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path: root/dpd/src/Symbol_align.py
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# -*- coding: utf-8 -*-
#
# DPD Computation Engine, Modulation Error Rate.
#
# http://www.opendigitalradio.org
# Licence: The MIT License, see notice at the end of this file

import datetime
import os
import logging

try:
    logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename)
except:
    logging_path = "/tmp/"

import numpy as np
import scipy
import matplotlib

matplotlib.use('agg')
import matplotlib.pyplot as plt


def _remove_outliers(x, stds=5):
    deviation_from_mean = np.abs(x - np.mean(x))
    inlier_idxs = deviation_from_mean < stds * np.std(x)
    x = x[inlier_idxs]
    return x


def _calc_delta_angle(fft):
    # Introduce invariance against carrier
    angles = np.angle(fft) % (np.pi / 2.)

    # Calculate Angle difference and compensate jumps
    deltas_angle = np.diff(angles)
    deltas_angle[deltas_angle > np.pi / 4.] = \
        deltas_angle[deltas_angle > np.pi / 4.] - np.pi / 2.
    deltas_angle[-deltas_angle > np.pi / 4.] = \
        deltas_angle[-deltas_angle > np.pi / 4.] + np.pi / 2.
    deltas_angle = _remove_outliers(deltas_angle)

    delta_angle = np.mean(deltas_angle)

    return delta_angle


class Symbol_align:
    """
    Find the phase offset to the start of the DAB symbols in an
    unaligned dab signal.
    """

    def __init__(self, c, plot=False):
        self.c = c
        self.plot = plot
        pass

    def _calc_offset_to_first_symbol_without_prefix(self, tx):
        tx_orig = tx[0:-self.c.T_U]
        tx_cut_prefix = tx[self.c.T_U:]

        tx_product = np.abs(tx_orig - tx_cut_prefix)
        tx_product_avg = np.correlate(
            tx_product,
            np.ones(self.c.T_C),
            mode='valid')
        tx_product_avg_min_filt = \
            scipy.ndimage.filters.minimum_filter1d(
                tx_product_avg,
                int(1.5 * self.c.T_S)
            )
        peaks = np.ravel(np.where(tx_product_avg == tx_product_avg_min_filt))

        offset = peaks[np.argmin([tx_product_avg[peak] for peak in peaks])]

        if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot:
            dt = datetime.datetime.now().isoformat()
            fig_path = logging_path + "/" + dt + "_Symbol_align.svg"

            fig = plt.figure(figsize=(9, 9))

            ax = fig.add_subplot(4, 1, 1)
            ax.plot(tx_product)
            ylim = ax.get_ylim()
            for peak in peaks:
                ax.plot((peak, peak), (ylim[0], ylim[1]))
                if peak == offset:
                    ax.text(peak, ylim[0] + 0.3 * np.diff(ylim), "offset", rotation=90)
                else:
                    ax.text(peak, ylim[0] + 0.2 * np.diff(ylim), "peak", rotation=90)
            ax.set_xlabel("Sample")
            ax.set_ylabel("Conj. Product")
            ax.set_title("Difference with shifted self")

            ax = fig.add_subplot(4, 1, 2)
            ax.plot(tx_product_avg)
            ylim = ax.get_ylim()
            for peak in peaks:
                ax.plot((peak, peak), (ylim[0], ylim[1]))
                if peak == offset:
                    ax.text(peak, ylim[0] + 0.3 * np.diff(ylim), "offset", rotation=90)
                else:
                    ax.text(peak, ylim[0] + 0.2 * np.diff(ylim), "peak", rotation=90)
            ax.set_xlabel("Sample")
            ax.set_ylabel("Conj. Product")
            ax.set_title("Moving Average")

            ax = fig.add_subplot(4, 1, 3)
            ax.plot(tx_product_avg_min_filt)
            ylim = ax.get_ylim()
            for peak in peaks:
                ax.plot((peak, peak), (ylim[0], ylim[1]))
                if peak == offset:
                    ax.text(peak, ylim[0] + 0.3 * np.diff(ylim), "offset", rotation=90)
                else:
                    ax.text(peak, ylim[0] + 0.2 * np.diff(ylim), "peak", rotation=90)
            ax.set_xlabel("Sample")
            ax.set_ylabel("Conj. Product")
            ax.set_title("Min Filter")

            ax = fig.add_subplot(4, 1, 4)
            tx_product_crop = tx_product[peaks[0] - 50:peaks[0] + 50]
            x = range(tx_product.shape[0])[peaks[0] - 50:peaks[0] + 50]
            ax.plot(x, tx_product_crop)
            ylim = ax.get_ylim()
            ax.plot((peaks[0], peaks[0]), (ylim[0], ylim[1]))
            ax.set_xlabel("Sample")
            ax.set_ylabel("Conj. Product")
            ax.set_title("Difference with shifted self")

            fig.tight_layout()
            fig.savefig(fig_path)
            plt.close(fig)

        # "offset" measures where the shifted signal matches the
        # original signal. Therefore we have to subtract the size
        # of the shift to find the offset of the symbol start.
        return (offset + self.c.T_C) % self.c.T_S

    def _delta_angle_to_samples(self, angle):
        return - angle / self.c.phase_offset_per_sample

    def _calc_sample_offset(self, sig):
        assert sig.shape[0] == self.c.T_U, \
            "Input length is not a Symbol without cyclic prefix"

        fft = np.fft.fftshift(np.fft.fft(sig))
        fft_crop = np.delete(fft[self.c.FFT_start:self.c.FFT_end], self.c.FFT_delete)
        delta_angle = _calc_delta_angle(fft_crop)
        delta_sample = self._delta_angle_to_samples(delta_angle)
        delta_sample_int = np.round(delta_sample).astype(int)
        error = np.abs(delta_sample_int - delta_sample)
        if error > 0.1:
            raise RuntimeError("Could not calculate " \
                               "sample offset. Error {}".format(error))
        return delta_sample_int

    def calc_offset(self, tx):
        """Calculate the offset the first symbol"""
        off_sym = self._calc_offset_to_first_symbol_without_prefix(
            tx)
        off_sam = self._calc_sample_offset(
            tx[off_sym:off_sym + self.c.T_U])
        off = (off_sym + off_sam) % self.c.T_S

        assert self._calc_sample_offset(tx[off:off + self.c.T_U]) == 0, \
            "Failed to calculate offset"
        return off

    def crop_symbol_without_cyclic_prefix(self, tx):
        off = self.calc_offset(tx)
        return tx[
               off:
               off + self.c.T_U
               ]

# The MIT License (MIT)
#
# Copyright (c) 2017 Andreas Steger
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.