Resolved issues with Python 3.5 compatibility

Fixed some bugs that occurred in Python 3.5 but were not present in
Python 2.7. Most compatiblity issues were caused by incompatible type
casting of numpy arrays.

python/led.py

@@ -15,7 +15,7 @@ pixels = np.tile(1, (3, config.N_PIXELS))
 
 def update():
     global pixels, _prev_pixels
-    pixels = np.clip(pixels, 0, 255)
+    pixels = np.clip(pixels, 0, 255).astype(int)
     m = ''
     p = _gamma[pixels] if config.GAMMA_CORRECTION else np.copy(pixels)
     for i in range(config.N_PIXELS):

python/visualization.py

@@ -75,13 +75,13 @@ b_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
                        alpha_decay=0.25, alpha_rise=0.99)
 p_filt = dsp.ExpFilter(np.tile(1, (3, config.N_PIXELS // 2)),
                        alpha_decay=0.05, alpha_rise=0.8)
-p = np.tile(1, (3, config.N_PIXELS // 2))
+p = np.tile(1.0, (3, config.N_PIXELS // 2))
 gain = dsp.ExpFilter(np.tile(0.01, config.N_FFT_BINS),
                      alpha_decay=0.001, alpha_rise=0.99)
 
 
 def largest_indices(ary, n):
-    """Returns the n largest indices from a numpy array."""
+    """Returns indices of the n largest values in the given a numpy array"""
     flat = ary.flatten()
     indices = np.argpartition(flat, -n)[-n:]
     indices = indices[np.argsort(-flat[indices])]
@@ -89,11 +89,12 @@ def largest_indices(ary, n):
 
 
 def visualize_max(y):
+    """Experimental sandbox effect. Not recommended for use"""
     y = np.copy(interpolate(y, config.N_PIXELS // 2)) * 255.0
     ind = largest_indices(y, 15)
-    y[ind] *= -1
+    y[ind] *= -1.0
-    y[y > 0] = 0
+    y[y > 0] = 0.0
-    y[ind] *= -1
+    y[ind] *= -1.0
     # Blur the color channels with different strengths
     r = gaussian_filter1d(y, sigma=0.25)
     g = gaussian_filter1d(y, sigma=0.10)
@@ -111,14 +112,15 @@ def visualize_max(y):
     led.pixels[0, :] = pixel_r
     led.pixels[1, :] = pixel_g
     led.pixels[2, :] = pixel_b
+    led.update()
     # Update the GUI plots
     GUI.curve[0][0].setData(y=pixel_r)
     GUI.curve[0][1].setData(y=pixel_g)
     GUI.curve[0][2].setData(y=pixel_b)
-    led.update()
 
 
 def visualize_scroll(y):
+    """Effect that originates in the center and scrolls outwards"""
     global p
     y = gaussian_filter1d(y, sigma=1.0)**3.0
     y = np.copy(y)
@@ -134,11 +136,17 @@ def visualize_scroll(y):
     p[0, 0] = r
     p[1, 0] = g
     p[2, 0] = b
+    # Update the LED strip
     led.pixels = np.concatenate((p[:, ::-1], p), axis=1)
     led.update()
+    # Update the GUI plots
+    GUI.curve[0][0].setData(y=np.concatenate((p[0, :][::-1], p[0, :])))
+    GUI.curve[0][1].setData(y=np.concatenate((p[1, :][::-1], p[1, :])))
+    GUI.curve[0][2].setData(y=np.concatenate((p[2, :][::-1], p[2, :])))
 
