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diff --git a/python/gui/dpd/Align.py b/python/gui/dpd/Align.py
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+# -*- coding: utf-8 -*-
+#
+# DPD Computation Engine, utility to do subsample alignment.
+#
+#   Copyright (c) 2017 Andreas Steger
+#   Copyright (c) 2018 Matthias P. Braendli
+#
+#    http://www.opendigitalradio.org
+#
+#   This file is part of ODR-DabMod.
+#
+#   ODR-DabMod is free software: you can redistribute it and/or modify
+#   it under the terms of the GNU General Public License as
+#   published by the Free Software Foundation, either version 3 of the
+#   License, or (at your option) any later version.
+#
+#   ODR-DabMod is distributed in the hope that it will be useful,
+#   but WITHOUT ANY WARRANTY; without even the implied warranty of
+#   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#   GNU General Public License for more details.
+#
+#   You should have received a copy of the GNU General Public License
+#   along with ODR-DabMod.  If not, see <http://www.gnu.org/licenses/>.
+import datetime
+import os
+import numpy as np
+from scipy import optimize
+import matplotlib.pyplot as plt
+
+def gen_omega(length):
+    if (length % 2) == 1:
+        raise ValueError("Needs an even length array.")
+
+    halflength = int(length / 2)
+    factor = 2.0 * np.pi / length
+
+    omega = np.zeros(length, dtype=np.float)
+    for i in range(halflength):
+        omega[i] = factor * i
+
+    for i in range(halflength, length):
+        omega[i] = factor * (i - length)
+
+    return omega
+
+
+def subsample_align(sig, ref_sig, plot_location=None):
+    """Do subsample alignment for sig relative to the reference signal
+    ref_sig. The delay between the two must be less than sample
+
+    Returns the aligned signal"""
+
+    n = len(sig)
+    if (n % 2) == 1:
+        raise ValueError("Needs an even length signal.")
+    halflen = int(n / 2)
+
+    fft_sig = np.fft.fft(sig)
+
+    omega = gen_omega(n)
+
+    def correlate_for_delay(tau):
+        # A subsample offset between two signals corresponds, in the frequency
+        # domain, to a linearly increasing phase shift, whose slope
+        # corresponds to the delay.
+        #
+        # Here, we build this phase shift in rotate_vec, and multiply it with
+        # our signal.
+
+        rotate_vec = np.exp(1j * tau * omega)
+        # zero-frequency is rotate_vec[0], so rotate_vec[N/2] is the
+        # bin corresponding to the [-1, 1, -1, 1, ...] time signal, which
+        # is both the maximum positive and negative frequency.
+        # I don't remember why we handle it differently.
+        rotate_vec[halflen] = np.cos(np.pi * tau)
+
+        corr_sig = np.fft.ifft(rotate_vec * fft_sig)
+
+        return -np.abs(np.sum(np.conj(corr_sig) * ref_sig))
+
+    optim_result = optimize.minimize_scalar(correlate_for_delay, bounds=(-1, 1), method='bounded',
+                                            options={'disp': True})
+
+    if optim_result.success:
+        best_tau = optim_result.x
+
+        if plot_location is not None:
+            corr = np.vectorize(correlate_for_delay)
+            ixs = np.linspace(-1, 1, 100)
+            taus = corr(ixs)
+
+            dt = datetime.datetime.now().isoformat()
+            tau_path = (plot_location + "/" + dt + "_tau.png")
+            plt.plot(ixs, taus)
+            plt.title("Subsample correlation, minimum is best: {}".format(best_tau))
+            plt.savefig(tau_path)
+            plt.close()
+
+        # Prepare rotate_vec = fft_sig with rotated phase
+        rotate_vec = np.exp(1j * best_tau * omega)
+        rotate_vec[halflen] = np.cos(np.pi * best_tau)
+        return np.fft.ifft(rotate_vec * fft_sig).astype(np.complex64)
+    else:
+        # print("Could not optimize: " + optim_result.message)
+        return np.zeros(0, dtype=np.complex64)
+
+def phase_align(sig, ref_sig, plot_location=None):
+    """Do phase alignment for sig relative to the reference signal
+    ref_sig.
+
+    Returns the aligned signal"""
+
+    angle_diff = (np.angle(sig) - np.angle(ref_sig)) % (2. * np.pi)
+
+    real_diffs = np.cos(angle_diff)
+    imag_diffs = np.sin(angle_diff)
+
+    if plot_location is not None:
+        dt = datetime.datetime.now().isoformat()
+        fig_path = plot_location + "/" + dt + "_phase_align.png"
+
+        plt.subplot(511)
+        plt.hist(angle_diff, bins=60, label="Angle Diff")
+        plt.xlabel("Angle")
+        plt.ylabel("Count")
+        plt.legend(loc=4)
+
+        plt.subplot(512)
+        plt.hist(real_diffs, bins=60, label="Real Diff")
+        plt.xlabel("Real Part")
+        plt.ylabel("Count")
+        plt.legend(loc=4)
+
+        plt.subplot(513)
+        plt.hist(imag_diffs, bins=60, label="Imaginary Diff")
+        plt.xlabel("Imaginary Part")
+        plt.ylabel("Count")
+        plt.legend(loc=4)
+
+        plt.subplot(514)
+        plt.plot(np.angle(ref_sig[:128]), label="ref_sig")
+        plt.plot(np.angle(sig[:128]), label="sig")
+        plt.xlabel("Angle")
+        plt.ylabel("Sample")
+        plt.legend(loc=4)
+
+    real_diff = np.median(real_diffs)
+    imag_diff = np.median(imag_diffs)
+
+    angle = np.angle(real_diff + 1j * imag_diff)
+
+    #logging.debug( "Compensating phase by {} rad, {} degree. real median {}, imag median {}".format( angle, angle*180./np.pi, real_diff, imag_diff))
+    sig = sig * np.exp(1j * -angle)
+
+    if plot_location is not None:
+        plt.subplot(515)
+        plt.plot(np.angle(ref_sig[:128]), label="ref_sig")
+        plt.plot(np.angle(sig[:128]), label="sig")
+        plt.xlabel("Angle")
+        plt.ylabel("Sample")
+        plt.legend(loc=4)
+        plt.tight_layout()
+        plt.savefig(fig_path)
+        plt.close()
+
+    return sig