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-rwxr-xr-xdpd/main.py17
-rw-r--r--dpd/src/Adapt.py9
-rw-r--r--dpd/src/Agc.py6
-rw-r--r--dpd/src/Dab_Util.py10
-rw-r--r--dpd/src/MER.py2
-rw-r--r--dpd/src/Model.py249
-rw-r--r--dpd/src/TX_Agc.py15
-rw-r--r--dpd/src/phase_align.py2
-rwxr-xr-xdpd/src/subsample_align.py2
9 files changed, 102 insertions, 210 deletions
diff --git a/dpd/main.py b/dpd/main.py
index ad42cb6..eef1eb8 100755
--- a/dpd/main.py
+++ b/dpd/main.py
@@ -61,7 +61,7 @@ parser.add_argument('--samplerate', default='8192000',
parser.add_argument('--coefs', default='poly.coef',
help='File with DPD coefficients, which will be read by ODR-DabMod',
required=False)
-parser.add_argument('--txgain', default=60,
+parser.add_argument('--txgain', default=70,
help='TX Gain',
required=False,
type=int)
@@ -69,7 +69,7 @@ parser.add_argument('--rxgain', default=30,
help='TX Gain',
required=False,
type=int)
-parser.add_argument('--digital_gain', default=0.2,
+parser.add_argument('--digital_gain', default=1,
help='Digital Gain',
required=False,
type=float)
@@ -107,17 +107,19 @@ if cli_args.load_poly:
model = Model.Model(coefs_am, coefs_pm, plot=True)
else:
model = Model.Model([1.0, 0, 0, 0, 0], [0, 0, 0, 0, 0], plot=True)
-adapt.set_txgain(digital_gain)
+adapt.set_coefs(model.coefs_am, model.coefs_pm)
+adapt.set_digital_gain(digital_gain)
adapt.set_txgain(txgain)
adapt.set_rxgain(rxgain)
-adapt.set_coefs(model.coefs_am, model.coefs_pm)
tx_gain = adapt.get_txgain()
rx_gain = adapt.get_rxgain()
+digital_gain = adapt.get_digital_gain()
dpd_coefs_am, dpd_coefs_pm = adapt.get_coefs()
logging.info(
- "TX gain {}, RX gain {}, dpd_coefs_am {}, dpd_coefs_pm {}".format(
- tx_gain, rx_gain, dpd_coefs_am, dpd_coefs_pm
+ "TX gain {}, RX gain {}, dpd_coefs_am {},"
+ " dpd_coefs_pm {}, digital_gain {}".format(
+ tx_gain, rx_gain, dpd_coefs_am, dpd_coefs_pm, digital_gain
)
)
@@ -131,7 +133,8 @@ for i in range(num_iter):
try:
txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median = meas.get_samples()
logging.debug("tx_ts {}, rx_ts {}".format(tx_ts, rx_ts))
- assert tx_ts - rx_ts < 1e-5, "Time Stamps do not match."
+ assert tx_ts - rx_ts < 1e-5, "Time stamps do not match."
+
if tx_agc.adapt_if_necessary(txframe_aligned):
continue
diff --git a/dpd/src/Adapt.py b/dpd/src/Adapt.py
index 5e05112..b4042d6 100644
--- a/dpd/src/Adapt.py
+++ b/dpd/src/Adapt.py
@@ -85,7 +85,7 @@ class Adapt:
# TODO this is specific to the B200
if gain < 0 or gain > 89:
raise ValueError("Gain has to be in [0,89]")
- return self.send_receive("set uhd txgain %d" % gain)
+ return self.send_receive("set uhd txgain %.4f" % float(gain))
def get_txgain(self):
"""Get the txgain value in dB for the ODR-DabMod."""
@@ -103,12 +103,12 @@ class Adapt:
# TODO this is specific to the B200
if gain < 0 or gain > 89:
raise ValueError("Gain has to be in [0,89]")
- return self.send_receive("set uhd rxgain %d" % gain)
+ return self.send_receive("set uhd rxgain %.4f" % float(gain))
def get_rxgain(self):
"""Get the rxgain value in dB for the ODR-DabMod."""
# TODO handle failure
- return int(self.send_receive("get uhd rxgain")[0])
+ return float(self.send_receive("get uhd rxgain")[0])
def set_digital_gain(self, gain):
"""Set a new rxgain for the ODR-DabMod.
