Rework GUI and DPDCE

1 files changed, 0 insertions, 131 deletions

diff --git a/python/dpd/src/TX_Agc.py b/python/dpd/src/TX_Agc.py
deleted file mode 100644
index 309193d..0000000
--- a/python/dpd/src/TX_Agc.py
+++ /dev/null
@@ -1,131 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# DPD Computation Engine,  Automatic Gain Control.
-#
-# http://www.opendigitalradio.org
-# Licence: The MIT License, see notice at the end of this file
-
-import datetime
-import os
-import logging
-import time
-import numpy as np
-import matplotlib
-
-matplotlib.use('agg')
-import matplotlib.pyplot as plt
-
-import src.Adapt as Adapt
-
-
-# TODO fix for float tx_gain
-class TX_Agc:
-    def __init__(self,
-                 adapt,
-                 c):
-        """
-        In order to avoid digital clipping, this class increases the
-        TX gain and reduces the digital gain. Digital clipping happens
-        when the digital analog converter receives values greater than
-        it's maximal output. This class solves that problem by adapting
-        the TX gain in a way that the peaks of the TX signal are in a
-        specified range. The TX gain is adapted accordingly. The TX peaks
-        are approximated by estimating it based on the signal median.
-
-        :param adapt: Instance of Adapt Class to update
-               txgain and coefficients
-        :param max_txgain: limit for TX gain
-        :param tx_median_threshold_max: if the median of TX is larger
-                than this value, then the digital gain is reduced
-        :param tx_median_threshold_min: if the median of TX is smaller
-                than this value, then the digital gain is increased
-        :param tx_median_target: The digital gain is reduced in a way that
-                the median TX value is expected to be lower than this value.
-        """
-
-        assert isinstance(adapt, Adapt.Adapt)
-        self.adapt = adapt
-        self.max_txgain = c.TAGC_max_txgain
-        self.txgain = self.max_txgain
-
-        self.tx_median_threshold_tolerate_max = c.TAGC_tx_median_max
-        self.tx_median_threshold_tolerate_min = c.TAGC_tx_median_min
-        self.tx_median_target = c.TAGC_tx_median_target
-
-    def _calc_new_tx_gain(self, tx_median):
-        delta_db = 20 * np.log10(self.tx_median_target / tx_median)
-        new_txgain = self.adapt.get_txgain() - delta_db
-        assert new_txgain < self.max_txgain, \
-            "TX_Agc failed. New TX gain of {} is too large.".format(
-                new_txgain
-            )
-        return new_txgain, delta_db
-
-    def _calc_digital_gain(self, delta_db):
-        digital_gain_factor = 10 ** (delta_db / 20.)
-        digital_gain = self.adapt.get_digital_gain() * digital_gain_factor
-        return digital_gain, digital_gain_factor
-
-    def _set_tx_gain(self, new_txgain):
-        self.adapt.set_txgain(new_txgain)
-        txgain = self.adapt.get_txgain()
-        return txgain
-
-    def _have_to_adapt(self, tx_median):
-        too_large = tx_median > self.tx_median_threshold_tolerate_max
-        too_small = tx_median < self.tx_median_threshold_tolerate_min
-        return too_large or too_small
-
-    def adapt_if_necessary(self, tx):
-        tx_median = np.median(np.abs(tx))
-
-        if self._have_to_adapt(tx_median):
-            # Calculate new values
-            new_txgain, delta_db = self._calc_new_tx_gain(tx_median)
-            digital_gain, digital_gain_factor = \
-                self._calc_digital_gain(delta_db)
-
-            # Set new values.
-            # Avoid temorary increase of output power with correct order
-            if digital_gain_factor < 1:
-                self.adapt.set_digital_gain(digital_gain)
-                time.sleep(0.5)
-                txgain = self._set_tx_gain(new_txgain)
-                time.sleep(1)
-            else:
-                txgain = self._set_tx_gain(new_txgain)
-                time.sleep(1)
-                self.adapt.set_digital_gain(digital_gain)
-                time.sleep(0.5)
-
-            logging.info(
-                "digital_gain = {}, txgain_new = {}, " \
-                "delta_db = {}, tx_median {}, " \
-                "digital_gain_factor = {}".
-                    format(digital_gain, txgain, delta_db,
-                           tx_median, digital_gain_factor))
-
-            return True
-        return False
-
-# The MIT License (MIT)
-#
-# Copyright (c) 2017 Andreas Steger
-#
-# 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.