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https://github.com/romanz/amodem.git
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126 lines
3.1 KiB
Python
126 lines
3.1 KiB
Python
import numpy as np
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from . import common
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class FIR(object):
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def __init__(self, h):
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self.h = np.array(h)
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self.x_state = [0] * len(self.h)
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def __call__(self, x):
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x_ = self.x_state
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h = self.h
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for v in x:
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x_ = [v] + x_[:-1]
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yield np.dot(x_, h)
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self.x_state = x_
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class Demux(object):
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def __init__(self, sampler, omegas, Nsym):
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self.Nsym = Nsym
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self.filters = [exp_iwt(-w, Nsym) / (0.5*self.Nsym) for w in omegas]
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self.filters = np.array(self.filters)
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self.sampler = sampler
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def __iter__(self):
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return self
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def next(self):
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frame = self.sampler.take(size=self.Nsym)
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if len(frame) == self.Nsym:
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return np.dot(self.filters, frame)
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else:
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raise StopIteration
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__next__ = next
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def exp_iwt(omega, n):
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return np.exp(1j * omega * np.arange(n))
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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 rms(x):
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return np.mean(np.abs(x) ** 2, axis=0) ** 0.5
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def coherence(x, omega):
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n = len(x)
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Hc = exp_iwt(-omega, n) / np.sqrt(0.5*n)
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norm_x = norm(x)
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if norm_x:
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return np.dot(Hc, x) / norm_x
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else:
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return 0.0
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def linear_regression(x, y):
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''' Find (a,b) such that y = a*x + b. '''
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x = np.array(x)
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y = np.array(y)
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mean_x = np.mean(x)
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mean_y = np.mean(y)
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x_ = x - mean_x
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y_ = y - mean_y
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a = np.dot(y_, x_) / np.dot(x_, x_)
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b = mean_y - a * mean_x
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return a, b
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class MODEM(object):
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def __init__(self, symbols):
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self.encode_map = {}
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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 = [int(i & (1 << j) != 0) for j in range(bits_per_symbol)]
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self.encode_map[tuple(bits)] = v
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self.symbols = symbols
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self.bits_per_symbol = bits_per_symbol
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bits_map = dict(item[::-1] for item in self.encode_map.items())
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self.decode_list = [(s, bits_map[s]) for s in self.symbols]
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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.encode_map[bits_tuple]
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def decode(self, symbols, error_handler=None):
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''' Maximum-likelihood decoding, using naive nearest-neighbour. '''
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symbols_vec = self.symbols
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_dec = self.decode_list
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for received in symbols:
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error = np.abs(symbols_vec - received)
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index = np.argmin(error)
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decoded, bits = _dec[index]
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if error_handler:
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error_handler(received=received, decoded=decoded)
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yield bits
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def prbs(reg, poly, bits):
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''' Simple pseudo-random number generator. '''
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mask = (1 << bits) - 1
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size = 0 # effective register size (in bits)
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while (poly >> size) > 1:
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size += 1
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while True:
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yield reg & mask
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reg = reg << 1
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if reg >> size:
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reg = reg ^ poly
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