diff options
Diffstat (limited to 'dpd/src/Model.py')
| -rw-r--r-- | dpd/src/Model.py | 358 |
1 files changed, 2 insertions, 356 deletions
diff --git a/dpd/src/Model.py b/dpd/src/Model.py index fe9c56b..7ce6171 100644 --- a/dpd/src/Model.py +++ b/dpd/src/Model.py @@ -1,357 +1,3 @@ # -*- coding: utf-8 -*- -# -# DPD Calculation Engine, model implementation. -# -# http://www.opendigitalradio.org -# Licence: The MIT License, see notice at the end of this file - -import datetime -import os -import logging - -logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename) - -import numpy as np -import matplotlib.pyplot as plt -from sklearn import linear_model - - -class Model: - """Calculates new coefficients using the measurement and the previous - coefficients""" - - def __init__(self, - c, - SA, - MER, - coefs_am, - coefs_pm, - learning_rate_am=0.1, - learning_rate_pm=0.1, - plot=False): - self.c = c - self.SA = SA - self.MER = MER - - self.learning_rate_am = learning_rate_am - self.learning_rate_pm = learning_rate_pm - - self.coefs_am = coefs_am - self.coefs_am_history = [coefs_am, ] - self.mses_am = [] - self.errs_am = [] - - self.tx_mers = [] - self.rx_mers = [] - - self.coefs_pm = coefs_pm - self.coefs_pm_history = [coefs_pm, ] - self.errs_pm = [] - - self.plot = plot - - def sample_uniformly(self, tx_dpd, rx_received, n_bins=5): - """This function returns tx and rx samples in a way - that the tx amplitudes have an approximate uniform - distribution with respect to the tx_dpd amplitudes""" - mask = np.logical_and((np.abs(tx_dpd) > 0.01), (np.abs(rx_received) > 0.01)) - tx_dpd = tx_dpd[mask] - rx_received = rx_received[mask] - - txframe_aligned_abs = np.abs(tx_dpd) - ccdf_min = 0 - ccdf_max = np.max(txframe_aligned_abs) - tx_hist, ccdf_edges = np.histogram(txframe_aligned_abs, - bins=n_bins, - range=(ccdf_min, ccdf_max)) - n_choise = np.min(tx_hist) - tx_choice = np.zeros(n_choise * n_bins, dtype=np.complex64) - rx_choice = np.zeros(n_choise * n_bins, dtype=np.complex64) - - for idx, bin in enumerate(tx_hist): - indices = np.where((txframe_aligned_abs >= ccdf_edges[idx]) & - (txframe_aligned_abs <= ccdf_edges[idx + 1]))[0] - indices_choise = np.random.choice(indices, - n_choise, - replace=False) - rx_choice[idx * n_choise:(idx + 1) * n_choise] = \ - rx_received[indices_choise] - tx_choice[idx * n_choise:(idx + 1) * n_choise] = \ - tx_dpd[indices_choise] - - assert isinstance(rx_choice[0], np.complex64), \ - "rx_choice is not complex64 but {}".format(rx_choice[0].dtype) - assert isinstance(tx_choice[0], np.complex64), \ - "tx_choice is not complex64 but {}".format(tx_choice[0].dtype) - - return tx_choice, rx_choice - - def dpd_amplitude(self, sig, coefs=None): - if coefs is None: - coefs = self.coefs_am - assert isinstance(sig[0], np.complex64), "Sig is not complex64 but {}".format(sig[0].dtype) - sig_abs = np.abs(sig) - A_sig = np.vstack([np.ones(sig_abs.shape), - sig_abs ** 1, - sig_abs ** 2, - sig_abs ** 3, - sig_abs ** 4, - ]).T - sig_dpd = sig * np.sum(A_sig * coefs, axis=1) - return sig_dpd, A_sig - - def dpd_phase(self, sig, coefs=None): - if coefs is None: - coefs = self.coefs_pm - assert isinstance(sig[0], np.complex64), "Sig is not complex64 but {}".format(sig[0].dtype) - sig_abs = np.abs(sig) - A_phase = np.vstack([np.ones(sig_abs.shape), - sig_abs ** 1, - sig_abs ** 2, - sig_abs ** 3, - sig_abs ** 4, - ]).T - phase_diff_est = np.sum(A_phase * coefs, axis=1) - return phase_diff_est, A_phase - - def _next_am_coefficent(self, tx_choice, rx_choice): - """Calculate new coefficients for AM/AM correction""" - rx_dpd, rx_A = self.dpd_amplitude(rx_choice) - rx_dpd = rx_dpd * ( - np.median(np.abs(tx_choice)) / - np.median(np.abs(rx_dpd))) - err = np.abs(rx_dpd) - np.abs(tx_choice) - mse = np.mean(np.abs((rx_dpd - tx_choice) ** 2)) - self.mses_am.append(mse) - self.errs_am.append(np.mean(err ** 2)) - - a_delta = np.linalg.lstsq(rx_A, err)[0] - new_coefs_am = self.coefs_am - self.learning_rate_am * a_delta - new_coefs_am = new_coefs_am * (self.coefs_am[0] / new_coefs_am[0]) - return new_coefs_am - - def _next_pm_coefficent(self, tx_choice, rx_choice): - """Calculate new coefficients for AM/PM correction - Assuming deviations smaller than pi/2""" - phase_diff_choice = np.angle( - (rx_choice * tx_choice.conjugate()) / - (np.abs(rx_choice) * np.abs(tx_choice)) - ) - plt.hist(phase_diff_choice) - plt.savefig('/tmp/hist_' + str(np.random.randint(0, 1000)) + '.svg') - plt.close() - phase_diff_est, phase_A = self.dpd_phase(rx_choice) - err_phase = phase_diff_est - phase_diff_choice - self.errs_pm.append(np.mean(np.abs(err_phase ** 2))) - - p_delta = np.linalg.lstsq(phase_A, err_phase)[0] - new_coefs_pm = self.coefs_pm - self.learning_rate_pm * p_delta - - return new_coefs_pm, phase_diff_choice - - def get_next_coefs(self, tx_dpd, rx_received): - # Check data type - assert tx_dpd[0].dtype == np.complex64, \ - "tx_dpd is not complex64 but {}".format(tx_dpd[0].dtype) - assert rx_received[0].dtype == np.complex64, \ - "rx_received is not complex64 but {}".format(rx_received[0].dtype) - # Check if signals have same normalization - normalization_error = np.abs(np.median(np.abs(tx_dpd)) - - np.median(np.abs(rx_received))) / ( - np.median(np.abs(tx_dpd)) + np.median(np.abs(rx_received))) - assert normalization_error < 0.01, "Non normalized signals" - - if logging.getLogger().getEffectiveLevel() == logging.DEBUG: - dt = datetime.datetime.now().isoformat() - tx_dpd.tofile(logging_path + "/tx_dpd_" + dt + ".iq") - rx_received.tofile(logging_path + "/rx_received_" + dt + ".iq") - - tx_choice, rx_choice = self.sample_uniformly(tx_dpd, rx_received) - new_coefs_am = self._next_am_coefficent(tx_choice, rx_choice) - new_coefs_pm, phase_diff_choice = self._next_pm_coefficent(tx_choice, rx_choice) - - logging.debug('txframe: min {:.2f}, max {:.2f}, ' \ - 'median {:.2f}; rxframe: min {:.2f}, max {:.2f}, ' \ - 'median {:.2f}; new coefs_am {};' \ - 'new_coefs_pm {}'.format( - np.min(np.abs(tx_dpd)), - np.max(np.abs(tx_dpd)), - np.median(np.abs(tx_dpd)), - np.min(np.abs(rx_choice)), - np.max(np.abs(rx_choice)), - np.median(np.abs(rx_choice)), - new_coefs_am, - new_coefs_pm)) - - if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot: - off = self.SA.calc_offset(tx_dpd) - tx_mer = self.MER.calc_mer(tx_dpd[off:off + self.c.T_U]) - rx_mer = self.MER.calc_mer(rx_received[off:off + self.c.T_U], debug=True) - self.tx_mers.append(tx_mer) - self.rx_mers.append(rx_mer) - - if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot: - dt = datetime.datetime.now().isoformat() - fig_path = logging_path + "/" + dt + "_Model.svg" - - fig = plt.figure(figsize=(2 * 6, 2 * 6)) - - i_sub = 1 - - ax = plt.subplot(4, 2, i_sub) - i_sub += 1 - ax.plot(np.abs(tx_dpd[:128]), - label="TX sent", - linestyle=":") - ax.plot(np.abs(rx_received[:128]), - label="RX received", - color="red") - ax.set_title("Synchronized Signals of Iteration {}" - .format(len(self.coefs_am_history))) - ax.set_xlabel("Samples") - ax.set_ylabel("Amplitude") - ax.text(0, 0, "TX (max {:01.3f}, mean {:01.3f}, " \ - "median {:01.3f})".format( - np.max(np.abs(tx_dpd)), - np.mean(np.abs(tx_dpd)), - np.median(np.abs(tx_dpd)) - ), size=8) - ax.legend(loc=4) - - ax = plt.subplot(4, 2, i_sub) - i_sub += 1 - ccdf_min, ccdf_max = 0, 1 - tx_hist, ccdf_edges = np.histogram(np.abs(tx_dpd), - bins=60, - range=(ccdf_min, ccdf_max)) - tx_hist_normalized = tx_hist.astype(float) / np.sum(tx_hist) - ccdf = 1.0 - np.cumsum(tx_hist_normalized) - ax.semilogy(ccdf_edges[:-1], ccdf, label="CCDF") - ax.semilogy(ccdf_edges[:-1], - tx_hist_normalized, - label="Histogram", - drawstyle='steps') - ax.legend(loc=4) - ax.set_ylim(1e-5, 2) - ax.set_title("Complementary Cumulative Distribution Function") - ax.set_xlabel("TX Amplitude") - ax.set_ylabel("Ratio of Samples larger than x") - - ax = plt.subplot(4, 2, i_sub) - i_sub += 1 - ax.semilogy(np.array(self.mses_am) + 1e-10, label="log(MSE)") - ax.semilogy(np.array(self.errs_am) + 1e-10, label="log(ERR)") - ax.legend(loc=4) - ax.set_title("MSE History") - ax.set_xlabel("Iterations") - ax.set_ylabel("MSE") - - ax = plt.subplot(4, 2, i_sub) - i_sub += 1 - ax.plot(self.tx_mers, label="TX MER") - ax.plot(self.rx_mers, label="RX MER") - ax.legend(loc=4) - ax.set_title("MER History") - ax.set_xlabel("Iterations") - ax.set_ylabel("MER") - - ax = plt.subplot(4, 2, i_sub) - rx_range = np.linspace(0, 1, num=100, dtype=np.complex64) - rx_range_dpd = self.dpd_amplitude(rx_range)[0] - rx_range_dpd_new = self.dpd_amplitude(rx_range, new_coefs_am)[0] - i_sub += 1 - ax.scatter( - np.abs(tx_choice), - np.abs(rx_choice), - s=0.1) - ax.plot(rx_range_dpd / self.coefs_am[0], rx_range, linewidth=0.25, label="current") - ax.plot(rx_range_dpd_new / self.coefs_am[0], rx_range, linewidth=0.25, label="next") - ax.set_ylim(0, 1) - ax.set_xlim(0, 1) - ax.legend() - ax.set_title("Amplifier Characteristic") - ax.set_xlabel("TX Amplitude") - ax.set_ylabel("RX Amplitude") - - ax = plt.subplot(4, 2, i_sub) - i_sub += 1 - coefs_am_history = np.array(self.coefs_am_history) - for idx, coef_hist in enumerate(coefs_am_history.T): - ax.plot(coef_hist, - label="Coef {}".format(idx), - linewidth=0.5) - ax.legend(loc=4) - ax.set_title("AM/AM Coefficient History") - ax.set_xlabel("Iterations") - ax.set_ylabel("Coefficient Value") - - phase_range = np.linspace(0, 1, num=100, dtype=np.complex64) - phase_range_dpd = self.dpd_phase(phase_range)[0] - phase_range_dpd_new = self.dpd_phase(phase_range, - coefs=new_coefs_pm)[0] - ax = plt.subplot(4, 2, i_sub) - i_sub += 1 - ax.scatter( - np.abs(tx_choice), - np.rad2deg(phase_diff_choice), - s=0.1) - ax.plot( - np.abs(phase_range), - np.rad2deg(phase_range_dpd), - linewidth=0.25, - label="current") - ax.plot( - np.abs(phase_range), - np.rad2deg(phase_range_dpd_new), - linewidth=0.25, - label="next") - ax.set_ylim(-180, 180) - ax.set_xlim(0, 1) - ax.legend() - ax.set_title("Amplifier Characteristic") - ax.set_xlabel("TX Amplitude") - ax.set_ylabel("Phase Difference") - - ax = plt.subplot(4, 2, i_sub) - i_sub += 1 - coefs_pm_history = np.array(self.coefs_pm_history) - for idx, coef_phase_hist in enumerate(coefs_pm_history.T): - ax.plot(coef_phase_hist, - label="Coef {}".format(idx), - linewidth=0.5) - ax.legend(loc=4) - ax.set_title("AM/PM Coefficient History") - ax.set_xlabel("Iterations") - ax.set_ylabel("Coefficient Value") - - fig.tight_layout() - fig.savefig(fig_path) - plt.close(fig) - - self.coefs_am = new_coefs_am - self.coefs_am_history.append(self.coefs_am) - self.coefs_pm = new_coefs_pm - self.coefs_pm_history.append(self.coefs_pm) - return self.coefs_am, self.coefs_pm - -# 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. +from src.Model_Poly import Poly +from src.Model_Lut import Lut |