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-rw-r--r--gui/dpd/Capture.py253
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diff --git a/gui/dpd/Capture.py b/gui/dpd/Capture.py
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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.path
-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 correlation_coefficient(sig_tx, sig_rx):
- return np.corrcoef(sig_tx, sig_rx)[0, 1]
-
-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, abs(off_meas)
-
-class Capture:
- """Capture samples from ODR-DabMod"""
- def __init__(self, samplerate, port, num_samples_to_request, plot_dir):
- self.samplerate = samplerate
- self.sizeof_sample = 8 # complex floats
- self.port = port
- self.num_samples_to_request = num_samples_to_request
- self.plot_dir = plot_dir
-
- # 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_n_bins = 64 # Number of bins between binning_start and binning_end
- self.binning_n_per_bin = 128 # Number of measurements pre bin
-
- self.rx_normalisation = 1.0
-
- self.clear_accumulated()
-
- def clear_accumulated(self):
- self.binning_start = 0.0
- self.binning_end = 1.0
-
- # 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 _plot_spectrum(self, signal, filename, title):
- fig = plt.figure()
- ax = plt.subplot(1, 1, 1)
-
- fft = np.fft.fftshift(np.fft.fft(signal))
- fft_db = 20 * np.log10(np.abs(fft))
-
- ax.plot(fft_db)
- ax.set_title(title)
- fig.tight_layout()
- fig.savefig(os.path.join(self.plot_dir, filename))
- plt.close(fig)
-
- def calibrate(self):
- txframe, tx_ts, rxframe, rx_ts = self.receive_tcp()
-
- # Normalize received signal with sent signal
- tx_median = np.median(np.abs(txframe))
- rx_median = np.median(np.abs(rxframe))
- self.rx_normalisation = tx_median / rx_median
-
- rxframe = rxframe * self.rx_normalisation
- txframe_aligned, rxframe_aligned, coarse_offset = align_samples(txframe, rxframe)
-
- self._plot_spectrum(rxframe[:8192], "rxframe.png", "RX Frame")
- self._plot_spectrum(txframe[:8192], "txframe.png", "RX Frame")
-
- return tx_ts, tx_median, rx_ts, rx_median, np.abs(coarse_offset), correlation_coefficient(txframe_aligned, rxframe_aligned)
-
- 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 calibrated normalisation
- rxframe = rxframe * self.rx_normalisation
- txframe_aligned, rxframe_aligned, coarse_offset = 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()
- fig.savefig(os.path.join(self.plot_dir, "pointcloud.png"))
- plt.close(fig)
-
- 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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