def main():
## parameters
dir_in = '/usr/local/home/yl8bc/duannas/'
filename_in_grid = 'nozzle_grid_test.h5'
filename_outlet = 'Test_Incompact3d/HLB_Nozzle/AcousticZgrid_k500.dat'
## output
dir_out = '../../data/nozzle/'
filename_out = 'nozzle_grid_test_try1'
## BELOW NO PARAMS ARE DEFINED
tstart = time.time()
## Read grid(uniform)
grid, vars_name = IO_util.read_hdf5(dir_out + filename_in_grid)
u = grid['x']
v = grid['z']
dset, zones_name, vars_name = util_tp.read_tp(dir_in + filename_outlet)
data_outlet, nodemap = util_tp.read_tp_zone(dset, 'SubZone', vars_name)
## intersections
nx, ny = u.shape
v[-1, :] = v[-1, -1] * normalized_flipped(data_outlet['z'], ny)
u, v = gridgen.cm_gridgen.gridgen_checker(u, v)
print 'Checker done. Elapsed time: %f' % (time.time() - tstart)
## Write grid
grid['x'] = u
grid['z'] = v
IO_util.write_hdf5(dir_out + filename_out, grid)
def main():
dir_in = '/usr/local/home/yl8bc/duannas/yl8bc/data/cone/run3_polar/REST/'
filename_in_grid = 'grid.h5'
stencilsize = 6
bc = [0, 0, 1, 1, 0, 1]
## end params
## read grid
grid_in = IO_util.read_hdf5(dir_in + filename_in_grid)
x = grid_in['x']
y = grid_in['y']
z = grid_in['z']
ny, nx, nz = x.shape
## extend grid
x, y, z = util_f.mod_metrics.extend_grid(x, y, z, stencilsize)
## compute grid der
grid_derivative = util_f.mod_metrics.compute_grid_derivative(
x, y, z, ny, nx, nz, stencilsize, bc)
grid_derivative = grid_derivative[:, (1, 0, 2), :, :, :]
## condition gridder
grid_derivative[(0, 2), 1, :, :, :] = 0.
grid_derivative[1, (0, 2), :, :, :] = 0.
grid_derivative = np.repeat(
grid_derivative[:, :,
stencilsize + ny / 2:stencilsize + ny / 2 + 1, :, :],
grid_derivative.shape[2],
axis=2)
grid_derivative[:, 1, :2, :, :] = 0.
grid_derivative[:, 1, -2:, :, :] = 0.
## out
grid_out = {'dxdi':grid_derivative[0,0,:,:,:], \
'dxdj':grid_derivative[0,1,:,:,:], \
'dxdk':grid_derivative[0,2,:,:,:], \
'dydi':grid_derivative[1,0,:,:,:], \
'dydj':grid_derivative[1,1,:,:,:], \
'dydk':grid_derivative[1,2,:,:,:], \
'dzdi':grid_derivative[2,0,:,:,:], \
'dzdj':grid_derivative[2,1,:,:,:], \
'dzdk':grid_derivative[2,2,:,:,:]}
IO_util.write_hdf5(dir_in + 'gridDerivative_f.h5', grid_out)
#x[:] = stretch_tanh(x, 2., type='left')
return
if __name__ == '__main__':
###---parameters----###
# Files required to start interpolation
# "filename_fluent": old file that contains Fluent data in .dat format
shape = (32, 100, 140)
dir_in = '../../data/grids/'
filename_in = 'cone_grid_flat_part2.h5'
# Files generated by the program
dir_out = '/usr/local/home/yl8bc/duannas/yl8bc/data/cone/run3_polar/REST/'
filename_out = 'grid'
## stack those grids
grid_in, names = IO_util.read_hdf5(dir_in+filename_in)
##
x0 = grid_in['x'][0,0]
xm = grid_in['x'][-1,0]
dist_bot = grid_in['x'][:,0].copy()
distribution = np.linspace(x0, xm, grid_in['x'].shape[0])