@@ -118,7 +118,8 @@ class Adapt:
gain : int
new RX gain, in the same format as ODR-DabMod's config file
"""
- return self.send_receive("set gain digital %.5f" % gain)
+ msg = "set gain digital %.5f" % gain
+ return self.send_receive(msg)
def get_digital_gain(self):
"""Get the rxgain value in dB for the ODR-DabMod."""
diff --git a/dpd/src/Agc.py b/dpd/src/Agc.py
index 1fd11c8..978b607 100644
--- a/dpd/src/Agc.py
+++ b/dpd/src/Agc.py
@@ -47,7 +47,7 @@ class Agc:
def run(self):
self.adapt.set_rxgain(self.rxgain)
- for i in range(3):
+ for i in range(2):
# Measure
txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median= \
self.measure.get_samples()
@@ -65,7 +65,7 @@ class Agc:
))
self.adapt.set_rxgain(self.rxgain)
- time.sleep(1)
+ time.sleep(0.5)
def plot_estimates(self):
"""Plots the estimate of for Max, Median, Mean for different
@@ -74,7 +74,7 @@ class Agc:
time.sleep(1)
dt = datetime.datetime.now().isoformat()
- fig_path = logging_path + "/" + dt + "_agc.pdf"
+ fig_path = logging_path + "/" + dt + "_agc.svg"
fig, axs = plt.subplots(2, 2, figsize=(3*6,1*6))
axs = axs.ravel()
diff --git a/dpd/src/Dab_Util.py b/dpd/src/Dab_Util.py
index 8a53004..a7403a3 100644
--- a/dpd/src/Dab_Util.py
+++ b/dpd/src/Dab_Util.py
@@ -45,7 +45,7 @@ class Dab_Util:
if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot:
dt = datetime.datetime.now().isoformat()
- corr_path = (logging_path + "/" + dt + "_tx_rx_corr.pdf")
+ corr_path = (logging_path + "/" + dt + "_tx_rx_corr.svg")
plt.plot(c, label="corr")
plt.legend()
plt.savefig(corr_path)
@@ -99,7 +99,7 @@ class Dab_Util:
if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot:
dt = datetime.datetime.now().isoformat()
- fig_path = logging_path + "/" + dt + "_sync_raw.pdf"
+ fig_path = logging_path + "/" + dt + "_sync_raw.svg"
fig, axs = plt.subplots(2)
axs[0].plot(np.abs(sig_tx[:128]), label="TX Frame")
@@ -143,7 +143,7 @@ class Dab_Util:
if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot:
dt = datetime.datetime.now().isoformat()
- fig_path = logging_path + "/" + dt + "_sync_sample_aligned.pdf"
+ fig_path = logging_path + "/" + dt + "_sync_sample_aligned.svg"
fig, axs = plt.subplots(2)
axs[0].plot(np.abs(sig_tx[:128]), label="TX Frame")
@@ -168,7 +168,7 @@ class Dab_Util:
if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
dt = datetime.datetime.now().isoformat()
- fig_path = logging_path + "/" + dt + "_sync_subsample_aligned.pdf"
+ fig_path = logging_path + "/" + dt + "_sync_subsample_aligned.svg"
fig, axs = plt.subplots(2)
axs[0].plot(np.abs(sig_tx[:128]), label="TX Frame")
@@ -192,7 +192,7 @@ class Dab_Util:
if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
dt = datetime.datetime.now().isoformat()
- fig_path = logging_path + "/" + dt + "_sync_phase_aligned.pdf"
+ fig_path = logging_path + "/" + dt + "_sync_phase_aligned.svg"
fig, axs = plt.subplots(2)
axs[0].plot(np.abs(sig_tx[:128]), label="TX Frame")
diff --git a/dpd/src/MER.py b/dpd/src/MER.py
index 2075ef5..00fcc23 100644
--- a/dpd/src/MER.py
+++ b/dpd/src/MER.py
@@ -77,7 +77,7 @@ class MER:
if debug:
