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# -*- coding: utf-8 -*-
#
# DPD Calculation Engine, model implementation for Amplitude and not Phase
#
# 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
def check_input_get_next_coefs(tx_dpd, rx_received):
is_float32 = lambda x: (isinstance(x, np.ndarray) and
x.dtype == np.float32 and
x.flags.contiguous)
assert is_float32(tx_dpd), \
"tx_dpd is not float32 but {}".format(tx_dpd[0].dtype)
assert is_float32(tx_dpd), \
"rx_received is not float32 but {}".format(tx_dpd[0].dtype)
class Model_AM:
"""Calculates new coefficients using the measurement and the previous
coefficients"""
def __init__(self,
c,
learning_rate_am=0.1,
plot=False):
self.c = c
self.learning_rate_am = learning_rate_am
self.plot = plot
def _plot(self, tx_dpd, rx_received, coefs_am, coefs_am_new):
if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot:
tx_range, rx_est = self.calc_line(coefs_am, 0, 0.6)
tx_range_new, rx_est_new = self.calc_line(coefs_am_new, 0, 0.6)
dt = datetime.datetime.now().isoformat()
fig_path = logging_path + "/" + dt + "_Model_AM.svg"
sub_rows = 1
sub_cols = 1
fig = plt.figure(figsize=(sub_cols * 6, sub_rows / 2. * 6))
i_sub = 0
i_sub += 1
ax = plt.subplot(sub_rows, sub_cols, i_sub)
ax.plot(tx_range, rx_est,
label="Estimated TX",
alpha=0.3,
color="gray")
ax.plot(tx_range_new, rx_est_new,
label="New Estimated TX",
color="red")
ax.scatter(tx_dpd, rx_received,
label="Binned Data",
color="blue",
s=0.1)
ax.set_title("Model_AM")
ax.set_xlabel("RX Amplitude")
ax.set_ylabel("TX Amplitude")
ax.legend(loc=4)
fig.tight_layout()
fig.savefig(fig_path)
plt.close(fig)
def poly(self, sig):
return np.array([sig ** i for i in range(1, 6)]).T
def fit_poly(self, tx_abs, rx_abs):
return np.linalg.lstsq(self.poly(rx_abs), tx_abs)[0]
def calc_line(self, coefs, min_amp, max_amp):
rx_range = np.linspace(min_amp, max_amp)
tx_est = np.sum(self.poly(rx_range) * coefs, axis=1)
return tx_est, rx_range
def get_next_coefs(self, tx_dpd, rx_received, coefs_am):
check_input_get_next_coefs(tx_dpd, rx_received)
coefs_am_new = self.fit_poly(tx_dpd, rx_received)
self._plot(tx_dpd, rx_received, coefs_am, coefs_am_new)
return coefs_am_new
# 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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