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