#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Fri Feb 23 10:51:54 2018 @author: shihweif """ import numpy as np from matplotlib import pyplot as plt from mpl_toolkits.basemap import Basemap, shiftgrid #from MEOF import MEOF #from EOF import EOF #from ModelResults import ModelResults class DrawFutureProjections: __lat_bnd = [-20, 20] __lon_bnd = [122, 290] __lat = np.arange(__lat_bnd[1]-(__lat_bnd[0])+1)+__lat_bnd[0] __lon = np.arange((__lon_bnd[1]-__lon_bnd[0])/1.5+1)*1.5+__lon_bnd[0] def __init__(self, *args, **kwargs): self.allmodelPlots = args[0] self.experiments = args[1] self.numOfExperiments = len(args[1]) self.Moderate = kwargs['Moderate'] if 'Moderate' in kwargs else False plt.switch_backend('agg') def plotNaturePaperFigure3(self): warmSST_all = [] coldSST_all = [] #ensoEvolution_all = [] ensoToSFCorr_all = [] for i in range(self.numOfExperiments): allModelP = self.allmodelPlots[i] warmSST, warmSST_obs = allModelP.getSSTTimeseries(box='warm-box') coldSST, coldSST_obs = allModelP.getSSTTimeseries(box='cold-box') ensoToSFCorr, ensoToSFCorr_obs = allModelP.getENSOToSFRelations() ensoToSFCorr_all.append(ensoToSFCorr) warmSST_all.append(warmSST) coldSST_all.append(coldSST) colors = ['C0','C1','C2','C3'] from matplotlib import gridspec import os from matplotlib import font_manager as fm, rcParams rcParams.update({'errorbar.capsize': 2}) #fpath = os.path.join(rcParams["datapath"], "/export/home/shihweif/ensoComplexity/fonts/Georgia.ttf") prop = fm.FontProperties(fname="fonts/Georgia.ttf") plt.close(0) plt.figure(0, figsize=(8,4)) gs = gridspec.GridSpec(2,5) axInds = plt.subplot(gs[:,3:]) axWarm = plt.subplot(gs[0,:3]) axCold = plt.subplot(gs[1,:3]) width = 0.5 X_obs = np.arange(warmSST_obs.shape[0])/12.+100+97 for i in range(self.numOfExperiments): asymmetry = ensoToSFCorr_all[i] meanAsySF = np.nanmean(asymmetry) stdAsySF = np.nanstd(asymmetry) axInds.bar(0.5+i,meanAsySF, width) axInds.errorbar(0.5+i, meanAsySF, yerr=stdAsySF, color='k', elinewidth=0.8) warmSST = warmSST_all[i] coldSST = coldSST_all[i] X = np.arange(self.allmodelPlots[i].years*12.)/12. if self.allmodelPlots[i].experiment == 'historical': X = X + 100 elif self.allmodelPlots[i].experiment == 'RCP45': X = X + 100 + 145 elif self.allmodelPlots[i].experiment == 'RCP85': X = X + 100 + 145 yMeansWarm = np.nanmean(warmSST, axis=0) yStdsWarm = np.nanstd(warmSST, axis=0) axWarm.plot(X,yMeansWarm, label=self.allmodelPlots[i].experiment, color=colors[i], linewidth=0.6) axWarm.fill_between(X, yMeansWarm-yStdsWarm, yMeansWarm+yStdsWarm, alpha=0.5, facecolor=colors[i]) yMeansCold = np.nanmean(coldSST, axis=0) yStdsCold = np.nanstd(coldSST, axis=0) axCold.plot(X,yMeansCold, label=self.allmodelPlots[i].experiment, color=colors[i], linewidth=0.6) axCold.fill_between(X, yMeansCold-yStdsCold, yMeansCold+yStdsCold, alpha=0.5, facecolor=colors[i]) axInds.bar(4.5,ensoToSFCorr_obs, width, color='k') axInds.set_title('Tendency to Multi-year or Cyclic ENSO', fontsize=10, fontproperties=prop) axInds.set_ylabel('Correlations', fontsize=10, fontproperties=prop) axInds.axhline(y=0, linestyle='-',color='k') axInds.set_xticks([0.5, 1.5, 2.5, 3.5, 4.5]) axInds.set_xticklabels(self.experiments+['Reanalysis'], fontsize=7, fontproperties=prop) yticks_c = [-0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6] axInds.set_yticks(yticks_c) axInds.set_yticklabels(["{:.1f}".format(ytick) for ytick in yticks_c], fontsize=8, fontproperties=prop) axInds.set_xlim(0,5) axInds.set_ylim(-0.6,0.6) text_prop = fm.FontProperties(fname="fonts/Georgia.ttf", size=10) axInds.text(.25, 0.85, 'Multi-year ENSO',horizontalalignment='center',verticalalignment='top', fontproperties=text_prop, transform=axInds.transAxes) axInds.text(.25, 0.15, 'ENSO