def plot_strained(c_array, plot_folder):
Truth = sumstat.TruthData(valid_folder=folder_valid, case='strain-relax')
Strain = workfunc.StrainTensor(folder_valid)
u0 = [1, 1, 0.36, -0.08, -0.28, 0, 0, 0]
# strain-relaxation
tspan = np.linspace(0.0775, 0.953, 500)
fig = plt.figure(figsize=(fig_width, fig_height))
ax = plt.gca()
ax.scatter(Truth.strain_relax_a11[:, 0],
Truth.strain_relax_a11[:, 1],
label='exp')
if len(c_array.shape) == 1:
c_array = np.array(c_array).reshape((1, -1))
for c in c_array:
Ynke = odeint(rans.rans_strain_relax,
u0,
tspan,
args=(c, Strain.strain_relax),
atol=1e-8,
mxstep=200)
plt.plot(tspan, Ynke[:, 2], 'm--', label='a11')
ax.set_xlabel(r'$\tau$')
ax.set_ylabel(r'$a$')
ax.axis(xmin=0, xmax=1, ymin=-1, ymax=1)
plt.legend(loc=0)
fig.subplots_adjust(left=0.15, right=0.95, bottom=0.14, top=0.95)
fig.savefig(os.path.join(plot_folder, 'compare_strained'))
plt.close('all')
def plot_decay(c_array, plot_folder):
Truth = sumstat.TruthData(valid_folder=folder_valid, case='decay')
u0 = [1, 1, 0.36, -0.08, -0.28, 0, 0, 0]
tspan = np.linspace(0, 0.3, 200)
fig = plt.figure(figsize=(0.8 * fig_width, 1.3 * fig_height))
ax = plt.gca()
ax.scatter(Truth.decay_a11[:, 0],
Truth.decay_a11[:, 1],
color='m',
label='exp')
ax.scatter(Truth.decay_a22[:, 0], Truth.decay_a22[:, 1], color='b')
ax.scatter(Truth.decay_a22[:, 0], Truth.decay_a33[:, 1], color='r')
if len(c_array.shape) == 1:
c_array = np.array(c_array).reshape((1, -1))
for c in c_array:
Ynke = odeint(rans.rans_decay,
u0,
tspan,
args=(c, ),
atol=1e-8,
mxstep=200)
plt.plot(tspan, Ynke[:, 2], 'm--', label='a11')
plt.plot(tspan, Ynke[:, 3], 'b--', label='a22')
plt.plot(tspan, Ynke[:, 4], 'r--', label='a33')
ax.set_xlabel(r'$\tau$')
ax.set_ylabel(r'$a$')
# ax.axis(xmin=0, xmax=0.5, ymin=-0.4, ymax=0.4)
plt.legend(loc=0)
fig.subplots_adjust(left=0.15, right=0.98, bottom=0.14, top=0.95)
fig.savefig(os.path.join(plot_folder, 'compare_decay'))
plt.close('all')
def plot_validation_nominal(c_array, plot_folder):
Truth = sumstat.TruthData(valid_folder=folder_valid,
case='validation_nominal')
s0 = 3.3
beta = 0.5
u0 = [1, 1, 0, 0, 0, 0, 0, 0]
# Periodic shear(five different frequencies)
tspan = np.linspace(0, 50 / s0, 500)
fig = plt.figure(figsize=(1 * fig_width, 1.3 * fig_height))
ax = plt.gca()
if len(c_array.shape) == 1:
c_array = np.array(c_array).reshape((1, -1))
for c in c_array:
Ynke = odeint(rans.rans_periodic,
u0,
tspan,
args=(c, s0, beta,
workfunc.StrainTensor.periodic_strain),
atol=1e-8,
mxstep=200)
ax.semilogy(s0 * tspan,
Ynke[:, 0],
label=r'$\omega/S_{max} = $' + f' {beta}')
ax.scatter(Truth.validation_nominal[:, 0],
Truth.validation_nominal[:, 1],
marker='o')
ax.set_xlabel(r'$S\cdot t$')
ax.set_ylabel(r'$k/k_0$')
ax.axis(xmin=0, ymin=0, xmax=51)
plt.legend(loc=2, labelspacing=0.2, borderpad=0.0)
fig.subplots_adjust(left=0.16, right=0.98, bottom=0.14, top=0.95)
fig.savefig(os.path.join(plot_folder, 'compare_periodic_nominal'))
