diff options
| -rwxr-xr-x | dpd/main.py | 4 | ||||
| -rw-r--r-- | dpd/src/Model.py | 36 |
2 files changed, 36 insertions, 4 deletions
diff --git a/dpd/main.py b/dpd/main.py index 560d142..ae18182 100755 --- a/dpd/main.py +++ b/dpd/main.py @@ -52,8 +52,8 @@ 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(82) tx_gain = adapt.get_txgain() rx_gain = adapt.get_rxgain() diff --git a/dpd/src/Model.py b/dpd/src/Model.py index f66ba8f..014b5ef 100644 --- a/dpd/src/Model.py +++ b/dpd/src/Model.py @@ -6,6 +6,7 @@ import logging 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 @@ -15,6 +16,31 @@ class Model: self.coefs = coefs def get_next_coefs(self, txframe_aligned, rxframe_aligned): + rx_abs = np.abs(rxframe_aligned) + A = np.vstack([rx_abs, + rx_abs**3, + rx_abs**5, + rx_abs**7, + rx_abs**9, + ]).T + y = np.abs(txframe_aligned) + + clf = Ridge(alpha=10) + clf.fit(A, y) + sol = clf.coef_ + + rx_range = np.linspace(0,1,50) + A_range = np.vstack([ + rx_range, + rx_range**3, + rx_range**5, + rx_range**7, + rx_range**9, + ]).T + y_est = np.sum(A_range * sol, axis=1) + + logging.debug("New coefficents {}".format(sol)) + if logging.getLogger().getEffectiveLevel() == logging.DEBUG: logging.debug("txframe: min %f, max %f, median %f" % (np.min(np.abs(txframe_aligned)), @@ -31,7 +57,7 @@ class Model: dt = datetime.datetime.now().isoformat() fig_path = "/tmp/" + dt + "_Model.pdf" - fig, axs = plt.subplots(4, figsize=(6,2*6)) + fig, axs = plt.subplots(5, figsize=(6,2*6)) ax = axs[0] ax.plot(np.abs(txframe_aligned[:128]), label="TX Frame") @@ -55,6 +81,11 @@ class Model: np.abs(rxframe_aligned[:1024]), s = 0.1 ) + ax.plot( + y_est, + rx_range, + linewidth=0.25 + ) ax.set_title("Amplifier Characteristic") ax.set_xlabel("TX Amplitude") ax.set_ylabel("RX Amplitude") @@ -82,7 +113,8 @@ class Model: mse = np.mean(np.abs(np.square(txframe_aligned[:1024] - rxframe_aligned[:1024]))) logging.debug("MSE: {}".format(mse)) - return self.coefs + sol = sol * 1.7/sol[0] + return sol # The MIT License (MIT) # |