From d5cbe10c0e2298b0e40161607a3da158249bdb82 Mon Sep 17 00:00:00 2001 From: "Matthias P. Braendli" Date: Tue, 4 Dec 2018 10:18:33 +0100 Subject: Move python stuff to folder --- python/dpd/src/TX_Agc.py | 131 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 131 insertions(+) create mode 100644 python/dpd/src/TX_Agc.py (limited to 'python/dpd/src/TX_Agc.py') diff --git a/python/dpd/src/TX_Agc.py b/python/dpd/src/TX_Agc.py new file mode 100644 index 0000000..309193d --- /dev/null +++ b/python/dpd/src/TX_Agc.py @@ -0,0 +1,131 @@ +# -*- 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. -- cgit v1.2.3