Rework GUI and DPDCE

1 files changed, 276 insertions, 0 deletions

diff --git a/python/dpd/old/show_spectrum.py b/python/dpd/old/show_spectrum.py
new file mode 100755
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+++ b/python/dpd/old/show_spectrum.py
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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.
+#
+# 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
+from scipy.misc import imsave
+
+SIZEOF_SAMPLE = 8 # complex floats
+
+# Constants for TM 1
+NbSymbols = 76
+NbCarriers = 1536
+Spacing = 2048
+NullSize = 2656
+SymSize = 2552
+FicSizeOut = 288
+
+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')
+    parser.add_argument('--constellation', action='store_true', help='Draw constellaton plot')
+    parser.add_argument('--samplerate', default='8192000', help='Sample rate',
+            required=False)
+
+    cli_args = parser.parse_args()
+
+    if cli_args.constellation:
+        plot_constellation_once(cli_args)
+    elif cli_args.animated:
+        plot_spectrum_animated(cli_args)
+    else:
+        plot_spectrum_once(cli_args)
+
+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)
+
+def recv_rxtx(port, num_samps_to_request):
+    tx_ts, txframe, rx_ts, rxframe = get_samples(port, num_samps_to_request)
+
+    # convert to complex doubles for more dynamic range
+    txframe = txframe.astype(np.complex128)
+    rxframe = rxframe.astype(np.complex128)
+
+    print("Received {} & {} frames at {} and {}".format(
+        len(txframe), len(rxframe), tx_ts, rx_ts))
+    return tx_ts, txframe, rx_ts, rxframe
+
+def get_spectrum(port, num_samps_to_request):
+    tx_ts, txframe, rx_ts, rxframe = recv_rxtx(port, num_samps_to_request)
+
+    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))
+    return tx_power, rx_power
+
+def remove_guard_intervals(frame, options):
+    """Remove the cyclic prefix. The frame needs to be aligned to the
+    end of the transmission frame. Transmission Mode 1 is assumed"""
+    oversample = int(int(options.samplerate) / 2048000)
+
+    # From the end, take 2048 samples, then skip 504 samples
+    frame = frame[::-1]
+
+    stride_len = Spacing * oversample
+    stride_advance = SymSize * oversample
+
+    # Truncate the frame to an integer length of strides
+    newlen = len(frame) - (len(frame) % stride_advance)
+    print("Truncating frame from {} to {}".format(len(frame), newlen))
+    frame = frame[:newlen]
+
+    # Remove the cyclic prefix
+    frame = frame.reshape(-1, stride_advance)[:,:stride_len].reshape(-1)
+
+    # Reverse again
+    return frame[::-1]
+
+
+def plot_constellation_once(options):
+    port = int(options.port)
+    num_samps_to_request = int(options.samps)
+
+    tx_ts, txframe, rx_ts, rxframe = recv_rxtx(port, num_samps_to_request)
+
+    frame = remove_guard_intervals(txframe, options)
+
+    oversample = int(int(options.samplerate) / 2048000)
+
+    n = Spacing * oversample # is also number of samples per symbol
+    if len(frame) % n != 0:
+        raise ValueError("Frame length doesn't contain exact number of symbols")
+    num_syms = int(len(frame) / n)
+    print("frame {} has {} symbols".format(len(frame), num_syms))
+    spectrums = np.array([np.fft.fftshift(np.fft.fft(frame[n*i:n*(i+1)], n)) for i in range(num_syms)])
+
+    def normalise(x):
+        """Normalise a real-valued array x to the range [0,1]"""
+        y = x + np.min(x)
+        return x / np.max(x)
+
+    imsave("spectrums.png", np.concatenate([
+        normalise(np.abs(spectrums)),
+        normalise(np.angle(spectrums))]))
+
+    # Only take bins that are supposed to contain energy
+    # i.e. the middle 1536 bins, excluding the bin at n/2
+    assert(n % 2 == 0)
+    n_half = int(n/2)
+    spectrums = np.concatenate(
+            [spectrums[...,n_half-768:n_half],
+             spectrums[...,n_half + 1:n_half + 769]], axis=1)
+
+    sym_indices = (np.tile(np.arange(num_syms-1).reshape(num_syms-1,1), (1,NbCarriers)) +
+                   np.tile(np.linspace(-0.4, 0.4, NbCarriers), (num_syms-1, 1) ) )
+    sym_indices = sym_indices.reshape(-1)
+    diff_angles = np.mod(np.diff(np.angle(spectrums, deg=1), axis=0), 360)
+    #sym_points = spectrums[:-1].reshape(-1)
+    # Set amplitude and phase of low power points to zero, avoid cluttering diagram
+    #sym_points[np.abs(sym_points) < np.mean(np.abs(sym_points)) * 0.1] = 0
+
+    print("ix {}  spec {} da {}".format(
+        sym_indices.shape, spectrums.shape, diff_angles.shape))
+
+    fig = pp.figure()
+
+    fig.suptitle("Constellation")
+    ax1 = fig.add_subplot(111)
+    ax1.set_title("TX")
+    ax1.scatter(sym_indices, diff_angles.reshape(-1), alpha=0.1)
+
+    pp.show()
+
+fft_size = 4096
+
+def plot_spectrum_once(options):
+    port = int(options.port)
+    num_samps_to_request = int(options.samps)
+    freqs = np.fft.fftshift(np.fft.fftfreq(fft_size, d=1./int(options.samplerate)))
+
+    tx_power, rx_power = get_spectrum(port, num_samps_to_request)
+    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(options):
+    port = int(options.port)
+    num_samps_to_request = int(options.samps)
+    freqs = np.fft.fftshift(np.fft.fftfreq(fft_size, d=1./int(options.samplerate)))
+
+    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_power, rx_power = get_spectrum(port, num_samps_to_request)
+
+        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.