aboutsummaryrefslogtreecommitdiffstats
path: root/python
diff options
context:
space:
mode:
authorMatthias P. Braendli <matthias.braendli@mpb.li>2018-12-22 13:32:34 +0100
committerMatthias P. Braendli <matthias.braendli@mpb.li>2018-12-22 13:32:34 +0100
commitb72f76d4154bb4c8bc356b624627e9d6bc4f7328 (patch)
treebc52c0f473b7d705f8fca280ea7f33538cd84be9 /python
parentaa3abebd804129f2eff361a1b4f21d0c04c61cfd (diff)
downloaddabmod-b72f76d4154bb4c8bc356b624627e9d6bc4f7328.tar.gz
dabmod-b72f76d4154bb4c8bc356b624627e9d6bc4f7328.tar.bz2
dabmod-b72f76d4154bb4c8bc356b624627e9d6bc4f7328.zip
GUI: fix path for adapt step
Diffstat (limited to 'python')
-rw-r--r--python/dpd/Adapt.py2
-rwxr-xr-xpython/dpdce.py6
2 files changed, 5 insertions, 3 deletions
diff --git a/python/dpd/Adapt.py b/python/dpd/Adapt.py
index 840aee9..8108375 100644
--- a/python/dpd/Adapt.py
+++ b/python/dpd/Adapt.py
@@ -232,7 +232,7 @@ class Adapt:
_write_lut_file(scalefactor, lut, self.coef_path)
else:
raise ValueError("Unknown predistorter '{}'".format(dpddata[0]))
- self.send_receive("set memlesspoly coeffile {}".format(self.coef_path))
+ return self.send_receive("set memlesspoly coeffile {}".format(self.coef_path))
def dump(self, path=None):
"""Backup current settings to a file"""
diff --git a/python/dpdce.py b/python/dpdce.py
index 90cd436..f855f9c 100755
--- a/python/dpdce.py
+++ b/python/dpdce.py
@@ -101,6 +101,7 @@ from dpd.MER import MER
from dpd.Measure_Shoulders import Measure_Shoulders
plot_path = os.path.realpath(plot_directory)
+coef_file = os.path.realpath(config['coef_file'])
c = GlobalConfig(samplerate, plot_path)
symbol_align = Symbol_align(c)
@@ -302,7 +303,7 @@ def engine_worker():
iteration = internal_data['n_runs']
internal_data['n_runs'] += 1
- adapt.set_predistorter(dpddata)
+ answer = adapt.set_predistorter(dpddata)
time.sleep(2)
@@ -324,7 +325,8 @@ def engine_worker():
lr = Heuristics.get_learning_rate(iteration)
- summary = [f"Signal measurements after iteration {iteration} with learning rate {lr}",
+ summary = [f"Set predistorter: {answer}",
+ f"Signal measurements after iteration {iteration} with learning rate {lr}",
f"TX MER {tx_mer}, RX MER {rx_mer}",
"Shoulders: TX {!r}, RX {!r}".format(tx_shoulder_tuple, rx_shoulder_tuple),
f"Mean-square error: {mse}",