def run_all_plots(): #************************************************************************ # Snow cover figure NH, Eurasia, NAmer = read_snow( DATALOC + "Robinson-snow-cover-{}.csv".format(settings.YEAR)) fig = plt.figure(figsize=(8, 5)) plt.clf() ax = plt.axes([0.10, 0.10, 0.86, 0.87]) utils.plot_ts_panel(ax, [NH, Eurasia, NAmer], "-", "cryosphere", loc=LEGEND_LOC) # sort formatting plt.xlim([1966, int(settings.YEAR) + 1]) plt.ylim([-1.9, 3.3]) ax.set_ylabel("Anomaly (Million km" + r'$^2$' + ")", fontsize=settings.FONTSIZE) for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) plt.savefig(settings.IMAGELOC + "SNW_ts{}".format(settings.OUTFMT)) plt.close() return # run_all_plots
def plot_modis_ts(axl, sos, sprt, dummy, label, anomalies, legend_loc): utils.plot_ts_panel(axl, [sos, dummy], "-", "phenological", loc=legend_loc) # make twin axr = axl.twinx() utils.plot_ts_panel(axr, [sprt], "-", "phenological", loc="") # prettify axl.set_ylim([-10, 10]) axr.set_ylim([3, -3]) # labels axl.text(0.02, 0.83, label, transform=axl.transAxes, fontsize=settings.FONTSIZE * 0.8) axl.text(0.47, 0.88, anomalies[0], transform=axl.transAxes) axl.text(0.47, 0.78, anomalies[1], transform=axl.transAxes) # ticks etc minorLocator = MultipleLocator(1) for ax in [axl]: utils.thicken_panel_border(ax) ax.set_yticks(ax.get_yticks()[1:-1]) ax.xaxis.set_minor_locator(minorLocator) for ax in [axr]: ax.yaxis.tick_right() utils.thicken_panel_border(ax) ax.set_yticks(ax.get_yticks()[1:-1]) ax.xaxis.set_minor_locator(minorLocator) axl.set_xlim([1999, 2020]) return # plot_modis_ts
def run_all_plots(): #************************************************************************ # GRACE timeseries if True: # model = read_ts(DATALOC + "glb_avg_tws_2003-2018_model.txt", "Model") # model.ls = "--" grace = read_ts(DATALOC + "avg_JPLM06v2_land.txt", "GRACE") grace_fo = utils.Timeseries("GRACE FO", grace.times[:], grace.data[:]) # post 2018 locs, = np.where(grace_fo.times > 2018) grace_fo.times = grace_fo.times[locs] grace_fo.data = grace_fo.data[locs] # pre 2018 locs, = np.where(grace.times < 2018) grace.times = grace.times[locs] grace.data = grace.data[locs] fig = plt.figure(figsize=(8, 5)) ax1 = plt.axes([0.1, 0.1, 0.88, 0.88]) utils.plot_ts_panel(ax1, [grace, grace_fo], "-", "hydrological", loc=LEGEND_LOC) #******************* # prettify ax1.set_ylim([-5.2, 2.3]) ax1.set_xlim([2003, int(settings.YEAR) + 1.3]) ax1.set_ylabel("Anomaly (cm)", fontsize=settings.FONTSIZE) for tick in ax1.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax1.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) plt.savefig(settings.IMAGELOC + "TWS_ts{}".format(settings.OUTFMT)) plt.close() #************************************************************************ # Hovmuller - Model if False: times, latitudes, data = read_hovmuller(DATALOC + "zonal_mean_tws.txt") bounds = np.array([-200, -12, -9, -6, -3, 0, 3, 6, 9, 12, 200]) utils.plot_hovmuller(settings.IMAGELOC + "TWS_hovmuller_grace", times, latitudes, data, settings.COLOURMAP_DICT["hydrological"], bounds, "Anomaly (cm)") #************************************************************************ # GRACE Hovmuller if True: times, latitudes, data = read_hovmuller_2017(DATALOC) bounds = np.array([-200, -12, -9, -6, -3, 0, 3, 6, 9, 12, 200]) utils.plot_hovmuller(settings.IMAGELOC + "TWS_hovmuller_grace", times, latitudes, data.T, settings.COLOURMAP_DICT["hydrological"], bounds, "Anomaly (cm)") #************************************************************************ # Difference Map if True: cube = read_map_data( DATALOC + "tws_changes_{}-{}_2.txt".format(settings.YEAR, int(settings.YEAR) - 1)) utils.plot_smooth_map_iris( settings.IMAGELOC + "p2.1_TWS_{}_diffs".format(settings.YEAR), cube, settings.COLOURMAP_DICT["hydrological"], bounds, "Difference between {} and {} Equivalent Depth of Water (cm)". format(settings.YEAR, int(settings.YEAR) - 1), figtext="(q) Terrestrial Water Storage") utils.plot_smooth_map_iris( settings.IMAGELOC + "TWS_{}_diffs".format(settings.YEAR), cube, settings.COLOURMAP_DICT["hydrological"], bounds, "Difference between {} and {} Equivalent Depth of Water (cm)". format(settings.YEAR, int(settings.YEAR) - 1)) return # run_all_plots
def run_all_plots(): #************************************************************************ # Global Map if True: bounds = [-1000, -160, -120, -80, -40, 0, 40, 80, 120, 160, 1000] cube = read_map(DATALOC + "map") utils.plot_smooth_map_iris( settings.IMAGELOC + "GLE_{}_anoms".format(settings.YEAR), cube, settings.COLOURMAP_DICT["hydrological"], bounds, "Anomalies from 1981-2010 (mm year" + r'$^{-1}$' + ")") utils.plot_smooth_map_iris( settings.IMAGELOC + "p2.1_GLE_{}_anoms".format(settings.YEAR), cube, settings.COLOURMAP_DICT["hydrological"], bounds, "Anomalies from 1981-2010 (mm year" + r'$^{-1}$' + ")", figtext="(s) Land Evaporation") # Evaporation Map if True: bounds = [-1000, -160, -120, -80, -40, 0, 40, 80, 120, 160, 1000] cube = read_map(DATALOC + "map_transp") utils.plot_smooth_map_iris( settings.IMAGELOC + "GLE_{}_transp".format(settings.YEAR), cube, settings.COLOURMAP_DICT["hydrological"], bounds, "Anomalies from 1981-2010 (mm year" + r'$^{-1}$' + ")") print("no other figures for {}'s report - author has made own".format( settings.YEAR)) sys.exit() #************************************************************************ # Timeseries figures if False: fig, ax1 = plt.subplots(figsize=(8, 5)) globe, NH, SH, SOI = read_time(DATALOC + "timeseries") utils.plot_ts_panel(ax1, [globe, NH, SH], "-", "hydrological", loc=LEGEND_LOC) for data in [globe, NH, SH]: slope, dummy, dummy = utils.median_pairwise_slopes( data.times, data.data, -99.9, 1.) fit_years, fit_values = utils.mpw_plot_points( slope, data.times, data.data) ax1.plot(fit_years, fit_values, c=settings.COLOURS["hydrological"][data.name], \ lw=2, ls="--") ax1.set_ylabel("Anomalies (mm year" + r'$^{-1}$' + ")", fontsize=settings.FONTSIZE) ax1.set_ylim([-29, 29]) for tick in ax1.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) ax2 = ax1.twinx() interpTimes = np.linspace(SOI.times[0], SOI.times[-1], 1000) interpData = np.interp(interpTimes, SOI.times, SOI.data) interpSOI = utils.Timeseries("SOI", interpTimes, interpData) ax2.fill_between(interpSOI.times, interpSOI.data, where=interpSOI.data >= 0, \ color='b', alpha=0.5, zorder=-1) ax2.fill_between(interpSOI.times, interpSOI.data, where=interpSOI.data <= 0, \ color='r', alpha=0.5, zorder=-1) ax2.set_xlim([1979, int(settings.YEAR) + 1]) ax2.set_ylim([-5, 5]) ax2.set_ylabel("SOI", fontsize=settings.FONTSIZE) for tick in ax1.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax2.yaxis.get_major_ticks(): tick.label2.set_fontsize(settings.FONTSIZE) utils.thicken_panel_border(ax2) plt.savefig(settings.IMAGELOC + "GLE_ts{}".format(settings.OUTFMT)) plt.close() #************************************************************************ # Hoemuller figure if False: bounds = [-100, -10, -8, -4, -2, 0, 2, 4, 8, 10, 100] times, latitudes, indata = read_hovmuller(DATALOC + "latitudinal") utils.plot_hovmuller(settings.IMAGELOC + "GLE_hovmuller", times, latitudes, indata, \ settings.COLOURMAP_DICT["hydrological"], bounds, \ "Anomaly (mm month"+r'$^{-1}$'+")") return # run_all_plots
def run_all_plots(): #************************************************************************ # Read in Australian # Data # aus_lat, aus_lon, aus_anom_8110, aus_trend_79_pres, aus_years, aus_anomalies, aus_clim_8110 = \ # read_australia("{}/u_Extracted_ANN_{}_v2.csv".format(DATALOC, settings.YEAR), start_year = 1979) # # read_australia("{}/u_Extracted_ANN_1974_{}_RD.csv".format(DATALOC, settings.YEAR)) # Timeseries figure if True: # ERA data era5_globe, era5_ocean, era5_land, era5tropics = utils.era5_ts_read( settings.REANALYSISLOC, "wnd", annual=True) land_era5_clim, land_era5_anoms = utils.calculate_climatology_and_anomalies_1d( era5_land, CLIMSTART, 2010) land_merra_anoms = utils.read_merra(settings.REANALYSISLOC + "MERRA-2_SfcAnom{}.dat".format(settings.YEAR), \ "wind", "L", anomalies=True) jra_actuals, jra_anoms = utils.read_jra55( settings.REANALYSISLOC + "JRA-55_ws10m_globalland_ts.txt", "windspeed") twenty_cr_actuals = utils.read_20cr( settings.REANALYSISLOC + "wspd10m.land.txt", "wind speed") dummy, twenty_cr_anoms = utils.calculate_climatology_and_anomalies_1d( twenty_cr_actuals, CLIMSTART, 2010) # Plot timeseries figure fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, figsize=(8, 13), sharex=True) print("{} {} {} {} {}".format("name", "mean", "anomaly", "trend/dec", "N station")) for region in all_regions: if region.name == "Australia": # use the Australian data years = aus_years anomalies = aus_anomalies print(anomalies) region.nstat = len(aus_lat) region.slope = np.mean( aus_trend_79_pres ) # just do the mean of all station slopes region.mean = np.mean(aus_clim_8110) else: # Read in the HadISD annual anomalies years, anomalies, m3, m10 = read_hadisd_annual_anomalies( region) # Print data for table print("{} {} {} {} {}".format(region.name, region.mean, anomalies[-1], region.slope * 10., region.nstat)) order = anomalies.argsort() ranks = order.argsort() + 1 print("{} highest {} lowest".format( len(ranks) - ranks[-1], ranks[-1])) # plot data if region.fname == "GlobalNoOz": ax1.plot(years, anomalies, c=region.color, label=region.name, lw=3, zorder=10) else: ax1.plot(years, anomalies, c=region.color, label=region.name, lw=2) if region.fname == "GlobalNoOz": ax3.plot(years, m3, c=region.color, lw=3, zorder=10) ax4.plot(years, m10, c=region.color, lw=3) elif region.name != "Australia": ax3.plot(years, m3, c=region.color, lw=2) ax4.plot(years, m10, c=region.color, lw=2) # plot reanalyses separately ax2.plot(land_era5_anoms.times, land_era5_anoms.data, c=settings.COLOURS["circulation"]["ERA5"], \ label="ERA5 (land only)", lw=2) ax2.plot(land_merra_anoms.times, land_merra_anoms.data, c=settings.COLOURS["circulation"]["MERRA-2"], \ label="MERRA-2 (land only)", lw=2) # ax2.plot(jra_anoms.times, jra_anoms.data, c=settings.COLOURS["circulation"]["JRA-55"], \ # label="JRA-55 (land only)", lw=2) ax2.plot(twenty_cr_anoms.times, twenty_cr_anoms.data, c=settings.COLOURS["circulation"]["20CRv3"], \ label="20CRv3 (land only)", lw=2) # finish off plot ax1.axhline(0, c='0.5', ls='--') ax2.axhline(0, c='0.5', ls='--') ax1.text(0.02, 0.9, "(a) In Situ - all Speeds", transform=ax1.transAxes, fontsize=settings.LABEL_FONTSIZE) ax2.text(0.02, 0.9, "(b) Reanalyses - all Speeds", transform=ax2.transAxes, fontsize=settings.LABEL_FONTSIZE) ax3.text(0.02, 0.9, "(c) In Situ >3 m s"+r'$^{-1}$'+" Winds", transform=ax3.transAxes, \ fontsize=settings.LABEL_FONTSIZE) ax4.text(0.02, 0.9, "(d) In Situ >10 m s"+r'$^{-1}$'+" Winds", transform=ax4.transAxes, \ fontsize=settings.LABEL_FONTSIZE) fig.text(0.02, 0.72, "Wind Anomaly (m s"+r'$^{-1}$'+")", va='center', rotation='vertical', \ fontsize=settings.LABEL_FONTSIZE) fig.text(0.02, 0.3, "Wind Frequency (% yr"+r'$^{-1}$'+")", va='center', rotation='vertical', \ fontsize=settings.LABEL_FONTSIZE) ax1.legend(loc="upper right", ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \ labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=(1.0, 0.9)) ax2.legend(loc="upper right", ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \ labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=(1.0, 0.9)) plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False) fig.subplots_adjust(right=0.96, top=0.99, bottom=0.03, hspace=0.001) for tick in ax4.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) plt.xlim([1970, int(settings.YEAR) + 1]) ax1.set_ylim([-0.39, 1.0]) ax2.set_ylim([-0.39, 1.0]) ax3.set_ylim([23, 68]) ax4.set_ylim([0, 6.5]) minorLocator = MultipleLocator(1) for ax in [ax1, ax2, ax3, ax4]: utils.thicken_panel_border(ax) ax.set_yticks(ax.get_yticks()[1:]) ax.xaxis.set_minor_locator(minorLocator) for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) ax.yaxis.set_ticks_position('left') plt.savefig(settings.IMAGELOC + "WND_land_ts{}".format(settings.OUTFMT)) plt.close() #************************************************************************ # HadISD Anomaly figure if True: # Read in HadISD station anomalies stn_id, hadisd_lons, hadisd_lats, mean8110, hadisd_anomaly_8110, hadisd_trend_79_pres = read_hadisd_global_summary( ) # combine together lats = hadisd_lats lons = hadisd_lons anom = hadisd_anomaly_8110 trend = hadisd_trend_79_pres * 10 # lats = np.append(hadisd_lats, aus_lat) # lons = np.append(hadisd_lons, aus_lon) # anom = combine_arrays((hadisd_anomaly_8110, aus_anom_8110)) # trend = combine_arrays((hadisd_trend_79_pres, aus_trend_79_pres)) * 10. # bounds = [-100, -0.8, -0.4, -0.2, -0.1, 0, 0.1, 0.2, 0.4, 0.8, 100] bounds = [-100, -0.4, -0.2, -0.1, -0.05, 0, 0.05, 0.1, 0.2, 0.4, 100] utils.scatter_plot_map(settings.IMAGELOC + "WND_{}_obs_trend".format(settings.YEAR), trend, \ lons, lats, settings.COLOURMAP_DICT["circulation_r"], bounds, "Trend from {}-{} (m s".format(TRENDSTART, settings.YEAR)+r'$^{-1}$'+" decade"+r'$^{-1}$)') bounds = [-100, -1.2, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 1.2, 100] utils.scatter_plot_map(settings.IMAGELOC + "WND_{}_obs_anomaly".format(settings.YEAR), anom, \ lons, lats, settings.COLOURMAP_DICT["circulation_r"], bounds, "Anomalies from {}-2010 (m s".format(CLIMSTART)+r'$^{-1}$)') total = float(len(anom.compressed())) pos, = np.ma.where(anom > 0) neg, = np.ma.where(anom < 0) print("Anomalies: positive {:5.3f} negative {:5.3f}".format( len(pos) / total, len(neg) / total)) pos, = np.ma.where(anom > 0.5) neg, = np.ma.where(anom < -0.5) print("Anomalies: positive {:5.3f} negative {:5.3f} (than 0.5)".format( len(pos) / total, len(neg) / total)) pos, = np.ma.where(anom > 1.0) neg, = np.ma.where(anom < -1.0) print("Anomalies: positive {:5.3f} negative {:5.3f} (than 1.0)".format( len(pos) / total, len(neg) / total)) total = float(len(trend.compressed())) pos, = np.ma.where(trend > 0) neg, = np.ma.where(trend < 0) print("Trends: positive {:5.3f} negative {:5.3f}".format( len(pos) / total, len(neg) / total)) #************************************************************************ # ERA5 + HadISD Anomaly figure if True: # Read in ERA anomalies cube_list = iris.load(settings.REANALYSISLOC + "era5_ws10_{}01-{}12_ann_ano.nc".format( settings.YEAR, settings.YEAR)) cube = cube_list[0] cube.coord('latitude').guess_bounds() cube.coord('longitude').guess_bounds() bounds = [-4, -1.2, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 1.2, 4] utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_era5_obs_anomaly".format(settings.YEAR), cube[0], \ settings.COLOURMAP_DICT["circulation_r"], bounds, "Anomalies from {}-2010 (m s".format(CLIMSTART)+r'$^{-1}$)', scatter = (lons, lats, anom)) utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_era5_anomaly".format(settings.YEAR), cube[0], \ settings.COLOURMAP_DICT["circulation_r"], bounds, "Anomalies from {}-2010 (m s".format(CLIMSTART)+r'$^{-1}$)') #************************************************************************ # MERRA Anomaly figure if True: anoms = read_ocean_ncdf( DATALOC + "rss_wind_trend_anomaly_SOTC_{}_updated.nc".format(settings.YEAR), "MERRA2_wind_anomaly_map_{}".format(settings.YEAR)) bounds = [-40, -1.2, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 1.2, 40] utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_merra2_anomaly", anoms, settings.COLOURMAP_DICT["circulation_r"], bounds,\ "Anomalies from 1981-2010 (m s"+r'$^{-1}$'+")") #************************************************************************ # MERRA + HadISD Anomaly figure if True: # Read in MERRA anomalies cube_list = iris.load( settings.REANALYSISLOC + "MERRA-2_SfcAnom_{}.nc".format(settings.YEAR), "10m Wind Speed Anomaly (1981-2010)") cube = cube_list[0] cube.coord('latitude').guess_bounds() cube.coord('longitude').guess_bounds() bounds = [-4, -1.2, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 1.2, 4] utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_merra_obs_anomaly".format(settings.YEAR), cube[0], \ settings.COLOURMAP_DICT["circulation_r"], bounds, \ "Anomalies from {}-2010 (m s".format(CLIMSTART)+r'$^{-1}$)', scatter=(lons, lats, anom)) utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_WND_{}_merra_obs_anomaly".format(settings.YEAR), cube[0], \ settings.COLOURMAP_DICT["circulation_r"], bounds, \ "Anomalies from {}-2010 (m s".format(CLIMSTART)+r'$^{-1}$)', figtext="(v) Surface Winds", scatter=(lons, lats, anom)) utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_merra_anomaly".format(settings.YEAR), cube[0], \ settings.COLOURMAP_DICT["circulation_r"], bounds, \ "Anomalies from {}-2010 (m s".format(CLIMSTART)+r'$^{-1}$)') #************************************************************************ # MERRA/RSS ocean + HadISD Anomaly figure if True: # Read in MERRA/RSS trends anomalies = read_ocean_ncdf( DATALOC + "rss_wind_trend_anomaly_SOTC_{}_updated.nc".format(settings.YEAR), "Merged_wind_anomaly_map_{}".format(settings.YEAR)) bounds = [-4, -1.2, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 1.2, 4] utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_merra-rss_anomaly".format(settings.YEAR), \ anomalies, settings.COLOURMAP_DICT["circulation_r"], bounds, \ "Anomalies from 1981-2010 (m s"+r'$^{-1}$)') utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_merra-rss_obs_anomaly".format(settings.YEAR), \ anomalies, settings.COLOURMAP_DICT["circulation_r"], bounds, \ "Anomalies from 1981-2010 (m s"+r'$^{-1}$)', scatter=(lons, lats, anom)) utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_WND_{}_merra-rss_obs_anomaly".format(settings.YEAR), \ anomalies, settings.COLOURMAP_DICT["circulation_r"], bounds, \ "Anomalies from 1981-2010 (m s"+r'$^{-1}$)', figtext="(v) Surface Winds", scatter=(lons, lats, anom)) #************************************************************************ # Ocean timeseries if True: satellite = read_ts_cube( DATALOC + "rss_wind_trend_anomaly_SOTC_{}.nc".format(settings.YEAR), "RSS_wind_global_annual_anom_ts", "Satellite MW Radiometers") satellite_clim, satellite_anom = utils.calculate_climatology_and_anomalies_1d( satellite, 1988, 2010) # print("NO IN SITU OCEAN DATA FOR 2016, using 2015 data") # nocs = read_ts_cube(DATALOC + "NOCSv2.0_oceanW_5by5_8110anoms_areaTS_FEB2016.nc", "Globally Average 70S-70N", "NOCSv2.0") # WASwind = read_ts_cube(DATALOC + "waswind_v1_0_1.monthly_areaTS_19502011.nc","Globally Averaged Anomalies 70S-70N", "WASwind") # print("FIXING WASWIND TIMES - DATAFILE HAS WRONG DESCRIPTOR") # WASwind.times = WASwind.times - (1973-1950) jra_actuals, jra_anoms = utils.read_jra55( settings.REANALYSISLOC + "JRA-55_ws10m_globalocean_ts.txt", "wind") era5_globe, era5_ocean, era5_land, era5tropics = utils.era5_ts_read( settings.REANALYSISLOC, "wnd", annual=True) ocean_era5_clim, ocean_era5_anoms = utils.calculate_climatology_and_anomalies_1d( era5_ocean, 1988, 2010) merra_anoms = utils.read_merra( settings.REANALYSISLOC + "MERRA-2_SfcAnom{}.dat".format(settings.YEAR), "wind", "O", anomalies=True) twenty_cr_actuals = utils.read_20cr( settings.REANALYSISLOC + "wspd10m.ocean.txt", "wind speed") dummy, twenty_cr_anoms = utils.calculate_climatology_and_anomalies_1d( twenty_cr_actuals, 1988, 2010) fig, (ax1) = plt.subplots(1, figsize=(8, 5), sharex=True) # Satellite # utils.plot_ts_panel(ax1, [satellite_anom], "-", "circulation", loc=LEGEND_LOC, bbox=BBOX) # In Situ # utils.plot_ts_panel(ax2, [nocs, WASwind], "-", "circulation", loc=LEGEND_LOC, bbox=BBOX) # ax2.set_ylabel("Anomaly (m s"+r'$^{-1}$'+")", fontsize = settings.FONTSIZE) # Reanalyses & Satellite single panel satellite_anom.lw = 4 satellite_anom.zorder = 10 utils.plot_ts_panel( ax1, [satellite_anom, ocean_era5_anoms, merra_anoms, twenty_cr_anoms], "-", "circulation", loc=LEGEND_LOC, bbox=BBOX) #******************* # prettify ax1.axhline(0, c='0.5', ls='--') plt.ylabel("Wind Anomaly (m s" + r'$^{-1}$' + ")", fontsize=settings.LABEL_FONTSIZE) ax1.legend(loc="upper right", ncol=1, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \ labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=(1.0, 0.99)) # sort formatting plt.xlim([1970, int(settings.YEAR) + 1]) for tick in ax1.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) # for ax in [ax1, ax2, ax3]: for ax in [ax1]: for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) ax.set_ylim([-0.28, 0.45]) ax.yaxis.set_ticks([-0.2, 0.0, 0.2, 0.4]) ax.yaxis.set_ticks_position('left') # sort labelling ax1.text(0.03, 0.9, "Satellites & Reanalyses", transform=ax1.transAxes, fontsize=settings.LABEL_FONTSIZE) # ax2.text(0.03, 0.9, "(b) In Situ", transform=ax2.transAxes, fontsize=settings.LABEL_FONTSIZE) # ax3.text(0.03, 0.9, "(b) Reanalyses", transform=ax3.transAxes, fontsize=settings.LABEL_FONTSIZE fig.subplots_adjust(right=0.95, top=0.95, hspace=0.001) plt.savefig(settings.IMAGELOC + "WND_ocean_ts{}".format(settings.OUTFMT)) plt.close() #************************************************************************ # Ocean maps if True: anoms = read_ocean_ncdf( DATALOC + "rss_wind_trend_anomaly_SOTC_{}_updated.nc".format(settings.YEAR), "RSS_wind_anomaly_map_{}".format(settings.YEAR)) bounds = [-40, -1.2, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 1.2, 40] utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_rss_anomaly".format(settings.YEAR), anoms, settings.COLOURMAP_DICT["circulation_r"], bounds,\ "Anomalies from 1981-2010 (m s"+r'$^{-1}$'+")") #************************************************************************ # MERRA/RSS ocean + HadISD Trend figure if True: # Read in MERRA/RSS trends trends = read_ocean_ncdf( DATALOC + "rss_wind_trend_anomaly_SOTC_{}_updated.nc".format(settings.YEAR), "Wind_trend_map") bounds = [-4, -0.8, -0.4, -0.2, -0.1, 0, 0.1, 0.2, 0.4, 0.8, 4] bounds = [-100, -0.4, -0.2, -0.1, -0.05, 0, 0.05, 0.1, 0.2, 0.4, 100] utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_merra-rss_trend".format(settings.YEAR), \ trends, settings.COLOURMAP_DICT["circulation_r"], bounds, \ "Trend from {}-{} (m s".format(TRENDSTART, settings.YEAR)+r'$^{-1}$'+" decade"+r'$^{-1}$)') utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_merra-rss_obs_trend".format(settings.YEAR), \ trends, settings.COLOURMAP_DICT["circulation_r"], bounds, \ "Trend from {}-{} (m s".format(TRENDSTART, settings.YEAR)+r'$^{-1}$'+" decade"+r'$^{-1}$)', scatter=(lons, lats, trend)) return # run_all_plots