cycle',horizontalalignment='center',verticalalignment='bottom', fontproperties=text_prop,transform=axInds.transAxes) axInds.text(-0.1, 1.06, '(c)',horizontalalignment='left',verticalalignment='top', fontsize=8,transform=axInds.transAxes) xticks = [0,50, 100, (100+39), (100+89), (100+145-5), (100+145+50-5), (100+145+94)] yticks = [24, 26, 28, 30] xLabelss = ['-100','-50','1861','1900','1950','2000','2050','2100'] axWarm.text(.01, 1.12, '(a)',horizontalalignment='left',verticalalignment='top', fontsize=8,transform=axWarm.transAxes) axWarm.set_title('SST in the Equatorial Eastern Pacific', fontsize=12, fontproperties=prop) axWarm.set_xlabel('Year', fontsize=10, fontproperties=prop) axWarm.set_ylabel('SST ($^\circ$C)', fontsize=10, fontproperties=prop) axWarm.plot(X_obs,warmSST_obs, label='Reanalysis', color='k', linewidth=0.4) axWarm.axhline(y=28, linestyle=':', color='k', linewidth=1) axWarm.set_xticks(xticks) axWarm.set_xticklabels(xLabelss, fontproperties=prop) axWarm.set_yticks(yticks) axWarm.set_yticklabels(["{:.1f}".format(ytick) for ytick in yticks], fontproperties=prop) axWarm.tick_params(labelsize=8) warmLegend = axWarm.legend(loc=2, fontsize=6, ncol=3) for legobj in warmLegend.legendHandles: legobj.set_linewidth(2.0) axCold.text(.01, 1.12, '(b)',horizontalalignment='left',verticalalignment='top', fontsize=8, transform=axCold.transAxes) axCold.set_title('SST in the Equatorial Central Pacific', fontsize=12, fontproperties=prop) axCold.set_xlabel('Year', fontsize=10, fontproperties=prop) axCold.set_ylabel('SST ($^\circ$C)', fontsize=10, fontproperties=prop) axCold.plot(X_obs,coldSST_obs, label='Reanalysis', color='k', linewidth=0.4) axCold.axhline(y=28, linestyle=':', color='k', linewidth=1) axCold.set_xticks(xticks) axCold.set_xticklabels(xLabelss, fontproperties=prop) axCold.set_yticklabels(axCold.get_yticks(), fontproperties=prop) axCold.tick_params(labelsize=8) coldLegend = axCold.legend(loc=2, fontsize=6, ncol=3) for legobj in coldLegend.legendHandles: legobj.set_linewidth(2.0) plt.tight_layout() plt.savefig('Figure/Figure2.png', bbox_inches='tight', dpi=800) def plotNaturePaperFigure4_SSTPropagation(self): from UnclassifiedComputations import UnclassifiedComputations unCom = UnclassifiedComputations() var_means_all = [] for i in range(self.numOfExperiments): allModelP = self.allmodelPlots[i] var_means, var_means_obs = allModelP.getSSTPropagationsFromSF() var_means_all.append(var_means) import os from matplotlib import font_manager as fm, rcParams #fpath = os.path.join(rcParams["datapath"], "/export/home/shihweif/ensoComplexity/fonts/Georgia.ttf") prop = fm.FontProperties(fname="fonts/Georgia.ttf") levels = np.arange(6)/5.+0.1 levels = (np.concatenate((-1*levels[::-1],levels)))/2. f,axes = plt.subplots(2,5,figsize=(10,6)) title_prop = fm.FontProperties(fname="fonts/Georgia.ttf", size=14) f.suptitle('Equtorial SST Evolutions for Strong SF Months', fontproperties=title_prop) for i in range(self.numOfExperiments): var_means = var_means_all[i] plot = unCom.plotVarSeasonalCompositeWithHaveHullerENLNOnsetOnly(np.nanmean(var_means, axis=0), axes[:,i+1], levels=levels, ifSST=True) axes[0,i+1].set_title(self.allmodelPlots[i].experiment, fontsize=10, fontproperties=prop) unCom.plotVarSeasonalCompositeWithHaveHullerENLNOnsetOnly(var_means_obs, axes[:,0], levels=levels*2, ifSST=True) axes[0,0].set_title('Reanalysis', fontsize=10, fontproperties=prop) plt.tight_layout() f.subplots_adjust(top=0.9) f.subplots_adjust(right=0.925) cbar_ax = f.add_axes([0.95, 0.05, 0.03, 0.83]) f.colorbar(plot, cax=cbar_ax, spacing='proportional', ticks=[-0.5,-0.4,-0.3,-0.2,-0.1,0,0.1,0.2,0.3,0.4,0.5]) cbar_ax.tick_params(labelsize=8) plt.savefig('Figure/Figure3.png', bbox_inches='tight', dpi=800) plt.close(f)