#distribution = grid_in['x'][:,0]
#stretch_func(distribution)
## interp by distribution
for key,var in grid_in.iteritems():
for n in range(0, grid_in['x'].shape[1]):
grid_in[key][:,n] = np.interp( distribution, dist_bot, var[:,n])
## intep to new shape
from util import IO_util, util_interp
import numpy as np
if __name__ == '__main__':
##---params --##
shape = (512, 128)
dir_in = './InitBody/'
filename_delta = 'BL.dat'
##
dir_out = '../../data/grids/'
filename_in_grid = 'cone_grid_parabolic_part2.h5'
filename_out = 'cone_para_part2'
##---params end----##
## Input
grid_in, names = IO_util.read_hdf5(dir_out+filename_in_grid)
delta = np.loadtxt(dir_in+filename_delta)
## assess delta
dist = np.linspace( grid_in['z'][0,0], grid_in['z'][0,-1], shape[1] )
for i in range(shape[0]):
grid_in['z'][i,:] = np.interp(
## output
IO_util.write_hdf5(dir_out+filename_out, grid_new)
start_over = True
if start_over:
#IC
idx_left, u[0,:],v[0,:] = gridgen.cm_gridgen.init_index(choice_left, shape[1], 1)
idx_right, u[-1,:],v[-1,:] = gridgen.cm_gridgen.init_index(choice_right, shape[1], 1)
idx_bottom, u[:,0],v[:,0] = gridgen.cm_gridgen.init_index(choice_bottom, shape[0], 1)
idx_top, u[:,-1],v[:,-1] = gridgen.cm_gridgen.init_index(choice_top, shape[0], 1)
u,v = gridgen.cm_gridgen.init_tfi(u, v)
grid = {'x':u, 'y':v}
IO_util.write_hdf5(dir_out+filename_output_hdf5, grid)
else:
idx_left = np.load('idx_left.npy')
idx_right = np.load('idx_right.npy')
idx_bottom = np.load('idx_bottom.npy')
idx_top = np.load('idx_top.npy')
grid, vars_name = IO_util.read_hdf5(dir_out+filename_output_hdf5+'.h5')
u = grid['x']
v = grid['y']
iloop = True
while iloop:
u,v,idx_left,idx_right,idx_bottom,idx_top,iconverge = gridgen.cm_gridgen.compute_grid(u,v,idx_left,idx_right,
idx_bottom,idx_top,
choice_left,choice_right,
choice_bottom,choice_top, 1e-8, .0, 100,
np.array([0,0,0,0],dtype=int),
np.array([0,0,0,0],dtype=int))
#iconverge = 1
np.save('idx_left', idx_left)
np.save('idx_right', idx_right)
from scipy import interpolate
if __name__ == '__main__':
##---params --##
shape = (2, 100, 140)
dir_in_old = '/usr/local/home/yl8bc/duannas/yl8bc/data/cone/run6_release/REST/'#'./InitBody/'
filename_grid_old = 'grid.h5'
filename_data_old = 'flowdata_00200000.h5'
dir_in_new = '/usr/local/home/yl8bc/duannas/yl8bc/data/cone/'
filename_grid_new = 'cone_grid_flat_part2.h5'
## out
dir_out = '/usr/local/home/yl8bc/duannas/yl8bc/data/cone/run7_2d/REST/'
#filename_out =
##---params end----##
## read data/grid
grid_old = IO_util.read_hdf5(dir_in_old+filename_grid_old)
data_old = IO_util.read_hdf5(dir_in_old+filename_data_old)
grid_new = IO_util.read_hdf5(dir_in_new+filename_grid_new)
grid_old = {'x':grid_old['x'], 'z':grid_old['z']}
## get IC
data_new = util_interp.structured_interp(grid_old, data_old, grid_new, robust=True, method='linear')
