delayfilter.set_input_port(1, generator, 0)
delayfilter.set_blend(blend)
delayfilter.set_feedback(feedback)
delayfilter.set_feedforward(feedforward)
outfilter = DoubleOutPointerFilter(output, False)
outfilter.set_input_sampling_rate(sample_rate)
outfilter.set_input_port(0, delayfilter, 0)
outfilter.process(input.shape[1])
return output
if __name__ == "__main__":
import numpy as np
samples = 2000000
freq_max = 20000
t = np.arange(samples, dtype=np.float64).reshape(1, -1) / sample_rate
d = np.sin(np.pi * (sample_rate * freq_max / samples * (t + 0.1)) * t)
np.savetxt("input.txt", d)
out = filter(d, feedforward=-1, blend=1, feedback=-0.5)
np.savetxt("output.txt", out)
plt.figure()
plot_me((d[0], out[0]), sample_rate)
plt.gcf().suptitle("Delay")
plt.legend()
plt.show()
pipelineend.set_input_sampling_rate(sample_rate)
pipelineend.add_filter(outfilterl)
pipelineend.add_filter(outfilterr)
pipelineend.process(inputl.shape[1])
return outputl, outputr
if __name__ == "__main__":
import numpy as np
samples = 2000000
freq_max = 20000
t = np.arange(samples, dtype=np.float64).reshape(1, -1) / sample_rate
d = np.sin(np.pi * (sample_rate * freq_max / samples * (t + .1)) * t)
dr = d[:,::-1].copy()
np.savetxt("input.txt", d)
outl, outr = filter(d, dr, blend_ch1=1, blend_ch2=1,
feedforward_ch1_ch1=-1, feedforward_ch2_ch2=-1, feedforward_ch1_ch2=.1, feedforward_ch2_ch1=.7,
feedback_ch1_ch1=-.5, feedback_ch1_ch2=.1, feedback_ch2_ch1=.3, feedback_ch2_ch2=-.1, )
np.savetxt("outputl.txt", outl)
np.savetxt("outputr.txt", outr)
plt.figure()
plot_me((d[0], outl[0]), sample_rate)
plt.gcf().suptitle("Delay Channel R")
plt.figure()
plot_me((dr[0], outr[0]), sample_rate)
plt.gcf().suptitle("Delay Channel L")
plt.show()
if __name__ == "__main__":
import numpy as np
size = 100000
samples = 2000000
freq_max = 20000
t = np.arange(samples, dtype=np.float64).reshape(1, -1) / sample_rate
d = np.sin(np.pi * (sample_rate * freq_max / samples * (t + .1)) * t)
np.savetxt("input.txt", d)
out = filter_32(d)
plt.figure()
plot_me((d[0], out[0]), sample_rate)
plt.gcf().suptitle("Oversampling 32")
np.savetxt("output32.txt", out)
out = filter_16(d)
plt.figure()
plot_me((d[0], out[0]), sample_rate)
plt.gcf().suptitle("Oversampling 16")
np.savetxt("output16.txt", out)
out = filter_8(d)
plt.figure()
plt.title("Oversampling 8")
plot_me((d[0], out[0]), sample_rate)
np.savetxt("output8.txt", out)
out = filter_4(d)
plt.figure()
plt.title("Oversampling 4")
pipelinesink = PipelineGlobalSinkFilter()
pipelinesink.set_input_sampling_rate(sample_rate)
pipelinesink.add_filter(out1filter)
pipelinesink.add_filter(out2filter)
pipelinesink.process(input.shape[1])
return output1, output2
if __name__ == "__main__":
import numpy as np
samples = 2000000
freq_max = 20000
t = np.arange(samples, dtype=np.float64).reshape(1, -1) / sample_rate
d = np.sin(np.pi * (sample_rate * freq_max / samples * (t + .1)) * t)
np.savetxt("input.txt", d)
out1, out2 = filter(d, feedforward=1, blend=0.7, feedback=-0.7)
np.savetxt("output1.txt", out1)
np.savetxt("output2.txt", out2)
plt.figure()
plot_me((d[0], out1[0]), sample_rate)
plt.gcf().suptitle("Stereo L")
plt.legend()
plt.figure()
plot_me((d[0], out2[0]), sample_rate)
plt.gcf().suptitle("Stereo R")
plt.legend()
plt.show()
