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')
