# Path: apicultor/constraints/dynamic_range.py
# def dyn_constraint_satis(audio, variables, gain):
#     assert ((type(variables) is list) and len(variables) == 2)
#     audio[variables[0] < variables[1]] = gain  #noise gating: anything below a threshold is silenced     
#     audio[variables[0] > variables[1]] = 1
#     return audio
# 
# Path: apicultor/sonification/Sonification.py
# def normalize(signal):
#     maximum_normalizing = np.max(np.abs(signal))/-1 
#     normalized = np.true_divide(signal,maximum_normalizing)
#     return normalized
# 
# def write_file(filename, fs, data):           
#     write(filename+'.wav', fs, data) 
#     call(['ffmpeg', '-i', filename+'.wav', filename+'.ogg', '-y'])
#     call(['rm', '-f', filename+'.wav'])


from ..constraints.dynamic_range import dyn_constraint_satis
from ..utils.algorithms import *
from ..sonification.Sonification import normalize, write_file
from scipy.fftpack import fft, ifft, fftfreq
from scipy.signal import lfilter, fftconvolve, firwin
from soundfile import read
import numpy as np
import os
import sys
import logging
                result = np.float32([h_sig_L, h_sig_R]).T               
                neg_angle = result[:,(1,0)]         
                panned = result + neg_angle
                normalized = normalize(panned)  
                del normalized                                          
                audio = mono_stereo(audio)          
                print(( "Rewriting without aliasing in %s"%f ))         
                song = sonify(audio, 44100)                     
                audio = song.IIR(audio, 44100/2, 'lowpass') #anti-aliasing filtering: erase frequencies higher than the sample rate being used
                print(( "Rewriting without DC in %s"%f ))                                  
                audio = song.IIR(audio, 40, 'highpass') #remove direct current on audio signal                       
                print(( "Rewriting with Equal Loudness contour in %s"%f ))                                
                audio = song.EqualLoudness(audio) #Equal-Loudness Contour  
                print(( "Rewriting with RIAA filter applied in %s"%f )) 
                riaa_filtered = biquad_filter(audio, abz)  #riaa filter 
                del audio
                normalized_riaa = normalize(riaa_filtered)                   
                del riaa_filtered                           
                print(( "Rewriting with Hum removal applied in %s"%f ))
                song.signal = np.float32(normalized_riaa)                
                without_hum = song.BandReject(np.float32(normalized_riaa), 50, 16) #remove undesired 50 hz hum     
                del normalized_riaa                                     
                print(( "Rewriting with subsonic rumble removal applied in %s"%f ))                          
                song.signal = without_hum                               
                without_rumble = song.IIR(song.signal, 20, 'highpass') #remove subsonic rumble                  
                del without_hum                                         
                db_mag = 20 * np.log10(abs(without_rumble)) #calculate silence if present in audio signal                         
                print(( "Rewriting without silence in %s"%f ))
                silence_threshold = -130 #complete silence              
                loud_audio = np.delete(without_rumble, np.where(db_mag < silence_threshold))#remove it

# Path: apicultor/constraints/dynamic_range.py
# def dyn_constraint_satis(audio, variables, gain):
#     assert ((type(variables) is list) and len(variables) == 2)
#     audio[variables[0] < variables[1]] = gain  #noise gating: anything below a threshold is silenced     
#     audio[variables[0] > variables[1]] = 1
#     return audio
# 
# Path: apicultor/sonification/Sonification.py
# def normalize(signal):
#     maximum_normalizing = np.max(np.abs(signal))/-1 
#     normalized = np.true_divide(signal,maximum_normalizing)
#     return normalized
# 
# def write_file(filename, fs, data):           
#     write(filename+'.wav', fs, data) 
#     call(['ffmpeg', '-i', filename+'.wav', filename+'.ogg', '-y'])
#     call(['rm', '-f', filename+'.wav'])


# Path: apicultor/machine_learning/quality.py
from ..constraints.dynamic_range import dyn_constraint_satis
from ..utils.algorithms import *
from ..sonification.Sonification import normalize, write_file
from scipy.fftpack import fft, ifft, fftfreq
from scipy.signal import lfilter, fftconvolve, firwin
from soundfile import read
import numpy as np
import os
import sys
import logging

                result = np.float32([h_sig_L, h_sig_R]).T               
                neg_angle = result[:,(1,0)]         
                panned = result + neg_angle
                normalized = normalize(panned)  
                del normalized                                          
                audio = mono_stereo(audio)          
                print(( "Rewriting without aliasing in %s"%f ))         
                song = sonify(audio, 44100)                     
                audio = song.IIR(audio, 44100/2, 'lowpass') #anti-aliasing filtering: erase frequencies higher than the sample rate being used
                print(( "Rewriting without DC in %s"%f ))                                  
                audio = song.IIR(audio, 40, 'highpass') #remove direct current on audio signal                       
                print(( "Rewriting with Equal Loudness contour in %s"%f ))                                
                audio = song.EqualLoudness(audio) #Equal-Loudness Contour  
                print(( "Rewriting with RIAA filter applied in %s"%f )) 
                riaa_filtered = biquad_filter(audio, abz)  #riaa filter 
                del audio
                normalized_riaa = normalize(riaa_filtered)                   
                del riaa_filtered                           
                print(( "Rewriting with Hum removal applied in %s"%f ))
                song.signal = np.float32(normalized_riaa)                
                without_hum = song.BandReject(np.float32(normalized_riaa), 50, 16) #remove undesired 50 hz hum     
                del normalized_riaa                                     
                print(( "Rewriting with subsonic rumble removal applied in %s"%f ))                          
                song.signal = without_hum                               
                without_rumble = song.IIR(song.signal, 20, 'highpass') #remove subsonic rumble                  
                del without_hum                                         
                db_mag = 20 * np.log10(abs(without_rumble)) #calculate silence if present in audio signal                         
                print(( "Rewriting without silence in %s"%f ))
                silence_threshold = -130 #complete silence              
                loud_audio = np.delete(without_rumble, np.where(db_mag < silence_threshold))#remove it

                write_file(subdir+'/'+os.path.splitext(f)[0], 44100, loud_audio)                                                              