plt.close('all')
def main(args):
# Initialization
if len(args) > 1:
input_path = args[1]
else:
input_path = os.path.join('../rans_ode', 'params.yml')
input = yaml.load(open(input_path, 'r'))
### Paths
# path = input['path']
# path = {'output': os.path.join('../runs_abc/', 'output/'), 'valid_data': '../rans_ode/valid_data/'}
path = {
'output': os.path.join('../', 'output/'),
'valid_data': '../rans_ode/valid_data/'
}
print(path)
logging.basicConfig(format="%(levelname)s: %(name)s: %(message)s",
handlers=[
logging.FileHandler("{0}/{1}.log".format(
path['output'], 'ABClog_postprocess0005')),
logging.StreamHandler()
],
level=logging.DEBUG)
logging.info('\n############# POSTPROCESSING ############')
x_list = [0.005, 0.01, 0.03, 0.05, 0.1, 0.3]
C_limits = np.loadtxt(os.path.join(path['output'], 'C_limits_init'))
N_params = 1
files_abc = glob.glob1(path['output'], "classic_abc*.npz")
files = [os.path.join(path['output'], i) for i in files_abc]
accepted = np.empty((0, N_params))
dist = np.empty((0, 1))
sum_stat = np.empty((0, len(np.load(files[0])['sumstat'][0])))
logging.info('Loading data')
for file in files:
logging.debug('loading {}'.format(file))
accepted = np.vstack((accepted, np.load(file)['C'][:, :N_params]))
sum_stat = np.vstack((sum_stat, np.load(file)['sumstat']))
dist = np.vstack((dist, np.load(file)['dist'].reshape((-1, 1))))
data = np.hstack((accepted, dist)).tolist()
np.savetxt(os.path.join(path['output'], '1d_dist_scatter'), data)
logging.info('\n############# Classic ABC ############')
for x in x_list:
logging.info('\n')
folder = os.path.join(path['output'], 'x_{}'.format(x * 100))
if not os.path.isdir(folder):
os.makedirs(folder)
print(folder)
print('min dist = ', np.min(dist))
eps = define_eps(data, x)
np.savetxt(os.path.join(folder, 'eps'), [eps])
abc_accepted = accepted[np.where(dist < eps)[0]]
logging.info('x = {}, eps = {}, N accepted = {} (total {})'.format(
x, eps, len(abc_accepted), len(dist)))
print(sum_stat.shape, sum_stat[np.where(dist < eps)[0], :].shape)
np.savez(os.path.join(path['output'], '1d_dist_scatter_{}'.format(x)),
C=abc_accepted,
dist=dist[np.where(dist < eps)].reshape((-1, 1)),
sumstat=sum_stat[np.where(dist < eps)[0], :])
num_bin_kde = 100
num_bin_raw = 20
##############################################################################
logging.info(
'1D raw histogram with {} bins per dimension'.format(num_bin_raw))
x, y = utils.pdf_from_array_with_x(abc_accepted,
bins=num_bin_raw,
range=C_limits)
np.savetxt(os.path.join(folder, 'histogram'), [x, y])
np.savetxt(os.path.join(folder, 'C_final_raw{}'.format(num_bin_raw)),
[x[np.argmax(y)]])
# ##############################################################################
logging.info('2D smooth marginals with {} bins per dimension'.format(
num_bin_kde))
Z = kdepy_fftkde(abc_accepted, [C_limits[0]], [C_limits[1]],
num_bin_kde)
