aboutsummaryrefslogtreecommitdiffstats
path: root/python/dpd/main.py
diff options
context:
space:
mode:
authorMatthias P. Braendli <matthias.braendli@mpb.li>2018-12-04 10:18:33 +0100
committerMatthias P. Braendli <matthias.braendli@mpb.li>2018-12-04 10:18:33 +0100
commitd5cbe10c0e2298b0e40161607a3da158249bdb82 (patch)
tree5f6a0ff40ce5b3dd39d0df1c348557b183b48a7e /python/dpd/main.py
parent594cb2691debaa7562fd7d76d3b224701ec087ea (diff)
downloaddabmod-d5cbe10c0e2298b0e40161607a3da158249bdb82.tar.gz
dabmod-d5cbe10c0e2298b0e40161607a3da158249bdb82.tar.bz2
dabmod-d5cbe10c0e2298b0e40161607a3da158249bdb82.zip
Move python stuff to folder
Diffstat (limited to 'python/dpd/main.py')
-rwxr-xr-xpython/dpd/main.py338
1 files changed, 338 insertions, 0 deletions
diff --git a/python/dpd/main.py b/python/dpd/main.py
new file mode 100755
index 0000000..9ea5a39
--- /dev/null
+++ b/python/dpd/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.