def run_all_plots(): #************************************************************************ # Timeseries figure (2 panels) if True: for region in ["global"]: if region == "global": raobcore, rich, ratpac, UAH, rss, era5, merra, jra = \ read_csv(DATALOC + "SotC_AnnTemps_2020_0520_LTTGL.csv") plt.clf() fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 10), sharex=True) # sondes utils.plot_ts_panel(ax1, [raobcore, rich, ratpac], "-", "temperature", loc=LEGEND_LOC) # satellites utils.plot_ts_panel(ax2, [UAH, rss], "-", "temperature", loc=LEGEND_LOC) ax2.set_ylabel("Anomaly ("+r'$^\circ$'+"C)", fontsize=settings.FONTSIZE) # reanalyses if region == "global": # jra_actuals, jra_anoms = utils.read_jra55(settings.REANALYSISLOC + "JRA-55_MSUch2LT_global_ts.txt", "temperature") # merra_actuals, merra_anoms = utils.read_merra_LT_LS(settings.REANALYSISLOC + "MERRA2_MSU_Tanom_ann_{}.dat".format(settings.YEAR), LT=True) # utils.plot_ts_panel(ax3, [erai, era5, jra_anoms, merra_anoms], "-", "temperature", loc=LEGEND_LOC) twenty_cr_actuals = utils.read_20cr(settings.REANALYSISLOC + "tlt.global.txt", "temperature") dummy, twenty_cr_anoms = utils.calculate_climatology_and_anomalies_1d(twenty_cr_actuals, 1981, 2010) utils.plot_ts_panel(ax3, [era5, jra, merra], "-", "temperature", loc=LEGEND_LOC) else: utils.plot_ts_panel(ax3, [era5], "-", "temperature", loc=LEGEND_LOC) # sort formatting plt.xlim([raobcore.times[0]-1, raobcore.times[-1]+1]) ax1.set_ylim([-0.89, 0.89]) ax2.set_ylim([-0.89, 0.89]) ax3.set_ylim([-0.89, 0.89]) for tick in ax3.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax1.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax2.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax3.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) # sort labelling ax1.text(0.02, 0.9, "(a) Radiosondes", transform=ax1.transAxes, fontsize=settings.LABEL_FONTSIZE) ax2.text(0.02, 0.9, "(b) Satellites", transform=ax2.transAxes, fontsize=settings.LABEL_FONTSIZE) ax3.text(0.02, 0.9, "(c) Reanalyses", transform=ax3.transAxes, fontsize=settings.LABEL_FONTSIZE) fig.subplots_adjust(right=0.98, top=0.98, bottom=0.05, hspace=0.001) plt.savefig(settings.IMAGELOC+"LTT_ts_{}{}".format(region, settings.OUTFMT)) plt.close() # #************************************************************************ # # Land Fraction - 2018 # high, low = read_hilo(DATALOC + "high_low.dat") # fig, ax1 = plt.subplots(1, figsize=(8, 5)) # ax1.plot(high.times, high.data, c="r", ls="-", lw=2, label=high.name) # ax1.plot(low.times, low.data, c="b", ls="-", lw=2, label=low.name) # ax1.legend(loc="upper right", ncol=2, frameon=False, prop={'size':settings.FONTSIZE}) # ax1.set_xlim([1978, int(settings.YEAR)+2]) # ax1.set_ylabel("Percentage of Global Area (%)", fontsize=settings.FONTSIZE) # for tick in ax1.yaxis.get_major_ticks(): # tick.label.set_fontsize(settings.FONTSIZE) # for tick in ax1.xaxis.get_major_ticks(): # tick.label.set_fontsize(settings.FONTSIZE) # utils.thicken_panel_border(ax1) # plt.savefig(settings.IMAGELOC+"LTT_land_area_ts{}".format(settings.OUTFMT)) # plt.close() #************************************************************************ # Read in ERA5 anomalies if False: cube_list = iris.load(DATALOC + "2019TLTAnom.nc") names = np.array([c.var_name for c in cube_list]) loc, = np.where(names == "tltAnnualAnom")[0] cube = cube_list[loc] bounds = np.array([-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]) bounds = np.array([-100, -1.6, -1.2, -0.8, -0.4, 0, 0.4, 0.8, 1.2, 1.6, 100]) bounds = np.array([-100, -2.0, -1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5, 2.0, 100]) cmap = settings.COLOURMAP_DICT["temperature"] utils.plot_smooth_map_iris(settings.IMAGELOC + "LTT_{}_anoms_era5".format(settings.YEAR), cube, cmap, \ bounds, "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="ERA5") utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_LTT_{}_anoms_era5".format(settings.YEAR), cube, \ cmap, bounds, "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", \ figtext="(e) Lower Tropospheric Temperature") #************************************************************************ # Read in ERA5 anomalies with record pixels if True: cube_list = iris.load(DATALOC + "2019TLTAnom_warmest.nc") names = np.array([c.var_name for c in cube_list]) loc, = np.where(names == "tltAnnualAnom")[0] cube = cube_list[loc] loc, = np.where(names == "recordMask")[0] record = cube_list[loc] record.coord("latitude").guess_bounds() record.coord("longitude").guess_bounds() lats, lons, data = [], [], [] for t, lat in enumerate(record.coord("latitude").points): for n, lon in enumerate(record.coord("longitude").points): if record.data[t, n] == 1: data += [cube.data[t, n]] lats += [np.mean(record.coord("latitude").bounds[t])] lons += [np.mean(record.coord("longitude").bounds[n])] bounds = np.array([-100, -2.0, -1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5, 2.0, 100]) cmap = settings.COLOURMAP_DICT["temperature"] utils.plot_smooth_map_iris(settings.IMAGELOC + "LTT_{}_anoms_era5".format(settings.YEAR), cube, cmap, \ bounds, "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="ERA5", \ scatter=(lons, lats, data), smarker="dots") utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_LTT_{}_anoms_era5".format(settings.YEAR), cube, \ cmap, bounds, "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", \ figtext="(e) Lower Tropospheric Temperature", \ scatter=(lons, lats, data), smarker="dots") #************************************************************************ # ERA-I Hovmuller # times, latitudes, data = utils.erai_2dts_read(settings.REANALYSISLOC, "ltt") # bounds = np.array([-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]) # bounds = np.array([-100, -1.6, -1.2, -0.8, -0.4, 0, 0.4, 0.8, 1.2, 1.6, 100]) # # sort the time axis # start = (times[0] - 101)/10000. # end = (times[-1] - 1201)/10000. # year = start # new_times = [] # months = (np.arange(12))/12. # while year <= end: # new_times += [np.array([year for i in range(12)]) + months] # year += 1 # new_times = np.array(new_times).reshape(-1) # # sort the climatology to 1981-2010 (monthly!) # # reshape to deal with monthly # data = data.reshape(-1, 12, data.shape[-1]) # new_times = new_times.reshape(-1, 12) # # extract climatology period # start_loc, = np.where(new_times[:, 0] == CLIMSTART) # end_loc, = np.where(new_times[:, 0] == CLIMEND) # clim_data = data[start_loc[0]:end_loc[0] + 1, :, :] # climatology = np.mean(clim_data, axis=0) # # make anomalies # data = np.array([data[i, :, :] - climatology for i in range(data.shape[0])]) # # return to original shapes # data = data.reshape(-1, data.shape[-1]) # new_times = new_times.reshape(-1) # utils.plot_hovmuller(settings.IMAGELOC + "LTT_hovmuller_erai", new_times, latitudes, data.T, \ # settings.COLOURMAP_DICT["temperature"], bounds, \ # "Anomaly ("+r'$^{\circ}$'+"C)", cosine=True) # version with MEI on top # mei = read_mei(os.path.join(DATALOC, "MEI.dat")) # utils.plot_hovmuller(settings.IMAGELOC + "LTT_hovmuller_era_MEI", new_times, latitudes, data.T, \ # settings.COLOURMAP_DICT["temperature"], bounds, \ # "Anomaly ("+r'$^{\circ}$'+"C)", cosine=True, extra_ts=mei) return # run_all_plots
def run_all_plots(): #************************************************************************ # Precipitation Timeseries if False: ghcn, gpcc, gpcp, ghcn2, erai, merra2 = read_land( DATALOC + "Land_insitu_timeseries-1979.dat") fig = plt.figure(figsize=(8, 5)) ax1 = plt.axes([0.1, 0.1, 0.85, 0.85]) # Land utils.plot_ts_panel(ax1, [ghcn, gpcc, gpcp, ghcn2, erai, merra2], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX) ax1.set_ylim([-60, 100]) ax1.yaxis.set_ticks([-50, -25, 0, 25, 50, 75, 100]) ax1.set_ylabel("Anomaly (mm)", fontsize=settings.FONTSIZE) for tick in ax1.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax1.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) # ax1.text(0.02, 0.9, "(a) Land in Situ", transform=ax1.transAxes, fontsize=settings.LABEL_FONTSIZE) plt.savefig(settings.IMAGELOC + "PCP_ts{}".format(settings.OUTFMT)) plt.close() # old 3-panel plot for 2015 report if False: fig = plt.figure(figsize=(8, 8)) ax1 = plt.axes([0.11, 0.68, 0.78, 0.3]) ax2 = plt.axes([0.11, 0.38, 0.78, 0.3], sharex=ax1) ax3 = plt.axes([0.11, 0.08, 0.78, 0.3], sharex=ax1) # Land utils.plot_ts_panel(ax1, [ghcn, gpcc, gpcp, ghcn2, erai, merra2], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX) # Ocean ocean_year = read_domain( DATALOC + "gpcp_v23_globalocean_ann_1979_2018", "GPCP") o_clim, o_anoms = utils.calculate_climatology_and_anomalies_1d( ocean_year, 1981, 2010) ax2.plot(o_anoms.times, o_anoms.data, c="b", ls="-", label=o_anoms.name, lw=2) # Nino and others land_year = read_domain(DATALOC + "gpcp_v23_globalland_ann_1979_2018", "GPCP") l_clim, l_anoms = utils.calculate_climatology_and_anomalies_1d( land_year, 1981, 2010) combined_year = read_domain( DATALOC + "gpcp_v23_globallandocean_ann_1979_2018", "GPCP") c_clim, c_anoms = utils.calculate_climatology_and_anomalies_1d( combined_year, 1981, 2010) nino = read_domain(DATALOC + "nino34_1979_2018_ann_anomaly", "Nino 3.4") ax3.plot(o_anoms.times, o_anoms.data, c="b", ls="-", label="{} Ocean".format(o_anoms.name), lw=2, zorder=10) ax3.plot(l_anoms.times, l_anoms.data, c="lime", ls="-", label="{} Land".format(l_anoms.name), lw=2, zorder=10) ax3.plot(c_anoms.times, c_anoms.data, c="r", ls="-", label="{} Land + Ocean".format(c_anoms.name), lw=2, zorder=10) ax3.plot([1960, 1961], [0, 0], c="k", label="Nino 3.4") ax4 = ax3.twinx() ax4.plot(nino.times, nino.data, c="k", zorder=1) ax4.fill_between(nino.times, nino.data, 0, color="0.5", label=nino.name, zorder=1) ax4.set_ylim([-1.9, 1.9]) ax3.patch.set_visible(False) ax3.set_zorder(ax4.get_zorder() + 1) #******************* # prettify minorLocator = MultipleLocator(1) ax1.set_ylim([-60, 100]) ax1.yaxis.set_ticks([-50, 0, 50, 100]) for ax in [ax2, ax3]: ax.set_ylim([-50, 50]) ax.axhline(0, c='0.5', ls='--') ax.legend(loc=LEGEND_LOC, ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \ labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=BBOX) ax.xaxis.set_minor_locator(minorLocator) ax.yaxis.set_ticks_position('left') utils.thicken_panel_border(ax) ax2.set_ylabel("Anomaly (mm yr" + r'$^{-1}$' + ")", fontsize=settings.FONTSIZE) plt.setp(ax1.get_xticklabels(), visible=False) plt.setp(ax2.get_xticklabels(), visible=False) ax4.set_xlim([1979, int(settings.YEAR) + 1]) ax4.yaxis.set_label_position("right") ax3.yaxis.set_tick_params(right=False) utils.thicken_panel_border(ax4) for ax in [ax1, ax2, ax3]: for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax3.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax4.yaxis.get_major_ticks(): tick.label2.set_fontsize(settings.FONTSIZE) # sort labelling ax1.text(0.02, 0.9, "(a) Land in Situ", transform=ax1.transAxes, fontsize=settings.LABEL_FONTSIZE) ax2.text(0.02, 0.9, "(b) Ocean", transform=ax2.transAxes, fontsize=settings.LABEL_FONTSIZE) ax3.text(0.02, 0.9, "(c) Globe", transform=ax3.transAxes, fontsize=settings.LABEL_FONTSIZE) plt.savefig(settings.IMAGELOC + "PCP_ts_3panel{}".format(settings.OUTFMT)) plt.close() # 3-panel plot for 2019 report if True: fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 8), sharex=True) # Land ghcn, gpcc, gpcp = read_land(DATALOC + "Land_insitu_timeseries-1979.dat") utils.plot_ts_panel(ax1, [ghcn, gpcc, gpcp], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX) # Ocean gpcp = read_ocean(DATALOC + "Ocean_insitu_timeseries-1979.dat") utils.plot_ts_panel(ax2, [gpcp], "-", "hydrological", loc="", bbox=BBOX) # Globe gpcp = read_ocean(DATALOC + "Global_insitu_timeseries-1979.dat") utils.plot_ts_panel(ax3, [gpcp], "-", "hydrological", loc="", bbox=BBOX) #******************* # prettify fig.subplots_adjust(right=0.98, bottom=0.08, top=0.98, hspace=0.001) minorLocator = MultipleLocator(1) ax1.set_ylim([-60, 100]) ax1.yaxis.set_ticks([-50, 0, 50, 100]) ax2.set_ylim([-29, 43]) ax3.set_ylim([-29, 43]) for ax in [ax1, ax2, ax3]: for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax3.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) ax2.set_ylabel("Anomaly (mm yr" + r'$^{-1}$' + ")", fontsize=settings.FONTSIZE) # sort labelling ax1.text(0.02, 0.9, "(a) Land in Situ", transform=ax1.transAxes, fontsize=settings.LABEL_FONTSIZE) ax2.text(0.02, 0.9, "(b) Ocean", transform=ax2.transAxes, fontsize=settings.LABEL_FONTSIZE) ax3.text(0.02, 0.9, "(c) Globe", transform=ax3.transAxes, fontsize=settings.LABEL_FONTSIZE) plt.savefig(settings.IMAGELOC + "PCP_ts_3panel{}".format(settings.OUTFMT)) plt.close() #************************************************************************ # GPCP map if True: cube = read_map( DATALOC + "GPCP_{}anomaly_base_1981-2000.txt".format(settings.YEAR)) bounds = [-2000, -400, -300, -200, -100, 0, 100, 200, 300, 400, 2000] utils.plot_smooth_map_iris( settings.IMAGELOC + "PCP_{}_anoms_gpcp".format(settings.YEAR), cube, settings.COLOURMAP_DICT["hydrological"], bounds, "Anomalies from 1981-2000 (mm)") utils.plot_smooth_map_iris( settings.IMAGELOC + "p2.1_PCP_{}_anoms_gpcp".format(settings.YEAR), cube, settings.COLOURMAP_DICT["hydrological"], bounds, "Anomalies from 1981-2000 (mm)", figtext="(k) Precipitation") #************************************************************************ # GPCP map - Ocean only # # Read Oceans # print "FIX YEAR" # cube = iris.load(DATALOC + "ocean_precipitation_sotc2015.nc", "anomaly_map")[0] # # add in the coordinates which haven't been stored sensibly # for c, coord in enumerate(["latitude", "longitude"]): # coord_cube = iris.load(DATALOC + "ocean_precipitation_sotc2015.nc", coord)[0] # if c == 0: # iris_coord = iris.coords.DimCoord(coord_cube.data[:,0], standard_name=coord, units='degrees') # elif c == 1: # iris_coord = iris.coords.DimCoord(coord_cube.data[0], standard_name=coord, units='degrees') # cube.add_dim_coord(iris_coord,c) # cube.coord(coord).guess_bounds() # bounds=[-2000, -400, -300, -200, -100, 0, 100, 200, 300, 400, 2000] # utils.plot_smooth_map_iris(settings.IMAGELOC + "PCP_{}_anoms_gpcp_ocean".format(settings.YEAR), cube, settings.COLOURMAP_DICT["hydrological"], bounds, "Anomalies from 1981-2000 (mm)") return # run_all_plots
def run_all_plots(): #*********************** # MODIS - centre cubelist = iris.load( os.path.join(data_loc, "MODIS.CMG.{}.SOS.EOS.Anomaly.nc".format(settings.YEAR))) for c, cube in enumerate(cubelist): if cube.name() == "SOS": sos_cube = cubelist[c] # deal with NANS sos_cube.data = np.ma.masked_where(sos_cube.data != sos_cube.data, sos_cube.data) # read in sites us_locations = read_us_phenocam(os.path.join(data_loc, "phenocam_locs.txt")) fig = plt.figure(figsize=(8, 12)) plt.clf() # set up plot settings BOUNDS = [-100, -20, -10, -5, -2, 0, 2, 5, 10, 20, 100] LABELS = "(c) Start of Season (SOS)" #, "(b) End of Season (EOS)"] # boundary circle theta = np.linspace(0, 2 * np.pi, 100) center, radius = [0.5, 0.5], 0.5 verts = np.vstack([np.sin(theta), np.cos(theta)]).T circle = mpath.Path(verts * radius + center) # axes for polar plot ax = plt.axes( [0.05, 0.01, 0.9, 0.65], projection=cartopy.crs.NorthPolarStereo(central_longitude=300.0)) plot_cube = sos_cube if settings.OUTFMT in [".eps", ".pdf"]: if plot_cube.coord("latitude").points.shape[0] > 90 or plot_cube.coord( "longitude").points.shape[0] > 360: regrid_size = 1.0 print( "Regridding cube for {} output to {} degree resolution".format( settings.OUTFMT, regrid_size)) print("Old Shape {}".format(plot_cube.data.shape)) plot_cube = utils.regrid_cube(plot_cube, regrid_size, regrid_size) print("New Shape {}".format(plot_cube.data.shape)) ax.gridlines() #draw_labels=True) ax.add_feature(cartopy.feature.LAND, zorder=0, facecolor="0.9", edgecolor="k") ax.coastlines() ax.set_boundary(circle, transform=ax.transAxes) cmap = settings.COLOURMAP_DICT["phenological_r"] norm = mpl.cm.colors.BoundaryNorm(BOUNDS, cmap.N) mesh = iris.plot.pcolormesh(plot_cube, cmap=cmap, norm=norm, axes=ax) # plot scatter COL = "yellow" ax.scatter(us_locations[1], us_locations[0], c=COL, s=100, edgecolor="k", transform=cartopy.crs.Geodetic(), zorder=10) ax.scatter(-2.9376, 54.3739, c=COL, s=100, edgecolor="k", transform=cartopy.crs.Geodetic(), zorder=10) # uk box region = [-10.0, 49.0, 3.0, 60.0] ax.plot([region[0], region[0]], [region[1], region[3]], c=COL, ls='-', lw=4, zorder=10, transform=cartopy.crs.PlateCarree()) ax.plot([region[2], region[2]], [region[1], region[3]], c=COL, ls='-', lw=4, zorder=10, transform=cartopy.crs.PlateCarree()) ax.plot([region[0], region[2]], [region[1], region[1]], c=COL, ls='-', lw=4, zorder=10, transform=cartopy.crs.Geodetic()) ax.plot([region[0], region[2]], [region[3], region[3]], c=COL, ls='-', lw=4, zorder=10, transform=cartopy.crs.Geodetic()) COL = "k" ax.plot([region[0], region[0]], [region[1], region[3]], c=COL, ls='-', lw=5, zorder=9, transform=cartopy.crs.PlateCarree()) ax.plot([region[2], region[2]], [region[1], region[3]], c=COL, ls='-', lw=5, zorder=9, transform=cartopy.crs.PlateCarree()) ax.plot([region[0], region[2]], [region[1], region[1]], c=COL, ls='-', lw=5, zorder=9, transform=cartopy.crs.Geodetic()) ax.plot([region[0], region[2]], [region[3], region[3]], c=COL, ls='-', lw=5, zorder=9, transform=cartopy.crs.Geodetic()) # label axes ax.text(-0.1, 1.0, LABELS, fontsize=settings.FONTSIZE * 0.8, transform=ax.transAxes) cb = plt.colorbar(mesh, orientation='horizontal', ticks=BOUNDS[1:-1], label="Anomaly (days)", drawedges=True, fraction=0.1, pad=0.05, aspect=15, shrink=0.8) # prettify cb.set_ticklabels(["{:g}".format(b) for b in BOUNDS[1:-1]]) cb.outline.set_linewidth(2) cb.dividers.set_color('k') cb.dividers.set_linewidth(2) ax.set_extent([-180, 180, 30, 90], cartopy.crs.PlateCarree()) for lat in range(30, 100, 10): ax.text(180, lat, '{}$^\circ$N'.format(lat), transform=cartopy.crs.Geodetic()) fig.subplots_adjust(bottom=0.05, top=0.95, left=0.04, right=0.95, wspace=0.02) del sos_cube del cubelist #*********************** # MODIS timeseries - 2018 sos_na, sos_ea, sprt_na_orig, sprt_ea_orig = read_modis_ts( os.path.join( data_loc, "MODIS.CMG.{}.SOS.EOS.SPRT.FALT.TS.csv".format(settings.YEAR))) dummy, sos_na = utils.calculate_climatology_and_anomalies_1d( sos_na, 2000, 2010) dummy, sos_ea = utils.calculate_climatology_and_anomalies_1d( sos_ea, 2000, 2010) dummy, sprt_na = utils.calculate_climatology_and_anomalies_1d( sprt_na_orig, 2000, 2010) dummy, sprt_ea = utils.calculate_climatology_and_anomalies_1d( sprt_ea_orig, 2000, 2010) ax = plt.axes([0.1, 0.7, 0.8, 0.15]) label = "(b) North America" anomalies = [ "2018 SOS Anomaly = 1.86 days", "2018 Spring T anomaly = -0.75 " + r'$^{\circ}$' + "C" ] plot_modis_ts(ax, sos_na, sprt_na, sprt_na_orig, label, anomalies, LEGEND_LOC) na_trend = -0.64 / 10 ax.plot([sos_na.times[0], sos_na.times[-1]], utils.trendline(na_trend, sos_na.times), ls="--", zorder=10, c="g", lw=2) print(utils.trendline(na_trend, sos_na.times)) ax1 = plt.axes([0.1, 0.85, 0.8, 0.15], sharex=ax) label = "(a) Eurasia" anomalies = [ "2018 SOS Anomaly = 2.01 days", "2018 Spring T anomaly = 0.13 " + r'$^{\circ}$' + "C" ] plot_modis_ts(ax1, sos_ea, sprt_ea, sprt_ea_orig, label, anomalies, "") ea_trend = -1.59 / 10 ax1.plot([sos_ea.times[0], sos_ea.times[-1]], utils.trendline(ea_trend, sos_ea.times), ls="--", zorder=10, c="g", lw=2) print(utils.trendline(ea_trend, sos_ea.times)) plt.setp(ax1.get_xticklabels(), visible=False) plt.setp(ax.get_xticklabels(), visible=True) fig.text(0.05, 0.92, "SOS Anomaly (days)", rotation="vertical") fig.text(0.95, 0.92, "Temperature Anomaly (" + r'$^{\circ}$' + "C)", rotation="vertical") plt.savefig(image_loc + "PHEN_modis_{}{}".format(settings.YEAR, settings.OUTFMT)) #*********************** # US timeseries - 2018 fig = plt.figure(figsize=(8, 10)) plt.clf() barrow, ozarks, turkey = read_us_phenocam_csv( os.path.join(data_loc, "Richardson PhenoCam plots for Sidebar.csv")) ax = plt.axes([0.1, 0.03, 0.35, 0.17]) plot_us_phenocam(ax, barrow, [160, 229], "Barrow") ax.text(0.05, 0.85, "(e) Barrow", transform=ax.transAxes) ax = plt.axes([0.1, 0.23, 0.35, 0.17]) plot_us_phenocam(ax, ozarks, [90, 159], "Ozarks", ylabel="Vegetation Greenness Index", legend=True) ax.text(0.05, 0.85, "(d) Ozarks", transform=ax.transAxes) ax = plt.axes([0.1, 0.43, 0.35, 0.17]) plot_us_phenocam(ax, turkey, [60, 129], "Turkey Point") ax.text(0.05, 0.85, "(c) Turkey Point", transform=ax.transAxes) ax = plt.axes([0.48, 0.02, 0.25, 0.2]) plot_images(ax, "barrow_2017_06_27_100002.jpg") ax = plt.axes([0.48, 0.22, 0.25, 0.2]) plot_images(ax, "missouriozarks_2017_04_30_163113.jpg") ax = plt.axes([0.48, 0.42, 0.25, 0.2]) plot_images(ax, "turkeypointenf39_2017_04_18_113106.jpg") ax = plt.axes([0.73, 0.02, 0.25, 0.2]) plot_images(ax, "barrow_2018_06_27_103003.jpg") ax = plt.axes([0.73, 0.22, 0.25, 0.2]) plot_images(ax, "missouriozarks_2018_04_30_163113.jpg") ax = plt.axes([0.73, 0.42, 0.25, 0.2]) plot_images(ax, "turkeypointenf39_2018_04_18_113302.jpg") plt.figtext(0.6, 0.02, "2017") plt.figtext(0.85, 0.02, "2018") #*********************** # UK timeseries - 2018 ax = plt.axes([0.1, 0.65, 0.85, 0.15]) oak = read_uk_oak_csv( os.path.join(data_loc, "UK Oak budburst 2000-2018.csv")) utils.plot_ts_panel(ax, [oak], "-", "phenological", loc="lower left") ax.set_ylim([94, 124]) ax.text(0.03, 0.8, "(b) Oak Budburst", transform=ax.transAxes) # fix the legend label to be italic handles, labels = ax.get_legend_handles_labels() labels[ 0] = "$\mathregular{\mathit{Quercus}}$" + " " + "$\mathregular{\mathit{robur}}$" ax.legend(handles, labels, frameon=False, loc="lower left") # need legend once have other panel's data north, south = read_windermere_csv( os.path.join(data_loc, "Windermere_2000-2018.csv")) ax2 = plt.axes([0.1, 0.8, 0.85, 0.15], sharex=ax) utils.plot_ts_panel(ax2, [north, south], "-", "phenological", loc="lower right") ax2.set_ylim([79, 159]) plt.setp(ax2.get_xticklabels(), visible=False) ax2.text(0.03, 0.8, "(a) Windermere", transform=ax2.transAxes) ax2.set_xlim([1998, 2020]) ax.set_xlim([1998, 2020]) fig.text(0.03, 0.835, "Day of year", rotation="vertical") plt.savefig(image_loc + "PHEN_timeseries_{}{}".format(settings.YEAR, settings.OUTFMT)) plt.close() return # run_all_plots
