-
-
Notifications
You must be signed in to change notification settings - Fork 26
/
generate_report_data.py
155 lines (121 loc) · 6.14 KB
/
generate_report_data.py
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
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
import os
import pickle
import matplotlib.pyplot as plt
from pprint import pprint
import numpy as np
from pa_result import PaResult
from visualization import generate_color_map, generate_3d_height_map
import statistics
from scipy.stats import gaussian_kde
from collections import Counter
from pathlib import Path
def plot_scatter(data_clean: list[tuple[float, float]], dataset: str, ax):
x, y = list(zip(*data_clean))
p = np.polyfit(x, y, 3)
trendline_x = np.linspace(min(x), max(x), 100)
# Calculate the R-squared value
y_pred = np.poly1d(p)(x)
ss_res = np.sum((y - y_pred) ** 2)
ss_tot = np.sum((y - np.mean(y)) ** 2)
r2 = 1 - (ss_res / ss_tot)
ax.set_title(dataset)
# Calculate the point density
xy = np.vstack([x, y])
z = gaussian_kde(xy)(xy)
ax.scatter(x, y, c=z)
ax.plot(trendline_x, np.poly1d(p)(trendline_x))
ax.set_xlabel("PA Value")
ax.set_ylabel("Deviation")
# add equation and R-squared annotation
# equation = f'y=({p[0]:.2e})x^3+{p[1]:.0f}x^2+{p[2]:.0f}x+{p[3]:.0f}'
equation = f"y=({p[0]:.2e})x^3" \
f"{'-' if p[1]<0 else '+'}{abs(p[1]):.0f}x^2" \
f"{'-' if p[2]<0 else '+'}{abs(p[2]):.0f}x" \
f"{'-' if p[3]<0 else '+'}{abs(p[3]):.0f}"
min_interpolated_value = trendline_x[np.argmin(np.poly1d(p)(trendline_x))]
min_text = f"Interpolated Minimum={min_interpolated_value:.3f}"
r2_text = f'R-squared={r2:.2f}'
ax.annotate(equation + '\n' + r2_text + '\n' + min_text, xy=(0.99, 0.98), xycoords='axes fraction', fontsize=9, ha='right', va='top')
def generate_consistency_chart(data_clean: list[tuple[float, float]], dataset:str, ax):
winning_results = []
for i in range(0, len(data_clean), 10):
x = i
individual_scan = list(sorted(data_clean[x:x+10], key=lambda x: x[1]))
pprint(individual_scan[0])
winning_results.append(individual_scan[0][0])
# pprint(data_clean[x:x+10])
counter = Counter(winning_results)
# fig, ax = plt.subplots()
ax.set_ylabel("Winning Frequency")
ax.set_xlabel("PA Value")
ax.bar(counter.keys(), counter.values(), width=0.06/10)
# Calculate the standard deviation of the winning results
std_dev = statistics.stdev(winning_results)
# Add the standard deviation as an annotation for the plot
ax.annotate(f"Standard deviation: {std_dev:.5e}", xy=(0.02, 0.95), xycoords='axes fraction',
fontsize=10, ha='left', va='top')
# fig.suptitle(dataset)
# return fig
def main():
datasets = [
("matte_black_ambient_light.pkl", "Matte Black Print Bed, Black Filament, Ambient Lighting"),
("matte_black_dark.pkl", "Matte Black Print Bed, Black Filament, No Ambient Light"),
("matte_white_ambient_light.pkl", "Matte Black Print Bed, White Filament, Ambient Lighting"),
("matte_white_dark.pkl", "Matte Black Print Bed, White Filament, No Ambient Light"),
("pei_black_ambient_light.pkl", "PEI Print Bed, Black Filament, Ambient Light"),
("pei_black_dark.pkl", "PEI Print Bed, Black Filament, No Ambient Light"),
("pei_white_ambient_light.pkl", "PEI Print Bed, White Filament, Ambient Light"),
("pei_white_dark.pkl", "PEI Print Bed, White Filament, No Ambient Light"),
("texture_black_ambient_light.pkl", "Textured Print Bed, Black Filament, Ambient Light"),
("texture_black_dark.pkl", "Textured Print Bed, Black Filament, No Ambient Light"),
("texture_white_ambient_light.pkl", "Textured Print Bed, White Filament, Ambient Light"),
("texture_white_dark.pkl", "Textured Print Bed, White Filament, No Ambient Light")
]
fig_scatter, axs_scatter = plt.subplots(3, 4)
fig_bar, axs_bar = plt.subplots(3, 4)
fig_scatter.set_figheight(10)
fig_scatter.set_figwidth(16)
fig_bar.set_figheight(10)
fig_bar.set_figwidth(16)
pad = 5 # in points
cols = ['Black Filament, Ambient Light', 'Black Filament, No Ambient Light', 'White Filament, Ambient Light', 'White Filament, No Ambient Light']
rows = ['Matte Black', 'PEI', 'Textured']
for axs in [axs_bar, axs_scatter]:
for ax, col in zip(axs[0], cols):
ax.annotate(col, xy=(0.5, 1), xytext=(0, pad),
xycoords='axes fraction', textcoords='offset points',
size='large', ha='center', va='baseline')
for ax, row in zip(axs[:,0], rows):
ax.annotate(row, xy=(0, 0.5), xytext=(-ax.yaxis.labelpad - pad, 0),
xycoords=ax.yaxis.label, textcoords='offset points',
size='large', ha='right', va='center')
for i, dataset in enumerate(datasets):
with open(dataset[0], "rb") as f:
# List of tuples where tuples are the PaValue and the PaResult
data: list[tuple[float, PaResult]] = pickle.load(f)
data_clean: list[tuple[float, float]] = list([(x, y.score) for x, y in data])
row = i // 4
col = i % 4
plot_scatter(data_clean, '', axs_scatter[row, col])
generate_consistency_chart(data_clean, dataset[1], axs_bar[row, col])
# Set the same limits for the x and y axes of all subplots
axs_scatter[row, col].set_ylim(0, 380)
axs_bar[row, col].set_xlim(0, 0.06)
axs_bar[row, col].set_ylim(0, 26)
for index, scan in enumerate(data):
print(f"{dataset[0]},{index},{scan[0]:.3f},{scan[1].score}")
# os.makedirs("scan_data_megadump/" + Path(dataset[0]).stem, exist_ok=True)
# chart = generate_3d_height_map(scan[1])
# chart.savefig("scan_data_megadump/" + Path(dataset[0]).stem + "/" + f"{index}_" + f"{scan[0]:.3f}_" + "3d.png")
# plt.close(chart)
# chart = generate_color_map(scan[1])
# chart.savefig("scan_data_megadump/" + Path(dataset[0]).stem + "/" + f"{index}_" + f"{scan[0]:.3f}_" + "color.png")
# plt.close(chart)
# Adjust the spacing between subplots
fig_scatter.tight_layout()
fig_bar.tight_layout()
# fig_scatter.savefig("scan_data_megadump/fig_scatter.png")
# fig_bar.savefig("scan_data_megadump/fig_bar.png")
plt.show()
if __name__=="__main__":
main()