path: root/dpd/main.py

#!/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 argparse
import matplotlib

matplotlib.use('GTKAgg')


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=1,
                    help='Digital Gain',
                    required=False,
                    type=float)
parser.add_argument('--target_median', default=0.05,
                    help='target_median',
                    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=1, 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('--plot',
                    help='Enable all plots, to be more selective choose plots in Const.py',
                    action="store_true")
parser.add_argument('--name', default="", type=str,
                    help='Name of the logging directory')

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_req = cli_args.samps
samplerate = cli_args.samplerate
num_iter = cli_args.iterations
target_median = cli_args.target_median
rxgain = cli_args.rxgain
txgain = cli_args.txgain
name = cli_args.name
plot = cli_args.plot

# Logging
import logging

dt = datetime.datetime.now().isoformat()
logging_path = '/tmp/dpd_{}'.format(dt).replace('.', '_').replace(':', '-')
if name: logging_path += '_' + name
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)

logging.info(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
from src.Agc import Agc
from src.TX_Agc import TX_Agc
from src.Symbol_align import Symbol_align
from src.Const import Const
from src.MER import MER
from src.Measure_Shoulders import Measure_Shoulders

c = Const(samplerate, target_median, plot)
SA = Symbol_align(c)
MER = MER(c)
MS = Measure_Shoulders(c)
meas = Measure(samplerate, port, num_req)
extStat = ExtractStatistic(c)
adapt = Adapt(port_rc, coef_path)

if cli_args.lut:
    model = Lut(c)
else:
    model = Poly(c)
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()
if dpddata[0] == "poly":
    coefs_am = dpddata[1]
    coefs_pm = dpddata[2]
    logging.info(
        "TX gain {}, RX gain {}, dpd_coefs_am {},"
        " dpd_coefs_pm {}, digital_gain {}".format(
            tx_gain, rx_gain, coefs_am, coefs_pm, digital_gain
        )
    )
elif dpddata[0] == "lut":
    scalefactor = dpddata[1]
    lut = dpddata[2]
    logging.info(
        "TX gain {}, RX gain {}, LUT scalefactor {},"
        " LUT {}, digital_gain {}".format(
            tx_gain, rx_gain, scalefactor, lut, digital_gain
        )
    )
else:
    logging.error("Unknown dpd data format {}".format(dpddata[0]))

tx_agc = TX_Agc(adapt, c)

# Automatic Gain Control
agc = Agc(meas, adapt, c)
agc.run()

state = "measure"
i = 0
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.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
            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:
                # 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) if c.MS_enable else None
                tx_shoulder_tuple = MS.average_shoulders(txframe_aligned) if c.MS_enable else None

                # 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 Exception as e:
        logging.error('Iteration {} failed.'.format(i))
        logging.error(traceback.format_exc())

# 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.