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-rwxr-xr-xpython/dpd/src/subsample_align.py111
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diff --git a/python/dpd/src/subsample_align.py b/python/dpd/src/subsample_align.py
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-# -*- coding: utf-8 -*-
-#
-# DPD Computation Engine, utility to do subsample alignment.
-#
-# http://www.opendigitalradio.org
-# Licence: The MIT License, see notice at the end of this file
-import datetime
-import logging
-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)
-
-# 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.