1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
|
# -*- coding: utf-8 -*-
#
# DPD Computation Engine,
# Extract statistic from received TX and RX 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 matplotlib.pyplot as plt
import datetime
import os
import logging
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"
def _phase_diff_value_per_bin(phase_diffs_values_lists):
phase_list = []
for values in phase_diffs_values_lists:
mean = np.mean(values) if len(values) > 0 else np.nan
phase_list.append(mean)
return phase_list
class ExtractStatistic:
"""Calculate a low variance RX value for equally spaced tx values
of a predefined range"""
def __init__(self, c):
self.c = c
# Number of measurements used to extract the statistic
self.n_meas = 0
# Boundaries for the bins
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
# List of rx values for each bin
self.rx_values_lists = []
for i in range(c.ES_n_bins):
self.rx_values_lists.append([])
# List of tx values for each bin
self.tx_values_lists = []
for i in range(c.ES_n_bins):
self.tx_values_lists.append([])
self.plot = c.ES_plot
def _plot_and_log(self, tx_values, rx_values, phase_diffs_values, phase_diffs_values_lists):
if self.plot and self.c.plot_location is not None:
dt = datetime.datetime.now().isoformat()
fig_path = self.c.plot_location + "/" + 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(tx_values, rx_values,
label="Estimated Values",
color="red")
for i, tx_value in enumerate(tx_values):
rx_values_list = self.rx_values_lists[i]
ax.scatter(np.ones(len(rx_values_list)) * tx_value,
np.abs(rx_values_list),
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(tx_values, np.rad2deg(phase_diffs_values),
label="Estimated Values",
color="red")
for i, tx_value in enumerate(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(tx_values):
rx_values_list = self.rx_values_lists[i]
num.append(len(rx_values_list))
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)
plt.close(fig)
def _rx_value_per_bin(self):
rx_values = []
for values in self.rx_values_lists:
mean = np.mean(np.abs(values)) if len(values) > 0 else np.nan
rx_values.append(mean)
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 extract(self, tx_dpd, rx):
"""Extract information from a new measurement and store them
in member variables."""
_check_input_extract(tx_dpd, rx)
self.n_meas += 1
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])
rx_values = self._rx_value_per_bin()
tx_values = self._tx_value_per_bin()
n_per_bin = np.array([len(values) for values in self.rx_values_lists])
# Index of first not filled bin, assumes that never all bins are filled
idx_end = np.argmin(n_per_bin == self.c.ES_n_per_bin)
phase_diffs_values_lists = self._phase_diff_list_per_bin()
phase_diffs_values = _phase_diff_value_per_bin(phase_diffs_values_lists)
self._plot_and_log(tx_values, rx_values, phase_diffs_values, phase_diffs_values_lists)
tx_values_crop = np.array(tx_values, dtype=np.float32)[:idx_end]
rx_values_crop = np.array(rx_values, dtype=np.float32)[:idx_end]
phase_diffs_values_crop = np.array(phase_diffs_values, dtype=np.float32)[:idx_end]
return tx_values_crop, rx_values_crop, phase_diffs_values_crop, 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.
|
