def main(): season_to_months = DEFAULT_SEASON_TO_MONTHS varnames = ["PR", "TT"] plot_utils.apply_plot_params(font_size=5, width_pt=None, width_cm=15, height_cm=4) reanalysis_driven_config = RunConfig(data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5", start_year=1980, end_year=2010, label="ERAI-CRCM5-L") bmp_info = analysis.get_basemap_info(r_config=reanalysis_driven_config) field_cmap = cm.get_cmap("jet", 10) vname_to_clevels = { "TT": np.arange(-30, 32, 2), "PR": np.arange(0, 6.5, 0.5) } vname_to_anusplin_path = { "TT": "/home/huziy/skynet3_rech1/anusplin_links", "PR": "/home/huziy/skynet3_rech1/anusplin_links" } vname_to_cru_path = { "TT": "/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc", "PR": "/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc" } xx_agg = None yy_agg = None for vname in varnames: # get anusplin obs climatology season_to_obs_anusplin = plot_performance_err_with_anusplin.get_seasonal_clim_obs_data( rconfig=reanalysis_driven_config, vname=vname, season_to_months=season_to_months, bmp_info=bmp_info) # get CRU obs values------------------------- bmp_info_agg, season_to_obs_cru = plot_performance_err_with_cru.get_seasonal_clim_obs_data( rconfig=reanalysis_driven_config, bmp_info=bmp_info, season_to_months=season_to_months, obs_path=vname_to_cru_path[vname], vname=vname ) if xx_agg is None: xx_agg, yy_agg = bmp_info_agg.get_proj_xy() # get model data seasonal_clim_fields_model = analysis.get_seasonal_climatology_for_runconfig(run_config=reanalysis_driven_config, varname=vname, level=0, season_to_months=season_to_months) ### biases_with_anusplin = OrderedDict() biases_with_cru = OrderedDict() nx_agg = 5 ny_agg = 5 season_to_clim_fields_model_agg = OrderedDict() for season, field in seasonal_clim_fields_model.items(): print(field.shape) season_to_clim_fields_model_agg[season] = aggregate_array(field, nagg_x=nx_agg, nagg_y=ny_agg) if vname == "PR": season_to_clim_fields_model_agg[season] *= 1.0e3 * 24 * 3600 biases_with_cru[season] = season_to_clim_fields_model_agg[season] - season_to_obs_cru[season] biases_with_anusplin[season] = season_to_clim_fields_model_agg[season] - aggregate_array(season_to_obs_anusplin[season], nagg_x=nx_agg, nagg_y=ny_agg) # Do the plotting fig = plt.figure() clevs = [c for c in np.arange(-0.5, 0.55, 0.05)] if vname == "PR" else np.arange(-2, 2.2, 0.2) gs = GridSpec(1, len(biases_with_cru) + 1, width_ratios=len(biases_with_cru) * [1., ] + [0.05, ]) col = 0 cs = None cmap = "seismic" fig.suptitle(r"$\left| \delta_{\rm Hopkinson} \right| - \left| \delta_{\rm CRU} \right|$") for season, cru_err in biases_with_cru.items(): anu_err = biases_with_anusplin[season] ax = fig.add_subplot(gs[0, col]) diff = np.abs(anu_err) - np.abs(cru_err) cs = bmp_info_agg.basemap.contourf(xx_agg, yy_agg, diff, levels=clevs, ax=ax, extend="both", cmap=cmap) bmp_info_agg.basemap.drawcoastlines(ax=ax, linewidth=0.3) good = diff[~diff.mask & ~np.isnan(diff)] n_neg = sum(good < 0) / sum(good > 0) print("season: {}, n-/n+ = {}".format(season, n_neg)) ax.set_title(season) ax.set_xlabel(r"$n_{-}/n_{+} = $" + "{:.1f}".format(n_neg) + "\n" + r"$\overline{\varepsilon} = $" + "{:.2f}".format(good.mean())) col += 1 ax = fig.add_subplot(gs[0, -1]) plt.colorbar(cs, cax=ax) ax.set_title("mm/day" if vname == "PR" else r"${\rm ^\circ C}$") fig.savefig(os.path.join(img_folder, "comp_anu_and_cru_biases_for_{}.png".format(vname)), bbox_inches="tight", dpi=common_plot_params.FIG_SAVE_DPI)