C_final_smooth = find_MAP_kde(Z, C_limits[0], C_limits[1])
grid = np.linspace(C_limits[0] - 1e-10, C_limits[1] + 1e-10,
num_bin_kde + 1)
# Z, C_final_smooth = gaussian_kde_scipy(abc_accepted, [C_limits[0]], [C_limits[1]], num_bin_kde)
# grid = np.linspace(C_limits[0], C_limits[1], num_bin_kde+1)
np.savetxt(os.path.join(folder, 'C_final_smooth'), C_final_smooth)
logging.info(
'Estimated parameters from joint pdf: {}'.format(C_final_smooth))
np.savez(os.path.join(folder, 'Z.npz'), Z=Z, grid=grid)
# for q in [0.05, 0.1, 0.25]:
# pp.marginal_confidence(N_params, folder, q)
# pp.marginal_confidence_joint(abc_accepted, folder, q)
####################################################################################################################
#
# ####################################################################################################################
logging.info('\n############# Regression ############')
path['regression_dist'] = os.path.join(path['output'], 'regression_dist')
path['regression_full'] = os.path.join(path['output'], 'regression_full')
if not os.path.isdir(path['regression_dist']):
os.makedirs(path['regression_dist'])
if not os.path.isdir(path['regression_full']):
os.makedirs(path['regression_full'])
Truth = sumstat.TruthData(valid_folder=path['valid_data'],
case=input['case'])
ind = np.argsort(dist[:, 0])
accepted = accepted[ind]
sum_stat = sum_stat[ind]
dist = dist[ind]
for x in x_list:
logging.info('\n')
n = int(x * len(accepted))
print('{} samples are taken for regression ({}% of {})'.format(
n, x * 100, len(accepted)))
samples = accepted[:n, :N_params]
dist_reg = dist[:n, -1].reshape((-1, 1))
#########################################################################
logging.info('Regression with distance')
folder = os.path.join(path['regression_dist'], 'x_{}'.format(x * 100))
if not os.path.isdir(folder):
os.makedirs(folder)
# new_samples, solution = regression(samples, (sum_stat[:n] - Truth.sumstat_true).reshape((-1, 1)),
# dist_reg, 1, folder)
new_samples, solution = regression(samples, dist_reg, dist_reg, 1,
folder)
limits = np.empty((N_params, 2))
for i in range(N_params):
limits[i, 0] = np.min(new_samples[:, i])
limits[i, 1] = np.max(new_samples[:, i])
if limits[i, 1] - limits[i, 0] < 1e-8:
logging.warning('too small new range')
limits[i, 0] -= 0.001
limits[i, 1] += 0.001
print('new limits = ', limits)
np.savetxt(os.path.join(folder, 'reg_limits'), limits)
num_bin_kde_reg = 20
logging.info('2D smooth marginals with {} bins per dimension'.format(
num_bin_kde_reg))
Z = kdepy_fftkde(new_samples, [limits[:, 0]], [limits[:, 1]],
num_bin_kde_reg)
C_final_smooth = find_MAP_kde(Z, [limits[:, 0]], [limits[:, 1]])
grid = np.linspace(limits[0, 0] - 1e-10, limits[0, 1] + 1e-10,
num_bin_kde_reg + 1)
# Z, C_final_smooth = gaussian_kde_scipy(abc_accepted, [C_limits[0]], [C_limits[1]], num_bin_kde)
# grid = np.linspace(C_limits[0], C_limits[1], num_bin_kde+1)
np.savetxt(os.path.join(folder, 'C_final_smooth'), C_final_smooth[0])
