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waveforms.py

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import os

import numpy as np

from scipy.io import wavfile

import matplotlib.pyplot as plt

from wave_array.mipmap.mipmap import Mipmap

def sine():

return np.sin([x * 2 * np.pi / Mipmap.L0_SIZE for x in range (Mipmap.L0_SIZE)]) * Mipmap.SAMPLE_MAX

def triangle():

wave = np.zeros(Mipmap.L0_SIZE)

for i in range(Mipmap.L0_SIZE):

if i < Mipmap.L0_SIZE // 4:

wave[i] = i * 4 / Mipmap.L0_SIZE * Mipmap.SAMPLE_MAX

elif i <= Mipmap.L0_SIZE * 3 // 4:

wave[i] = ((i - Mipmap.L0_SIZE // 4) * -4 / Mipmap.L0_SIZE + 1) * Mipmap.SAMPLE_MAX

else:

wave[i] = ((i - Mipmap.L0_SIZE * 3 // 4) * 4 / Mipmap.L0_SIZE - 1) * Mipmap.SAMPLE_MAX

return wave

def saw():

return np.array([(2.0 * (i + 0.5) / Mipmap.L0_SIZE + 1.0) % 2 - 1.0

for i in range(Mipmap.L0_SIZE)]) * Mipmap.SAMPLE_MAX

def square():

return np.concatenate(

(np.ones(Mipmap.L0_SIZE // 2), -np.ones(Mipmap.L0_SIZE // 2))) * Mipmap.SAMPLE_MAX

def acid():

wave = wavfile.read(f"{os.path.dirname(__file__)}\Acid.wav")[1][:2048]

wave *= Mipmap.SAMPLE_MAX / max(wave) # Normalize to sample size.

return wave

def main():

plt.figure()

plt.plot(sine())

plt.plot(triangle())

plt.plot(saw())

plt.plot(square())

plt.show()

if __name__ == '__main__':

main()