def main(): if not img_folder.is_dir(): img_folder.mkdir(parents=True) season_to_months = OrderedDict([ ("Winter (DJF)", (1, 2, 12)), ("Spring (MAM)", range(3, 6)), ("Summer (JJA)", range(6, 9)), ("Fall (SON)", range(9, 12)), ]) varnames = ["TT", "PR"] plot_utils.apply_plot_params(font_size=10, width_pt=None, width_cm=20, height_cm=17) # reanalysis_driven_config = RunConfig(data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5", # start_year=1980, end_year=2010, label="ERAI-CRCM5-L") # reanalysis_driven_config = RunConfig(data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.4_crcm5-hcd-rl.hdf5", start_year=1980, end_year=2010, label="ERAI-CRCM5-L(0.4)") nx_agg_model = 1 ny_agg_model = 1 nx_agg_anusplin = 4 ny_agg_anusplin = 4 gcm_driven_config = RunConfig( data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5", start_year=1980, end_year=2010, label="CanESM2-CRCM5-L") bmp_info = analysis.get_basemap_info(r_config=reanalysis_driven_config) xx, yy = bmp_info.get_proj_xy() field_cmap = cm.get_cmap("jet", 10) vname_to_clevels = { "TT": np.arange(-30, 32, 2), "PR": np.arange(0, 6.5, 0.5) } vname_to_anusplin_path = { "TT": "/home/huziy/skynet3_rech1/anusplin_links", "PR": "/home/huziy/skynet3_rech1/anusplin_links" } vname_to_cru_path = { "TT": "/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc", "PR": "/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc" } for vname in varnames: fig = plt.figure() ncols = len(season_to_months) gs = GridSpec(4, ncols + 1, width_ratios=ncols * [1., ] + [0.09, ]) clevels = vname_to_clevels[vname] # get anusplin obs climatology season_to_obs_anusplin = plot_performance_err_with_anusplin.get_seasonal_clim_obs_data( rconfig=reanalysis_driven_config, vname=vname, season_to_months=season_to_months, bmp_info=bmp_info, n_agg_x=nx_agg_anusplin, n_agg_y=ny_agg_anusplin) row = 0 # Plot CRU values------------------------- bmp_info_agg, season_to_obs_cru = plot_performance_err_with_cru.get_seasonal_clim_obs_data( rconfig=reanalysis_driven_config, bmp_info=bmp_info, season_to_months=season_to_months, obs_path=vname_to_cru_path[vname], vname=vname ) # Mask out the Great Lakes cru_mask = get_mask(bmp_info_agg.lons, bmp_info_agg.lats, shp_path=os.path.join(GL_SHP_FOLDER, "gl_cst.shp")) for season in season_to_obs_cru: season_to_obs_cru[season] = np.ma.masked_where(cru_mask > 0.5, season_to_obs_cru[season]) ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)] cs = None xx_agg, yy_agg = bmp_info_agg.get_proj_xy() for j, (season, obs_field) in enumerate(season_to_obs_cru.items()): ax = ax_list[j] cs = bmp_info_agg.basemap.contourf(xx_agg, yy_agg, obs_field.copy(), levels=clevels, ax=ax) bmp_info.basemap.drawcoastlines(ax=ax) bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax) ax.set_title(season) ax_list[0].set_ylabel("CRU") # plt.colorbar(cs, caax=ax_list[-1]) row += 1 # Plot ANUSPLIN values------------------------- ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)] cs = None for j, (season, obs_field) in enumerate(season_to_obs_anusplin.items()): ax = ax_list[j] cs = bmp_info.basemap.contourf(xx, yy, obs_field, levels=clevels, ax=ax) bmp_info.basemap.drawcoastlines(ax=ax) bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax) ax.set_title(season) ax_list[0].set_ylabel("Hopkinson") cb = plt.colorbar(cs, cax=fig.add_subplot(gs[:2, -1])) cb.ax.set_xlabel(infovar.get_units(vname)) _format_axes(ax_list, vname=vname) row += 1 # Plot model (CRCM) values------------------------- # ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)] # cs = None # # season_to_field_crcm = analysis.get_seasonal_climatology_for_runconfig(run_config=reanalysis_driven_config, # varname=vname, level=0, # season_to_months=season_to_months) # # for j, (season, crcm_field) in enumerate(season_to_field_crcm.items()): # ax = ax_list[j] # cs = bmp_info.basemap.contourf(xx, yy, crcm_field * 1000 * 24 * 3600, levels=clevels, ax=ax) # bmp_info.basemap.drawcoastlines(ax=ax) # bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax) # ax.set_title(season) # # ax_list[0].set_ylabel(reanalysis_driven_config.label) # cb = plt.colorbar(cs, cax=fig.add_subplot(gs[:2, -1])) # cb.ax.set_xlabel(infovar.get_units(vname)) # _format_axes(ax_list, vname=vname) # row += 1 # Plot (Model - CRU) Performance biases------------------------- ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)] cs = plot_performance_err_with_cru.compare_vars(vname_model=vname, vname_obs=None, r_config=reanalysis_driven_config, season_to_months=season_to_months, obs_path=vname_to_cru_path[vname], bmp_info_agg=bmp_info_agg, diff_axes_list=ax_list, mask_shape_file=os.path.join(GL_SHP_FOLDER, "gl_cst.shp"), nx_agg_model=nx_agg_model, ny_agg_model=ny_agg_model) ax_list[0].set_ylabel("{label}\n--\nCRU".format(label=reanalysis_driven_config.label)) _format_axes(ax_list, vname=vname) row += 1 # Plot performance+BFE errors with respect to CRU (Model - CRU)------------------------- # ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)] # plot_performance_err_with_cru.compare_vars(vname, vname_obs=None, obs_path=vname_to_cru_path[vname], # r_config=gcm_driven_config, # bmp_info_agg=bmp_info_agg, season_to_months=season_to_months, # axes_list=ax_list) # _format_axes(ax_list, vname=vname) # ax_list[0].set_ylabel("{label}\nvs\nCRU".format(label=gcm_driven_config.label)) # row += 1 # Plot performance errors with respect to ANUSPLIN (Model - ANUSPLIN)------------------------- ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)] plot_performance_err_with_anusplin.compare_vars(vname, {vname: season_to_obs_anusplin}, r_config=reanalysis_driven_config, bmp_info_agg=bmp_info, season_to_months=season_to_months, axes_list=ax_list) _format_axes(ax_list, vname=vname) ax_list[0].set_ylabel("{label}\n--\nHopkinson".format(label=reanalysis_driven_config.label)) row += 1 # Plot performance+BFE errors with respect to ANUSPLIN (Model - ANUSPLIN)------------------------- # ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)] # plot_performance_err_with_anusplin.compare_vars(vname, {vname: season_to_obs_anusplin}, # r_config=gcm_driven_config, # bmp_info_agg=bmp_info, season_to_months=season_to_months, # axes_list=ax_list) # _format_axes(ax_list, vname=vname) # ax_list[0].set_ylabel("{label}\nvs\nHopkinson".format(label=gcm_driven_config.label)) cb = plt.colorbar(cs, cax=fig.add_subplot(gs[-2:, -1])) cb.ax.set_xlabel(infovar.get_units(vname)) # Save the plot img_file = "{vname}_{sy}-{ey}_{sim_label}.png".format( vname=vname, sy=reanalysis_driven_config.start_year, ey=reanalysis_driven_config.end_year, sim_label=reanalysis_driven_config.label) img_file = img_folder.joinpath(img_file) with img_file.open("wb") as f: fig.savefig(f, bbox_inches="tight") plt.close(fig)
