import numpy as np
import scipy
import matplotlib.pyplot as plt
import src.subsample_align as sa
from scipy import signal
import logging

class Dab_Util:
    """Collection of methods that can be applied to an array
     complex IQ samples of a DAB signal
     """
    def __init__(self, sample_rate):
        """
        :param sample_rate: sample rate [sample/sec] to use for calculations
        """
        self.sample_rate = sample_rate
        self.dab_bandwidth = 1536000 #Bandwidth of a dab signal
        self.frame_ms = 96           #Duration of a Dab frame

    def lag(self, sig_orig, sig_rec):
        """
        Find lag between two signals
        Args:
            sig_orig: The signal that has been sent
            sig_rec: The signal that has been recored
        """
        off = sig_rec.shape[0]
        c = signal.correlate(sig_orig, sig_rec)
        return np.argmax(c) - off + 1

    def lag_upsampling(self, sig_orig, sig_rec, n_up):
        sig_orig_up = signal.resample(sig_orig, sig_orig.shape[0] * n_up)
        sig_rec_up  = signal.resample(sig_rec, sig_rec.shape[0] * n_up)
        l = self.lag(sig_orig_up, sig_rec_up)
        l_orig = float(l) / n_up
        return l_orig

    def subsample_align_upsampling(self, sig1, sig2, n_up=32):
        """
        Returns an aligned version of sig1 and sig2 by cropping and subsample alignment
        Using upsampling
        """
        assert(sig1.shape[0] == sig2.shape[0])

        if sig1.shape[0] % 2 == 1:
            sig1 = sig1[:-1]
            sig2 = sig2[:-1]

        sig1_up = signal.resample(sig1, sig1.shape[0] * n_up)
        sig2_up = signal.resample(sig2, sig2.shape[0] * n_up)

        off_meas = self.lag_upsampling(sig2_up, sig1_up, n_up=1)
        off = int(abs(off_meas))

        if off_meas > 0:
            sig1_up = sig1_up[:-off]
            sig2_up = sig2_up[off:]
        elif off_meas < 0:
            sig1_up = sig1_up[off:]
        sig2_up = sig2_up[:-off]

        sig1 = signal.resample(sig1_up, sig1_up.shape[0] / n_up).astype(np.complex64)
        sig2 = signal.resample(sig2_up, sig2_up.shape[0] / n_up).astype(np.complex64)
        return sig1, sig2

    def subsample_align(self, sig1, sig2):
        """
        Returns an aligned version of sig1 and sig2 by cropping and subsample alignment
        """
        logging.debug("Sig1_orig: %d %s, Sig2_orig: %d %s" % (len(sig1), sig1.dtype, len(sig2), sig2.dtype))
        off_meas = self.lag_upsampling(sig2, sig1, n_up=1)
        off = int(abs(off_meas))

        if off_meas > 0:
            sig1 = sig1[:-off]
            sig2 = sig2[off:]
        elif off_meas < 0:
            sig1 = sig1[off:]
            sig2 = sig2[:-off]

        if off % 2 == 1:
            sig1 = sig1[:-1]
            sig2 = sig2[:-1]

        sig2 = sa.subsample_align(sig2, sig1)
        logging.debug("Sig1_cut: %d %s, Sig2_cut: %d %s, off: %d" % (len(sig1), sig1.dtype, len(sig2), sig2.dtype, off))
        return sig1, sig2

    def fromfile(self, filename, offset=0, length=None):
        if length is None:
            return np.memmap(filename, dtype=np.complex64, mode='r', offset=64/8*offset)
        else:
            return np.memmap(filename, dtype=np.complex64, mode='r', offset=64/8*offset, shape=length)