research/conditional-analysis-dns
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added comments to fluctuation script

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This commit is contained in:
ignis 2021-04-02 14:47:51 +09:00
parent 6032ce5452
commit b43035918b
1 changed files with 10 additions and 5 deletions
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15
fluctuation_product_u_ddxc.py
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@ -15,14 +15,16 @@ parser.add_argument("-c", "--case", help="target case name", required=True)
args = parser.parse_args() args = parser.parse_args()
params = vars(args) params = vars(args)
# Case Parameters
cases = dnstool.case_library() cases = dnstool.case_library()
casename = params["case"] casename = params["case"]
case = cases[casename] case = cases[casename]
nx, ny, nz = case.shape nx, ny, nz = case.shape
# Initialize Compact Scheme object
cs = CompactScheme(nx, ny, nz, False, True, True, 4, 2, 2) cs = CompactScheme(nx, ny, nz, False, True, True, 4, 2, 2)
# Open Files
cfile = "c.dat" cfile = "c.dat"
ufile = "u.dat" ufile = "u.dat"
ddxcfile = "ddxc.dat" ddxcfile = "ddxc.dat"
@ -33,29 +35,32 @@ u = np.memmap(ufile, mode="r", dtype=np.double).reshape((nx,-1,ny,nz))
ddxc = np.memmap(ddxcfile, mode="r", dtype=np.double).reshape((nx,-1,ny,nz)) ddxc = np.memmap(ddxcfile, mode="r", dtype=np.double).reshape((nx,-1,ny,nz))
storage = np.memmap(resfile, mode='w+', dtype=np.double, shape=c.shape) storage = np.memmap(resfile, mode='w+', dtype=np.double, shape=c.shape)
# Read pre-calculated Conditional Means
batch_cmean_ddxc_c = np.load('./cmean_ddxc_given_c.npz') batch_cmean_ddxc_c = np.load('./cmean_ddxc_given_c.npz')
print(*batch_cmean_ddxc_c.keys())
cstar_ddxc_c = batch_cmean_ddxc_c['cstar'] cstar_ddxc_c = batch_cmean_ddxc_c['cstar']
cmean_ddxc_c = batch_cmean_ddxc_c['cmean'] cmean_ddxc_c = batch_cmean_ddxc_c['cmean']
batch_cmean_u_c = np.load('./cmean_u_given_c.npz') batch_cmean_u_c = np.load('./cmean_u_given_c.npz')
cstar_u_c = batch_cmean_u_c['cstar'] cstar_u_c = batch_cmean_u_c['cstar']
cmean_u_c = batch_cmean_u_c['cmean'] cmean_u_c = batch_cmean_u_c['cmean']
# Compute product of conditional fluctuations at each x index
for xidx in range(nx): for xidx in range(nx):
print (datetime.datetime.now(), xidx) print (datetime.datetime.now(), xidx) # Print wall time and x index
# Calculate Conditional u Fluctuation
u_mean_at_x = np.interp (c[xidx], cstar_u_c[xidx], cmean_u_c[xidx]) u_mean_at_x = np.interp (c[xidx], cstar_u_c[xidx], cmean_u_c[xidx])
u_at_x = u[xidx] u_at_x = u[xidx]
u_flux_at_x = u_at_x - u_mean_at_x u_flux_at_x = u_at_x - u_mean_at_x
# Calculate Conditional d/dx(c) Fluctuation
ddxc_mean_at_x = np.interp (c[xidx], cstar_ddxc_c[xidx], cmean_ddxc_c[xidx]) ddxc_mean_at_x = np.interp (c[xidx], cstar_ddxc_c[xidx], cmean_ddxc_c[xidx])
ddxc_at_x = u[xidx] ddxc_at_x = u[xidx]
ddxc_flux_at_x = ddxc_at_x - ddxc_mean_at_x ddxc_flux_at_x = ddxc_at_x - ddxc_mean_at_x
# Save u' * (d/dx(c))'
storage[xidx,:,:,:] = u_flux_at_x * ddxc_flux_at_x storage[xidx,:,:,:] = u_flux_at_x * ddxc_flux_at_x
# Flush all write to disk
storage.flush() storage.flush()