Add phase dpd, cleanup

1 files changed, 205 insertions, 92 deletions

diff --git a/dpd/src/Model.py b/dpd/src/Model.py
index 61c487e..4ccf0be 100644
--- a/dpd/src/Model.py
+++ b/dpd/src/Model.py
@@ -8,119 +8,174 @@
 import datetime
 import os
 import logging
+
 logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename)
 
 import numpy as np
 import matplotlib.pyplot as plt
 
+
 class Model:
     """Calculates new coefficients using the measurement and the old
     coefficients"""
 
-    def __init__(self, c, SA, MER, coefs_am, coefs_pm, plot=False):
+    def __init__(self,
+                 c,
+                 SA,
+                 MER,
+                 coefs_am,
+                 coefs_pm,
+                 learning_rate_am=0.1,
+                 learning_rate_pm=0.1,
+                 plot=False):
         self.c = c
         self.SA = SA
         self.MER = MER
 
+        self.learning_rate_am = learning_rate_am
+        self.learning_rate_pm = learning_rate_pm
+
         self.coefs_am = coefs_am
-        self.coefs_history = [coefs_am, ]
-        self.mses = []
-        self.errs = []
+        self.coefs_am_history = [coefs_am, ]
+        self.mses_am = []
+        self.errs_am = []
+
         self.tx_mers = []
         self.rx_mers = []
 
         self.coefs_pm = coefs_pm
         self.coefs_pm_history = [coefs_pm, ]
-        self.errs_phase = [0, ]
+        self.errs_pm = []
 
-        self.plot=plot
+        self.plot = plot
 
-    def sample_uniformly(self, tx_dpd, rx_received, n_bins=4):
+    def sample_uniformly(self, tx_dpd, rx_received, n_bins=5):
         """This function returns tx and rx samples in a way
         that the tx amplitudes have an approximate uniform 
         distribution with respect to the tx_dpd amplitudes"""
+        mask = np.logical_and((np.abs(tx_dpd) > 0.01), (np.abs(rx_received) > 0.01))
+        tx_dpd = tx_dpd[mask]
+        rx_received = rx_received[mask]
+
         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,
                                            bins=n_bins,
                                            range=(ccdf_min, ccdf_max))
-        n_choise  = np.min(tx_hist)
+        n_choise = np.min(tx_hist)
         tx_choice = np.zeros(n_choise * n_bins, dtype=np.complex64)
         rx_choice = np.zeros(n_choise * n_bins, dtype=np.complex64)
 
         for idx, bin in enumerate(tx_hist):
             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] = rx_received[indices_choise]
-            tx_choice[idx*n_choise:(idx+1)*n_choise] = tx_dpd[indices_choise]
+                               (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] = \
+                rx_received[indices_choise]
+            tx_choice[idx * n_choise:(idx + 1) * n_choise] = \
+                tx_dpd[indices_choise]
+
+        assert isinstance(rx_choice[0], np.complex64), \
+            "rx_choice is not complex64 but {}".format(rx_choice[0].dtype)
+        assert isinstance(tx_choice[0], np.complex64), \
+            "tx_choice is not complex64 but {}".format(tx_choice[0].dtype)
+
         return tx_choice, rx_choice
 
-    def amplitude_predistortion(self, sig):
+    def dpd_amplitude(self, sig, coefs=None):
+        if coefs is None:
+            coefs = self.coefs_am
+        assert isinstance(sig[0], np.complex64), "Sig is not complex64 but {}".format(sig[0].dtype)
         sig_abs = np.abs(sig)
         A_sig = np.vstack([np.ones(sig_abs.shape),
-                          sig_abs ** 1,
-                          sig_abs ** 2,
-                          sig_abs ** 3,
-                          sig_abs ** 4,
-                          ]).T
-        sig_dpd = sig * np.sum(A_sig * self.coefs_am, axis=1)
+                           sig_abs ** 1,
+                           sig_abs ** 2,
+                           sig_abs ** 3,
+                           sig_abs ** 4,
+                           ]).T
+        sig_dpd = sig * np.sum(A_sig * coefs, 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)
+    def dpd_phase(self, sig, coefs=None):
+        if coefs is None:
+            coefs = self.coefs_pm
+        assert isinstance(sig[0], np.complex64), "Sig is not complex64 but {}".format(sig[0].dtype)
+        sig_abs = np.abs(sig)
+        A_phase = np.vstack([np.ones(sig_abs.shape),
+                             sig_abs ** 1,
+                             sig_abs ** 2,
+                             sig_abs ** 3,
+                             sig_abs ** 4,
+                             ]).T
+        phase_diff_est = np.sum(A_phase * coefs, axis=1)
+        return phase_diff_est, A_phase
 
