Move python stuff to folder

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diff --git a/python/dpd/main.py b/python/dpd/main.py
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+#!/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.