data_new.update(grid_new)
## distribution
x0 = data_new['x'][0,0]
xm = data_new['x'][-1,0]
dist_old = data_new['x'][:,0].copy()
dist_new = np.linspace(x0, xm, data_new['x'].shape[0])
def main():
###params###
mrange = range(1, 99, 1)
mmax = len(mrange)
nmax = 75
dir_in = '/usr/local/home/yl8bc/data_local/cone/run3_Col_native/REST/'
filename_in_grid = 'grid.h5'
filename_in_data = 'flowdata_00024000.h5'
## files output:
dir_out = '/usr/local/home/yl8bc/yl8bc/data/cone/FLUENT/'
filename_out_profiles = 'profiles_dns_GCL'
filename_out_integrals = 'integrals_dns_GCL'
##--below no param is defined--##
tstart = time.time()
keys_change = {
'X': 'x',
'Y': 'z',
'X Velocity': 'uave',
'Y Velocity': 'wave',
'Pressure': 'pave',
'Density': 'rhoave',
'Temperature': 'tave'
}
grid_keys = ['x', 'z']
## read grid and data
grid = IO_util.read_hdf5(dir_in + filename_in_grid)
data = IO_util.read_hdf5(dir_in + filename_in_data)
print('Data read. Time elapsed: %f secs' % (time.time() - tstart))
## slice
data.update(grid)
data.pop('time')
sdata = util_data.structured_data(data, ['y', 'x', 'z'])
sdata = sdata.slice_data(0, 0)
## wall normal profiles
profiles_out = sdata.get_wall_normal_profiles('right', mrange, nmax)
## integral
delta = np.zeros(mmax)
delta_star = np.zeros(mmax)
theta = np.zeros(mmax)
for n in range(mmax):
wd = profiles_out['wd'][n, :]
x = profiles_out['x'][n, :]
z = profiles_out['z'][n, :]
u = profiles_out['u'][n, :]
w = profiles_out['w'][n, :]
rho = util_flow.get_rho(profiles_out['p'][n, :],
profiles_out['T'][n, :])
up = util_flow.get_up_2d(u, w, x, z)
delta[n] = util_flow.get_delta(wd, up)
delta_star[n] = util_flow.get_delta_star(wd, up, rho)
theta[n] = util_flow.get_theta(wd, up, rho)
int_out = {}
int_out['x'] = profiles_out['x'][:, 0]
int_out['z'] = profiles_out['z'][:, 0]
int_out['delta'] = delta
int_out['delta*'] = delta_star
int_out['theta'] = theta
## out
IO_util.write_hdf5(dir_out + filename_out_profiles, profiles_out)
IO_util.write_hdf5(dir_out + filename_out_integrals, int_out)
def main():
###---parameters----###
shape0 = (32, 256) #(i,k)(this is only used in orthogonal gridgen)
shape1 = (96, 256)
shape_out = (16, 100, 140) #(j,i,k)(this is FINAL SHAPE)
xl = -30.e-4 # (xl,yl) is top-left point of whole domain
yl = .12
# skip options(skip gridgen)
skip_shock = False
skip_freestream = False
# Files required to start interpolation
dir_in = './InitBody/'
filename_in_body = 'ConeSurfGeometry.dat'
filename_in_shock = 'ConeShockGeometry.dat'
dir_IC = '/usr/local/home/yl8bc/yl8bc/data/cone/run7_Col_2d/REST/'
filename_in_grid_IC = 'grid.h5'
filename_in_data_IC = 'flowdata_00050000.h5'
# Files generated by the program
dir_out = '/usr/local/home/yl8bc/duannas/yl8bc/data/cone/run11_GCL/REST/'
filename_out_shock = 'cone_grid_flat_shock.h5'
