Ny = 256 Re = 1000 Ny_test = 192 Re_test = 550 # Filenames filepath_mean = 'data/channel/re1000/LM_Channel_1000_mean_prof.dat' filepath_fluc = 'data/channel/re1000/LM_Channel_1000_vel_fluc_prof.dat' filepath_tke = 'data/channel/re1000/LM_Channel_1000_RSTE_k_prof.dat' filepath_mean_test = 'data/channel/re0550/LM_Channel_0550_mean_prof.dat' filepath_fluc_test = 'data/channel/re0550/LM_Channel_0550_vel_fluc_prof.dat' filepath_tke_test = 'data/channel/re0550/LM_Channel_0550_RSTE_k_prof.dat' # Load data y_train, U_train, dUdy_train = ld.load_mean_data(filepath_mean, Ny) uus_train, tke_train = ld.load_fluc_data(filepath_fluc, Ny) eps_train = ld.load_tke_data(filepath_tke, Ny) y_raw, U_raw, dUdy_raw = ld.load_mean_data(filepath_mean_test, Ny_test) uus_raw, tke_raw = ld.load_fluc_data(filepath_fluc_test, Ny_test) eps_raw = ld.load_tke_data(filepath_tke_test, Ny_test) # Filter for synthetic RANS y_filt_train = pd.rans_filter(y_train, fsize) U_filt_train = pd.rans_filter(U_train, fsize) dUdy_filt_train = pd.rans_filter(dUdy_train, fsize) uus_filt_train = pd.rans_filter(uus_train, fsize) tke_filt_train = pd.rans_filter(tke_train, fsize) eps_filt_train = pd.rans_filter(eps_train, fsize)
# Input/Settings seed_no = 3 np.random.seed(seed_no) fsize = 3 Ny = 384 Re = 2000 # Filenames filepath_mean = 'data/channel/re2000/LM_Channel_2000_mean_prof.dat' filepath_fluc = 'data/channel/re2000/LM_Channel_2000_vel_fluc_prof.dat' filepath_tke = 'data/channel/re2000/LM_Channel_2000_RSTE_k_prof.dat' # Load data y_raw, U_raw, dUdy_raw = ld.load_mean_data(filepath_mean, Ny) uus_raw, tke_raw = ld.load_fluc_data(filepath_fluc, Ny) eps_raw = ld.load_tke_data(filepath_tke, Ny) # Filter for synthetic RANS y_filt = pd.rans_filter(y_raw, fsize) U_filt = pd.rans_filter(U_raw, fsize) dUdy_filt = pd.rans_filter(dUdy_raw, fsize) uus_filt = pd.rans_filter(uus_raw, fsize) tke_filt = pd.rans_filter(tke_raw, fsize) eps_filt = pd.rans_filter(eps_raw, fsize) # Shuffle data shuffler = np.random.permutation(Ny) y_filt_sh = y_filt[shuffler]
filepath_mean1 = 'data/channel/re0550/LM_Channel_0550_mean_prof.dat' filepath_fluc1 = 'data/channel/re0550/LM_Channel_0550_vel_fluc_prof.dat' filepath_mean2 = 'data/channel/re1000/LM_Channel_1000_mean_prof.dat' filepath_fluc2 = 'data/channel/re1000/LM_Channel_1000_vel_fluc_prof.dat' # Filenames - couette filepath_mean3 = 'data/couette/re0220/LM_Couette_R0220_100PI_mean_prof.dat' filepath_fluc3 = 'data/couette/re0220/LM_Couette_R0220_100PI_vel_fluc_prof.dat' filepath_mean4 = 'data/couette/re0500/LM_Couette_R0500_100PI_mean_prof.dat' filepath_fluc4 = 'data/couette/re0500/LM_Couette_R0500_100PI_vel_fluc_prof.dat' # Filenames - shs filepath_shs = 'data/shs/tbnn_stats.npz' # Load data y1, U1 = ld.load_mean_data(filepath_mean1, Ny1)[0:2] tke1 = ld.load_fluc_data(filepath_fluc1, Ny1)[1] y2, U2 = ld.load_mean_data(filepath_mean2, Ny2)[0:2] tke2 = ld.load_fluc_data(filepath_fluc2, Ny2)[1] y3, U3 = ld.load_mean_data(filepath_mean3, Ny3)[0:2] tke3 = ld.load_fluc_data(filepath_fluc3, Ny3)[1] y4, U4 = ld.load_mean_data(filepath_mean4, Ny4)[0:2] tke4 = ld.load_fluc_data(filepath_fluc4, Ny4)[1] _, y5, U5, _, _, tke5, _ = ld.load_shs_data(filepath_shs) # Plot plt.figure()
