#!/usr/bin/env python # -*- coding: utf-8 -*- # # DPD Calculation Engine main file. # # http://www.opendigitalradio.org # Licence: The MIT License, see notice at the end of this file """This Python script is the main file for ODR-DabMod's DPD Computation Engine. This engine calculates and updates the parameter of the digital predistortion module of ODR-DabMod.""" import datetime import os import time import matplotlib matplotlib.use('GTKAgg') import logging dt = datetime.datetime.now().isoformat() logging_path = "/tmp/dpd_{}".format(dt).replace(".", "_").replace(":", "-") os.makedirs(logging_path) logging.basicConfig(format='%(asctime)s - %(module)s - %(levelname)s - %(message)s', datefmt='%Y-%m-%d %H:%M:%S', filename='{}/dpd.log'.format(logging_path), filemode='w', level=logging.DEBUG) # also log up to INFO to console console = logging.StreamHandler() console.setLevel(logging.INFO) # set a format which is simpler for console use formatter = logging.Formatter('%(asctime)s - %(module)s - %(levelname)s - %(message)s') # tell the handler to use this format console.setFormatter(formatter) # add the handler to the root logger logging.getLogger('').addHandler(console) import numpy as np import traceback import src.Measure as Measure import src.Model as Model import src.Adapt as Adapt import src.Agc as Agc import src.TX_Agc as TX_Agc import src.Symbol_align import src.const import src.MER import argparse parser = argparse.ArgumentParser( description="DPD Computation Engine for ODR-DabMod") parser.add_argument('--port', default='50055', help='port of DPD server to connect to (default: 50055)', required=False) parser.add_argument('--rc-port', default='9400', help='port of ODR-DabMod ZMQ Remote Control to connect to (default: 9400)', required=False) parser.add_argument('--samplerate', default='8192000', help='Sample rate', required=False) parser.add_argument('--coefs', default='poly.coef', help='File with DPD coefficients, which will be read by ODR-DabMod', required=False) parser.add_argument('--txgain', default=74, help='TX Gain', required=False, type=int) parser.add_argument('--rxgain', default=30, help='TX Gain', required=False, type=int) parser.add_argument('--digital_gain', default=1, help='Digital Gain', required=False, type=float) parser.add_argument('--samps', default='81920', help='Number of samples to request from ODR-DabMod', required=False) parser.add_argument('-i', '--iterations', default='1', help='Number of iterations to run', required=False) parser.add_argument('-l', '--load-poly', help='Load existing polynomial', action="store_true") cli_args = parser.parse_args() port = int(cli_args.port) port_rc = int(cli_args.rc_port) coef_path = cli_args.coefs digital_gain = cli_args.digital_gain txgain = cli_args.txgain rxgain = cli_args.rxgain num_req = int(cli_args.samps) samplerate = int(cli_args.samplerate) num_iter = int(cli_args.iterations) SA = src.Symbol_align.Symbol_align(samplerate) MER = src.MER.MER(samplerate) c = src.const.const(samplerate) meas = Measure.Measure(samplerate, port, num_req) adapt = Adapt.Adapt(port_rc, coef_path) coefs_am, coefs_pm = adapt.get_coefs() if cli_args.load_poly: model = Model.Model(c, SA, MER, coefs_am, coefs_pm, plot=True) else: model = Model.Model(c, SA, MER, [1.0, 0, 0, 0, 0], [0, 0, 0, 0, 0], plot=True) adapt.set_coefs(model.coefs_am, model.coefs_pm) adapt.set_digital_gain(digital_gain) adapt.set_txgain(txgain) adapt.set_rxgain(rxgain) tx_gain = adapt.get_txgain() rx_gain = adapt.get_rxgain() digital_gain = adapt.get_digital_gain() dpd_coefs_am, dpd_coefs_pm = adapt.get_coefs() logging.info( "TX gain {}, RX gain {}, dpd_coefs_am {}," " dpd_coefs_pm {}, digital_gain {}".format( tx_gain, rx_gain, dpd_coefs_am, dpd_coefs_pm, digital_gain ) ) tx_agc = TX_Agc.TX_Agc(adapt) # Automatic Gain Control agc = Agc.Agc(meas, adapt) agc.run() for i in range(num_iter): try: txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median = meas.get_samples() logging.debug("tx_ts {}, rx_ts {}".format(tx_ts, rx_ts)) assert tx_ts - rx_ts < 1e-5, "Time stamps do not match." if tx_agc.adapt_if_necessary(txframe_aligned): continue coefs_am, coefs_pm = model.get_next_coefs(txframe_aligned, rxframe_aligned) adapt.set_coefs(coefs_am, coefs_pm) off = SA.calc_offset(txframe_aligned) tx_mer = MER.calc_mer(txframe_aligned[off:off + c.T_U]) rx_mer = MER.calc_mer(rxframe_aligned[off:off + c.T_U], debug=True) logging.info("MER with lag in it. {}: TX {}, RX {}". format(i, tx_mer, rx_mer)) except Exception as e: logging.warning("Iteration {} failed.".format(i)) logging.warning(traceback.format_exc()) # The MIT License (MIT) # # Copyright (c) 2017 Andreas Steger, 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.