def plot_with_conf_intrv(out_predic,
exact_data,
x_field,
num_mc_trials,
num_points_in_each_data_set,
x_label,
y_label,
xlim=None,
ylim=None,
add_to_save=None):
assert out_predic.shape[0] == num_points_in_each_data_set
assert out_predic.shape[1] == num_mc_trials
assert len(exact_data) == num_points_in_each_data_set
std = np.std(out_predic, axis=1)
mean = np.mean(out_predic, axis=1)
assert len(std) == num_points_in_each_data_set
assert len(mean) == num_points_in_each_data_set
plt = plot_x_y(x_all=[x_field, x_field],
y_all=[mean, exact_data],
x_label=x_label,
y_label=y_label,
legend_all=['Mean Predictions', 'Experiment'],
add_leg=False,
plot_linestyle=['-', '--'],
plot_linecolor=['b', 'r'],
xlim=xlim,
ylim=ylim,
need_return_plt=True,
add_to_save=None)
plt.fill_between(x_field,
mean - 1.96 * std,
mean + 1.96 * std,
color='b',
alpha=.3) #std curves.
plt.legend(loc="best", prop={'size': 28})
plt.tight_layout()
if add_to_save:
plt.savefig(add_to_save)
plt.close()
else:
plt.show()
scaled_pred_data = scaler_exp.transform(scaled_pred_data)
err = (scaled_exp_data - scaled_pred_data)**2
err = np.sum(err, axis=0)
temp = np.zeros(num_anns)
for jj, sub_graph_output in enumerate(sub_graph_outputs):
for ff in sub_graph_output:
temp[jj] += err[features_pred_map[ff]]
temp /= (num_out_feature_each_ann * num_loads)
error_anns[j, :] = temp
temp = root_folder + case['main_graph_name'] + '/data_postproc/'
# sub grapg by subgraph
for jj, sub_graph_output in enumerate(sub_graph_outputs):
train_ecdf = error_anns[train_data_set_ids, jj]
train_ecdf = np.array(train_ecdf)
train_ecdf = np.sort(train_ecdf)
y_train = np.array(range(len(train_ecdf))) / len(train_ecdf)
test_ecdf = error_anns[test_data_set_ids, jj]
test_ecdf = np.array(test_ecdf)
test_ecdf = np.sort(test_ecdf)
y_test = np.array(range(len(test_ecdf))) / len(test_ecdf)
plot_x_y(x_all=[train_ecdf, test_ecdf],
y_all=[y_train, y_test],
x_label='scaled MSE',
y_label='eCDF',
legend_all=['Train', 'Test'],
add_to_save=temp +
'setwise_{}.png'.format(case['output_name'][jj]),
xscale='log')
for i, case in enumerate(ann_cases):
folder_add = root_folder + case['main_graph_name'] + '/data_postproc/'
temp = np.load(folder_add + 'train_setwise_ecdf_{}.npy'.format(field))
train_ecdf, y_train = temp[:, 0], temp[:, 1]
train_ecdf_all_ann_cases.append(train_ecdf)
y_train_all_ann_cases.append(y_train)
temp = np.load(folder_add + 'test_setwise_ecdf_{}.npy'.format(field))
test_ecdf, y_test = temp[:, 0], temp[:, 1]
test_ecdf_all_ann_cases.append(test_ecdf)
y_test_all_ann_cases.append(y_test)
plot_x_y(
x_all=[
train_ecdf_all_ann_cases[0], test_ecdf_all_ann_cases[0],
train_ecdf_all_ann_cases[1], test_ecdf_all_ann_cases[1]
],
y_all=[
y_train_all_ann_cases[0], y_test_all_ann_cases[0],
y_train_all_ann_cases[1], y_test_all_ann_cases[1]
],
x_label='Scaled MSE',
y_label='eCDF',
legend_all=[
'train-no graph info', 'test-no graph info',
'train-with graph info', 'test-with graph info'
],
plot_linestyle=['-', '--', '-', '--'],
plot_linecolor=['k', 'k', 'b', 'b'],
xscale='log',
add_to_save=folder_add + 'compare_setwise_ecdf_{}.png'.format(field),
)
p_exp = -(sig_eig1_exp + sig_eig2_exp + sig_eig3_exp) / 3.