def main(): """ Everything is aggregated to the CRU resolution before calculating biases """ season_to_months = DEFAULT_SEASON_TO_MONTHS varnames = ["PR", "TT"] plot_utils.apply_plot_params(font_size=5, width_pt=None, width_cm=15, height_cm=4) # reanalysis_driven_config = RunConfig(data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5", # start_year=1980, end_year=2010, label="ERAI-CRCM5-L") reanalysis_driven_config = RunConfig( data_path= "/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.4_crcm5-hcd-rl.hdf5", start_year=1980, end_year=2010, label="ERAI-CRCM5-RL_0.4deg") bmp_info = analysis.get_basemap_info(r_config=reanalysis_driven_config) field_cmap = cm.get_cmap("jet", 10) vname_to_clevels = { "TT": np.arange(-30, 32, 2), "PR": np.arange(0, 6.5, 0.5) } vname_to_anusplin_path = { "TT": "/home/huziy/skynet3_rech1/anusplin_links", "PR": "/home/huziy/skynet3_rech1/anusplin_links" } vname_to_cru_path = { "TT": "/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc", "PR": "/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc" } xx_agg = None yy_agg = None for vname in varnames: # get anusplin obs climatology season_to_obs_anusplin = plot_performance_err_with_anusplin.get_seasonal_clim_obs_data( rconfig=reanalysis_driven_config, vname=vname, season_to_months=season_to_months, bmp_info=bmp_info) # get CRU obs values------------------------- bmp_info_agg, season_to_obs_cru = plot_performance_err_with_cru.get_seasonal_clim_obs_data( rconfig=reanalysis_driven_config, bmp_info=bmp_info, season_to_months=season_to_months, obs_path=vname_to_cru_path[vname], vname=vname) if xx_agg is None: xx_agg, yy_agg = bmp_info_agg.get_proj_xy() # get model data seasonal_clim_fields_model = analysis.get_seasonal_climatology_for_runconfig( run_config=reanalysis_driven_config, varname=vname, level=0, season_to_months=season_to_months) ### biases_with_anusplin = OrderedDict() biases_with_cru = OrderedDict() nx_agg_anusplin = 4 ny_agg_anusplin = 4 nx_agg_model = 1 ny_agg_model = 1 season_to_clim_fields_model_agg = OrderedDict() for season, field in seasonal_clim_fields_model.items(): print(field.shape) season_to_clim_fields_model_agg[season] = aggregate_array( field, nagg_x=nx_agg_model, nagg_y=ny_agg_model) if vname == "PR": season_to_clim_fields_model_agg[season] *= 1.0e3 * 24 * 3600 biases_with_cru[season] = season_to_clim_fields_model_agg[ season] - season_to_obs_cru[season] biases_with_anusplin[season] = season_to_clim_fields_model_agg[ season] - aggregate_array(season_to_obs_anusplin[season], nagg_x=nx_agg_anusplin, nagg_y=ny_agg_anusplin) # Do the plotting fig = plt.figure() clevs = [c for c in np.arange(-0.5, 0.55, 0.05) ] if vname == "PR" else np.arange(-2, 2.2, 0.2) gs = GridSpec(1, len(biases_with_cru) + 1, width_ratios=len(biases_with_cru) * [ 1., ] + [ 0.05, ]) col = 0 cs = None cmap = "seismic" fig.suptitle( r"$\left| \delta_{\rm Hopkinson} \right| - \left| \delta_{\rm CRU} \right|$" ) for season, cru_err in biases_with_cru.items(): anu_err = biases_with_anusplin[season] ax = fig.add_subplot(gs[0, col]) diff = np.abs(anu_err) - np.abs(cru_err) cs = bmp_info_agg.basemap.contourf(xx_agg, yy_agg, diff, levels=clevs, ax=ax, extend="both", cmap=cmap) bmp_info_agg.basemap.drawcoastlines(ax=ax, linewidth=0.3) good = diff[~diff.mask & ~np.isnan(diff)] n_neg = sum(good < 0) / sum(good > 0) print("season: {}, n-/n+ = {}".format(season, n_neg)) ax.set_title(season) ax.set_xlabel(r"$n_{-}/n_{+} = $" + "{:.1f}".format(n_neg) + "\n" + r"$\overline{\varepsilon} = $" + "{:.2f}".format(good.mean())) col += 1 ax = fig.add_subplot(gs[0, -1]) plt.colorbar(cs, cax=ax) ax.set_title("mm/day" if vname == "PR" else r"${\rm ^\circ C}$") fig.savefig(os.path.join( img_folder, "comp_anu_and_cru_biases_for_{}_{}.png".format( vname, reanalysis_driven_config.label)), bbox_inches="tight", dpi=common_plot_params.FIG_SAVE_DPI)