def run_all_plots(): cubelist = iris.load( os.path.join(DATALOC, "era5_lic_anom_1979_2019_NH.nc".format(settings.YEAR))) names = np.array([c.name() for c in cubelist]) LABELS = { "Ice Start": "(a) Ice On", "Ice End": "(b) Ice Off", "Ice Duration": "(c) Ice Duration" } COLORS = { "Ice Start": plt.cm.RdBu_r, "Ice End": plt.cm.RdBu, "Ice Duration": plt.cm.RdBu } BOUNDS = [-100, -20, -10, -5, -2, 0, 2, 5, 10, 20, 100] for name in names: if name == "Ice Depth": continue c, = np.where(names == name)[0] cube = cubelist[c] fig = plt.figure(figsize=(8, 9.5)) plt.clf() # boundary circle theta = np.linspace(0, 2 * np.pi, 100) center, radius = [0.5, 0.5], 0.5 verts = np.vstack([np.sin(theta), np.cos(theta)]).T circle = mpath.Path(verts * radius + center) # axes for polar plot ax = plt.axes( [0.01, 0.02, 0.98, 0.98], projection=cartopy.crs.NorthPolarStereo(central_longitude=300.0)) plot_cube = cube # regrid depending on output format if settings.OUTFMT in [".eps", ".pdf"]: if plot_cube.coord( "latitude").points.shape[0] > 90 or plot_cube.coord( "longitude").points.shape[0] > 360: regrid_size = 1.0 print("Regridding cube for {} output to {} degree resolution". format(settings.OUTFMT, regrid_size)) print("Old Shape {}".format(plot_cube.data.shape)) plot_cube = utils.regrid_cube(plot_cube, regrid_size, regrid_size) print("New Shape {}".format(plot_cube.data.shape)) # prettify ax.gridlines() #draw_labels=True) ax.add_feature(cartopy.feature.LAND, zorder=0, facecolor="0.9", edgecolor="k") ax.coastlines() ax.set_boundary(circle, transform=ax.transAxes) cmap = COLORS[name] norm = mpl.cm.colors.BoundaryNorm(BOUNDS, cmap.N) mesh = iris.plot.pcolormesh(plot_cube, cmap=cmap, norm=norm, axes=ax) # label axes ax.text(0.01, 1.0, "{}".format(LABELS[name]), fontsize=settings.FONTSIZE, transform=ax.transAxes) cb = plt.colorbar(mesh, orientation='horizontal', ticks=BOUNDS[1:-1], drawedges=True, fraction=0.1, pad=0.01, aspect=20, shrink=0.8) # prettify cb.set_label(label="Anomaly (days)", fontsize=settings.FONTSIZE) cb.ax.tick_params(axis='x', labelsize=settings.FONTSIZE, direction='in', size=0) cb.set_ticklabels(["{:g}".format(b) for b in BOUNDS[1:-1]]) cb.outline.set_linewidth(2) cb.dividers.set_color('k') cb.dividers.set_linewidth(2) ax.set_extent([-180, 180, 30, 90], cartopy.crs.PlateCarree()) for lat in range(30, 100, 10): ax.text(180, lat, '{}$^\circ$N'.format(lat), transform=cartopy.crs.Geodetic()) plt.savefig(settings.IMAGELOC + "LIC_map_{}_{}{}".format( settings.YEAR, "".join(name.split()), settings.OUTFMT)) #************************************************************************ # and temperatures BOUNDS = [-100, -4, -3, -2, -1, 0, 1, 2, 3, 4, 100] cubelist = iris.load(os.path.join(DATALOC, "amaps.nc")) cube = cubelist[0] fig = plt.figure(figsize=(8, 9.5)) plt.clf() # boundary circle theta = np.linspace(0, 2 * np.pi, 100) center, radius = [0.5, 0.5], 0.5 verts = np.vstack([np.sin(theta), np.cos(theta)]).T circle = mpath.Path(verts * radius + center) # axes for polar plot ax = plt.axes( [0.01, 0.02, 0.98, 0.98], projection=cartopy.crs.NorthPolarStereo(central_longitude=300.0)) plot_cube = cube # regrid depending on output format if settings.OUTFMT in [".eps", ".pdf"]: if plot_cube.coord("latitude").points.shape[0] > 90 or plot_cube.coord( "longitude").points.shape[0] > 360: regrid_size = 1.0 print( "Regridding cube for {} output to {} degree resolution".format( settings.OUTFMT, regrid_size)) print("Old Shape {}".format(plot_cube.data.shape)) plot_cube = utils.regrid_cube(plot_cube, regrid_size, regrid_size) print("New Shape {}".format(plot_cube.data.shape)) # prettify ax.gridlines() #draw_labels=True) ax.add_feature(cartopy.feature.LAND, zorder=0, facecolor="0.9", edgecolor="k") ax.coastlines() ax.set_boundary(circle, transform=ax.transAxes) cmap = plt.cm.RdBu_r norm = mpl.cm.colors.BoundaryNorm(BOUNDS, cmap.N) mesh = iris.plot.pcolormesh(plot_cube, cmap=cmap, norm=norm, axes=ax) # label axes ax.text(0.01, 1.0, "(d) Nov-Apr Air Temperature", fontsize=settings.FONTSIZE, transform=ax.transAxes) cb = plt.colorbar(mesh, orientation='horizontal', ticks=BOUNDS[1:-1], drawedges=True, fraction=0.1, pad=0.01, aspect=20, shrink=0.8) # prettify cb.set_label(label="Anomaly ($^\circ$C)", fontsize=settings.FONTSIZE) cb.ax.tick_params(axis='x', labelsize=settings.FONTSIZE, direction='in', size=0) cb.set_ticklabels(["{:g}".format(b) for b in BOUNDS[1:-1]]) cb.outline.set_linewidth(2) cb.dividers.set_color('k') cb.dividers.set_linewidth(2) ax.set_extent([-180, 180, 30, 90], cartopy.crs.PlateCarree()) for lat in range(30, 100, 10): ax.text(180, lat, '{}$^\circ$N'.format(lat), transform=cartopy.crs.Geodetic()) plt.savefig( settings.IMAGELOC + "LIC_map_{}_{}{}".format(settings.YEAR, "AirT", settings.OUTFMT)) #************************************************************************ # Timeseries era_start, era_end, era_length = read_column_csv( os.path.join(DATALOC, "era5_lic_anom_1979_{}_NH.csv".format(settings.YEAR))) situ_start, situ_end, situ_length = read_column_csv(os.path.join( DATALOC, "situ_lic_anom_1979_{}_NH.csv".format(settings.YEAR)), era=False) plt.clf() fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 10), sharex=True) # start utils.plot_ts_panel(ax1, [era_start, situ_start], "-", "cryosphere", loc="") # end utils.plot_ts_panel(ax2, [era_end, situ_end], "-", "cryosphere", loc="") ax2.set_ylabel("Anomaly (day)", fontsize=settings.FONTSIZE) # duration utils.plot_ts_panel(ax3, [era_length, situ_length], "-", "cryosphere", loc="lower left") # sort formatting for tick in ax3.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax1.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax2.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax3.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) # sort labelling ax1.text(0.02, 0.9, "(a) Start of Ice Cover", transform=ax1.transAxes, fontsize=settings.LABEL_FONTSIZE) ax2.text(0.02, 0.9, "(b) End of Ice Cover", transform=ax2.transAxes, fontsize=settings.LABEL_FONTSIZE) ax3.text(0.02, 0.9, "(c) Duration of Ice Cover", transform=ax3.transAxes, fontsize=settings.LABEL_FONTSIZE) fig.subplots_adjust(bottom=0.05, right=0.95, top=0.95, hspace=0.001) plt.savefig(settings.IMAGELOC + "LIC_ts_{}{}".format(settings.YEAR, settings.OUTFMT)) #************************************************************************ # Timeseries indata = read_continuous_csv(os.path.join(DATALOC, "great_lakes_anom.csv")) fig = plt.figure(figsize=(8, 6)) plt.clf() ax = plt.axes([0.1, 0.05, 0.88, 0.9]) utils.plot_ts_panel(ax, indata, "-", "cryosphere", loc="") fig.text(0.02, 0.5, "Maximum ice cover (anomaly, %)", va='center', rotation='vertical', ha="center", fontsize=settings.FONTSIZE) for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) plt.savefig(settings.IMAGELOC + "LIC_GL_ts_{}{}".format(settings.YEAR, settings.OUTFMT)) return # run_all_plots
def run_all_plots(): #********************************************* # Timeseries plot if False: (hadisdhLQ, hadcruhLQ, hadcruhextLQ, daiLQ, eraiLQ, era5LQ, merraLQ, jraLQ, era5_mskLQ, merra_mskLQ, cr20LQ) = \ read_ts(DATALOC + "HUM_timeseries_ALL{}.txt".format(settings.YEAR), "q", "L") (hadisdhMQ, hadcruhMQ, daiMQ, nocsMQ, hoapsMQ, eraiMQ, era5MQ, merraMQ, jraMQ, cr20MQ) = \ read_ts(DATALOC + "HUM_timeseries_ALL{}.txt".format(settings.YEAR), "q", "M") (hadisdhLR, hadcruhLR, hadcruhextLR, daiLR, eraiLR, era5LR, merraLR, jraLR, era5_mskLR, merra_mskLR, cr20LR) = \ read_ts(DATALOC + "HUM_timeseries_ALL{}.txt".format(settings.YEAR), "rh", "L") (hadisdhMR, hadcruhMR, daiMR, nocsMR, hoapsMR, eraiMR, era5MR, merraMR, jraMR, cr20MR) = \ read_ts(DATALOC + "HUM_timeseries_ALL{}.txt".format(settings.YEAR), "rh", "M") COLOURS = settings.COLOURS["hydrological"] fig = plt.figure(figsize=(14, 12)) # manually set up the 8 axes w = 0.45 # width h = 0.24 # height c = 0.53 # centre line of plots ax1 = plt.axes([c-w, 0.99-h, w, h]) ax2 = plt.axes([c, 0.99-h, w, h]) ax3 = plt.axes([c-w, 0.99-(2*h), w, h], sharex=ax1) ax4 = plt.axes([c, 0.99-(2*h), w, h], sharex=ax2) ax5 = plt.axes([c-w, 0.99-(3*h), w, h], sharex=ax1) ax6 = plt.axes([c, 0.99-(3*h), w, h], sharex=ax2) ax7 = plt.axes([c-w, 0.99-(4*h), w, h], sharex=ax1) ax8 = plt.axes([c, 0.99-(4*h), w, h], sharex=ax2) if int(settings.YEAR) < 2019: # in situ utils.plot_ts_panel(ax1, [hadisdhLQ, hadcruhLQ, hadcruhextLQ, daiLQ, era5_mskLQ], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX) utils.plot_ts_panel(ax3, [hadisdhMQ, hadcruhMQ, daiMQ, nocsMQ, hoapsMQ], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX) utils.plot_ts_panel(ax5, [hadisdhLR, hadcruhLR, hadcruhextLR, daiLR, era5_mskLR], "-", "hydrological", loc="") utils.plot_ts_panel(ax7, [hadisdhMR, hadcruhMR, daiMR], "-", "hydrological", loc="") # reanalyses utils.plot_ts_panel(ax2, [eraiLQ, era5LQ, merraLQ, jraLQ], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX) utils.plot_ts_panel(ax4, [eraiMQ, era5MQ, merraMQ, jraMQ], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX) utils.plot_ts_panel(ax6, [eraiLR, era5LR, jraLR], "-", "hydrological", loc="") utils.plot_ts_panel(ax8, [eraiMR, era5MR, jraMR], "-", "hydrological", loc="") else: # in situ utils.plot_ts_panel(ax1, [hadisdhLQ], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX) utils.plot_ts_panel(ax3, [hadisdhMQ, nocsMQ], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX) utils.plot_ts_panel(ax5, [hadisdhLR], "-", "hydrological", loc="") utils.plot_ts_panel(ax7, [hadisdhMR], "-", "hydrological", loc="") # hadisdh uncertainties in due course # reanalyses utils.plot_ts_panel(ax2, [era5LQ, merraLQ, jraLQ, cr20LQ], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX) utils.plot_ts_panel(ax4, [era5MQ, merraMQ, jraMQ, cr20MQ], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX) utils.plot_ts_panel(ax6, [era5LR, jraLR, cr20LR], "-", "hydrological", loc="") utils.plot_ts_panel(ax8, [era5MR, jraMR, cr20MR], "-", "hydrological", loc="") # prettify ax1.set_xlim([1957, int(settings.YEAR)+2]) ax2.set_xlim([1957, int(settings.YEAR)+2]) ax1.set_xticklabels(["", "1960", "1970", "1980", "1990", "2000", "2010", ""]) for ax in [ax1, ax2, ax3, ax4]: ax.set_ylim([-0.39, 0.8]) for ax in [ax5, ax6, ax7, ax8]: ax.set_ylim([-1.8, 1.5]) for ax in [ax1, ax3, ax5, ax7]: for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax7.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax8.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) plt.setp([a.get_xticklabels() for a in fig.axes[:-2]], visible=False) plt.setp(ax2.get_yticklabels(), visible=False) plt.setp(ax4.get_yticklabels(), visible=False) plt.setp(ax6.get_yticklabels(), visible=False) plt.setp(ax8.get_yticklabels(), visible=False) ax1.text(0.02, 0.85, "(a) In Situ Land q", transform=ax1.transAxes, fontsize=settings.FONTSIZE) ax2.text(0.02, 0.85, "(b) Reanalyses Land q", transform=ax2.transAxes, fontsize=settings.FONTSIZE) ax3.text(0.02, 0.85, "(c) In Situ Ocean q", transform=ax3.transAxes, fontsize=settings.FONTSIZE) ax4.text(0.02, 0.85, "(d) Reanalyses Ocean q", transform=ax4.transAxes, fontsize=settings.FONTSIZE) ax5.text(0.02, 0.85, "(e) In Situ Land RH", transform=ax5.transAxes, fontsize=settings.FONTSIZE) ax6.text(0.02, 0.85, "(f) Reanalyses Land RH", transform=ax6.transAxes, fontsize=settings.FONTSIZE) ax7.text(0.02, 0.85, "(g) In Situ Ocean RH", transform=ax7.transAxes, fontsize=settings.FONTSIZE) ax8.text(0.02, 0.85, "(h) Reanalyses Ocean RH", transform=ax8.transAxes, fontsize=settings.FONTSIZE) plt.figtext(0.01, 0.75, "Specific Humidity (g kg"+r'$^{-1}$'+")", va='center', rotation='vertical', fontsize=settings.FONTSIZE) plt.figtext(0.01, 0.25, "Relative Humidity (%rh)", va='center', rotation='vertical', fontsize=settings.FONTSIZE) fig.subplots_adjust(right=0.98, top=0.95, bottom=0.05, hspace=0.001) plt.savefig(settings.IMAGELOC + "HUM_ts{}".format(settings.OUTFMT)) plt.close() #********************************************* # Timeseries uncertainty plot if True: (hadisdhLQ, hadisdhLQ_l, hadisdhLQ_u, era5LQ, merraLQ, jraLQ, era5_mskLQ, merra_mskLQ, cr20LQ) = \ read_ts_unc(DATALOC + "HUM_timeseries_ALL{}_unc.txt".format(settings.YEAR), "q", "L") (hadisdhMQ, hadisdhMQ_l, hadisdhMQ_u, nocsMQ, era5MQ, merraMQ, jraMQ, cr20MQ) = \ read_ts_unc(DATALOC + "HUM_timeseries_ALL{}_unc.txt".format(settings.YEAR), "q", "M") (hadisdhLR, hadisdhLR_l, hadisdhLR_u, era5LR, merraLR, jraLR, era5_mskLR, merra_mskLR, cr20LR) = \ read_ts_unc(DATALOC + "HUM_timeseries_ALL{}_unc.txt".format(settings.YEAR), "rh", "L") (hadisdhMR, hadisdhMR_l, hadisdhMR_u, nocsMR, era5MR, merraMR, jraMR, cr20MR) = \ read_ts_unc(DATALOC + "HUM_timeseries_ALL{}_unc.txt".format(settings.YEAR), "rh", "M") COLOURS = settings.COLOURS["hydrological"] fig = plt.figure(figsize=(14, 12)) # manually set up the 8 axes w = 0.45 # width h = 0.24 # height c = 0.53 # centre line of plots ax1 = plt.axes([c-w, 0.99-h, w, h]) ax2 = plt.axes([c, 0.99-h, w, h]) ax3 = plt.axes([c-w, 0.99-(2*h), w, h], sharex=ax1) ax4 = plt.axes([c, 0.99-(2*h), w, h], sharex=ax2) ax5 = plt.axes([c-w, 0.99-(3*h), w, h], sharex=ax1) ax6 = plt.axes([c, 0.99-(3*h), w, h], sharex=ax2) ax7 = plt.axes([c-w, 0.99-(4*h), w, h], sharex=ax1) ax8 = plt.axes([c, 0.99-(4*h), w, h], sharex=ax2) # in situ utils.plot_ts_panel(ax1, [hadisdhLQ], "-", "hydrological", loc="",) ax1.fill_between(hadisdhLQ.times, hadisdhLQ_u.data, hadisdhLQ_l.data, color='0.8', label="") utils.plot_ts_panel(ax3, [hadisdhMQ, nocsMQ], "-", "hydrological", loc="") ax3.fill_between(hadisdhMQ.times, hadisdhMQ_u.data, hadisdhMQ_l.data, color='0.8', label="") utils.plot_ts_panel(ax5, [hadisdhLR], "-", "hydrological", loc="") ax5.fill_between(hadisdhLR.times, hadisdhLR_u.data, hadisdhLR_l.data, color='0.8', label="") utils.plot_ts_panel(ax7, [hadisdhMR], "-", "hydrological", loc="") ax7.fill_between(hadisdhMR.times, hadisdhMR_u.data, hadisdhMR_l.data, color='0.8', label="") # reanalyses utils.plot_ts_panel(ax2, [era5LQ, merraLQ, jraLQ, cr20LQ], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX) utils.plot_ts_panel(ax4, [era5MQ, merraMQ, jraMQ, cr20MQ], "-", "hydrological", loc=LEGEND_LOC, bbox=BBOX) utils.plot_ts_panel(ax6, [era5LR, jraLR, cr20LR], "-", "hydrological", loc="") utils.plot_ts_panel(ax8, [era5MR, jraMR, cr20MR], "-", "hydrological", loc="") # fix legend unc_patch = ax1.fill(np.NaN, np.NaN, '0.8', zorder = 1, alpha=0.7) for ax in [ax1, ax3]: lines = [] labels = [] for line in ax.get_lines(): if line.get_label() == "HadISDH": lines += [(line, unc_patch[0])] labels += [line.get_label()] else: lines += [line] labels += [line.get_label()] ax.legend(lines, labels, \ loc=LEGEND_LOC, ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \ labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=BBOX) # prettify ax1.set_xlim([1957, int(settings.YEAR)+2]) ax2.set_xlim([1957, int(settings.YEAR)+2]) ax1.set_xticklabels(["", "1960", "1970", "1980", "1990", "2000", "2010", ""]) for ax in [ax1, ax2, ax3, ax4]: ax.set_ylim([-0.39, 0.8]) for ax in [ax5, ax6, ax7, ax8]: ax.set_ylim([-1.8, 1.5]) for ax in [ax1, ax3, ax5, ax7]: for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax7.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax8.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) plt.setp([a.get_xticklabels() for a in fig.axes[:-2]], visible=False) plt.setp(ax2.get_yticklabels(), visible=False) plt.setp(ax4.get_yticklabels(), visible=False) plt.setp(ax6.get_yticklabels(), visible=False) plt.setp(ax8.get_yticklabels(), visible=False) ax1.text(0.02, 0.85, "(a) In Situ Land q", transform=ax1.transAxes, fontsize=settings.FONTSIZE) ax2.text(0.02, 0.85, "(b) Reanalyses Land q", transform=ax2.transAxes, fontsize=settings.FONTSIZE) ax3.text(0.02, 0.85, "(c) In Situ Ocean q", transform=ax3.transAxes, fontsize=settings.FONTSIZE) ax4.text(0.02, 0.85, "(d) Reanalyses Ocean q", transform=ax4.transAxes, fontsize=settings.FONTSIZE) ax5.text(0.02, 0.85, "(e) In Situ Land RH", transform=ax5.transAxes, fontsize=settings.FONTSIZE) ax6.text(0.02, 0.85, "(f) Reanalyses Land RH", transform=ax6.transAxes, fontsize=settings.FONTSIZE) ax7.text(0.02, 0.85, "(g) In Situ Ocean RH", transform=ax7.transAxes, fontsize=settings.FONTSIZE) ax8.text(0.02, 0.85, "(h) Reanalyses Ocean RH", transform=ax8.transAxes, fontsize=settings.FONTSIZE) plt.figtext(0.01, 0.75, "Specific Humidity (g kg"+r'$^{-1}$'+")", va='center', rotation='vertical', fontsize=settings.FONTSIZE) plt.figtext(0.01, 0.25, "Relative Humidity (%rh)", va='center', rotation='vertical', fontsize=settings.FONTSIZE) fig.subplots_adjust(right=0.98, top=0.95, bottom=0.05, hspace=0.001) plt.savefig(settings.IMAGELOC + "HUM_ts_unc{}".format(settings.OUTFMT)) plt.close() input("stOP") #********************************************* # Map plots ## RH bounds = [-50, -12, -9, -6, -3, 0, 3, 6, 9, 12, 50] # RH HadISDH if False: cube = read_maps(DATALOC + "HUMrh_anomalymap_HADISDHland{}.txt".format(settings.YEAR), "HadISDH RH", None, footer=True) utils.plot_smooth_map_iris(settings.IMAGELOC + "HUM_RH_hadisdh_land", cube, settings.COLOURMAP_DICT["hydrological"], \ bounds, "Anomalies from 1981-2010 (%rh)", figtext="", title="") # RH HadISDH Land and Marine if True: cube = read_maps(DATALOC + "HUMrh_anomalymap_HADISDH{}.txt".format(settings.YEAR), "HadISDH RH", None, footer=True) utils.plot_smooth_map_iris(settings.IMAGELOC + "HUM_RH_hadisdh_combined", cube, settings.COLOURMAP_DICT["hydrological"], \ bounds, "Anomalies from 1981-2010 (%rh)", figtext="", title="") utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_HUM_RH_hadisdh_combined", cube, settings.COLOURMAP_DICT["hydrological"], \ bounds, "Anomalies from 1981-2010 (%rh)", figtext="(h) Surface Relative Humidity", title="") # RH ERA if True: cube = read_maps(DATALOC + "HUMrh_anomalymap_ERA5{}.txt".format(settings.YEAR), "ERA-I RH", None, footer=True) utils.plot_smooth_map_iris(settings.IMAGELOC + "HUM_RH_era5", cube, settings.COLOURMAP_DICT["hydrological"], \ bounds, "Anomalies from 1981-2010 (%rh)", figtext="", title="") # utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_HUM_RH_era5", cube, settings.COLOURMAP_DICT["hydrological"], \ # bounds, "Anomalies from 1981-2010 (%rh)", figtext="(o) Surface Relative Humidity", title="") # RH MERRA if True: cube = read_maps(DATALOC + "HUMrh_anomalymap_MERRA2{}.txt".format(settings.YEAR), "MERRA2 RH", None, footer=True) utils.plot_smooth_map_iris(settings.IMAGELOC + "HUM_RH_merra", cube, settings.COLOURMAP_DICT["hydrological"], \ bounds, "Anomalies from 1981-2010 (%rh)", figtext="", title="") ## Q bounds = [-20., -2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2, 20] # q HadISDH if False: cube = read_maps(DATALOC + "HUMq_anomalymap_HADISDHland{}.txt".format(settings.YEAR), "HadISDH q", "g/kg", footer=True) utils.plot_smooth_map_iris(settings.IMAGELOC + "HUM_q_hadisdh_land", cube, settings.COLOURMAP_DICT["hydrological"], \ bounds, "Anomalies from 1981-2010 (g kg"+r'$^{-1}$'+")", figtext="", title="") # utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_HUM_q_hadisdh_land", cube, settings.COLOURMAP_DICT["hydrological"], \ # bounds, "Anomalies from 1981-2010 (g kg"+r'$^{-1}$'+")", figtext="(n) Surface Specific Humidity", title="") # q HadISDH Land and Marine if True: cube = read_maps(DATALOC + "HUMq_anomalymap_HADISDH{}.txt".format(settings.YEAR), "HadISDH q", "g/kg", footer=True) utils.plot_smooth_map_iris(settings.IMAGELOC + "HUM_q_hadisdh_combined", cube, settings.COLOURMAP_DICT["hydrological"], \ bounds, "Anomalies from 1981-2010 (g kg"+r'$^{-1}$'+")", figtext="", title="") utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_HUM_q_hadisdh_combined", cube, settings.COLOURMAP_DICT["hydrological"], \ bounds, "Anomalies from 1981-2010 (g kg"+r'$^{-1}$'+")", figtext="(g) Surface Specific Humidity", title="") # q ERA if True: cube = read_maps(DATALOC + "HUMq_anomalymap_ERA5{}.txt".format(settings.YEAR), "ERA-I q", "g/kg", footer=True) utils.plot_smooth_map_iris(settings.IMAGELOC + "HUM_q_era5", cube, settings.COLOURMAP_DICT["hydrological"], \ bounds, "Anomalies from 1981-2010 (g kg"+r'$^{-1}$'+")", figtext="", title="") # MERRA? if True: cube = read_maps(DATALOC + "HUMq_anomalymap_MERRA2{}.txt".format(settings.YEAR), "MERRA2 q", "g/kg", footer=True) utils.plot_smooth_map_iris(settings.IMAGELOC + "HUM_q_merra", cube, settings.COLOURMAP_DICT["hydrological"], \ bounds, "Anomalies from 1981-2010 (g kg"+r'$^{-1}$'+")", figtext="", title="") return # run_all_plots
def run_all_plots(): #************************************************************************ # Cloudiness timeseries if True: plt.clf() fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 6.5), sharex=True) infilename = os.path.join( DATALOC, "{}_global_cloudiness_timeseries_v2.txt".format(settings.YEAR)) # anomalies patmosx, hirs, misr, modis, calipso, ceres, satcorps, clara_a2, patmosdx, cci = \ read_ts(infilename, anomaly=True) utils.plot_ts_panel(ax1, [ patmosx, hirs, misr, modis, calipso, ceres, satcorps, clara_a2, patmosdx, cci ], "-", "hydrological", loc="") ax1.text(0.02, 0.9, "(a) Satellite - Anomalies", transform=ax1.transAxes, fontsize=settings.FONTSIZE) # actuals patmosx, hirs, misr, modis, calipso, ceres, satcorps, clara_a2, patmosdx, cci = \ read_ts(infilename) utils.plot_ts_panel(ax2, [ patmosx, hirs, misr, modis, calipso, ceres, satcorps, clara_a2, patmosdx, cci ], "-", "hydrological", loc=LEGEND_LOC, ncol=3) ax2.text(0.02, 0.9, "(b) Satellite - Actual", transform=ax2.transAxes, fontsize=settings.FONTSIZE) #******************* # prettify ax1.set_ylabel("Anomaly (%)", fontsize=settings.FONTSIZE) ax2.set_ylabel("(%)", fontsize=settings.FONTSIZE) for tick in ax2.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) minorLocator = MultipleLocator(1) for ax in [ax1, ax2]: utils.thicken_panel_border(ax) ax.set_yticks(ax.get_yticks()[1:]) ax.xaxis.set_minor_locator(minorLocator) for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False) fig.subplots_adjust(right=0.96, top=0.99, bottom=0.04, left=0.09, hspace=0.001) plt.xlim([hirs.times[0] - 1, hirs.times[-1] + 1]) ax1.set_ylim([-4.4, 6.9]) ax2.set_ylim([0, 95]) plt.savefig(settings.IMAGELOC + "CLD_ts{}".format(settings.OUTFMT)) plt.close() #************************************************************************ # make a version using the full base period of the data and only the pre2000 datasets if False: plt.clf() fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 6.5), sharex=True) # anomalies patmosx, hirs, misr, modis, calipso, ceres, satcorps, clara_a2, patmosdx, cci = \ read_ts(DATALOC + "{}_global_cloudiness_timeseries.txt".format(settings.YEAR), \ anomaly=True, fullbase=True) utils.plot_ts_panel(ax1, [patmosx, hirs, satcorps, clara_a2, cci], "-", "hydrological", loc="") ax1.text(0.02, 0.9, "(a) Satellite - Anomalies", transform=ax1.transAxes, fontsize=settings.FONTSIZE) # actuals patmosx, hirs, misr, modis, calipso, ceres, satcorps, clara_a2, patmosdx, cci = \ read_ts(DATALOC + "{}_global_cloudiness_timeseries.txt".format(settings.YEAR)) utils.plot_ts_panel(ax2, [patmosx, hirs, satcorps, clara_a2, cci], "-", "hydrological", \ loc=LEGEND_LOC, ncol=3) ax2.text(0.02, 0.9, "(b) Satellite - Actual", transform=ax2.transAxes, fontsize=settings.FONTSIZE) #******************* # prettify ax1.set_ylabel("Anomaly (%)", fontsize=settings.FONTSIZE) ax2.set_ylabel("(%)", fontsize=settings.FONTSIZE) for tick in ax2.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) minorLocator = MultipleLocator(1) for ax in [ax1, ax2]: utils.thicken_panel_border(ax) ax.set_yticks(ax.get_yticks()[1:]) ax.xaxis.set_minor_locator(minorLocator) for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False) fig.subplots_adjust(right=0.95, top=0.95, hspace=0.001) plt.xlim([hirs.times[0] - 1, hirs.times[-1] + 1]) ax1.set_ylim([-4.9, 4.4]) ax2.set_ylim([0, 95]) plt.savefig(settings.IMAGELOC + "CLD_ts_fullbaseperiod{}".format(settings.OUTFMT)) plt.close() #************************************************************************ # Cloudiness map if True: mapfile_dict = scipy.io.readsav( DATALOC + "patmosx_global_monthly_cloudiness_anomaly_map_{}.sav".format( settings.YEAR)) annual_anoms = mapfile_dict["annual_anom"] * 100. djf_anoms = mapfile_dict["djf_anom"] * 100. jja_anoms = mapfile_dict["jja_anom"] * 100. mam_anoms = mapfile_dict["mam_anom"] * 100. son_anoms = mapfile_dict["son_anom"] * 100. lats = mapfile_dict["lat"] lons = mapfile_dict["lon"] cube = utils.make_iris_cube_2d(annual_anoms, lats[:, 0], lons[0], "CLD_anom", "%") bounds = [-100, -15, -10, -5, -2.5, 0, 2.5, 5, 10, 15, 100] utils.plot_smooth_map_iris(settings.IMAGELOC + "CLD_{}_anoms".format(settings.YEAR), cube, \ settings.COLOURMAP_DICT["hydrological"], bounds, \ "Anomalies from 1981-2010 (%)") utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_CLD_{}_anoms".format(settings.YEAR), \ cube, settings.COLOURMAP_DICT["hydrological"], bounds, \ "Anomalies from 1981-2010 (%)", figtext="(n) Cloudiness") #************************************************************************ # Cloudiness Seasonal Map if True: cubelist = [] for season in [djf_anoms, mam_anoms, jja_anoms, son_anoms]: cube = utils.make_iris_cube_2d(season, lats[:, 0], lons[0], "CLD_anom", "%") cubelist += [cube] utils.plot_smooth_map_iris_multipanel(settings.IMAGELOC + "CLD_{}_anoms_seasons".format(settings.YEAR), \ cubelist, settings.COLOURMAP_DICT["hydrological"], \ bounds, "Anomaly (%)", shape=(2, 2), \ title=["DJF", "MAM", "JJA", "SON"], \ figtext=["(a)", "(b)", "(c)", "(d)"]) #************************************************************************ # Cloudiness Hovmoller if True: data_dict = scipy.io.readsav( DATALOC + "patmosx_global_monthly_cloudiness_hovmuller_{}.sav".format( settings.YEAR)) lats = data_dict["latitude"] times = data_dict["hov_time"] anoms = data_dict["hov_anom"] * 100. utils.plot_hovmuller(settings.IMAGELOC + "CLD_hovmuller", times, lats, anoms, settings.COLOURMAP_DICT["hydrological"], bounds, "Anomaly (%)") return # run_all_plots
