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.
This commit is contained in:
Scott Lawson
parent
da338ae8f4
commit
e874ed3b2c
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@ -15,7 +15,7 @@ pixels = np.tile(1, (3, config.N_PIXELS))
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def update():
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global pixels, _prev_pixels
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pixels = np.clip(pixels, 0, 255)
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pixels = np.clip(pixels, 0, 255).astype(int)
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m = ''
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p = _gamma[pixels] if config.GAMMA_CORRECTION else np.copy(pixels)
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for i in range(config.N_PIXELS):
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@ -75,13 +75,13 @@ b_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
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alpha_decay=0.25, alpha_rise=0.99)
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p_filt = dsp.ExpFilter(np.tile(1, (3, config.N_PIXELS // 2)),
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alpha_decay=0.05, alpha_rise=0.8)
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p = np.tile(1, (3, config.N_PIXELS // 2))
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p = np.tile(1.0, (3, config.N_PIXELS // 2))
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gain = dsp.ExpFilter(np.tile(0.01, config.N_FFT_BINS),
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alpha_decay=0.001, alpha_rise=0.99)
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def largest_indices(ary, n):
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"""Returns the n largest indices from a numpy array."""
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"""Returns indices of the n largest values in the given a numpy array"""
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flat = ary.flatten()
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indices = np.argpartition(flat, -n)[-n:]
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indices = indices[np.argsort(-flat[indices])]
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@ -89,11 +89,12 @@ def largest_indices(ary, n):
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def visualize_max(y):
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"""Experimental sandbox effect. Not recommended for use"""
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y = np.copy(interpolate(y, config.N_PIXELS // 2)) * 255.0
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ind = largest_indices(y, 15)
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y[ind] *= -1
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y[y > 0] = 0
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y[ind] *= -1
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y[ind] *= -1.0
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y[y > 0] = 0.0
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y[ind] *= -1.0
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# Blur the color channels with different strengths
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r = gaussian_filter1d(y, sigma=0.25)
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g = gaussian_filter1d(y, sigma=0.10)
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@ -111,14 +112,15 @@ def visualize_max(y):
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led.pixels[0, :] = pixel_r
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led.pixels[1, :] = pixel_g
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led.pixels[2, :] = pixel_b
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led.update()
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# Update the GUI plots
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GUI.curve[0][0].setData(y=pixel_r)
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GUI.curve[0][1].setData(y=pixel_g)
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GUI.curve[0][2].setData(y=pixel_b)
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led.update()
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def visualize_scroll(y):
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"""Effect that originates in the center and scrolls outwards"""
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global p
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y = gaussian_filter1d(y, sigma=1.0)**3.0
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y = np.copy(y)
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@ -134,11 +136,17 @@ def visualize_scroll(y):
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p[0, 0] = r
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p[1, 0] = g
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p[2, 0] = b
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# Update the LED strip
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led.pixels = np.concatenate((p[:, ::-1], p), axis=1)
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led.update()
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# Update the GUI plots
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GUI.curve[0][0].setData(y=np.concatenate((p[0, :][::-1], p[0, :])))
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GUI.curve[0][1].setData(y=np.concatenate((p[1, :][::-1], p[1, :])))
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GUI.curve[0][2].setData(y=np.concatenate((p[2, :][::-1], p[2, :])))
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def visualize_energy(y):
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"""Effect that expands from the center with increasing sound energy"""
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global p
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y = gaussian_filter1d(y, sigma=1.0)**3.0
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gain.update(y)
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@ -147,22 +155,28 @@ def visualize_energy(y):
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r = int(np.mean(y[:len(y) // 3]))
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g = int(np.mean(y[len(y) // 3: 2 * len(y) // 3]))
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b = int(np.mean(y[2 * len(y) // 3:]))
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p[0, :r] = 255
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p[0, r:] = 0
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p[1, :g] = 255
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p[1, g:] = 0
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p[2, :b] = 255
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p[2, b:] = 0
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p[0, :r] = 255.0
