-
Notifications
You must be signed in to change notification settings - Fork 0
/
haar.py
56 lines (42 loc) · 1.39 KB
/
haar.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
import numpy as np
from matplotlib import pyplot as plt
data_len = 400
HAAR_COEFF = 8
signal = np.sin(4*np.pi*np.linspace(0,1,data_len)) + 0.0*np.random.randn(data_len)
def haar(signal,sig_digits):
new_signal = np.zeros(len(signal))
# print int(np.log2(len(signal)))
for level in xrange(int(np.log2(len(signal)))):
new_signal_len = len(signal)/2
new_signal = np.zeros(new_signal_len)
for j in xrange(new_signal_len):
new_signal[j]=signal[2*j]+signal[2*j+1]
if new_signal_len==sig_digits:
break;
signal = new_signal
return new_signal
def interprolate(signal, desired_len):
scale=len(signal)*1.0/desired_len
print "scale", scale
new_signal = np.zeros(desired_len)
for index in xrange(desired_len):
new_index = scale*index
down = int(new_index)
up = np.ceil(new_index)
if (up==len(signal)):
up -= 1
new_signal[index] = (up-new_index)*signal[up]+(new_index-down)*signal[down]
return new_signal
def round_power_two(x):
return 1<<(x-1).bit_length()
def interprolate(signal, desired_len):
return np.interp(np.linspace(0,1,desired_len),np.linspace(0,1,len(signal)),signal)
def haar_transform(signal):
new_data = haar(interprolate(signal,round_power_two(len(signal))),HAAR_COEFF)
dmax=np.amax(new_data)
dmin=np.amin(new_data)
if (dmax==dmin):
return np.zeros(len(new_data))
return 2*((new_data - dmin)*1.0/(dmax - dmin))-1
# plt.plot(haar_transform(signal))
# plt.show()