diff options
| -rwxr-xr-x | dpd/main.py | 99 | ||||
| -rw-r--r-- | dpd/src/TX_Agc.py | 2 |
2 files changed, 53 insertions, 48 deletions
diff --git a/dpd/main.py b/dpd/main.py index aa96a8c..176f80f 100755 --- a/dpd/main.py +++ b/dpd/main.py @@ -12,45 +12,11 @@ predistortion module of ODR-DabMod.""" import datetime import os +import argparse import matplotlib 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 numpy as np -import traceback -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 -from src.Symbol_align import Symbol_align -from src.Const import Const -from src.MER import MER -from src.Measure_Shoulders import Measure_Shoulders -import argparse -import src.Heuristics as Heur parser = argparse.ArgumentParser( description="DPD Computation Engine for ODR-DabMod") @@ -94,9 +60,10 @@ parser.add_argument('-L', '--lut', 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() -logging.info(cli_args) port = cli_args.port port_rc = cli_args.rc_port @@ -108,21 +75,59 @@ 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.Measure(samplerate, port, num_req) -extStat = ExtractStatistic.ExtractStatistic(c) -adapt = Adapt.Adapt(port_rc, coef_path) +meas = Measure(samplerate, port, num_req) +extStat = ExtractStatistic(c) +adapt = Adapt(port_rc, coef_path) if cli_args.lut: - model = Model.Lut(c) + model = Lut(c) else: - model = Model.Poly(c) + model = Poly(c) adapt.set_predistorter(model.get_dpd_data()) adapt.set_digital_gain(digital_gain) @@ -164,10 +169,10 @@ elif dpddata[0] == "lut": else: logging.error("Unknown dpd data format {}".format(dpddata[0])) -tx_agc = TX_Agc.TX_Agc(adapt, c) +tx_agc = TX_Agc(adapt, c) # Automatic Gain Control -agc = Agc.Agc(meas, adapt, c) +agc = Agc(meas, adapt, c) agc.run() state = "measure" @@ -184,7 +189,7 @@ while i < num_iter: # Extract usable data from measurement tx, rx, phase_diff, n_per_bin = extStat.extract(txframe_aligned, rxframe_aligned) - n_meas = Heur.get_n_meas(i) + n_meas = Heuristics.get_n_meas(i) if extStat.n_meas >= n_meas: # Use as many measurements nr of runs state = 'model' else: @@ -193,10 +198,10 @@ while i < num_iter: # Model elif state == 'model': # Calculate new model parameters and delete old measurements - lr = Heur.get_learning_rate(i) + lr = Heuristics.get_learning_rate(i) model.train(tx, rx, phase_diff, lr=lr) dpddata = model.get_dpd_data() - extStat = ExtractStatistic.ExtractStatistic(c) + extStat = ExtractStatistic(c) state = 'adapt' # Adapt diff --git a/dpd/src/TX_Agc.py b/dpd/src/TX_Agc.py index 7b74c8f..2602ea6 100644 --- a/dpd/src/TX_Agc.py +++ b/dpd/src/TX_Agc.py @@ -90,7 +90,7 @@ class TX_Agc: # Set new values. # Avoid temorary increase of output power with correct order - if digital_gain_factor < 0: + if digital_gain_factor < 1: self.adapt.set_digital_gain(digital_gain) time.sleep(0.5) txgain = self._set_tx_gain(new_txgain) |