-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathFunctions_overlap.py
179 lines (143 loc) · 6.6 KB
/
Functions_overlap.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
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
import numpy as np
import os
import matplotlib.pyplot as plt
from matplotlib.cm import ScalarMappable
def execute(file1):
#File open for trace
file_path1 = file1
if not os.path.exists(file_path1):
print(f"File '{file_path1}' does not exist.")
else:
with open(file_path1, "r") as file1:
lines1 = file1.readlines()
num_lines1 = len(lines1)
t_actual = []
for line1 in lines1:
columns1 = line1.strip().split()
if len(columns1) >= 2:
t_actual.append(float(columns1[1]))
t_actual = np.array(t_actual)
return t_actual
def execute1(file2):
#File open for Comp_wavelet
file_path2 = file2
if not os.path.exists(file_path2):
print(f"File '{file_path2}' does not exist.")
else:
with open(file_path2, "r") as file2:
lines2 = file2.readlines()
num_lines2 = len(lines2)
t1 = []
for line2 in lines2:
columns2 = line2.strip().split()
if len(columns2) >= 2:
t1.append(float(columns2[1]))
t1 = np.array(t1)
return t1
#Function which calculates corr & phase angle
def calculate_cross_correlation(t1, t_actual):
cross_correlation_percentages = []
phase_differences=[]
t1_length = len(t1)
t_actual_length = len(t_actual)
part_length = 20
length = []
length_value = []
for i in range(0, t_actual_length, part_length):
t1_part = t_actual[i:i+part_length]
t_actual_part = t_actual[i:i+part_length]
t1_part = np.array(t1_part)
phase = np.angle(t1_part)
new_phase = phase + np.pi/2
rotated_wave = np.abs(t1_part) * np.exp(1j * new_phase)
t1_part = np.real(rotated_wave)
j = 1400 + 2*i
length.append(j)
length_value.append(t_actual[i])
#Normalisation is not required in this data
#t1_norm = (t1_part - np.mean(t1_part)) / np.std(t1_part)
#t_actual_norm = (t_actual_part - np.mean(t_actual_part)) / np.std(t_actual_part)
#autocorrelation
#t1_auto = np.corrcoef(t1_part,rowvar=True)
#t_actual_auto = np.corrcoef(t_actual_part,rowvar=True)
#Corr
cross_correlation = np.correlate(t1_part, t_actual_part, "full")
max_cross_correlation = np.max(cross_correlation)
peak_index = np.argmax(cross_correlation)
#Convert peak position to time lag
lag = peak_index - len(t1_part) + 1
sampling_rate = 20
#Convert lag to phase difference
frequency = 1 / sampling_rate
phase_difference = 360 * frequency * lag
condition_met = False
#if(phase_difference != 90 or phase_difference != -90 and not condition_met):
# condition_met = True
# part_length = 40
#continue
#%age
cross_correlation_percentage = (max_cross_correlation / (np.linalg.norm(t1_part) * np.linalg.norm(t_actual_part))) * 100
cross_correlation_percentages.append(cross_correlation_percentage)
phase_differences.append(phase_difference)
return cross_correlation_percentages,phase_differences, length, length_value
#t1 = np.array([-0.393105, -0.822565, -1.32783, -1.84869, -2.30533, -2.52846, -2.40997, -1.97941, -1.24355, -0.517542, 0.0990675, 1.13414, 2.17622, 3.01957, 3.55358, 3.72256, 3.47835, 2.7966, 1.96388, 1.23775])
#t1 = np.array([0.865257, 2.09646, 3.55949, 5.0869, 6.39701, 7.34355, 7.71642, 7.22113, 5.87831, 3.78083, 1.15362, -1.68, -4.35489, -6.53074, -7.95374, -8.48222, -8.08787, -7.15307, -5.63978, -3.91215])
#t1 = np.array([-0.0413589, -0.0744249, -0.108228, -0.161074, -0.224722, -0.291076, -0.334128, -0.333282, -0.271283, -0.153474, -0.00260863, 0.153474, 0.271283, 0.333282, 0.334128, 0.291076, 0.224722, 0.161074, 0.108228,0.0744249])
#cross_correlation_percentages,phase_differences,length,length_value = calculate_cross_correlation(t1, t_actual)
# for i, percentage in enumerate(cross_correlation_percentages):
# print(f"Cross-correlation percentage for part {i+1}: {percentage}")
# for i, phase in enumerate(phase_differences):
# print(f"Phase for part {i+1}: {phase}")
#print(length)
#print(length_value)
#print(phase_differences)
#plotting
def plot(length, length_value, phase_difference):
filename = 'data.txt'
delimiter = ' , '
new_list = [num for num in range(length[0], length[-1]+1, 2)]
patterns = [[var]*20 for var in phase_difference]
phase_list = [item for pattern in patterns for item in pattern]
with open(filename, 'w') as file:
for item1, item2, item3 in zip(new_list, length_value, phase_list):
line = f"{item1}{delimiter}{item2}{delimiter}{item3}\n"
file.write(line)
data = np.loadtxt(filename, delimiter=',')
time = data[:, 0]
amplitude = data[:, 1]
phase = data[:, 2]
#phase = [-x for x in phase]
scaling_factor = 5 #scaling factor changed as needed
amplitude_scaled = amplitude.copy()
amplitude_scaled[phase == -90] *= scaling_factor
amplitude_scaled[phase == 90] *= scaling_factor
amplitude_scaled[phase == 90] *= scaling_factor
#amplitude_scaled[phase == 0] *= scaling_factor
#amplitude_scaled[phase == 180] *= scaling_factor
#amplitude_scaled[phase == -180] *= scaling_factor
fig, ax = plt.subplots(figsize=(10, 6))
time = time[::-1]
phase = phase[::-1]
amplitude_scaled = amplitude_scaled[::-1]
line_offsets = np.arange(len(time))
plt.fill_betweenx(line_offsets, phase - amplitude_scaled, phase + amplitude_scaled,
where=(amplitude_scaled > 0), facecolor='red', interpolate=True, alpha=0.5)
plt.fill_betweenx(line_offsets, phase - amplitude_scaled, phase + amplitude_scaled,
where=(amplitude_scaled < 0), facecolor='blue', interpolate=True, alpha=0.5)
cmap = plt.cm.bwr
norm = plt.Normalize(vmin=np.min(amplitude_scaled), vmax=np.max(amplitude_scaled))
sm = ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
cbar = plt.colorbar(sm, ax=ax, label='Amplitude')
cbar.ax.invert_yaxis()
plt.xlabel('Phase (Degrees)')
plt.ylabel('Time (ms)')
plt.title('Variable Density Wiggle Plot')
tick_interval = max(1, int(len(time) / 2))
ax.set_yticks(np.arange(0, len(time), tick_interval))
ax.set_yticklabels(time[::tick_interval])
ax.set_xticks(np.arange(-180, 181, 60))
ax.set_xticklabels(np.arange(-180, 181, 60))
plt.axvline(x=2, color='red', linestyle='dotted')
# Show the plot
plt.show()