dt = datetime.datetime.now().isoformat()
- fig_path = logging_path + "/" + dt + "_MER.pdf"
+ fig_path = logging_path + "/" + dt + "_MER.svg"
MERs = []
for i, (x, y) in enumerate(self._split_in_carrier(
diff --git a/dpd/src/Model.py b/dpd/src/Model.py
index dc526c5..bf52287 100644
--- a/dpd/src/Model.py
+++ b/dpd/src/Model.py
@@ -32,11 +32,11 @@ class Model:
self.plot=plot
- def sample_uniformly(self, txframe_aligned, rxframe_aligned, n_bins=4):
+ def sample_uniformly(self, tx_dpd, rx_received, n_bins=4):
"""This function returns tx and rx samples in a way
that the tx amplitudes have an approximate uniform
- distribution with respect to the txframe_aligned amplitudes"""
- txframe_aligned_abs = np.abs(txframe_aligned)
+ distribution with respect to the tx_dpd amplitudes"""
+ txframe_aligned_abs = np.abs(tx_dpd)
ccdf_min = 0
ccdf_max = np.max(txframe_aligned_abs)
tx_hist, ccdf_edges = np.histogram(txframe_aligned_abs,
@@ -50,219 +50,111 @@ class Model:
indices = np.where((txframe_aligned_abs >= ccdf_edges[idx]) &
(txframe_aligned_abs <= ccdf_edges[idx+1]))[0]
indices_choise = np.random.choice(indices, n_choise, replace=False)
- rx_choice[idx*n_choise:(idx+1)*n_choise] = rxframe_aligned[indices_choise]
- tx_choice[idx*n_choise:(idx+1)*n_choise] = txframe_aligned[indices_choise]
+ rx_choice[idx*n_choise:(idx+1)*n_choise] = rx_received[indices_choise]
+ tx_choice[idx*n_choise:(idx+1)*n_choise] = tx_dpd[indices_choise]
return tx_choice, rx_choice
- def get_next_coefs(self, txframe_aligned, rxframe_aligned):
- tx_choice, rx_choice = self.sample_uniformly(txframe_aligned, rxframe_aligned)
+ def amplitude_predistortion(self, sig):
+ sig_abs = np.abs(sig)
+ A_sig = np.vstack([np.ones(sig_abs.shape),
+ sig_abs ** 2,
+ sig_abs ** 4,
+ sig_abs ** 6,
+ sig_abs ** 8,
+ ]).T
+ sig_dpd = sig * np.sum(A_sig * self.coefs_am, axis=1)
+ return sig_dpd, A_sig
+
+ def dpd_phase(self, tx):
+ tx_abs = np.abs(tx)
+ tx_A_complex = np.vstack([tx,
+ tx * tx_abs ** 1,
+ tx * tx_abs ** 2,
+ tx * tx_abs ** 3,
+ tx * tx_abs ** 4,
+ ]).T
+ tx_dpd = np.sum(tx_A_complex * self.coefs_pm, axis=1)
+ return tx_dpd
+
+ def get_next_coefs(self, tx_dpd, rx_received):
+ normalization_error = np.abs(np.median(np.abs(tx_dpd)) - np.median(np.abs(rx_received)))/(np.median(np.abs(tx_dpd)) + np.median(np.abs(rx_received)))
+ assert normalization_error < 0.01, "Non normalized signals"
+ tx_choice, rx_choice = self.sample_uniformly(tx_dpd, rx_received)
# Calculate new coefficients for AM/AM correction
- rx_abs = np.abs(rx_choice)
- rx_A = np.vstack([rx_abs,
- rx_abs ** 3,
- rx_abs ** 5,
- rx_abs ** 7,
- rx_abs ** 9,
- ]).T
- rx_dpd = np.sum(rx_A * self.coefs_am, axis=1)
+ rx_dpd, rx_A = self.amplitude_predistortion(rx_choice)
rx_dpd = rx_dpd * (
- np.median(np.abs(tx_choice)) / np.median(np.abs(rx_dpd)))
+ np.median(np.abs(tx_choice)) /
+ np.median(np.abs(rx_dpd)))
- err = rx_dpd - np.abs(tx_choice)
+ err = np.abs(rx_dpd) - np.abs(tx_choice)
self.errs.append(np.mean(np.abs(err ** 2)))
+ mse = np.mean(np.abs((rx_dpd - tx_choice)**2))
+ self.mses.append(mse)
+
a_delta = np.linalg.lstsq(rx_A, err)[0]
