(outFileName) write_image_xml(config=config, filePrefix=self.filePrefix, componentName='Ocean', componentSubdirectory='ocean', galleryGroup=self.galleryGroup, groupSubtitle=self.groupSubtitle, groupLink=self.groupLink, gallery=self.galleryName, thumbnailDescription='{} {}'.format( regionNameInTitle, self.thumbnailSuffix), imageDescription=self.imageCaption, imageCaption=self.imageCaption)
def run_task(self): # {{{ """ Performs analysis of the time-series output of sea-surface temperature (SST). """ # Authors # ------- # Xylar Asay-Davis, Milena Veneziani self.logger.info("\nPlotting SST time series...") self.logger.info(' Load SST data...') config = self.config calendar = self.calendar mainRunName = config.get('runs', 'mainRunName') preprocessedReferenceRunName = \ config.get('runs', 'preprocessedReferenceRunName') preprocessedInputDirectory = config.get('oceanPreprocessedReference', 'baseDirectory') movingAveragePoints = config.getint('timeSeriesSST', 'movingAveragePoints') regions = config.getExpression('regions', 'regions') plotTitles = config.getExpression('regions', 'plotTitles') regionsToPlot = config.getExpression('timeSeriesSST', 'regions') regionIndicesToPlot = [ regions.index(region) for region in regionsToPlot ] outputDirectory = build_config_full_path(config, 'output', 'timeseriesSubdirectory') make_directories(outputDirectory) dsSST = open_mpas_dataset(fileName=self.inputFile, calendar=calendar, variableList=self.variableList, startDate=self.startDate, endDate=self.endDate) yearStart = days_to_datetime(dsSST.Time.min(), calendar=calendar).year yearEnd = days_to_datetime(dsSST.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) if self.controlConfig is not None: baseDirectory = build_config_full_path(self.controlConfig, 'output', 'timeSeriesSubdirectory') controlFileName = '{}/{}.nc'.format( baseDirectory, self.mpasTimeSeriesTask.fullTaskName) 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) dsRefSST = open_mpas_dataset(fileName=controlFileName, calendar=calendar, variableList=self.variableList, startDate=controlStartDate, endDate=controlEndDate) else: dsRefSST = None if preprocessedReferenceRunName != 'None': self.logger.info(' Load in SST for a preprocesses reference ' 'run...') inFilesPreprocessed = '{}/SST.{}.year*.nc'.format( preprocessedInputDirectory, preprocessedReferenceRunName) outFolder = '{}/preprocessed'.format(outputDirectory) make_directories(outFolder) outFileName = '{}/sst.nc'.format(outFolder) combine_time_series_with_ncrcat(inFilesPreprocessed, outFileName, logger=self.logger) dsPreprocessed = open_mpas_dataset(fileName=outFileName, calendar=calendar, timeVariableNames='xtime') yearEndPreprocessed = days_to_datetime(dsPreprocessed.Time.max(), calendar=calendar).year if yearStart <= yearEndPreprocessed: dsPreprocessedTimeSlice = \ dsPreprocessed.sel(Time=slice(timeStart, timeEnd)) else: self.logger.warning('Preprocessed time series ends before the ' 'timeSeries startYear and will not be ' 'plotted.') preprocessedReferenceRunName = 'None' self.logger.info(' Make plots...') for regionIndex in regionIndicesToPlot: region = regions[regionIndex] title = '{} SST'.format(plotTitles[regionIndex]) xLabel = 'Time [years]' yLabel = r'[$\degree$C]' varName = self.variableList[0] SST = dsSST[varName].isel(nOceanRegions=regionIndex) filePrefix = self.filePrefixes[region] outFileName = '{}/{}.png'.format(self.plotsDirectory, filePrefix) lineColors = ['k'] lineWidths = [3] fields = [SST] legendText = [mainRunName] if dsRefSST is not None: refSST = dsRefSST[varName].isel(nOceanRegions=regionIndex) fields.append(refSST) lineColors.append('r') lineWidths.append(1.5) controlRunName = self.controlConfig.get('runs', 'mainRunName') legendText.append(controlRunName) if preprocessedReferenceRunName != 'None': SST_v0 = dsPreprocessedTimeSlice.SST fields.append(SST_v0) lineColors.append('purple') lineWidths.append(1.5) legendText.append(preprocessedReferenceRunName) 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, fields, movingAveragePoints, title, xLabel, yLabel, calendar=calendar, lineColors=lineColors, lineWidths=lineWidths, legendText=legendText, firstYearXTicks=firstYearXTicks, yearStrideXTicks=yearStrideXTicks) savefig(outFileName) caption = 'Running Mean of {} Sea Surface Temperature'.format( region) write_image_xml(config=config, filePrefix=filePrefix, componentName='Ocean', componentSubdirectory='ocean', galleryGroup='Time Series', groupLink='timeseries', thumbnailDescription='{} SST'.format(region), imageDescription=caption, imageCaption=caption)