forked from buddhabrot/fusion-zauberstab
115 lines
2.5 KiB
Python
115 lines
2.5 KiB
Python
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import matplotlib.pyplot as plt
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import numpy as np
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n_plots = 3
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titlex = 0.5
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titley = 0.5
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endtime = 4
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size = 500
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#fig, axs = plt.subplots(n_plots, 1, sharex=True)
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fig = plt.figure(figsize=(10,4))
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gs = fig.add_gridspec(n_plots, hspace=0)
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axs = gs.subplots()
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##############################################
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time = np.linspace(0,endtime,size)
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audio = 1023*np.random.random(size=(size))
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audio = 512+(audio-512)*(0.2)*np.sin(np.linspace(0,endtime*2*3.14*2, size))+(audio-512)*0.3
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for i in range(size):
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if i < size/2:
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audio[i] = 0.5*audio[i]+256
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axs[0].set_xlim((-0,4))
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axs[0].plot(time,audio)
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axs[0].set_ylabel("Mikrofon\n-signal", x=titlex, y=titley)
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audio_norm = audio-512
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spc = size/endtime
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audio_norm = np.array(audio_norm)
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audio_squared = np.square(audio_norm)
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audio_squared_filtered = list()
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state = 0
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for sample in audio_squared:
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state += (sample-state)*0.01
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audio_squared_filtered.append(state)
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audio_squared_filtered2 = list()
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state = 0
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for sample in audio_squared:
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state += (sample-state)*0.1
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audio_squared_filtered2.append(state)
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audio_squared = np.array(audio_squared)
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audio_squared_filtered = np.array(audio_squared_filtered)
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audio_squared_filtered2 = np.array(audio_squared_filtered2)
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axs[1].plot(time, audio_squared)
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axs[1].plot(time, audio_squared_filtered, "k--")
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axs[1].plot(time, audio_squared_filtered2, "k")
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axs[1].set_ylabel("Signal\n-energie\n(gefiltert)", x=titlex, y=titley)
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axs[1].set_xlim((-0,4))
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n_LED = 500
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animation = np.zeros((size, n_LED))
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for i in range(size):
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for j in range(n_LED):
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if i == 0:
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animation[i,j] = 0
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else:
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if j == 0:
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animation[i,j] = 10 if audio_squared_filtered2[i]*1.15 < audio_squared_filtered[i] else 255
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elif j == 1:
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animation[i,j] = 10 if audio_squared_filtered2[i]*1.15 < audio_squared_filtered[i] else 255
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elif j == 2:
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animation[i,j] = 10 if audio_squared_filtered2[i]*1.15 < audio_squared_filtered[i] else 255
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else:
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animation[i,j] = (animation[i-1,j-1]+animation[i-1,j-2]+animation[i-1,j-3])*0.33
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print(repr(animation))
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animation = animation.T
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animation = np.flip(animation, axis=0)
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axs[2].imshow(animation, cmap="Blues")
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axs[2].set_aspect('auto')
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axs[2].set_ylabel("LED\nLicht-\nverlauf", x=titlex, y=titley)
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for i in range(n_plots):
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axs[i].set_yticks(())
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axs[i].set_xticks(())
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#axs[2].set_xlabel("Zeit")
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plt.savefig("fackel.svg")
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plt.savefig("fackel.png", dpi=500)
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plt.show()
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