def main(): if not img_folder.is_dir(): img_folder.mkdir(parents=True) season_to_months = OrderedDict([ ("Winter (DJF)", (1, 2, 12)), ("Spring (MAM)", range(3, 6)), ("Summer (JJA)", range(6, 9)), ("Fall (SON)", range(9, 12)), ]) varnames = ["TT", "PR"] plot_utils.apply_plot_params(font_size=10, width_pt=None, width_cm=20, height_cm=17) # reanalysis_driven_config = RunConfig(data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5", # start_year=1980, end_year=2010, label="ERAI-CRCM5-L") # reanalysis_driven_config = RunConfig( data_path= "/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.4_crcm5-hcd-rl.hdf5", start_year=1980, end_year=2010, label="ERAI-CRCM5-L(0.4)") nx_agg_model = 1 ny_agg_model = 1 nx_agg_anusplin = 4 ny_agg_anusplin = 4 gcm_driven_config = RunConfig( data_path= "/RESCUE/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5", start_year=1980, end_year=2010, label="CanESM2-CRCM5-L") bmp_info = analysis.get_basemap_info(r_config=reanalysis_driven_config) xx, yy = bmp_info.get_proj_xy() field_cmap = cm.get_cmap("jet", 10) vname_to_clevels = { "TT": np.arange(-30, 32, 2), "PR": np.arange(0, 6.5, 0.5) } vname_to_anusplin_path = { "TT": "/home/huziy/skynet3_rech1/anusplin_links", "PR": "/home/huziy/skynet3_rech1/anusplin_links" } vname_to_cru_path = { "TT": "/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc", "PR": "/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc" } for vname in varnames: fig = plt.figure() ncols = len(season_to_months) gs = GridSpec(4, ncols + 1, width_ratios=ncols * [ 1., ] + [ 0.09, ]) clevels = vname_to_clevels[vname] # get anusplin obs climatology season_to_obs_anusplin = plot_performance_err_with_anusplin.get_seasonal_clim_obs_data( rconfig=reanalysis_driven_config, vname=vname, season_to_months=season_to_months, bmp_info=bmp_info, n_agg_x=nx_agg_anusplin, n_agg_y=ny_agg_anusplin) row = 0 # Plot CRU values------------------------- bmp_info_agg, season_to_obs_cru = plot_performance_err_with_cru.get_seasonal_clim_obs_data( rconfig=reanalysis_driven_config, bmp_info=bmp_info, season_to_months=season_to_months, obs_path=vname_to_cru_path[vname], vname=vname) # Mask out the Great Lakes cru_mask = get_mask(bmp_info_agg.lons, bmp_info_agg.lats, shp_path=os.path.join(GL_SHP_FOLDER, "gl_cst.shp")) for season in season_to_obs_cru: season_to_obs_cru[season] = np.ma.masked_where( cru_mask > 0.5, season_to_obs_cru[season]) ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)] cs = None xx_agg, yy_agg = bmp_info_agg.get_proj_xy() for j, (season, obs_field) in enumerate(season_to_obs_cru.items()): ax = ax_list[j] cs = bmp_info_agg.basemap.contourf(xx_agg, yy_agg, obs_field.copy(), levels=clevels, ax=ax) bmp_info.basemap.drawcoastlines(ax=ax) bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax) ax.set_title(season) ax_list[0].set_ylabel("CRU") # plt.colorbar(cs, caax=ax_list[-1]) row += 1 # Plot ANUSPLIN values------------------------- ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)] cs = None for j, (season, obs_field) in enumerate(season_to_obs_anusplin.items()): ax = ax_list[j] cs = bmp_info.basemap.contourf(xx, yy, obs_field, levels=clevels, ax=ax) bmp_info.basemap.drawcoastlines(ax=ax) bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax) ax.set_title(season) ax_list[0].set_ylabel("Hopkinson") cb = plt.colorbar(cs, cax=fig.add_subplot(gs[:2, -1])) cb.ax.set_xlabel(infovar.get_units(vname)) _format_axes(ax_list, vname=vname) row += 1 # Plot model (CRCM) values------------------------- # ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)] # cs = None # # season_to_field_crcm = analysis.get_seasonal_climatology_for_runconfig(run_config=reanalysis_driven_config, # varname=vname, level=0, # season_to_months=season_to_months) # # for j, (season, crcm_field) in enumerate(season_to_field_crcm.items()): # ax = ax_list[j] # cs = bmp_info.basemap.contourf(xx, yy, crcm_field * 1000 * 24 * 3600, levels=clevels, ax=ax) # bmp_info.basemap.drawcoastlines(ax=ax) # bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax) # ax.set_title(season) # # ax_list[0].set_ylabel(reanalysis_driven_config.label) # cb = plt.colorbar(cs, cax=fig.add_subplot(gs[:2, -1])) # cb.ax.set_xlabel(infovar.get_units(vname)) # _format_axes(ax_list, vname=vname) # row += 1 # Plot (Model - CRU) Performance biases------------------------- ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)] cs = plot_performance_err_with_cru.compare_vars( vname_model=vname, vname_obs=None, r_config=reanalysis_driven_config, season_to_months=season_to_months, obs_path=vname_to_cru_path[vname], bmp_info_agg=bmp_info_agg, diff_axes_list=ax_list, mask_shape_file=os.path.join(GL_SHP_FOLDER, "gl_cst.shp"), nx_agg_model=nx_agg_model, ny_agg_model=ny_agg_model) ax_list[0].set_ylabel( "{label}\n--\nCRU".format(label=reanalysis_driven_config.label)) _format_axes(ax_list, vname=vname) row += 1 # Plot performance+BFE errors with respect to CRU (Model - CRU)------------------------- # ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)] # plot_performance_err_with_cru.compare_vars(vname, vname_obs=None, obs_path=vname_to_cru_path[vname], # r_config=gcm_driven_config, # bmp_info_agg=bmp_info_agg, season_to_months=season_to_months, # axes_list=ax_list) # _format_axes(ax_list, vname=vname) # ax_list[0].set_ylabel("{label}\nvs\nCRU".format(label=gcm_driven_config.label)) # row += 1 # Plot performance errors with respect to ANUSPLIN (Model - ANUSPLIN)------------------------- ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)] plot_performance_err_with_anusplin.compare_vars( vname, {vname: season_to_obs_anusplin}, r_config=reanalysis_driven_config, bmp_info_agg=bmp_info, season_to_months=season_to_months, axes_list=ax_list) _format_axes(ax_list, vname=vname) ax_list[0].set_ylabel("{label}\n--\nHopkinson".format( label=reanalysis_driven_config.label)) row += 1 # Plot performance+BFE errors with respect to ANUSPLIN (Model - ANUSPLIN)------------------------- # ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)] # plot_performance_err_with_anusplin.compare_vars(vname, {vname: season_to_obs_anusplin}, # r_config=gcm_driven_config, # bmp_info_agg=bmp_info, season_to_months=season_to_months, # axes_list=ax_list) # _format_axes(ax_list, vname=vname) # ax_list[0].set_ylabel("{label}\nvs\nHopkinson".format(label=gcm_driven_config.label)) cb = plt.colorbar(cs, cax=fig.add_subplot(gs[-2:, -1])) cb.ax.set_xlabel(infovar.get_units(vname)) # Save the plot img_file = "{vname}_{sy}-{ey}_{sim_label}.png".format( vname=vname, sy=reanalysis_driven_config.start_year, ey=reanalysis_driven_config.end_year, sim_label=reanalysis_driven_config.label) img_file = img_folder.joinpath(img_file) with img_file.open("wb") as f: fig.savefig(f, bbox_inches="tight") plt.close(fig)