logging.info('Estimated parameters from joint pdf: {}'.format(
C_final_smooth[0]))
np.savez(os.path.join(folder, 'Z.npz'), Z=Z, grid=grid)
# for q in [0.05, 0.1, 0.25]:
# pp.marginal_confidence(N_params, folder, q)
##########################################################################
logging.info('Regression with full summary statistics')
folder = os.path.join(path['regression_full'], 'x_{}'.format(x * 100))
if not os.path.isdir(folder):
os.makedirs(folder)
new_samples, _ = regression(samples, sum_stat[:n] - Truth.sumstat_true,
dist_reg, 1, folder)
limits = np.empty((N_params, 2))
for i in range(N_params):
limits[i, 0] = np.min(new_samples[:, i])
limits[i, 1] = np.max(new_samples[:, i])
if limits[i, 1] - limits[i, 0] < 1e-8:
logging.warning('too small new range')
limits[i, 0] -= 0.001
limits[i, 1] += 0.001
print('new limits = ', limits)
np.savetxt(os.path.join(folder, 'reg_limits'), limits)
num_bin_kde_reg = 20
logging.info('2D smooth marginals with {} bins per dimension'.format(
num_bin_kde_reg))
Z = kdepy_fftkde(new_samples, [limits[:, 0]], [limits[:, 1]],
num_bin_kde_reg)
C_final_smooth = find_MAP_kde(Z, [limits[:, 0]], [limits[:, 1]])
grid = np.linspace(limits[0, 0] - 1e-10, limits[0, 1] + 1e-10,
num_bin_kde_reg + 1)
# Z, C_final_smooth = gaussian_kde_scipy(abc_accepted, [C_limits[0]], [C_limits[1]], num_bin_kde)
# grid = np.linspace(C_limits[0], C_limits[1], num_bin_kde+1)
np.savetxt(os.path.join(folder, 'C_final_smooth'), C_final_smooth[0])
logging.info('Estimated parameters from joint pdf: {}'.format(
C_final_smooth[0]))
np.savez(os.path.join(folder, 'Z.npz'), Z=Z, grid=grid)
# for q in [0.05, 0.1, 0.25]:
# pp.marginal_confidence(N_params, folder, q)
logging.info('\n#############Done############')
def plot_impulsive(c_array, plot_folder):
Truth = sumstat.TruthData(valid_folder=folder_valid, case='impulsive_k')
Strain = workfunc.StrainTensor(valid_folder=folder_valid)
u0 = [1, 1, 0, 0, 0, 0, 0, 0]
# axisymmetric expansion
if len(c_array.shape) == 1:
c_array = np.array(c_array).reshape((1, -1))
fig = plt.figure(figsize=(0.8 * fig_width, 1.3 * fig_height))
ax = plt.gca()
fig2 = plt.figure(figsize=(0.8 * fig_width, 1.3 * fig_height))
ax2 = plt.gca()
for c in c_array:
tspan1 = np.linspace(0, 1.5 / np.abs(Strain.axi_exp[0]), 200)
Ynke1 = odeint(rans.rans_impulsive,
u0,
tspan1,
args=(c, Strain.axi_exp),
atol=1e-8,
mxstep=200)
# axisymmetric contraction
tspan2 = np.linspace(0, 1.5 / np.abs(Strain.axi_con[0]), 200)
Ynke2 = odeint(rans.rans_impulsive,
u0,
tspan2,
args=(c, Strain.axi_con),
atol=1e-8,
mxstep=200)
# pure shear
tspan3 = np.linspace(0, 5 / (2 * Strain.pure_shear[3]), 200)
Ynke3 = odeint(rans.rans_impulsive,
u0,
tspan3,
args=(c, Strain.pure_shear),
atol=1e-8,
mxstep=200)
# plane strain
tspan4 = np.linspace(0, 1.5 / Strain.plane_strain[0], 200)
