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# -*- coding: utf-8 -*-
import datetime
import os
import logging
logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename)
from pynverse import inversefunc
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
self.coefs_history = [coefs,]
self.mses = [0,]
self.errs = [0,]
def get_next_coefs(self, txframe_aligned, rxframe_aligned):
rx_abs = np.abs(rxframe_aligned)
rx_A = np.vstack([rx_abs,
rx_abs**3,
rx_abs**5,
rx_abs**7,
rx_abs**9,
]).T
rx_dpd = np.sum(rx_A * self.coefs, axis=1)
rx_dpd = rx_dpd * (
np.median(np.abs(txframe_aligned)) / np.median(np.abs(rx_dpd)))
err = rx_dpd - np.abs(txframe_aligned)
self.errs.append(np.mean(np.abs(err**2)))
a_delta = np.linalg.lstsq(rx_A, err)[0]
new_coefs = self.coefs - 0.1 * a_delta
logging.debug("a_delta {}".format(a_delta))
logging.debug("new coefs {}".format(new_coefs))
tx_abs = np.abs(rxframe_aligned)
tx_A = np.vstack([tx_abs,
tx_abs**3,
tx_abs**5,
tx_abs**7,
tx_abs**9,
]).T
tx_dpd = np.sum(tx_A * new_coefs, axis=1)
tx_dpd_norm = tx_dpd * (
np.median(np.abs(txframe_aligned)) / np.median(np.abs(tx_dpd)))
rx_A_complex = np.vstack([rxframe_aligned,
rxframe_aligned * rx_abs**2,
rxframe_aligned * rx_abs**4,
rxframe_aligned * rx_abs**6,
rxframe_aligned * rx_abs**8,
]).T
rx_post_distored = np.sum(rx_A_complex * self.coefs, axis=1)
rx_post_distored = rx_post_distored * (
np.median(np.abs(txframe_aligned)) /
np.median(np.abs(rx_post_distored)))
mse = np.mean(np.abs((txframe_aligned - rx_post_distored)**2))
logging.debug("MSE: {}".format(mse))
self.mses.append(mse)
def dpd(tx):
tx_abs = np.abs(tx)
tx_A_complex = np.vstack([tx,
tx * tx_abs**2,
tx * tx_abs**4,
tx * tx_abs**6,
tx * tx_abs**8,
]).T
tx_dpd = np.sum(tx_A_complex * self.coefs, axis=1)
return tx_dpd
tx_inverse_dpd = inversefunc(dpd, y_values=txframe_aligned[:128])
tx_inverse_dpd = tx_inverse_dpd * (
np.median(np.abs(txframe_aligned)) /
np.median(np.abs(tx_inverse_dpd))
)
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(7, figsize=(6,3*6))
ax = axs[0]
ax.plot(np.abs(txframe_aligned[:128]),
label="TX sent",
linestyle=":")
ax.plot(np.abs(tx_inverse_dpd[:128]),
label="TX inverse dpd",
color="green")
ax.plot(np.abs(rxframe_aligned[:128]),
label="RX received",
color="red")
ax.set_title("Synchronized Signals of Iteration {}".format(len(self.coefs_history)))
ax.set_xlabel("Samples")
ax.set_ylabel("Amplitude")
ax.legend(loc=4)
ax = axs[1]
ax.plot(np.real(txframe_aligned[:128]),
label="TX sent",
linestyle=":")
ax.plot(np.real(tx_inverse_dpd[:128]),
label="TX inverse dpd",
color="green")
ax.plot(np.real(rxframe_aligned[:128]),
label="RX received",
color="red")
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.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]")
ax = axs[4]
ax.plot(np.abs(txframe_aligned[:128]),
label="TX Frame",
linestyle=":",
linewidth=0.5)
ax.plot(np.abs(rxframe_aligned[:128]),
label="RX Frame",
linestyle="--",
linewidth=0.5)
ax.plot(np.abs(rx_dpd[:128]),
label="RX DPD Frame",
linestyle="-.",
linewidth=0.5)
ax.plot(np.abs(tx_dpd_norm[:128]),
label="TX DPD Frame Norm",
linestyle="-.",
linewidth=0.5)
ax.legend(loc=4)
ax.set_title("RX DPD")
ax.set_xlabel("Samples")
ax.set_ylabel("Amplitude")
ax = axs[5]
coefs_history = np.array(self.coefs_history)
for idx, coef_hist in enumerate(coefs_history.T):
ax.plot(coef_hist,
label="Coef {}".format(idx),
linewidth=0.5)
ax.legend(loc=4)
ax.set_title("Coefficient History")
ax.set_xlabel("Iterations")
ax.set_ylabel("Coefficient Value")
ax = axs[6]
coefs_history = np.array(self.coefs_history)
ax.plot(self.mses, label="MSE")
ax.plot(self.errs, label="ERR")
ax.legend(loc=4)
ax.set_title("MSE History")
ax.set_xlabel("Iterations")
ax.set_ylabel("MSE")
fig.tight_layout()
fig.savefig(fig_path)
fig.clf()
self.coefs = new_coefs
self.coefs_history.append(self.coefs)
return self.coefs
# 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.
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