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# -*- coding: utf-8 -*-
#
# DPD Computation Engine, model implementation using polynomial
#
# http://www.opendigitalradio.org
# Licence: The MIT License, see notice at the end of this file
import os
import logging
import numpy as np
import matplotlib.pyplot as plt
def assert_np_float32(array):
assert isinstance(array, np.ndarray), type(array)
assert array.dtype == np.float32, array.dtype
assert array.flags.contiguous
assert not any(np.isnan(array))
def _check_input_get_next_coefs(tx_abs, rx_abs, phase_diff):
assert_np_float32(tx_abs)
assert_np_float32(rx_abs)
assert_np_float32(phase_diff)
assert tx_abs.shape == rx_abs.shape, \
"tx_abs.shape {}, rx_abs.shape {}".format(
tx_abs.shape, rx_abs.shape)
assert tx_abs.shape == phase_diff.shape, \
"tx_abs.shape {}, phase_diff.shape {}".format(
tx_abs.shape, phase_diff.shape)
class Poly:
"""Calculates new coefficients using the measurement and the previous
coefficients"""
def __init__(self, c, learning_rate_am=1.0, learning_rate_pm=1.0):
self.c = c
self.learning_rate_am = learning_rate_am
self.learning_rate_pm = learning_rate_pm
self.reset_coefs()
def plot(self, plot_location, title):
if self._am_plot_data is not None and self._pm_plot_data is not None:
tx_dpd, rx_received, coefs_am, coefs_am_new = self._am_plot_data
tx_range, rx_est = self._am_calc_line(coefs_am, 0, 0.6)
tx_range_new, rx_est_new = self._am_calc_line(coefs_am_new, 0, 0.6)
sub_rows = 2
sub_cols = 1
fig = plt.figure(figsize=(sub_cols * 6, sub_rows / 2. * 6))
i_sub = 0
# AM subplot
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=1)
ax.set_title("Model AM and PM {}".format(title))
ax.set_xlabel("TX Amplitude")
ax.set_ylabel("RX Amplitude")
ax.set_xlim(0, 1.0)
ax.legend(loc=4)
# PM sub plot
tx_dpd, phase_diff, coefs_pm, coefs_pm_new = self._pm_plot_data
tx_range, phase_diff_est = self._pm_calc_line(coefs_pm, 0, 0.6)
tx_range_new, phase_diff_est_new = self._pm_calc_line(coefs_pm_new, 0, 0.6)
i_sub += 1
ax = plt.subplot(sub_rows, sub_cols, i_sub)
ax.plot(tx_range, phase_diff_est,
label="Estimated Phase Diff",
alpha=0.3,
color="gray")
ax.plot(tx_range_new, phase_diff_est_new,
label="New Estimated Phase Diff",
color="red")
ax.scatter(tx_dpd, phase_diff,
label="Binned Data",
color="blue",
s=1)
ax.set_xlabel("TX Amplitude")
ax.set_ylabel("Phase DIff")
ax.set_xlim(0, 1.0)
ax.legend(loc=4)
fig.tight_layout()
fig.savefig(plot_location)
plt.close(fig)
def reset_coefs(self):
self.coefs_am = np.zeros(5, dtype=np.float32)
self.coefs_am[0] = 1
self.coefs_pm = np.zeros(5, dtype=np.float32)
def train(self, tx_abs, rx_abs, phase_diff, lr=None):
"""
:type tx_abs: np.ndarray
:type rx_abs: np.ndarray
:type phase_diff: np.ndarray
:type lr: float
"""
_check_input_get_next_coefs(tx_abs, rx_abs, phase_diff)
coefs_am_new = self._am_get_next_coefs(tx_abs, rx_abs, self.coefs_am)
coefs_pm_new = self._pm_get_next_coefs(tx_abs, phase_diff, self.coefs_pm)
self.coefs_am = self.coefs_am + (coefs_am_new - self.coefs_am) * self.learning_rate_am
self.coefs_pm = self.coefs_pm + (coefs_pm_new - self.coefs_pm) * self.learning_rate_pm
def get_dpd_data(self):
return "poly", self.coefs_am, self.coefs_pm
def _am_calc_line(self, coefs, min_amp, max_amp):
rx_range = np.linspace(min_amp, max_amp)
tx_est = np.sum(self._am_poly(rx_range) * coefs, axis=1)
return tx_est, rx_range
def _am_poly(self, sig):
return np.array([sig ** i for i in range(1, 6)]).T
def _am_fit_poly(self, tx_abs, rx_abs):
return np.linalg.lstsq(self._am_poly(rx_abs), tx_abs, rcond=None)[0]
def _am_get_next_coefs(self, tx_dpd, rx_received, coefs_am):
"""Calculate the next AM/AM coefficients using the extracted
statistic of TX and RX amplitude"""
coefs_am_new = self._am_fit_poly(tx_dpd, rx_received)
coefs_am_new = coefs_am + \
self.learning_rate_am * (coefs_am_new - coefs_am)
self._am_plot_data = (tx_dpd, rx_received, coefs_am, coefs_am_new)
return coefs_am_new
def _pm_poly(self, sig):
return np.array([sig ** i for i in range(0, 5)]).T
def _pm_calc_line(self, coefs, min_amp, max_amp):
tx_range = np.linspace(min_amp, max_amp)
phase_diff = np.sum(self._pm_poly(tx_range) * coefs, axis=1)
return tx_range, phase_diff
def _discard_small_values(self, tx_dpd, phase_diff):
""" Assumes that the phase for small tx amplitudes is zero"""
mask = tx_dpd < self.c.MPM_tx_min
phase_diff[mask] = 0
return tx_dpd, phase_diff
def _pm_fit_poly(self, tx_abs, phase_diff):
return np.linalg.lstsq(self._pm_poly(tx_abs), phase_diff, rcond=None)[0]
def _pm_get_next_coefs(self, tx_dpd, phase_diff, coefs_pm):
"""Calculate the next AM/PM coefficients using the extracted
statistic of TX amplitude and phase difference"""
tx_dpd, phase_diff = self._discard_small_values(tx_dpd, phase_diff)
coefs_pm_new = self._pm_fit_poly(tx_dpd, phase_diff)
coefs_pm_new = coefs_pm + self.learning_rate_pm * (coefs_pm_new - coefs_pm)
self._pm_plot_data = (tx_dpd, phase_diff, coefs_pm, coefs_pm_new)
return coefs_pm_new
# The MIT License (MIT)
#
# Copyright (c) 2017 Andreas Steger
# Copyright (c) 2018 Matthias P. Brandli
#
# 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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