new_coefs = self.coefs_am - 0.1 * a_delta
new_coefs = new_coefs * (self.coefs_am[0] / new_coefs[0])
+ assert np.abs(self.coefs_am[0] / new_coefs[0] - 1) < 0.1, \
+ "Too large change in first " \
+ "coefficient. {}, {}".format(self.coefs_am[0], new_coefs[0])
logging.debug("a_delta {}".format(a_delta))
logging.debug("new coefs_am {}".format(new_coefs))
- # Calculate new coefficients for AM/PM correction
- phase_diff_rad = ((
- (np.angle(tx_choice) -
- np.angle(rx_choice) +
- np.pi) % (2 * np.pi)) -
- np.pi
- )
-
- tx_abs = np.abs(tx_choice)
- tx_abs_A = np.vstack([tx_abs,
- tx_abs ** 2,
- tx_abs ** 3,
- tx_abs ** 4,
- tx_abs ** 5,
- ]).T
- phase_dpd = np.sum(tx_abs_A * self.coefs_pm, axis=1)
-
- err_phase = phase_dpd - phase_diff_rad
- self.errs_phase.append(np.mean(np.abs(err_phase ** 2)))
- a_delta = np.linalg.lstsq(tx_abs_A, err_phase)[0]
- new_coefs_pm = self.coefs_pm - 0.1 * a_delta
- logging.debug("a_delta {}".format(a_delta))
- logging.debug("new new_coefs_pm {}".format(new_coefs_pm))
-
- def dpd_phase(tx):
- tx_abs = np.abs(tx)
- tx_A_complex = np.vstack([tx,
- tx * tx_abs ** 1,
- tx * tx_abs ** 2,
- tx * tx_abs ** 3,
- tx * tx_abs ** 4,
- ]).T
- tx_dpd = np.sum(tx_A_complex * self.coefs_pm, axis=1)
- return tx_dpd
-
- tx_range = np.linspace(0, 2)
- phase_range_dpd = dpd_phase(tx_range)
-
- rx_A_complex = np.vstack([rx_choice,
- rx_choice * rx_abs ** 2,
- rx_choice * rx_abs ** 4,
- rx_choice * rx_abs ** 6,
- rx_choice * rx_abs ** 8,
- ]).T
- rx_post_distored = np.sum(rx_A_complex * self.coefs_am, axis=1)
- rx_post_distored = rx_post_distored * (
- np.median(np.abs(tx_choice)) /
- np.median(np.abs(rx_post_distored)))
- mse = np.mean(np.abs((tx_choice - rx_post_distored) ** 2))
- logging.debug("MSE: {}".format(mse))
- self.mses.append(mse)
-
- def dpd(tx):
- tx_abs = np.abs(tx)
- tx_A_complex = np.vstack([tx,
- tx * tx_abs ** 2,
- tx * tx_abs ** 4,
- tx * tx_abs ** 6,
- tx * tx_abs ** 8,
- ]).T
- tx_dpd = np.sum(tx_A_complex * self.coefs_am, axis=1)
- return tx_dpd
-
rx_range = np.linspace(0, 1, num=100)
- rx_range_dpd = dpd(rx_range)
+ rx_range_dpd = self.amplitude_predistortion(rx_range)[0]
rx_range = rx_range[(rx_range_dpd > 0) & (rx_range_dpd < 2)]
rx_range_dpd = rx_range_dpd[(rx_range_dpd > 0) & (rx_range_dpd < 2)]
- logging.debug("txframe: min %f, max %f, median %f" %
- (np.min(np.abs(txframe_aligned)),
- np.max(np.abs(txframe_aligned)),
- np.median(np.abs(txframe_aligned))
- ))
-
- logging.debug("rxframe: min %f, max %f, median %f" %
- (np.min(np.abs(rx_choice)),
- np.max(np.abs(rx_choice)),
- np.median(np.abs(rx_choice))
- ))
+ logging.debug('txframe: min {:.2f}, max {:.2f}, ' \
+ 'median {:.2f}; rxframe: min {:.2f}, max {:.2f}, ' \
+ 'median {:.2f}; a_delta {}; new coefs_am {}'.format(
+ np.min(np.abs(tx_dpd)),
+ np.max(np.abs(tx_dpd)),
+ np.median(np.abs(tx_dpd)),
+ np.min(np.abs(rx_choice)),
+ np.max(np.abs(rx_choice)),
+ np.median(np.abs(rx_choice)),
+ a_delta,
+ new_coefs))
if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot:
dt = datetime.datetime.now().isoformat()
- fig_path = logging_path + "/" + dt + "_Model.pdf"
