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Commit 3c3a23a0 authored by Cristian Lalescu's avatar Cristian Lalescu
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tweak statistics cache 

basic stats can now be read even in the absence of the main simname.h5
file, using just the cache file.
parent 39a161c2
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bfps/DNS.py
View file @ 3c3a23a0
...@@ -233,76 +233,102 @@ class DNS(_code): ...@@ -233,76 +233,102 @@ class DNS(_code):
""" """
if len(list(self.statistics.keys())) > 0: if len(list(self.statistics.keys())) > 0:
return None return None
self.read_parameters() if not os.path.exists(self.get_data_file_name()):
with self.get_data_file() as data_file: if os.path.exists(self.get_cache_file_name()):
if 'moments' not in data_file['statistics'].keys(): self.read_parameters(fname = self.get_cache_file_name())
return None with self.get_cache_file() as pp_file:
iter0 = min((data_file['statistics/moments/velocity'].shape[0] * for k in ['t',
self.parameters['niter_stat']-1), 'energy(t)',
iter0) 'energy(k)',
if type(iter1) == type(None): 'enstrophy(t)',
iter1 = data_file['iteration'].value 'enstrophy(k)',
else:
iter1 = min(data_file['iteration'].value, iter1)
ii0 = iter0 // self.parameters['niter_stat']
ii1 = iter1 // self.parameters['niter_stat']
self.statistics['kshell'] = data_file['kspace/kshell'].value
self.statistics['nshell'] = data_file['kspace/nshell'].value
for kk in [-1, -2]:
if (self.statistics['kshell'][kk] == 0):
self.statistics['kshell'][kk] = np.nan
self.statistics['kM'] = data_file['kspace/kM'].value
self.statistics['dk'] = data_file['kspace/dk'].value
computation_needed = True
pp_file = h5py.File(self.get_postprocess_file_name(), 'a')
if 'ii0' in pp_file.keys():
computation_needed = not (ii0 == pp_file['ii0'].value and
ii1 == pp_file['ii1'].value)
if computation_needed:
for k in ['t', 'vel_max(t)', 'renergy(t)',
'energy(t)', 'enstrophy(t)',
'energy(k)', 'enstrophy(k)',
'energy(t, k)',
'enstrophy(t, k)',
'R_ij(t)', 'R_ij(t)',
'ii0', 'ii1', 'iter0', 'iter1']: 'vel_max(t)',
'renergy(t)']:
if k in pp_file.keys(): if k in pp_file.keys():
del pp_file[k] self.statistics[k] = pp_file[k].value
if computation_needed: self.statistics['kM'] = pp_file['kspace/kM'].value
pp_file['iter0'] = iter0 self.statistics['dk'] = pp_file['kspace/dk'].value
pp_file['iter1'] = iter1 self.statistics['kshell'] = pp_file['kspace/kshell'].value
pp_file['ii0'] = ii0 self.statistics['nshell'] = pp_file['kspace/nshell'].value
pp_file['ii1'] = ii1 else:
pp_file['t'] = (self.parameters['dt']* self.read_parameters()
self.parameters['niter_stat']* with self.get_data_file() as data_file:
(np.arange(ii0, ii1+1).astype(np.float))) if 'moments' not in data_file['statistics'].keys():
phi_ij = data_file['statistics/spectra/velocity_velocity'][ii0:ii1+1] return None
pp_file['R_ij(t)'] = np.sum(phi_ij, axis = 1) iter0 = min((data_file['statistics/moments/velocity'].shape[0] *
energy_tk = ( self.parameters['niter_stat']-1),
phi_ij[:, :, 0, 0] + iter0)
phi_ij[:, :, 1, 1] + if type(iter1) == type(None):
phi_ij[:, :, 2, 2])/2 iter1 = data_file['iteration'].value
pp_file['energy(t)'] = np.sum(energy_tk, axis = 1) else:
pp_file['energy(k)'] = np.mean(energy_tk, axis = 0)*(4*np.pi*self.statistics['kshell']**2) / (self.statistics['dk']*self.statistics['nshell']) iter1 = min(data_file['iteration'].value, iter1)
enstrophy_tk = ( ii0 = iter0 // self.parameters['niter_stat']
