def time_serie_Arctic(path, var): yr, xr, time, depth = loading.extracting_coord(path) TorS = loading.extracting_var(path, var) make_plot.points_on_map(xr, yr, var, basin) S = loading.extracting_var(path, 'sal') TorS[np.where(S == 0.)] = np.nan i_zmax = np.max(np.where(depth < simu.zmax)) print(np.shape(TorS)) mean_Arctic = np.mean(np.nanmean(TorS[:, 0:i_zmax, :], axis=0), axis=0) return time, depth, mean_Arctic
def time_serie_Arctic_2D(path, var, lat_min, basin): yr, xr, time = loading.extracting_coord_2D(path) ice = loading.extracting_var(path, var) S = loading.extracting_var(path, 'sal') ice[np.where(S[:, :, 0, :] == 0.)] = np.nan if basin == 'undefined': mask = loading.latitudinal_band_mask(yr, lat_min, 90) else: mask = loading.Ocean_mask(xr, yr, basin) make_plot.points_on_map(xr[mask], yr[mask], var, basin) arctic = ice[mask, :] mean_Arctic = np.nanmean(arctic, axis=0) return time, mean_Arctic
def time_serie_Arctic(path, var, lat_min, basin): yr, xr, time, depth = loading.extracting_coord(path) TorS = loading.extracting_var(path, var) print(np.shape(TorS)) S = loading.extracting_var(path, 'sal') TorS[np.where(S == 0.)] = np.nan if basin == 'undefined': mask = loading.latitudinal_band_mask(yr, lat_min, 90) else: mask = loading.Ocean_mask(xr, yr, basin) make_plot.points_on_map(xr[mask], yr[mask], var, basin) arctic = TorS[mask, :, :] print(np.nanmax(arctic)) mean_Arctic = np.nanmean(arctic, axis=0) return time, depth, mean_Arctic
def time_serie_Arctic_2D(path, var_name, lat_min, basin): yr, xr, time = loading.extracting_coord_2D(path) VAR = loading.extracting_var(path, var_name) S = loading.extracting_var(path, 'sal') VAR[np.where(S[:, :, 0, :] == 0.)] = 0 #np.nan print(np.max(VAR), np.min(VAR)) VAR[np.where(VAR > 1E10)] = 0 #np.nan print(np.max(VAR), np.min(VAR)) if basin == 'undefined': mask = loading.latitudinal_band_mask(yr, lat_min, 90) else: mask = loading.Ocean_mask(xr, yr, basin) make_plot.points_on_map(xr[mask], yr[mask], var_name, basin) VAR[np.where(VAR == 0)] = np.nan arctic = VAR[mask, :] mean_Arctic = np.nansum(arctic, axis=0) return time, mean_Arctic
simu = yearly_LongPeriod(option, var, y1, y2, basin, lat_min, month) yr, xr, time, depth = loading.extracting_coord(simu.path) VarArray_simuRho = loading.extracting_var(simu.path, 'rho') # Practical Salinity index_y1 = np.min( np.where(simu.first_year + time[:] / (3600 * 24 * 364.5) > simu.y1)) index_y2 = np.min( np.where(simu.first_year + time[:] / (3600 * 24 * 364.5) > y2)) if basin == 'undefined': mask = loading.latitudinal_band_mask(yr, lat_min, 90) else: mask = loading.Ocean_mask(xr, yr, basin) make_plot.points_on_map(xr[mask], yr[mask], var, basin) VarArray_simuRho[np.where(VarArray_simuRho == 0)] = np.nan region = VarArray_simuRho[mask, :, :] mean_region = np.nanmean(region, axis=0) #make_plot.vertical_profile(mean_region[:,index_y1],mean_region[:,index_y2],y1,y2,var,simu.vmin,simu.vmax,depth,simu.max_depth,basin,lat_min,simu.output_file) mean_region_pro = mean_region[:, index_y1:index_y2 + 1] MEAN = np.zeros_like((mean_region_pro[:, 0])) MEAN2 = np.zeros_like((mean_region_pro[:, 0])) sig = np.zeros_like(mean_region_pro[:, 0]) MEAN = np.nanmean(mean_region_pro, axis=1) MEAN2 = np.nanmean(mean_region_pro**2, axis=1) #for i in range(0,np.size(MEAN2[0,:])):
simu = From1950to2100(option, var, y1, y2, comparison, lat_min, basin) yr, xr, time, depth, X = loading.extracting_coord_1D(simu.path) VAR = loading.extracting_var(simu.path, var) if var == 'sal': VAR[np.where(VAR < 32.)] = np.nan elif var == 'density': VAR[np.where(VAR < 18)] = np.nan else: S = loading.extracting_var(simu.path, 'sal') VAR[S == 0] = np.nan print(np.nanmin(VAR)) make_plot.points_on_map(xr, yr, var, basin) index_y1 = np.min( np.where(simu.first_year + time[:] / (3600 * 24 * 364.5) > simu.y1)) index_y2 = np.min( np.where(simu.first_year + time[:] / (3600 * 24 * 364.5) > simu.y2)) if var == 'Uorth': make_plot.one_sec( 100 * np.mean(VAR[:, :, index_y1:index_y1 + 29], axis=2).transpose(), var, X, depth, simu.max_depth, y1, simu.output_file, simu.vmin, simu.vmax, basin) else: make_plot.one_sec( np.mean(VAR[:, :, index_y1:index_y1 + 29], axis=2).transpose(), var, X, depth, simu.max_depth, y1, simu.output_file, simu.vmin, simu.vmax, basin)