def plot_bfe_row_for_var(finfo_to_season_to_diff=None, ax_list=None, season_titles=False, varname="", basemap_info=None): cmap = cm.get_cmap("RdBu_r", 20) assert isinstance(basemap_info, BasemapInfo) xx, yy = None, None cs = None for finfo, season_to_diff in finfo_to_season_to_diff.items(): assert isinstance(finfo, FieldInfo) if finfo.varname != varname: continue for season in season_to_diff: season_to_diff[season] = infovar.get_to_plot(varname, season_to_diff[season], difference=True, lons=basemap_info.lons, lats=basemap_info.lats) clevs = get_diff_levels(season_to_diff, ncolors=cmap.N, varname=varname) for i, (season, diff) in enumerate(season_to_diff.items()): ax = ax_list[i] if xx is None or yy is None: xx, yy = basemap_info.get_proj_xy() print(diff.shape) cs = basemap_info.basemap.contourf(xx, yy, diff[:], cmap=cmap, levels=clevs, extend="both", ax=ax) basemap_info.basemap.drawcoastlines(ax=ax) # ax.set_aspect("auto") basemap_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax) if season_titles: ax.set_title(season) if i == 0: ax.set_ylabel(infovar.get_long_display_label_for_var(finfo.varname)) if finfo.varname in ["I5", ] and season.lower() in ["summer"]: ax.set_visible(False) ax = ax_list[-1] # ax.set_aspect(30) ax.set_title(infovar.get_units(varname)) plt.colorbar(cs, cax=ax_list[-1])
def _plot_row(vname="", level=0, config_dict=None, plot_cc_only_for=None, mark_significance=True): """ if plot_cc_only_for is not None, should be equal to the label of the simulation to be plotted """ lons, lats = config_dict.lons, config_dict.lats bmp = config_dict.basemap """ :type bmp: mpl_toolkits.basemap.Basemap """ xx, yy = bmp(lons, lats) lons[lons > 180] -= 360 fig = config_dict.fig gs = config_dict.gs """:type : matplotlib.gridspec.GridSpec """ nrows_subplots, ncols_subplots = gs.get_geometry() label_base = config_dict.label_base label_modif = config_dict.label_modif the_row = config_dict.the_row season_to_months = config_dict.season_to_months if "+" in vname or "-" in vname: op = "+" if "+" in vname else "-" vname1, vname2 = vname.split(op) vname1 = vname1.strip() vname2 = vname2.strip() current_base = {} future_base = {} current_modif = {} future_modif = {} # vname1 current_base1 = compute_seasonal_means_for_each_year(config_dict["Current"][label_base], var_name=vname1, level=level, season_to_months=season_to_months) future_base1 = compute_seasonal_means_for_each_year(config_dict["Future"][label_base], var_name=vname1, level=level, season_to_months=season_to_months) current_modif1 = compute_seasonal_means_for_each_year(config_dict["Current"][label_modif], var_name=vname1, level=level, season_to_months=season_to_months) future_modif1 = compute_seasonal_means_for_each_year(config_dict["Future"][label_modif], var_name=vname1, level=level, season_to_months=season_to_months) # vname2 current_base2 = compute_seasonal_means_for_each_year(config_dict["Current"][label_base], var_name=vname2, level=level, season_to_months=season_to_months) future_base2 = compute_seasonal_means_for_each_year(config_dict["Future"][label_base], var_name=vname2, level=level, season_to_months=season_to_months) current_modif2 = compute_seasonal_means_for_each_year(config_dict["Current"][label_modif], var_name=vname2, level=level, season_to_months=season_to_months) future_modif2 = compute_seasonal_means_for_each_year(config_dict["Future"][label_modif], var_name=vname2, level=level, season_to_months=season_to_months) for season in current_base1: current_base[season] = eval("current_base2[season]{}current_base1[season]".format(op)) future_base[season] = eval("future_base2[season]{}future_base1[season]".format(op)) current_modif[season] = eval("current_modif2[season]{}current_modif1[season]".format(op)) future_modif[season] = eval("future_modif2[season]{}future_modif1[season]".format(op)) else: current_base = compute_seasonal_means_for_each_year(config_dict["Current"][label_base], var_name=vname, level=level, season_to_months=season_to_months) future_base = compute_seasonal_means_for_each_year(config_dict["Future"][label_base], var_name=vname, level=level, season_to_months=season_to_months) current_modif = compute_seasonal_means_for_each_year(config_dict["Current"][label_modif], var_name=vname, level=level, season_to_months=season_to_months) future_modif = compute_seasonal_means_for_each_year(config_dict["Future"][label_modif], var_name=vname, level=level, season_to_months=season_to_months) # Calculate the differences in cc signal season_to_diff = OrderedDict() season_to_plot_diff = OrderedDict() diff_max = 0 print(list(current_base.keys())) # Get the ranges for colorbar and calculate p-values print("------------------ impacts on projected changes to {} -----------------------".format(vname)) season_to_pvalue = OrderedDict() for season in list(current_base.keys()): _, pvalue_current = ttest_ind(current_modif[season], current_base[season], axis=0, equal_var=False) _, pvalue_future = ttest_ind(future_modif[season], future_base[season], axis=0, equal_var=False) if plot_cc_only_for is None: season_to_pvalue[season] = np.minimum(pvalue_current, pvalue_future) season_to_diff[season] = (future_modif[season] - current_modif[season]) - \ (future_base[season] - current_base[season]) else: if plot_cc_only_for == label_base: _, season_to_pvalue[season] = ttest_ind(future_base[season], current_base[season], axis=0, equal_var=False) c_data = current_base[season] f_data = future_base[season] else: _, season_to_pvalue[season] = ttest_ind(future_modif[season], current_modif[season], axis=0, equal_var=False) c_data = current_modif[season] f_data = future_modif[season] season_to_diff[season] = f_data - c_data # Convert units if required if vname in config_dict.multipliers: season_to_diff[season] *= config_dict.multipliers[vname] field_to_plot = infovar.get_to_plot(vname, season_to_diff[season].mean(axis=0), lons=lons, lats=lats) season_to_plot_diff[season] = field_to_plot print("{}: {}".format(season, season_to_plot_diff[season].mean())) if hasattr(field_to_plot, "mask"): diff_max = max(np.percentile(np.abs(field_to_plot[~field_to_plot.mask]), 95), diff_max) else: diff_max = max(np.percentile(np.abs(field_to_plot), 