data_file['statistics/spectra/vorticity_vorticity'][ii0:ii1+1, :, 0, 0] + ii1 = iter1 // self.parameters['niter_stat']
data_file['statistics/spectra/vorticity_vorticity'][ii0:ii1+1, :, 1, 1] + self.statistics['kshell'] = data_file['kspace/kshell'].value
data_file['statistics/spectra/vorticity_vorticity'][ii0:ii1+1, :, 2, 2])/2 self.statistics['nshell'] = data_file['kspace/nshell'].value
pp_file['enstrophy(t)'] = np.sum(enstrophy_tk, axis = 1) for kk in [-1, -2]:
pp_file['enstrophy(k)'] = np.mean(enstrophy_tk, axis = 0)*(4*np.pi*self.statistics['kshell']**2) / self.statistics['nshell'] if (self.statistics['kshell'][kk] == 0):
pp_file['vel_max(t)'] = data_file['statistics/moments/velocity'][ii0:ii1+1, 9, 3] self.statistics['kshell'][kk] = np.nan
pp_file['renergy(t)'] = data_file['statistics/moments/velocity'][ii0:ii1+1, 2, 3]/2 self.statistics['kM'] = data_file['kspace/kM'].value
for k in ['t', self.statistics['dk'] = data_file['kspace/dk'].value
'energy(t)', computation_needed = True
'energy(k)', pp_file = h5py.File(self.get_postprocess_file_name(), 'a')
'enstrophy(t)', if not ('parameters' in pp_file.keys()):
'enstrophy(k)', data_file.copy('parameters', pp_file)
'R_ij(t)', data_file.copy('kspace', pp_file)
'vel_max(t)', if 'ii0' in pp_file.keys():
'renergy(t)']: computation_needed = not (ii0 == pp_file['ii0'].value and
if k in pp_file.keys(): ii1 == pp_file['ii1'].value)
self.statistics[k] = pp_file[k].value if computation_needed:
self.compute_time_averages() for k in ['t', 'vel_max(t)', 'renergy(t)',
'energy(t)', 'enstrophy(t)',
'energy(k)', 'enstrophy(k)',
'energy(t, k)',
'enstrophy(t, k)',
'R_ij(t)',
'ii0', 'ii1', 'iter0', 'iter1']:
if k in pp_file.keys():
del pp_file[k]
if computation_needed:
pp_file['iter0'] = iter0
pp_file['iter1'] = iter1
pp_file['ii0'] = ii0
pp_file['ii1'] = ii1
pp_file['t'] = (self.parameters['dt']*
self.parameters['niter_stat']*
(np.arange(ii0, ii1+1).astype(np.float)))
phi_ij = data_file['statistics/spectra/velocity_velocity'][ii0:ii1+1]
pp_file['R_ij(t)'] = np.sum(phi_ij, axis = 1)
energy_tk = (
phi_ij[:, :, 0, 0] +
phi_ij[:, :, 1, 1] +
phi_ij[:, :, 2, 2])/2
pp_file['energy(t)'] = np.sum(energy_tk, axis = 1)
pp_file['energy(k)'] = np.mean(energy_tk, axis = 0)*(4*np.pi*self.statistics['kshell']**2) / (self.statistics['dk']*self.statistics['nshell'])
enstrophy_tk = (
data_file['statistics/spectra/vorticity_vorticity'][ii0:ii1+1, :, 0, 0] +
data_file['statistics/spectra/vorticity_vorticity'][ii0:ii1+1, :, 1, 1] +
data_file['statistics/spectra/vorticity_vorticity'][ii0:ii1+1, :, 2, 2])/2
pp_file['enstrophy(t)'] = np.sum(enstrophy_tk, axis = 1)
pp_file['enstrophy(k)'] = np.mean(enstrophy_tk, axis = 0)*(4*np.pi*self.statistics['kshell']**2) / (self.statistics['dk']*self.statistics['nshell'])
pp_file['vel_max(t)'] = data_file['statistics/moments/velocity'][ii0:ii1+1, 9, 3]
pp_file['renergy(t)'] = data_file['statistics/moments/velocity'][ii0:ii1+1, 2, 3]/2
for k in ['t',
'energy(t)',
'energy(k)',
'enstrophy(t)',
'enstrophy(k)',
'R_ij(t)',
'vel_max(t)',
'renergy(t)']:
if k in pp_file.keys():
self.statistics[k] = pp_file[k].value
# sanity check --- Parseval theorem check
assert(np.max(np.abs(
self.statistics['renergy(t)'] -
self.statistics['energy(t)']) / self.statistics['energy(t)']) < 1e-5)
self.compute_time_averages()
return None return None
def compute_Reynolds_stress_invariants( def compute_Reynolds_stress_invariants(
self): self):
......
