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# -*- coding: utf-8 -*-
#
# DPD Calculation Engine,
# Extract statistic from data to use in Model
#
# http://www.opendigitalradio.org
# Licence: The MIT License, see notice at the end of this file
import numpy as np
import pickle
import matplotlib.pyplot as plt
import datetime
import os
import logging
logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename)
def _check_input_extract(tx_dpd, rx_received):
# Check data type
assert tx_dpd[0].dtype == np.complex64, \
"tx_dpd is not complex64 but {}".format(tx_dpd[0].dtype)
assert rx_received[0].dtype == np.complex64, \
"rx_received is not complex64 but {}".format(rx_received[0].dtype)
# Check if signals have same normalization
normalization_error = np.abs(np.median(np.abs(tx_dpd)) -
np.median(np.abs(rx_received))) / (
np.median(np.abs(tx_dpd)) + np.median(np.abs(rx_received)))
assert normalization_error < 0.01, "Non normalized signals"
class ExtractStatistic:
"""Calculate a low variance RX value for equally spaced tx values
of a predefined range"""
def __init__(self,
c,
plot=False):
self.c = c
self.tx_boundaries = np.linspace(c.ES_start, c.ES_end, c.ES_n_bins + 1)
self.n_per_bin = c.ES_n_per_bin
self.rx_values_lists = []
for i in range(c.ES_n_bins):
self.rx_values_lists.append([])
self.tx_values_lists = []
for i in range(c.ES_n_bins):
self.tx_values_lists.append([])
self.tx_values = self._tx_value_per_bin()
self.rx_values = []
for i in range(c.ES_n_bins):
self.rx_values.append(None)
self.plot = plot
def _plot_and_log(self):
if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot:
phase_diffs_values_lists = self._phase_diff_list_per_bin()
phase_diffs_values = self._phase_diff_value_per_bin(phase_diffs_values_lists)
dt = datetime.datetime.now().isoformat()
fig_path = logging_path + "/" + dt + "_ExtractStatistic.png"
sub_rows = 3
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(self.tx_values, self.rx_values,
label="Estimated Values",
color="red")
for i, tx_value in enumerate(self.tx_values):
rx_values = self.rx_values_lists[i]
ax.scatter(np.ones(len(rx_values)) * tx_value,
np.abs(rx_values),
s=0.1,
color="black")
ax.set_title("Extracted Statistic")
ax.set_xlabel("TX Amplitude")
ax.set_ylabel("RX Amplitude")
ax.set_ylim(0, 0.8)
ax.set_xlim(0, 1.1)
ax.legend(loc=4)
i_sub += 1
ax = plt.subplot(sub_rows, sub_cols, i_sub)
ax.plot(self.tx_values, np.rad2deg(phase_diffs_values),
label="Estimated Values",
color="red")
for i, tx_value in enumerate(self.tx_values):
phase_diff = phase_diffs_values_lists[i]
ax.scatter(np.ones(len(phase_diff)) * tx_value,
np.rad2deg(phase_diff),
s=0.1,
color="black")
ax.set_xlabel("TX Amplitude")
ax.set_ylabel("Phase Difference")
ax.set_ylim(-60,60)
ax.set_xlim(0, 1.1)
ax.legend(loc=4)
num = []
for i, tx_value in enumerate(self.tx_values):
rx_values = self.rx_values_lists[i]
num.append(len(rx_values))
i_sub += 1
ax = plt.subplot(sub_rows, sub_cols, i_sub)
ax.plot(num)
ax.set_xlabel("TX Amplitude")
ax.set_ylabel("Number of Samples")
ax.set_ylim(0, self.n_per_bin * 1.2)
fig.tight_layout()
fig.savefig(fig_path)
fig.clf()
pickle.dump(self.rx_values_lists, open("/tmp/rx_values", "wb"))
pickle.dump(self.tx_values, open("/tmp/tx_values", "wb"))
def _rx_value_per_bin(self):
rx_values = []
for values in self.rx_values_lists:
rx_values.append(np.mean(np.abs(values)))
return rx_values
def _tx_value_per_bin(self):
tx_values = []
for start, end in zip(self.tx_boundaries, self.tx_boundaries[1:]):
tx_values.append(np.mean((start, end)))
return tx_values
def _phase_diff_list_per_bin(self):
phase_values_lists = []
for tx_list, rx_list in zip(self.tx_values_lists, self.rx_values_lists):
phase_diffs = []
for tx, rx in zip(tx_list, rx_list):
phase_diffs.append(np.angle(rx * tx.conjugate()))
phase_values_lists.append(phase_diffs)
return phase_values_lists
def _phase_diff_value_per_bin(self, phase_diffs_values_lists):
phase_list = []
for values in phase_diffs_values_lists:
phase_list.append(np.mean(values))
return phase_list
def extract(self, tx_dpd, rx):
_check_input_extract(tx_dpd, rx)
tx_abs = np.abs(tx_dpd)
for i, (tx_start, tx_end) in enumerate(zip(self.tx_boundaries, self.tx_boundaries[1:])):
mask = (tx_abs > tx_start) & (tx_abs < tx_end)
n_add = max(0, self.n_per_bin - len(self.rx_values_lists[i]))
self.rx_values_lists[i] += \
list(rx[mask][:n_add])
self.tx_values_lists[i] += \
list(tx_dpd[mask][:n_add])
self.rx_values = self._rx_value_per_bin()
self.tx_values = self._tx_value_per_bin()
self._plot_and_log()
n_per_bin = [len(values) for values in self.rx_values_lists]
return np.array(self.tx_values, dtype=np.float32), \
np.array(self.rx_values, dtype=np.float32), \
n_per_bin
# 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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