def run_all_plots(): if True: for index in INDICES: # dummy so far # NYEARS = 66 # rank_bounds = [-1,1,2,3,NYEARS-2,NYEARS-1,NYEARS,100] rank_bounds = [-4.5, -3.5, -2.5, -1.5, 1.5, 2.5, 3.5, 4.5] # sort the bounds and colourbars if index in ["TX90p", "TN90p"]: bounds = [-100, -40, -30, -20, -10, 0, 10, 20, 30, 40, 100] cmap = settings.COLOURMAP_DICT["temperature"] elif index in ["TX10p", "TN10p"]: bounds = [-100, -40, -30, -20, -10, 0, 10, 20, 30, 40, 100] cmap = settings.COLOURMAP_DICT["temperature_r"] elif index in ["TXx", "TNx", "TXn", "TNn"]: bounds = [-100, -6, -4, -2, -1, 0, 1, 2, 4, 6, 100] cmap = settings.COLOURMAP_DICT["temperature"] cube_list = iris.load( DATALOC + "GHCND_{}_1951-{}_RegularGrid_global_2.5x2.5deg_LSmask.nc". format(index, int(settings.YEAR) + 1)) names = np.array([cube.name() for cube in cube_list]) #************* # plot annual map selected_cube, = np.where(names == "Ann")[0] cube = cube_list[selected_cube] cube.coord('latitude').guess_bounds() cube.coord('longitude').guess_bounds() if index in ["TX90p", "TN90p", "TX10p", "TN10p"]: # change from % to days cube.data = cube.data * 3.65 cube = ApplyClimatology(cube) # select the year to plot years = GetYears(cube) loc, = np.where(years == SELECTED_YEAR) utils.plot_smooth_map_iris( settings.IMAGELOC + "TEX_{}_{}_anoms_ghcndex".format(index, settings.YEAR), cube[loc[0]], cmap, bounds, "Anomalies from 1961-90 ({})".format(INDEX_UNITS[index]), title="{} - {}".format(index, INDEX_LABELS[index]), figtext=FIGURE_LABELS[index]) if index == "TX90p": utils.plot_smooth_map_iris( settings.IMAGELOC + "p2.1_TEX_{}_{}_anoms_ghcndex".format( index, settings.YEAR), cube[loc[0]], cmap, bounds, "Anomalies from 1961-90 ({})".format(INDEX_UNITS[index]), title="", figtext="(c) Warm Days", save_netcdf_filename="{}{}_for_NOAA_{}.nc".format( DATALOC, index, dt.datetime.strftime(dt.datetime.now(), "%d-%b-%Y"))) if index == "TN10p": utils.plot_smooth_map_iris( settings.IMAGELOC + "p2.1_TEX_{}_{}_anoms_ghcndex".format( index, settings.YEAR), cube[loc[0]], cmap, bounds, "Anomalies from 1961-90 ({})".format(INDEX_UNITS[index]), title="", figtext="(d) Cool Nights", save_netcdf_filename="{}{}_for_NOAA_{}.nc".format( DATALOC, index, dt.datetime.strftime(dt.datetime.now(), "%d-%b-%Y"))) rank_cube = get_ranks(cube) plot_rank_map( settings.IMAGELOC + "TEX_{}_{}_rank_ghcndex".format(index, settings.YEAR), rank_cube[loc[0]], cmap, rank_bounds, "Rank", title="{} - {}".format(index, INDEX_LABELS[index])) #************* # plot season maps (2x2) season_list = [] for season in SEASONS: # extract each month month_data = [] months = SEASON_DICT[season] for month in months: selected_cube, = np.where(names == month)[0] cube = cube_list[selected_cube] if month == "Dec": # need to extract from previous year - cheat by rolling data around cube.data = np.roll(cube.data, 1, axis=0) cube.data.mask[ 0, :, :] = True # and mask out the previous years' if index in ["TX90p", "TN90p", "TX10p", "TN10p"]: # change from % to days cube.data = cube.data * ( 3.65 / 4.) # assume a season is 1/4 of a year month_data += [cube.data] # finished getting all months, make a dummy cube to populate month_data = np.ma.array(month_data) season_cube = copy.deepcopy(cube) # take appropriate seasonal value if index in ["TX90p", "TN90p", "TX10p", "TN10p"]: season_cube.data = np.ma.mean(month_data, axis=0) elif index in ["TXx", "TNx"]: season_cube.data = np.ma.max(month_data, axis=0) elif index in ["TXn", "TNn"]: season_cube.data = np.ma.min(month_data, axis=0) # mask if fewer that 2 months present nmonths_locs = np.ma.count(month_data, axis=0) season_cube.data = np.ma.masked_where(nmonths_locs < 2, season_cube.data) # make anomalies season_cube = ApplyClimatology(season_cube) # fix for plotting season_cube.coord('latitude').guess_bounds() season_cube.coord('longitude').guess_bounds() # select the year to plot years = GetYears(cube) loc, = np.where(years == SELECTED_YEAR) # add to list season_list += [season_cube[loc[0]]] # sort the bounds and colourbars if index in ["TX90p", "TN90p"]: bounds = [-100, -10, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 100] elif index in ["TX10p", "TN10p"]: bounds = [-100, -10, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 100] elif index in ["TXx", "TNx", "TXn", "TNn"]: bounds = [-100, -6, -4, -2, -1, 0, 1, 2, 4, 6, 100] # pass to plotting routine utils.plot_smooth_map_iris_multipanel( settings.IMAGELOC + "TEX_{}_{}_seasons_ghcndex".format(index, settings.YEAR), season_list, cmap, bounds, "Anomalies from 1961-90 ({})".format(INDEX_UNITS[index]), shape=(2, 2), title=SEASONS, figtext=SEASON_LABELS[index], figtitle="{} - {}".format(index, INDEX_LABELS[index])) #************* # timeseries obs if True: for index_pair in [["TX90p", "TN10p"], ["TN90p", "TX10p"]]: fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 6.5), sharex=True) ax3 = ax1.twinx() ax4 = ax2.twinx() axes = (ax1, ax2, ax3, ax4) for ix, index in enumerate(index_pair): index_ts, cover_ts = obtain_timeseries( DATALOC + "GHCND_{}_1951-{}_RegularGrid_global_2.5x2.5deg_LSmask.nc". format(index, int(settings.YEAR) + 1), "Ann", "GHCNDEX", index) utils.plot_ts_panel(axes[ix], [index_ts], "-", "temperature", loc="", bbox=BBOX) # no legend as single line axes[ix].text(0.02, 0.9, "({}) {}".format(string.ascii_lowercase[ix], index), transform=axes[ix].transAxes, fontsize=settings.FONTSIZE) # red tickmarks axes[ix].tick_params(axis='y', colors='red', direction="in") # and smoothed index_ts.data.fill_value = -99.9 smoothed = binomialfilter(index_ts.data.filled(), -99.9, 5, pad=False) smoothed = np.ma.masked_where(smoothed == -99.9, smoothed) axes[ix].plot(index_ts.times, smoothed, "r--", lw=LW) # print the index, the current anomaly and the rank information print(index, index_ts.data.compressed()[-1] / 36.5, np.argsort(np.argsort(index_ts.data.compressed())) + 1) axes[ix + 2].plot(cover_ts.times, cover_ts.data, "k:", lw=2) axes[ix + 2].yaxis.set_label_position("right") axes[ix + 2].yaxis.set_ticks_position('right') # prettify plt.xlim([1950, int(settings.YEAR) + 1]) axes[0].set_ylim([15, None]) if "X" in index: axes[1].set_ylim([19, 59]) elif "N" in index: axes[1].set_ylim([11, 59]) ax3.set_ylim([0, 98]) ax4.set_ylim([0, 98]) fig.text(0.03, 0.5, "Number of Days", va='center', rotation='vertical', fontsize=settings.FONTSIZE, color="r") fig.text(0.97, 0.5, "% land covered", va='center', rotation='vertical', fontsize=settings.FONTSIZE) for ax in axes: for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) tick.label2.set_fontsize(settings.FONTSIZE) for tick in axes[1].xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) fig.subplots_adjust(left=0.1, right=0.9, top=0.98, bottom=0.05, hspace=0.001) plt.savefig(settings.IMAGELOC + "TEX_{}+{}_ts_ghcndex{}".format( index_pair[0], index_pair[1], settings.OUTFMT)) plt.close() # #************* # # timeseries ERA-Int # for index_pair in [["TX90p", "TN10p"], ["TN90p", "TX10p"]]: # fig, axes = plt.subplots(2, figsize=(8, 6.5), sharex=True) # for ix, index in enumerate(index_pair): # index_ts, cover_ts = obtain_timeseries(DATALOC + "ERA-Int_1979-{}_{}_LSmask.nc".format(settings.YEAR, index), "Annual", "ERA-Interim", index) # utils.plot_ts_panel(axes[ix], [index_ts], "-", "temperature", loc = LEGEND_LOC, bbox = BBOX) # axes[ix].text(0.02, 0.9, "({}) {}".format(string.ascii_lowercase[ix], index), transform = axes[ix].transAxes, fontsize = settings.FONTSIZE) # # and smoothed # index_ts.data.fill_value = -99.9 # smoothed = binomialfilter(index_ts.data.filled(), -99.9, 5, pad = False) # smoothed = np.ma.masked_where(smoothed == -99.9, smoothed) # axes[ix].plot(index_ts.times, smoothed, "r--", lw = LW) # # prettify # plt.xlim([1950,2019]) # axes[0].set_ylim([15,None]) # axes[1].set_ylim([16,59]) # fig.text(0.03, 0.5, "Number of Days", va='center', rotation='vertical', fontsize = settings.FONTSIZE) # for ax in axes: # for tick in ax.yaxis.get_major_ticks(): # tick.label.set_fontsize(settings.FONTSIZE) # for tick in axes[1].xaxis.get_major_ticks(): # tick.label.set_fontsize(settings.FONTSIZE) # fig.subplots_adjust(right = 0.95, top = 0.95, bottom = 0.05, hspace = 0.001) # plt.savefig(settings.IMAGELOC+"TEX_{}+{}_ts_erai{}".format(index_pair[0], index_pair[1], settings.OUTFMT)) # plt.close() # #************* # # ERA-Int Maps (annual only) # for index in INDICES: # # sort the bounds and colourbars # if index in ["TX90p", "TN90p"]: # bounds = [-100, -30, -20, -10, -5, 0, 5, 10, 20, 30, 100] # cmap=settings.COLOURMAP_DICT["temperature"] # elif index in ["TX10p", "TN10p"]: # bounds = [-100, -30, -20, -10, -5, 0, 5, 10, 20, 30, 100] # cmap=settings.COLOURMAP_DICT["temperature_r"] # elif index in ["TXx", "TNx", "TXn", "TNn"]: # bounds = [-100, -6, -4, -2, -1, 0, 1, 2, 4, 6, 100] # cmap=settings.COLOURMAP_DICT["temperature"] # cube_list = iris.load(DATALOC + "ERA-Int_1979-{}_{}_LSmask.nc".format(settings.YEAR, index)) # names = np.array([cube.name() for cube in cube_list]) # #************* # # plot annual map # selected_cube, = np.where(names == "Annual")[0] # cube = cube_list[selected_cube] # cube.coord('latitude').guess_bounds() # cube.coord('longitude').guess_bounds() # if index in ["TX90p", "TN90p", "TX10p", "TN10p"]: # # change from % to days # cube.data = cube.data * 3.65 # cube = ApplyClimatology(cube) # # select the year to plot # years = GetYears(cube) # loc, = np.where(years == SELECTED_YEAR) # utils.plot_smooth_map_iris(settings.IMAGELOC + "TEX_{}_{}_anoms_erai".format(index, settings.YEAR), cube[loc[0]], cmap, bounds, "Anomalies from 1981-2010 ({})".format(INDEX_UNITS[index]), title = "ERA-Interim {} - {}".format(index, INDEX_LABELS[index]), figtext = FIGURE_LABELS[index]) # #************* # # plot season maps (2x2) # season_list = [] # for season in SEASONS: # # extract each month # month_data = [] # months = SEASON_DICT_ERA[season] # for month in months: # selected_cube, = np.where(names == month)[0] # cube = cube_list[selected_cube] # if month == "December": # # need to extract from previous year - cheat by rolling data around # cube.data = np.roll(cube.data, 1, axis = 0) # cube.data.mask[0,:,:] = True # and mask out the previous years' # if index in ["TX90p", "TN90p", "TX10p", "TN10p"]: # # change from % to days # cube.data = cube.data * (3.65/4.) # assume a season is 1/4 of a year # month_data += [cube.data] # # finished getting all months, make a dummy cube to populate # month_data = np.ma.array(month_data) # season_cube = copy.deepcopy(cube) # # take appropriate seasonal value # if index in ["TX90p", "TN90p", "TX10p", "TN10p"]: # season_cube.data = np.ma.mean(month_data, axis = 0) # elif index in ["TXx", "TNx"]: # season_cube.data = np.ma.max(month_data, axis = 0) # elif index in ["TXn", "TNn"]: # season_cube.data = np.ma.min(month_data, axis = 0) # # mask if fewer that 2 months present # nmonths_locs = np.ma.count(month_data, axis = 0) # season_cube.data = np.ma.masked_where(nmonths_locs < 2, season_cube.data) # # make anomalies # season_cube = ApplyClimatology(season_cube) # # fix for plotting # season_cube.coord('latitude').guess_bounds() # season_cube.coord('longitude').guess_bounds() # # select the year to plot # years = GetYears(cube) # loc, = np.where(years == SELECTED_YEAR) # # add to list # season_list += [season_cube[loc[0]]] # # sort the bounds and colourbars # if index in ["TX90p", "TN90p"]: # bounds = [-100, -10, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 100] # elif index in ["TX10p", "TN10p"]: # bounds = [-100, -10, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 100] # elif index in ["TXx", "TNx", "TXn", "TNn"]: # bounds = [-100, -6, -4, -2, -1, 0, 1, 2, 4, 6, 100] # # pass to plotting routine # utils.plot_smooth_map_iris_multipanel(settings.IMAGELOC + "TEX_{}_{}_seasons_erai".format(index, settings.YEAR), season_list, cmap, bounds, "Anomalies from 1981-2010 ({})".format(INDEX_UNITS[index]), shape = (2,2), title = SEASONS, figtext = SEASON_LABELS[index], figtitle = "{} - {}".format(index, INDEX_LABELS[index])) #************* # timeseries ERA5 ERA5LOCTEMP = "/data/users/rdunn/reanalyses/data/era5/v20200409/indices/" # ERA5LOCTEMP = "/scratch/rdunn/reanalyses/era5/final/" if True: for index_pair in [["TX90p", "TN10p"], ["TN90p", "TX10p"]]: fig, axes = plt.subplots(2, figsize=(8, 6.5), sharex=True) for ix, index in enumerate(index_pair): index_ts, cover_ts = obtain_timeseries( ERA5LOCTEMP + "ERA5_{}_1979-{}.nc".format(index, settings.YEAR), "Ann", "ERA5", index, is_era5=True) utils.plot_ts_panel(axes[ix], [index_ts], "-", "temperature", loc=LEGEND_LOC, bbox=BBOX) axes[ix].text(0.02, 0.9, "({}) {}".format(string.ascii_lowercase[ix], index), transform=axes[ix].transAxes, fontsize=settings.FONTSIZE) # and smoothed index_ts.data.fill_value = -99.9 smoothed = binomialfilter(index_ts.data.filled(), -99.9, 5, pad=False) smoothed = np.ma.masked_where(smoothed == -99.9, smoothed) axes[ix].plot(index_ts.times, smoothed, "r--", lw=LW) # prettify plt.xlim([1950, int(settings.YEAR) + 1]) axes[0].set_ylim([15, None]) axes[1].set_ylim([16, 59]) fig.text(0.03, 0.5, "Number of Days", va='center', rotation='vertical', fontsize=settings.FONTSIZE) for ax in axes: for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in axes[1].xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) fig.subplots_adjust(right=0.95, top=0.95, bottom=0.05, hspace=0.001) plt.savefig(settings.IMAGELOC + "TEX_{}+{}_ts_era5{}".format( index_pair[0], index_pair[1], settings.OUTFMT)) plt.close() #************* # ERA Maps (annual only) if True: for index in INDICES: # sort the bounds and colourbars if index in ["TX90p", "TN90p"]: # bounds = [-100, -30, -20, -10, -5, 0, 5, 10, 20, 30, 100] bounds = [-100, -40, -30, -20, -10, 0, 10, 20, 30, 40, 100] cmap = settings.COLOURMAP_DICT["temperature"] elif index in ["TX10p", "TN10p"]: # bounds = [-100, -30, -20, -10, -5, 0, 5, 10, 20, 30, 100] bounds = [-100, -40, -30, -20, -10, 0, 10, 20, 30, 40, 100] cmap = settings.COLOURMAP_DICT["temperature_r"] elif index in ["TXx", "TNx", "TXn", "TNn"]: bounds = [-100, -6, -4, -2, -1, 0, 1, 2, 4, 6, 100] cmap = settings.COLOURMAP_DICT["temperature"] cube_list = iris.load( ERA5LOCTEMP + "ERA5_{}_1979-{}.nc".format(index, settings.YEAR)) names = np.array([cube.var_name for cube in cube_list]) #************* # plot annual map selected_cube, = np.where(names == "Ann")[0] cube = cube_list[selected_cube] cube.coord('latitude').guess_bounds() cube.coord('longitude').guess_bounds() if index in ["TX90p", "TN90p", "TX10p", "TN10p"]: # change from % to days cube.data = cube.data * 3.65 cube = ApplyClimatology(cube, is_era5=True) # select the year to plot years = GetYears(cube, is_era5=True) loc, = np.where(years == SELECTED_YEAR) utils.plot_smooth_map_iris( settings.IMAGELOC + "TEX_{}_{}_anoms_era5".format(index, settings.YEAR), cube[loc[0]], cmap, bounds, "Anomalies from 1981-2010 ({})".format(INDEX_UNITS[index]), title="ERA5 {} - {}".format(index, INDEX_LABELS[index]), figtext=FIGURE_LABELS[index]) #************* # plot season maps (2x2) season_list = [] for season in SEASONS: # extract each month month_data = [] months = SEASON_DICT[season] for month in months: selected_cube, = np.where(names == month)[0] cube = cube_list[selected_cube] if month == "December": # need to extract from previous year - cheat by rolling data around cube.data = np.roll(cube.data, 1, axis=0) cube.data.mask[ 0, :, :] = True # and mask out the previous years' if index in ["TX90p", "TN90p", "TX10p", "TN10p"]: # change from % to days cube.data = cube.data * ( 3.65 / 4.) # assume a season is 1/4 of a year month_data += [cube.data] # finished getting all months, make a dummy cube to populate month_data = np.ma.array(month_data) season_cube = copy.deepcopy(cube) # take appropriate seasonal value if index in ["TX90p", "TN90p", "TX10p", "TN10p"]: season_cube.data = np.ma.mean(month_data, axis=0) elif index in ["TXx", "TNx"]: season_cube.data = np.ma.max(month_data, axis=0) elif index in ["TXn", "TNn"]: season_cube.data = np.ma.min(month_data, axis=0) # mask if fewer that 2 months present nmonths_locs = np.ma.count(month_data, axis=0) season_cube.data = np.ma.masked_where(nmonths_locs < 2, season_cube.data) # make anomalies season_cube = ApplyClimatology(season_cube, is_era5=True) # fix for plotting season_cube.coord('latitude').guess_bounds() season_cube.coord('longitude').guess_bounds() # select the year to plot years = GetYears(cube, is_era5=True) loc, = np.where(years == SELECTED_YEAR) # add to list season_list += [season_cube[loc[0]]] # sort the bounds and colourbars if index in ["TX90p", "TN90p"]: bounds = [-100, -10, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 100] elif index in ["TX10p", "TN10p"]: bounds = [-100, -10, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 100] elif index in ["TXx", "TNx", "TXn", "TNn"]: bounds = [-100, -6, -4, -2, -1, 0, 1, 2, 4, 6, 100] # pass to plotting routine utils.plot_smooth_map_iris_multipanel( settings.IMAGELOC + "TEX_{}_{}_seasons_era5".format(index, settings.YEAR), season_list, cmap, bounds, "Anomalies from 1981-2010 ({})".format(INDEX_UNITS[index]), shape=(2, 2), title=SEASONS, figtext=SEASON_LABELS[index], figtitle="ERA5 {} - {}".format(index, INDEX_LABELS[index])) #************* # timeseries obs with uncertainties if True: for index_pair in [["TX90p", "TN10p"], ["TN90p", "TX10p"]]: fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 6.5), sharex=True) ax3 = ax1.twinx() ax4 = ax2.twinx() axes = (ax1, ax2, ax3, ax4) for ix, index in enumerate(index_pair): index_ts, cover_ts = obtain_timeseries( DATALOC + "GHCND_{}_1951-{}_RegularGrid_global_2.5x2.5deg_LSmask.nc". format(index, int(settings.YEAR) + 1), "Ann", "GHCNDEX", index) utils.plot_ts_panel(axes[ix], [index_ts], "-", "temperature", loc="", bbox=BBOX) # no legend as single line axes[ix].text(0.02, 0.9, "({}) {}".format(string.ascii_lowercase[ix], index), transform=axes[ix].transAxes, fontsize=settings.FONTSIZE) # obs cube cube_list = iris.load( DATALOC + "GHCND_{}_1951-{}_RegularGrid_global_2.5x2.5deg_LSmask.nc". format(index, int(settings.YEAR) + 1)) names = np.array([cube.var_name for cube in cube_list]) selected_cube, = np.where(names == "Ann")[0] ghcndex_cube = cube_list[selected_cube] ghcndex_cube.coord('latitude').guess_bounds() ghcndex_cube.coord('longitude').guess_bounds() ghcndex_cube = fix_time_coord(ghcndex_cube) if index in ["TX90p", "TN90p", "TX10p", "TN10p"]: # change from % to days ghcndex_cube.data = ghcndex_cube.data * 3.65 # era5 cube cube_list = iris.load( ERA5LOCTEMP + "ERA5_{}_1979-{}.nc".format(index, settings.YEAR)) names = np.array([cube.var_name for cube in cube_list]) selected_cube, = np.where(names == "Ann")[0] era5_cube = cube_list[selected_cube] era5_cube.coord('latitude').guess_bounds() era5_cube.coord('longitude').guess_bounds() if index in ["TX90p", "TN90p", "TX10p", "TN10p"]: # change from % to days era5_cube.data = era5_cube.data * 3.65 # need to regrid era5_cube = era5_cube.regrid( ghcndex_cube, iris.analysis.Linear(extrapolation_mode="mask")) coverage_offset, coverage_stdev = compute_coverage_error( ghcndex_cube, era5_cube) coverage_stdev *= 2. # 90%, 2s.d. axes[ix].fill_between(index_ts.times, index_ts.data - coverage_stdev, index_ts.data + coverage_stdev, color='mistyrose', label="ERA5 coverage uncertainty") # red tickmarks axes[ix].tick_params(axis='y', colors='red', direction="in") # and smoothed index_ts.data.fill_value = -99.9 smoothed = binomialfilter(index_ts.data.filled(), -99.9, 5, pad=False) smoothed = np.ma.masked_where(smoothed == -99.9, smoothed) axes[ix].plot(index_ts.times, smoothed, "r--", lw=LW) # print the index, the current anomaly and the rank information print(index, index_ts.data.compressed()[-1] / 36.5, np.argsort(np.argsort(index_ts.data.compressed())) + 1) axes[ix + 2].plot(cover_ts.times, cover_ts.data, "k:", lw=2) axes[ix + 2].yaxis.set_label_position("right") axes[ix + 2].yaxis.set_ticks_position('right') # prettify plt.xlim([1950, int(settings.YEAR) + 1]) axes[0].set_ylim([15, None]) if "X" in index: axes[1].set_ylim([19, 59]) elif "N" in index: axes[1].set_ylim([11, 59]) ax3.set_ylim([0, 98]) ax4.set_ylim([0, 98]) fig.text(0.03, 0.5, "Number of Days", va='center', rotation='vertical', fontsize=settings.FONTSIZE, color="r") fig.text(0.97, 0.5, "% land covered", va='center', rotation='vertical', fontsize=settings.FONTSIZE) for ax in axes: for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) tick.label2.set_fontsize(settings.FONTSIZE) for tick in axes[1].xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) fig.subplots_adjust(left=0.1, right=0.9, top=0.98, bottom=0.05, hspace=0.001) plt.savefig(settings.IMAGELOC + "TEX_{}+{}_ts_ghcndex_uncertainties{}".format( index_pair[0], index_pair[1], settings.OUTFMT)) plt.close() return # run_all_plots