bfps/NavierStokes.py
View file @ 3c3a23a0
...@@ -584,79 +584,105 @@ class NavierStokes(_fluid_particle_base): ...@@ -584,79 +584,105 @@ class NavierStokes(_fluid_particle_base):
""" """
if len(list(self.statistics.keys())) > 0: if len(list(self.statistics.keys())) > 0:
return None return None
self.read_parameters() if not os.path.exists(self.get_data_file_name()):
with self.get_data_file() as data_file: if os.path.exists(self.get_cache_file_name()):
if 'moments' not in data_file['statistics'].keys(): self.read_parameters(fname = self.get_cache_file_name())
return None with self.get_cache_file() as pp_file:
iter0 = min((data_file['statistics/moments/velocity'].shape[0] * for k in ['t',
self.parameters['niter_stat']-1), 'energy(t)',
iter0) 'energy(k)',
if type(iter1) == type(None): 'enstrophy(t)',
iter1 = data_file['iteration'].value 'enstrophy(k)',
else:
iter1 = min(data_file['iteration'].value, iter1)
ii0 = iter0 // self.parameters['niter_stat']
ii1 = iter1 // self.parameters['niter_stat']
self.statistics['kshell'] = data_file['kspace/kshell'].value
self.statistics['nshell'] = data_file['kspace/nshell'].value
for kk in [-1, -2]:
if (self.statistics['kshell'][kk] == 0):
self.statistics['kshell'][kk] = np.nan
self.statistics['kM'] = data_file['kspace/kM'].value
self.statistics['dk'] = data_file['kspace/dk'].value
computation_needed = True
pp_file = h5py.File(self.get_postprocess_file_name(), 'a')
if 'ii0' in pp_file.keys():
computation_needed = not (ii0 == pp_file['ii0'].value and
ii1 == pp_file['ii1'].value)
if computation_needed:
for k in ['t', 'vel_max(t)', 'renergy(t)',
'energy(t)', 'enstrophy(t)',
'energy(k)', 'enstrophy(k)',
'energy(t, k)',
'enstrophy(t, k)',
'R_ij(t)', 'R_ij(t)',
'ii0', 'ii1', 'iter0', 'iter1']: 'vel_max(t)',
'renergy(t)']:
if k in pp_file.keys(): if k in pp_file.keys():
del pp_file[k] self.statistics[k] = pp_file[k].value
if computation_needed: self.statistics['kM'] = pp_file['kspace/kM'].value
pp_file['iter0'] = iter0 self.statistics['dk'] = pp_file['kspace/dk'].value
pp_file['iter1'] = iter1 self.statistics['kshell'] = pp_file['kspace/kshell'].value
pp_file['ii0'] = ii0 self.statistics['nshell'] = pp_file['kspace/nshell'].value
pp_file['ii1'] = ii1 else:
pp_file['t'] = (self.parameters['dt']* self.read_parameters()
self.parameters['niter_stat']* with self.get_data_file() as data_file:
(np.arange(ii0, ii1+1).astype(np.float))) if 'moments' not in data_file['statistics'].keys():
phi_ij = data_file['statistics/spectra/velocity_velocity'][ii0:ii1+1] return None
pp_file['R_ij(t)'] = np.sum(phi_ij, axis = 1) iter0 = min((data_file['statistics/moments/velocity'].shape[0] *
energy_tk = ( self.parameters['niter_stat']-1),
phi_ij[:, :, 0, 0] + iter0)
phi_ij[:, :, 1, 1] + if type(iter1) == type(None):
phi_ij[:, :, 2, 2])/2 iter1 = data_file['iteration'].value
pp_file['energy(t)'] = np.sum(energy_tk, axis = 1) else:
pp_file['energy(k)'] = np.mean(energy_tk, axis = 0)*(4*np.pi*self.statistics['kshell']**2) / (self.statistics['dk']*self.statistics['nshell']) iter1 = min(data_file['iteration'].value, iter1)
enstrophy_tk = ( ii0 = iter0 // self.parameters['niter_stat']
data_file['statistics/spectra/vorticity_vorticity'][ii0:ii1+1, :, 0, 0] + ii1 = iter1 // self.parameters['niter_stat']
data_file['statistics/spectra/vorticity_vorticity'][ii0:ii1+1, :, 1, 1] + self.statistics['kshell'] = data_file['kspace/kshell'].value
data_file['statistics/spectra/vorticity_vorticity'][ii0:ii1+1, :, 2, 2])/2 self.statistics['nshell'] = data_file['kspace/nshell'].value
pp_file['enstrophy(t)'] = np.sum(enstrophy_tk, axis = 1) for kk in [-1, -2]:
pp_file['enstrophy(k)'] = np.mean(enstrophy_tk, axis = 0)*(4*np.pi*self.statistics['kshell']**2) / (self.statistics['dk']*self.statistics['nshell']) if (self.statistics['kshell'][kk] == 0):
pp_file['vel_max(t)'] = data_file['statistics/moments/velocity'][ii0:ii1+1, 9, 3] self.statistics['kshell'][kk] = np.nan
