GUI: Automatically iterate captures and show model plots

6 files changed, 98 insertions, 88 deletions

diff --git a/python/dpd/Model_AM.py b/python/dpd/Model_AM.py
index 75b226f..b07a5a5 100644
--- a/python/dpd/Model_AM.py
+++ b/python/dpd/Model_AM.py
@@ -42,22 +42,18 @@ class Model_AM:
     """Calculates new coefficients using the measurement and the previous
     coefficients"""
 
-    def __init__(self,
-                 c,
-                 learning_rate_am=1,
-                 plot=False):
+    def __init__(self, c, learning_rate_am=1):
         self.c = c
-
         self.learning_rate_am = learning_rate_am
-        self.plot = plot
+        self._plot_data = None
+
+    def plot(self, plot_location, title):
+        if self._plot_data is not None:
+            tx_dpd, rx_received, coefs_am, coefs_am_new = self._plot_data
 
-    def _plot(self, tx_dpd, rx_received, coefs_am, coefs_am_new):
-        if self.plot and self.c.plot_location is not None:
             tx_range, rx_est = calc_line(coefs_am, 0, 0.6)
             tx_range_new, rx_est_new = calc_line(coefs_am_new, 0, 0.6)
 
-            dt = datetime.datetime.now().isoformat()
-            fig_path = self.c.plot_location + "/" + dt + "_Model_AM.png"
             sub_rows = 1
             sub_cols = 1
             fig = plt.figure(figsize=(sub_cols * 6, sub_rows / 2. * 6))
@@ -76,14 +72,14 @@ class Model_AM:
                        label="Binned Data",
                        color="blue",
                        s=1)
-            ax.set_title("Model_AM")
+            ax.set_title("Model_AM {}".format(title))
             ax.set_xlabel("TX Amplitude")
             ax.set_ylabel("RX Amplitude")
             ax.set_xlim(-0.5, 1.5)
             ax.legend(loc=4)
 
             fig.tight_layout()
-            fig.savefig(fig_path)
+            fig.savefig(plot_location)
             plt.close(fig)
 
     def get_next_coefs(self, tx_dpd, rx_received, coefs_am):
@@ -95,13 +91,14 @@ class Model_AM:
         coefs_am_new = coefs_am + \
                        self.learning_rate_am * (coefs_am_new - coefs_am)
 
-        self._plot(tx_dpd, rx_received, coefs_am, coefs_am_new)
+        self._plot_data = (tx_dpd, rx_received, coefs_am, coefs_am_new)
 
         return coefs_am_new
 
 # The MIT License (MIT)
 #
 # Copyright (c) 2017 Andreas Steger
+# Copyright (c) 2018 Matthias P. Braendli
 #
 # Permission is hereby granted, free of charge, to any person obtaining a copy
 # of this software and associated documentation files (the "Software"), to deal
diff --git a/python/dpd/Model_PM.py b/python/dpd/Model_PM.py
index 7b80bf3..40fa1d4 100644
--- a/python/dpd/Model_PM.py
+++ b/python/dpd/Model_PM.py
@@ -28,22 +28,18 @@ class Model_PM:
     """Calculates new coefficients using the measurement and the previous
     coefficients"""
 
-    def __init__(self,
-                 c,
-                 learning_rate_pm=1,
-                 plot=False):
+    def __init__(self, c, learning_rate_pm=1):
         self.c = c
-
         self.learning_rate_pm = learning_rate_pm
-        self.plot = plot
+        self._plot_data = None
+
+    def plot(self, plot_location, title):
+        if self._plot_data is not None:
+            tx_dpd, phase_diff, coefs_pm, coefs_pm_new = self._plot_data
 
-    def _plot(self, tx_dpd, phase_diff, coefs_pm, coefs_pm_new):
-        if self.plot and self.c.plot_location is not None:
             tx_range, phase_diff_est = self.calc_line(coefs_pm, 0, 0.6)
             tx_range_new, phase_diff_est_new = self.calc_line(coefs_pm_new, 0, 0.6)
 