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p[0, r:] = 0.0
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p[1, :g] = 255.0
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p[1, g:] = 0.0
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p[2, :b] = 255.0
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p[2, b:] = 0.0
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p_filt.update(p)
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p = p_filt.value.astype(int)
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p[0, :] = gaussian_filter1d(p[0, :], sigma=4.0)
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p[1, :] = gaussian_filter1d(p[1, :], sigma=4.0)
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p[2, :] = gaussian_filter1d(p[2, :], sigma=4.0)
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# Update LED pixel arrays
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led.pixels = np.concatenate((p[:, ::-1], p), axis=1)
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led.update()
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# Update the GUI plots
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GUI.curve[0][0].setData(y=np.concatenate((p[0, :][::-1], p[0, :])))
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GUI.curve[0][1].setData(y=np.concatenate((p[1, :][::-1], p[1, :])))
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GUI.curve[0][2].setData(y=np.concatenate((p[2, :][::-1], p[2, :])))
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def visualize_spectrum(y):
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"""Effect that maps the Mel filterbank frequencies onto the LED strip"""
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y = np.copy(interpolate(y, config.N_PIXELS // 2)) * 255.0
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# Blur the color channels with different strengths
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r = gaussian_filter1d(y, sigma=0.25)
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@ -179,11 +193,11 @@ def visualize_spectrum(y):
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led.pixels[0, :] = pixel_r
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led.pixels[1, :] = pixel_g
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led.pixels[2, :] = pixel_b
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led.update()
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# Update the GUI plots
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GUI.curve[0][0].setData(y=pixel_r)
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GUI.curve[0][1].setData(y=pixel_g)
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GUI.curve[0][2].setData(y=pixel_b)
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led.update()
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mel_gain = dsp.ExpFilter(np.tile(1e-1, config.N_FFT_BINS),
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@ -194,7 +208,7 @@ volume = dsp.ExpFilter(config.MIN_VOLUME_THRESHOLD,
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def microphone_update(stream):
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global y_roll, prev_rms, prev_exp
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# Normalize new audio samples
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# Retrieve and normalize the new audio samples
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y = np.fromstring(stream.read(samples_per_frame,
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exception_on_overflow=False), dtype=np.int16)
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y = y / 2.0**15
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@ -209,19 +223,25 @@ def microphone_update(stream):
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led.pixels = np.tile(0, (3, config.N_PIXELS))
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led.update()
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else:
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# Transform audio input into the frequency domain
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XS, YS = dsp.fft(y_data, window=np.hamming)
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# Remove half of the FFT data because of symmetry
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YS = YS[:len(YS) // 2]
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XS = XS[:len(XS) // 2]
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# Construct a Mel filterbank from the FFT data
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YS = np.atleast_2d(np.abs(YS)).T * dsp.mel_y.T
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# Scale data to values more suitable for visualization
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YS = np.sum(YS, axis=0)**2.0
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mel = YS**0.5
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mel = gaussian_filter1d(mel, sigma=1.0)
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# Normalize the Mel filterbank to make it volume independent
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mel_gain.update(np.max(mel))
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mel = mel / mel_gain.value
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# Visualize the filterbank output
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visualize_spectrum(mel)
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# visualize_max(mel)
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# visualize_scroll(mel)
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# visualize_energy(mel)
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visualize_energy(mel)
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GUI.app.processEvents()
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print('FPS {:.0f} / {:.0f}'.format(frames_per_second(), config.FPS))
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@ -235,8 +255,8 @@ y_roll = np.random.rand(config.N_ROLLING_HISTORY, samples_per_frame) / 1e16
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if __name__ == '__main__':
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import pyqtgraph as pg
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# Create GUI plot for visualizing LED strip output
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GUI = gui.GUI(width=800, height=400, title='Audio Visualization')
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# Audio plot
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GUI.add_plot('Color Channels')
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r_pen = pg.mkPen((255, 30, 30, 200), width=6)
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g_pen = pg.mkPen((30, 255, 30, 200), width=6)
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