pp_file['renergy(t)'] = data_file['statistics/moments/velocity'][ii0:ii1+1, 2, 3]/2 self.statistics['kM'] = data_file['kspace/kM'].value
if 'trS2_Q_R' in data_file['statistics/moments'].keys(): self.statistics['dk'] = data_file['kspace/dk'].value
pp_file['mean_trS2(t)'] = data_file['statistics/moments/trS2_Q_R'][:, 1, 0] computation_needed = True
for k in ['t', pp_file = h5py.File(self.get_postprocess_file_name(), 'a')
'energy(t)', if not ('parameters' in pp_file.keys()):
'energy(k)', data_file.copy('parameters', pp_file)
'enstrophy(t)', data_file.copy('kspace', pp_file)
'enstrophy(k)', if 'ii0' in pp_file.keys():
'R_ij(t)', computation_needed = not (ii0 == pp_file['ii0'].value and
'vel_max(t)', ii1 == pp_file['ii1'].value)
'renergy(t)', if computation_needed:
'mean_trS2(t)']: for k in ['t', 'vel_max(t)', 'renergy(t)',
if k in pp_file.keys(): 'energy(t)', 'enstrophy(t)',
self.statistics[k] = pp_file[k].value 'energy(k)', 'enstrophy(k)',
self.compute_time_averages() 'energy(t, k)',
'enstrophy(t, k)',
'R_ij(t)',
'ii0', 'ii1', 'iter0', 'iter1']:
if k in pp_file.keys():
del pp_file[k]
if computation_needed:
pp_file['iter0'] = iter0
pp_file['iter1'] = iter1
pp_file['ii0'] = ii0
pp_file['ii1'] = ii1
pp_file['t'] = (self.parameters['dt']*
self.parameters['niter_stat']*
(np.arange(ii0, ii1+1).astype(np.float)))
phi_ij = data_file['statistics/spectra/velocity_velocity'][ii0:ii1+1]
pp_file['R_ij(t)'] = np.sum(phi_ij, axis = 1)
energy_tk = (
phi_ij[:, :, 0, 0] +
phi_ij[:, :, 1, 1] +
phi_ij[:, :, 2, 2])/2
pp_file['energy(t)'] = np.sum(energy_tk, axis = 1)
pp_file['energy(k)'] = np.mean(energy_tk, axis = 0)*(4*np.pi*self.statistics['kshell']**2) / (self.statistics['dk']*self.statistics['nshell'])
enstrophy_tk = (
data_file['statistics/spectra/vorticity_vorticity'][ii0:ii1+1, :, 0, 0] +
data_file['statistics/spectra/vorticity_vorticity'][ii0:ii1+1, :, 1, 1] +
data_file['statistics/spectra/vorticity_vorticity'][ii0:ii1+1, :, 2, 2])/2
pp_file['enstrophy(t)'] = np.sum(enstrophy_tk, axis = 1)
pp_file['enstrophy(k)'] = np.mean(enstrophy_tk, axis = 0)*(4*np.pi*self.statistics['kshell']**2) / (self.statistics['dk']*self.statistics['nshell'])
pp_file['vel_max(t)'] = data_file['statistics/moments/velocity'][ii0:ii1+1, 9, 3]
pp_file['renergy(t)'] = data_file['statistics/moments/velocity'][ii0:ii1+1, 2, 3]/2
if 'trS2_Q_R' in data_file['statistics/moments'].keys():
pp_file['mean_trS2(t)'] = data_file['statistics/moments/trS2_Q_R'][:, 1, 0]
for k in ['t',
'energy(t)',
'energy(k)',
'enstrophy(t)',
'enstrophy(k)',
'R_ij(t)',
'vel_max(t)',
'renergy(t)',
'mean_trS2(t)']:
if k in pp_file.keys():
self.statistics[k] = pp_file[k].value
# sanity check --- Parseval theorem check
assert(np.max(np.abs(
self.statistics['renergy(t)'] -
self.statistics['energy(t)']) / self.statistics['energy(t)']) < 1e-5)
self.compute_time_averages()
return None return None
def compute_Reynolds_stress_invariants( def compute_Reynolds_stress_invariants(
self): self):
......
bfps/_base.py
View file @ 3c3a23a0
...@@ -224,8 +224,10 @@ class _base(object): ...@@ -224,8 +224,10 @@ class _base(object):
ofile[group + '/' + k][...] = parameters[k] ofile[group + '/' + k][...] = parameters[k]
ofile.close() ofile.close()
return None return None
def read_parameters(self): def read_parameters(self, fname = None):
with h5py.File(os.path.join(self.work_dir, self.simname + '.h5'), 'r') as data_file: if type(fname) == type(None):
fname = os.path.join(self.work_dir, self.simname + '.h5')
with h5py.File(fname, 'r') as data_file:
for k in data_file['parameters'].keys(): for k in data_file['parameters'].keys():
if k in self.parameters.keys(): if k in self.parameters.keys():
if type(self.parameters[k]) in [int, str, float]: if type(self.parameters[k]) in [int, str, float]:
......
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