sonification 0.0.9

Map data to sound allowing it to be interpreted it in an auditory manner

sonification

Map data to sound allowing it to be interpreted it in an auditory manner

To install: pip install sonification

Examples

import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
import simpleaudio as sa

def preprocess_dataframe(df):
    df = df.copy()
    # Normalize numerical columns
    scaler = MinMaxScaler()
    for column in df.select_dtypes(include=np.number).columns:
        df[column] = scaler.fit_transform(df[[column]])
    
    # Ensure durations are not zero
    if 'duration' in df.columns:
        df['duration'] = df['duration'] + 0.01  # Adding a small value to ensure durations are not zero
    
    # Encode categorical columns
    label_encoders = {}
    for column in df.select_dtypes(include='object').columns:
        le = LabelEncoder()
        df[column] = le.fit_transform(df[column])
        label_encoders[column] = le
    
    return df, label_encoders

def generate_tone(frequency, duration, volume, sample_rate=44100):
    t = np.linspace(0, duration, int(sample_rate * duration), False)
    wave = volume * np.sin(frequency * t * 2 * np.pi)
    return wave

def map_features_to_audio(df, pitch_col, duration_col, volume_col, sample_rate=44100):
    waveform = np.array([])
    
    for index, row in df.iterrows():
        pitch = row[pitch_col]
        duration = row[duration_col]
        volume = row[volume_col]
        
        frequency = 440 + pitch * 440  # Example: Map pitch to frequency
        wave = generate_tone(frequency, duration, volume, sample_rate)
        
        # print(f"Row {index}: pitch={pitch}, duration={duration}, volume={volume}, frequency={frequency}, wave_len={len(wave)}")
        
        waveform = np.concatenate([waveform, wave])
    
    print(f"Final waveform length: {len(waveform)}")
    return waveform, sample_rate

def save_or_return_audio(waveform, sample_rate, filepath=None):
    if filepath:
        # Normalize waveform to int16 range
        waveform_int16 = np.int16(waveform / np.max(np.abs(waveform)) * 32767)
        sa.WaveObject(waveform_int16, 1, 2, sample_rate).save(filepath)
    else:
        return waveform, sample_rate

def sonification_dataframe(df, pitch_col, duration_col, volume_col, sample_rate=44100, filepath=None):
    df, label_encoders = preprocess_dataframe(df)
    waveform, sr = map_features_to_audio(df, pitch_col, duration_col, volume_col, sample_rate)
    return save_or_return_audio(waveform, sr, filepath)

# Example usage:
df = pd.DataFrame({
    'pitch': [0.2, 0.4, 0.6, 0.8],
    'duration': [0.5, 0.5, 0.5, 0.5],
    'volume': [0.5, 0.7, 0.9, 1.0]
})
waveform, sr = sonification_dataframe(df, 'pitch', 'duration', 'volume')

# To play the audio
if waveform is not None and len(waveform) > 0:
    play_obj = sa.play_buffer(np.int16(waveform / np.max(np.abs(waveform)) * 32767), 1, 2, sr)
    play_obj.wait_done()