Repository navigation
Expand file tree
/
Copy pathplot_holographic_example.py
More file actions
140 lines (106 loc) · 3.59 KB
/
Copy pathplot_holographic_example.py
File metadata and controls
140 lines (106 loc) · 3.59 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
import numpy as np
import scipy.io as sio
import matplotlib.pyplot as plt
# Plot Forward Model Results
n_plots = 3 # number of examples to plot
samp_index = [0,1,2,3]
# Load the test results for the multi-fidelity forward model
results = sio.loadmat('results/holographic_forward_model_examples.mat')
x_exp_test = results['target']
y_exp_lf_test = results['low_fidelity']
y_exp_hf_test = results['ground_truth']
samples = results['samples']
samples[samples<0.0]=0.0
mu = results['mean']
sig = results['standard_deviation']
cost_plot = results['cost'][0]
plt.figure()
plt.plot(cost_plot)
plt.title('Multi-Fidelity Forward Model Cost')
plt.figure()
for i in range(n_plots):
target_i = x_exp_test[i,:]
target_i = np.reshape(target_i,(28,28))
lf_i = y_exp_lf_test[i,:]
lf_i = np.reshape(lf_i,(28,28))
hf_i = y_exp_hf_test[i,:]
hf_i = np.reshape(hf_i,(28,28))
mu_i = mu[i,:]
mu_i = np.reshape(mu_i,(28,28))
sig_i = sig[i,:]
sig_i = np.reshape(sig_i,(28,28))
samp_i = samples[i,:,:]
samples_i = np.zeros((28*2,28*2))
for j in range(4):
samp_ij = samp_i[:,samp_index[j]]
samp_ij = np.reshape(samp_ij,(28,28))
if j<2:
samples_i[j*28:(j+1)*28,0:28] = samp_ij
else:
samples_i[(j-2)*28:(j-1)*28,28:2*28] = samp_ij
plt.subplot(n_plots,6,i*6+1)
plt.imshow(target_i)
plt.title('Target')
plt.subplot(n_plots,6,i*6+2)
plt.imshow(lf_i)
plt.title('Low Fidelity Measurements')
plt.subplot(n_plots,6,i*6+3)
plt.imshow(hf_i)
plt.title('High Fidelity Measurements')
plt.subplot(n_plots,6,i*6+4)
plt.imshow(mu_i)
plt.title('Emulated Mean')
plt.subplot(n_plots,6,i*6+5)
plt.imshow(sig_i)
plt.title('Emulated STD')
plt.subplot(n_plots,6,i*6+6)
plt.imshow(samples_i)
plt.title('Emulated Samples')
# Plot Inverse Model Results
n_plots = 3 # number of examples to plot
samp_index = [0,1,2,3]
# Load the test results for the multi-fidelity forward model
results = sio.loadmat('results/holographic_inverse_model_examples.mat')
x_exp_test = results['target']
y_exp_hf_test = results['measurements']
samples = results['samples']
mu = results['mean']
sig = results['standard_deviation']
cost_plot = results['cost'][0]
plt.figure()
plt.plot(cost_plot)
plt.title('Inverse Model Model Cost')
plt.figure()
for i in range(n_plots):
target_i = x_exp_test[i,:]
target_i = np.reshape(target_i,(28,28))
hf_i = y_exp_hf_test[i,:]
hf_i = np.reshape(hf_i,(28,28))
mu_i = mu[i,:]
mu_i = np.reshape(mu_i,(28,28))
sig_i = sig[i,:]
sig_i = np.reshape(sig_i,(28,28))
samp_i = samples[i,:,:]
samples_i = np.zeros((28*2,28*2))
for j in range(4):
samp_ij = samp_i[:,samp_index[j]]
samp_ij = np.reshape(samp_ij,(28,28))
if j<2:
samples_i[j*28:(j+1)*28,0:28] = samp_ij
else:
samples_i[(j-2)*28:(j-1)*28,28:2*28] = samp_ij
plt.subplot(n_plots,5,i*5+1)
plt.imshow(target_i)
plt.title('Target')
plt.subplot(n_plots,5,i*5+2)
plt.imshow(hf_i)
plt.title('High Fidelity Measurements')
plt.subplot(n_plots,5,i*5+3)
plt.imshow(mu_i)
plt.title('Reconstructed Mean')
plt.subplot(n_plots,5,i*5+4)
plt.imshow(sig_i)
plt.title('Reconstructed STD')
plt.subplot(n_plots,5,i*5+5)
plt.imshow(samples_i)
plt.title('Reconstructed Samples')