+ fig_path = logging_path + "/" + dt + "_Model.svg"
- fig = plt.figure(figsize=(3*6, 1.5 * 6))
+ fig = plt.figure(figsize=(3*6, 6))
- ax = plt.subplot(3,3,1)
- ax.plot(np.abs(txframe_aligned[:128]),
+ ax = plt.subplot(2,3,1)
+ ax.plot(np.abs(tx_dpd[:128]),
label="TX sent",
linestyle=":")
- ax.plot(np.abs(rxframe_aligned[:128]),
+ ax.plot(np.abs(rx_received[:128]),
label="RX received",
color="red")
ax.set_title("Synchronized Signals of Iteration {}".format(len(self.coefs_history)))
ax.set_xlabel("Samples")
ax.set_ylabel("Amplitude")
ax.text(0, 0, "TX (max {:01.3f}, mean {:01.3f}, median {:01.3f})".format(
- np.max(np.abs(txframe_aligned)),
- np.mean(np.abs(txframe_aligned)),
- np.median(np.abs(txframe_aligned))
+ np.max(np.abs(tx_dpd)),
+ np.mean(np.abs(tx_dpd)),
+ np.median(np.abs(tx_dpd))
), size = 8)
ax.legend(loc=4)
- ax = plt.subplot(3,3,2)
- ax.plot(np.real(txframe_aligned[:128]),
- label="TX sent",
- linestyle=":")
- ax.plot(np.real(rxframe_aligned[:128]),
- label="RX received",
- color="red")
- ax.set_title("Synchronized Signals")
- ax.set_xlabel("Samples")
- ax.set_ylabel("Real Part")
- ax.legend(loc=4)
-
- ax = plt.subplot(3,3,3)
- ax.plot(np.abs(txframe_aligned[:128]),
- label="TX Frame",
- linestyle=":",
- linewidth=0.5)
- ax.plot(np.abs(rxframe_aligned[:128]),
- label="RX Frame",
- linestyle="--",
- linewidth=0.5)
-
- rx_abs = np.abs(rxframe_aligned)
- rx_A = np.vstack([rx_abs,
- rx_abs ** 3,
- rx_abs ** 5,
- rx_abs ** 7,
- rx_abs ** 9,
- ]).T
- rx_dpd = np.sum(rx_A * self.coefs_am, axis=1)
- rx_dpd = rx_dpd * (
- np.median(np.abs(tx_choice)) / np.median(np.abs(rx_dpd)))
-
- ax.plot(np.abs(rx_dpd[:128]),
- label="RX DPD Frame",
- linestyle="-.",
- linewidth=0.5)
-
- tx_abs = np.abs(np.abs(txframe_aligned[:128]))
- tx_A = np.vstack([tx_abs,
- tx_abs ** 3,
- tx_abs ** 5,
- tx_abs ** 7,
- tx_abs ** 9,
- ]).T
- tx_dpd = np.sum(tx_A * new_coefs, axis=1)
- tx_dpd_norm = tx_dpd * (
- np.median(np.abs(tx_choice)) / np.median(np.abs(tx_dpd)))
-
- ax.plot(np.abs(tx_dpd_norm[:128]),
- label="TX DPD Frame Norm",
- linestyle="-.",
- linewidth=0.5)
- ax.legend(loc=4)
- ax.set_title("RX DPD")
- ax.set_xlabel("Samples")
- ax.set_ylabel("Amplitude")
-
- ax = plt.subplot(3,3,4)
+ ax = plt.subplot(2,3,2)
ax.scatter(
- np.abs(tx_choice[:1024]),
- np.abs(rx_choice[:1024]),
+ np.abs(tx_choice),
+ np.abs(rx_choice),
s=0.1)
ax.plot(rx_range_dpd / self.coefs_am[0], rx_range, linewidth=0.25)
ax.set_title("Amplifier Characteristic")
ax.set_xlabel("TX Amplitude")
ax.set_ylabel("RX Amplitude")
- ax = plt.subplot(3,3,5)
- ax.scatter(
- np.abs(tx_choice[:1024]),
- phase_diff_rad[:1024] * 180 / np.pi,
- s=0.1
- )
- ax.plot(tx_range, phase_range_dpd * 180 / np.pi, linewidth=0.25)
- ax.set_title("Amplifier Characteristic")
- ax.set_xlabel("TX Amplitude")
- ax.set_ylabel("Phase Difference [deg]")
-
- ax = plt.subplot(3,3,6)
+ ax = plt.subplot(2,3,3)
ccdf_min, ccdf_max = 0, 1
- tx_hist, ccdf_edges = np.histogram(np.abs(txframe_aligned),
+ tx_hist, ccdf_edges = np.histogram(np.abs(tx_dpd),
bins=60,
range=(ccdf_min, ccdf_max))
