# Try to average with values at previous steps
n_hist = 40
n_step = 150
n_hist = min(n_hist, n_step)
w = np.exp(-np.linspace(0.0, 5.0, n_hist))
w = w / np.sum(w)
print("Performing time average")
# smooth_kin_ener *= w[0]
kin_ener *= w[0]
for k in range(n_hist - 1):
vel_x, vel_z = dm.read_velocity_file(folder_name + '/' + base_name +
'{:05d}'.format(n_step - k) + '.dat')
tmp = 0.5 * np.multiply(
rho,
np.multiply(vel_x, vel_x) + np.multiply(vel_z, vel_z))
# tmp = dm.convolute(kin_ener, smoother)
kin_ener += w[k + 1] * tmp
p = 2.0
# r_mol = 0.39876
r_mol = p * 0.09584
smoother = dm.smooth_kernel(r_mol, hx, hz)
smooth_kin_ener = dm.convolute(kin_ener, smoother)
plt.pcolormesh(X, Z, smooth_kin_ener, cmap=cm.jet)
plt.colorbar()
plt.axis('scaled')
plt.xlim([0, Lx])
plt.ylim([0, Lz])
plt.show()
# CREATING MESHGRID
print("Creating meshgrid")
vel_x, vel_z = dm.read_velocity_file(folder_name+'/'+file_root+'00001.dat')
Nx = vel_x.shape[0]
Nz = vel_x.shape[1]
hx = Lx/Nx
hz = Lz/Nz
x = hx*np.arange(0.0,Nx,1.0, dtype=float)
z = hz*np.arange(0.0,Nz,1.0, dtype=float)
X, Z = np.meshgrid(x, z, sparse=False, indexing='ij')
# INITIALIZING SMOOTHING KERNEL
p = 2.0
r_mol = p*0.09584
smoother = dm.smooth_kernel(r_mol, hx, hz)
# Tecnically unnecessary ...
smoother_den = dm.smooth_kernel(r_mol/p, hx, hz)
# TIME AVERAGING
n_aver = 25
n_init = 1
n_fin = 1000
dt = 8.0
# dt = 25.0
# dt = 0.5*25.0
n_dump = 10
print("Producing movie of the kinetic energy")
FFMpegWriter = manimation.writers['ffmpeg']
metadata = dict( title='Meniscus kinetic energy Ca=1.0', \
nx = Nx
nz = Nz
# CHANGE IN MAIN DENSMAP.PY !!!
x = hx * np.arange(0.0, nx, 1.0, dtype=float) + 0.5 * hx
z = hz * np.arange(0.0, nz, 1.0, dtype=float) + 0.5 * hz
X, Z = np.meshgrid(x, z, sparse=False, indexing='ij')
# PARAMETERS TO TUNE
r_h2o = 0.09584
# r_pro = 0.17
# r_half = 0.5 * ( r_h2o + r_pro )
# alpha = 3.0
alpha = 1.0
smoother = dm.smooth_kernel(alpha * r_h2o, hx, hz)
# smoother = dm.smooth_kernel(alpha*r_half, hz, hz)
smooth_density_array = dm.convolute(density_array, smoother)
# PARAMETERS TO TUNE
# aka 'max_vapour_density'
delta_th = 2.0
bulk_density = dm.detect_bulk_density(smooth_density_array, delta_th)
intf_contour = dm.detect_contour(smooth_density_array, 0.5 * bulk_density, hx,
hz)
density_indicator = dm.density_indicator(smooth_density_array,
0.5 * bulk_density)
# Detecting dimensions
dummy_file = '20nm/flow_sit_f/flow_00125.dat'
density_array = dm.read_density_file(dummy_file, bin='y')
Nx = density_array.shape[0]
Nz = density_array.shape[1]
hx = Lx/Nx
hz = Lz/Nz
nx = Nx
nz = Nz
x = hx*np.arange(0.0, nx, 1.0, dtype=float)
z = hz*np.arange(0.0, nz, 1.0, dtype=float)
X, Z = np.meshgrid(x, z, sparse=False, indexing='ij')
# Smoothing kernel once for all
smoother = dm.smooth_kernel(r_h2o, hx, hz)
master_folder = '20nm/'
folders_sit = 'flow_sit'
folders_rec = 'flow_rec'
files_sit = []
files_rec = []
# files_sit.append(master_folder+'flow_sit_f/flow_00125.dat')
# files_rec.append(master_folder+'flow_rec_f/flow_00375.dat')
files_sit.append(master_folder+'flow_sit_f/')
files_rec.append(master_folder+'flow_rec_f/')
for k in [2,1] :
files_sit.append(master_folder+folders_sit+'_h'+str(k)+'/')
files_rec.append(master_folder+folders_rec+'_h'+str(k)+'/')
