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# -*- 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.
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