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# -*- coding: utf-8 -*-
#
# DPD Computation Engine, Capture TX signal and RX feedback using ODR-DabMod's
# DPD Server.
#
# Copyright (c) 2017 Andreas Steger
# Copyright (c) 2018 Matthias P. Braendli
#
# http://www.opendigitalradio.org
#
# This file is part of ODR-DabMod.
#
# ODR-DabMod is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# ODR-DabMod is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with ODR-DabMod. If not, see <http://www.gnu.org/licenses/>.
import socket
import struct
import os
import logging
import numpy as np
from scipy import signal
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import io
from . import Align as sa
def align_samples(sig_tx, sig_rx):
"""
Returns an aligned version of sig_tx and sig_rx by cropping, subsample alignment and
correct phase offset
"""
# Coarse sample-level alignment
c = np.abs(signal.correlate(sig_rx, sig_tx))
off_meas = np.argmax(c) - sig_tx.shape[0] + 1
off = int(abs(off_meas))
if off_meas > 0:
sig_tx = sig_tx[:-off]
sig_rx = sig_rx[off:]
elif off_meas < 0:
sig_tx = sig_tx[off:]
sig_rx = sig_rx[:-off]
if off % 2 == 1:
sig_tx = sig_tx[:-1]
sig_rx = sig_rx[:-1]
# Fine subsample alignment and phase offset
sig_rx = sa.subsample_align(sig_rx, sig_tx)
sig_rx = sa.phase_align(sig_rx, sig_tx)
return sig_tx, sig_rx
class Capture:
"""Capture samples from ODR-DabMod"""
def __init__(self, samplerate, port, num_samples_to_request):
self.samplerate = samplerate
self.sizeof_sample = 8 # complex floats
self.port = port
self.num_samples_to_request = num_samples_to_request
# Before we run the samples through the model, we want to accumulate
# them into bins depending on their amplitude, and keep only n_per_bin
# samples to avoid that the polynomial gets overfitted in the low-amplitude
# part, which is less interesting than the high-amplitude part, where
# non-linearities become apparent.
self.binning_start = 0.0
self.binning_end = 1.0
self.binning_n_bins = 64 # Number of bins between binning_start and binning_end
self.binning_n_per_bin = 128 # Number of measurements pre bin
self.target_median = 0.05
self.median_max = self.target_median * 1.4
self.median_min = self.target_median / 1.4
# axis 0: bins
# axis 1: 0=tx, 1=rx
self.accumulated_bins = [np.zeros((0, 2), dtype=np.complex64) for i in range(self.binning_n_bins)]
def _recv_exact(self, sock, num_bytes):
"""Receive an exact number of bytes from a socket. This is
a wrapper around sock.recv() that can return less than the number
of requested bytes.
Args:
sock (socket): Socket to receive data from.
num_bytes (int): Number of bytes that will be returned.
"""
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 receive_tcp(self):
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.settimeout(4)
s.connect(('localhost', self.port))
logging.debug("Send version")
s.sendall(b"\x01")
logging.debug("Send request for {} samples".format(self.num_samples_to_request))
s.sendall(struct.pack("=I", self.num_samples_to_request))
logging.debug("Wait for TX metadata")
num_samps, tx_second, tx_pps = struct.unpack("=III", self._recv_exact(s, 12))
tx_ts = tx_second + tx_pps / 16384000.0
if num_samps > 0:
logging.debug("Receiving {} TX samples".format(num_samps))
txframe_bytes = self._recv_exact(s, num_samps * self.sizeof_sample)
txframe = np.fromstring(txframe_bytes, dtype=np.complex64)
else:
txframe = np.array([], dtype=np.complex64)
logging.debug("Wait for RX metadata")
rx_second, rx_pps = struct.unpack("=II", self._recv_exact(s, 8))
rx_ts = rx_second + rx_pps / 16384000.0
if num_samps > 0:
logging.debug("Receiving {} RX samples".format(num_samps))
rxframe_bytes = self._recv_exact(s, num_samps * self.sizeof_sample)
rxframe = np.fromstring(rxframe_bytes, dtype=np.complex64)
else:
rxframe = np.array([], dtype=np.complex64)
if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
logging.debug('txframe: min {}, max {}, median {}'.format(
np.min(np.abs(txframe)),
np.max(np.abs(txframe)),
np.median(np.abs(txframe))))
logging.debug('rxframe: min {}, max {}, median {}'.format(
np.min(np.abs(rxframe)),
np.max(np.abs(rxframe)),
np.median(np.abs(rxframe))))
logging.debug("Disconnecting")
s.close()
return txframe, tx_ts, rxframe, rx_ts
def get_samples(self):
"""Connect to ODR-DabMod, retrieve TX and RX samples, load
into numpy arrays, and return a tuple
(txframe_aligned, tx_ts, tx_median, rxframe_aligned, rx_ts, rx_median)
"""
txframe, tx_ts, rxframe, rx_ts = self.receive_tcp()
# Normalize received signal with sent signal
tx_median = np.median(np.abs(txframe))
if self.median_max < tx_median:
raise ValueError("TX median {} too high, decrease digital_gain!".format(tx_median))
elif tx_median < self.median_min:
raise ValueError("TX median {} too low, increase digital_gain!".format(tx_median))
else:
rx_median = np.median(np.abs(rxframe))
rxframe = rxframe / rx_median * tx_median
txframe_aligned, rxframe_aligned = align_samples(txframe, rxframe)
self._bin_and_accumulate(txframe_aligned, rxframe_aligned)
return txframe_aligned, tx_ts, tx_median, rxframe_aligned, rx_ts, rx_median
def bin_histogram(self):
return [b.shape[0] for b in self.accumulated_bins]
def pointcloud_png(self):
fig = plt.figure()
ax = plt.subplot(1, 1, 1)
for b in self.accumulated_bins:
if b:
ax.scatter(
np.abs(b[0]),
np.abs(b[1]),
s=0.1,
color="black")
ax.set_title("Captured and Binned Samples")
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)
fig.tight_layout()
buf = io.BytesIO()
fig.savefig(buf)
plt.close(fig)
return buf.getvalue()
def _bin_and_accumulate(self, txframe, rxframe):
"""Bin the samples and extend the accumulated samples"""
bin_edges = np.linspace(self.binning_start, self.binning_end, self.binning_n_bins)
minsize = self.num_samples_to_request
for i, (tx_start, tx_end) in enumerate(zip(bin_edges, bin_edges[1:])):
txframe_abs = np.abs(txframe)
indices = np.bitwise_and(tx_start < txframe_abs, txframe_abs <= tx_end)
txsamples = np.asmatrix(txframe[indices])
rxsamples = np.asmatrix(rxframe[indices])
binned_sample_pairs = np.concatenate((txsamples, rxsamples)).T
missing_in_bin = self.binning_n_per_bin - self.accumulated_bins[i].shape[0]
num_to_append = min(missing_in_bin, binned_sample_pairs.shape[0])
print("Handling bin {} {}-{}, {} available, {} missing".format(i, tx_start, tx_end, binned_sample_pairs.shape[0], missing_in_bin))
if num_to_append:
print("Appending {} to bin {} with shape {}".format(num_to_append, i, self.accumulated_bins[i].shape))
self.accumulated_bins[i] = np.concatenate((self.accumulated_bins[i], binned_sample_pairs[:num_to_append,...]))
print("{} now has shape {}".format(i, self.accumulated_bins[i].shape))
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