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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# This is an example tool that shows how to connect to ODR-DabMod's dpd TCP server
# and get samples from there.
#
# Since the TX and RX samples are not perfectly aligned, the tool has to align them properly,
# which is done in two steps: First on sample-level using a correlation, then with subsample
# accuracy using a FFT approach.
#
# It requires SciPy and matplotlib.
#
# Copyright (C) 2017 Matthias P. Braendli
# http://www.opendigitalradio.org
# Licence: The MIT License, see notice at the end of this file
import sys
import socket
import struct
import numpy as np
import matplotlib.pyplot as pp
from matplotlib.animation import FuncAnimation
import argparse
SIZEOF_SAMPLE = 8 # complex floats
def main():
parser = argparse.ArgumentParser(description="Plot the spectrum of ODR-DabMod's DPD feedback")
parser.add_argument('--samps', default='10240', help='Number of samples to request at once',
required=False)
parser.add_argument('--port', default='50055',
help='port to connect to ODR-DabMod DPD (default: 50055)',
required=False)
parser.add_argument('--animated', action='store_true', help='Enable real-time animation')
cli_args = parser.parse_args()
port = int(cli_args.port)
num_samps_to_request = int(cli_args.samps)
if cli_args.animated:
plot_spectrum_animated(port, num_samps_to_request)
else:
plot_spectrum_once(port, num_samps_to_request)
def recv_exact(sock, num_bytes):
bufs = []
while num_bytes > 0:
b = sock.recv(num_bytes)
if len(b) == 0:
break
num_bytes -= len(b)
bufs.append(b)
return b''.join(bufs)
def get_samples(port, num_samps_to_request):
"""Connect to ODR-DabMod, retrieve TX and RX samples, load
into numpy arrays, and return a tuple
(tx_timestamp, tx_samples, rx_timestamp, rx_samples)
where the timestamps are doubles, and the samples are numpy
arrays of complex floats, both having the same size
"""
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect(('localhost', port))
print("Send version");
s.sendall(b"\x01")
print("Send request for {} samples".format(num_samps_to_request))
s.sendall(struct.pack("=I", num_samps_to_request))
print("Wait for TX metadata")
num_samps, tx_second, tx_pps = struct.unpack("=III", recv_exact(s, 12))
tx_ts = tx_second + tx_pps / 16384000.0
if num_samps > 0:
print("Receiving {} TX samples".format(num_samps))
txframe_bytes = recv_exact(s, num_samps * SIZEOF_SAMPLE)
txframe = np.fromstring(txframe_bytes, dtype=np.complex64)
else:
txframe = np.array([], dtype=np.complex64)
print("Wait for RX metadata")
rx_second, rx_pps = struct.unpack("=II", recv_exact(s, 8))
rx_ts = rx_second + rx_pps / 16384000.0
if num_samps > 0:
print("Receiving {} RX samples".format(num_samps))
rxframe_bytes = recv_exact(s, num_samps * SIZEOF_SAMPLE)
rxframe = np.fromstring(rxframe_bytes, dtype=np.complex64)
else:
rxframe = np.array([], dtype=np.complex64)
print("Disconnecting")
s.close()
return (tx_ts, txframe, rx_ts, rxframe)
sampling_rate = 8192000
fft_size = 4096
freqs = np.fft.fftshift(np.fft.fftfreq(fft_size, d=1./sampling_rate))
def plot_spectrum_once(port, num_samps_to_request):
tx_ts, txframe, rx_ts, rxframe = get_samples(port, num_samps_to_request)
print("Received {} & {} frames at {} and {}".format(
len(txframe), len(rxframe), tx_ts, rx_ts))
print("Calculate TX and RX spectrum assuming 8192000 samples per second")
tx_spectrum = np.fft.fftshift(np.fft.fft(txframe, fft_size))
tx_power = 20*np.log10(np.abs(tx_spectrum))
rx_spectrum = np.fft.fftshift(np.fft.fft(rxframe, fft_size))
rx_power = 20*np.log10(np.abs(rx_spectrum))
fig = pp.figure()
fig.suptitle("TX and RX spectrum")
ax1 = fig.add_subplot(211)
ax1.set_title("TX")
ax1.plot(freqs, tx_power, 'r')
ax2 = fig.add_subplot(212)
ax2.set_title("RX")
ax2.plot(freqs, rx_power, 'b')
pp.show()
def plot_spectrum_animated(port, num_samps_to_request):
fig, axes = pp.subplots(2, sharex=True)
line1, = axes[0].plot(freqs, np.ones(len(freqs)), 'r', animated=True)
axes[0].set_title("TX")
line2, = axes[1].plot(freqs, np.ones(len(freqs)), 'b', animated=True)
axes[1].set_title("RX")
lines = [line1, line2]
axes[0].set_ylim(-30, 50)
axes[1].set_ylim(-60, 40)
def update(frame):
tx_ts, txframe, rx_ts, rxframe = get_samples(port, num_samps_to_request)
print("Received {} & {} frames at {} and {}".format(
len(txframe), len(rxframe), tx_ts, rx_ts))
print("Calculate TX and RX spectrum assuming 8192000 samples per second")
tx_spectrum = np.fft.fftshift(np.fft.fft(txframe, fft_size))
tx_power = 20*np.log10(np.abs(tx_spectrum))
rx_spectrum = np.fft.fftshift(np.fft.fft(rxframe, fft_size))
rx_power = 20*np.log10(np.abs(rx_spectrum))
lines[0].set_ydata(tx_power)
lines[1].set_ydata(rx_power)
return lines
ani = FuncAnimation(fig, update, blit=True)
pp.show()
main()
# The MIT License (MIT)
#
# Copyright (c) 2017 Matthias P. Braendli
#
# 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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