#!/bin/python
# -*- coding: utf-8 -*-
# vim: set fileencoding=<Laurel_Yanny.py> :
"""
This function to explain Laurel & Yanny hearing illusion

"""

__version_info__ = ('12', '04', '2020')
__version__ = '_'.join(__version_info__)
__author__ = 'Hitham Jleed'
print (__doc__)  
print ('version: ',__version__) 
print ('author: ',__author__) 
import numpy as np

# matplotlib for displaying the output
import matplotlib.pyplot as plt
import matplotlib.style as ms
ms.use('seaborn-muted')

# Librosa for audio
import librosa
# And the display module for visualization
import librosa.display

# Read the audio file. sr is the default sampling rate of 22050
y, sr = librosa.load('audio.wav')

def plot_time(x,sr,mytitle,savedname):    
    Time = np.linspace(0, len(x) / sr, num=len(x)) # define time in seconds
    plt.figure(figsize=(12,4))
    plt.plot(Time, x)
    plt.title(mytitle)
    plt.grid(True)
    plt.tight_layout()
    plt.savefig(savedname, bbox_inches='tight')   
    plt.show()
    
def display_mel_spectogram(y, sr,mytitle,savedname):
    S = librosa.feature.melspectrogram(y, sr=sr, n_mels=128)
    # Convert to log scale (dB)
    log_S = librosa.power_to_db(S, ref=np.max)
    # Make a new figure
    plt.figure(figsize=(12,4))
    # Display the spectrogram on a mel scale
    # sample rate and hop length parameters are used to render the time axis
    librosa.display.specshow(log_S, sr=sr, x_axis='time', y_axis='mel')
    # draw a color bar
    plt.colorbar(format='%+02.0f dB')
    # Make the figure layout compact
    plt.title(mytitle)
    plt.grid(True)
    plt.tight_layout()
    plt.savefig(savedname, bbox_inches='tight')   
    plt.show()
	
# display the original signal  
plot_time(y,sr,'original','orig_time.png')
display_mel_spectogram(y, sr,'original','orig_spec.png')

# Define a function which wil apply a butterworth bandpass filter
from scipy.signal import butter, lfilter
def butter_bandpass(lowcut, highcut, fs, order=5):
    nyq = 0.5 * fs
    low = lowcut / nyq
    high = highcut / nyq
    b, a = butter(order, [low, high], btype='band')
    return b, a
def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):
    b, a = butter_bandpass(lowcut, highcut, fs, order=order)
    y = lfilter(b, a, data)
    return y

# Now use butter_bandpass_filter to split the signal about the 1024 hz mark
signal_upper = butter_bandpass_filter(y, 2548, 6500, sr)
librosa.output.write_wav('Yanny.wav', signal_upper, sr)
# display Yanny signal  
plot_time(signal_upper,sr,'Yanny','Yanny_time.png')
display_mel_spectogram(signal_upper, sr,'Yanny','Yanny_spec.png')

# the lower part
signal_lower = butter_bandpass_filter(y, 50, 1024, sr)
librosa.output.write_wav('Laurel.wav', signal_lower, sr)
# display Yanny signal  
plot_time(signal_lower,sr,'Laurel','Laurel_time.png')
display_mel_spectogram(signal_lower, sr,'Laurel','Laurel_spec.png')