import numpy as np
from numpy import linalg

import common
from config import Ts, Nsym


class Filter(object):
    def __init__(self, b, a):
        self.b = np.array(b) / a[0]
        self.a = np.array(a[1:]) / a[0]

    def __call__(self, x):
        x_ = [0] * len(self.b)
        y_ = [0] * len(self.a)
        for v in x:
            x_ = [v] + x_[:-1]
            y = np.dot(x_, self.b) - np.dot(y_, self.a)
            y_ = [y] + y_[1:]
            yield y


def train(S, training):
    A = np.array([S[1:], S[:-1], training[:-1]]).T
    b = training[1:]
    b0, b1, a1 = linalg.lstsq(A, b)[0]
    return Filter(b=[b0, b1], a=[1, -a1])


class QAM(object):
    def __init__(self, symbols):
        self._enc = {}
        symbols = np.array(list(symbols))
        bits_per_symbol = np.log2(len(symbols))
        bits_per_symbol = np.round(bits_per_symbol)
        N = (2 ** bits_per_symbol)
        assert N == len(symbols)
        bits_per_symbol = int(bits_per_symbol)

        for i, v in enumerate(symbols):
            bits = tuple(int(i & (1 << j) != 0) for j in range(bits_per_symbol))
            self._enc[bits] = v

        self._dec = {v: k for k, v in self._enc.items()}
        self.symbols = symbols
        self.bits_per_symbol = bits_per_symbol

    def encode(self, bits):
        for _, bits_tuple in common.iterate(bits, self.bits_per_symbol, tuple):
            yield self._enc[bits_tuple]

    def decode(self, symbols, error_handler=None):
        for s in symbols:
            index = np.argmin(np.abs(s - self.symbols))
            S = self.symbols[index]
            if error_handler:
                error_handler(received=s, decoded=S)
            yield self._dec[S]


class MODEM(object):
    def __init__(self, config):
        self.qam = QAM(config.symbols)
        self.baud = config.baud
        self.freqs = config.frequencies
        self.bits_per_baud = self.qam.bits_per_symbol * len(self.freqs)
        self.modem_bps = self.baud * self.bits_per_baud


def clip(x, lims):
    return min(max(x, lims[0]), lims[1])


def power(x):
    return np.dot(x.conj(), x).real / len(x)


def exp_iwt(freq, n):
    iwt = 2j * np.pi * freq * np.arange(n) * Ts
    return np.exp(iwt)


def norm(x):
    return np.sqrt(np.dot(x.conj(), x).real)


def coherence(x, freq):
    n = len(x)
    Hc = exp_iwt(-freq, n) / np.sqrt(0.5*n)
    norm_x = norm(x)
    if norm_x:
        return np.dot(Hc, x) / norm_x
    else:
        return 0.0


def extract_symbols(x, freq, offset=0):
    Hc = exp_iwt(-freq, Nsym) / (0.5*Nsym)

    for _, symbol in common.iterate(x, Nsym):
        yield np.dot(Hc, symbol)


def linear_regression(x, y):
    ''' Find (a,b) such that y = a*x + b. '''
    x = np.array(x)
    y = np.array(y)
    ones = np.ones(len(x))
    M = np.array([x, ones]).T
    a, b = linalg.lstsq(M, y)[0]
    return a, b