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105 lines
2.6 KiB
Python
105 lines
2.6 KiB
Python
import numpy as np
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from numpy import linalg
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import common
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class Filter(object):
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def __init__(self, b, a):
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self.b = np.array(b) / a[0]
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self.a = np.array(a[1:]) / a[0]
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def __call__(self, x):
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x_ = [0] * len(self.b)
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y_ = [0] * len(self.a)
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for v in x:
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x_ = [v] + x_[:-1]
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y = np.dot(x_, self.b) - np.dot(y_, self.a)
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y_ = [y] + y_[1:]
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yield y
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def train(S, training):
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A = np.array([S[1:], S[:-1], training[:-1]]).T
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b = training[1:]
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b0, b1, a1 = linalg.lstsq(A, b)[0]
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return Filter(b=[b0, b1], a=[1, -a1])
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class QAM(object):
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def __init__(self, symbols):
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self._enc = {}
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symbols = np.array(list(symbols))
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bits_per_symbol = np.log2(len(symbols))
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bits_per_symbol = np.round(bits_per_symbol)
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N = (2 ** bits_per_symbol)
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assert N == len(symbols)
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bits_per_symbol = int(bits_per_symbol)
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for i, v in enumerate(symbols):
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bits = tuple(int(i & (1 << j) != 0) for j in range(bits_per_symbol))
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self._enc[bits] = v
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self._dec = {v: k for k, v in self._enc.items()}
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self.symbols = symbols
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self.bits_per_symbol = bits_per_symbol
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def encode(self, bits):
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for _, bits_tuple in common.iterate(bits, self.bits_per_symbol, tuple):
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yield self._enc[bits_tuple]
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def decode(self, symbols, error_handler=None):
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for s in symbols:
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index = np.argmin(np.abs(s - self.symbols))
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S = self.symbols[index]
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if error_handler:
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error_handler(received=s, decoded=S)
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yield self._dec[S]
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modulator = QAM(common.symbols)
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bits_per_baud = modulator.bits_per_symbol * len(common.frequencies)
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modem_bps = common.baud * bits_per_baud
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def clip(x, lims):
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return min(max(x, lims[0]), lims[1])
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def power(x):
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return np.dot(x.conj(), x).real / len(x)
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def exp_iwt(freq, n):
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iwt = 2j * np.pi * freq * np.arange(n) * common.Ts
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return np.exp(iwt)
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def norm(x):
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return np.sqrt(np.dot(x.conj(), x).real)
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def coherence(x, freq):
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n = len(x)
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Hc = exp_iwt(-freq, n) / np.sqrt(0.5*n)
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return np.dot(Hc, x) / norm(x)
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def extract_symbols(x, freq, offset=0):
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Hc = exp_iwt(-freq, common.Nsym) / (0.5*common.Nsym)
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func = lambda y: np.dot(Hc, y)
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iterator = common.iterate(x, common.Nsym, func=func)
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for _, symbol in iterator:
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yield symbol
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def drift(S):
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x = np.arange(len(S))
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x = x - np.mean(x)
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y = np.unwrap(np.angle(S)) / (2*np.pi)
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mean_y = np.mean(y)
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y = y - mean_y
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a = np.dot(x, y) / np.dot(x, x)
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return a, mean_y
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