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authorMatthias P. Braendli <matthias.braendli@mpb.li>2017-09-14 16:30:52 +0200
committerMatthias P. Braendli <matthias.braendli@mpb.li>2017-09-14 16:30:52 +0200
commitef78d66f6b3afc47121f7352d961943fa29d1518 (patch)
treef3d02bbef9e60d4d4ed89527e84ae802857acfdc /dpd/src/Model.py
parent1ca5368f547c429bf0d86dac78162310e1d2b032 (diff)
parentbf32d4e1efb87eb7a51207281f2565ee54e1aee2 (diff)
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Merge LUT into 'next_memless'
Diffstat (limited to 'dpd/src/Model.py')
-rw-r--r--dpd/src/Model.py389
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