-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathconv.py
133 lines (95 loc) · 3.99 KB
/
conv.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
'''
Author: J. Rafid Siddiqui
Azad-Academy
https://www.azaditech.com
'''
#==================================================================
import numpy as np
from scipy import signal
from matplotlib import pyplot as plt
from matplotlib.animation import FuncAnimation
from matplotlib.patches import Rectangle
class Convolution:
def __init__(self,X,F,G,mode='full',kernel_window_width=4,style='lines',show_area=False):
self.fig = plt.figure()
self.paused = False
self.X = X
self.F = F
self.G = G
self.Y = np.empty((3,len(X)))
self.fig.canvas.mpl_connect('button_press_event', self.pause)
self.fig.canvas.manager.set_window_title('Convolution')
self.ax = self.fig.axes
self.p = [None]*3
self.pf = [None]*3
self.show_area = show_area
self.style = style
self.animation = None
self.kernel_window_width = kernel_window_width
self.cur_pos = None
self.mode=mode
self.styles = ['lines','bars']
def convolve(self):
c = ['b-','r--','g:']
lbls = ['$f(x)$','$g(x)$','$f(x)*g(x)$']
self.cur_pos = np.max(self.X)-self.kernel_window_width
self.Y[0] = self.F(self.X)
self.Y[1] = self.G(self.X)
self.Y[2] = signal.convolve(self.Y[0],self.Y[1],'same')
self.Y[2] = self.Y[2]/np.max(self.Y[2])#int(self.X.shape[0]/2)
if(self.mode=='window'):
self.Y[2]=np.zeros(self.X.shape)
for i in range(3):
if(self.show_area):
self.pf[i] = plt.fill_between(self.X, self.Y[i],step='mid',alpha=0.4)
if(self.style=='lines'):
self.p[i] = plt.plot(self.X, self.Y[i],c[i],linewidth=2,label=lbls[i])
else:
if(i==0):
self.p[i] = plt.bar(self.X, self.Y[i],label=lbls[i],alpha=0.4)
else:
self.p[i] = plt.plot(self.X, self.Y[i],c[i],linewidth=2,label=lbls[i])
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.legend(bbox_to_anchor=(0.5, 1.2), loc='upper center',ncol=3)
plt.tight_layout()
self.fig.canvas.manager.set_window_title('Convolution')
return self.Y[2]
def animate(self,f=200,fps=30):
self.animation = FuncAnimation(self.fig, self.update, frames=f, blit=False, interval=1000/fps, repeat=False)
self.fig.canvas.draw()
def pause(self):
if self.paused:
self.animation.resume()
else:
self.animation.pause()
self.paused ^= True
def update(self,t):
if(self.mode=='window'):
w_min = self.cur_pos
w_max = self.cur_pos + self.kernel_window_width
X = np.linspace(w_min,w_max,self.X.shape[0])
Yg = self.G(X+0.05*t)
Yf = self.F(X)
self.Y[1] = self.G(self.X+0.05*t)
self.Y[2] = np.convolve(Yf,Yg,'same')
self.Y[2] = self.Y[2]/int(self.X.shape[0]/2)
self.p[1][0].set_data(self.X, self.Y[1])
self.p[2][0].set_data(X, self.Y[2])
self.fig.gca().collections.clear()
self.fig.gca().fill_between(X, self.Y[2],step='mid',alpha=0.4,facecolor='g')
self.cur_pos -= 0.05
elif(self.mode=='full'):
X = self.X + 0.05*t
self.Y[1] = self.G(X)
if(self.mode=='both'):
self.Y[0] = self.F(X)
plt.xlim([np.min(X),np.max(X)])
else:
self.Y[0] = self.F(self.X)
self.Y[2] = signal.convolve(self.Y[0],self.Y[1],'same')
self.Y[2] = self.Y[2]/np.max(self.Y[2])
self.p[1][0].set_data(self.X, self.Y[1])
self.p[2][0].set_data(self.X, self.Y[2])
return self.p