Merge LUT into 'next_memless'

1 files changed, 2 insertions, 387 deletions

diff --git a/dpd/src/Model.py b/dpd/src/Model.py
index a23f0ce..7ce6171 100644
--- a/dpd/src/Model.py
+++ b/dpd/src/Model.py
@@ -1,388 +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 PolyModel:
-    """Calculates new coefficients using the measurement and the old
-    coefficients"""
-
-    def __init__(self,
-                 c,
-                 SA,
-                 MER,
-                 coefs_am,
-                 coefs_pm,
-                 learning_rate_am=1.,
-                 learning_rate_pm=1.,
-                 plot=False):
-        logging.debug("Initialising Poly Model")
-        self.c = c
-        self.SA = SA
-        self.MER = MER
-
-        self.learning_rate_am = learning_rate_am
-        self.learning_rate_pm = learning_rate_pm
-
-        if coefs_am is None:
-            self.coefs_am = [1.0, 0, 0, 0, 0]
-        else:
-            self.coefs_am = coefs_am
-        self.coefs_am_history = [coefs_am, ]
-        self.mses_am = []
-        self.errs_am = []
-
-        self.tx_mers = []
-        self.rx_mers = []
-
-        if coefs_pm is None:
-            self.coefs_pm = [0, 0, 0, 0, 0]
-        else:
-            self.coefs_pm = coefs_pm
-        self.coefs_pm_history = [coefs_pm, ]
-        self.errs_pm = []
-
-        self.plot = plot
-
-    def train(self, tx_dpd, rx_received):
-        """Give new training data to the model"""
-        # 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"
-
-        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(-60, 60)
-            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)
-            fig.clf()
-
-        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)
-
-    def get_dpd_data(self):
-        return "poly", self.coefs_am, self.coefs_pm
-
-    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))
-
-        reg = linear_model.Ridge(alpha=0.00001)
-        reg.fit(rx_A, err)
-        a_delta = reg.coef_
-        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.clf()
-        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)))
-
-        reg = linear_model.Ridge(alpha=0.00001)
-        reg.fit(phase_A, err_phase)
-        p_delta = reg.coef_
-        new_coefs_pm = self.coefs_pm - self.learning_rate_pm * p_delta
-
-        return new_coefs_pm, phase_diff_choice
-
-class LutModel:
-    """Implements a model that calculates lookup table coefficients"""
-
-    def __init__(self,
-                 c,
-                 SA,
-                 MER,
-                 learning_rate=1.,
-                 plot=False):
-        logging.debug("Initialising LUT Model")
-        self.c = c
-        self.SA = SA
-        self.MER = MER
-        self.learning_rate = learning_rate
-        self.plot = plot
-
-    def train(self, tx_dpd, rx_received):
-        pass
-
-    def get_dpd_data(self):
-        return ("lut", np.ones(32, dtype=np.complex64))
-
-# The MIT License (MIT)
-#
-# Copyright (c) 2017 Andreas Steger
-# Copyright (c) 2017 Matthias P. Braendli
-#
-# 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