Move python stuff to folder

1 files changed, 0 insertions, 336 deletions

diff --git a/dpd/main.py b/dpd/main.py
deleted file mode 100755
index 10a56fc..0000000
--- a/dpd/main.py
+++ /dev/null
@@ -1,336 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-#
-# DPD Computation 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 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.