filename_out_freestream = 'cone_grid_flat_freestream.h5'
filename_out_full = 'grid.h5'
filename_out_IC = 'flowdata_00000000.h5'
filename_out_gd = 'gridDerivative_f.h5'
filename_out_mi = 'metric_identities_f.h5'
###---no parameters below---#
if skip_shock:
grid_tmp = IO_util.read_hdf5(dir_out + filename_out_shock)
u_s = grid_tmp['x']
v_s = grid_tmp['z']
else:
## define wall
body = np.loadtxt(dir_in + filename_in_body, skiprows=2)
#poly = np.polyfit(body[21:35,0], body[21:35,1], 10)
#body[21:35,1] = np.polyval(poly, body[21:35,0])
## define shock
shock = np.loadtxt(dir_in + filename_in_shock, skiprows=2)
## compute shock part
u_s, v_s = flat_shock.gridgen(shape0, body, shock,
dir_out + filename_out_shock)
## compute full grid
if skip_freestream:
grid_tmp = IO_util.read_hdf5(dir_out + filename_out_freestream)
u_f = grid_tmp['x']
v_f = grid_tmp['z']
else:
u_f, v_f = flat_freestream.gridgen(shape1[0], xl, yl, u_s, v_s,
dir_out + filename_out_freestream)
## combine grids
u, v = flat_combined.gridgen(u_f, v_f, u_s, v_s, shape1[0] - 8, shape1[0])
grid_out = {'x': u, 'z': v}
## IC
grid_IC = IO_util.read_hdf5(dir_IC + filename_in_grid_IC)
data_IC = IO_util.read_hdf5(dir_IC + filename_in_data_IC)
data_out = IC_by_interp.ICgen(grid_IC, data_IC, grid_out)
## reshape in 2d
grid_out = reshape.reshape_by_index(shape_out[1:], grid_out)
data_out = reshape.reshape_by_index(shape_out[1:], data_out)
## redistribute
slc_axis = np.s_[:, 0]
dist_old = grid_out['x'][slc_axis].copy()
pdist_new = dist.pdist(dist_old[0], dist_old[-1], dist_old.shape[0])
dist_new = pdist_new.tanh(2.5, type='right')
grid_out = reshape.redistribute(dist_new, dist_old, grid_out)
data_out = reshape.redistribute(dist_new, dist_old, data_out)
## expand
grid_out = reshape.expand_spanwise(shape_out[0],
grid_out,
dim_new=1,
span_name='y')
data_out = reshape.expand_spanwise(shape_out[0], data_out, dim_new=1)
## polar treatment
grid_out['z'] = shift.Colonius_shift(grid_out['z'], 2)
## regulate
slc_body = np.s_[-1, :, :]
data_out['v'][:] = 0.
data_out['T'][slc_body] = 400.
## inlet
data_inlet = {
key: data_out[key][0:1, :, :]
for key in ['T', 'p', 'u', 'v', 'w']
}
# RANS
data_inlet['p'][:] = 6878.1
data_inlet['u'][:] = 1508.7
data_inlet['v'][:] = 0.
data_inlet['w'][:] = 0.
data_inlet['T'][:] = 202.08
## ducros
# ducros = sensor.ducros(data_out['u'], data_out['v'], data_out['w'], grid_out, \
# u_inf=1508., delta_in = 0.125)
# dist_old = grid_out['x'][:,0,0].copy()
# dist_new = dist.ptrans(dist_old).expo(.5, 50)
# grid_out = reshape.redistribute(dist_new, dist_old, grid_out, dim=0)
# data_out = reshape.redistribute(dist_new, dist_old, data_out, dim=0)
## metrICs
grid_tmp, grid_derivative = m_gd.analytically_2d(grid_out,
bc_g=[0, 0, 0, 0, 0, 0],
bc_gd=[0, 0, 1, 1, 0, 1])
#grid_tmp, grid_derivative = m_gd.native(grid_out, bc_g=[0,0,0,0,0,0], bc_gd=[1,1, 0,0, 0,1])