q1_exp = sig_eig1_exp - sig_eig3_exp
q2_exp = sig_eig1_exp - sig_eig2_exp
q3_exp = sig_eig2_exp - sig_eig3_exp
p_pred = -(sig_eig1_pred + sig_eig2_pred + sig_eig3_pred) / 3.
q1_pred = sig_eig1_pred - sig_eig3_pred
q2_pred = sig_eig1_pred - sig_eig2_pred
q3_pred = sig_eig2_pred - sig_eig3_pred
folder_to_save = folder_add + 'figures/'
if not os.path.exists(folder_to_save):
os.system("mkdir {}".format(folder_to_save))
plot_x_y([np.log10(p_exp), np.log10(p_pred)],
[void_ratio_exp, void_ratio_pred],
r'$\log10(p [\mathrm{KPa}] )$', 'Void Ratio',
['Experiment', 'Prediction'],
folder_to_save + 'log10p_voidRatio.png')
max_y = max(max(q1_exp), max(q2_exp), max(q3_exp))
min_y = min(min(q1_exp), min(q2_exp), min(q3_exp))
range_y = max_y - min_y
plot_x_y([eps_11, eps_11], [q1_exp, q1_pred],
'Axial Strain',
r'$q_1 [\mathrm{KPa}]$', ['Experiment', 'Prediction'],
ylim=[min_y - 0.05 * range_y, max_y + 0.05 * range_y],
add_to_save=folder_to_save + 'eps1_q1.png')
plot_x_y([eps_11, eps_11], [q2_exp, q2_pred],
'Axial Strain',
r'$q_2 [\mathrm{KPa}]$', ['Experiment', 'Prediction'],
ylim=[min_y - 0.05 * range_y, max_y + 0.05 * range_y],
folder_add = root_folder + case['main_graph_name'] + '/data_postproc/'
temp = np.load(folder_add + 'train_pointwise_ecdf_all.npy')
train_ecdf, y_train = temp[:, 0], temp[:, 1]
train_ecdf_all_ann_cases.append(train_ecdf)
y_train_all_ann_cases.append(y_train)
temp = np.load(folder_add + 'test_pointwise_ecdf_all.npy')
test_ecdf, y_test = temp[:, 0], temp[:, 1]
test_ecdf_all_ann_cases.append(test_ecdf)
y_test_all_ann_cases.append(y_test)
plot_x_y(
x_all=[
train_ecdf_all_ann_cases[0], test_ecdf_all_ann_cases[0],
train_ecdf_all_ann_cases[1], test_ecdf_all_ann_cases[1]
],
y_all=[
y_train_all_ann_cases[0], y_test_all_ann_cases[0],
y_train_all_ann_cases[1], y_test_all_ann_cases[1]
],
x_label='Scaled MSE',
y_label='eCDF',
legend_all=[
'train-no graph info', 'test-no graph info', 'train-with graph info',
'test-with graph info'
],
plot_linestyle=['-', '--', '-', '--'],
plot_linecolor=['k', 'k', 'r', 'r'],
xscale='log',
leg_loc='upper left',
add_to_save=folder_add + 'compare_pointwise_ecdf_all.png',
)
root_folder = './results/'
ann_cases = [
# 'ANN_graphs_bulk_plasticity_classical_without_fabric',
'ANN_graphs_bulk_plasticity_classical_with_fabric',
# 'ANN_graphs_bulk_plasticity_new_fabric_with_classical_graph',
]
data_sets_to_analyze = np.array(range(59))
num_loads = 501
q123_exp = np.zeros((num_loads, 3))
q123_pred = np.zeros((num_loads, 3))
pq_exp = np.zeros((num_loads, 2))
pq_pred = np.zeros((num_loads, 2))
for i, case in enumerate(ann_cases):
for data_set in data_sets_to_analyze:
folder_add = root_folder + case + '/data_postproc/dataset_{}/'.format(
data_set)
exp_data = pd.read_csv(folder_add + 'data_experiment.csv',
delimiter=',')