-            dt = datetime.datetime.now().isoformat()
-            fig_path = self.c.plot_location + "/" + dt + "_Model_PM.png"
             sub_rows = 1
             sub_cols = 1
             fig = plt.figure(figsize=(sub_cols * 6, sub_rows / 2. * 6))
@@ -62,13 +58,13 @@ class Model_PM:
                        label="Binned Data",
                        color="blue",
                        s=1)
-            ax.set_title("Model_PM")
+            ax.set_title("Model_PM {}".format(title))
             ax.set_xlabel("TX Amplitude")
             ax.set_ylabel("Phase DIff")
             ax.legend(loc=4)
 
             fig.tight_layout()
-            fig.savefig(fig_path)
+            fig.savefig(plot_location)
             plt.close(fig)
 
     def _discard_small_values(self, tx_dpd, phase_diff):
@@ -97,13 +93,14 @@ class Model_PM:
         coefs_pm_new = self.fit_poly(tx_dpd, phase_diff)
 
         coefs_pm_new = coefs_pm + self.learning_rate_pm * (coefs_pm_new - coefs_pm)
-        self._plot(tx_dpd, phase_diff, coefs_pm, coefs_pm_new)
+        self._plot_data = (tx_dpd, phase_diff, coefs_pm, coefs_pm_new)
 
         return coefs_pm_new
 
 # The MIT License (MIT)
 #
 # Copyright (c) 2017 Andreas Steger
+# Copyright (c) 2018 Matthias P. Braendli
 #
 # Permission is hereby granted, free of charge, to any person obtaining a copy
 # of this software and associated documentation files (the "Software"), to deal
diff --git a/python/dpd/Model_Poly.py b/python/dpd/Model_Poly.py
index c8f6135..ca39492 100644
--- a/python/dpd/Model_Poly.py
+++ b/python/dpd/Model_Poly.py
@@ -36,20 +36,20 @@ class Poly:
     """Calculates new coefficients using the measurement and the previous
     coefficients"""
 
-    def __init__(self,
-                 c,
-                 learning_rate_am=1.0,
-                 learning_rate_pm=1.0):
+    def __init__(self, c, learning_rate_am=1.0, learning_rate_pm=1.0):
         self.c = c
-        self.plot = c.MDL_plot
 
         self.learning_rate_am = learning_rate_am
         self.learning_rate_pm = learning_rate_pm
 
         self.reset_coefs()
 
-        self.model_am = Model_AM.Model_AM(c, plot=self.plot)
-        self.model_pm = Model_PM.Model_PM(c, plot=self.plot)
+        self.model_am = Model_AM.Model_AM(c)
+        self.model_pm = Model_PM.Model_PM(c)
+
+    def plot(self, am_plot_location, pm_plot_location, title):
+        self.model_am.plot(am_plot_location, title)
+        self.model_pm.plot(pm_plot_location, title)
 
     def reset_coefs(self):
         self.coefs_am = np.zeros(5, dtype=np.float32)
diff --git a/python/dpdce.py b/python/dpdce.py
index a9ed140..e601d9c 100755
--- a/python/dpdce.py
+++ b/python/dpdce.py
@@ -142,6 +142,8 @@ internal_data = {
         }
 results = {
         'statplot': None,
+        'amplot': None,
+        'pmplot': None,
         'tx_median': 0,
         'rx_median': 0,
         'state': 'Idle',
@@ -203,71 +205,76 @@ def engine_worker():
                     results['stateprogress'] = 0
                     n_runs = internal_data['n_runs']
 
-                # Get Samples and check gain
-                txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median, tx_median = meas.get_samples()
-                # TODO Check TX median
-
-                with lock:
-                    results['stateprogress'] = 20
-                    results['summary'] = ["Captured {} samples".format(len(txframe_aligned)),
-                        "TX/RX median: {} / {}".format(tx_median, rx_median)]
+                while True:
+                    # Get Samples and check gain
+                    txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median, tx_median = meas.get_samples()
+                    # TODO Check TX median
 
-                # Extract usable data from measurement
-                tx, rx, phase_diff, n_per_bin = extStat.extract(txframe_aligned, rxframe_aligned)
-
-                time = datetime.datetime.utcnow()
+                    with lock:
+                        results['stateprogress'] += 5
+                        results['summary'] = ["Captured {} samples".format(len(txframe_aligned)),
+                            "TX/RX median: {} / {}".format(tx_median, rx_median)]
 
-                plot_file = "stats_{}.png".format(time.strftime("%s"))
-                extStat.plot(os.path.join(plot_path, plot_file), time.strftime("%Y-%m-%dT%H%M%S"))
+                    # Extract usable data from measurement
+                    tx, rx, phase_diff, n_per_bin = extStat.extract(txframe_aligned, rxframe_aligned)
 