tx_hist_normalized = tx_hist.astype(float)/np.sum(tx_hist)
@@ -278,7 +170,7 @@ class Model:
ax.set_xlabel("TX Amplitude")
ax.set_ylabel("Ratio of Samples larger than x")
- ax = plt.subplot(3,3,7)
+ ax = plt.subplot(2,3,4)
coefs_history = np.array(self.coefs_history)
for idx, coef_hist in enumerate(coefs_history.T):
ax.plot(coef_hist,
@@ -289,7 +181,7 @@ class Model:
ax.set_xlabel("Iterations")
ax.set_ylabel("Coefficient Value")
- ax = plt.subplot(3,3,8)
+ ax = plt.subplot(2,3,5)
coefs_history = np.array(self.coefs_pm_history)
for idx, coef_hist in enumerate(coefs_history.T):
ax.plot(coef_hist,
@@ -300,8 +192,7 @@ class Model:
ax.set_xlabel("Iterations")
ax.set_ylabel("Coefficient Value")
- ax = plt.subplot(3,3,9)
- coefs_history = np.array(self.coefs_history)
+ ax = plt.subplot(2,3,6)
ax.plot(self.mses, label="MSE")
ax.plot(self.errs, label="ERR")
ax.legend(loc=4)
@@ -315,8 +206,6 @@ class Model:
self.coefs_am = new_coefs
self.coefs_history.append(self.coefs_am)
- self.coefs_pm = new_coefs_pm
- self.coefs_pm_history.append(self.coefs_pm)
return self.coefs_am, self.coefs_pm
# The MIT License (MIT)
diff --git a/dpd/src/TX_Agc.py b/dpd/src/TX_Agc.py
index 6f4cee8..c3d0698 100644
--- a/dpd/src/TX_Agc.py
+++ b/dpd/src/TX_Agc.py
@@ -24,10 +24,10 @@ import src.Adapt as Adapt
class TX_Agc:
def __init__(self,
adapt,
- max_txgain=80,
- tx_median_target=0.1,
- tx_median_threshold_max=0.15,
- tx_median_threshold_min=0.05):
+ max_txgain=85,
+ tx_median_target=0.04,
+ tx_median_threshold_max=0.07,
+ tx_median_threshold_min=0.02):
"""
In order to avoid digital clipping, this class increases the
TX gain and reduces the digital gain. Digital clipping happens
@@ -62,8 +62,7 @@ class TX_Agc:
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 = \
- np.floor(20 * np.log10(self.tx_median_target / tx_median)).astype(int)
+ 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(
@@ -77,8 +76,8 @@ class TX_Agc:
self.adapt.set_digital_gain(digital_gain)
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))
diff --git a/dpd/src/phase_align.py b/dpd/src/phase_align.py
index f8e38c4..7f82392 100644
--- a/dpd/src/phase_align.py
+++ b/dpd/src/phase_align.py
@@ -28,7 +28,7 @@ def phase_align(sig, ref_sig, plot=False):
if logging.getLogger().getEffectiveLevel() == logging.DEBUG and plot:
dt = datetime.datetime.now().isoformat()
- fig_path = logging_path + "/" + dt + "_phase_align.pdf"
+ fig_path = logging_path + "/" + dt + "_phase_align.svg"
plt.subplot(511)
plt.hist(angle_diff, bins=60, label="Angle Diff")
diff --git a/dpd/src/subsample_align.py b/dpd/src/subsample_align.py
index cc2b975..6d1cd2a 100755
--- a/dpd/src/subsample_align.py
+++ b/dpd/src/subsample_align.py
@@ -74,7 +74,7 @@ def subsample_align(sig, ref_sig, plot=False):
taus = corr(ixs)
dt = datetime.datetime.now().isoformat()
- tau_path = (logging_path + "/" + dt + "_tau.pdf")
+ tau_path = (logging_path + "/" + dt + "_tau.svg")
plt.plot(ixs, taus)
plt.title("Subsample correlation, minimum is best: {}".format(best_tau))
plt.savefig(tau_path)
generated by cgit v1.2.3 (git 2.39.1) at 2024-12-26 20:36:47 +0000