pred_data = pd.read_csv(folder_add + 'data_prediction_ave_over_1.csv',
delimiter=',')
features_pred = list(pred_data.columns)
folder_to_save = folder_add + 'figures/'
if not os.path.exists(folder_to_save):
os.system("mkdir {}".format(folder_to_save))
for feature_name in features_pred:
plot_x_y([range(num_loads), range(num_loads)],
[exp_data[feature_name], pred_data[feature_name]],
'load step', feature_name, ['experiment', 'prediction'],
folder_to_save + 'loadStep_{}.png'.format(feature_name))
scaled_exp_data = scaler_exp.transform(scaled_exp_data)
scaled_pred_data = scaler_exp.transform(scaled_pred_data)
err = (scaled_exp_data - scaled_pred_data)**2
err = np.sum(err, axis=1)
err /= len(features_pred)
ecdf_all.append(err)
train_ecdf = []
for i in train_data_set_ids:
train_ecdf = train_ecdf + list(ecdf_all[i])
train_ecdf = np.array(train_ecdf)
train_ecdf = np.sort(train_ecdf)
y_train = np.array(range(len(train_ecdf))) / len(train_ecdf)
test_ecdf = []
for i in test_data_set_ids:
test_ecdf = test_ecdf + list(ecdf_all[i])
test_ecdf = np.array(test_ecdf)
test_ecdf = np.sort(test_ecdf)
y_test = np.array(range(len(test_ecdf))) / len(test_ecdf)
plot_x_y(x_all=[train_ecdf, test_ecdf],
y_all=[y_train, y_test],
x_label='mse',
y_label='eCDF',
legend_all=['train', 'test'],
add_to_save=None,
xscale='log')
for i in train_data_set_ids:
train_ecdf = train_ecdf + list(ecdf_all[i])
train_ecdf = np.array(train_ecdf)
train_ecdf = np.sort(train_ecdf)
y_train = np.array(range(len(train_ecdf))) / len(train_ecdf)
test_ecdf = []
for i in test_data_set_ids:
test_ecdf = test_ecdf + list(ecdf_all[i])
test_ecdf = np.array(test_ecdf)
test_ecdf = np.sort(test_ecdf)
y_test = np.array(range(len(test_ecdf))) / len(test_ecdf)
ecdf_all_2.append(train_ecdf)
ecdf_all_2.append(test_ecdf)
y_all.append(y_train)
y_all.append(y_test)
# plot_x_y(x_all=[train_ecdf, test_ecdf],y_all=[y_train, y_test], x_label='mse', y_label='eCDF', legend_all=['train', 'test'], add_to_save=None)
plot_x_y(x_all=ecdf_all_2,
y_all=y_all,
x_label='mse',
y_label='eCDF',
legend_all=[
'train-no graph info', 'test-no graph info',
'train-with graph info', 'test-with graph info'
],
plot_linestyle=['-', '--', '-', '--'],
plot_linecolor=['k', 'k', 'b', 'b'],
xscale='log',
add_to_save=None)
exit()
temp = all_errors[test_id]
temp = temp[:, ff_ids]
temp = np.sum(temp, axis=1) / len(ff_ids)
assert len(temp) == num_loads
test_ecdf = test_ecdf + list(temp)
test_ecdf = np.array(test_ecdf)
test_ecdf = np.sort(test_ecdf)
if len(test_ecdf) > 1: assert test_ecdf[0] <= test_ecdf[1]
y_test = np.array(range(len(test_ecdf))) / len(test_ecdf)
temp = np.zeros((len(train_ecdf), 2))
temp[:, 0], temp[:, 1] = train_ecdf, y_train
np.save(
temp_root_folder +
'train_pointwise_ecdf_{}.npy'.format(case['output_name'][jj]),
temp)
temp = np.zeros((len(test_ecdf), 2))