-        # Calculate new coefficients for AM/AM correction
-        rx_dpd, rx_A = self.amplitude_predistortion(rx_choice)
+    def _next_am_coefficent(self, tx_choice, rx_choice):
+        """Calculate new coefficients for AM/AM correction"""
+        rx_dpd, rx_A = self.dpd_amplitude(rx_choice)
         rx_dpd = rx_dpd * (
             np.median(np.abs(tx_choice)) /
             np.median(np.abs(rx_dpd)))
-
         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)
+        mse = np.mean(np.abs((rx_dpd - tx_choice) ** 2))
+        self.mses_am.append(mse)
+        self.errs_am.append(np.mean(err**2))
 
         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))
-
-        rx_range = np.linspace(0, 1, num=100)
-        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)]
+        new_coefs_am = self.coefs_am - self.learning_rate_am * a_delta
+        new_coefs_am = new_coefs_am * (self.coefs_am[0] / new_coefs_am[0])
+        return new_coefs_am
+
+    def _next_pm_coefficent(self, tx_choice, rx_choice):
+        """Calculate new coefficients for AM/PM correction
+        Assuming deviations smaller than pi/2"""
+        phase_diff_choice = np.angle(
+            (rx_choice * tx_choice.conjugate()) /
+            (np.abs(rx_choice) * np.abs(tx_choice))
+        )
+        plt.hist(phase_diff_choice)
+        plt.savefig('/tmp/hist_' + str(np.random.randint(0,1000)) + '.svg')
+        plt.clf()
+        phase_diff_est, phase_A = self.dpd_phase(rx_choice)
+        err_phase = phase_diff_est - phase_diff_choice
+        self.errs_pm.append(np.mean(np.abs(err_phase ** 2)))
+
+        p_delta = np.linalg.lstsq(phase_A, err_phase)[0]
+        new_coefs_pm = self.coefs_pm - self.learning_rate_pm * p_delta
+
+        return new_coefs_pm, phase_diff_choice
+
+    def get_next_coefs(self, tx_dpd, rx_received):
+        # Check data type
+        assert tx_dpd[0].dtype == np.complex64, \
+            "tx_dpd is not complex64 but {}".format(tx_dpd[0].dtype)
+        assert rx_received[0].dtype == np.complex64, \
+            "rx_received is not complex64 but {}".format(rx_received[0].dtype)
+        # Check if signals have same normalization
+        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)
+        new_coefs_am = self._next_am_coefficent(tx_choice, rx_choice)
+        new_coefs_pm, phase_diff_choice = self._next_pm_coefficent(tx_choice, rx_choice)
 
         logging.debug('txframe: min {:.2f}, max {:.2f}, ' \
                       'median {:.2f}; rxframe: min {:.2f}, max {:.2f}, ' \
-                      'median {:.2f}; a_delta {}; new coefs_am {}'.format(
+                      'median {:.2f}; new coefs_am {};' \
+                      'new_coefs_pm {}'.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))
+            new_coefs_am,
+            new_coefs_pm))
 
         if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot:
             off = self.SA.calc_offset(tx_dpd)
@@ -133,41 +188,37 @@ class Model:
             dt = datetime.datetime.now().isoformat()
             fig_path = logging_path + "/" + dt + "_Model.svg"
 
-            fig = plt.figure(figsize=(3*6, 6))
+            fig = plt.figure(figsize=(2 * 6, 2 * 6))
+
+            i_sub = 1
 
-            ax = plt.subplot(2,3,1)
+            ax = plt.subplot(4, 2, i_sub)
+            i_sub += 1
             ax.plot(np.abs(tx_dpd[:128]),
                     label="TX sent",
                     linestyle=":")
             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_title("Synchronized Signals of Iteration {}"
+                         .format(len(self.coefs_am_history)))
             ax.set_xlabel("Samples")
             ax.set_ylabel("Amplitude")
-            ax.text(0, 0, "TX (max {:01.3f}, mean {:01.3f}, median {:01.3f})".format(
+            ax.text(0, 0, "TX (max {:01.3f}, mean {:01.3f}, " \
+                          "median {:01.3f})".format(
                 np.max(np.abs(tx_dpd)),
                 np.mean(np.abs(tx_dpd)),
                 np.median(np.abs(tx_dpd))
-            ), size = 8)
+            ), size=8)
             ax.legend(loc=4)
 
