Visible to audible calculations

visible_to_audible.py

import wave
import numpy as np

sample_rate = 44100

def create_audio(freq):
    """
    Create audio sample
    """
    n_seconds = 2
    time_points = np.linspace(0, n_seconds, n_seconds*sample_rate)
    audio = 0.5 * np.sin(2 * np.pi * freq * time_points)
    return audio

hydrogen_spectra = [
    410, #nm: Violet
    434, #nm: Blue
    486, #nm: Blue-green
    656, #nm: Red
]

a0 = create_audio(hydrogen_spectra[0])
a1 = create_audio(hydrogen_spectra[1])
a2 = create_audio(hydrogen_spectra[2])
a3 = create_audio(hydrogen_spectra[3])
a_all = (a0 + a1 + a2 + a3)/len(hydrogen_spectra)


# Put the channels together
a = np.concatenate([a0, a1, a2, a3, a_all])
audio = np.array([a, a]).T

# Convert to (little-endian) 16 bit integers.
audio = (audio * (2 ** 15 - 1)).astype("<h")

with wave.open("sound1.wav", "w") as f:
    # 2 Channels.
    f.setnchannels(2)
    # 2 bytes per sample.
    f.setsampwidth(2)
    f.setframerate(sample_rate)
    f.writeframes(audio.tobytes())


def linear_interpolation(x):
    """
    Extrapolates a linear interpolation for visual wavelengths into the audible spectrum
    """
    visible_range = [700, 400] # x nm wavelength range of visible light
    audio_range = [20, 20000] # y Hz

    return (audio_range[1]-audio_range[0])/(visible_range[1]-visible_range[0]) * (x - visible_range[0]) # audio_range[0]

This came out of that Terrence Howard Joe Rogan interview to try to make sense of some conversion factor between the frequencies to shift things from the atomic to audible spectrum and what precisely the claims were for the color and sound mapping.