mm, Ji = m_mm.compute_mesh_metrics(grid_derivative)
I = m_mm.compute_metric_identities(mm, Ji)
## transpose
grid_out = {
key: np.swapaxes(value, 0, 1)
for key, value in grid_out.items()
}
data_out = {
key: np.swapaxes(value, 0, 1)
for key, value in data_out.items()
}
data_inlet = {
key: np.swapaxes(value, 0, 1)
for key, value in data_inlet.items()
}
grid_derivative = np.swapaxes(grid_derivative, 2, 3)
mm = np.swapaxes(mm, 2, 3)
I = np.swapaxes(I, 1, 2)
ducros = np.swapaxes(ducros, 0, 1)
## time
IC_by_interp.add_time(data_out)
## out
IO_util.write_hdf5(dir_out + filename_out_full, grid_out)
IO_util.write_hdf5(dir_out + filename_out_IC, data_out)
grid_derivative = m_gd.pack_grid_derivative(grid_derivative)
IO_util.write_hdf5(dir_out + filename_out_gd, grid_derivative)
I = m_mm.pack_metric_identities(I)
IO_util.write_hdf5(dir_out + filename_out_mi, I)
IO_util.write_hdf5(dir_out + 'inlet', data_inlet)
data_out.update(grid_out)
data_out.update(grid_derivative)
data_out.update({'ducros': ducros})
IO_util.write_hdf5(dir_out + 'vis', data_out)
from util import util_cyl, IO_util
## params
dir_in = '../' # directory in
filename_in_grid = 'grid.h5'
filename_in_data = 'flowdata_00000000.h5'
dir_out = './' # directory out
filename_out_grid = 'grid.h5'
filename_out_data = 'flowdata_00000000.h5'
if_cart_to_cyl = True # if converting cartesian to cylidrical? set True/False
## end params
## read files
grid = IO_util.read_hdf5(dir_in + filename_in_grid)
data = IO_util.read_hdf5(dir_in + filename_in_data)
## convert
if if_cart_to_cyl:
x, y, z = util_cyl.coord_cart_to_cyl(grid['x'], grid['y'], grid['z'])
u, v, w = util_cyl.vel_cart_to_cyl(data['u'], data['v'], data['w'], x, y,
z)
else:
x, y, z = util_cyl.coord_cyl_to_cart(grid['x'], grid['y'], grid['z'])
u, v, w = util_cyl.vel_cyl_to_cart(data['u'], data['v'], data['w'],
grid['x'], grid['y'], grid['z'])
##out
grid['x'] = x
grid['y'] = y
grid['z'] = z
data['u'] = u
def main():
'''
Note:
The boundary thickness calculation relies on a clear profile that converges. current code cannot intelligently tell which wall normal profile is "perfect". Thus boundary layer thickness might yield to poor quality if the profile is badly extracted.
'''
###params##
mrange = range(100, 300, 100) ## along the wall at which index to extract
nmax = 75 ## along wall normal direction how many nodes to extract
fluid_type = 'nitrogen'
dir_in = './'
filename_in_data = 'structured_tec.plt'
is_in_tecplot = True # Input is of tecplot format ??? False to be HDF5, True to be Tecplot format
## files output---------------------------------!
dir_out = './'
filename_out_profiles = 'profiles'
filename_out_integrals = 'integrals'
## names of variables---------------------------!
name_x = 'X' # name of these variables!