temp[:, 0], temp[:, 1] = test_ecdf, y_test
np.save(
temp_root_folder +
'test_pointwise_ecdf_{}.npy'.format(case['output_name'][jj]), temp)
plot_x_y(
x_all=[train_ecdf, test_ecdf],
y_all=[y_train, y_test],
x_label='mse',
y_label='eCDF',
legend_all=['train', 'test'],
xscale='log',
add_to_save=temp_root_folder +
'pointwise_{}.png'.format(case['output_name'][jj]),
)
fabric_pred = pred_data[target_fields].values
folder_to_save = folder_add + 'figures/'
if not os.path.exists(folder_to_save):
os.system("mkdir {}".format(folder_to_save))
temp_max = np.max(fabric_exp, axis=0)
assert len(temp_max) == len(target_fields)
temp_max = np.max(temp_max)
temp_min = np.min(fabric_exp, axis=0)
assert len(temp_min) == len(target_fields)
temp_min = np.min(temp_min)
range_val = abs(temp_max - temp_min)
for col_id in range(fabric_exp.shape[1]):
y_exp = fabric_exp[:, col_id]
y_pred = fabric_pred[:, col_id]
plot_x_y(
[eps_11, eps_11],
[y_exp, y_pred],
'Axial Strain',
'Fabric {}'.format(comp_mapped[col_id]),
['Experiment', 'Prediction'],
ylim=[
temp_min - 0.05 * range_val, temp_max + 0.05 * range_val
],
add_to_save=folder_to_save +
'eps1_fab{}.png'.format(comp_mapped[col_id]),
)
for j, data_set in enumerate(data_sets_to_analyze):
folder_add = root_folder + case['main_graph_name'] +'/data_postproc/dataset_{}/'.format(data_set)
exp_data = pd.read_csv(folder_add + 'data_experiment.csv', delimiter=',')
pred_data = pd.read_csv(folder_add + 'data_prediction_ave_over_1.csv', delimiter=',')
# features_pred = list(pred_data.columns)
num_rows = exp_data.shape[0]
assert num_rows == num_loads
scaled_exp_data = exp_data[features_pred].values
scaled_pred_data = pred_data[features_pred].values
scaled_exp_data = scaler_exp.transform(scaled_exp_data)
scaled_pred_data = scaler_exp.transform(scaled_pred_data)
err = (scaled_exp_data - scaled_pred_data)**2
num_elem = err.shape[0] * err.shape[1]
error_sets[j] = np.sum(err) / num_elem
temp = root_folder + case['main_graph_name'] +'/data_postproc/'
train_ecdf = error_sets[train_data_set_ids]
train_ecdf = np.array(train_ecdf)
train_ecdf = np.sort(train_ecdf)
y_train = np.array(range(len(train_ecdf))) / len(train_ecdf)
test_ecdf = error_sets[test_data_set_ids]
test_ecdf = np.array(test_ecdf)
test_ecdf = np.sort(test_ecdf)
y_test = np.array(range(len(test_ecdf))) / len(test_ecdf)
plot_x_y(x_all=[train_ecdf, test_ecdf],y_all=[y_train, y_test], x_label='scaled MSE', y_label='eCDF',
legend_all=['Train', 'Test'],xscale='log',
add_to_save=temp+'setwise_ecdf_all.png'
)
np.save(
temp_root_folder +
'train_setwise_ecdf_{}.npy'.format(case['output_name'][jj]), temp)
temp = np.zeros((len(test_ecdf), 2))
temp[:, 0], temp[:, 1] = test_ecdf, y_test
np.save(
temp_root_folder +
'test_setwise_ecdf_{}.npy'.format(case['output_name'][jj]), temp)