 
 def visualize_energy(y):
+    """Effect that expands from the center with increasing sound energy"""
     global p
     y = gaussian_filter1d(y, sigma=1.0)**3.0
     gain.update(y)
@@ -147,22 +155,28 @@ def visualize_energy(y):
     r = int(np.mean(y[:len(y) // 3]))
     g = int(np.mean(y[len(y) // 3: 2 * len(y) // 3]))
     b = int(np.mean(y[2 * len(y) // 3:]))
-    p[0, :r] = 255
+    p[0, :r] = 255.0
-    p[0, r:] = 0
+    p[0, r:] = 0.0
-    p[1, :g] = 255
+    p[1, :g] = 255.0
-    p[1, g:] = 0
+    p[1, g:] = 0.0
-    p[2, :b] = 255
+    p[2, :b] = 255.0
-    p[2, b:] = 0
+    p[2, b:] = 0.0
     p_filt.update(p)
     p = p_filt.value.astype(int)
     p[0, :] = gaussian_filter1d(p[0, :], sigma=4.0)
     p[1, :] = gaussian_filter1d(p[1, :], sigma=4.0)
     p[2, :] = gaussian_filter1d(p[2, :], sigma=4.0)
+    # Update LED pixel arrays
     led.pixels = np.concatenate((p[:, ::-1], p), axis=1)
     led.update()
+    # Update the GUI plots
+    GUI.curve[0][0].setData(y=np.concatenate((p[0, :][::-1], p[0, :])))
+    GUI.curve[0][1].setData(y=np.concatenate((p[1, :][::-1], p[1, :])))
+    GUI.curve[0][2].setData(y=np.concatenate((p[2, :][::-1], p[2, :])))
 
 
 def visualize_spectrum(y):
+    """Effect that maps the Mel filterbank frequencies onto the LED strip"""
     y = np.copy(interpolate(y, config.N_PIXELS // 2)) * 255.0
     # Blur the color channels with different strengths
     r = gaussian_filter1d(y, sigma=0.25)
@@ -179,11 +193,11 @@ def visualize_spectrum(y):
     led.pixels[0, :] = pixel_r
     led.pixels[1, :] = pixel_g
     led.pixels[2, :] = pixel_b
+    led.update()
     # Update the GUI plots
     GUI.curve[0][0].setData(y=pixel_r)
     GUI.curve[0][1].setData(y=pixel_g)
     GUI.curve[0][2].setData(y=pixel_b)
-    led.update()
 
 
 mel_gain = dsp.ExpFilter(np.tile(1e-1, config.N_FFT_BINS),
@@ -194,7 +208,7 @@ volume = dsp.ExpFilter(config.MIN_VOLUME_THRESHOLD,
 
 def microphone_update(stream):
     global y_roll, prev_rms, prev_exp
-    # Normalize new audio samples
+    # Retrieve and normalize the new audio samples
     y = np.fromstring(stream.read(samples_per_frame,
                                   exception_on_overflow=False), dtype=np.int16)
     y = y / 2.0**15
@@ -209,19 +223,25 @@ def microphone_update(stream):
         led.pixels = np.tile(0, (3, config.N_PIXELS))
         led.update()
     else:
+        # Transform audio input into the frequency domain
         XS, YS = dsp.fft(y_data, window=np.hamming)
+        # Remove half of the FFT data because of symmetry
         YS = YS[:len(YS) // 2]
         XS = XS[:len(XS) // 2]
+        # Construct a Mel filterbank from the FFT data
         YS = np.atleast_2d(np.abs(YS)).T * dsp.mel_y.T
+        # Scale data to values more suitable for visualization
         YS = np.sum(YS, axis=0)**2.0
         mel = YS**0.5
         mel = gaussian_filter1d(mel, sigma=1.0)
+        # Normalize the Mel filterbank to make it volume independent
         mel_gain.update(np.max(mel))
         mel = mel / mel_gain.value
+        # Visualize the filterbank output
         visualize_spectrum(mel)
         # visualize_max(mel)
         # visualize_scroll(mel)
-        # visualize_energy(mel)
+        visualize_energy(mel)
     GUI.app.processEvents()
     print('FPS {:.0f} / {:.0f}'.format(frames_per_second(), config.FPS))
 
@@ -235,8 +255,8 @@ y_roll = np.random.rand(config.N_ROLLING_HISTORY, samples_per_frame) / 1e16
 
 if __name__ == '__main__':
     import pyqtgraph as pg
+    # Create GUI plot for visualizing LED strip output
     GUI = gui.GUI(width=800, height=400, title='Audio Visualization')
-    # Audio plot
     GUI.add_plot('Color Channels')
     r_pen = pg.mkPen((255, 30, 30, 200), width=6)
     g_pen = pg.mkPen((30, 255, 30, 200), width=6)