name_z = 'Y'
name_u = 'u'
name_w = 'w'
name_p = 'p'
name_T = 'T'
## --------------------------------------
keys_change = {name_x: 'x', name_z: 'z'}
grid_keys = ['x', 'z']
## end params
tstart = time.time()
## read grid and data
if is_in_tecplot:
data = util_tp.read_tp(dir_in + filename_in_data, order='F')
data = next(iter(data.values()))
else:
data = IO_util.read_hdf5(dir_in + filename_in_data)
print('Data read. Time elapsed: %f secs' % (time.time() - tstart))
## slice
data = util_data.change_dict(data, keys_change=keys_change)
sdata = util_data.structured_data(data, grid_keys)
## wall normal profiles
profiles_out = sdata.get_wall_normal_profiles('top', mrange, nmax)
## integral
mmax = len(mrange)
delta = np.zeros(mmax)
delta_star = np.zeros(mmax)
theta = np.zeros(mmax)
tauw = np.zeros(mmax)
utau = np.zeros(mmax)
ztau = np.zeros(mmax)
for n in range(mmax):
wd = profiles_out['wd'][n, :]
x = profiles_out['x'][n, :]
z = profiles_out['z'][n, :]
u = profiles_out[name_u][n, :]
w = profiles_out[name_w][n, :]
p = profiles_out[name_p][n, :]
T = profiles_out[name_T][n, :]
rho = util_flow.get_rho(profiles_out[name_p][n, :],
profiles_out[name_T][n, :],
fluid_type=fluid_type)
up = util_flow.get_up_2d(u, w, x, z)
delta[n] = util_flow.get_delta(wd, up)
delta_star[n] = util_flow.get_delta_star(wd, up, rho)
theta[n] = util_flow.get_theta(wd, up, rho)
mu = util_flow.get_mu_Sutherland(T[0], fluid_type=fluid_type)
tauw[n] = np.abs(mu * up[1] / wd[1])
utau[n] = np.sqrt(tauw[n] / rho[0])
ztau[n] = mu / rho[0] / utau[n]
int_out = {}
int_out['x'] = profiles_out['x'][:, 0]
int_out['z'] = profiles_out['z'][:, 0]
int_out['delta'] = delta
int_out['delta*'] = delta_star
int_out['theta'] = theta
int_out['tauw'] = tauw
int_out['utau'] = utau
int_out['ztau'] = ztau
## out
IO_util.write_hdf5(dir_out + filename_out_profiles, profiles_out)
IO_util.write_hdf5(dir_out + filename_out_integrals, int_out)
IO_util.write_ascii_point(dir_out + filename_out_profiles, profiles_out)
IO_util.write_ascii_point(dir_out + filename_out_integrals, int_out)
def main():
###---parameters----###
shape0 = (32, 256) #(i,k)
shape1 = (96, 256)
shape_out = (16, 100, 140) #(j,i,k)
xl = -30.e-4
yl = .12
# skip options
skip_shock = True #False
skip_freestream = True #False
# Files required to start interpolation
dir_in = './InitBody/'
filename_in_body = 'ConeSurfGeometry.dat'
filename_in_shock = 'ConeShockGeometry.dat'
dir_IC = '/usr/local/home/yl8bc/yl8bc/data/cone/run9/REST/'
filename_in_grid_IC = 'grid.h5'
filename_in_data_IC = 'flowdata_00100000.h5'
# Files generated by the program
dir_out = '/usr/local/home/yl8bc/duannas/yl8bc/data/cone/run7_2d_Lele/REST/'
filename_out_shock = 'cone_grid_flat_shock.h5'
filename_out_freestream = 'cone_grid_flat_freestream.h5'
filename_out_full = 'grid.h5'
filename_out_IC = 'flowdata_00000000.h5'
filename_out_gd = 'gridDerivative_f.h5'
###---no parameters below---#
if skip_shock:
grid_tmp = IO_util.read_hdf5(dir_out + filename_out_shock)
u_s = grid_tmp['x']
v_s = grid_tmp['z']
else:
## define wall
body = np.loadtxt(dir_in + filename_in_body, skiprows=2)
## define shock
shock = np.loadtxt(dir_in + filename_in_shock, skiprows=2)
## compute shock part
u_s, v_s = flat_shock.gridgen(shape0, body, shock,
dir_out + filename_out_shock)
## compute full grid
if skip_freestream:
grid_tmp = IO_util.read_hdf5(dir_out + filename_out_freestream)
u_f = grid_tmp['x']
v_f = grid_tmp['z']
else:
u_f, v_f = flat_freestream.gridgen(shape1[0], xl, yl, u_s, v_s,
dir_out + filename_out_freestream)
## combine grids
u, v = flat_combined.gridgen(u_f, v_f, u_s, v_s, shape1[0] - 8, shape1[0])
grid_out = {'x': u, 'z': v}
## IC
grid_IC = IO_util.read_hdf5(dir_IC + filename_in_grid_IC)
data_IC = IO_util.read_hdf5(dir_IC + filename_in_data_IC)
data_out = IC_by_interp.ICgen(grid_IC, data_IC, grid_out)
## reshape
grid_out = reshape.reshape_by_index(shape_out[1:], grid_out)
data_out = reshape.reshape_by_index(shape_out[1:], data_out)
## redistribute
slc_axis = np.s_[:, 0]
dist_old = grid_out['x'][slc_axis].copy()
pdist_new = dist.pdist(dist_old[0], dist_old[-1], dist_old.shape[0])
dist_new = pdist_new.tanh(1.5, type='right')
grid_out = reshape.redistribute(dist_new, dist_old, grid_out)
data_out = reshape.redistribute(dist_new, dist_old, data_out)
## expand
grid_out = reshape.expand_spanwise(shape_out[0], grid_out, span_name='y')
data_out = reshape.expand_spanwise(shape_out[0], data_out)
## polar treatment
#grid_out['z'] = shift.Colonius_shift(grid_out['z'], 2)
## regulate
slc_body = np.s_[:, -1, :]
data_out['v'][:] = 0.
