diff options
Diffstat (limited to 'dpd')
| -rwxr-xr-x | dpd/main.py | 17 | ||||
| -rw-r--r-- | dpd/poly_am.coef | 8 | ||||
| -rw-r--r-- | dpd/poly_pm.coef | 10 | ||||
| -rw-r--r-- | dpd/src/Dab_Util.py | 17 | ||||
| -rw-r--r-- | dpd/src/Model.py | 153 | ||||
| -rw-r--r-- | dpd/src/phase_align.py | 9 | ||||
| -rwxr-xr-x | dpd/src/subsample_align.py | 11 |
7 files changed, 183 insertions, 42 deletions
diff --git a/dpd/main.py b/dpd/main.py index 560d142..4d2b93d 100755 --- a/dpd/main.py +++ b/dpd/main.py @@ -10,10 +10,16 @@ This engine calculates and updates the parameter of the digital predistortion module of ODR-DabMod.""" +import datetime +import os + import logging +dt = datetime.datetime.now().isoformat() +logging_path = "/tmp/dpd_{}".format(dt).replace(".","_").replace(":","-") +os.makedirs(logging_path) logging.basicConfig(format='%(asctime)s - %(module)s - %(levelname)s - %(message)s', datefmt='%Y-%m-%d %H:%M:%S', - filename='/tmp/dpd.log', + filename='{}/dpd.log'.format(logging_path), filemode='w', level=logging.DEBUG) @@ -32,7 +38,7 @@ parser.add_argument('--rc-port', default='9400', parser.add_argument('--samplerate', default='8192000', help='Sample rate', required=False) -parser.add_argument('--coefs', default='dpdpoly.coef', +parser.add_argument('--coefs', default='poly.coef', help='File with DPD coefficients, which will be read by ODR-DabMod', required=False) parser.add_argument('--samps', default='10240', @@ -52,8 +58,9 @@ meas = Measure.Measure(samplerate, port, num_req) adapt = Adapt.Adapt(port_rc, coef_path) coefs = adapt.get_coefs() #model = Model.Model(coefs) -model = Model.Model([0.8, 0, 0, 0, 0]) -adapt.set_txgain(60) +model = Model.Model([2.2, 0, 0, 0, 0]) +adapt.set_txgain(79) +adapt.set_rxgain(15+20) tx_gain = adapt.get_txgain() rx_gain = adapt.get_rxgain() @@ -64,7 +71,7 @@ logging.info( ) ) -for i in range(10): +for i in range(500): txframe_aligned, tx_ts, rxframe_aligned, rx_ts = meas.get_samples() logging.debug("tx_ts {}, rx_ts {}".format(tx_ts, rx_ts)) coefs = model.get_next_coefs(txframe_aligned, rxframe_aligned) diff --git a/dpd/poly_am.coef b/dpd/poly_am.coef index ce11551..ff09e5a 100644 --- a/dpd/poly_am.coef +++ b/dpd/poly_am.coef @@ -1,6 +1,6 @@ 5 2.2 -2.34402815751 --25.0487363081 -93.1884045291 --85.3795993469 +1.16110192544 +-13.7895532562 +55.107515965 +-53.8583673922 diff --git a/dpd/poly_pm.coef b/dpd/poly_pm.coef index 2a5bf1c..ae91b12 100644 --- a/dpd/poly_pm.coef +++ b/dpd/poly_pm.coef @@ -1,6 +1,6 @@ 5 -0.126147521763 --0.507247679586 -0.645526051204 --0.313225683513 -0.0522039980935 +0.141477421551 +-0.654864288614 +1.00568534673 +-0.588530075442 +0.0935391293974 diff --git a/dpd/src/Dab_Util.py b/dpd/src/Dab_Util.py index 1f88ae4..175b744 100644 --- a/dpd/src/Dab_Util.py +++ b/dpd/src/Dab_Util.py @@ -1,15 +1,18 @@ # -*- coding: utf-8 -*- +import datetime +import os +import logging +logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename) + import numpy as np import scipy import matplotlib matplotlib.use('agg') import matplotlib.pyplot as plt -import datetime import src.subsample_align as sa import src.phase_align as pa from scipy import signal -import logging class Dab_Util: """Collection of methods that can be applied to an array @@ -35,7 +38,7 @@ class Dab_Util: if logging.getLogger().getEffectiveLevel() == logging.DEBUG: dt = datetime.datetime.now().isoformat() - corr_path = ("/tmp/" + dt + "_tx_rx_corr.pdf") + corr_path = (logging_path + "/" + dt + "_tx_rx_corr.pdf") plt.plot(c, label="corr") plt.legend() plt.savefig(corr_path) @@ -89,7 +92,7 @@ class Dab_Util: if logging.getLogger().getEffectiveLevel() == logging.DEBUG: dt = datetime.datetime.now().isoformat() - fig_path = "/tmp/" + dt + "_sync_raw.pdf" + fig_path = logging_path + "/" + dt + "_sync_raw.pdf" fig, axs = plt.subplots(2) axs[0].plot(np.abs(sig1[:128]), label="TX Frame") @@ -127,7 +130,7 @@ class Dab_Util: if logging.getLogger().getEffectiveLevel() == logging.DEBUG: dt = datetime.datetime.now().isoformat() - fig_path = "/tmp/" + dt + "_sync_sample_aligned.pdf" + fig_path = logging_path + "/" + dt + "_sync_sample_aligned.pdf" fig, axs = plt.subplots(2) axs[0].plot(np.abs(sig1[:128]), label="TX Frame") @@ -152,7 +155,7 @@ class Dab_Util: if logging.getLogger().getEffectiveLevel() == logging.DEBUG: dt = datetime.datetime.now().isoformat() - fig_path = "/tmp/" + dt + "_sync_subsample_aligned.pdf" + fig_path = logging_path + "/" + dt + "_sync_subsample_aligned.pdf" fig, axs = plt.subplots(2) axs[0].plot(np.abs(sig1[:128]), label="TX Frame") @@ -176,7 +179,7 @@ class Dab_Util: if logging.getLogger().getEffectiveLevel() == logging.DEBUG: dt = datetime.datetime.now().isoformat() - fig_path = "/tmp/" + dt + "_sync_phase_aligned.pdf" + fig_path = logging_path + "/" + dt + "_sync_phase_aligned.pdf" fig, axs = plt.subplots(2) axs[0].plot(np.abs(sig1[:128]), label="TX Frame") diff --git a/dpd/src/Model.py b/dpd/src/Model.py index f66ba8f..8df3925 100644 --- a/dpd/src/Model.py +++ b/dpd/src/Model.py @@ -1,11 +1,16 @@ # -*- coding: utf-8 -*- -import numpy as np import datetime +import os import logging +logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename) + +from pynverse import inversefunc +import numpy as np import matplotlib matplotlib.use('agg') import matplotlib.pyplot as plt +from sklearn.linear_model import Ridge class Model: """Calculates new coefficients using the measurement and the old @@ -13,8 +18,76 @@ class Model: def __init__(self, coefs): self.coefs = coefs + self.coefs_history = [coefs,] + self.mses = [0,] + self.errs = [0,] def get_next_coefs(self, txframe_aligned, rxframe_aligned): + dt = datetime.datetime.now().isoformat() + txframe_aligned.tofile(logging_path + "/txframe_" + dt + ".iq") + rxframe_aligned.tofile(logging_path + "/rxframe_" + dt + ".iq") + 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, axis=1) + rx_dpd = rx_dpd * ( + np.median(np.abs(txframe_aligned)) / np.median(np.abs(rx_dpd))) + + err = rx_dpd - np.abs(txframe_aligned) + self.errs.append(np.mean(np.abs(err**2))) + + a_delta = np.linalg.lstsq(rx_A, err)[0] + new_coefs = self.coefs - 0.1 * a_delta + new_coefs = new_coefs * (self.coefs[0] / new_coefs[0]) + logging.debug("a_delta {}".format(a_delta)) + logging.debug("new coefs {}".format(new_coefs)) + + tx_abs = np.abs(rxframe_aligned) + 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(txframe_aligned)) / np.median(np.abs(tx_dpd))) + + rx_A_complex = np.vstack([rxframe_aligned, + rxframe_aligned * rx_abs**2, + rxframe_aligned * rx_abs**4, + rxframe_aligned * rx_abs**6, + rxframe_aligned * rx_abs**8, + ]).T + rx_post_distored = np.sum(rx_A_complex * self.coefs, axis=1) + rx_post_distored = rx_post_distored * ( + np.median(np.abs(txframe_aligned)) / + np.median(np.abs(rx_post_distored))) + mse = np.mean(np.abs((txframe_aligned - 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, axis=1) + return tx_dpd + tx_inverse_dpd = inversefunc(dpd, y_values=txframe_aligned[:128]) + tx_inverse_dpd = tx_inverse_dpd * ( + np.median(np.abs(txframe_aligned)) / + np.median(np.abs(tx_inverse_dpd)) + ) + if logging.getLogger().getEffectiveLevel() == logging.DEBUG: logging.debug("txframe: min %f, max %f, median %f" % (np.min(np.abs(txframe_aligned)), @@ -29,21 +102,35 @@ class Model: )) dt = datetime.datetime.now().isoformat() - fig_path = "/tmp/" + dt + "_Model.pdf" + fig_path = logging_path + "/" + dt + "_Model.pdf" - fig, axs = plt.subplots(4, figsize=(6,2*6)) + fig, axs = plt.subplots(7, figsize=(6,3*6)) ax = axs[0] - ax.plot(np.abs(txframe_aligned[:128]), label="TX Frame") - ax.plot(np.abs(rxframe_aligned[:128]), label="RX Frame") - ax.set_title("Synchronized Signals") + ax.plot(np.abs(txframe_aligned[:128]), + label="TX sent", + linestyle=":") + ax.plot(np.abs(tx_inverse_dpd[:128]), + label="TX inverse dpd", + color="green") + ax.plot(np.abs(rxframe_aligned[: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.legend(loc=4) ax = axs[1] - ax.plot(np.real(txframe_aligned[:128]), label="TX Frame") - ax.plot(np.real(rxframe_aligned[:128]), label="RX Frame") + ax.plot(np.real(txframe_aligned[:128]), + label="TX sent", + linestyle=":") + ax.plot(np.real(tx_inverse_dpd[:128]), + label="TX inverse dpd", + color="green") + 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") @@ -53,8 +140,7 @@ class Model: ax.scatter( np.abs(txframe_aligned[:1024]), np.abs(rxframe_aligned[:1024]), - s = 0.1 - ) + s = 0.1) ax.set_title("Amplifier Characteristic") ax.set_xlabel("TX Amplitude") ax.set_ylabel("RX Amplitude") @@ -75,13 +161,54 @@ class Model: ax.set_xlabel("TX Amplitude") ax.set_ylabel("Phase Difference [deg]") + ax = axs[4] + 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) + ax.plot(np.abs(rx_dpd[:128]), + label="RX DPD Frame", + linestyle="-.", + linewidth=0.5) + 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 = axs[5] + coefs_history = np.array(self.coefs_history) + for idx, coef_hist in enumerate(coefs_history.T): + ax.plot(coef_hist, + label="Coef {}".format(idx), + linewidth=0.5) + ax.legend(loc=4) + ax.set_title("Coefficient History") + ax.set_xlabel("Iterations") + ax.set_ylabel("Coefficient Value") + + ax = axs[6] + coefs_history = np.array(self.coefs_history) + ax.plot(self.mses, label="MSE") + ax.plot(self.errs, label="ERR") + ax.legend(loc=4) + ax.set_title("MSE History") + ax.set_xlabel("Iterations") + ax.set_ylabel("MSE") + fig.tight_layout() fig.savefig(fig_path) fig.clf() - mse = np.mean(np.abs(np.square(txframe_aligned[:1024] - rxframe_aligned[:1024]))) - logging.debug("MSE: {}".format(mse)) - + self.coefs = new_coefs + self.coefs_history.append(self.coefs) return self.coefs # The MIT License (MIT) diff --git a/dpd/src/phase_align.py b/dpd/src/phase_align.py index bea0b82..f03184b 100644 --- a/dpd/src/phase_align.py +++ b/dpd/src/phase_align.py @@ -1,9 +1,12 @@ +import datetime +import os +import logging +logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename) + import numpy as np from scipy import signal, optimize import sys import matplotlib.pyplot as plt -import datetime -import logging def phase_align(sig, ref_sig): @@ -19,7 +22,7 @@ def phase_align(sig, ref_sig): if logging.getLogger().getEffectiveLevel() == logging.DEBUG: dt = datetime.datetime.now().isoformat() - fig_path = "/tmp/" + dt + "_phase_align.pdf" + fig_path = logging_path + "/" + dt + "_phase_align.pdf" 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 0dc78c1..0a51593 100755 --- a/dpd/src/subsample_align.py +++ b/dpd/src/subsample_align.py @@ -1,8 +1,11 @@ +import datetime +import os +import logging +logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename) + import numpy as np from scipy import signal, optimize -import sys import matplotlib.pyplot as plt -import datetime def gen_omega(length): if (length % 2) == 1: @@ -59,15 +62,13 @@ def subsample_align(sig, ref_sig): if optim_result.success: best_tau = optim_result.x - #print("Found subsample delay = {}".format(best_tau)) - if 1: corr = np.vectorize(correlate_for_delay) ixs = np.linspace(-1, 1, 100) taus = corr(ixs) dt = datetime.datetime.now().isoformat() - tau_path = ("/tmp/" + dt + "_tau.pdf") + tau_path = (logging_path + "/" + dt + "_tau.pdf") plt.plot(ixs, taus) plt.title("Subsample correlation, minimum is best: {}".format(best_tau)) plt.savefig(tau_path) |