def adaptive_epsopt(dirlist, title, norm, Bs, ax):
e, Nb = [], []
ref = 'ref_flusi/T5_2048_double/flusiphi_000002500000.h5'
if len(dirlist) == 0:
raise ValueError('no data')
for d in dirlist:
if (os.path.isfile(d + '/fullphi_000002500000.h5')):
# compute error
err = wabbit_tools.wabbit_error_vs_flusi(
d + '/fullphi_000002500000.h5', ref, norm=norm)
e.append(err)
# compute number of points
N, Bs = wabbit_tools.fetch_Nblocks_RHS_dir(d, return_Bs=True)
Nb.append((Bs - 1) * np.sqrt(N))
# sort data by number of blocks
Nb, e = zip(*sorted(zip(Nb, e)))
plt.loglog(Nb,
e,
label=title + " [%2.2f]" %
(wabbit_tools.convergence_order(Nb, e)),
marker='o')
def equidistant(rootdir, title, norm, ax):
dirsx = glob.glob(rootdir + '*')
Nblocks, e = [], []
ref = 'ref_flusi/T5_2048_double/flusiphi_000002500000.h5'
if len(dirsx) == 0:
raise ValueError('no data')
for d in dirsx:
if (os.path.isfile(d + '/fullphi_000002500000.h5')):
err = wabbit_tools.wabbit_error_vs_flusi(
d + '/fullphi_000002500000.h5', ref, norm=norm)
N, Bs = wabbit_tools.fetch_Nblocks_dir(d, return_Bs=True)
# append to plot-lists
Nblocks.append((Bs - 1) * np.sqrt(N))
e.append(err)
Nblocks, e = zip(*sorted(zip(Nblocks, e)))
ax.loglog(Nblocks,
e,
label=title + " [%2.2f]" %
(wabbit_tools.convergence_order(Nblocks, e)),
marker='o')
def do_test(rootdir, J, name, ax1, ax2):
dirsx = glob.glob(rootdir + '*' + 'Jmax' + J + '*')
EPS = []
err = []
Nblocks = []
reffile = 'ref_flusi/T5_2048_double/flusiphi_000002500000.h5'
for d in dirsx:
if (os.path.isfile(d + '/fullphi_000002500000.h5')):
e = wabbit_tools.wabbit_error_vs_flusi(d +
'/fullphi_000002500000.h5',
reffile,
norm=norm)
err.append(e)
Nblocks.append(wabbit_tools.fetch_compression_rate_dir(d))
# Nblocks.append( wabbit_tools.fetch_Nblocks_dir(d) )
EPS.append(wabbit_tools.fetch_eps_dir(d))
# sort the lists (by eps)
EPS, err, Nblocks = zip(*sorted(zip(EPS, err, Nblocks)))
# name = name +" [%2.2f]" % (wabbit_tools.convergence_order(EPS,err))
ax1.loglog(EPS, err, label=name, marker='o')
ax2.semilogx(EPS, Nblocks, label=name, marker='o')
def do_test2(rootdir, tt, name, reffile):
dirsx = glob.glob(rootdir)
tcpu=[]
err=[]
f = '/fullphi_00000'+tt+'00000.h5'
for d in dirsx:
if (os.path.isfile(d+f)):
e = wabbit_tools.wabbit_error_vs_flusi( d+f, reffile, norm=norm )
err.append(e)
ncpu = len( glob.glob(d+'/*times.dat') )
d = insect_tools.load_t_file(d+'/timesteps_info.t')
tcpu.append( np.sum( d[:,1] )*float(ncpu) )
else:
warnings.warn("No data in that dir="+d+f)
# sort the lists (by eps)
err, tcpu = zip(*sorted(zip(err, tcpu )))
plt.figure(1)
plt.loglog( err, tcpu, label=name, marker='o')
l2plt = [0] * len(Jmax_list)
linfplt = [0] * len(Jmax_list)
for j, jmax_dir in enumerate(Jmax_dir_list):
for i, eps_dir in enumerate(eps_dir_list):
print(i)
fname_dense = glob.glob(jmax_dir + "/" + eps_dir + '/' + quantity +
'-dense_*' + config_id + '*.h5')
fname_sparse = glob.glob(jmax_dir + "/" + eps_dir + '/' + quantity +
'-sparse_*' + config_id + '*.h5')
fname_ref = glob.glob(jmax_dir + "/" + eps_dir + '/' + quantity +
'_*' + config_id + '*.h5')
# compute Lp errors
l2error[i] = wt.wabbit_error_vs_flusi(fname_dense[0],
fname_flusi[0],
norm=2,
dim=2)
# l2error[i] = wt.wabbit_error_vs_(fname_ref[0],fname_dense[0], norm=2, dim=2)
linferror[i] = wt.wabbit_error_vs_flusi(fname_dense[0],
fname_flusi[0],
norm=np.inf,
dim=2)
# compute compression
Nblocks[i] = sum(wt.block_level_distribution_file(fname_sparse[0]))
# compute number of dense blokcs
Nblocksdense = sum(wt.block_level_distribution_file(fname_dense[0]))
##############################################################################
# Plot