def _draw_zonal_plot(self, timmean=True, vmin=None, vmax=None, fontsize=8): """ calculate zonal statistics and add to zonal axis Parameters ---------- timmean : bool temporal mean for zonal plot [default=True] vmin : float minimum value for zonal plot vmax : float maximum value for zonal plot """ ZP = ZonalPlot(ax=self.zax, dir='y') if self.x.ndim == 2: pass elif self.x.ndim == 3: nt, ny, nx = self.x.shape ZP.plot(self.x, timmean=timmean, show_ylabel=False) # set limits if ((vmin is None) & (vmax is None)): vmin = self.zax.get_xlim()[0] vmax = self.zax.get_xlim()[1] # symmetry if neg. and positive limits if (vmin < 0.) & (vmax > 0.): val = max(abs(vmin), abs(vmax)) vmin = -val vmax = val if vmin is None: vmin = self.zax.get_xlim()[0] if vmax is None: vmax = self.zax.get_xlim()[1] self.zax.set_xlim(vmin, vmax) # set only first and last label self.zax.set_xticks([vmin, vmax]) self.zax.plot([0, 0], self.zax.get_ylim(), linestyle='-', color='grey') for tick in self.zax.xaxis.get_major_ticks(): tick.label.set_fontsize(fontsize)
map_plot(S, use_basemap=True, ax=ax2) map_plot(I, use_basemap=True, ax=ax3) map_plot(P, use_basemap=True, ax=ax4) f.suptitle('Example of temporal correlation analysis results', size=20) print 'Calculate climatology and plot ...' # get_climatology() returns 12 values which are then used for plotting map_season(D.get_climatology(return_object=True), use_basemap=True, vmin=-20., vmax=30.) print 'Map difference between datasets ...' map_difference(D, P) print 'ZonalPlot ...' Z=ZonalPlot() Z.plot(D) #~ print 'Hovmoeller diagrams ...' #~ hm = HovmoellerPlot(D) #~ hm.plot(climits=[-20.,30.]) #~ print '... generate Hovmoeller plot from deseasonalized anomalies' #~ ha=HovmoellerPlot(D.get_deseasonalized_anomaly(base='all')) #~ ha.plot(climits=[-2.,2.], cmap='RdBu_r') plt.show() r = raw_input("Press Enter to continue...") plt.close('all')
def test_ZonalPlot(self): Z = ZonalPlot() Z.plot(self.D)
map_plot(I, use_basemap=True, ax=ax3) map_plot(P, use_basemap=True, ax=ax4) f.suptitle('Example of temporal correlation analysis results', size=20) print 'Calculate climatology and plot ...' # get_climatology() returns 12 values which are then used for plotting map_season(D.get_climatology(return_object=True), use_basemap=True, vmin=-20., vmax=30.) print 'Map difference between datasets ...' map_difference(D, P) print 'ZonalPlot ...' Z = ZonalPlot() Z.plot(D) #~ print 'Hovmoeller diagrams ...' #~ hm = HovmoellerPlot(D) #~ hm.plot(climits=[-20.,30.]) #~ print '... generate Hovmoeller plot from deseasonalized anomalies' #~ ha=HovmoellerPlot(D.get_deseasonalized_anomaly(base='all')) #~ ha.plot(climits=[-2.,2.], cmap='RdBu_r') plt.show() r = raw_input("Press Enter to continue...") plt.close('all')