diff options
Diffstat (limited to 'dpd/src')
| -rw-r--r-- | dpd/src/Model.py | 83 |
1 files changed, 51 insertions, 32 deletions
diff --git a/dpd/src/Model.py b/dpd/src/Model.py index b962140..64c8857 100644 --- a/dpd/src/Model.py +++ b/dpd/src/Model.py @@ -27,10 +27,6 @@ class Model: self.errs_phase = [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") - # Calculate new coefficients for AM/AM correction rx_abs = np.abs(rxframe_aligned) rx_A = np.vstack([rx_abs, @@ -148,9 +144,9 @@ class Model: dt = datetime.datetime.now().isoformat() fig_path = logging_path + "/" + dt + "_Model.pdf" - fig, axs = plt.subplots(8, figsize=(6, 4 * 6)) + fig = plt.figure(figsize=(3*6, 1.5 * 6)) - ax = axs[0] + ax = plt.subplot(3,3,1) ax.plot(np.abs(txframe_aligned[:128]), label="TX sent", linestyle=":") @@ -160,9 +156,14 @@ class Model: 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)) + ), size = 8) ax.legend(loc=4) - ax = axs[1] + ax = plt.subplot(3,3,2) ax.plot(np.real(txframe_aligned[:128]), label="TX sent", linestyle=":") @@ -174,28 +175,7 @@ class Model: ax.set_ylabel("Real Part") ax.legend(loc=4) - ax = axs[2] - ax.scatter( - np.abs(txframe_aligned[:1024]), - np.abs(rxframe_aligned[:1024]), - 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 = axs[3] - ax.scatter( - np.abs(txframe_aligned[: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 = axs[4] + ax = plt.subplot(3,3,3) ax.plot(np.abs(txframe_aligned[:128]), label="TX Frame", linestyle=":", @@ -217,7 +197,46 @@ class Model: ax.set_xlabel("Samples") ax.set_ylabel("Amplitude") - ax = axs[5] + ax = plt.subplot(3,3,4) + ax.scatter( + np.abs(txframe_aligned[:1024]), + np.abs(rxframe_aligned[:1024]), + 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(txframe_aligned[: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) + ccdf_min, ccdf_max = 0, 1 + tx_hist, ccdf_edges = np.histogram(np.abs(txframe_aligned), + bins=60, + range=(ccdf_min, ccdf_max)) + tx_hist_normalized = tx_hist.astype(float)/np.sum(tx_hist) + ccdf = 1.0 - np.cumsum(tx_hist_normalized) + ax.semilogy(ccdf_edges[:-1], ccdf, label="CCDF") + ax.semilogy(ccdf_edges[:-1], + tx_hist_normalized, + label="Histogram", + drawstyle='steps') + ax.legend(loc=4) + ax.set_ylim(1e-5,2) + ax.set_title("Complementary Cumulative Distribution Function") + ax.set_xlabel("TX Amplitude") + ax.set_ylabel("Ratio of Samples larger than x") + + ax = plt.subplot(3,3,7) coefs_history = np.array(self.coefs_history) for idx, coef_hist in enumerate(coefs_history.T): ax.plot(coef_hist, @@ -228,7 +247,7 @@ class Model: ax.set_xlabel("Iterations") ax.set_ylabel("Coefficient Value") - ax = axs[6] + ax = plt.subplot(3,3,8) coefs_history = np.array(self.coefs_pm_history) for idx, coef_hist in enumerate(coefs_history.T): ax.plot(coef_hist, @@ -239,7 +258,7 @@ class Model: ax.set_xlabel("Iterations") ax.set_ylabel("Coefficient Value") - ax = axs[7] + ax = plt.subplot(3,3,9) coefs_history = np.array(self.coefs_history) ax.plot(self.mses, label="MSE") ax.plot(self.errs, label="ERR") |