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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# DPD Calculation Engine, apply stored configuration.
#
# 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 matplotlib
import glob
import natsort
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 src.Measure as Measure
import src.Model as Model
import src.ExtractStatistic as ExtractStatistic
import src.Adapt as Adapt
import src.Agc as Agc
import src.TX_Agc as TX_Agc
import argparse
import src.const
import src.Symbol_align
import src.Measure_Shoulders
import src.MER
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='/tmp/poly.coef',
help='File with DPD coefficients, which will be read by ODR-DabMod',
required=False)
parser.add_argument('--txgain', default=75,
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', type=int,
help='Number of samples to request from ODR-DabMod',
required=False)
parser.add_argument('--target_median', default=0.1,
help='target_median',
required=False,
type=float)
parser.add_argument('--searchpath', default='./stored', type=str,
help='Path to search .pkl files with stored configuration'
'for adapt',
required=False)
parser.add_argument('-L', '--lut',
help='Use lookup table instead of polynomial predistorter',
action="store_true")
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
txgain = cli_args.txgain
rxgain = cli_args.rxgain
num_req = cli_args.samps
samplerate = cli_args.samplerate
searchpath = cli_args.searchpath
target_median = cli_args.target_median
c = src.const.const(samplerate, target_median)
SA = src.Symbol_align.Symbol_align(c)
MER = src.MER.MER(c)
MS = src.Measure_Shoulders.Measure_Shoulder(c, plot=False)
meas = Measure.Measure(samplerate, port, num_req)
extStat = ExtractStatistic.ExtractStatistic(c, plot=True)
adapt = Adapt.Adapt(port_rc, coef_path)
dpddata = adapt.get_predistorter()
if cli_args.lut:
model = Model.Lut(c, plot=True)
else:
model = Model.Poly(c, plot=True)
adapt.set_predistorter(model.get_dpd_data())
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()
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.TX_Agc(adapt, c)
# Automatic Gain Control
agc = Agc.Agc(meas, adapt, c)
agc.run()
import os
paths = natsort.natsorted(glob.glob(searchpath + "/*.pkl"))
print(paths)
for i, path in enumerate(paths):
print(i, path)
adapt.load(path)
dpddata_after = adapt.get_predistorter()
coefs_am, coefs_pm = model.reset_coefs()
adapt.set_predistorter(("poly", coefs_am, coefs_pm))
print("Loaded configuration without pre-distortion")
raw_input("Key for pre-distortion ")
adapt.set_predistorter(dpddata_after)
print("Pre-distortion done")
raw_input("Key for next ")
# 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.
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