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diff --git a/python/dpd/TX_Agc.py b/python/dpd/TX_Agc.py
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+# -*- 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.