def load_from_nh(set_of_dir, nh): set_of_dir_nh = set_of_dir.filter(nh=nh, f=0) path = set_of_dir_nh.path_larger_t_start() sim = solveq2d.create_sim_plot_from_dir(path) tmin, tmax, tstatio = baseSW1lw.tminmaxstatio_from_sim(sim, VERBOSE=True) (dico_time_means, dico_results) = sim.output.spatial_means.compute_time_means(tstatio) c2 = sim.param['c2'] c = np.sqrt(c2) EK = dico_time_means['EK'] Fr = np.sqrt(2 * EK / c2) EA = dico_time_means['EA'] EKr = dico_time_means['EKr'] EKd = EK - EKr E = EK + EA epsK = dico_time_means['epsK'] epsA = dico_time_means['epsA'] eps = epsK + epsA epsK_tot = dico_time_means['epsK_tot'] epsA_tot = dico_time_means['epsA_tot'] Enorm = np.sqrt(eps * Lf * c) print 'c = {0:8.1f}, Fr = {1:8.3f}, eps = {2:8.2f}'.format(c, Fr, eps) return locals()
def spectra_from_namedir(nh): set_of_dir_nh = set_of_dir.filter(nh=nh) path = set_of_dir_nh.path_larger_t_start() sim = solveq2d.create_sim_plot_from_dir(path) tmin, tmax, tstatio = baseSW1lw.tminmaxstatio_from_sim(sim, VERBOSE=True) dico_results = sim.output.spectra.load1D_mean(tmin=tstatio) kh = dico_results['kh'] spectEK = (dico_results['spectrum1Dkx_EK'] + dico_results['spectrum1Dkx_EK']) / 2 spectEA = (dico_results['spectrum1Dkx_EA'] + dico_results['spectrum1Dkx_EA']) / 2 spectEKr = (dico_results['spectrum1Dkx_EKr'] + dico_results['spectrum1Dkx_EKr']) / 2 spectEKd = spectEK - spectEKr spectE = spectEK + spectEA dico1, dico2 = sim.output.spatial_means.compute_time_means(tstatio=tstatio) epsK = dico1['epsK'] epsA = dico1['epsA'] eps = epsK + epsA EK = dico1['EK'] U = np.sqrt(2 * EK) c = np.sqrt(sim.param['c2']) return locals()
def sprectra_from_namedir(name_dir_results): path_dir_results = set_of_dir_results.path_dirs[name_dir_results] sim = solveq2d.create_sim_plot_from_dir(path_dir_results) dict_results = sim.output.spectra.load2D_mean(tmin=tmin) kh = dict_results['kh'] EK = dict_results['spectrum2D_EK'] EA = dict_results['spectrum2D_EA'] EKr = dict_results['spectrum2D_EKr'] EKd = EK - EKr return kh, EKr, EKd
def print1sim(set_of_dir, f): """Print informations on one simulation.""" print('\n\n') set_of_dir = set_of_dir.filter(f=f) path = set_of_dir.path_larger_t_start() sim = solveq2d.create_sim_plot_from_dir(path) param = sim.param nx = param['nx'] c = np.sqrt(sim.param['c2']) nu8 = param['nu_8'] f = sim.param['f'] tmin, tmax, tstatio = baseSW1lw.tminmaxstatio_from_sim(sim, VERBOSE=True) dico1, dico2 = sim.output.spatial_means.compute_time_means(tstatio=tstatio) epsK = dico1['epsK'] epsA = dico1['epsA'] eps = epsK + epsA Ff = eps**(1. / 3) * kf**(-1. / 3) / c n = 8 kd8 = (eps / nu8**3)**(1. / (3 * n - 2)) kmax = param['coef_dealiasing'] * np.pi * nx / param['Lx'] minh, maxu = minhmaxu_from_sim(sim) ftable.write('\n') ftable.write('{0:4d} & {1:4.0f} & '.format(nx, c)) ftable.write('{0:7.1e} & '.format(nu8)) if f > 0: Ro = eps**(1. / 3) * kf**(2. / 3) / f Bu = (kf * c / f)**2 print('Ro = {0:.2f}; Bu = {1:.2f}'.format(Ro, Bu)) ftable.write('{0:4.1f} & {1:6.2g} & {2:6.2g} & '.format(f, Ro, Bu)) else: ftable.write(' 0 & $\infty$ & $\infty$ & ') ftable.write('{0:4.2f} & '.format(eps)) ftable.write('{0:.2f} & '.format(kmax / kd8)) ftable.write('{0:3.0f} & '.format(kd8 / kf)) ftable.write('{0:6.3f} & '.format(Ff)) ftable.write('{0:4.2f} & {1:4.2f} '.format(minh, maxu / c)) ftable.write('\\\\')
