Add DPD page to web gui

1 files changed, 197 insertions, 0 deletions

diff --git a/gui/dpd/Capture.py b/gui/dpd/Capture.py
new file mode 100644
index 0000000..d6d0307
--- /dev/null
+++ b/gui/dpd/Capture.py
@@ -0,0 +1,197 @@
+# -*- 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
+
+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
+
+        # axis 1: 0=tx, 1=rx
+        self.accumulated_samples = np.zeros((0, 2), dtype=np.complex64)
+
+    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
+        rx_median = np.median(np.abs(rxframe))
+        tx_median = np.median(np.abs(txframe))
+        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 num_accumulated(self):
+        return self.accumulated_samples.shape[0]
+
+    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)
+
+        binned_sample_pairs = {}
+
+        minsize = self.num_samples_to_request
+
+        for i, (tx_start, tx_end) in enumerate(zip(bin_edges, bin_edges[1:])):
+            indices = np.bitwise_and(tx_start < txframe, txframe <= tx_end)
+            binned_sample_pairs[i] = (txframe[indices], rxframe[indices])
+            len_bin = len(txframe[indices])
+
+            #TODO this doesn't work, the min is always 0
+            if len_bin < minsize:
+                minsize = len_bin
+
+        # axis 0: bins, axis 1: sample index, axis 2: tx(0) and rx(1)
+        samples = np.zeros((self.binning_n_bins, len_bin, 2), dtype=np.complex64)
+
+        for i in binned_sample_pairs:
+            tx, rx = binned_sample_pairs[i]
+            new_samples = np.array((tx[:minsize], rx[:minsize]), dtype=np.complex64)
+            self.accumulated_samples = np.concatenate((self.accumulated_samples, new_samples.T))
+