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| -rwxr-xr-x | dpd/store_received.py | 157 |
1 files changed, 157 insertions, 0 deletions
diff --git a/dpd/store_received.py b/dpd/store_received.py new file mode 100755 index 0000000..902f607 --- /dev/null +++ b/dpd/store_received.py @@ -0,0 +1,157 @@ +#!/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 +import os +import time +import src.dab_util as du + +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('--out_dir', default='/tmp/record', help='Output directory', + required=False) + parser.add_argument('--count', default='1', help='Number of recordings', + required=False) + parser.add_argument('--verbose', type=int, default=0, help='Level of verbosity', + required=False) + + parser.add_argument('--animated', action='store_true', help='Enable real-time animation') + + + cli_args = parser.parse_args() + + if not os.path.isdir(cli_args.out_dir): + os.mkdir(cli_args.out_dir) + + port = int(cli_args.port) + num_samps_to_request = int(cli_args.samps) + + for i in range(int(cli_args.count)): + if i>0: + time.sleep(0.1) + + tx_ts, txframe, rx_ts, rxframe = get_samples(port, num_samps_to_request) + + txframe_aligned, rxframe_aligned = du.subsample_align(txframe, rxframe) + + if cli_args.verbose >= 1: + n_up = 32 + lag = du.lag_upsampling(txframe, rxframe, n_up) + lag_aligned = du.lag_upsampling(txframe_aligned, rxframe_aligned, n_up) + print("Lag from %d times oversampled signal:" % n_up) + print("Before alignment: %.2f" % lag) + print("After alignment: %.2f" % lag_aligned) + print("") + + txframe_aligned.tofile("%s/%d_tx_record.iq" % (cli_args.out_dir, i)) + rxframe_aligned.tofile("%s/%d_rx_record.iq" % (cli_args.out_dir, i)) + + +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)) + +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. |