fusion-zauberstab/make_artikel/grafiken/illustrationen/beaterkennung.py
2022-11-14 17:33:34 +01:00

49 lines
832 B
Python

import matplotlib.pyplot as plt
import numpy as np
n_plots = 5
endtime = 4
size = 1000
time = np.linspace(0,endtime,size)
audio = 1023*np.random.random(size=(size))
audio = 512+(audio-512)*(0.2)*np.sin(np.linspace(0,endtime*2*3.14*2, size))+(audio-512)*0.3
fig, axs = plt.subplots(n_plots, 1)
axs[0].plot(time,audio)
audio_norm = audio-512
axs[1].plot(time,audio_norm)
spc = size/endtime
audio_norm = np.array(audio_norm)
audio_squared = np.square(audio_norm)
axs[2].plot(time, audio_squared)
spc = int(size/endtime/40)
chunks = list()
chunktimes = list()
energy = 0
i = 0
for sample, timepoint in zip(audio_squared, time):
energy += sample
i += 1
if i > spc:
i = 0
chunks.append(energy)
chunktimes.append(timepoint)
energy = 0
axs[3].plot(chunktimes, chunks)
plt.show()