diff options
| -rw-r--r-- | dpd/src/TX_Agc.py | 66 |
1 files changed, 47 insertions, 19 deletions
diff --git a/dpd/src/TX_Agc.py b/dpd/src/TX_Agc.py index b9226b2..7b74c8f 100644 --- a/dpd/src/TX_Agc.py +++ b/dpd/src/TX_Agc.py @@ -20,7 +20,8 @@ import matplotlib.pyplot as plt import src.Adapt as Adapt -#TODO fix for float tx_gain + +# TODO fix for float tx_gain class TX_Agc: def __init__(self, adapt, @@ -45,7 +46,6 @@ class TX_Agc: 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 @@ -55,31 +55,59 @@ class TX_Agc: 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 tx_median > self.tx_median_threshold_tolerate_max or\ - tx_median < self.tx_median_threshold_tolerate_min: - 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 - ) - self.adapt.set_txgain(new_txgain) - txgain = self.adapt.get_txgain() - - digital_gain_factor = 10 ** (delta_db / 20.) - digital_gain = self.adapt.get_digital_gain() * digital_gain_factor - self.adapt.set_digital_gain(digital_gain) + + 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 < 0: + 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 = {}, txgain_new = {}, " \ + "delta_db = {}, tx_median {}, " \ "digital_gain_factor = {}". format(digital_gain, txgain, delta_db, tx_median, digital_gain_factor)) - time.sleep(1) return True return False |