-            ax = plt.subplot(2,3,2)
-            ax.scatter(
-                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(2,3,3)
+            ax = plt.subplot(4, 2, i_sub)
+            i_sub += 1
             ccdf_min, ccdf_max = 0, 1
             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)
+                                               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],
@@ -175,23 +226,22 @@ class Model:
                         label="Histogram",
                         drawstyle='steps')
             ax.legend(loc=4)
-            ax.set_ylim(1e-5,2)
+            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(2,3,4)
-            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 = plt.subplot(4, 2, i_sub)
+            i_sub += 1
+            ax.semilogy(np.array(self.mses_am) + 1e-10, label="log(MSE)")
+            ax.semilogy(np.array(self.errs_am) + 1e-10, label="log(ERR)")
             ax.legend(loc=4)
-            ax.set_title("AM/AM Coefficient History")
+            ax.set_title("MSE History")
             ax.set_xlabel("Iterations")
-            ax.set_ylabel("Coefficient Value")
+            ax.set_ylabel("MSE")
 
-            ax = plt.subplot(2,3,5)
+            ax = plt.subplot(4, 2, i_sub)
+            i_sub += 1
             ax.plot(self.tx_mers, label="TX MER")
             ax.plot(self.rx_mers, label="RX MER")
             ax.legend(loc=4)
@@ -199,20 +249,83 @@ class Model:
             ax.set_xlabel("Iterations")
             ax.set_ylabel("MER")
 
-            ax = plt.subplot(2,3,6)
-            ax.plot(self.mses, label="MSE")
-            ax.plot(self.errs, label="ERR")
+            ax = plt.subplot(4, 2, i_sub)
+            rx_range = np.linspace(0, 1, num=100, dtype=np.complex64)
+            rx_range_dpd = self.dpd_amplitude(rx_range)[0]
+            rx_range_dpd_new = self.dpd_amplitude(rx_range, new_coefs_am)[0]
+            i_sub += 1
+            ax.scatter(
+                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, label="current")
+            ax.plot(rx_range_dpd_new / self.coefs_am[0], rx_range, linewidth=0.25, label="next")
+            ax.set_ylim(0, 1)
+            ax.set_xlim(0, 1)
+            ax.legend()
+            ax.set_title("Amplifier Characteristic")
+            ax.set_xlabel("TX Amplitude")
+            ax.set_ylabel("RX Amplitude")
+
+            ax = plt.subplot(4, 2, i_sub)
+            i_sub += 1
+            coefs_am_history = np.array(self.coefs_am_history)
+            for idx, coef_hist in enumerate(coefs_am_history.T):
+                ax.plot(coef_hist,
+                        label="Coef {}".format(idx),
+                        linewidth=0.5)
+            ax.legend(loc=4)
+            ax.set_title("AM/AM Coefficient History")
+            ax.set_xlabel("Iterations")
+            ax.set_ylabel("Coefficient Value")
+
+            phase_range = np.linspace(0, 1, num=100, dtype=np.complex64)
+            phase_range_dpd = self.dpd_phase(phase_range)[0]
+            phase_range_dpd_new = self.dpd_phase(phase_range,
+                                                 coefs=new_coefs_pm)[0]
+            ax = plt.subplot(4, 2, i_sub)
+            i_sub += 1
+            ax.scatter(
+                np.abs(tx_choice),
+                np.rad2deg(phase_diff_choice),
+                s=0.1)
+            ax.plot(
+                np.abs(phase_range),
+                np.rad2deg(phase_range_dpd),
+                linewidth=0.25,
+                label="current")
+            ax.plot(
+                np.abs(phase_range),
+                np.rad2deg(phase_range_dpd_new),
+                linewidth=0.25,
+                label="next")
+            ax.set_ylim(-60, 60)
+            ax.set_xlim(0, 1)
+            ax.legend()
+            ax.set_title("Amplifier Characteristic")
+            ax.set_xlabel("TX Amplitude")
+            ax.set_ylabel("Phase Difference")
+
+            ax = plt.subplot(4, 2, i_sub)
+            i_sub += 1
+            coefs_pm_history = np.array(self.coefs_pm_history)
+            for idx, coef_phase_hist in enumerate(coefs_pm_history.T):
+                ax.plot(coef_phase_hist,
+                        label="Coef {}".format(idx),
+                        linewidth=0.5)
             ax.legend(loc=4)
-            ax.set_title("MSE History")
+            ax.set_title("AM/PM Coefficient History")
             ax.set_xlabel("Iterations")
-            ax.set_ylabel("MSE")
+            ax.set_ylabel("Coefficient Value")
 
             fig.tight_layout()
             fig.savefig(fig_path)
             fig.clf()
 
-        self.coefs_am = new_coefs
-        self.coefs_history.append(self.coefs_am)
+        self.coefs_am = new_coefs_am
+        self.coefs_am_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)