def run_all_plots(): # #*********************** # # Three panel timeseries - UK, DWD, Windermere # chlorophyll = read_lake_csv(os.path.join(data_loc, "timeseries", "Windermere.csv")) # betula_out = read_dwd_betula(os.path.join(data_loc, "timeseries", "dwd-data_per180112_Betula_pendula_leaf_out.csv")) # quercus_out, quercus_fall = read_dwd_quercus(os.path.join(data_loc, "timeseries", "dwd-data_per-180112_Quercus robur leave unf.csv")) # betula_fall, fagus_out, fagus_fall = read_dwd_fagus(os.path.join(data_loc, "timeseries", "dwd-data_per180112_Fagus sylvatica and Betula pendula leaf data.csv")) # alder, chestnut, oak, beech = read_uk_csv(os.path.join(data_loc, "timeseries", "details_for_climate_report.csv")) # long_oak = read_uk_oak_csv(os.path.join(data_loc, "timeseries", "NatureCalendar_pedunculate_oak_first_leaf_1753_1999.csv")) # fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 7), sharex=True) # # DWD data - budburst # utils.plot_ts_panel(ax1, [betula_out, fagus_out, quercus_out], "-", "phenological", loc="") # lines, labels = ax1.get_legend_handles_labels() # newlabels = [] # for ll in labels: # newlabels += [r'${}$'.format(ll)] # ax1.legend(lines, newlabels, loc="lower left", ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, labelspacing=0.1, columnspacing=0.5) # ax4 = plt.axes([0.84, 0.84, 0.15, 0.15]) # img = mpimg.imread(os.path.join(data_loc, 'DWD_oak_leaf.jpg')) # ax4.imshow(img) # ax4.set_xticks([]) # ax4.set_yticks([]) # # UK data - budburst # utils.plot_ts_panel(ax2, [alder, chestnut, beech, long_oak, oak], "-", "phenological", loc="") # ax2.plot(long_oak.times, long_oak.data, ls=None, marker="o", c=settings.COLOURS["phenological"][long_oak.name], markeredgecolor=settings.COLOURS["phenological"][long_oak.name]) # lines, labels = ax2.get_legend_handles_labels() # newlabels = [] # for ll in labels: # newlabels += [r'${}$'.format(ll)] # ax2.legend(lines, newlabels, loc="lower left", ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, labelspacing=0.1, columnspacing=0.5) # ax5 = plt.axes([0.84, 0.54, 0.15, 0.15]) # img = mpimg.imread(os.path.join(data_loc, '61d1c433-b65c-47bc-b831-7e44ab74a895.jpg')) # ax5.imshow(img) # ax5.set_xticks([]) # ax5.set_yticks([]) # # Windermere # utils.plot_ts_panel(ax3, [chlorophyll], "-", "phenological", loc="lower left") # ax6 = plt.axes([0.84, 0.23, 0.15, 0.15]) # img = mpimg.imread(os.path.join(data_loc, 'Asterionella and aulacoseira 05 May 2017.jpg')) # ax6.imshow(img) # ax6.set_xticks([]) # ax6.set_yticks([]) # # prettify # ax1.set_ylim([70, 160]) # ax2.set_ylim([70, 160]) # ax3.set_ylim([70, 160]) # ax2.set_ylabel("Day of year", fontsize=settings.FONTSIZE) # ax1.text(0.02, 0.88, "(a) Germany - tree leaf unfurling", transform=ax1.transAxes, fontsize=settings.FONTSIZE) # ax2.text(0.02, 0.88, "(b) UK - tree bud burst & leaf out", transform=ax2.transAxes, fontsize=settings.FONTSIZE) # ax3.text(0.02, 0.88, "(c) UK - Windermere - plankton", transform=ax3.transAxes, fontsize=settings.FONTSIZE) # for tick in ax3.xaxis.get_major_ticks(): # tick.label.set_fontsize(settings.FONTSIZE) # minorLocator = MultipleLocator(100) # for ax in [ax1, ax2, ax3]: # utils.thicken_panel_border(ax) # ax.set_yticks(ax.get_yticks()[1:-1]) # ax.xaxis.set_minor_locator(minorLocator) # for tick in ax.yaxis.get_major_ticks(): # tick.label.set_fontsize(settings.FONTSIZE) # ax1.set_xlim([1950, 2020]) # plt.setp([a.get_xticklabels() for a in [ax1, ax2]], visible=False) # fig.subplots_adjust(right=0.95, top=0.95, bottom=0.05, hspace=0.001) # plt.savefig(image_loc+"PHEN_tree_ts{}".format(settings.OUTFMT)) # plt.close() # #*********************** # # Barlett start/end timeseries - image inset # # Hand-copied data from "Bartlett Gcc Transition Dates 2008-2017.xlsx" # # up, down, diff = read_bartlett_green(os.path.join(data_loc, "timeseries", "Bartlett_greenupdown.dat")) # # fig = plt.figure(figsize = (8, 9)) # # plt.clf() # # # make axes by hand # # ax1 = plt.axes([0.15,0.3,0.8,0.65]) # # ax2 = plt.axes([0.15,0.1,0.8,0.2], sharex = ax1) # # print "need inset axes for images" # # # plot data # # ax1.plot(up.times, up.data, c = "g", ls = "-", lw = 2, label = "Green up") # # ax1.plot(down.times, down.data, c = "brown", ls = "-", lw = 2, label = "Green down") # # ax1.fill_between(up.times, up.data, down.data, color = "0.75") # # ax2.plot(diff.times, diff.data, c = "c", ls = "-", lw = 2) # # ax1.legend(loc="upper right", ncol = 1, frameon = False, prop = {'size':settings.LEGEND_FONTSIZE}, labelspacing = 0.1, columnspacing = 0.5) # # # sort axes # # ax1.set_xlim([2008, 2018]) # # ax2.set_ylim([140,180]) # # ax1.text(0.02, 0.93, "(a) Bartlett seasons", transform = ax1.transAxes, fontsize = settings.FONTSIZE) # # ax2.text(0.02, 0.85, "(b) Difference", transform = ax2.transAxes, fontsize = settings.FONTSIZE) # # # prettify # # ax1.set_ylabel("Day of Year", fontsize = settings.FONTSIZE) # # ax2.set_ylabel("Days", fontsize = settings.FONTSIZE) # # minorLocator = MultipleLocator(100) # # for ax in [ax1, ax2]: # # utils.thicken_panel_border(ax) # # ax.set_yticks(ax.get_yticks()[1:-1]) # # ax.xaxis.set_minor_locator(minorLocator) # # for tick in ax.yaxis.get_major_ticks(): # # tick.label.set_fontsize(settings.FONTSIZE) # # for tick in ax.xaxis.get_major_ticks(): # # tick.label.set_fontsize(settings.FONTSIZE) # # plt.setp(ax1.get_xticklabels(), visible=False) # # plt.savefig(image_loc+"PHEN_bartlett_ts{}".format(settings.OUTFMT)) # # plt.close() # #*********************** # # Barlett start/end timeseries - image inset # # Hand-copied data from "Bartlett Gcc Transition Dates 2008-2017.xlsx" # # https://matplotlib.org/examples/pylab_examples/broken_axis.html # up, down, diff = read_bartlett_green(os.path.join(data_loc, "timeseries", "Bartlett_greenupdown.dat")) # fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 9), sharex=True) # print("need inset axes for images") # # plot same on both # ax1.plot(up.times, up.data, c="g", ls="-", lw=2, label="Green up", marker="o") # ax1.plot(down.times, down.data, c="brown", ls="-", lw=2, label="Green down", marker="o") # ax1.fill_between(up.times, up.data, down.data, color="lightgreen") # ax2.plot(up.times, up.data, c="g", ls="-", lw=2, marker="o") # ax2.plot(down.times, down.data, c="brown", ls="-", lw=2, marker="o") # ax2.fill_between(up.times, up.data, down.data, color="lightgreen") # ax1.invert_yaxis() # ax2.invert_yaxis() # # hide the spines between ax and ax2 # ax1.spines['bottom'].set_visible(False) # ax2.spines['top'].set_visible(False) # ax1.xaxis.tick_top() # ax1.tick_params(labeltop='off') # don't put tick labels at the top # ax2.xaxis.tick_bottom() # # and the difference # ax3.plot(diff.times, diff.data, c="c", ls="-", lw=2, marker="o") # # plot the legend # ax1.legend(loc="upper right", ncol=1, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, labelspacing=0.1, columnspacing=0.5) # # prettify # ax1.set_xlim([2007, int(settings.YEAR)+1]) # ax2.set_ylabel("Day of Year", fontsize=settings.FONTSIZE) # ax3.set_ylabel("Days", fontsize=settings.FONTSIZE) # ax1.text(0.02, 0.88, "(a) Bartlett seasons", transform=ax1.transAxes, fontsize=settings.FONTSIZE) # ax3.text(0.02, 0.85, "(b) Difference", transform=ax3.transAxes, fontsize=settings.FONTSIZE) # # zoom in # ax1.set_ylim([150, 100]) # ax2.set_ylim([300, 250]) # ax3.set_ylim([136, 179]) # minorLocator = MultipleLocator(100) # for ax in [ax1, ax2, ax3]: # utils.thicken_panel_border(ax) # ax.set_yticks(ax.get_yticks()[1:-1]) # ax.xaxis.set_minor_locator(minorLocator) # for tick in ax.yaxis.get_major_ticks(): # tick.label.set_fontsize(settings.FONTSIZE) # for tick in ax.xaxis.get_major_ticks(): # tick.label.set_fontsize(settings.FONTSIZE) # plt.setp(ax1.get_xticklabels(), visible=False) # fig.subplots_adjust(right=0.95, top=0.95, hspace=0.001) # plt.savefig(image_loc+"PHEN_bartlett_ts_gap{}".format(settings.OUTFMT)) # plt.close() # #*********************** # # Bartlett GPP & Duke separate # fig = plt.figure(figsize=(8, 6.5)) # plt.clf() # ax1 = plt.axes([0.12, 0.52, 0.78, 0.44]) # ax2 = ax1.twinx() # ax3 = plt.axes([0.12, 0.08, 0.78, 0.44], sharex=ax1) # gpp, gcc = read_bartlett_gpp(os.path.join(data_loc, "timeseries", "Bartlett GPP Flux and Canopy Greenness.csv")) # bartlett, duke = read_bartlett_duke(os.path.join(data_loc, "timeseries", "Bartlett 2008-2017 and Duke 2017 Camera Greenness.csv")) # utils.plot_ts_panel(ax1, [gcc], "-", "phenological", loc="") # utils.plot_ts_panel(ax2, [gpp], "-", "phenological", loc="") # utils.plot_ts_panel(ax3, [bartlett, duke], "-", "phenological", loc="upper right", ncol=1) # ax3.axhline(0.36, c='0.5', ls='--') # ax3.axvline(185, c='0.5', ls=":") # # fix the legend # lines1, labels1 = ax1.get_legend_handles_labels() # lines2, labels2 = ax2.get_legend_handles_labels() # ax2.legend(lines1 + lines2, labels1 + labels2, loc="upper right", ncol=1, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, labelspacing=0.1, columnspacing=0.5) # # prettify # ax2.yaxis.set_label_position("right") # ax2.yaxis.set_ticks_position('right') # ax1.set_ylim([0.34, 0.48]) # ax3.set_ylim([0.34, 0.48]) # ax2.set_ylim([-2, 12]) # ax1.set_ylabel("Canopy Greeness", fontsize=settings.FONTSIZE) # ax2.set_ylabel("Gross Primary Productivity", fontsize=settings.FONTSIZE) # ax3.set_ylabel("Canopy Greeness", fontsize=settings.FONTSIZE) # ax1.text(0.02, 0.88, "(c)", transform=ax1.transAxes, fontsize=settings.FONTSIZE) # ax3.text(0.02, 0.85, "(d)", transform=ax3.transAxes, fontsize=settings.FONTSIZE) # for tick in ax3.xaxis.get_major_ticks(): # tick.label.set_fontsize(settings.FONTSIZE) # minorLocator = MultipleLocator(100) # for ax in [ax1, ax3]: # utils.thicken_panel_border(ax) # ax.set_yticks(ax.get_yticks()[1:-1]) # ax.xaxis.set_minor_locator(minorLocator) # for tick in ax.yaxis.get_major_ticks(): # tick.label.set_fontsize(settings.FONTSIZE) # utils.thicken_panel_border(ax2) # ax2.set_yticks(ax2.get_yticks()[1:-1]) # for tick in ax2.yaxis.get_major_ticks(): # tick.label2.set_fontsize(settings.FONTSIZE) # plt.setp(ax1.get_xticklabels(), visible=False) # plt.setp(ax2.get_xticklabels(), visible=False) # fig.subplots_adjust(right=0.95, top=0.95, hspace=0.001) # ax4 = plt.axes([0.14, 0.65, 0.15, 0.15]) # img = mpimg.imread(os.path.join(data_loc, 'bbc7_2017_01_20_140005.jpg')) # ax4.imshow(img) # ax4.set_xticks([]) # ax4.set_yticks([]) # ax4.set_title("January") # ax5 = plt.axes([0.72, 0.65, 0.15, 0.15]) # img = mpimg.imread(os.path.join(data_loc, 'bbc7_2017_07_17_130005.jpg')) # ax5.imshow(img) # ax5.set_xticks([]) # ax5.set_yticks([]) # ax5.set_title("July") # ax6 = plt.axes([0.14, 0.25, 0.15, 0.15]) # img = mpimg.imread(os.path.join(data_loc, 'bbc7_2017_07_04_124505.jpg')) # ax6.imshow(img) # ax6.set_xticks([]) # ax6.set_yticks([]) # ax6.set_title("Bartlett") # ax7 = plt.axes([0.72, 0.25, 0.15, 0.15]) # img = mpimg.imread(os.path.join(data_loc, 'dukehw_2017_07_04_120110.jpg')) # ax7.imshow(img) # ax7.set_xticks([]) # ax7.set_yticks([]) # ax7.set_title("Duke") # plt.savefig(image_loc+"PHEN_Bartlett_GPP_Duke_separate{}".format(settings.OUTFMT)) # plt.close() # #*********************** # # Bartlett GPP & Duke combined # # fig = plt.figure(figsize = (8, 5)) # # plt.clf() # # ax1 = plt.axes([0.12, 0.1, 0.78, 0.8]) # # ax2 = ax1.twinx() # # gpp, gcc = read_bartlett_gpp(os.path.join(data_loc, "timeseries", "Bartlett GPP Flux and Canopy Greenness.csv")) # # bartlett, duke = read_bartlett_duke(os.path.join(data_loc, "timeseries", "Bartlett 2008-2017 and Duke 2017 Camera Greenness.csv")) # # utils.plot_ts_panel(ax1, [gcc, duke], "-", "phenological", loc = "") # # utils.plot_ts_panel(ax2, [gpp], "-", "phenological", loc = "") # # # fix the legend # # lines1, labels1 = ax1.get_legend_handles_labels() # # lines2, labels2 = ax2.get_legend_handles_labels() # # # have to remove duplicate label # # ax2.legend(lines1 + lines2, labels1 + labels2, loc="upper right", ncol = 1, frameon = False, prop = {'size':settings.LEGEND_FONTSIZE}, labelspacing = 0.1, columnspacing = 0.5) # # # prettify # # ax2.yaxis.set_label_position("right") # # ax1.set_ylim([0.34, 0.48]) # # ax2.set_ylim([-2, 12]) # # ax1.set_ylabel("Canopy Greeness", fontsize = settings.FONTSIZE) # # ax2.set_ylabel("Gross Primary Productivity", fontsize = settings.FONTSIZE) # # for tick in ax1.xaxis.get_major_ticks(): # # tick.label.set_fontsize(settings.FONTSIZE) # # minorLocator = MultipleLocator(100) # # for ax in [ax1, ax3]: # # utils.thicken_panel_border(ax) # # ax.set_yticks(ax.get_yticks()[1:]) # # ax.xaxis.set_minor_locator(minorLocator) # # for tick in ax.yaxis.get_major_ticks(): # # tick.label.set_fontsize(settings.FONTSIZE) # # utils.thicken_panel_border(ax2) # # ax2.set_yticks(ax2.get_yticks()[1:-1]) # # for tick in ax2.yaxis.get_major_ticks(): # # tick.label2.set_fontsize(settings.FONTSIZE) # # fig.subplots_adjust(right = 0.95, top = 0.95, hspace = 0.001) # # plt.savefig(image_loc+"PHEN_Bartlett_GPP_Duke_combined{}".format(settings.OUTFMT)) # # plt.close() # #*********************** # # UK Map # import cartopy.feature as cfeature # land_50m = cfeature.NaturalEarthFeature('physical', 'land', '50m', # edgecolor='face', # facecolor=cfeature.COLORS['land']) # species, days, lats, lons = read_uk_map_csv(os.path.join(data_loc, "timeseries", "UK_Leafout_4Trees.csv")) # cmap = plt.cm.YlGn # bounds = np.arange(80, 160, 10) # norm = mpl.cm.colors.BoundaryNorm(bounds, cmap.N) # fig = plt.figure(figsize=(8, 10.5)) # plt.clf() # ax = plt.axes([0.05, 0.05, 0.9, 0.9], projection=cartopy.crs.LambertConformal(central_longitude=-7.5)) # ax.gridlines() #draw_labels=True) # ax.add_feature(land_50m, zorder=0, facecolor="0.9", edgecolor="k") # ax.set_extent([-10, 4, 48, 60], cartopy.crs.PlateCarree()) # scat = ax.scatter(lons, lats, c=days, transform=cartopy.crs.PlateCarree(), cmap=cmap, norm=norm, s=25, edgecolor='0.5', linewidth='0.5', zorder=10) # utils.thicken_panel_border(ax) # cb = plt.colorbar(scat, orientation='horizontal', ticks=bounds[1:-1], label="Day of year", drawedges=True, fraction=0.1, pad=0.05, aspect=15, shrink=0.8) # # prettify # cb.set_ticklabels(["{:g}".format(b) for b in bounds[1:-1]]) # cb.outline.set_linewidth(2) # cb.dividers.set_color('k') # cb.dividers.set_linewidth(2) # plt.savefig(image_loc + "PHEN_UK_map{}".format(settings.OUTFMT)) # plt.close() #*********************** # MODIS cubelist = iris.load(os.path.join(data_loc, "MODIS.CMG.{}.SOS.EOS.Anomaly.nc".format(settings.YEAR))) for c, cube in enumerate(cubelist): if cube.name() == "EOS": eos_cube = cubelist[c] elif cube.name() == "SOS": sos_cube = cubelist[c] elif cube.name() == "MAX": max_cube = cubelist[c] # deal with NANS # eos_cube.data = np.ma.masked_where(eos_cube.data != eos_cube.data, eos_cube.data) sos_cube.data = np.ma.masked_where(sos_cube.data != sos_cube.data, sos_cube.data) # max_cube.data = np.ma.masked_where(max_cube.data != max_cube.data, max_cube.data) # set up a 1 x 2 set of axes (2018 only needs one panel) fig = plt.figure(figsize=(8, 8)) plt.clf() # set up plot settings BOUNDS = [[-100, -20, -10, -5, -2, 0, 2, 5, 10, 20, 100]]#, [-100, -20, -10, -5, -2, 0, 2, 5, 10, 20, 100]] CMAPS = [settings.COLOURMAP_DICT["phenological_r"]]#, settings.COLOURMAP_DICT["phenological"]] CUBES = [sos_cube]# eos_cube] LABELS = ["(a) Start of Season (SOS)"]#, "(b) End of Season (EOS)"] # boundary circle theta = np.linspace(0, 2*np.pi, 100) center, radius = [0.5, 0.5], 0.5 verts = np.vstack([np.sin(theta), np.cos(theta)]).T circle = mpath.Path(verts * radius + center) # spin through axes for a in range(1): ax = plt.subplot(1, 1, a+1, projection=cartopy.crs.NorthPolarStereo()) plot_cube = CUBES[a] if settings.OUTFMT in [".eps", ".pdf"]: if plot_cube.coord("latitude").points.shape[0] > 90 or plot_cube.coord("longitude").points.shape[0] > 360: regrid_size = 1.0 print("Regridding cube for {} output to {} degree resolution".format(settings.OUTFMT, regrid_size)) print("Old Shape {}".format(plot_cube.data.shape)) plot_cube = utils.regrid_cube(plot_cube, regrid_size, regrid_size) print("New Shape {}".format(plot_cube.data.shape)) ax.gridlines() #draw_labels=True) ax.add_feature(cartopy.feature.LAND, zorder=0, facecolor="0.9", edgecolor="k") ax.coastlines() ax.set_boundary(circle, transform=ax.transAxes) ax.set_extent([-180, 180, 45, 90], cartopy.crs.PlateCarree()) ext = ax.get_extent() # save the original extent cmap = CMAPS[a] norm = mpl.cm.colors.BoundaryNorm(BOUNDS[a], cmap.N) mesh = iris.plot.pcolormesh(plot_cube, cmap=cmap, norm=norm, axes=ax) ax.set_extent(ext, ax.projection) # fix the extent change from colormesh ax.text(-0.1, 1.0, LABELS[a], fontsize=settings.FONTSIZE * 0.8, transform=ax.transAxes) cb = plt.colorbar(mesh, orientation='horizontal', ticks=BOUNDS[a][1:-1], label="Anomaly (days)", drawedges=True, fraction=0.1, pad=0.05, aspect=15, shrink=0.8) # prettify cb.set_ticklabels(["{:g}".format(b) for b in BOUNDS[a][1:-1]]) cb.outline.set_linewidth(2) cb.dividers.set_color('k') cb.dividers.set_linewidth(2) ax.set_extent([-180, 180, 45, 90], cartopy.crs.PlateCarree()) fig.subplots_adjust(bottom=0.05, top=0.95, left=0.04, right=0.95, wspace=0.02) plt.title("") plt.savefig(image_loc + "PHEN_modis_polar{}".format(settings.OUTFMT)) plt.close() del eos_cube del sos_cube del cubelist #*********************** # MODIS LAI # max_cube.data = np.ma.masked_where(max_cube.data <= -9000, max_cube.data) # if settings.OUTFMT in [".eps", ".pdf"]: # if max_cube.coord("latitude").points.shape[0] > 180 or max_cube.coord("longitude").points.shape[0] > 360: # regrid_size = 1.0 # print("Regridding cube for {} output to {} degree resolution".format(settings.OUTFMT, regrid_size)) # print("Old Shape {}".format(max_cube.data.shape)) # max_cube = utils.regrid_cube(max_cube, regrid_size, regrid_size) # print("New Shape {}".format(max_cube.data.shape)) # fig = plt.figure(figsize=(8, 8)) # plt.clf() # ax = plt.axes([0.05, 0.05, 0.9, 0.9], projection=cartopy.crs.NorthPolarStereo()) # ax.gridlines() #draw_labels=True) # ax.add_feature(cartopy.feature.LAND, zorder=0, facecolor="0.9", edgecolor="k") # ax.coastlines() # ax.set_boundary(circle, transform=ax.transAxes) # ax.set_extent([-180, 180, 45, 90], cartopy.crs.PlateCarree()) # ext = ax.get_extent() # save the original extent # cmap = settings.COLOURMAP_DICT["phenological_r"] # bounds = [-100, -5, -3, -2, -1, 0, 1, 2, 3, 5, 100] # norm = mpl.cm.colors.BoundaryNorm(bounds, cmap.N) # mesh = iris.plot.pcolormesh(max_cube, cmap=cmap, norm=norm, axes=ax) # ax.set_extent(ext, ax.projection) # fix the extent change from colormesh # ax.text(-0.1, 1.0, "Max Leaf Area Index", fontsize=settings.FONTSIZE * 0.8, transform=ax.transAxes) # cb = plt.colorbar(mesh, orientation='horizontal', ticks=bounds[1:-1], label="Anomaly (m"+r'$^2'+"/m"+r'$^2'+")", drawedges=True, fraction=0.1, pad=0.05, aspect=15, shrink=0.8) # # prettify # cb.set_ticklabels(["{:g}".format(b) for b in bounds[1:-1]]) # cb.outline.set_linewidth(2) # cb.dividers.set_color('k') # cb.dividers.set_linewidth(2) # ax.set_extent([-180, 180, 45, 90], cartopy.crs.PlateCarree()) # plt.savefig(image_loc + "PHEN_modis_lai{}".format(settings.OUTFMT)) # plt.close() #*********************** # MODIS timeseries - 2017 # sos, eos, lai = read_modis_ts(os.path.join(data_loc, "MODIS.CMG.{}.SOS.EOS.MAX.AnomalyTS.csv".format(settings.YEAR))) # plt.clf() # fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 4), sharex=True) # # plot same on both # ax1.plot(sos.times, sos.data, c="g", ls="-", lw=2, label="Start of Season", marker="o") # ax1.plot(eos.times, eos.data, c="brown", ls="-", lw=2, label="End of Season", marker="o") # ax1.fill_between(eos.times, eos.data, sos.data, color="lightgreen") # ax2.plot(sos.times, sos.data, c="g", ls="-", lw=2, marker="o") # ax2.plot(eos.times, eos.data, c="brown", ls="-", lw=2, marker="o") # ax2.fill_between(eos.times, eos.data, sos.data, color="lightgreen") # # invert so green down is on the bottom # ax1.invert_yaxis() # ax2.invert_yaxis() # # hide the spines between ax and ax2 # ax1.spines['bottom'].set_visible(False) # ax2.spines['top'].set_visible(False) # ax1.xaxis.tick_top() # ax1.tick_params(labeltop='off') # don't put tick labels at the top # ax2.xaxis.tick_bottom() # # plot the legend # ax1.legend(loc="upper right", ncol=1, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, labelspacing=0.1, columnspacing=0.5) # # prettify # ax1.set_xlim([1999, 2018]) # ax2.set_ylabel("Day of Year", fontsize=settings.FONTSIZE) # # zoom in # ax1.set_ylim([160, 110]) # ax2.set_ylim([300, 250]) # # set label # ax1.text(-0.1, 0.92, "(c)", transform=ax1.transAxes, fontsize=settings.FONTSIZE) # minorLocator = MultipleLocator(100) # for ax in [ax1, ax2]: # utils.thicken_panel_border(ax) # ax.set_yticks(ax.get_yticks()[1:-1]) # ax.xaxis.set_minor_locator(minorLocator) # for tick in ax.yaxis.get_major_ticks(): # tick.label.set_fontsize(settings.FONTSIZE) # for tick in ax.xaxis.get_major_ticks(): # tick.label.set_fontsize(settings.FONTSIZE) # plt.setp(ax1.get_xticklabels(), visible=False) # fig.subplots_adjust(right=0.95, top=0.95, hspace=0.001) # plt.savefig(image_loc+"PHEN_modis_ts{}".format(settings.OUTFMT)) # plt.close() #*********************** # MODIS timeseries - 2018 sos_na, sos_ea, sprt_na, sprt_ea = read_modis_ts(os.path.join(data_loc, "MODIS.CMG.{}.SOS.EOS.SPRT.FALT.TS.csv".format(settings.YEAR))) dummy, sos_na = utils.calculate_climatology_and_anomalies_1d(sos_na, 2000, 2010) dummy, sos_ea = utils.calculate_climatology_and_anomalies_1d(sos_ea, 2000, 2010) fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 6.5), sharex=True) # North America # use un-anomalised spring T to get legend without the data utils.plot_ts_panel(ax1, [sos_na, sprt_na], "-", "phenological", loc=LEGEND_LOC) # Eurasia utils.plot_ts_panel(ax2, [sos_ea, sprt_ea], "-", "phenological", loc=LEGEND_LOC) # make twin axes for Spring T dummy, sprt_na = utils.calculate_climatology_and_anomalies_1d(sprt_na, 2000, 2010) dummy, sprt_ea = utils.calculate_climatology_and_anomalies_1d(sprt_ea, 2000, 2010) ax3 = ax1.twinx() utils.plot_ts_panel(ax3, [sprt_na], "-", "phenological", loc="") ax4 = ax2.twinx() utils.plot_ts_panel(ax4, [sprt_ea], "-", "phenological", loc="") # prettify ax1.set_ylim([-8, 8]) ax2.set_ylim([-8, 8]) ax3.set_ylim([2, -2]) ax4.set_ylim([2, -2]) # labels ax1.text(0.02, 0.88, "(a) North America", transform=ax1.transAxes, fontsize=settings.FONTSIZE) ax2.text(0.02, 0.88, "(b) Eurasia", transform=ax2.transAxes, fontsize=settings.FONTSIZE) ax1.text(0.47, 0.88, "2018 SOS Anomaly = 1.86 days", transform=ax1.transAxes, fontsize=settings.FONTSIZE*0.8) ax1.text(0.47, 0.78, "2018 Spring T anomaly = -0.75 "+r'$^{\circ}$'+"C", transform=ax1.transAxes, fontsize=settings.FONTSIZE*0.8) ax2.text(0.47, 0.88, "2018 SOS Anomaly = 2.01 days", transform=ax2.transAxes, fontsize=settings.FONTSIZE*0.8) ax2.text(0.47, 0.78, "2018 Spring T anomaly = 0.13 "+r'$^{\circ}$'+"C", transform=ax2.transAxes, fontsize=settings.FONTSIZE*0.8) fig.text(0.01, 0.5, "SOS Anomaly (days)", va='center', rotation='vertical', fontsize = settings.FONTSIZE) fig.text(0.95, 0.5, "Temperature Anomaly ("+r'$^{\circ}$'+"C)", va='center', rotation='vertical', fontsize = settings.FONTSIZE) # ticks and labels for tick in ax2.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) minorLocator = MultipleLocator(1) for ax in [ax1, ax2]: utils.thicken_panel_border(ax) ax.set_yticks(ax.get_yticks()[1:-1]) ax.xaxis.set_minor_locator(minorLocator) for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for ax in [ax3, ax4]: ax.yaxis.tick_right() utils.thicken_panel_border(ax) ax.set_yticks(ax.get_yticks()[1:-1]) ax.xaxis.set_minor_locator(minorLocator) for tick in ax.yaxis.get_major_ticks(): tick.label2.set_fontsize(settings.FONTSIZE) # final settings ax1.set_xlim([1999, 2020]) plt.setp([a.get_xticklabels() for a in [ax1]], visible=False) fig.subplots_adjust(left=0.1, right=0.85, top=0.95, bottom=0.05, hspace=0.001) plt.savefig(image_loc+"PHEN_modis_ts{}".format(settings.OUTFMT)) plt.close() return # run_all_plots