Ynke4 = odeint(rans.rans_impulsive,
u0,
tspan4,
args=(c, Strain.plane_strain),
atol=1e-8,
mxstep=200)
ax.plot(np.abs(Strain.axi_exp[0]) * tspan1,
Ynke1[:, 0],
label='axisymmetric expansion')
ax.scatter(Truth.axi_exp_k[:, 0], Truth.axi_exp_k[:, 1], marker='o')
ax.plot(np.abs(Strain.axi_con[0]) * tspan2,
Ynke2[:, 0],
label='axisymmetric contraction')
ax.scatter(Truth.axi_con_k[:, 0], Truth.axi_con_k[:, 1], marker='o')
ax.plot(2 * Strain.pure_shear[3] * tspan3,
Ynke3[:, 0],
label='pure shear')
ax.scatter(Truth.shear_k[:, 0], Truth.shear_k[:, 1], marker='o')
ax.plot(np.abs(Strain.plane_strain[0]) * tspan4,
Ynke4[:, 0],
label='plain strain')
ax.scatter(Truth.plane_k[:, 0], Truth.plane_k[:, 1], marker='o')
# gradient
x_diff = np.abs(Strain.axi_exp[0]) * tspan1
ax2.plot(np.abs(Strain.axi_exp[0]) * tspan1,
np.gradient(Ynke1[:, 0], x_diff),
label='axisymmetric expansion')
ax2.scatter(Truth.axi_exp_k[:, 0],
np.gradient(Truth.axi_exp_k[:, 1], Truth.axi_exp_k[:, 0]),
marker='o')
x_diff = np.abs(Strain.axi_con[0]) * tspan2
ax2.plot(np.abs(Strain.axi_con[0]) * tspan2,
np.gradient(Ynke2[:, 0], x_diff),
label='axisymmetric contraction')
ax2.scatter(Truth.axi_con_k[:, 0],
np.gradient(Truth.axi_con_k[:, 1], Truth.axi_con_k[:, 0]),
marker='o')
x_diff = 2 * Strain.pure_shear[3] * tspan3
ax2.plot(2 * Strain.pure_shear[3] * tspan3,
np.gradient(Ynke3[:, 0], x_diff),
label='pure shear')
ax2.scatter(Truth.shear_k[:, 0],
np.gradient(Truth.shear_k[:, 1], Truth.shear_k[:, 0]),
marker='o')
x_diff = np.abs(Strain.plane_strain[0]) * tspan4
ax2.plot(np.abs(Strain.plane_strain[0]) * tspan4,
np.gradient(Ynke4[:, 0], x_diff),
label='plain strain')
ax2.scatter(Truth.plane_k[:, 0],
np.gradient(Truth.plane_k[:, 1], Truth.plane_k[:, 0]),
marker='o')
ax.set_xlabel(r'$S\cdot t$')
ax.set_ylabel(r'$k/k_0$')
# ax.axis(xmin=0, xmax=5, ymin=0, ymax=2.5)
plt.legend()
fig.subplots_adjust(left=0.13, right=0.98, bottom=0.14, top=0.95)
fig.savefig(os.path.join(plot_folder, 'compare_impulsive_k'))
Truth = sumstat.TruthData(valid_folder=folder_valid, case='impulsive_a')
ax2.set_xlabel(r'$S\cdot t$')
ax2.set_ylabel(r'$k/k_0$')
# ax.axis(xmin=0, xmax=5, ymin=0, ymax=2.5)
plt.legend()
fig2.subplots_adjust(left=0.13, right=0.98, bottom=0.14, top=0.95)
fig2.savefig(os.path.join(plot_folder, 'compare_impulsive_k_gradient'))
for c in c_array:
fig = plt.figure(figsize=(0.8 * fig_width, 1.3 * fig_height))
ax = plt.gca()
ax.plot(np.abs(Strain.axi_exp[0]) * tspan1,
Ynke1[:, 2],
label=r'axisymmetric expansion $a_{11}$')
ax.scatter(Truth.axi_exp_a[:, 0], Truth.axi_exp_a[:, 1], marker='o')
ax.plot(np.abs(Strain.axi_con[0]) * tspan2,
Ynke2[:, 2],
label=r'axisymmetric contraction $a_{11}$')
ax.scatter(Truth.axi_con_a[:, 0], Truth.axi_con_a[:, 1], marker='o')
ax.plot(np.abs(Strain.plane_strain[0]) * tspan4,
Ynke4[:, 2],
label=r'plain strain $a_{11}$')
ax.scatter(Truth.plane_a11[:, 0], Truth.plane_a11[:, 1], marker='o')
