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Diffstat (limited to 'python/dpdce.py')
| -rwxr-xr-x | python/dpdce.py | 337 |
1 files changed, 337 insertions, 0 deletions
diff --git a/python/dpdce.py b/python/dpdce.py new file mode 100755 index 0000000..da1b6fb --- /dev/null +++ b/python/dpdce.py @@ -0,0 +1,337 @@ +#!/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 server mode. + +This engine calculates and updates the parameter of the digital +predistortion module of ODR-DabMod.""" + +import sys +import os +import argparse +import configparser +import matplotlib +matplotlib.use('Agg') + +parser = argparse.ArgumentParser( + description="DPD Computation Engine for ODR-DabMod") +parser.add_argument('--config', default="gui-dpdce.ini", type=str, + help='Location of configuration filename (default: gui-dpdce.ini)', + required=False) +parser.add_argument('-s', '--status', action="store_true", + help='Display the currently running DPD settings.') +parser.add_argument('-r', '--reset', action="store_true", + help='Reset the DPD settings to the defaults, and set digital gain to 0.01') + +cli_args = parser.parse_args() +allconfig = configparser.ConfigParser() +allconfig.read(cli_args.config) +config = allconfig['dpdce'] + +# removed options: +# txgain, rxgain, digital_gain, target_median, iterations, lut, enable-txgain-agc, plot, measure + +control_port = config['control_port'] +dpd_port = config['dpd_port'] +rc_port = config['rc_port'] +samplerate = config['samplerate'] +samps = config['samps'] +coef_file = config['coef_file'] +log_folder = config['log_folder'] + +import logging +import datetime + +save_logs = False + +# Simple usage scenarios don't need to clutter /tmp +if save_logs: + dt = datetime.datetime.now().isoformat() + logging_path = '/tmp/dpd_{}'.format(dt).replace('.', '_').replace(':', '-') + 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 = "" + +logging.info("DPDCE starting up"); + +import socket +from lib import yamlrpc +import numpy as np +import traceback +from threading import Thread, Lock +from queue import Queue +from dpd.Model import Poly +import dpd.Heuristics as Heuristics +from dpd.Measure import Measure +from dpd.ExtractStatistic import ExtractStatistic +from dpd.Adapt import Adapt, dpddata_to_str +from dpd.RX_Agc import Agc +from dpd.Symbol_align import Symbol_align +from dpd.GlobalConfig import GlobalConfig +from dpd.MER import MER +from dpd.Measure_Shoulders import Measure_Shoulders + +c = GlobalConfig(config, logging_path) +symbol_align = Symbol_align(c) +mer = MER(c) +meas_shoulders = Measure_Shoulders(c) +meas = Measure(c, samplerate, dpd_port, samps) +extStat = ExtractStatistic(c) +adapt = Adapt(rc_port, coef_file, logging_path) + +model = Poly(c) + +# Do not touch settings on startup +tx_gain = adapt.get_txgain() +rx_gain = adapt.get_rxgain() +digital_gain = adapt.get_digital_gain() +dpddata = adapt.get_predistorter() + +logging.info("ODR-DabMod currently running with TXGain {}, RXGain {}, digital gain {} and {}".format( + tx_gain, rx_gain, digital_gain, dpddata_to_str(dpddata))) + +if cli_args.status: + sys.exit(0) + +if cli_args.reset: + adapt.set_digital_gain(0.01) + adapt.set_rxgain(0) + adapt.set_predistorter(model.get_dpd_data()) + logging.info("DPD Settings were reset to default values.") + sys.exit(0) + +cmd_socket = yamlrpc.Socket(bind_port=config.getint(control_port)) + +# The following is accessed by both threads and need to be locked +settings = { + 'rx_gain': rx_gain, + 'tx_gain': tx_gain, + 'digital_gain': digital_gain, + 'dpddata': dpddata, + } +results = { + 'tx_median': 0, + 'rx_median': 0, + 'state': 'idle', + } +lock = Lock() +command_queue = Queue(maxsize=1) + +# Automatic Gain Control for the RX gain +agc = Agc(meas, adapt, c) + +def engine_worker(): + try: + while True: + cmd = command_queue.get() + + if cmd == "quit": + break + elif cmd == "calibrate": + with lock: + results['state'] = 'rx agc' + + agc.run() + + txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median = self.measure.get_samples() + + with lock: + settings['rx_gain'] = adapt.get_rxgain() + settings['digital_gain'] = adapt.get_digital_gain() + results['tx_median'] = tx_median + results['rx_median'] = rx_median + results['state'] = 'idle' + + finally: + with lock: + results['state'] = 'terminated' + + +engine = Thread(target=engine_worker) +engine.start() + +try: + while True: + addr, msg_id, method, params = cmd_socket.receive_request() + + if method == 'get_settings': + with lock: + cmd_socket.send_success_response(addr, msg_id, settings) + elif method == 'get_results': + with lock: + cmd_socket.send_success_response(addr, msg_id, results) + elif method == 'calibrate': + command_queue.put('calibrate') + else: + cmd_socket.send_error_response(addr, msg_id, "request not understood") +finally: + command_queue.put('quit') + engine.join() + +# Make code below unreachable +sys.exit(0) + +def measure_once(): + 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 = symbol_align.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 = meas_shoulders.average_shoulders(rxframe_aligned) + #tx_shoulder_tuple = meas_shoulders.average_shoulders(txframe_aligned) + +state = 'report' +i = 0 +n_meas = None +num_iter = 10 +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() + # TODO Check TX median + + # 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 + + model.train(tx, rx, phase_diff, lr=Heuristics.get_learning_rate(i)) + 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 = symbol_align.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 = meas_shoulders.average_shoulders(rxframe_aligned) + tx_shoulder_tuple = meas_shoulders.average_shoulders(txframe_aligned) + + lr = Heuristics.get_learning_rate(i) + + # 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) 2018 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. |