def run_all_plots(): #************************************************************************ # Timeseries - 2016 version if False: fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 6.5), sharex=True) gfed = read_csv(DATALOC + "timeseries_NAme", "GFED3.1") gfas3 = read_csv(DATALOC + "timeseries_NAme", "GFAS1p3") gfas0 = read_csv(DATALOC + "timeseries_NAme", "GFAS1p0") utils.plot_ts_panel(ax1, [gfed, gfas3, gfas0], "-", "land_surface", loc=LEGEND_LOC) ax1.set_ylabel("Tg(C) per month", fontsize=settings.FONTSIZE) ax1.set_ylim([0, 100]) gfed = read_csv(DATALOC + "timeseries_TAsi", "GFED3.1") gfas3 = read_csv(DATALOC + "timeseries_TAsi", "GFAS1p3") gfas0 = read_csv(DATALOC + "timeseries_TAsi", "GFAS1p0") utils.plot_ts_panel(ax2, [gfed, gfas3, gfas0], "-", "land_surface", loc=LEGEND_LOC) ax2.set_ylabel("Tg(C) per month", fontsize=settings.FONTSIZE) ax2.set_ylim([0, 450]) # sort formatting plt.xlim([1997, 2017]) for tick in ax2.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for ax in [ax1, ax2]: for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) # sort labelling ax1.text(0.02, 0.9, "(a) North America", transform=ax1.transAxes, fontsize=settings.LABEL_FONTSIZE) ax2.text(0.02, 0.9, "(b) Tropical Asia", transform=ax2.transAxes, fontsize=settings.LABEL_FONTSIZE) fig.subplots_adjust(right=0.95, top=0.95, hspace=0.001) plt.savefig(settings.IMAGELOC + "BOB_ts{}".format(settings.OUTFMT)) plt.close() #************************************************************************ # Timeseries - 2017, 2018 version if True: fig = plt.figure(figsize=(8, 5)) plt.clf() ax = plt.axes([0.11, 0.09, 0.88, 0.89]) gfed = read_gfed_csv(DATALOC + "data4Johannes.txt", "global") gfas = read_csv(DATALOC + "timeseries_glob", "GFAS1p4") utils.plot_ts_panel(ax, [gfed, gfas], "-", "land_surface", loc=LEGEND_LOC) ax.set_ylabel("Tg(C) month" + r'$^{-1}$', fontsize=settings.FONTSIZE) ax.set_ylim([0, 840]) # sort formatting plt.xlim([1997, int(settings.YEAR) + 2]) for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) # sort labelling ax.text(0.02, 0.9, "Global", transform=ax.transAxes, fontsize=settings.LABEL_FONTSIZE) plt.savefig(settings.IMAGELOC + "BOB_ts{}".format(settings.OUTFMT)) plt.close() #************************************************************************ # Timeseries - 2019 version if True: majorLocator = MultipleLocator(5) fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, figsize=(8, 10), sharex=True) data = read_csv(DATALOC + "timeseries_TAsi", "GFAS1p4") utils.plot_ts_panel(ax1, [data], "-", "land_surface", loc="") ax1.text(0.02, 0.9, "(a) Tropical Asia", transform=ax1.transAxes, fontsize=settings.LABEL_FONTSIZE) ax1.set_ylim([0, 160]) data = read_csv(DATALOC + "timeseries_Arct", "GFAS1p4") utils.plot_ts_panel(ax2, [data], "-", "land_surface", loc="") ax2.text(0.02, 0.9, "(b) Arctic", transform=ax2.transAxes, fontsize=settings.LABEL_FONTSIZE) ax2.set_ylim([0, 12]) data = read_csv(DATALOC + "timeseries_SEAu", "GFAS1p4") utils.plot_ts_panel(ax3, [data], "-", "land_surface", loc="") ax3.text(0.02, 0.9, "(c) NSW & Victoria", transform=ax3.transAxes, fontsize=settings.LABEL_FONTSIZE) ax3.set_ylim([0, 16]) data = read_csv(DATALOC + "timeseries_SAme", "GFAS1p4") utils.plot_ts_panel(ax4, [data], "-", "land_surface", loc="") ax4.text(0.02, 0.9, "(d) Southern America", transform=ax4.transAxes, fontsize=settings.LABEL_FONTSIZE) ax4.set_ylim([0, 160]) # sort formatting plt.xlim([2003, int(settings.YEAR) + 2]) fig.text(0.03, 0.5, "Tg(C) per month", fontsize=settings.FONTSIZE, rotation="vertical") ax4.xaxis.set_major_locator(majorLocator) for tick in ax4.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for ax in [ax1, ax2, ax3, ax4]: for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) # sort labelling fig.subplots_adjust(right=0.96, top=0.98, bottom=0.05, hspace=0.001) plt.savefig(settings.IMAGELOC + "BOB_regional_ts{}".format(settings.OUTFMT)) plt.close() #************************************************************************ # Global Anomalies if True: # cube = read_data(DATALOC + "anomaly{}from2003-2015_smooth.txt".format(settings.YEAR), 2.1021584473146504e-05) # cube_list = iris.load(DATALOC + "anomaly{}from2003-{}_coarse20.grb".format(settings.YEAR, int(settings.YEAR)-1)) cube_list = iris.load( DATALOC + "anomaly{}from2003-{}_coarse20.grb".format(settings.YEAR, 2010)) # 2016 - these grib files have 6 identical fields (email from J Kaiser 10/3/2017) cube = cube_list[0] # convert from kg(C) m-2 s-1 to g(C) m-2 a-1 print("remove factor 1.14 after 2018 run") cube.data = cube.data * 1000 * 60 * 60 * 24 * 365. * GFAS_FACTOR bounds = [-1000, -160, -80, -40, -10, -5, 5, 10, 40, 80, 160, 1000] bounds = [-1000, -100, -40, -10, -5, -1, 1, 5, 10, 40, 100, 1000] utils.plot_smooth_map_iris( settings.IMAGELOC + "BOB_anomalies{}".format(settings.YEAR), cube, settings.COLOURMAP_DICT["land_surface"], bounds, "Anomalies from 2003-{} (g C m".format(2010) + r'$^{-2}$' + " yr" + r'$^{-1}$' + ")") utils.plot_smooth_map_iris( settings.IMAGELOC + "p2.1_BOB_anomalies{}".format(settings.YEAR), cube, settings.COLOURMAP_DICT["land_surface"], bounds, "Anomalies from 2003-{} (g C m".format(2010) + r'$^{-2}$' + " yr" + r'$^{-1}$' + ")", figtext="(af) Carbon Emissions from Biomass Burning") #************************************************************************ # Global actuals if True: # cube = read_data(DATALOC + "year2016_smooth.txt", 0) cube_list = iris.load(DATALOC + "year{}_coarse20.grb".format(settings.YEAR)) cube = cube_list[0] # convert from kg(C) m-2 s-1 to g(C) m-2 a-1 print("remove factor 1.14 after 2018 run") cube.data = cube.data * 1000 * 60 * 60 * 24 * 365. * GFAS_FACTOR bounds = [0, 5, 10, 40, 80, 120, 160, 200, 240, 500] bounds = [0, 1, 5, 10, 40, 80, 120, 160, 200, 500] # adjust to mask out <1 gC/m2/yr (Feb 2018) cmap = plt.cm.YlOrBr cmaplist = [cmap(i) for i in range(cmap.N)] for i in range(30): cmaplist[i] = (0.75, 0.75, 0.75, 1.0) cmap = cmap.from_list('Custom cmap', cmaplist, cmap.N) cube.data = np.ma.masked_where(cube.data < 1, cube.data) print("using hard coded sequential colourmap") utils.plot_smooth_map_iris( settings.IMAGELOC + "BOB_actuals{}".format(settings.YEAR), cube, cmap, bounds, "g C m" + r'$^{-2}$' + " yr" + r'$^{-1}$' + "") #************************************************************************ # Global climatology if False: cube_list = iris.load(DATALOC + "clim2003-2015_smooth.grb") cube = cube_list[0] # convert from kg(C) m-2 s-1 to g(C) m-2 a-1 cube.data = cube.data * 1000 * 60 * 60 * 24 * 365. bounds = [0, 5, 10, 40, 80, 120, 160, 200, 240, 500] print("using hard coded sequential colourmap") utils.plot_smooth_map_iris( settings.IMAGELOC + "BOB_climatology{}".format(settings.YEAR), cube, plt.cm.YlOrBr, bounds, "g C m" + r'$^{-2}$' + " yr" + r'$^{-1}$' + "") return # run_all_plots
def run_all_plots(): #************************************************************************ # Timeseries - 2016 if False: grasp, erai, era_presat, merra, jra55 = read_uaw_ts(DATALOC + "20N-40N300.nc", smooth=True) qbo = read_QBO(DATALOC + "qbo_1908_2015_REC_ERA40_ERAINT.txt") fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(5, figsize=(8, 12), sharex=True) # Observations utils.plot_ts_panel(ax1, [grasp], "-", "circulation", loc=LEGEND_LOC, ncol=2, extra_labels=[" (0.02)"]) # Reanalyses utils.plot_ts_panel( ax2, [erai, era_presat, jra55, merra], "-", "circulation", loc=LEGEND_LOC, ncol=2, extra_labels=[" (-0.20)", " (0.33)", " (-0.13)", " (-0.07)"]) grasp, erai, era_presat, merra, jra55 = read_uaw_ts(DATALOC + "10S-10N50.nc", smooth=False) # Observations utils.plot_ts_panel(ax3, [qbo, grasp], "-", "circulation", loc=LEGEND_LOC, ncol=2, extra_labels=["", " (-0.31)"]) ax3.set_ylabel("Zonal Anomaly (m s" + r'$^{-1}$' + ")", fontsize=settings.FONTSIZE) # Reanalyses utils.plot_ts_panel( ax4, [erai, era_presat, jra55, merra], "-", "circulation", loc=LEGEND_LOC, ncol=2, extra_labels=[" (-0.33)", " (-0.16)", " (-0.37)", " (0.30)"]) fig.subplots_adjust(left=0.11, right=0.99, top=0.99, hspace=0.001) # turn on 4th axis ticks for tick in ax4.get_xticklabels(): tick.set_visible(True) # delete the 5th axis and recreate - to break the sharex link fig.delaxes(ax5) ax5 = fig.add_subplot(515) pos = ax5.get_position() new_pos = [pos.x0, pos.y0 - 0.05, pos.width, pos.height] ax5.set_position(new_pos) # Obs & Reanalyses utils.plot_ts_panel(ax5, [grasp, erai, jra55, merra], "-", "circulation", loc=LEGEND_LOC, ncol=2) # sort formatting for ax in [ax4, ax5]: for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for ax in [ax1, ax2, ax3, ax4, ax5]: for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) # x + y limit ax1.set_xlim([1930, 2017.9]) ax1.set_ylim([-13, 6]) ax1.yaxis.set_ticks([-10, -5, 0]) ax2.set_ylim([-3.8, 3.8]) ax2.yaxis.set_ticks([-2, 0, 2, 4]) ax3.set_ylim([-34, 24]) ax4.set_ylim([-34, 24]) ax5.set_ylim([-34, 24]) ax5.set_xlim([2000, 2017.9]) # sort labelling ax1.text(0.02, 0.87, "(a) Observations 20" + r'$^\circ$' + " - 40" + r'$^\circ$' + "N 300hPa", transform=ax1.transAxes, fontsize=settings.LABEL_FONTSIZE) ax2.text(0.02, 0.87, "(b) Reanalyses 20" + r'$^\circ$' + " - 40" + r'$^\circ$' + "N 300hPa", transform=ax2.transAxes, fontsize=settings.LABEL_FONTSIZE) ax3.text(0.02, 0.87, "(c) Observations & Reconstructions 10" + r'$^\circ$' + "S - 10" + r'$^\circ$' + "N 50hPa", transform=ax3.transAxes, fontsize=settings.LABEL_FONTSIZE) ax4.text(0.02, 0.87, "(d) Reanalyses 10" + r'$^\circ$' + "S - 10" + r'$^\circ$' + "N 50hPa", transform=ax4.transAxes, fontsize=settings.LABEL_FONTSIZE) ax5.text(0.02, 0.87, "(e) Observations & Reanalyses 10" + r'$^\circ$' + "S - 10" + r'$^\circ$' + "N 50hPa", transform=ax5.transAxes, fontsize=settings.LABEL_FONTSIZE) plt.savefig(settings.IMAGELOC + "UAW_ts{}".format(settings.OUTFMT)) #************************************************************************ # Timeseries - 2018 if True: plt.figure(figsize=(8, 5)) plt.clf() ax = plt.axes([0.12, 0.10, 0.87, 0.87]) # Globe # grasp, erai, cera, merra, jra55 = read_uaw_ts(DATALOC + "Globe850.nc", annual=True) era5, erai, merra, jra55 = read_uaw_ts(DATALOC + "Globe850_v2.nc", annual=True) utils.plot_ts_panel(ax, [merra, erai, era5, jra55], "-", "circulation", \ loc=LEGEND_LOC, ncol=2, extra_labels=[" (0.03)", " (0.07)", \ " (0.03)", " (0.06)"]) # sort formatting for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) # x + y limit ax.set_xlim([1958, int(settings.YEAR) + 0.9]) ax.set_ylim([-0.39, 1.0]) ax.yaxis.set_ticks_position('left') ax.set_ylabel("Wind Anomaly (m s" + r'$^{-1}$' + ")", fontsize=settings.LABEL_FONTSIZE) # # sort labelling ax.text(0.02, 0.87, "Globe 850hPa", transform=ax.transAxes, fontsize=settings.LABEL_FONTSIZE) plt.savefig(settings.IMAGELOC + "UAW_globe_ts{}".format(settings.OUTFMT)) #******* # Tropics timeseries if False: fig = plt.figure(figsize=(8, 5)) plt.clf() ax = plt.axes([0.10, 0.10, 0.87, 0.87]) # 10N to 10S grasp, erai, cera, merra, jra55 = read_uaw_ts(DATALOC + "10S-10N50.nc") utils.plot_ts_panel(ax, [merra, erai, jra55, grasp], "-", "circulation",\ loc=LEGEND_LOC, ncol=2, extra_labels=[" (0.17)", " (-0.40)", \ " (-0.51)", " (-0.30)"]) # sort formatting for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) # x + y limit ax.set_xlim([2000, int(settings.YEAR) + 2]) ax.set_ylim([-28, 18]) ax.yaxis.set_ticks_position('left') ax.set_ylabel("Wind Anomaly (m s" + r'$^{-1}$' + ")", fontsize=settings.LABEL_FONTSIZE) # # sort labelling ax.text(0.02, 0.87, "10"+r'$^\circ$'+"S - 10"+r'$^\circ$'+"N 50hPa", \ transform=ax.transAxes, fontsize=settings.LABEL_FONTSIZE) plt.savefig(settings.IMAGELOC + "UAW_tropics_ts{}".format(settings.OUTFMT)) #************************************************************************ # Global Map - ERA5 Anomaly figure if True: # Read in ERA5 anomalies # IRIS doesn't like the Conventions attribute ncfile = ncdf.Dataset(DATALOC + "ERA5_850_u.nc", 'r') var = ncfile.variables["u"][:] # this is a masked array lons = ncfile.variables["longitude"][:] lats = ncfile.variables["latitude"][:] ncfile.close() # monthly data, so take mean print("not complete year used in 2019") mean = np.mean(var[7:], axis=0) cube = utils.make_iris_cube_2d(mean, lats, lons, "UAW_ANOM", "m/s") bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100] utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_UAW_{}_anoms_era5".format(settings.YEAR), \ cube, settings.COLOURMAP_DICT["circulation"], bounds, \ "Anomalies from 1981-2010 (m s"+r'$^{-1}$'+")", \ figtext="(w) Upper Air (850-hPa) Eastward Winds (ASOND)") utils.plot_smooth_map_iris(settings.IMAGELOC + "UAW_{}_anoms_era5".format(settings.YEAR), \ cube, settings.COLOURMAP_DICT["circulation"], bounds, \ "Anomalies from 1981-2010 (m s"+r'$^{-1}$'+")") #************************************************************************ # QBO plot - https://www.geo.fu-berlin.de/en/met/ag/strat/produkte/qbo/index.html if False: levels = np.array([70., 50., 40., 30., 20., 15., 10.]) times = [] dttimes = [] data = np.zeros((levels.shape[0], 13)) factor = 0.1 j = 0 with open(DATALOC + "qbo.dat", "r") as infile: for line in infile: line = line.split() if len(line) > 0: # get current year try: if int(line[1][:2]) >= int(settings.YEAR[-2:]) and int( line[1][:2]) <= int(settings.YEAR[-2:]) + 1: month = int(line[1][-2:]) times += [month] dttimes += [ dt.datetime(int(settings.YEAR), month, 1) ] data[:, j] = [float(i) * factor for i in line[2:]] j += 1 except ValueError: pass data = np.array(data) times = np.array(times) times[-1] += 12 # And now plot cmap = settings.COLOURMAP_DICT["circulation"] bounds = [ -100., -45., -30., -15., -10., -5., 0., 5., 10., 15., 30., 45., 100 ] norm = mpl.cm.colors.BoundaryNorm(bounds, cmap.N) fig = plt.figure(figsize=(8, 8)) plt.clf() ax = plt.axes([0.12, 0.07, 0.8, 0.9]) times, levels = np.meshgrid(times, levels) con = plt.contourf(times, levels, data, bounds, cmap=cmap, norm=norm, vmax=bounds[-1], vmin=bounds[1]) plt.ylabel("Pressure (hPa)", fontsize=settings.FONTSIZE) plt.xlabel(settings.YEAR, fontsize=settings.FONTSIZE) plt.xticks(times[0], [dt.datetime.strftime(d, "%b") for d in dttimes], fontsize=settings.FONTSIZE * 0.8) plt.xlim([1, 13]) plt.ylim([70, 10]) ax.set_yscale("log", subsy=[]) plt.gca().yaxis.set_major_locator( matplotlib.ticker.MultipleLocator(10)) plt.gca().yaxis.set_minor_locator(matplotlib.ticker.NullLocator()) plt.gca().yaxis.set_major_formatter( matplotlib.ticker.ScalarFormatter()) plt.yticks(np.arange(70, 0, -10), ["{}".format(l) for l in np.arange(70, 0, -10)], fontsize=settings.FONTSIZE) # colourbar and prettify cb = plt.colorbar(con, orientation='horizontal', pad=0.1, fraction=0.05, aspect=30, \ ticks=bounds[1:-1], label="zonal wind (m/s)", drawedges=True) cb.set_ticklabels(["{:g}".format(b) for b in bounds[1:-1]]) cb.ax.tick_params(axis='x', labelsize=settings.FONTSIZE * 0.6, direction='in') cb.set_label(label="zonal wind (m/s)", fontsize=settings.FONTSIZE * 0.6) # cb.outline.set_color('k') cb.outline.set_linewidth(2) cb.dividers.set_color('k') cb.dividers.set_linewidth(2) utils.thicken_panel_border(ax) plt.savefig(settings.IMAGELOC + "UAW_QBO_levels{}".format(settings.OUTFMT)) #************************************************************************ # https://www.geo.fu-berlin.de/met/ag/strat/produkte/qbo/singapore2019.dat if True: levels = [] times = np.arange(1, 13, 1) data = [] factor = 0.1 j = 0 with open(DATALOC + "singapore{}.dat".format(settings.YEAR), "r") as infile: read = False for line in infile: line = line.split() if len(line) == 0: continue if line[0] == "hPa": read = True continue elif read: if len(line) > 0: levels += [int(line[0])] data += [[float(l) * 0.1 for l in line[1:]]] else: continue # convert ot arrays and reorder levels = np.array(levels) levels = levels[::-1] data = np.array(data) data = data[::-1, :] # And now plot cmap = settings.COLOURMAP_DICT["circulation"] bounds = [ -100., -45., -30., -15., -10., -5., 0., 5., 10., 15., 30., 45., 100 ] norm = mpl.cm.colors.BoundaryNorm(bounds, cmap.N) fig = plt.figure(figsize=(8, 8)) plt.clf() ax = plt.axes([0.12, 0.07, 0.85, 0.9]) times, levels = np.meshgrid(times, levels) con = plt.contourf(times, levels, data, bounds, cmap=cmap, norm=norm, vmax=bounds[-1], vmin=bounds[1]) plt.ylabel("Pressure (hPa)", fontsize=settings.FONTSIZE) plt.xlabel(settings.YEAR, fontsize=settings.FONTSIZE) dttimes = [ dt.datetime(int(settings.YEAR), m + 1, 1) for m in range(12) ] plt.xticks(times[0], [dt.datetime.strftime(d, "%b") for d in dttimes], fontsize=settings.FONTSIZE) plt.xlim([1, 12]) plt.ylim([100, 10]) ax.set_yscale("log", subsy=[]) plt.gca().yaxis.set_major_locator( matplotlib.ticker.MultipleLocator(10)) plt.gca().yaxis.set_minor_locator(matplotlib.ticker.NullLocator()) plt.gca().yaxis.set_major_formatter( matplotlib.ticker.ScalarFormatter()) plt.yticks(np.arange(100, 0, -10), ["{}".format(l) for l in np.arange(100, 0, -10)], fontsize=settings.FONTSIZE) # colourbar and prettify cb = plt.colorbar(con, orientation='horizontal', pad=0.1, fraction=0.05, aspect=30, \ ticks=bounds[1:-1], drawedges=True) cb.set_ticklabels(["{:g}".format(b) for b in bounds[1:-1]]) cb.set_label(label="zonal wind (m/s)", fontsize=settings.FONTSIZE) cb.ax.tick_params(axis='x', labelsize=settings.FONTSIZE, direction='in') cb.set_label(label="zonal wind (m/s)", fontsize=settings.FONTSIZE) # cb.outline.set_color('k') cb.outline.set_linewidth(2) cb.dividers.set_color('k') cb.dividers.set_linewidth(2) utils.thicken_panel_border(ax) plt.savefig(settings.IMAGELOC + "UAW_levels{}".format(settings.OUTFMT)) #************************************************************************ # 200hPa winds in 1980 and 2018 if False: cube_list = iris.load(DATALOC + "ws200_spread_197901_201801.nc") names = np.array([str(cube.var_name) for cube in cube_list]) # hard coded labels mu = {"1980": "1.8", "2018": "1.0"} rms = {"1980": "2.0", "2018": "1.1"} label = {"1980": "(a)", "2018": "(b)"} bounds = [0, 0.5, 1, 1.5, 2, 2.5, 3.0, 100] for name in names: print(name) cube_index, = np.where(names == name) cube = cube_list[cube_index[0]] year = name.split("_")[-1][:4] utils.plot_smooth_map_iris( settings.IMAGELOC + "UAW_200hPa_Jan{}".format(year), cube, plt.cm.BuPu, bounds, "m/s", figtext="{} January {}, mean={}, RMS={}".format( label[year], year, mu[year], rms[year])) #************************************************************************ # Plots if False: label = {"1980": "(c)", "2018": "(d)"} for year in ["1980", "2018"]: cube_list = iris.load(DATALOC + "v200_zonal_{}01.nc".format(year)) for cube in cube_list: if cube.var_name == "products": names = cube elif cube.var_name == "v200_array": data_array = cube latitudes = data_array.coord("latitude").points plt.figure() ax = plt.axes([0.13, 0.13, 0.85, 0.85]) COLOURS = { "ERA5 ens. mean": "red", "ERA5 ensemble": "orange", "ERA5 HRES": "orange", "JRA55": "c", "MERRA-2": "m", "ERA-Interim": "orange" } for name, data in zip(names.data, data_array.data): str_name = "".join(str(name.compressed(), "latin-1").rstrip()) # manually fix names if str_name == "ERAI": str_name = "ERA-Interim" elif str_name == "ERA5 ens mean": str_name = "ERA5 ens. mean" if str_name[:3] == "mem": plt.plot(latitudes[1:], data[1:], c="orange") elif str_name == "ERA-Interim": plt.plot(latitudes[1:], data[1:], c=COLOURS[str_name], label=str_name, lw=2, ls="--") else: plt.plot(latitudes[1:], data[1:], c=COLOURS[str_name], label=str_name, lw=2) plt.legend(loc="upper right", ncol=1, frameon=False) plt.xlabel("Latitude", fontsize=settings.FONTSIZE * 0.8) plt.ylabel("m/s", fontsize=settings.FONTSIZE * 0.8) plt.text(0.03, 0.92, "{} January {}".format(label[year], year), transform=ax.transAxes, fontsize=settings.FONTSIZE * 0.8) plt.xlim([-90, 90]) plt.xticks(np.arange(-90, 120, 30)) plt.ylim([-1, 4]) for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE * 0.8) for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE * 0.8) utils.thicken_panel_border(ax) plt.savefig(settings.IMAGELOC + "UAW_200hPa_Jan{}_ts{}".format(year, settings.OUTFMT)) return
def run_all_plots(): if True: #*************** # Figure 1 euro, africa, tibet, canada = read_ts(DATALOC + "BAMS2020Fig1_data_LSWT.csv") # euro_fit, africa_fit, tibet_fit, canada_fit = read_ts(DATALOC + "Fig1_lines_LSWT.csv") euro_fit = utils.Timeseries("Lake", [1994, 2020], [-0.5136, (2020 - 1994) * 0.0386 - 0.5136]) africa_fit = utils.Timeseries( "Lake", [1994, 2020], [0.0204, (2020 - 1994) * 0.0036 + 0.0204]) tibet_fit = utils.Timeseries("Lake", [1994, 2020], [0.0878, (2020 - 1994) * 0.0017 + 0.0878]) canada_fit = utils.Timeseries( "Lake", [1994, 2020], [-0.2018, (2020 - 1994) * 0.0223 - 0.2018]) fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, figsize=(8, 10), sharex=True) #*************** # the timeseries LEGEND_LOC = "" utils.plot_ts_panel(ax1, [euro], "-", "temperature", loc=LEGEND_LOC) ax1.plot(euro_fit.times, euro_fit.data, c=settings.COLOURS["temperature"][euro_fit.name], lw=2, ls="--") ax1.text(1995, 0.8, "Europe, 127 lakes", fontsize=settings.FONTSIZE) utils.plot_ts_panel(ax2, [africa], "-", "temperature", loc=LEGEND_LOC) ax2.plot(africa_fit.times, africa_fit.data, c=settings.COLOURS["temperature"][africa_fit.name], lw=2, ls="--") ax2.text(1995, 0.8, "Africa, 68 lakes", fontsize=settings.FONTSIZE) utils.plot_ts_panel(ax3, [tibet], "-", "temperature", loc=LEGEND_LOC) ax3.plot(tibet_fit.times, tibet_fit.data, c=settings.COLOURS["temperature"][tibet_fit.name], lw=2, ls="--") ax3.text(1995, 0.8, "Tibetan Plateau, 106 lakes", fontsize=settings.FONTSIZE) utils.plot_ts_panel(ax4, [canada], "-", "temperature", loc=LEGEND_LOC) ax4.plot(canada_fit.times, canada_fit.data, c=settings.COLOURS["temperature"][canada_fit.name], lw=2, ls="--") ax4.text(1995, 0.8, "Canada, 244 lakes", fontsize=settings.FONTSIZE) # prettify for ax in [ax1, ax2, ax3, ax4]: ax.axhline(0, c='0.5', ls='--') utils.thicken_panel_border(ax) ax.set_ylim([-1, 1.2]) ax.set_xlim([euro.times[0] - 1, int(settings.YEAR) + 1]) ax.yaxis.set_ticks_position('left') for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax4.