# -*- coding: utf-8 -*- import datetime import os import logging logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename) import numpy as np import matplotlib matplotlib.use('agg') import matplotlib.pyplot as plt from sklearn.linear_model import Ridge class Model: """Calculates new coefficients using the measurement and the old coefficients""" def __init__(self, coefs): self.coefs = coefs def get_next_coefs(self, txframe_aligned, rxframe_aligned): rx_abs = np.abs(rxframe_aligned) A = np.vstack([rx_abs, rx_abs**3, rx_abs**5, rx_abs**7, rx_abs**9, ]).T y = np.abs(txframe_aligned) clf = Ridge(alpha=10) clf.fit(A, y) sol = clf.coef_ rx_range = np.linspace(0,1,50) A_range = np.vstack([ rx_range, rx_range**3, rx_range**5, rx_range**7, rx_range**9, ]).T y_est = np.sum(A_range * sol, axis=1) logging.debug("New coefficents {}".format(sol)) if logging.getLogger().getEffectiveLevel() == logging.DEBUG: logging.debug("txframe: min %f, max %f, median %f" % (np.min(np.abs(txframe_aligned)), np.max(np.abs(txframe_aligned)), np.median(np.abs(txframe_aligned)) )) logging.debug("rxframe: min %f, max %f, median %f" % (np.min(np.abs(rxframe_aligned)), np.max(np.abs(rxframe_aligned)), np.median(np.abs(rxframe_aligned)) )) dt = datetime.datetime.now().isoformat() fig_path = logging_path + "/" + dt + "_Model.pdf" fig, axs = plt.subplots(4, figsize=(6,2*6)) ax = axs[0] ax.plot(np.abs(txframe_aligned[:128]), label="TX Frame") ax.plot(np.abs(rxframe_aligned[:128]), label="RX Frame") ax.set_title("Synchronized Signals") ax.set_xlabel("Samples") ax.set_ylabel("Amplitude") ax.legend(loc=4) ax = axs[1] ax.plot(np.real(txframe_aligned[:128]), label="TX Frame") ax.plot(np.real(rxframe_aligned[:128]), label="RX Frame") ax.set_title("Synchronized Signals") ax.set_xlabel("Samples") ax.set_ylabel("Real Part") ax.legend(loc=4) ax = axs[2] ax.scatter( np.abs(txframe_aligned[:1024]), np.abs(rxframe_aligned[:1024]), s = 0.1 ) ax.plot( y_est, rx_range, linewidth=0.25 ) ax.set_title("Amplifier Characteristic") ax.set_xlabel("TX Amplitude") ax.set_ylabel("RX Amplitude") ax = axs[3] angle_diff_rad = (( (np.angle(txframe_aligned[:1024]) - np.angle(rxframe_aligned[:1024]) + np.pi) % (2 * np.pi)) - np.pi ) ax.scatter( np.abs(txframe_aligned[:1024]), angle_diff_rad * 180 / np.pi, s = 0.1 ) ax.set_title("Amplifier Characteristic") ax.set_xlabel("TX Amplitude") ax.set_ylabel("Phase Difference [deg]") fig.tight_layout() fig.savefig(fig_path) fig.clf() mse = np.mean(np.abs(np.square(txframe_aligned[:1024] - rxframe_aligned[:1024]))) logging.debug("MSE: {}".format(mse)) sol = sol * 1.7/sol[0] return sol # 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.