ax.plot(np.abs(Strain.plane_strain[0]) * tspan4,
Ynke4[:, 3],
label=r'plain strain $a_{22}$')
ax.scatter(Truth.plane_a22[:, 0], Truth.plane_a22[:, 1], marker='o')
ax.set_xlabel(r'$S\cdot t$')
ax.set_ylabel(r'$a$')
# ax.axis(xmin=0, xmax=1.5, ymin=0, ymax=2.5)
plt.legend()
fig.subplots_adjust(left=0.15, right=0.98, bottom=0.14, top=0.95)
fig.savefig(os.path.join(plot_folder, 'compare_impulsive_a'))
plt.close('all')
def plot_experiment(plot_folder, indices=None):
# impulsive
Truth = sumstat.TruthData(valid_folder=folder_valid, case='impulsive_k')
fig = plt.figure(figsize=(0.8 * fig_width, 1 * fig_height))
ax = plt.gca()
ax.scatter(Truth.axi_exp_k[:, 0],
Truth.axi_exp_k[:, 1],
marker='o',
label='axisymmetric expansion')
ax.scatter(Truth.axi_con_k[:, 0],
Truth.axi_con_k[:, 1],
marker='o',
label='axisymmetric contraction')
ax.scatter(Truth.shear_k[:, 0],
Truth.shear_k[:, 1],
marker='o',
label='pure shear')
ax.scatter(Truth.plane_k[:, 0],
Truth.plane_k[:, 1],
marker='o',
label='plain strain')
if indices is not None:
truth = np.vstack(
(Truth.axi_exp_k, Truth.axi_con_k, Truth.shear_k, Truth.plane_k))
ax.scatter(truth[indices, 0], truth[indices, 1], color='k', marker='o')
ax.set_xlabel(r'$S\cdot t$')
ax.set_ylabel(r'$k/k_0$')
# ax.axis(xmin=0, xmax=5, ymin=0, ymax=2.5)
plt.legend(frameon=True)
fig.subplots_adjust(left=0.13, right=0.98, bottom=0.14, top=0.95)
fig.savefig(os.path.join(plot_folder, 'impulsive_k'))
Truth = sumstat.TruthData(valid_folder=folder_valid, case='impulsive_a')
fig = plt.figure(figsize=(0.8 * fig_width, 1.3 * fig_height))
ax = plt.gca()
ax.scatter(Truth.axi_exp_a[:, 0],
Truth.axi_exp_a[:, 1],
marker='o',
label=r'axisymmetric expansion $a_{11}$')
ax.scatter(Truth.axi_con_a[:, 0],
Truth.axi_con_a[:, 1],
marker='o',
label=r'axisymmetric contraction $a_{11}$')
ax.scatter(Truth.plane_a11[:, 0],
Truth.plane_a11[:, 1],
marker='o',
label=r'plain strain $a_{11}$')
ax.scatter(Truth.plane_a22[:, 0],
Truth.plane_a22[:, 1],
marker='o',
label=r'plain strain $a_{22}$')
ax.set_xlabel(r'$S\cdot t$')
ax.set_ylabel(r'$a$')
# ax.axis(xmin=0, xmax=1.5, ymin=0, ymax=2.5)
plt.legend()
fig.subplots_adjust(left=0.15, right=0.98, bottom=0.14, top=0.95)
fig.savefig(os.path.join(plot_folder, 'impulsive_a'))
# Decay
Truth = sumstat.TruthData(valid_folder=folder_valid, case='decay')
fig = plt.figure(figsize=(0.8 * fig_width, 1.3 * fig_height))
ax = plt.gca()
ax.scatter(Truth.decay_a11[:, 0],
2 * Truth.decay_a11[:, 1],
marker='o',
label=r'$a_{11}$')
ax.scatter(Truth.decay_a22[:, 0],
2 * Truth.decay_a22[:, 1],
marker='o',
label=r'$a_{22}$')
ax.scatter(Truth.decay_a33[:, 0],
2 * Truth.decay_a33[:, 1],
marker='o',
label=r'$a_{33}$')
ax.set_xlabel(r'$S\cdot t$')
ax.set_ylabel(r'$a$')
# ax.axis(xmin=0, xmax=1.5, ymin=0, ymax=2.5)
plt.legend()
fig.subplots_adjust(left=0.15, right=0.98, bottom=0.14, top=0.95)
fig.savefig(os.path.join(plot_folder, 'decay_a'))
plt.close('all')