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) fig.text(0.01, 0.35, "Anomaly from 1996-2016 (" + r'$^\circ$' + "C)", fontsize=settings.FONTSIZE, rotation="vertical") fig.subplots_adjust(bottom=0.03, right=0.96, top=0.99, hspace=0.001) plt.savefig(settings.IMAGELOC + "LKT_ts{}".format(settings.OUTFMT)) plt.close() #*************** # Anomaly Scatter map if True: anomalies = read_lakes(DATALOC + "PlateX_data_LSWT.csv") bounds = [-8, -2, -1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5, 2, 8] # bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100] lons = np.arange(-90, 120, 30) lats = np.arange(-180, 210, 30) dummy = np.ma.zeros((len(lats), len(lons))) dummy.mask = np.ones(dummy.shape) cube = utils.make_iris_cube_2d(dummy, lats, lons, "blank", "m") utils.plot_smooth_map_iris(settings.IMAGELOC + "LKT_anomaly", cube, settings.COLOURMAP_DICT["temperature"], \ bounds, "Anomalies from 1996-2016 ("+r"$^{\circ}$"+"C)", \ scatter=[anomalies[1], anomalies[0], anomalies[2]], figtext="", title="") utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_LKT_anomaly", cube, settings.COLOURMAP_DICT["temperature"], \ bounds, "Anomalies from 1996-2016 ("+r"$^{\circ}$"+"C)", \ scatter=[anomalies[1], anomalies[0], anomalies[2]], \ figtext="(b) Lake Temperatures", title="") #*************** # Insets Scatter map if True: fig = plt.figure(figsize=(8, 7)) plt.clf() anomalies = read_lakes(DATALOC + "Fig2_data_LSWT.csv") bounds = [-8, -2, -1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5, 2, 8] # bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100] cmap = settings.COLOURMAP_DICT["temperature"] norm = mpl.cm.colors.BoundaryNorm(bounds, cmap.N) this_cmap = copy.copy(cmap) # first_cube = iris.load(DATALOC + "amaps_1st_quarter_2018_250km.nc")[0] # third_cube = iris.load(DATALOC + "amaps_3rd_quarter_2018_250km.nc")[0] # annual_cube = iris.load(DATALOC + "amaps_annual_2018_250km.nc")[0] cube = iris.load(DATALOC + "lswt_anom_1979_2019.nc")[0] if settings.OUTFMT in [".eps", ".pdf"]: if cube.coord("latitude").points.shape[0] > 180 or cube.coord( "longitude").points.shape[0] > 360: regrid_size = 1.0 print("Regridding cube for {} output to {} degree resolution". format(settings.OUTFMT, regrid_size)) print("Old Shape {}".format(cube.data.shape)) plot_cube = utils.regrid_cube(cube, regrid_size, regrid_size) print("New Shape {}".format(plot_cube.data.shape)) else: plot_cube = copy.deepcopy(cube) else: plot_cube = copy.deepcopy(cube) # make axes by hand axes = ([0.01, 0.55, 0.59, 0.41], [0.565, 0.45, 0.47, 0.50], [0.01, 0.13, 0.59, 0.41], [0.61, 0.07, 0.38, 0.41], [0.1, 0.1, 0.8, 0.03]) # Europe ax = plt.axes(axes[0], projection=cartopy.crs.PlateCarree()) ax.gridlines() #draw_labels=True) ax.add_feature(cartopy.feature.LAND, zorder=0, facecolor="0.9", edgecolor="k") ax.coastlines(resolution="50m") #ax.add_feature(cartopy.feature.BORDERS.with_scale('110m'), linewidth=.5) ax.set_extent([-25, 40, 34, 72], cartopy.crs.PlateCarree()) mesh = iris.plot.pcolormesh(plot_cube, cmap=this_cmap, norm=norm, axes=ax) plt.scatter(anomalies[1], anomalies[0], c=anomalies[2], cmap=this_cmap, norm=norm, s=25, \ transform=cartopy.crs.Geodetic(), edgecolor='0.1', linewidth=0.5, zorder=10) ax.text(0.05, 1.05, "(a) Europe", fontsize=settings.FONTSIZE * 0.8, transform=ax.transAxes) utils.thicken_panel_border(ax) # Africa ax = plt.axes(axes[1], projection=cartopy.crs.PlateCarree()) ax.gridlines() #draw_labels=True) ax.add_feature(cartopy.feature.LAND, zorder=0, facecolor="0.9", edgecolor="k") ax.coastlines(resolution="50m") #ax.add_feature(cartopy.feature.BORDERS.with_scale('110m'), linewidth=.5) ax.set_extent([-19, 43, -40, 33], cartopy.crs.PlateCarree()) # lat_constraint = utils.latConstraint([25, 90]) # nh_cube = third_cube.extract(lat_constraint) # lat_constraint = utils.latConstraint([-90, -25]) # sh_cube = first_cube.extract(lat_constraint) # lat_constraint = utils.latConstraint([-25, 25]) # trop_cube = annual_cube.extract(lat_constraint) # mesh = iris.plot.pcolormesh(nh_cube, cmap=this_cmap, norm=norm, axes=ax) # mesh = iris.plot.pcolormesh(trop_cube, cmap=this_cmap, norm=norm, axes=ax) # mesh = iris.plot.pcolormesh(sh_cube, cmap=this_cmap, norm=norm, axes=ax) mesh = iris.plot.pcolormesh(plot_cube, cmap=this_cmap, norm=norm, axes=ax) plt.scatter(anomalies[1], anomalies[0], c=anomalies[2], cmap=this_cmap, norm=norm, s=25, \ transform=cartopy.crs.Geodetic(), edgecolor='0.1', linewidth=0.5, zorder=10) ax.text(0.05, 1.05, "(b) Africa", fontsize=settings.FONTSIZE * 0.8, transform=ax.transAxes) utils.thicken_panel_border(ax) # Canada ax = plt.axes(axes[2], projection=cartopy.crs.PlateCarree()) ax.gridlines() #draw_labels=True) ax.add_feature(cartopy.feature.LAND, zorder=0, facecolor="0.9", edgecolor="k") ax.coastlines(resolution="50m") #ax.add_feature(cartopy.feature.BORDERS.with_scale('110m'), linewidth=.5) ax.set_extent([-140, -55, 42, 82], cartopy.crs.PlateCarree()) mesh = iris.plot.pcolormesh(plot_cube, cmap=this_cmap, norm=norm, axes=ax) plt.scatter(anomalies[1], anomalies[0], c=anomalies[2], cmap=this_cmap, norm=norm, s=25, \ transform=cartopy.crs.Geodetic(), edgecolor='0.1', linewidth=0.5, zorder=10) ax.text(0.05, 1.05, "(c) Canada", fontsize=settings.FONTSIZE * 0.8, transform=ax.transAxes) utils.thicken_panel_border(ax) # Tibet ax = plt.axes(axes[3], projection=cartopy.crs.PlateCarree()) ax.gridlines() #draw_labels=True) ax.add_feature(cartopy.feature.LAND, zorder=0, facecolor="0.9", edgecolor="k") ax.coastlines(resolution="50m") ax.add_feature(cartopy.feature.BORDERS.with_scale('50m'), linewidth=.5) ax.set_extent([78, 102, 28, 39], cartopy.crs.PlateCarree()) mesh = iris.plot.pcolormesh(plot_cube, cmap=this_cmap, norm=norm, axes=ax) plt.scatter(anomalies[1], anomalies[0], c=anomalies[2], cmap=this_cmap, norm=norm, s=25, \ transform=cartopy.crs.Geodetic(), edgecolor='0.1', linewidth=0.5, zorder=10) ax.text(0.05, 1.05, "(d) Tibetan Plateau", fontsize=settings.FONTSIZE * 0.8, transform=ax.transAxes) utils.thicken_panel_border(ax) # colourbar cb = plt.colorbar(mesh, cax=plt.axes(axes[4]), orientation='horizontal', ticks=bounds[1:-1], drawedges=True) # prettify cb.ax.tick_params(axis='x', labelsize=settings.FONTSIZE, direction='in', size=0) cb.set_label(label="Anomalies from 1996-2016 (" + r"$^{\circ}$" + "C)", fontsize=settings.FONTSIZE) cb.set_ticklabels(["{:g}".format(b) for b in bounds[1:-1]]) cb.outline.set_linewidth(2) cb.dividers.set_color('k') cb.dividers.set_linewidth(2) plt.savefig(settings.IMAGELOC + "LKT_Regions_scatter_map{}".format(settings.OUTFMT)) plt.close()
def run_all_plots(): #************************************************************************ # Upper Tropospheric Humidity timeseries figure if True: HIRSSTART = 1979 MWSTART = 1999 ERASTART = 1979 # smooth by 3 months hirs = read_ts(DATALOC + "hirs_data.aa", HIRSSTART, "HIRS", smooth=3) mw = read_ts(DATALOC + "mw_data.aa", MWSTART, "Microwave", smooth=3) era5 = read_ts(DATALOC + "era5_data.aa", ERASTART, "ERA5", smooth=3) fig = plt.figure(figsize=(8, 5)) ax = plt.axes([0.11, 0.08, 0.86, 0.90]) utils.plot_ts_panel(ax, [hirs, mw, era5], "-", "hydrological", loc=LEGEND_LOC, ncol=3) #******************* # prettify fig.text(0.01, 0.5, "Anomalies (% rh)", va='center', rotation='vertical', fontsize=settings.FONTSIZE) plt.ylim([-2.0, 2.0]) plt.xlim([1979, int(settings.YEAR) + 1.5]) for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) plt.savefig(settings.IMAGELOC + "UTH_ts{}".format(settings.OUTFMT)) plt.close() #************************************************************************ # HIRS map if True: cube = read_map(DATALOC, "hirs") bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100] # utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_UTH_{}_anoms_hirs".format(settings.YEAR), cube, settings.COLOURMAP_DICT["hydrological"], bounds, "Anomalies from 2001-2010 (% rh)", figtext = "(n) Upper Tropospheric Humidity") utils.plot_smooth_map_iris( settings.IMAGELOC + "UTH_{}_anoms_hirs".format(settings.YEAR), cube, settings.COLOURMAP_DICT["hydrological"], bounds, "Anomalies from 2001-2010 (% rh)") #************************************************************************ # MW map if True: cube = read_map(DATALOC, "mw") bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100] utils.plot_smooth_map_iris( settings.IMAGELOC + "UTH_{}_anoms_mw".format(settings.YEAR), cube, settings.COLOURMAP_DICT["hydrological"], bounds, "Anomalies from 2001-2010 (% rh)") utils.plot_smooth_map_iris( settings.IMAGELOC + "p2.1_UTH_{}_anoms_mw".format(settings.YEAR), cube, settings.COLOURMAP_DICT["hydrological"], bounds, "Anomalies from 2001-2010 (% rh)", figtext="(j) Upper Tropospheric Humidity") return # run_all_plots
def run_all_plots(): if True: # multipanel timeseries COLOURS = settings.COLOURS["temperature"] fig, (ax1, ax2, ax3, ax4, ax5, ax6) = plt.subplots(6, figsize=(8, 19), sharex=True) # ERA5 era5_globe, era5_ocean, era5_land, era5tropics = utils.era5_ts_read( settings.REANALYSISLOC, "sat", annual=True) land_era5_clim, land_era5_anoms = utils.calculate_climatology_and_anomalies_1d( era5_land, 1981, 2010) ocean_era5_clim, ocean_era5_anoms = utils.calculate_climatology_and_anomalies_1d( era5_ocean, 1981, 2010) global_era5_clim, global_era5_anoms = utils.calculate_climatology_and_anomalies_1d( era5_globe, 1981, 2010) #******************* # in situ L+O noaa, nasa, jma = read_global_t(DATALOC + "{}_LO.csv".format(IS_timeseries_root)) hadcrut = read_hadcrut_crutem(DATALOC + "hadcrut4.1981-2010.csv") p0 = ax1.plot(noaa.times, noaa.data, c=COLOURS[noaa.name], ls='-', label=noaa.name, lw=LW) p1 = ax1.plot(nasa.times, nasa.data, c=COLOURS[nasa.name], ls='-', label=nasa.name, lw=LW) # p2 = ax1.plot(jma.times, jma.data, c=COLOURS[jma.name], ls='-', label=jma.name, lw=LW) p3 = ax1.plot(hadcrut.times, hadcrut.data, c=COLOURS[hadcrut.name], ls='-', label=hadcrut.name, lw=LW) ax1.fill_between(hadcrut.times, hadcrut.lower, hadcrut.upper, \ where=hadcrut.upper > hadcrut.lower, color='0.5', alpha=0.7) p4 = ax1.fill(np.NaN, np.NaN, '0.5', alpha=0.7) ax1.axhline(0, c='0.5', ls='--') ax1.legend([p0[0], p1[0], (p3[0], p4[0])], [noaa.name, nasa.name, hadcrut.name], \ loc=LEGEND_LOC, ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \ labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=BBOX) ax1.text(0.02, 0.9, "(a) In Situ Land and Ocean", transform=ax1.transAxes, fontsize=settings.FONTSIZE) utils.thicken_panel_border(ax1) # ax1.yaxis.set_ticks_position('left') #******************* # reanalysis L+O merra = utils.read_merra( settings.REANALYSISLOC + "MERRA-2_SfcAnom{}.dat".format(settings.YEAR), "temperature", "LO") jra_actuals, jra_anoms = utils.read_jra55( settings.REANALYSISLOC + "JRA-55_tmp2m_global_ts.txt", "temperature") twenty_cr_actuals = utils.read_20cr( settings.REANALYSISLOC + "global.2mt.skt.txt", "temperature") dummy, twenty_cr_anoms = utils.calculate_climatology_and_anomalies_1d( twenty_cr_actuals, 1981, 2010) twenty_cr_anoms.zorder = -1 # 2018 no MERRA utils.plot_ts_panel(ax2, [jra_anoms, global_era5_anoms], "-", "temperature", loc=LEGEND_LOC, bbox=BBOX) ax2.text(0.02, 0.9, "(b) Reanalysis Land and Ocean", transform=ax2.transAxes, fontsize=settings.FONTSIZE) #******************* # in situ L noaa, nasa, jma = read_global_t(DATALOC + "{}_L.csv".format(IS_timeseries_root)) crutem = read_hadcrut_crutem(DATALOC + "crutem4_new_logo.1981-2010.csv") p0 = ax3.plot(noaa.times, noaa.data, ls='-', c=COLOURS[noaa.name], label=noaa.name, lw=LW) p1 = ax3.plot(nasa.times, nasa.data, ls='-', c=COLOURS[nasa.name], label=nasa.name, lw=LW) # p2 = ax3.plot(jma.times, jma.data, ls='-', c=COLOURS[jma.name], label=jma.name, lw=LW) # p3 = ax3.plot(berkeley.times, berkeley.data, ls = '-', c = COLOURS[berkeley.name], label = berkeley.name, lw = LW) p4 = ax3.plot(crutem.times, crutem.data, ls='-', c=COLOURS[crutem.name], label=crutem.name, lw=LW) ax3.fill_between(crutem.times, crutem.lower, crutem.upper, \ where=crutem.upper > crutem.lower, color='0.5', alpha=0.7) p5 = ax1.fill(np.NaN, np.NaN, '0.5', alpha=0.7) ax3.axhline(0, c='0.5', ls='--') ax3.legend([p0[0], p1[0], (p4[0], p5[0])], [noaa.name, nasa.name, crutem.name], \ loc=LEGEND_LOC, ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \ labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=BBOX) ax3.text(0.02, 0.9, "(c) In Situ Land only", transform=ax3.transAxes, fontsize=settings.FONTSIZE) utils.thicken_panel_border(ax3) # ax3.yaxis.set_ticks_position('left') #******************* # reanalysis L merra = utils.read_merra( settings.REANALYSISLOC + "MERRA-2_SfcAnom{}.dat".format(settings.YEAR), "temperature", "L") jra_actual, jra_anoms = utils.read_jra55( settings.REANALYSISLOC + "JRA-55_tmp2m_globalland_ts.txt", "temperature") twenty_cr_actuals = utils.read_20cr( settings.REANALYSISLOC + "air2mland.txt", "temperature") dummy, twenty_cr_anoms = utils.calculate_climatology_and_anomalies_1d( twenty_cr_actuals, 1981, 2010) twenty_cr_anoms.zorder = -1 # 2018 - No MERRA utils.plot_ts_panel(ax4, [jra_anoms, land_era5_anoms], "-", "temperature", loc=LEGEND_LOC, bbox=BBOX) ax4.text(0.02, 0.9, "(d) Reanalysis Land only", transform=ax4.transAxes, fontsize=settings.FONTSIZE) #******************* # in situ O noaa, nasa, jma = read_global_t(DATALOC + "{}_O.csv".format(IS_timeseries_root)) hadsst = read_hadcrut_crutem(DATALOC + "hadsst3_new_logo.1981-2010.csv") p0 = ax5.plot(noaa.times, noaa.data, ls='-', c=COLOURS[noaa.name], label=noaa.name, lw=LW) p1 = ax5.plot(nasa.times, nasa.data, ls='-', c=COLOURS[nasa.name], label=nasa.name, lw=LW) # p2 = ax5.plot(jma.times, jma.data, ls='-', c=COLOURS[jma.name], label=jma.name, lw=LW) p3 = ax5.plot(hadsst.times, hadsst.data, ls='-', c=COLOURS[hadsst.name], label=hadsst.name, lw=LW) ax5.fill_between(hadsst.times, hadsst.lower, hadsst.upper, \ where=hadsst.upper > hadsst.lower, color='0.5', alpha=0.7) p4 = ax1.fill(np.NaN, np.NaN, '0.5', alpha=0.7) ax5.axhline(0, c='0.5', ls='--') ax5.legend([p0[0], p1[0], (p3[0], p4[0])], [noaa.name, nasa.name, hadsst.name], \ loc=LEGEND_LOC, ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \ labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=BBOX) ax5.text(0.02, 0.9, "(e) In Situ Ocean only", transform=ax5.transAxes, fontsize=settings.FONTSIZE) utils.thicken_panel_border(ax5) # ax5.yaxis.set_ticks_position('left') #******************* # reanalysis O merra = utils.read_merra( settings.REANALYSISLOC + "MERRA-2_SfcAnom{}.dat".format(settings.YEAR), "temperature", "O") jra_actual, jra_anoms = utils.read_jra55( settings.REANALYSISLOC + "JRA-55_tmp2m_globalocean_ts.txt", "temperature") twenty_cr_actuals = utils.read_20cr( settings.REANALYSISLOC + "airsktocean.txt", "temperature") dummy, twenty_cr_anoms = utils.calculate_climatology_and_anomalies_1d( twenty_cr_actuals, 1981, 2010) twenty_cr_anoms.zorder = -1 # 2018 no MERRA utils.plot_ts_panel(ax6, [jra_anoms, ocean_era5_anoms], "-", "temperature", loc=LEGEND_LOC, bbox=BBOX) ax6.text(0.02, 0.9, "(f) Reanalysis Ocean only", transform=ax6.transAxes, fontsize=settings.FONTSIZE) #******************* # prettify fig.text(0.03, 0.5, "Anomalies (" + r'$^{\circ}$' + "C)", va='center', rotation='vertical', fontsize=settings.FONTSIZE) plt.xlim([1900, int(settings.YEAR) + 2]) minorLocator = MultipleLocator(5) for ax in [ax1, ax2, ax3, ax4, ax5, ax6]: ax.set_ylim(YLIM) for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) ax.xaxis.set_minor_locator(minorLocator) for tick in ax6.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) fig.subplots_adjust(right=0.96, top=0.995, bottom=0.02, hspace=0.001) plt.savefig(settings.IMAGELOC + "SAT_ts{}".format(settings.OUTFMT)) plt.close() #************************************************************************ # ERA5 Anomaly figure if True: # Read in ERA anomalies cube_list = iris.load(settings.REANALYSISLOC + "era5_t2m_{}01-{}12_ann_ano.nc".format( settings.YEAR, settings.YEAR)) for cube in cube_list: if cube.var_name == "T2M": break cube.coord('latitude').guess_bounds() cube.coord('longitude').guess_bounds() bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100] utils.plot_smooth_map_iris( settings.IMAGELOC + "SAT_{}_anoms_era5".format(settings.YEAR), cube[0], settings.COLOURMAP_DICT["temperature"], bounds, "Anomalies from 1981-2010 (" + r'$^{\circ}$' + "C)", title="ERA5") #************************************************************************ # MERRA2 Anomaly figure if True: cube_list = iris.load(settings.REANALYSISLOC + "MERRA-2_SfcAnom_{}.nc".format(settings.YEAR)) for cube in cube_list: if cube.var_name == "t2ma": break cube.coord('latitude').guess_bounds() cube.coord('longitude').guess_bounds() bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100] utils.plot_smooth_map_iris(settings.IMAGELOC + "SAT_{}_anoms_merra".format(settings.YEAR), cube[0], \ settings.COLOURMAP_DICT["temperature"], bounds, \ "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="MERRA-2") #************************************************************************ # HadCRUT4 Anomaly figure if True: cube_list = iris.load(DATALOC + "HadCRUT.4.6.0.0.median.nc") for cube in cube_list: if cube.var_name == "temperature_anomaly": break cube.coord('latitude').guess_bounds() cube.coord('longitude').guess_bounds() # restrict to 1851 to last full year date_constraint = utils.periodConstraint( cube, dt.datetime(1850, 1, 1), dt.datetime(int(settings.YEAR) + 1, 1, 1)) cube = cube.extract(date_constraint) # convert to 1981-2010 climatology. clim_constraint = utils.periodConstraint(cube, dt.datetime(1981, 1, 1), dt.datetime(2011, 1, 1)) clim_cube = cube.extract(clim_constraint) clim_data = clim_cube.data.reshape(-1, 12, clim_cube.data.shape[-2], clim_cube.data.shape[-1]) # more than 15 years present climatology = np.ma.mean(clim_data, axis=0) nyears = np.ma.count(clim_data, axis=0) climatology = np.ma.masked_where(nyears <= 15, climatology) # Kate keeps GT 15. # extract final year final_year_constraint = utils.periodConstraint( cube, dt.datetime(int(settings.YEAR), 1, 1), dt.datetime(int(settings.YEAR) + 1, 1, 1)) final_year_cube = cube.extract(final_year_constraint) final_year_cube.data = final_year_cube.data - climatology # more than 6 months present annual_cube = final_year_cube.collapsed(['time'], iris.analysis.MEAN) nmonths = np.ma.count(final_year_cube.data, axis=0) annual_cube.data = np.ma.masked_where(nmonths <= 6, annual_cube.data) bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100] utils.plot_smooth_map_iris(settings.IMAGELOC + "SAT_{}_anoms_hadcrut4".format(settings.YEAR), annual_cube, \ settings.COLOURMAP_DICT["temperature"], bounds, \ "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="HadCRUT 4.6") #************************************************************************ # NOAA data Anomaly figure - incl plate 2.1 if True: cube = read_noaa_mlost(DATALOC + "mlost-box.ytd.12.1981-2010bp.txt", int(settings.YEAR)) bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100] utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_SAT_{}_anoms_noaa".format(settings.YEAR), cube, \ settings.COLOURMAP_DICT["temperature"], bounds, \ "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", \ figtext="(a) Surface Temperature", \ save_netcdf_filename="{}MLOST_for_NOAA_{}.nc".format(DATALOC, dt.datetime.strftime(dt.datetime.now(), "%d-%b-%Y"))) utils.plot_smooth_map_iris(settings.IMAGELOC + "SAT_{}_anoms_noaa".format(settings.YEAR), cube, \ settings.COLOURMAP_DICT["temperature"], bounds, \ "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="NOAAGlobalTemp") #************************************************************************ # JRA55 data Anomaly figure if True: cube_list = iris.load(settings.REANALYSISLOC + "jra55_t2m_{}01-{}12_ann_ano.nc".format( settings.YEAR, settings.YEAR)) for cube in cube_list: if cube.var_name == "T2M": break cube.coord('latitude').guess_bounds() cube.coord('longitude').guess_bounds() bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100] utils.plot_smooth_map_iris(settings.IMAGELOC + "SAT_{}_anoms_jra55".format(settings.YEAR), cube[0,0], \ settings.COLOURMAP_DICT["temperature"], bounds, \ "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="JRA-55") #************************************************************************ # NASA GISS Anomaly figure if True: #cube = read_nasa_giss(DATALOC + "nasa-2015-anomalies-wrt1981-2010bp") cube = iris.load(DATALOC + "gistemp1200_GHCNv4_ERSSTv5.nc")[0] # convert to 1981-2010 climatology. clim_constraint = utils.periodConstraint(cube, dt.datetime(1981, 1, 1), dt.datetime(2011, 1, 1)) clim_cube = cube.extract(clim_constraint) clim_data = clim_cube.data.reshape(-1, 12, clim_cube.data.shape[-2], clim_cube.data.shape[-1]) # more than 15 years present climatology = np.ma.mean(clim_data, axis=0) nyears = np.ma.count(clim_data, axis=0) climatology = np.ma.masked_where(nyears <= 15, climatology) # Kate keeps GT 15. # extract final year final_year_constraint = utils.periodConstraint(cube, dt.datetime(int(settings.YEAR), 1, 1), \ dt.datetime(int(settings.YEAR)+1, 1, 1)) final_year_cube = cube.extract(final_year_constraint) final_year_cube.data = final_year_cube.data - climatology # more than 6 months present annual_cube = final_year_cube.collapsed(['time'], iris.analysis.MEAN) nmonths = np.ma.count(final_year_cube.data, axis=0) annual_cube.data = np.ma.masked_where(nmonths <= 6, annual_cube.data) bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100] utils.plot_smooth_map_iris(settings.IMAGELOC + "SAT_{}_anoms_nasa".format(settings.YEAR), annual_cube, \ settings.COLOURMAP_DICT["temperature"], bounds, \ "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="NASA GISS") return # run_all_plots
def plot_modis_ts(axl, sos, sprt, dummy, label, anomalies, legend_loc): utils.plot_ts_panel(axl, [sos, dummy], "-", "phenological", loc=legend_loc) # make twin axr = axl.twinx() utils.plot_ts_panel(axr, [sprt], "-", "phenological", loc="") # prettify axl.set_ylim([-10, 10]) # labels axl.text(-0.17, 1.08, label, transform=axl.transAxes, fontsize=settings.FONTSIZE) axl.text(0.4, 0.88, anomalies[0], transform=axl.transAxes, fontsize=settings.FONTSIZE) axl.text(0.4, 0.78, anomalies[1], transform=axl.transAxes, fontsize=settings.FONTSIZE) # ticks etc minorLocator = MultipleLocator(1) majorLocator = MultipleLocator(5) for ax in [axl]: if "EOS" in anomalies[0]: axr.set_ylim([-3, 3]) ax.set_ylabel("EOS Anomaly (days)", fontsize=settings.FONTSIZE, color='g') elif "SOS" in anomalies[0]: axr.set_ylim([3, -3]) ax.set_ylabel("SOS Anomaly (days)", fontsize=settings.FONTSIZE, color='g') ax.tick_params(axis='y', color='g') for ax in [axr]: ax.yaxis.tick_right() ax.set_ylabel("Temperature Anomaly (" + r'$^{\circ}$' + "C)", fontsize=settings.FONTSIZE, color='m') ax.tick_params(axis='y', color='m') ax.yaxis.set_tick_params(right=True, which="both", width=2, direction="in") for ax in [axl, axr]: utils.thicken_panel_border(ax) ax.set_yticks(ax.get_yticks()[1:-1]) ax.xaxis.set_minor_locator(minorLocator) ax.xaxis.set_major_locator(majorLocator) for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) tick.label2.set_fontsize(settings.FONTSIZE) for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) ax.set_xlim([1999, int(settings.YEAR) + 1]) return # plot_modis_ts