def load_from_namedir(set_of_dir, name_dir_results, tstatio): path_dir_results = set_of_dir.path_dirs[name_dir_results] sim = solveq2d.create_sim_plot_from_dir(path_dir_results) (dico_time_means, dico_results ) = sim.output.spatial_means.compute_time_means(tstatio) c2 = sim.param['c2'] EK = dico_time_means['EK'] Fr = np.sqrt(2*EK/c2) EA = dico_time_means['EA'] EKr = dico_time_means['EKr'] EKd = EK - EKr E = EK + EA epsK = dico_time_means['epsK'] epsA = dico_time_means['epsA'] eps = epsK + epsA epsK_tot = dico_time_means['epsK_tot'] epsA_tot = dico_time_means['epsA_tot'] c = np.sqrt(set_of_dir.dico_c2[name_dir_results]) print 'c = {0:8.1f}, Fr = {1:8.3f}, eps = {2:8.2f}'.format( c, Fr, eps) return locals()
SAVE_FIG=SAVE_FIG, FOR_BEAMER=False, fontsize=19) dir_base = create_fig.path_base_dir + '/Results_SW1lw' str_resol = repr(resol) str_to_find_path = (dir_base + '/Pure_standing_waves_' + str_resol + '*/SE2D*c=' + repr(c)) + '_*' print str_to_find_path paths_dir = glob.glob(str_to_find_path) print paths_dir sim = solveq2d.create_sim_plot_from_dir(paths_dir[0]) tmin = sim.output.spatial_means.first_saved_time() tstatio = tmin + 4. dico_results = sim.output.spectra.load1D_mean(tmin=tstatio) kh = dico_results['kh'] EK = (dico_results['spectrum1Dkx_EK'] + dico_results['spectrum1Dkx_EK']) / 2 EA = (dico_results['spectrum1Dkx_EA'] + dico_results['spectrum1Dkx_EA']) / 2 EKr = (dico_results['spectrum1Dkx_EKr'] + dico_results['spectrum1Dkx_EKr']) / 2 E_tot = EK + EA EKd = EK - EKr
def load_from_path(path): sim = solveq2d.create_sim_plot_from_dir(path) dico = sim.output.spatial_means.load() c = np.sqrt(sim.param.c2) return dico, c
EK = np.empty([nb_runs]) EA = np.empty([nb_runs]) EKr = np.empty([nb_runs]) epsK = np.empty([nb_runs]) epsA = np.empty([nb_runs]) epsK_tot = np.empty([nb_runs]) epsA_tot = np.empty([nb_runs]) irun = -1 for c2, name_solver in tuple_loop: path_dir = set_of_dir_results.one_path_from_values(solver=name_solver, c2=c2) irun += 1 sim = solveq2d.create_sim_plot_from_dir(name_dir=path_dir) (dict_time_means, dict_results) = sim.output.spatial_means.compute_time_means(tstatio) arr_c2[irun] = sim.param['c2'] EK[irun] = dict_time_means['EK'] Froude_numbers[irun] = 2 * EK[irun] / sim.param['c2'] EA[irun] = dict_time_means['EA'] EKr[irun] = dict_time_means['EKr'] epsK[irun] = dict_time_means['epsK'] epsA[irun] = dict_time_means['epsA'] epsK_tot[irun] = dict_time_means['epsK_tot'] epsA_tot[irun] = dict_time_means['epsA_tot']
def load_from_namedir(set_of_dir, name_dir_results): path_dir_results = set_of_dir.path_dirs[name_dir_results] sim = solveq2d.create_sim_plot_from_dir(path_dir_results) dico = sim.output.spatial_means.load() c = np.sqrt(sim.param.c2) return dico, c
short_name_article='SW1l', SAVE_FIG=SAVE_FIG, FOR_BEAMER=False, fontsize=19 ) name_dir_results = ( create_fig.path_base_dir+'/Results_for_article_SW1l' '/Approach_runs_2048x2048' '/SE2D_SW1lexlin_forcing_L=50.x50._2048x2048_c2=400_f=0_2013-05-29_23-54-57' ) sim = solveq2d.create_sim_plot_from_dir(name_dir_results) tmin = 30 dict_results = sim.output.spectra.load2D_mean(tmin=tmin) kh = dict_results['kh'] EK = dict_results['spectrum2D_EK'] EA = dict_results['spectrum2D_EA'] EKr = dict_results['spectrum2D_EKr'] E_tot = EK + EA EKd = EK - EKr Edlin = dict_results['spectrum2D_Edlin'] fig, ax1 = create_fig.figure_axe(name_file=name_file) ax1.set_xscale('log') ax1.set_yscale('log')