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Diffstat (limited to 'python/dpd/old/show_spectrum.py')
| -rwxr-xr-x | python/dpd/old/show_spectrum.py | 276 |
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 index 0000000..f23dba2 --- /dev/null +++ b/python/dpd/old/show_spectrum.py @@ -0,0 +1,276 @@ +#!/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. |