From d5cbe10c0e2298b0e40161607a3da158249bdb82 Mon Sep 17 00:00:00 2001 From: "Matthias P. Braendli" Date: Tue, 4 Dec 2018 10:18:33 +0100 Subject: Move python stuff to folder --- dpd/main.py | 336 ------------------------------------------------------------ 1 file changed, 336 deletions(-) delete mode 100755 dpd/main.py (limited to 'dpd/main.py') diff --git a/dpd/main.py b/dpd/main.py deleted file mode 100755 index 10a56fc..0000000 --- a/dpd/main.py +++ /dev/null @@ -1,336 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# -# DPD Computation 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 sys -import datetime -import os -import argparse -import matplotlib - -matplotlib.use('Agg') - -parser = argparse.ArgumentParser( - description="DPD Computation Engine for ODR-DabMod") -parser.add_argument('--port', default=50055, type=int, - help='port of DPD server to connect to (default: 50055)', - required=False) -parser.add_argument('--rc-port', default=9400, type=int, - help='port of ODR-DabMod ZMQ Remote Control to connect to (default: 9400)', - required=False) -parser.add_argument('--samplerate', default=8192000, type=int, - 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=-1, - help='TX Gain, -1 to leave unchanged', - required=False, - type=int) -parser.add_argument('--rxgain', default=30, - help='TX Gain, -1 to leave unchanged', - required=False, - type=int) -parser.add_argument('--digital_gain', default=0.01, - help='Digital Gain', - required=False, - type=float) -parser.add_argument('--target_median', default=0.05, - help='The target median for the RX and TX AGC', - required=False, - type=float) -parser.add_argument('--samps', default='81920', type=int, - help='Number of samples to request from ODR-DabMod', - required=False) -parser.add_argument('-i', '--iterations', default=10, type=int, - help='Number of iterations to run', - required=False) -parser.add_argument('-L', '--lut', - help='Use lookup table instead of polynomial predistorter', - action="store_true") -parser.add_argument('--enable-txgain-agc', - help='Enable the TX gain AGC', - action="store_true") -parser.add_argument('--plot', - help='Enable all plots, to be more selective choose plots in GlobalConfig.py', - action="store_true") -parser.add_argument('--name', default="", type=str, - help='Name of the logging directory') -parser.add_argument('-r', '--reset', action="store_true", - help='Reset the DPD settings to the defaults.') -parser.add_argument('-s', '--status', action="store_true", - help='Display the currently running DPD settings.') -parser.add_argument('--measure', action="store_true", - help='Only measure metrics once') - -cli_args = parser.parse_args() - -port = cli_args.port -port_rc = cli_args.rc_port -coef_path = cli_args.coefs -digital_gain = cli_args.digital_gain -num_iter = cli_args.iterations -rxgain = cli_args.rxgain -txgain = cli_args.txgain -name = cli_args.name -plot = cli_args.plot - -# Logging -import logging - -# Simple usage scenarios don't need to clutter /tmp -if not (cli_args.status or cli_args.reset or cli_args.measure): - dt = datetime.datetime.now().isoformat() - logging_path = '/tmp/dpd_{}'.format(dt).replace('.', '_').replace(':', '-') - if name: - logging_path += '_' + name - print("Logs and plots written to {}".format(logging_path)) - 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) -else: - dt = datetime.datetime.now().isoformat() - logging.basicConfig(format='%(asctime)s - %(module)s - %(levelname)s - %(message)s', - datefmt='%Y-%m-%d %H:%M:%S', - level=logging.INFO) - logging_path = None - -logging.info("DPDCE starting up with options: {}".format(cli_args)) - -import numpy as np -import traceback -from src.Model import Lut, Poly -import src.Heuristics as Heuristics -from src.Measure import Measure -from src.ExtractStatistic import ExtractStatistic -from src.Adapt import Adapt, dpddata_to_str -from src.RX_Agc import Agc -from src.TX_Agc import TX_Agc -from src.Symbol_align import Symbol_align -from src.GlobalConfig import GlobalConfig -from src.MER import MER -from src.Measure_Shoulders import Measure_Shoulders - -c = GlobalConfig(cli_args, logging_path) -SA = Symbol_align(c) -MER = MER(c) -MS = Measure_Shoulders(c) -meas = Measure(c, cli_args.samplerate, port, cli_args.samps) -extStat = ExtractStatistic(c) -adapt = Adapt(c, port_rc, coef_path) - -if cli_args.status: - txgain = adapt.get_txgain() - rxgain = adapt.get_rxgain() - digital_gain = adapt.get_digital_gain() - dpddata = dpddata_to_str(adapt.get_predistorter()) - - logging.info("ODR-DabMod currently running with TXGain {}, RXGain {}, digital gain {} and {}".format( - txgain, rxgain, digital_gain, dpddata)) - sys.exit(0) - -if cli_args.lut: - model = Lut(c) -else: - model = Poly(c) - -# Models have the default settings on startup -adapt.set_predistorter(model.get_dpd_data()) -adapt.set_digital_gain(digital_gain) - -# Set RX Gain -if rxgain == -1: - rxgain = adapt.get_rxgain() -else: - adapt.set_rxgain(rxgain) - -# Set TX Gain -if txgain == -1: - txgain = adapt.get_txgain() -else: - adapt.set_txgain(txgain) - -tx_gain = adapt.get_txgain() -rx_gain = adapt.get_rxgain() -digital_gain = adapt.get_digital_gain() - -dpddata = adapt.get_predistorter() - -logging.info("TX gain {}, RX gain {}, digital_gain {}, {!s}".format( - tx_gain, rx_gain, digital_gain, dpddata_to_str(dpddata))) - -if cli_args.reset: - logging.info("DPD Settings were reset to default values.") - sys.exit(0) - -# Automatic Gain Control -agc = Agc(meas, adapt, c) -agc.run() - -if cli_args.measure: - txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median = meas.get_samples() - - print("TX signal median {}".format(np.median(np.abs(txframe_aligned)))) - print("RX signal median {}".format(rx_median)) - - tx, rx, phase_diff, n_per_bin = extStat.extract(txframe_aligned, rxframe_aligned) - - off = SA.calc_offset(txframe_aligned) - print("off {}".format(off)) - tx_mer = MER.calc_mer(txframe_aligned[off:off + c.T_U], debug_name='TX') - print("tx_mer {}".format(tx_mer)) - rx_mer = MER.calc_mer(rxframe_aligned[off:off + c.T_U], debug_name='RX') - print("rx_mer {}".format(rx_mer)) - - mse = np.mean(np.abs((txframe_aligned - rxframe_aligned) ** 2)) - print("mse {}".format(mse)) - - digital_gain = adapt.get_digital_gain() - print("digital_gain {}".format(digital_gain)) - - #rx_shoulder_tuple = MS.average_shoulders(rxframe_aligned) - #tx_shoulder_tuple = MS.average_shoulders(txframe_aligned) - sys.exit(0) - -# Disable TXGain AGC by default, so that the experiments are controlled -# better. -tx_agc = None -if cli_args.enable_txgain_agc: - tx_agc = TX_Agc(adapt, c) - -state = 'report' -i = 0 -lr = None -n_meas = None -while i < num_iter: - try: - # Measure - if state == 'measure': - # Get Samples and check gain - txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median = meas.get_samples() - if tx_agc and tx_agc.adapt_if_necessary(txframe_aligned): - continue - - # Extract usable data from measurement - tx, rx, phase_diff, n_per_bin = extStat.extract(txframe_aligned, rxframe_aligned) - - n_meas = Heuristics.get_n_meas(i) - if extStat.n_meas >= n_meas: # Use as many measurements nr of runs - state = 'model' - else: - state = 'measure' - - # Model - elif state == 'model': - # Calculate new model parameters and delete old measurements - if any([x is None for x in [tx, rx, phase_diff]]): - logging.error("No data to calculate model") - state = 'measure' - continue - - lr = Heuristics.get_learning_rate(i) - model.train(tx, rx, phase_diff, lr=lr) - dpddata = model.get_dpd_data() - extStat = ExtractStatistic(c) - state = 'adapt' - - # Adapt - elif state == 'adapt': - adapt.set_predistorter(dpddata) - state = 'report' - - # Report - elif state == 'report': - try: - txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median = meas.get_samples() - - # Store all settings for pre-distortion, tx and rx - adapt.dump() - - # Collect logging data - off = SA.calc_offset(txframe_aligned) - tx_mer = MER.calc_mer(txframe_aligned[off:off + c.T_U], debug_name='TX') - rx_mer = MER.calc_mer(rxframe_aligned[off:off + c.T_U], debug_name='RX') - mse = np.mean(np.abs((txframe_aligned - rxframe_aligned) ** 2)) - tx_gain = adapt.get_txgain() - rx_gain = adapt.get_rxgain() - digital_gain = adapt.get_digital_gain() - tx_median = np.median(np.abs(txframe_aligned)) - rx_shoulder_tuple = MS.average_shoulders(rxframe_aligned) - tx_shoulder_tuple = MS.average_shoulders(txframe_aligned) - - # Generic logging - logging.info(list((name, eval(name)) for name in - ['i', 'tx_mer', 'tx_shoulder_tuple', 'rx_mer', - 'rx_shoulder_tuple', 'mse', 'tx_gain', - 'digital_gain', 'rx_gain', 'rx_median', - 'tx_median', 'lr', 'n_meas'])) - - # Model specific logging - if dpddata[0] == 'poly': - coefs_am = dpddata[1] - coefs_pm = dpddata[2] - logging.info('It {}: coefs_am {}'. - format(i, coefs_am)) - logging.info('It {}: coefs_pm {}'. - format(i, coefs_pm)) - elif dpddata[0] == 'lut': - scalefactor = dpddata[1] - lut = dpddata[2] - logging.info('It {}: LUT scalefactor {}, LUT {}'. - format(i, scalefactor, lut)) - except: - logging.error('Iteration {}: Report failed.'.format(i)) - logging.error(traceback.format_exc()) - i += 1 - state = 'measure' - - except: - logging.error('Iteration {} failed.'.format(i)) - logging.error(traceback.format_exc()) - i += 1 - state = 'measure' - -# The MIT License (MIT) -# -# Copyright (c) 2017 Andreas Steger -# 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. -- cgit v1.2.3