data_out['T'][slc_body] = 400.
## time
IC_by_interp.add_time(data_out)
## inlet
data_inlet = {
key: data_out[key][:, 0:1, :]
for key in ['T', 'p', 'u', 'v', 'w']
}
# RANS
data_inlet['p'][:] = 6878.1
data_inlet['u'][:] = 1508.7 #570.
data_inlet['v'][:] = 0.
data_inlet['w'][:] = 0.
data_inlet['T'][:] = 202.08
## metrICs
grid_tmp, grid_derivative = m_gd.analytically_2d(grid_out,
bc_g=[0, 0, 0, 0, 1, 0],
bc_gd=[0, 0, 1, 1, 0, 1])
## out
IO_util.write_hdf5(dir_out + filename_out_full, grid_out)
IO_util.write_hdf5(dir_out + 'ghost', grid_tmp)
IO_util.write_hdf5(dir_out + filename_out_IC, data_out)
grid_derivative = m_gd.pack_grid_derivative(grid_derivative)
IO_util.write_hdf5(dir_out + filename_out_gd, grid_derivative)
IO_util.write_hdf5(dir_out + 'inlet', data_inlet)
data_out.update(grid_tmp)
data_out.update(grid_derivative)
IO_util.write_hdf5(dir_out + 'vis', data_out)
['pave', 'p2', 'tauw', 'delta'],
order='F')
tauw = 0.5 * (stat_in['tauw'][:, 0] + stat_in['tauw'][:, -1])
delta = 0.5 * (stat_in['delta'][:, 0] + stat_in['delta'][:, -1])
## calculation
stat_out = {}
stat_out['prms'] = np.sqrt(np.abs(stat_in['p2'] - stat_in['pave']**2))
prms_tauw_vs_x = stat_out['prms'][:, index_k_vs_x] / tauw
#
prms_tauw_vs_z = np.mean(stat_out['prms'][ibe:ien, :] /
tauw[ibe:ien, np.newaxis],
axis=0)
## read grid atg walls
grid, gridname = IO_util.read_hdf5(dir_in + filename_grid,
vars_name=['x', 'z'])
x = grid['x'][:, 0]
z = np.mean(grid['z'][ibe:ien, :] / delta[ibe:ien, np.newaxis], axis=0)
## plots
fig, axe = plt.subplots(1, 1)
axe.plot(x, prms_tauw_vs_x)
util_plot.labels(axe, 'x/m', r'$p_{rms}/\tau_{w}$')
util_plot.title(axe, r'$p_{rms}/\tau_{w}$ along k = %d' % index_k_vs_x)
fig.savefig(dir_out + 'prms_tauw_vs_x.png')
fig, axe = plt.subplots(1, 1)
axe.plot(z, prms_tauw_vs_z)
util_plot.labels(axe, r'$z/\delta$', r'$p_{rms}/\tau_{w}$')
util_plot.title(axe,
r'$p_{rms}/\tau_{w}$ in window i = %d~%d' % (ibe, ien))
fig.savefig(dir_out + 'prms_tauw_vs_z.png')