alpha1 = 1e-3
alpha2 = 1e-4
alpha3 = 1e-4
beta = 2.5e-3
# Compute velocity and source.
vel, k, res, fun, converged = cmscr1d_img(imgp, alpha0, alpha1, alpha2, alpha3,
beta, 'mesh')
# Plot and save figures.
path = os.path.join(*[resultpath, 'cmscr1d'])
if not os.path.exists(resultpath):
os.makedirs(resultpath)
ph.saveimage(path, name, imgp)
ph.savevelocity(path, name, img, vel)
ph.savesource(path, name, k)
# Perform linear regression on k = k_on + k_off * imgp.
slope, intercept, r_value, p_value, std_err = stats.linregress(
imgp.flatten(), k.flatten())
print(('Linear regression: k_on={0:.3f}, ' + 'k_off={1:.3f}').format(
intercept, -slope))
# Set font style.
font = {
'family': 'sans-serif',
'serif': ['DejaVu Sans'],
'weight': 'normal',
'size': 20
# Compute source.
source = mp.k_on - mp.k_off * ca * rho
# Set name and create folder.
name = 'mechanical_model'
resfolder = os.path.join(resultpath, name)
if not os.path.exists(resfolder):
os.makedirs(resfolder)
# Plot and save figures.
ph.saveimage(resfolder, '{0}-rho'.format(name), rho)
ph.saveimage(resfolder, '{0}-ca'.format(name), ca)
ph.saveimage(resfolder, '{0}-v'.format(name), v)
ph.saveimage(resfolder, '{0}-sigma'.format(name), sigma)
ph.saveimage(resfolder, '{0}-k'.format(name), source)
ph.savevelocity(resfolder, '{0}-v'.format(name), ca, v, T=sp.T)
# Plot and save concentration profile.
fig, ax = plt.subplots(figsize=(10, 5))
for t in np.arange(0, sp.m, 59):
plt.plot(ca[t, :], label='t={:.2f}'.format(sp.T * t / (sp.m - 1)))
ax.legend(loc='best')
fig.tight_layout()
plt.show()
# Save figure.
fig.savefig(os.path.join(resfolder, '{0}-ca-profile.png'.format(name)),
dpi=dpi,
bbox_inches='tight')
plt.close(fig)
# Perform linear regression on k = k_on + k_off * img * rho.
ax.set_title('c vs k')
plt.xlabel('c')
plt.ylabel('k')
fig.tight_layout()
plt.show()
plt.close(fig)
resfolder = os.path.join(os.path.join(resultpath, gen), dat)
# Plot and save figures.
path = os.path.join(*[resultpath, 'cmscr1d', gen, dat])
if not os.path.exists(resultpath):
os.makedirs(resultpath)
ph.saveimage(path, name, imgp)
ph.savevelocity(path, name, imgp, v)
ph.savesource(path, name, k)
# Create model and solver parameters.
mp = solver.ModelParams()
mp.t_cut = float('Inf')
mp.k_on = 0
mp.k_off = 0
eta = 0.5
xi = 0.1
chi = 1.5
sp = solver.SolverParams()
sp.n = n
sp.m = m
sp.T = 1