95), diff_max) print("--------------------------------------------------------") img = None locator = MaxNLocator(nbins=10, symmetric=True) clevels = locator.tick_values(-diff_max, diff_max) bn = BoundaryNorm(clevels, len(clevels) - 1) cmap = cm.get_cmap("RdBu_r", len(clevels) - 1) for col, season in enumerate(current_base.keys()): ax = fig.add_subplot(gs[the_row, col]) if not col: ax.set_ylabel(infovar.get_long_display_label_for_var(vname)) if not the_row: ax.set_title(season) img = bmp.pcolormesh(xx, yy, season_to_plot_diff[season].copy(), vmin=-diff_max, vmax=diff_max, cmap=cmap, norm=bn, ax=ax) # logging good_vals = season_to_plot_diff[season] good_vals = good_vals[~good_vals.mask] print("------" * 10) print("{}: min={}; max={}; area-avg={};".format(season, good_vals.min(), good_vals.max(), good_vals.mean())) bmp.readshapefile(quebec_info.BASIN_BOUNDARIES_DERIVED_10km[:-4], "basin_edge", ax=ax) p = season_to_pvalue[season] if hasattr(season_to_plot_diff[season], "mask"): p = np.ma.masked_where(season_to_plot_diff[season].mask, p) if plot_cc_only_for is not None and mark_significance: cs = bmp.contourf(xx, yy, p, hatches=["..."], levels=[0.05, 1], colors='none') if (col == ncols_subplots - 2) and (the_row == nrows_subplots - 1): # create a legend for the contour set artists, labels = cs.legend_elements() labels = ["not significant"] ax.legend(artists, labels, handleheight=1, loc="upper right", bbox_to_anchor=(1.0, -0.05), borderaxespad=0., frameon=False) bmp.drawcoastlines(ax=ax, linewidth=0.4) if vname in ["I5"] and season.lower() in ["summer"]: ax.set_visible(False) cb = plt.colorbar(img, cax=fig.add_subplot(gs[the_row, len(current_base)]), extend="both") if hasattr(config_dict, "name_to_units") and vname in config_dict.name_to_units: cb.ax.set_title(config_dict.name_to_units[vname]) else: cb.ax.set_title(infovar.get_units(vname))
def compare(paths=None, path_to_control_data=None, control_label="", labels=None, varnames=None, levels=None, months_of_interest=None, start_year=None, end_year=None): """ Comparing 2D fields :param paths: paths to the simulation results :param varnames: :param labels: Display name for each simulation (number of labels should be equal to the number of paths) :param path_to_control_data: the path with which the comparison done i.e. a in the following formula delta = (x - a)/a * 100% generates one image file per variable (in the folder images_for_lake-river_paper): compare_varname_<control_label>_<label1>_..._<labeln>_startyear_endyear.png """ # get coordinate data (assumes that all the variables and runs have the same coordinates) lons2d, lats2d, basemap = analysis.get_basemap_from_hdf(file_path=path_to_control_data) x, y = basemap(lons2d, lats2d) lake_fraction = analysis.get_array_from_file(path=path_to_control_data, var_name="lake_fraction") if lake_fraction is None: lake_fraction = np.zeros(lons2d.shape) ncolors = 10 # +1 to include white diff_cmap = cm.get_cmap("RdBu_r", ncolors + 1) for var_name, level in zip(varnames, levels): sfmt = infovar.get_colorbar_formatter(var_name) control_means = analysis.get_mean_2d_fields_for_months(path=path_to_control_data, var_name=var_name, months=months_of_interest, start_year=start_year, end_year=end_year, level=level) control_mean = np.mean(control_means, axis=0) fig = plt.figure() assert isinstance(fig, Figure) gs = gridspec.GridSpec(2, len(paths) + 1, wspace=0.5) # plot the control ax = fig.add_subplot(gs[0, 0]) assert isinstance(ax, Axes) ax.set_title("{0}".format(control_label)) ax.set_ylabel("Mean: $X_{0}$") to_plot = infovar.get_to_plot(var_name, control_mean, lake_fraction=lake_fraction, mask_oceans=True, lons=lons2d, lats=lats2d) # determine colorabr extent and spacing field_cmap, field_norm = infovar.get_colormap_and_norm_for(var_name, to_plot, ncolors=ncolors) basemap.pcolormesh(x, y, to_plot, cmap=field_cmap, norm=field_norm) cb = basemap.colorbar(format=sfmt) assert isinstance(cb, Colorbar) # cb.ax.set_ylabel(infovar.get_units(var_name)) units = infovar.get_units(var_name) info = "Variable:" \ "\n{0}" \ "\nPeriod: {1}-{2}" \ "\nMonths: {3}" \ "\nUnits: {4}" info = info.format(infovar.get_long_name(var_name), start_year, end_year, ",".join([datetime(2001, m, 1).strftime("%b") for m in months_of_interest]), units) ax.annotate(info, xy=(0.1, 0.3), xycoords="figure fraction") sel_axes = [ax] for the_path, the_label, column in zip(paths, labels, list(range(1, len(paths) + 1))): means_for_years = analysis.get_mean_2d_fields_for_months(path=the_path, var_name=var_name, months=months_of_interest, start_year=start_year, end_year=end_year) the_mean = np.mean(means_for_years, axis=0) # plot the mean value ax = fig.add_subplot(gs[0, column]) sel_axes.append(ax) ax.set_title("{0}".format(the_label)) to_plot = infovar.get_to_plot(var_name, the_mean, lake_fraction=lake_fraction, mask_oceans=True, lons=lons2d, lats=lats2d) basemap.pcolormesh(x, y, to_plot, cmap=field_cmap, norm=field_norm) ax.set_ylabel("Mean: $X_{0}$".format(column)) cb = basemap.colorbar(format=sfmt) # cb.ax.set_ylabel(infovar.get_units(var_name)) # plot the difference ax = fig.add_subplot(gs[1, column]) sel_axes.append(ax) ax.set_ylabel("$X_{0} - X_0$".format(column)) # #Mask only if the previous plot (means) is masked thediff = the_mean - control_mean if hasattr(to_plot, "mask"): to_plot = np.ma.masked_where(to_plot.mask, thediff) else: to_plot = thediff if var_name == "PR": # convert to mm/day to_plot = infovar.get_to_plot(var_name, to_plot, mask_oceans=False) vmin = np.ma.min(to_plot) vmax = np.ma.max(to_plot) d = max(abs(vmin), abs(vmax)) vmin = -d vmax = d field_norm, bounds, vmn_nice, vmx_nice = infovar.get_boundary_norm(vmin, vmax, diff_cmap.N, exclude_zero=False) basemap.pcolormesh(x, y, to_plot, cmap=diff_cmap, norm=field_norm, vmin=vmn_nice, vmax=vmx_nice) cb = basemap.colorbar(format=sfmt) t, pval = ttest_ind(means_for_years, control_means, axis=0) sig = pval < 0.1 basemap.contourf(x, y, sig.astype(int), nlevels=2, hatches=["+", None], colors="none") # cb.ax.set_ylabel(infovar.get_units(var_name)) # plot coastlines for the_ax in sel_axes: basemap.drawcoastlines(ax=the_ax, linewidth=common_plot_params.COASTLINE_WIDTH) # depends on the compared simulations and the months of interest fig_file_name = "compare_{0}_{1}_{2}_months-{3}.jpeg".format(var_name, control_label, "_".join(labels), "-".join([str(m) for m in months_of_interest])) figpath = os.path.join(images_folder, fig_file_name) fig.savefig(figpath, dpi=cpp.FIG_SAVE_DPI, bbox_inches="tight") plt.close(fig)