-                with lock:
-                    results['statplot'] = "dpd/" + plot_file
-                    results['stateprogress'] = 30
-                    results['summary'] += ["Extracted Statistics".format(tx_median, rx_median)]
-
-                n_meas = Heuristics.get_n_meas(n_runs)
-                if extStat.n_meas >= n_meas:  # Use as many measurements nr of runs
-                    if any(x is None for x in [tx, rx, phase_diff]):
-                        with lock:
-                            results['summary'] += ["Error! No data to calculate model"]
-                            results['state'] = 'Idle'
-                            results['stateprogress'] = 0
-                    else:
-                        with lock:
-                            results['state'] = 'Capture + Model'
-                            results['stateprogress'] = 40
-                            results['summary'] += ["Training model"]
-
-                        model.train(tx, rx, phase_diff, lr=Heuristics.get_learning_rate(n_runs))
-
-                        with lock:
-                            results['state'] = 'Capture + Model'
-                            results['stateprogress'] = 60
-                            results['summary'] += ["Getting DPD data"]
-
-                        dpddata = model.get_dpd_data()
-                        with lock:
-                            internal_data['dpddata'] = dpddata
-                            internal_data['n_runs'] = 0
-
-                            results['state'] = 'Capture + Model'
-                            results['stateprogress'] = 80
-                            results['summary'] += ["Reset statistics"]
-
-                        extStat = ExtractStatistic(c)
-
-                        with lock:
-                            results['state'] = 'Idle'
-                            results['stateprogress'] = 100
-                            results['summary'] += ["New DPD coefficients calculated"]
+                    time = datetime.datetime.utcnow()
+                    plot_file = "stats_{}.png".format(time.strftime("%s"))
+                    extStat.plot(os.path.join(plot_path, plot_file), time.strftime("%Y-%m-%dT%H%M%S"))
+                    n_meas = Heuristics.get_n_meas(n_runs)
 
                     with lock:
+                        results['statplot'] = "dpd/" + plot_file
+                        results['stateprogress'] += 5
+                        results['summary'] += ["Extracted Statistics".format(tx_median, rx_median),
+                                "Runs: {}/{}".format(extStat.n_meas, n_meas)]
                         internal_data['n_runs'] += 1
+                    if extStat.n_meas >= n_meas:
+                        break
+
+                if any(x is None for x in [tx, rx, phase_diff]):
+                    with lock:
+                        results['summary'] += ["Error! No data to calculate model"]
+                        results['state'] = 'Idle'
+                        results['stateprogress'] = 0
                 else:
                     with lock:
+                        results['state'] = 'Capture + Model'
+                        results['stateprogress'] = 60
+                        results['summary'] += ["Training model"]
+
+                    model.train(tx, rx, phase_diff, lr=Heuristics.get_learning_rate(n_runs))
+
+                    time = datetime.datetime.utcnow()
+                    am_plot_file = "model_am_{}.png".format(time.strftime("%s"))
+                    pm_plot_file = "model_pm_{}.png".format(time.strftime("%s"))
+                    model.plot(
+                            os.path.join(plot_path, am_plot_file),
+                            os.path.join(plot_path, pm_plot_file),
+                            time.strftime("%Y-%m-%dT%H%M%S"))
+
+                    with lock:
+                        results['amplot'] = "dpd/" + am_plot_file
+                        results['pmplot'] = "dpd/" + pm_plot_file
+                        results['state'] = 'Capture + Model'
+                        results['stateprogress'] = 70
+                        results['summary'] += ["Getting DPD data"]
+
+                    dpddata = model.get_dpd_data()
+                    with lock:
+                        internal_data['dpddata'] = dpddata
+                        internal_data['n_runs'] = 0
+
+                        results['state'] = 'Capture + Model'
+                        results['stateprogress'] = 80
+                        results['summary'] += ["Reset statistics"]
+
+                    extStat = ExtractStatistic(c)
+
+                    with lock:
                         results['state'] = 'Idle'
                         results['stateprogress'] = 100
-                        results['summary'] += ["More data required to train model"]
+                        results['summary'] += ["New DPD coefficients calculated"]
 
     finally:
         with lock:
diff --git a/python/gui/static/js/odr-predistortion.js b/python/gui/static/js/odr-predistortion.js
index e9f7c96..ff82142 100644
--- a/python/gui/static/js/odr-predistortion.js
+++ b/python/gui/static/js/odr-predistortion.js
@@ -39,6 +39,13 @@ function resultrefresh() {
         if (data['statplot']) {
             $('#dpdcapturestats').attr('src', data['statplot']);
         }
+
+        if (data['amplot']) {
+            $('#dpdamplot').attr('src', data['amplot']);
+        }
+        if (data['pmplot']) {
+            $('#dpdpmplot').attr('src', data['pmplot']);
+        }
     });
 
     jqxhr.always(function() {
diff --git a/python/gui/templates/predistortion.html b/python/gui/templates/predistortion.html
index adbba7e..e21c688 100644
--- a/python/gui/templates/predistortion.html
+++ b/python/gui/templates/predistortion.html
@@ -42,6 +42,8 @@
         <div class="panel-heading">Capture Statistics</div>
         <div class="panel-body">
           <img id="dpdcapturestats" />
+          <img id="dpdamplot" />
+          <img id="dpdpmplot" />
         </div>
       </div>