add_to_save = temp_root_folder + 'setwise_ecdf_{}.png'.format(
case['output_name'][jj])
plt = plot_x_y(
x_all=[train_ecdf, test_ecdf],
y_all=[y_train, y_test],
x_label='scaled MSE',
y_label='eCDF',
legend_all=['Train', 'Test'],
xscale='log',
need_return_plt=True,
# add_to_save=add_to_save
)
# y_len = abs(plt.gca().get_ylim()[1] - plt.gca().get_ylim()[0])
# x_len = abs(plt.gca().get_xlim()[1] - plt.gca().get_xlim()[0])
# fig_size = plt.gcf().get_size_inches()
for key, val in train_extra_point.items():
plt.plot(val[0], val[1], 'ro')
plt.text(val[0], val[1], '{}'.format(key), fontsize=22, c='r')
for key, val in test_extra_point.items():
plt.plot(val[0], val[1], 'ro')
plt.text(val[0], val[1], '{}'.format(key), fontsize=22, c='r')
plt.savefig(add_to_save)
plt.close()
temp_root_folder = root_folder + case['main_graph_name'] +'/data_postproc/'
train_ecdf = []
for train_id in train_data_set_ids:
temp = all_errors[train_id]
train_ecdf = train_ecdf + list(temp)
train_ecdf = np.array(train_ecdf)
train_ecdf = np.sort(train_ecdf)
if len(train_ecdf)>1: assert train_ecdf[0]<= train_ecdf[1]
y_train = np.array(range(len(train_ecdf))) / len(train_ecdf)
test_ecdf = []
for test_id in test_data_set_ids:
temp = all_errors[test_id]
test_ecdf = test_ecdf + list(temp)
test_ecdf = np.array(test_ecdf)
test_ecdf = np.sort(test_ecdf)
if len(test_ecdf)>1: assert test_ecdf[0]<= test_ecdf[1]
y_test = np.array(range(len(test_ecdf))) / len(test_ecdf)
temp = np.zeros((len(train_ecdf), 2))
temp[:,0], temp[:,1] = train_ecdf, y_train
np.save(temp_root_folder+'train_pointwise_ecdf_all.npy', temp)
temp = np.zeros((len(test_ecdf), 2))
temp[:,0], temp[:,1] = test_ecdf, y_test
np.save(temp_root_folder+'test_pointwise_ecdf_all.npy', temp)
plot_x_y(x_all=[train_ecdf, test_ecdf],y_all=[y_train, y_test], x_label='scaled MSE', y_label='eCDF',
legend_all=['train', 'test'], xscale='log',
add_to_save=temp_root_folder + 'pointwise_all.png',
)
pq_pred = np.zeros((num_loads, 2))
for i, case in enumerate(ann_cases):
for data_set in data_sets_to_analyze:
folder_add = root_folder + case + '/data_postproc/dataset_{}/'.format(
data_set)
exp_data = pd.read_csv(folder_add + 'data_experiment.csv',
delimiter=',')
pred_data = pd.read_csv(folder_add + 'data_prediction_ave_over_1.csv',
delimiter=',')
eps_11 = -exp_data['eps11'].values
fabric_exp = exp_data[target_fields].values
fabric_pred = pred_data[target_fields].values
folder_to_save = folder_add + 'figures/'
if not os.path.exists(folder_to_save):
os.system("mkdir {}".format(folder_to_save))
for col_id in range(fabric_exp.shape[1]):
y_exp = fabric_exp[:, col_id]
y_pred = fabric_pred[:, col_id]
plot_x_y(
[eps_11, eps_11],
[y_exp, y_pred],
'Axial Strain',
'{}'.format(comp_mapped[col_id]),
['Experiment', 'Prediction'],
add_to_save=folder_to_save +
'eps1_{}.png'.format(target_fields[col_id]),
)