def run_all_plots(): #*********************** # MODIS - centre if True: cubelist = iris.load( os.path.join( DATALOC, "MODIS.CMG.{}.SOS.EOS.Anomaly.nc".format(settings.YEAR))) names = np.array([c.name() for c in cubelist]) # set up plot settings BOUNDS = [-100, -20, -10, -5, -2, 0, 2, 5, 10, 20, 100] LABELS = { "SOS": "(c) Start of Season (SOS)", "EOS": "(d) End of Season (EOS)" } for season in ["SOS", "EOS"]: c, = np.where(names == season)[0] cube = cubelist[c] # deal with NANS cube.data = np.ma.masked_where(cube.data != cube.data, cube.data) fig = plt.figure(figsize=(8, 11)) plt.clf() # boundary circle theta = np.linspace(0, 2 * np.pi, 100) center, radius = [0.5, 0.5], 0.5 verts = np.vstack([np.sin(theta), np.cos(theta)]).T circle = mpath.Path(verts * radius + center) # axes for polar plot ax = plt.axes([0.01, 0.02, 0.98, 0.65], projection=cartopy.crs.NorthPolarStereo( central_longitude=300.0)) plot_cube = cube # regrid depending on output format if settings.OUTFMT in [".eps", ".pdf"]: if plot_cube.coord( "latitude").points.shape[0] > 90 or plot_cube.coord( "longitude").points.shape[0] > 360: regrid_size = 1.0 print( "Regridding cube for {} output to {} degree resolution" .format(settings.OUTFMT, regrid_size)) print("Old Shape {}".format(plot_cube.data.shape)) plot_cube = utils.regrid_cube(plot_cube, regrid_size, regrid_size) print("New Shape {}".format(plot_cube.data.shape)) # prettify ax.gridlines() #draw_labels=True) ax.add_feature(cartopy.feature.LAND, zorder=0, facecolor="0.9", edgecolor="k") ax.coastlines() ax.set_boundary(circle, transform=ax.transAxes) if season == "SOS": cmap = settings.COLOURMAP_DICT["phenological_r"] elif season == "EOS": cmap = settings.COLOURMAP_DICT["phenological"] norm = mpl.cm.colors.BoundaryNorm(BOUNDS, cmap.N) mesh = iris.plot.pcolormesh(plot_cube, cmap=cmap, norm=norm, axes=ax) # # read in sites if season == "EOS": pass elif season == "SOS": lake_locations = read_us_phenocam( os.path.join(DATALOC, "lake_coords.csv")) # scatter COL = "chartreuse" ax.scatter(lake_locations[1], lake_locations[0], c=COL, s=100, edgecolor="k", transform=cartopy.crs.Geodetic(), zorder=10) COL = "m" # Harvard Forest - 2019 ax.scatter(-72.17, 42.54, c=COL, s=100, edgecolor="k", transform=cartopy.crs.Geodetic(), zorder=10) # uk box COL = "y" region = [-10.0, 49.0, 3.0, 60.0] ax.plot([region[0], region[0]], [region[1], region[3]], c=COL, ls='-', lw=4, zorder=10, transform=cartopy.crs.PlateCarree()) ax.plot([region[2], region[2]], [region[1], region[3]], c=COL, ls='-', lw=4, zorder=10, transform=cartopy.crs.PlateCarree()) ax.plot([region[0], region[2]], [region[1], region[1]], c=COL, ls='-', lw=4, zorder=10, transform=cartopy.crs.Geodetic()) ax.plot([region[0], region[2]], [region[3], region[3]], c=COL, ls='-', lw=4, zorder=10, transform=cartopy.crs.Geodetic()) # COL = "k" # ax.plot([region[0], region[0]], [region[1], region[3]], c=COL, ls='-', lw=5, zorder=9, transform=cartopy.crs.PlateCarree()) # ax.plot([region[2], region[2]], [region[1], region[3]], c=COL, ls='-', lw=5, zorder=9, transform=cartopy.crs.PlateCarree()) # ax.plot([region[0], region[2]], [region[1], region[1]], c=COL, ls='-', lw=5, zorder=9, transform=cartopy.crs.Geodetic()) # ax.plot([region[0], region[2]], [region[3], region[3]], c=COL, ls='-', lw=5, zorder=9, transform=cartopy.crs.Geodetic()) # label axes ax.text(-0.1, 1.0, LABELS[season], fontsize=settings.FONTSIZE, transform=ax.transAxes) cb = plt.colorbar(mesh, orientation='horizontal', ticks=BOUNDS[1:-1], drawedges=True, fraction=0.1, pad=0.01, aspect=20, shrink=0.8) # prettify cb.set_label(label="Anomaly (days)", fontsize=settings.FONTSIZE) cb.ax.tick_params(axis='x', labelsize=settings.FONTSIZE, direction='in', size=0) cb.set_ticklabels(["{:g}".format(b) for b in BOUNDS[1:-1]]) cb.outline.set_linewidth(2) cb.dividers.set_color('k') cb.dividers.set_linewidth(2) ax.set_extent([-180, 180, 30, 90], cartopy.crs.PlateCarree()) for lat in range(30, 100, 10): ax.text(180, lat, '{}$^\circ$N'.format(lat), transform=cartopy.crs.Geodetic()) fig.subplots_adjust(bottom=0.05, top=0.95, left=0.04, right=0.95, wspace=0.02) del cube #*********************** # MODIS timeserise sos_nh, eos_nh, sprt_nh_orig, falt_nh_orig = read_modis_ts( os.path.join( DATALOC, "MODIS.CMG.{}.SOS.EOS.SPRT.FALT.TS.csv".format( settings.YEAR))) dummy, sos_nh = utils.calculate_climatology_and_anomalies_1d( sos_nh, 2000, 2010) dummy, eos_nh = utils.calculate_climatology_and_anomalies_1d( eos_nh, 2000, 2010) dummy, sprt_nh = utils.calculate_climatology_and_anomalies_1d( sprt_nh_orig, 2000, 2010) dummy, falt_nh = utils.calculate_climatology_and_anomalies_1d( falt_nh_orig, 2000, 2010) ax = plt.axes([0.15, 0.73, 0.75, 0.23]) if season == "SOS": label = "(a) Start of Season" anomalies = [ "{} SOS Anomaly = -4.3 days".format(settings.YEAR), "{} Spr. T anomaly = 0.19 ".format(settings.YEAR) + r'$^{\circ}$' + "C" ] plot_modis_ts(ax, sos_nh, sprt_nh, sprt_nh_orig, label, anomalies, LEGEND_LOC) elif season == "EOS": label = "(b) End of Season" anomalies = [ "{} EOS Anomaly = 2.4 days".format(settings.YEAR), "{} Fall T anomaly = -0.53 ".format(settings.YEAR) + r'$^{\circ}$' + "C" ] plot_modis_ts(ax, eos_nh, falt_nh, falt_nh_orig, label, anomalies, LEGEND_LOC) plt.savefig(settings.IMAGELOC + "PHEN_modis_{}_{}{}".format( settings.YEAR, season, settings.OUTFMT)) del cubelist #*********************** # US timeseries - 2018 if True: fig = plt.figure(figsize=(8, 9.5)) plt.clf() modis_sos, modis_eos, pheno_sos, pheno_eos = read_us_phenocam_csv( os.path.join(DATALOC, "Richardson Data for SOC 2019 Figures.csv")) # images ax = plt.axes([0.01, 0.66, 0.49, 0.3]) plot_images(ax, "HarvardForest_20190511.jpg") ax = plt.axes([0.5, 0.66, 0.49, 0.3]) plot_images(ax, "HarvardForest_20191024.jpg") # timeseries ax = plt.axes([0.11, 0.35, 0.84, 0.3]) plot_us_phenocam(ax, modis_eos, pheno_eos, sos=False) # ax.text(0.05, 0.85, "(a)", transform=ax.transAxes, fontsize=settings.FONTSIZE) ax.set_ylim([275, 369]) plt.setp(ax.get_xticklabels(), visible=False) ax = plt.axes([0.11, 0.05, 0.84, 0.3]) plot_us_phenocam(ax, modis_sos, pheno_sos) # ax.text(0.05, 0.85, "(b)", transform=ax.transAxes, fontsize=settings.FONTSIZE) ax.set_ylim([101, 144]) fig.text(0.02, 0.97, "(a)", transform=ax.transAxes, fontsize=settings.FONTSIZE) fig.text(0.02, 0.3, "Day of year", rotation="vertical", fontsize=settings.FONTSIZE) plt.savefig( settings.IMAGELOC + "PHEN_UStimeseries_{}{}".format(settings.YEAR, settings.OUTFMT)) plt.close() #*********************** # US timeseries - 2018 if False: fig = plt.figure(figsize=(8, 7)) plt.clf() modis_sos, modis_eos, pheno_sos, pheno_eos = read_us_phenocam_csv( os.path.join(DATALOC, "Richardson Data for SOC 2019 Figures.csv")) # timeseries ax = plt.axes([0.11, 0.5, 0.64, 0.45]) plot_us_phenocam(ax, modis_eos, pheno_eos, sos=False) ax.text(0.05, 0.85, "(c)", transform=ax.transAxes, fontsize=settings.FONTSIZE) ax.set_ylim([275, 369]) plt.setp(ax.get_xticklabels(), visible=False) ax = plt.axes([0.75, 0.5, 0.25, 0.4]) plot_images(ax, "HarvardForest_20191024.jpg") ax = plt.axes([0.11, 0.05, 0.64, 0.45]) plot_us_phenocam(ax, modis_sos, pheno_sos) ax.text(0.05, 0.85, "(d)", transform=ax.transAxes, fontsize=settings.FONTSIZE) ax.set_ylim([101, 144]) ax = plt.axes([0.75, 0.05, 0.25, 0.4]) plot_images(ax, "HarvardForest_20190511.jpg") fig.text(0.02, 0.4, "Day of year", rotation="vertical", fontsize=settings.FONTSIZE) plt.savefig( settings.IMAGELOC + "PHEN_UStimeseries_{}{}".format(settings.YEAR, settings.OUTFMT)) plt.close() #*********************** # UK timeseries - 2018 if True: from matplotlib.ticker import MultipleLocator majorLocator = MultipleLocator(5) fig = plt.figure(figsize=(8, 9.5)) plt.clf() # images ax = plt.axes([0.01, 0.66, 0.48, 0.3]) plot_images(ax, "Sarah Burgess first leaf.jpg") ax = plt.axes([0.5, 0.66, 0.48, 0.3]) plot_images(ax, "Judith Garforth oak bare tree 2019.jpg") sos_uk, eos_uk = read_modis_uk_ts( os.path.join( DATALOC, "MODIS.CMG.{}.SOS.EOS.SPRT.FALT.TS.UK_DH.csv".format( settings.YEAR))) oak_sos, oak_eos = read_uk_oak_csv( os.path.join(DATALOC, "UK_Oakleaf_data.csv")) # timeseries ax = plt.axes([0.11, 0.35, 0.84, 0.3]) utils.plot_ts_panel(ax, [oak_eos, eos_uk], "-", "phenological", loc="center left") # ax.text(0.05, 0.85, "(c)", transform=ax.transAxes, fontsize=settings.FONTSIZE) ax.set_ylim([210, 354]) plt.setp(ax.get_xticklabels(), visible=False) for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) ax.xaxis.set_major_locator(majorLocator) # naughtily, manually tweak the upper oak plot for line in ax.get_lines(): if line.get_color() == "g": line.set_color("#d95f0e") leg = ax.get_legend() for line in leg.get_lines(): if line.get_color() == "g": line.set_color("#d95f0e") ax = plt.axes([0.11, 0.05, 0.84, 0.3]) utils.plot_ts_panel(ax, [oak_sos, sos_uk], "-", "phenological", loc="center left") # ax.text(0.05, 0.85, "(d)", transform=ax.transAxes, fontsize=settings.FONTSIZE) ax.set_ylim([66, 132]) for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) ax.xaxis.set_major_locator(majorLocator) fig.text(0.02, 0.97, "(b)", transform=ax.transAxes, fontsize=settings.FONTSIZE) fig.text(0.02, 0.3, "Day of year", rotation="vertical", fontsize=settings.FONTSIZE) plt.savefig( settings.IMAGELOC + "PHEN_UKtimeseries_{}{}".format(settings.YEAR, settings.OUTFMT)) plt.close() #*********************** # Lake Boxplot if True: import pandas as pd df = pd.read_csv(DATALOC + "LakeData_forRobert.csv") # rename columns cols = [] for col in df.columns: if len(col.split()) >= 2: df.rename(columns={col: col.split()[0]}, inplace=True) cols += [col.split()[0]] fig = plt.figure(figsize=(8, 7)) plt.clf() ax = plt.axes([0.1, 0.25, 0.89, 0.74]) df.boxplot( column=cols, ax=ax, grid=False, ) # messily pull out 2019 this_year = df.iloc[-1] this_year = this_year.to_frame() plt.plot(np.arange(11) + 1, this_year[19][1:], "ro") for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) tick.label.set_rotation("vertical") for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) utils.thicken_panel_border(ax) plt.ylabel("Day of year", fontsize=settings.FONTSIZE) plt.savefig( settings.IMAGELOC + "PHEN_lakes_boxplot_{}{}".format(settings.YEAR, settings.OUTFMT)) plt.close() return # run_all_plots
def run_all_plots(): #************************************************************************ # Timeseries figures if True: fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 10), sharex=True) UAH, rss, ratpac, raobcore, rich, noaa, jra, merra = read_csv( DATALOC + "SotC_AnnTemps_2020_0220_LSTGL.csv") # ssu2, ncar = read_ssu_csv(DATALOC + "2018_LTT_LST_SSU_date0401_SSU.csv") eral, erao, eralo = read_era5(DATALOC + "ERA5_TLS_GLOBAL") eralo_ann = utils.Timeseries("ERA5", np.reshape(eralo.times, [-1, 12])[:, 0], utils.annual_average(eralo.data)) # Sondes [no RATPAC for 2019] utils.plot_ts_panel(ax1, [raobcore, rich], "-", "temperature", loc=LEGEND_LOC) # satellites utils.plot_ts_panel(ax2, [UAH, noaa, rss], "-", "temperature", loc=LEGEND_LOC) # reanalyses jra_actuals, jra_anoms = utils.read_jra55( settings.REANALYSISLOC + "JRA-55_MSUch4_global_ts.txt", "temperature") merra_actuals, merra_anoms = utils.read_merra_LT_LS( settings.REANALYSISLOC + "MERRA2_MSU_Tanom_ann_{}.dat".format(settings.YEAR), LS=True) utils.plot_ts_panel(ax3, [eralo_ann, jra_anoms, merra_anoms], "-", "temperature", loc=LEGEND_LOC) # ax3.set_ylabel("Anomaly ("+r'$^\circ$'+"C)", fontsize=settings.FONTSIZE) # Upper Stratosphere # utils.plot_ts_panel(ax4, [ssu2, ncar], "-", "temperature", loc=LEGEND_LOC) # sort formatting plt.xlim([1957, raobcore.times[-1] + 1]) for tick in ax3.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for ax in [ax1, ax2, ax3]: for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) ax.set_ylim([-1.2, 2.2]) # sort labelling ax1.text(0.02, 0.88, "(a) Radiosondes", transform=ax1.transAxes, \ fontsize=settings.LABEL_FONTSIZE) ax2.text(0.02, 0.88, "(b) Satellites", transform=ax2.transAxes, \ fontsize=settings.LABEL_FONTSIZE) ax3.text(0.02, 0.88, "(c) Reanalyses", transform=ax3.transAxes, \ fontsize=settings.LABEL_FONTSIZE) fig.text(0.01, 0.45, "Anomaly (" + r'$^\circ$' + "C)", fontsize=settings.FONTSIZE, rotation="vertical") fig.subplots_adjust(right=0.98, top=0.98, bottom=0.04, hspace=0.001) plt.savefig(settings.IMAGELOC + "LST_ts{}".format(settings.OUTFMT)) plt.close() #************************************************************************ # Timeseries figures if True: fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 10), sharex=True) ssu1, ssu1_2, ssu2, ssu2_2, ssu3, ssu3_2 = read_ssu(DATALOC + "SSU.dat") # SSU3 utils.plot_ts_panel(ax1, [ssu3, ssu3_2], "-", "temperature", loc="") # SSU2 utils.plot_ts_panel(ax2, [ssu2, ssu2_2], "-", "temperature", loc="") # SSU1 utils.plot_ts_panel(ax3, [ssu1, ssu1_2], "-", "temperature", loc=LEGEND_LOC) # sort formatting plt.xlim([1957, ssu1.times[-1] + 2]) for tick in ax3.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for ax in [ax1, ax2, ax3]: for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) ax.set_ylim([-1.3, 2.2]) # sort labelling ax1.text(0.02, 0.88, "(a) SSU3", transform=ax1.transAxes, \ fontsize=settings.LABEL_FONTSIZE) ax2.text(0.02, 0.88, "(b) SSU2", transform=ax2.transAxes, \ fontsize=settings.LABEL_FONTSIZE) ax3.text(0.02, 0.88, "(c) SSU1", transform=ax3.transAxes, \ fontsize=settings.LABEL_FONTSIZE) fig.text(0.01, 0.45, "Anomaly (" + r'$^\circ$' + "C)", fontsize=settings.FONTSIZE, rotation="vertical") fig.subplots_adjust(right=0.98, top=0.98, bottom=0.04, hspace=0.001) plt.savefig(settings.IMAGELOC + "LST_SSU_ts{}".format(settings.OUTFMT)) plt.close() #************************************************************************ # Combined Timeseries figures if True: fig = plt.figure(figsize=(12, 8)) # manually set up the 10 axes w = 0.42 h = 0.31 c = 0.51 ax1 = plt.axes([c - w, 0.99 - h, w, h]) ax2 = plt.axes([c, 0.99 - h, w, h]) ax3 = plt.axes([c - w, 0.99 - (2 * h), w, h], sharex=ax1) ax4 = plt.axes([c, 0.99 - (2 * h), w, h], sharex=ax2) ax5 = plt.axes([c - w, 0.99 - (3 * h), w, h], sharex=ax1) ax6 = plt.axes([c, 0.99 - (3 * h), w, h], sharex=ax2) UAH, rss, ratpac, raobcore, rich, noaa, jra, merra = read_csv( DATALOC + "SotC_AnnTemps_2020_0220_LSTGL.csv") # ssu2, ncar = read_ssu_csv(DATALOC + "2018_LTT_LST_SSU_date0401_SSU.csv") eral, erao, eralo = read_era5(DATALOC + "ERA5_TLS_GLOBAL") eralo_ann = utils.Timeseries("ERA5", np.reshape(eralo.times, [-1, 12])[:, 0], utils.annual_average(eralo.data)) # update to ERA5.1 during revisions for SotC2019 era51 = np.genfromtxt(DATALOC + "ERA5_1_update.dat") eralo_ann = utils.Timeseries("ERA5", era51[:, 0], era51[:, 1]) # Sondes [no RATPAC for 2019] utils.plot_ts_panel(ax2, [raobcore, rich, ratpac], "-", "temperature", loc=LEGEND_LOC) # satellites utils.plot_ts_panel(ax4, [UAH, noaa, rss], "-", "temperature", loc=LEGEND_LOC) # reanalyses jra_actuals, jra_anoms = utils.read_jra55( settings.REANALYSISLOC + "JRA-55_MSUch4_global_ts.txt", "temperature") merra_actuals, merra_anoms = utils.read_merra_LT_LS( settings.REANALYSISLOC + "MERRA2_MSU_Tanom_ann_{}.dat".format(settings.YEAR), LS=True) utils.plot_ts_panel(ax6, [eralo_ann, jra_anoms, merra_anoms], "-", "temperature", loc=LEGEND_LOC) ssu1, ssu1_2, ssu2, ssu2_2, ssu3, ssu3_2 = read_ssu(DATALOC + "SSU.dat") # SSU3 utils.plot_ts_panel(ax1, [ssu3, ssu3_2], "-", "temperature", loc="") # SSU2 utils.plot_ts_panel(ax3, [ssu2, ssu2_2], "-", "temperature", loc="") # SSU1 utils.plot_ts_panel(ax5, [ssu1, ssu1_2], "-", "temperature", loc=LEGEND_LOC) # sort labelling for ax in [ax2, ax4, ax6]: ax.yaxis.tick_right() for tick in ax.yaxis.get_major_ticks(): tick.label2.set_fontsize(settings.FONTSIZE) for ax in [ax1, ax3, ax5]: ax.yaxis.tick_right() ax.yaxis.set_ticks_position('left') ax2.text(0.02, 0.88, "(d) Radiosondes", transform=ax2.transAxes, \ fontsize=settings.LABEL_FONTSIZE) ax4.text(0.02, 0.88, "(e) Satellites", transform=ax4.transAxes, \ fontsize=settings.LABEL_FONTSIZE) ax6.text(0.02, 0.88, "(f) Reanalyses", transform=ax6.transAxes, \ fontsize=settings.LABEL_FONTSIZE) ax1.text(0.02, 0.88, "(a) SSU3", transform=ax1.transAxes, \ fontsize=settings.LABEL_FONTSIZE) ax3.text(0.02, 0.88, "(b) SSU2", transform=ax3.transAxes, \ fontsize=settings.LABEL_FONTSIZE) ax5.text(0.02, 0.88, "(c) SSU1", transform=ax5.transAxes, \ fontsize=settings.LABEL_FONTSIZE) plt.setp([a.get_xticklabels() for a in fig.axes[:-2]], visible=False) # sort formatting ax1.set_xlim([1957, raobcore.times[-1] + 3]) ax2.set_xlim([1957, raobcore.times[-1] + 3]) for ax in [ax5, ax6]: for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for ax in [ax1, ax2, ax3, ax4, ax5, ax6]: for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) ax.set_ylim([-1.2, 2.2]) fig.text(0.01, 0.55, "Anomaly (" + r'$^\circ$' + "C)", fontsize=settings.FONTSIZE, rotation="vertical") fig.subplots_adjust(right=0.98, top=0.98, bottom=0.04, hspace=0.001) plt.savefig(settings.IMAGELOC + "LST_combined_ts{}".format(settings.OUTFMT)) plt.close() #************************************************************************ # Polar Figure if False: fig, ax1 = plt.subplots(figsize=(8, 5)) north, south = read_polar(DATALOC + "polar_data_{}.dat".format(settings.YEAR)) ax1.plot(north.times, north.data, c="b", ls="-", label=north.name) ax1.plot(south.times, south.data, c="r", ls="-", label=south.name) ax1.legend(loc=LEGEND_LOC, frameon=False, prop={'size': settings.FONTSIZE}) ax1.set_xlim([1978, 2018 + 2]) ax1.set_ylabel("Anomaly (" + r'$^\circ$' + "C)", fontsize=settings.FONTSIZE) utils.thicken_panel_border(ax1) plt.savefig(settings.IMAGELOC + "LST_polar_ts{}".format(settings.OUTFMT)) for tick in ax1.yaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) for tick in ax1.xaxis.get_major_ticks(): tick.label.set_fontsize(settings.FONTSIZE) plt.close() #************************************************************************ # Polar and QBO Timeseries figures # fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 10), sharex=True) # north, south, qbo = read_qbo_csv(DATALOC + "SOC_Strat_Data_QBO.csv") # noaa_qbo = read_qbo_ncdf(DATALOC + "qbo_noaa.nc", "NOAA v4.0") # uah_qbo = read_qbo_ncdf(DATALOC + "qbo_uah.nc", "UAH v6.0") # rss_qbo = read_qbo_ncdf(DATALOC + "qbo_rss.nc", "RSS v3.3") # utils.plot_ts_panel(ax1, [north], "-", "temperature", loc="") # utils.plot_ts_panel(ax2, [south], "-", "temperature", loc="") # utils.plot_ts_panel(ax3, [noaa_qbo, uah_qbo, rss_qbo], "-", "temperature", loc="", lw=1) # lines3, labels3 = ax3.get_legend_handles_labels() # plt.figlegend(lines3, labels3, "lower center", frameon=False, ncol=3, fontsize=settings.FONTSIZE) # ax1.set_ylabel("Anomaly ("+r'$^\circ$'+"C)", fontsize=settings.FONTSIZE) # ax2.set_ylabel("Anomaly ("+r'$^\circ$'+"C)", fontsize=settings.FONTSIZE) # ax3.set_ylabel("QBO Index", fontsize=settings.FONTSIZE) # ax1.text(0.02, 0.88, "(a) North Polar Pentad Anomalies", transform=ax1.transAxes, \ # fontsize=settings.LABEL_FONTSIZE) # ax2.text(0.02, 0.88, "(b) South Polar Pentad Anomalies", transform=ax2.transAxes, \ # fontsize=settings.LABEL_FONTSIZE) # ax3.text(0.02, 0.88, "(c) QBO from Lower Stratospheric Temperature", transform=ax3.transAxes, \ # fontsize=settings.LABEL_FONTSIZE) # ax3.text(-0.15, 0.88, "West", transform=ax3.transAxes, fontsize=settings.LABEL_FONTSIZE) # ax3.text(-0.15, 0.01, "East", transform=ax3.transAxes, fontsize=settings.LABEL_FONTSIZE) # # sort formatting # plt.xlim([1978, int(settings.YEAR)+1]) # for tick in ax3.xaxis.get_major_ticks(): # tick.label.set_fontsize(settings.FONTSIZE) # for ax in [ax1, ax2, ax3]: # for tick in ax.yaxis.get_major_ticks(): # tick.label.set_fontsize(settings.FONTSIZE) # ax1.set_ylim([-10, 19]) # ax2.set_ylim([-10, 23]) # ax3.set_ylim([-1.1, 1.4]) # fig.subplots_adjust(right=0.95, top=0.95, hspace=0.001) # plt.savefig(settings.IMAGELOC+"LST_qbo_ts{}".format(settings.OUTFMT)) # plt.close() #************************************************************************ # MERRA Trop and Strat Timeseries figures # fourh, threeh, twofiveh, twoh, onefiveh, fourtwoav, oneh, seventy, fifty, thirty, twenty, ten, seventytwentyav = read_merra_monthly(DATALOC + "MERRA2_400_20_temp_anom_1980-{}.txt".format(settings.YEAR)) # fourtwoav.data = np.ma.masked_where(fourtwoav.times < 1994, fourtwoav.data) # seventytwentyav.data = np.ma.masked_where(seventytwentyav.times < 1994, seventytwentyav.data) # fourtwoav.times = np.ma.masked_where(fourtwoav.times < 1994, fourtwoav.times) # seventytwentyav.times = np.ma.masked_where(seventytwentyav.times < 1994, seventytwentyav.times) # fig, (ax1, ax2) = plt.subplots(2, figsize = (8, 8), sharex=True) # # trop # fit = utils.fit_plot_points(0.025, -50.01, fourtwoav.times) # ax1.plot(fourh.times, fit, c="0.5", lw = 2, ls = "-", label = "fit", zorder = 10) # utils.plot_ts_panel(ax1, [fourh, threeh, twofiveh, twoh, fourtwoav], "-", "lst", loc = LEGEND_LOC) # # strat # fit = utils.fit_plot_points(-0.0014, 2.4527, seventytwentyav.times) # ax2.plot(fourh.times, fit, c="0.5", lw = 2, ls = "-", label = "fit", zorder = 10) # utils.plot_ts_panel(ax2, [seventy, fifty, thirty, twenty, seventytwentyav], "-", "lst", loc = LEGEND_LOC) # # sort formatting # plt.xlim([1978, fourh.times[-1]+1]) # ax1.set_ylim([-1.7,1.5]) # ax2.set_ylim([-2.0,2.7]) # for tick in ax2.xaxis.get_major_ticks(): # tick.label.set_fontsize(settings.FONTSIZE) # for ax in [ax1, ax2]: # for tick in ax.yaxis.get_major_ticks(): # tick.label.set_fontsize(settings.FONTSIZE) # # sort labelling # ax1.text(0.02, 0.9, "(a) MERRA-2 Troposphere", transform = ax1.transAxes, fontsize = settings.LABEL_FONTSIZE) # ax2.text(0.02, 0.9, "(b) MERRA-2 Stratosphere", transform = ax2.transAxes, fontsize = settings.LABEL_FONTSIZE) # ax1.text(0.7, 0.9, "y=0.025x - 50.01", transform = ax1.transAxes, fontsize = settings.LABEL_FONTSIZE*0.8) # ax2.text(0.7, 0.9, "y=-0.0014x + 2.4527", transform = ax2.transAxes, fontsize = settings.LABEL_FONTSIZE*0.8) # ax1.set_ylabel("Anomaly ("+r'$^\circ$'+"C)", fontsize = settings.FONTSIZE) # ax2.set_ylabel("Anomaly ("+r'$^\circ$'+"C)", fontsize = settings.FONTSIZE) # fig.subplots_adjust(right = 0.95, top = 0.95, hspace = 0.001) # plt.savefig(settings.IMAGELOC+"LST_merra_ts{}".format(settings.OUTFMT)) # plt.close() #************************************************************************ # Zonal figures # fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (8, 6.5), sharey=True) # cfsr, merra, era, jra = read_zonal(DATALOC + "TLS_Reanal_zonal_trends_1994-{}.txt".format(settings.YEAR), "R") # star, uah, rss = read_zonal(DATALOC + "TLS_Satellite_zonal_trends_1994-{}.txt".format(settings.YEAR), "S") # # Reanalyses # utils.plot_ts_panel(ax1, [cfsr, merra, era, jra], "--", "temperature", loc = "lower left", ncol = 1) # # Satellite # utils.plot_ts_panel(ax2, [star, uah, rss], "--", "temperature", loc = "center right", ncol = 1) # ax1.axvline(0,color = "0.5", ls = "--") # ax2.axvline(0,color = "0.5", ls = "--") # # sort formatting # plt.ylim([-90,90]) # ax1.set_ylabel("Latitude", fontsize = settings.LABEL_FONTSIZE) # ax1.set_xlabel("Trend ("+r'$^\circ$'+"C decade"+r'$^{-1}$'+")", fontsize = settings.FONTSIZE) # ax2.set_xlabel("Trend ("+r'$^\circ$'+"C decade"+r'$^{-1}$'+")", fontsize = settings.FONTSIZE) # for tick in ax1.yaxis.get_major_ticks(): # tick.label.set_fontsize(settings.FONTSIZE) # for ax in [ax1, ax2]: # ax.set_xlim([-0.3,0.6]) # ax.set_xticks(np.arange(-0.2,0.8,0.2)) # ax.set_yticks(np.arange(-90,120,30)) # for tick in ax.xaxis.get_major_ticks(): # tick.label.set_fontsize(settings.FONTSIZE) # # sort labelling # ax1.text(0.02, 0.9, "(a) Radiosondes", transform = ax1.transAxes, fontsize = settings.LABEL_FONTSIZE) # ax2.text(0.02, 0.9, "(b) Satellites", transform = ax2.transAxes, fontsize = settings.LABEL_FONTSIZE) # fig.subplots_adjust(right = 0.95, top = 0.95, hspace = 0.001) # plt.savefig(settings.IMAGELOC+"LST_profiles{}".format(settings.OUTFMT)) # plt.close() #************************************************************************ # ERA5 Anomaly figure if True: # Read in ERA anomalies cube_list = iris.load(settings.REANALYSISLOC + "era5_tls_{}01-{}12_ann_ano.nc".format( settings.YEAR, settings.YEAR)) cube = cube_list[0] cube.coord('latitude').guess_bounds() cube.coord('longitude').guess_bounds() bounds = [-4, -1.2, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 1.2, 4] utils.plot_smooth_map_iris(settings.IMAGELOC + "LST_{}_anoms_era5".format(settings.YEAR), cube[0], \ settings.COLOURMAP_DICT["temperature"], bounds, "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="ERA5") utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_LST_{}_anoms_era5".format(settings.YEAR), cube[0], \ settings.COLOURMAP_DICT["temperature"], bounds, "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", \ figtext="(f) Lower Stratosphere Temperature") #************************************************************************ # merra Anomaly figure if False: import netCDF4 as ncdf ncfile = ncdf.Dataset(settings.REANALYSISLOC + "merra2_tls_ANNUAL_anom.nc") var = ncfile.variables["TLS ANOM"][:] # this is a masked array nlons = ncfile.variables["LONGITUDES"][:] nlats = ncfile.variables["LATITUDES"][:] cube = utils.make_iris_cube_2d(var, nlats, nlons, "LST", "C") utils.plot_smooth_map_iris( settings.IMAGELOC + "LST_{}_anoms_merra".format(settings.YEAR), cube, settings.COLOURMAP_DICT["temperature"], bounds, "Anomalies from 1981-2010 (" + r'$^{\circ}$' + "C)") #************************************************************************ # 2015 MERRA seasonal figure # import netCDF4 as ncdf # month_list = [] # for month in MONTHS: # print month # # IRIS doesn't like the whitespace in the "TLS ANOM" # ncfile=ncdf.Dataset(settings.REANALYSISLOC + "merra2_tls_{}_anom.nc".format(month.upper()),'r') # var=ncfile.variables["TLS ANOM"][:] # this is a masked array # lons = ncfile.variables["LONGITUDES"][:] # lats = ncfile.variables["LATITUDES"][:] # ncfile.close() # cube = utils.make_iris_cube_2d(var, lats, lons, "TLS_ANOM", "C") # month_list += [cube] # # pass to plotting routine # utils.plot_smooth_map_iris_multipanel(settings.IMAGELOC + "LST_{}_monthly_merra".format(settings.YEAR), month_list, \ # settings.COLOURMAP_DICT["temperature"], bounds, \ # "Anomaly ("+r'$^{\circ}$'+"C)", shape = (6,2), \ # title = MONTHS, \ # figtext = ["(a)","(b)","(c)","(d)", "(e)","(f)","(g)","(h)","(i)","(j)","(k)","(l)"]) return # run_all_plots