def plot_control_and_differences_in_one_panel_for_all_seasons_for_all_vars( varnames=None, levels=None, season_to_months=None, start_year=None, end_year=None): season_list = list(season_to_months.keys()) pvalue_max = 0.1 # crcm5-r vs crcm5-hcd-r # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-r_spinup.hdf" # control_label = "CRCM5-R" # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r_spinup2.hdf", ] # labels = ["CRCM5-HCD-R"] # crcm5-hcd-rl vs crcm5-hcd-r # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r_spinup2.hdf" # control_label = "CRCM5-HCD-R" # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl_spinup.hdf", ] # labels = ["CRCM5-HCD-RL"] # compare simulations with and without interflow # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl_spinup.hdf" # control_label = "CRCM5-HCD-RL" # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_do_not_discard_small.hdf", ] # labels = ["CRCM5-HCD-RL-INTFL"] # very high hydr cond # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_do_not_discard_small.hdf" # control_label = "CRCM5-HCD-RL-INTFL" ## # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_sani-10000.hdf", ] # labels = ["CRCM5-HCD-RL-INTFL-sani=10000"] # Interflow effect # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl_spinup.hdf" # control_label = "CRCM5-HCD-RL" # ## # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_spinup_ITFS.hdf5", ] # labels = ["ITFS"] # total lake effect # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-r.hdf5" # control_label = "CRCM5-NL" # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5", ] # labels = ["CRCM5-L2", ] # lake effect (lake-atm interactions) # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-r.hdf5" # control_label = "CRCM5-R" # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r.hdf5", ] # labels = ["CRCM5-HCD-R", ] # lake effect (lake-river interactions) # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r.hdf5" # control_label = "CRCM5-L1" # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5", ] # labels = ["CRCM5-HCD-L2", ] # interflow effect () control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5" control_label = "CRCM5-L2" paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS.hdf5", ] labels = ["CRCM5-L2I", ] # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS_avoid_truncation1979-1989.hdf5", ] # labels = ["CRCM5-HCD-RL-INTFb", ] # interflow effect (avoid truncation and bigger slopes) # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS.hdf5" # control_label = "CRCM5-HCD-RL-INTF" # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS_avoid_truncation1979-1989.hdf5", ] # labels = ["CRCM5-HCD-RL-INTF-improved", ] # row_labels = [ r"{} vs {}".format(s, control_label) for s in labels ] print(labels) # varnames = ["QQ", ] # levels = [None, ] assert len(levels) == len(varnames) lons2d, lats2d, basemap = analysis.get_basemap_from_hdf(file_path=control_path) x, y = basemap(lons2d, lats2d) # save the domain properties for reuse domain_props = DomainProperties() domain_props.basemap = basemap domain_props.lons2d = lons2d domain_props.lats2d = lats2d domain_props.x = x domain_props.y = y lake_fraction = analysis.get_array_from_file(path=control_path, var_name=infovar.HDF_LAKE_FRACTION_NAME) dpth_to_bedrock = analysis.get_array_from_file(path=control_path, var_name=infovar.HDF_DEPTH_TO_BEDROCK_NAME) assert dpth_to_bedrock is not None if lake_fraction is None: lake_fraction = np.zeros(lons2d.shape) ncolors = 10 # +1 to include white diff_cmap = cm.get_cmap("RdBu", ncolors + 1) # Do the plotting for each variable fig = plt.figure() assert isinstance(fig, Figure) # plot the control data ncols = len(season_list) + 1 # +1 is for the colorbar gs = gridspec.GridSpec(len(varnames), ncols, width_ratios=[1.0, ] * (ncols - 1) + [0.07], top=0.95) lev_width_3d = np.ones(dpth_to_bedrock.shape + infovar.soil_layer_widths_26_to_60.shape) lev_width_3d *= infovar.soil_layer_widths_26_to_60[np.newaxis, np.newaxis, :] lev_bot_3d = lev_width_3d.cumsum(axis=2) correction = -lev_bot_3d + dpth_to_bedrock[:, :, np.newaxis] # Apply the correction only at points where the layer bottom is lower than # the bedrock lev_width_3d[correction < 0] += correction[correction < 0] lev_width_3d[lev_width_3d < 0] = 0 # plot the plots one file per variable for var_name, level, the_row in zip(varnames, levels, list(range(len(varnames)))): sfmt = infovar.get_colorbar_formatter(var_name) season_to_control_mean = {} label_to_season_to_difference = {} label_to_season_to_significance = {} try: # Calculate the difference for each season, and save the results to dictionaries # to access later when plotting for season, months_of_interest in season_to_months.items(): print("working on season: {0}".format(season)) control_means = analysis.get_mean_2d_fields_for_months(path=control_path, var_name=var_name, months=months_of_interest, start_year=start_year, end_year=end_year, level=level) control_mean = np.mean(control_means, axis=0) control_mean = infovar.get_to_plot(var_name, control_mean, lake_fraction=domain_props.lake_fraction, lons=lons2d, lats=lats2d, level_width_m=lev_width_3d[:, :, level]) # multiply by the number of days in a season for PR and TRAF to convert them into mm from mm/day if var_name in ["PR", "TRAF", "TDRA"]: control_mean *= get_num_days(months_of_interest) infovar.change_units_to(varnames=[var_name, ], new_units=r"${\rm mm}$") season_to_control_mean[season] = control_mean print("calculated mean from {0}".format(control_path)) # calculate the difference for each simulation for the_path, the_label in zip(paths, row_labels): modified_means = analysis.get_mean_2d_fields_for_months(path=the_path, var_name=var_name, months=months_of_interest, start_year=start_year, end_year=end_year, level=level) tval, pval = ttest_ind(modified_means, control_means, axis=0, equal_var=False) significance = ((pval <= pvalue_max) & (~control_mean.mask)).astype(int) print("pval ranges: {} to {}".format(pval.min(), pval.max())) modified_mean = np.mean(modified_means, axis=0) if the_label not in label_to_season_to_difference: label_to_season_to_difference[the_label] = OrderedDict() label_to_season_to_significance[the_label] = OrderedDict() modified_mean = infovar.get_to_plot(var_name, modified_mean, lake_fraction=domain_props.lake_fraction, lons=lons2d, lats=lats2d, level_width_m=lev_width_3d[:, :, level]) # multiply by the number of days in a season for PR and TRAF to convert them into mm from mm/day if var_name in ["PR", "TRAF", "TDRA"]: modified_mean *= get_num_days(months_of_interest) diff_vals = modified_mean - control_mean print("diff ranges: min: {0}; max: {1}".format(diff_vals.min(), diff_vals.max())) label_to_season_to_difference[the_label][season] = diff_vals label_to_season_to_significance[the_label][season] = significance print("Calculated mean and diff from {0}".format(the_path)) except NoSuchNodeError: print("Could not find {0}, skipping...".format(var_name)) continue for the_label, data in label_to_season_to_difference.items(): axes = [] for col in range(ncols): axes.append(fig.add_subplot(gs[the_row, col])) # Set season titles if the_row == 0: for the_season, ax in zip(season_list, axes): ax.set_title(the_season) _plot_row(axes, data, the_label, var_name, increments=True, domain_props=domain_props, season_list=season_list, significance=label_to_season_to_significance[the_label]) var_label = infovar.get_long_display_label_for_var(var_name) if var_name in ["I1"]: var_label = "{}\n{} layer".format(var_label, ordinal(level + 1)) axes[0].set_ylabel(var_label) fig.suptitle("({}) vs ({})".format(labels[0], control_label), font_properties=FontProperties(weight="bold")) folderpath = os.path.join(images_folder, "seasonal_mean_maps/{0}_vs_{1}_for_{2}_{3}-{4}".format( "_".join(labels), control_label, "-".join(list(season_to_months.keys())), start_year, end_year)) if not os.path.isdir(folderpath): os.mkdir(folderpath) imname = "{0}_{1}.png".format("-".join(varnames), "_".join(labels + [control_label])) impath = os.path.join(folderpath, imname) fig.savefig(impath, bbox_inches="tight")
def plot_control_and_differences_in_one_panel_for_all_seasons(varnames=None, levels=None, season_to_months=None, start_year=None, end_year=None): season_list = list(season_to_months.keys()) pvalue_max = 0.1 # crcm5-r vs crcm5-hcd-r # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-r_spinup.hdf" # control_label = "CRCM5-R" # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r_spinup2.hdf", ] # labels = ["CRCM5-HCD-R"] # crcm5-hcd-rl vs crcm5-hcd-r # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r_spinup2.hdf" # control_label = "CRCM5-HCD-R" # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl_spinup.hdf", ] # labels = ["CRCM5-HCD-RL"] # compare simulations with and without interflow # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl_spinup.hdf" # control_label = "CRCM5-HCD-RL" # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_do_not_discard_small.hdf", ] # labels = ["CRCM5-HCD-RL-INTFL"] # very high hydr cond # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_do_not_discard_small.hdf" # control_label = "CRCM5-HCD-RL-INTFL" ## # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_sani-10000.hdf", ] # labels = ["CRCM5-HCD-RL-INTFL-sani=10000"] # Interflow effect # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl_spinup.hdf" # control_label = "CRCM5-HCD-RL" # ## # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_spinup_ITFS.hdf5", ] # labels = ["ITFS"] # lake effect (lake-atm interactions) # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-r.hdf5" # control_label = "CRCM5-NL" # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r.hdf5", ] # labels = ["CRCM5-L1", ] # lake effect (lake-atm interactions) radiative fluxes # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-r_radiation_fluxes.hdf5" # control_label = "CRCM5-NL" # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r-1980-2010_radiation_fluxes.hdf5", ] # labels = ["CRCM5-L1", ] # lake effect (lake-river interactions) control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r.hdf5" control_label = "CRCM5-L1" paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5", ] labels = ["CRCM5-L2", ] # interflow effect () # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5" # control_label = "CRCM5-L2" # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS.hdf5", ] # labels = ["CRCM5-L2I", ] # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS_avoid_truncation1979-1989.hdf5", ] # labels = ["CRCM5-L2I-short", ] # interflow effect (avoid truncation and bigger slopes) # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS.hdf5" # control_label = "CRCM5-HCD-RL-INTF" # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS_avoid_truncation1979-1989.hdf5", ] # labels = ["CRCM5-HCD-RL-INTF-improved", ] # row_labels = [ r"({}) - ({})".format(s, control_label) for s in labels ] print(labels) # varnames = ["QQ", ] # levels = [None, ] assert len(levels) == len(varnames) lons2d, lats2d, basemap = analysis.get_basemap_from_hdf(file_path=control_path) x, y = basemap(lons2d, lats2d) # save the domain properties for reuse domain_props = DomainProperties() domain_props.basemap = basemap domain_props.lons2d = lons2d domain_props.lats2d = lats2d domain_props.x = x domain_props.y = y lake_fraction = analysis.get_array_from_file(path=control_path, var_name="lake_fraction") if lake_fraction is None: lake_fraction = np.zeros(lons2d.shape) ncolors = 50 # change to 10 for the paper plots # +1 to include white diff_cmap = cm.get_cmap("RdBu", ncolors + 1) # plot the plots one file per variable for var_name, level in zip(varnames, levels): sfmt = infovar.get_colorbar_formatter(var_name) season_to_control_mean = {} label_to_season_to_difference = {} label_to_season_to_significance = {} try: # Calculate the difference for each season, and save the results to dictionaries # to access later when plotting for season, months_of_interest in season_to_months.items(): print("working on season: {0}".format(season)) control_means = analysis.get_mean_2d_fields_for_months(path=control_path, var_name=var_name, months=months_of_interest, start_year=start_year, end_year=end_year, level=level) control_mean = np.mean(control_means, axis=0) control_mean = infovar.get_to_plot(var_name, control_mean, lake_fraction=domain_props.lake_fraction, lons=lons2d, lats=lats2d) # multiply by the number of days in a season for PR and TRAF to convert them into mm from mm/day if var_name in ["PR", "TRAF", "TDRA"]: control_mean *= get_num_days(months_of_interest) season_to_control_mean[season] = control_mean print("calculated mean from {0}".format(control_path)) # calculate the difference for each simulation for the_path, the_label in zip(paths, row_labels): modified_means = analysis.get_mean_2d_fields_for_months(path=the_path, var_name=var_name, months=months_of_interest, start_year=start_year, end_year=end_year, level=level) tval, pval = ttest_ind(modified_means, control_means, axis=0, equal_var=False) significance = ((pval <= pvalue_max) & (~control_mean.mask)).astype(int) print("pval ranges: {} to {}".format(pval.min(), pval.max())) modified_mean = np.mean(modified_means, axis=0) if the_label not in label_to_season_to_difference: label_to_season_to_difference[the_label] = OrderedDict() label_to_season_to_significance[the_label] = OrderedDict() modified_mean = infovar.get_to_plot(var_name, modified_mean, lake_fraction=domain_props.lake_fraction, lons=lons2d, lats=lats2d) # multiply by the number of days in a season for PR and TRAF to convert them into mm from mm/day if var_name in ["PR", "TRAF", "TDRA"]: modified_mean *= get_num_days(months_of_interest) diff_vals = modified_mean - control_mean print("diff ranges: min: {0}; max: {1}".format(diff_vals.min(), diff_vals.max())) label_to_season_to_difference[the_label][season] = diff_vals label_to_season_to_significance[the_label][season] = significance print("Calculated mean and diff from {0}".format(the_path)) except NoSuchNodeError: print("Could not find {0}, skipping...".format(var_name)) continue # Do the plotting for each variable fig = plt.figure() assert isinstance(fig, Figure) # plot the control data ncols = len(season_to_control_mean) + 1 # +1 is for the colorbar gs = gridspec.GridSpec(len(paths) + 1, ncols, width_ratios=[1.0, ] * (ncols - 1) + [0.07]) axes = [] for col in range(ncols): axes.append(fig.add_subplot(gs[0, col])) _plot_row(axes, season_to_control_mean, control_label, var_name, domain_props=domain_props, season_list=season_list) the_row = 1 for the_label, data in label_to_season_to_difference.items(): axes = [] for col in range(ncols): axes.append(fig.add_subplot(gs[the_row, col])) _plot_row(axes, data, the_label, var_name, increments=True, domain_props=domain_props, season_list=season_list, significance=label_to_season_to_significance[the_label]) the_row += 1 for the_ax, the_season in zip(axes, season_list): the_ax.set_title(the_season) folderpath = os.path.join(images_folder, "seasonal_mean_maps/{0}_vs_{1}_for_{2}_{3}-{4}".format( "_".join(labels), control_label, "-".join(list(season_to_months.keys())), start_year, end_year)) if not os.path.isdir(folderpath): os.mkdir(folderpath) imname = "{0}_{1}.png".format(var_name, "_".join(labels + [control_label])) impath = os.path.join(folderpath, imname) fig.savefig(impath, bbox_inches="tight", dpi=cpp.FIG_SAVE_DPI, transparent=True)
def compare(paths=None, path_to_control_data=None, control_label="", labels=None, varnames=None, levels=None, months_of_interest=None, start_year=None, end_year=None): """ Comparing 2D fields :param paths: paths to the simulation results :param varnames: :param labels: Display name for each simulation (number of labels should be equal to the number of paths) :param path_to_control_data: the path with which the comparison done i.e. a in the following formula delta = (x - a)/a * 100% generates one image file per variable (in the folder images_for_lake-river_paper): compare_varname_<control_label>_<label1>_..._<labeln>_startyear_endyear.png """ # get coordinate data (assumes that all the variables and runs have the same coordinates) lons2d, lats2d, basemap = analysis.get_basemap_from_hdf( file_path=path_to_control_data) x, y = basemap(lons2d, lats2d) lake_fraction = analysis.get_array_from_file(path=path_to_control_data, var_name="lake_fraction") if lake_fraction is None: lake_fraction = np.zeros(lons2d.shape) ncolors = 10 # +1 to include white diff_cmap = cm.get_cmap("RdBu_r", ncolors + 1) for var_name, level in zip(varnames, levels): sfmt = infovar.get_colorbar_formatter(var_name) control_means = analysis.get_mean_2d_fields_for_months( path=path_to_control_data, var_name=var_name, months=months_of_interest, start_year=start_year, end_year=end_year, level=level) control_mean = np.mean(control_means, axis=0) fig = plt.figure() assert isinstance(fig, Figure) gs = gridspec.GridSpec(2, len(paths) + 1, wspace=0.5) # plot the control ax = fig.add_subplot(gs[0, 0]) assert isinstance(ax, Axes) ax.set_title("{0}".format(control_label)) ax.set_ylabel("Mean: $X_{0}$") to_plot = infovar.get_to_plot(var_name, control_mean, lake_fraction=lake_fraction, mask_oceans=True, lons=lons2d, lats=lats2d) # determine colorabr extent and spacing field_cmap, field_norm = infovar.get_colormap_and_norm_for( var_name, to_plot, ncolors=ncolors) basemap.pcolormesh(x, y, to_plot, cmap=field_cmap, norm=field_norm) cb = basemap.colorbar(format=sfmt) assert isinstance(cb, Colorbar) # cb.ax.set_ylabel(infovar.get_units(var_name)) units = infovar.get_units(var_name) info = "Variable:" \ "\n{0}" \ "\nPeriod: {1}-{2}" \ "\nMonths: {3}" \ "\nUnits: {4}" info = info.format( infovar.get_long_name(var_name), start_year, end_year, ",".join([ datetime(2001, m, 1).strftime("%b") for m in months_of_interest ]), units) ax.annotate(info, xy=(0.1, 0.3), xycoords="figure fraction") sel_axes = [ax] for the_path, the_label, column in zip(paths, labels, list(range(1, len(paths) + 1))): means_for_years = analysis.get_mean_2d_fields_for_months( path=the_path, var_name=var_name, months=months_of_interest, start_year=start_year, end_year=end_year) the_mean = np.mean(means_for_years, axis=0) # plot the mean value ax = fig.add_subplot(gs[0, column]) sel_axes.append(ax) ax.set_title("{0}".format(the_label)) to_plot = infovar.get_to_plot(var_name, the_mean, lake_fraction=lake_fraction, mask_oceans=True, lons=lons2d, lats=lats2d) basemap.pcolormesh(x, y, to_plot, cmap=field_cmap, norm=field_norm) ax.set_ylabel("Mean: $X_{0}$".format(column)) cb = basemap.colorbar(format=sfmt) # cb.ax.set_ylabel(infovar.get_units(var_name)) # plot the difference ax = fig.add_subplot(gs[1, column]) sel_axes.append(ax) ax.set_ylabel("$X_{0} - X_0$".format(column)) # #Mask only if the previous plot (means) is masked thediff = the_mean - control_mean if hasattr(to_plot, "mask"): to_plot = np.ma.masked_where(to_plot.mask, thediff) else: to_plot = thediff if var_name == "PR": # convert to mm/day to_plot = infovar.get_to_plot(var_name, to_plot, mask_oceans=False) vmin = np.ma.min(to_plot) vmax = np.ma.max(to_plot) d = max(abs(vmin), abs(vmax)) vmin = -d vmax = d field_norm, bounds, vmn_nice, vmx_nice = infovar.get_boundary_norm( vmin, vmax, diff_cmap.N, exclude_zero=False) basemap.pcolormesh(x, y, to_plot, cmap=diff_cmap, norm=field_norm, vmin=vmn_nice, vmax=vmx_nice) cb = basemap.colorbar(format=sfmt) t, pval = ttest_ind(means_for_years, control_means, axis=0) sig = pval < 0.1 basemap.contourf(x, y, sig.astype(int), nlevels=2, hatches=["+", None], colors="none") # cb.ax.set_ylabel(infovar.get_units(var_name)) # plot coastlines for the_ax in sel_axes: basemap.drawcoastlines( ax=the_ax, linewidth=common_plot_params.COASTLINE_WIDTH) # depends on the compared simulations and the months of interest fig_file_name = "compare_{0}_{1}_{2}_months-{3}.jpeg".format( var_name, control_label, "_".join(labels), "-".join([str(m) for m in months_of_interest])) figpath = os.path.join(images_folder, fig_file_name) fig.savefig(figpath, dpi=cpp.FIG_SAVE_DPI, bbox_inches="tight") plt.close(fig)
def plot_control_and_differences_in_one_panel_for_all_seasons_for_all_vars( varnames=None, levels=None, season_to_months=None, start_year=None, end_year=None): season_list = list(season_to_months.keys()) pvalue_max = 0.1 # crcm5-r vs crcm5-hcd-r # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-r_spinup.hdf" # control_label = "CRCM5-R" # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r_spinup2.hdf", ] # labels = ["CRCM5-HCD-R"] # crcm5-hcd-rl vs crcm5-hcd-r # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r_spinup2.hdf" # control_label = "CRCM5-HCD-R" # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl_spinup.hdf", ] # labels = ["CRCM5-HCD-RL"] # compare simulations with and without interflow # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl_spinup.hdf" # control_label = "CRCM5-HCD-RL" # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_do_not_discard_small.hdf", ] # labels = ["CRCM5-HCD-RL-INTFL"] # very high hydr cond # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_do_not_discard_small.hdf" # control_label = "CRCM5-HCD-RL-INTFL" ## # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_sani-10000.hdf", ] # labels = ["CRCM5-HCD-RL-INTFL-sani=10000"] # Interflow effect # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl_spinup.hdf" # control_label = "CRCM5-HCD-RL" # ## # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_spinup_ITFS.hdf5", ] # labels = ["ITFS"] # total lake effect # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-r.hdf5" # control_label = "CRCM5-NL" # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5", ] # labels = ["CRCM5-L2", ] # lake effect (lake-atm interactions) # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-r.hdf5" # control_label = "CRCM5-R" # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r.hdf5", ] # labels = ["CRCM5-HCD-R", ] # lake effect (lake-river interactions) # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r.hdf5" # control_label = "CRCM5-L1" # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5", ] # labels = ["CRCM5-HCD-L2", ] # interflow effect () control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5" control_label = "CRCM5-L2" paths = [ "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS.hdf5", ] labels = [ "CRCM5-L2I", ] # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS_avoid_truncation1979-1989.hdf5", ] # labels = ["CRCM5-HCD-RL-INTFb", ] # interflow effect (avoid truncation and bigger slopes) # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS.hdf5" # control_label = "CRCM5-HCD-RL-INTF" # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS_avoid_truncation1979-1989.hdf5", ] # labels = ["CRCM5-HCD-RL-INTF-improved", ] # row_labels = [r"{} vs {}".format(s, control_label) for s in labels] print(labels) # varnames = ["QQ", ] # levels = [None, ] assert len(levels) == len(varnames) lons2d, lats2d, basemap = analysis.get_basemap_from_hdf( file_path=control_path) x, y = basemap(lons2d, lats2d) # save the domain properties for reuse domain_props = DomainProperties() domain_props.basemap = basemap domain_props.lons2d = lons2d domain_props.lats2d = lats2d domain_props.x = x domain_props.y = y lake_fraction = analysis.get_array_from_file( path=control_path, var_name=infovar.HDF_LAKE_FRACTION_NAME) dpth_to_bedrock = analysis.get_array_from_file( path=control_path, var_name=infovar.HDF_DEPTH_TO_BEDROCK_NAME) assert dpth_to_bedrock is not None if lake_fraction is None: lake_fraction = np.zeros(lons2d.shape) ncolors = 10 # +1 to include white diff_cmap = cm.get_cmap("RdBu", ncolors + 1) # Do the plotting for each variable fig = plt.figure() assert isinstance(fig, Figure) # plot the control data ncols = len(season_list) + 1 # +1 is for the colorbar gs = gridspec.GridSpec(len(varnames), ncols, width_ratios=[ 1.0, ] * (ncols - 1) + [0.07], top=0.95) lev_width_3d = np.ones(dpth_to_bedrock.shape + infovar.soil_layer_widths_26_to_60.shape) lev_width_3d *= infovar.soil_layer_widths_26_to_60[np.newaxis, np.newaxis, :] lev_bot_3d = lev_width_3d.cumsum(axis=2) correction = -lev_bot_3d + dpth_to_bedrock[:, :, np.newaxis] # Apply the correction only at points where the layer bottom is lower than # the bedrock lev_width_3d[correction < 0] += correction[correction < 0] lev_width_3d[lev_width_3d < 0] = 0 # plot the plots one file per variable for var_name, level, the_row in zip(varnames, levels, list(range(len(varnames)))): sfmt = infovar.get_colorbar_formatter(var_name) season_to_control_mean = {} label_to_season_to_difference = {} label_to_season_to_significance = {} try: # Calculate the difference for each season, and save the results to dictionaries # to access later when plotting for season, months_of_interest in season_to_months.items(): print("working on season: {0}".format(season)) control_means = analysis.get_mean_2d_fields_for_months( path=control_path, var_name=var_name, months=months_of_interest, start_year=start_year, end_year=end_year, level=level) control_mean = np.mean(control_means, axis=0) control_mean = infovar.get_to_plot( var_name, control_mean, lake_fraction=domain_props.lake_fraction, lons=lons2d, lats=lats2d, level_width_m=lev_width_3d[:, :, level]) # multiply by the number of days in a season for PR and TRAF to convert them into mm from mm/day if var_name in ["PR", "TRAF", "TDRA"]: control_mean *= get_num_days(months_of_interest) infovar.change_units_to(varnames=[ var_name, ], new_units=r"${\rm mm}$") season_to_control_mean[season] = control_mean print("calculated mean from {0}".format(control_path)) # calculate the difference for each simulation for the_path, the_label in zip(paths, row_labels): modified_means = analysis.get_mean_2d_fields_for_months( path=the_path, var_name=var_name, months=months_of_interest, start_year=start_year, end_year=end_year, level=level) tval, pval = ttest_ind(modified_means, control_means, axis=0, equal_var=False) significance = ((pval <= pvalue_max) & (~control_mean.mask)).astype(int) print("pval ranges: {} to {}".format( pval.min(), pval.max())) modified_mean = np.mean(modified_means, axis=0) if the_label not in label_to_season_to_difference: label_to_season_to_difference[the_label] = OrderedDict( ) label_to_season_to_significance[ the_label] = OrderedDict() modified_mean = infovar.get_to_plot( var_name, modified_mean, lake_fraction=domain_props.lake_fraction, lons=lons2d, lats=lats2d, level_width_m=lev_width_3d[:, :, level]) # multiply by the number of days in a season for PR and TRAF to convert them into mm from mm/day if var_name in ["PR", "TRAF", "TDRA"]: modified_mean *= get_num_days(months_of_interest) diff_vals = modified_mean - control_mean print("diff ranges: min: {0}; max: {1}".format( diff_vals.min(), diff_vals.max())) label_to_season_to_difference[the_label][ season] = diff_vals label_to_season_to_significance[the_label][ season] = significance print("Calculated mean and diff from {0}".format(the_path)) except NoSuchNodeError: print("Could not find {0}, skipping...".format(var_name)) continue for the_label, data in label_to_season_to_difference.items(): axes = [] for col in range(ncols): axes.append(fig.add_subplot(gs[the_row, col])) # Set season titles if the_row == 0: for the_season, ax in zip(season_list, axes): ax.set_title(the_season) _plot_row(axes, data, the_label, var_name, increments=True, domain_props=domain_props, season_list=season_list, significance=label_to_season_to_significance[the_label]) var_label = infovar.get_long_display_label_for_var(var_name) if var_name in ["I1"]: var_label = "{}\n{} layer".format(var_label, ordinal(level + 1)) axes[0].set_ylabel(var_label) fig.suptitle("({}) vs ({})".format(labels[0], control_label), font_properties=FontProperties(weight="bold")) folderpath = os.path.join( images_folder, "seasonal_mean_maps/{0}_vs_{1}_for_{2}_{3}-{4}".format( "_".join(labels), control_label, "-".join(list(season_to_months.keys())), start_year, end_year)) if not os.path.isdir(folderpath): os.mkdir(folderpath) imname = "{0}_{1}.png".format("-".join(varnames), "_".join(labels + [control_label])) impath = os.path.join(folderpath, imname) fig.savefig(impath, bbox_inches="tight")
def plot_bfe_row_for_var(finfo_to_season_to_diff=None, ax_list=None, season_titles=False, varname="", basemap_info=None): cmap = cm.get_cmap("RdBu_r", 20) assert isinstance(basemap_info, BasemapInfo) xx, yy = None, None cs = None for finfo, season_to_diff in finfo_to_season_to_diff.items(): assert isinstance(finfo, FieldInfo) if finfo.varname != varname: continue for season in season_to_diff: season_to_diff[season] = infovar.get_to_plot( varname, season_to_diff[season], difference=True, lons=basemap_info.lons, lats=basemap_info.lats) clevs = get_diff_levels(season_to_diff, ncolors=cmap.N, varname=varname) for i, (season, diff) in enumerate(season_to_diff.items()): ax = ax_list[i] if xx is None or yy is None: xx, yy = basemap_info.get_proj_xy() print(diff.shape) cs = basemap_info.basemap.contourf(xx, yy, diff[:], cmap=cmap, levels=clevs, extend="both", ax=ax) basemap_info.basemap.drawcoastlines(ax=ax) # ax.set_aspect("auto") basemap_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax) if season_titles: ax.set_title(season) if i == 0: ax.set_ylabel( infovar.get_long_display_label_for_var(finfo.varname)) if finfo.varname in [ "I5", ] and season.lower() in ["summer"]: ax.set_visible(False) ax = ax_list[-1] # ax.set_aspect(30) ax.set_title(infovar.get_units(varname)) plt.colorbar(cs, cax=ax_list[-1])
def plot_control_and_differences_in_one_panel_for_all_seasons( varnames=None, levels=None, season_to_months=None, start_year=None, end_year=None): season_list = list(season_to_months.keys()) pvalue_max = 0.1 # crcm5-r vs crcm5-hcd-r # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-r_spinup.hdf" # control_label = "CRCM5-R" # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r_spinup2.hdf", ] # labels = ["CRCM5-HCD-R"] # crcm5-hcd-rl vs crcm5-hcd-r # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r_spinup2.hdf" # control_label = "CRCM5-HCD-R" # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl_spinup.hdf", ] # labels = ["CRCM5-HCD-RL"] # compare simulations with and without interflow # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl_spinup.hdf" # control_label = "CRCM5-HCD-RL" # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_do_not_discard_small.hdf", ] # labels = ["CRCM5-HCD-RL-INTFL"] # very high hydr cond # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_do_not_discard_small.hdf" # control_label = "CRCM5-HCD-RL-INTFL" ## # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_sani-10000.hdf", ] # labels = ["CRCM5-HCD-RL-INTFL-sani=10000"] # Interflow effect # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl_spinup.hdf" # control_label = "CRCM5-HCD-RL" # ## # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_spinup_ITFS.hdf5", ] # labels = ["ITFS"] # lake effect (lake-atm interactions) # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-r.hdf5" # control_label = "CRCM5-NL" # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r.hdf5", ] # labels = ["CRCM5-L1", ] # lake effect (lake-atm interactions) radiative fluxes # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-r_radiation_fluxes.hdf5" # control_label = "CRCM5-NL" # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r-1980-2010_radiation_fluxes.hdf5", ] # labels = ["CRCM5-L1", ] # lake effect (lake-river interactions) control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r.hdf5" control_label = "CRCM5-L1" paths = [ "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5", ] labels = [ "CRCM5-L2", ] # interflow effect () # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5" # control_label = "CRCM5-L2" # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS.hdf5", ] # labels = ["CRCM5-L2I", ] # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS_avoid_truncation1979-1989.hdf5", ] # labels = ["CRCM5-L2I-short", ] # interflow effect (avoid truncation and bigger slopes) # control_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS.hdf5" # control_label = "CRCM5-HCD-RL-INTF" # # paths = ["/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS_avoid_truncation1979-1989.hdf5", ] # labels = ["CRCM5-HCD-RL-INTF-improved", ] # row_labels = [r"({}) - ({})".format(s, control_label) for s in labels] print(labels) # varnames = ["QQ", ] # levels = [None, ] assert len(levels) == len(varnames) lons2d, lats2d, basemap = analysis.get_basemap_from_hdf( file_path=control_path) x, y = basemap(lons2d, lats2d) # save the domain properties for reuse domain_props = DomainProperties() domain_props.basemap = basemap domain_props.lons2d = lons2d domain_props.lats2d = lats2d domain_props.x = x domain_props.y = y lake_fraction = analysis.get_array_from_file(path=control_path, var_name="lake_fraction") if lake_fraction is None: lake_fraction = np.zeros(lons2d.shape) ncolors = 50 # change to 10 for the paper plots # +1 to include white diff_cmap = cm.get_cmap("RdBu", ncolors + 1) # plot the plots one file per variable for var_name, level in zip(varnames, levels): sfmt = infovar.get_colorbar_formatter(var_name) season_to_control_mean = {} label_to_season_to_difference = {} label_to_season_to_significance = {} try: # Calculate the difference for each season, and save the results to dictionaries # to access later when plotting for season, months_of_interest in season_to_months.items(): print("working on season: {0}".format(season)) control_means = analysis.get_mean_2d_fields_for_months( path=control_path, var_name=var_name, months=months_of_interest, start_year=start_year, end_year=end_year, level=level) control_mean = np.mean(control_means, axis=0) control_mean = infovar.get_to_plot( var_name, control_mean, lake_fraction=domain_props.lake_fraction, lons=lons2d, lats=lats2d) # multiply by the number of days in a season for PR and TRAF to convert them into mm from mm/day if var_name in ["PR", "TRAF", "TDRA"]: control_mean *= get_num_days(months_of_interest) season_to_control_mean[season] = control_mean print("calculated mean from {0}".format(control_path)) # calculate the difference for each simulation for the_path, the_label in zip(paths, row_labels): modified_means = analysis.get_mean_2d_fields_for_months( path=the_path, var_name=var_name, months=months_of_interest, start_year=start_year, end_year=end_year, level=level) tval, pval = ttest_ind(modified_means, control_means, axis=0, equal_var=False) significance = ((pval <= pvalue_max) & (~control_mean.mask)).astype(int) print("pval ranges: {} to {}".format( pval.min(), pval.max())) modified_mean = np.mean(modified_means, axis=0) if the_label not in label_to_season_to_difference: label_to_season_to_difference[the_label] = OrderedDict( ) label_to_season_to_significance[ the_label] = OrderedDict() modified_mean = infovar.get_to_plot( var_name, modified_mean, lake_fraction=domain_props.lake_fraction, lons=lons2d, lats=lats2d) # multiply by the number of days in a season for PR and TRAF to convert them into mm from mm/day if var_name in ["PR", "TRAF", "TDRA"]: modified_mean *= get_num_days(months_of_interest) diff_vals = modified_mean - control_mean print("diff ranges: min: {0}; max: {1}".format( diff_vals.min(), diff_vals.max())) label_to_season_to_difference[the_label][ season] = diff_vals label_to_season_to_significance[the_label][ season] = significance print("Calculated mean and diff from {0}".format(the_path)) except NoSuchNodeError: print("Could not find {0}, skipping...".format(var_name)) continue # Do the plotting for each variable fig = plt.figure() assert isinstance(fig, Figure) # plot the control data ncols = len(season_to_control_mean) + 1 # +1 is for the colorbar gs = gridspec.GridSpec(len(paths) + 1, ncols, width_ratios=[ 1.0, ] * (ncols - 1) + [0.07]) axes = [] for col in range(ncols): axes.append(fig.add_subplot(gs[0, col])) _plot_row(axes, season_to_control_mean, control_label, var_name, domain_props=domain_props, season_list=season_list) the_row = 1 for the_label, data in label_to_season_to_difference.items(): axes = [] for col in range(ncols): axes.append(fig.add_subplot(gs[the_row, col])) _plot_row(axes, data, the_label, var_name, increments=True, domain_props=domain_props, season_list=season_list, significance=label_to_season_to_significance[the_label]) the_row += 1 for the_ax, the_season in zip(axes, season_list): the_ax.set_title(the_season) folderpath = os.path.join( images_folder, "seasonal_mean_maps/{0}_vs_{1}_for_{2}_{3}-{4}".format( "_".join(labels), control_label, "-".join(list(season_to_months.keys())), start_year, end_year)) if not os.path.isdir(folderpath): os.mkdir(folderpath) imname = "{0}_{1}.png".format(var_name, "_".join(labels + [control_label])) impath = os.path.join(folderpath, imname) fig.savefig(impath, bbox_inches="tight", dpi=cpp.FIG_SAVE_DPI, transparent=True)