diff --git a/recv.py b/recv.py
index 4b38b96..dd58214 100755
--- a/recv.py
+++ b/recv.py
@@ -79,8 +79,8 @@ def receive_prefix(symbols):
 def train_receiver(symbols, freqs):
     filters = {}
     full_scale = len(freqs)
-    training_bits = np.array(train.equalizer)
-    expected = full_scale * training_bits
+    training = np.array(train.equalizer)
+    expected = full_scale * training
     if pylab:
         pylab.figure()
 
@@ -90,16 +90,15 @@ def train_receiver(symbols, freqs):
         filt = sigproc.train(S, expected)
         filters[freq] = filt
 
-        S = filt(S)
-        y = np.array(list(S)).real
+        S = list(filt(S))
+        y = np.array(S)
         if pylab:
             pylab.subplot(HEIGHT, WIDTH, i+1)
-            pylab.plot(y, '-', expected, '-')
-            pylab.title('Train: $F_c = {}Hz$'.format(freq))
+            show.constellation(y, 'Train: $F_c = {}Hz$'.format(freq))
 
-        train_result = y > 0.5 * full_scale
-        if not all(train_result == training_bits):
-            return ValueError('#{} training failed on {} Hz'.format(i, freq))
+        train_result = np.round(y)
+        if not all(train_result == training):
+            raise ValueError('#{} training failed on {} Hz'.format(i, freq))
 
         noise = y - expected
         Pnoise = sigproc.power(noise)
@@ -114,6 +113,7 @@ def demodulate(symbols, filters, freqs):
     symbol_list = []
 
     generators = split(symbols, n=len(freqs))
+    print filters
     for freq, S in zip(freqs, generators):
         S = filters[freq](S)
 
@@ -204,7 +204,10 @@ if __name__ == '__main__':
     p = argparse.ArgumentParser()
     p.add_argument('fname')
     args = p.parse_args()
-    main(fname=args.fname)
-
-    if pylab:
-        pylab.show()
+    try:
+        main(fname=args.fname)
+    except Exception as e:
+        log.exception(e)
+    finally:
+        if pylab:
+            pylab.show()
diff --git a/show.py b/show.py
index 7edfb0a..145a530 100755
--- a/show.py
+++ b/show.py
@@ -4,23 +4,25 @@ import numpy as np
 
 import sigproc
 
+
 def constellation(y, title):
     theta = np.linspace(0, 2*np.pi, 1000)
     pylab.plot(y.real, y.imag, '.')
     pylab.plot(np.cos(theta), np.sin(theta), ':')
     points = np.array(sigproc.modulator.symbols)
-    pylab.plot(points.real, points.imag, 'o')
+    pylab.plot(points.real, points.imag, '+')
     pylab.grid('on')
     pylab.axis('equal')
     pylab.title(title)
 
+
 def spectrogram(t, x, Fs, NFFT=256):
     ax1 = pylab.subplot(211)
     pylab.plot(t, x)
 
     pylab.subplot(212, sharex=ax1)
-    Pxx, freqs, bins, im = pylab.specgram(x,
-        NFFT=NFFT, Fs=Fs, noverlap=NFFT/2, cmap=pylab.cm.gist_heat)
+    pylab.specgram(x, NFFT=NFFT, Fs=Fs, noverlap=NFFT/2,
+                   cmap=pylab.cm.gist_heat)
 
 if __name__ == '__main__':
     import sys
@@ -33,4 +35,3 @@ if __name__ == '__main__':
         spectrogram(t, x, common.Fs)
 
     pylab.show()
-
diff --git a/train.py b/train.py
index f6bf6cb..5a6d852 100644
--- a/train.py
+++ b/train.py
@@ -1,2 +1,16 @@
+import itertools
+
 prefix = [1]*400 + [0]*50
-equalizer = ([1]*10 + [0]*10)*10 + [0]*20
+
+
+def _equalizer_sequence():
+    res = []
+
+    symbols = [1, 1j, -1, -1j]
+    for s in itertools.islice(itertools.cycle(symbols), 100):
+        res.extend([s]*1 + [0]*1)
+
+    res.extend([0]*20)
+    return res
+
+equalizer = _equalizer_sequence()