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
|
from os import path
import json
import functools
import numpy as np
import numbers
import math
from scipy import signal as sig
import scipy.fftpack
sampling_rate = 10 # sp/s
# From https://github.com/mubeta06/python/blob/master/signal_processing/sp/gold.py
preferred_pairs = {5:[[2],[1,2,3]], 6:[[5],[1,4,5]], 7:[[4],[4,5,6]],
8:[[1,2,3,6,7],[1,2,7]], 9:[[5],[3,5,6]],
10:[[2,5,9],[3,4,6,8,9]], 11:[[9],[3,6,9]]}
def gen_gold(seq1, seq2):
gold = [seq1, seq2]
for shift in range(len(seq1)):
gold.append(seq1 ^ np.roll(seq2, -shift))
return gold
def gold(n):
n = int(n)
if not n in preferred_pairs:
raise KeyError('preferred pairs for %s bits unknown' % str(n))
t0, t1 = preferred_pairs[n]
(seq0, _st0), (seq1, _st1) = sig.max_len_seq(n, taps=t0), sig.max_len_seq(n, taps=t1)
return gen_gold(seq0, seq1)
def modulate(data, nbits=5):
# 0, 1 -> -1, 1
mask = np.array(gold(nbits))*2 - 1
sel = mask[data>>1]
data_lsb_centered = ((data&1)*2 - 1)
signal = (np.multiply(sel, np.tile(data_lsb_centered, (2**nbits-1, 1)).T).flatten() + 1) // 2
return np.hstack([ np.zeros(len(mask)), signal, np.zeros(len(mask)) ])
def load_noise_meas_params(capture_file):
with open(capture_file, 'rb') as f:
meas_data = np.copy(np.frombuffer(f.read(), dtype='float32'))
meas_data -= np.mean(meas_data)
return (meas_data,)
def mains_noise_measured(seed, n, meas_data):
last_valid = len(meas_data) - n
st = np.random.RandomState(seed)
start = st.randint(last_valid)
return meas_data[start:start+n] + 50.00
def load_noise_synth_params(specfile):
with open(specfile) as f:
d = json.load(f)
return {'spl_x': np.linspace(*d['x_spec']),
'spl_N': d['x_spec'][2],
'psd_spl': (d['t'], d['c'], d['k']) }
def mains_noise_synthetic(seed, n, psd_spl, spl_N, spl_x):
st = np.random.RandomState(seed)
noise = st.normal(size=spl_N) * 2
spec = scipy.fftpack.fft(noise) **2
spec *= np.exp(scipy.interpolate.splev(spl_x, psd_spl))
spec **= 1/2
renoise = scipy.fftpack.ifft(spec)
return renoise[10000:][:n] + 50.00
@functools.lru_cache()
def load_noise_gen(url):
schema, refpath = url.split('://')
if not path.isabs(refpath):
refpath = path.abspath(path.join(path.dirname(__file__), refpath))
if schema == 'meas':
return mains_noise_measured, load_noise_meas_params(refpath)
elif schema == 'synth':
return mains_noise_synthetic, load_noise_synth_params(refpath)
else:
raise ValueError('Invalid schema', schema)
|
