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Diffstat (limited to 'python/dpd/old/main.py')
| -rwxr-xr-x | python/dpd/old/main.py | 338 |
1 files changed, 338 insertions, 0 deletions
diff --git a/python/dpd/old/main.py b/python/dpd/old/main.py new file mode 100755 index 0000000..9ea5a39 --- /dev/null +++ b/python/dpd/old/main.py @@ -0,0 +1,338 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# DPD Computation Engine standalone 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 running +in stand-alone mode. + +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. |