# 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
    noise_floor = np.exp(2.0 * noise_power.mean())                                    
    mn = song.magnitude_spectrum
    e_n = energy(mn)   
    pin = 0                
    output = np.zeros(len(audio))
    hold_time = 0
    ca = 0
    cr = 0
    amp = audio.max()
    while pin < pend:
        selection = pin+2048
        song.frame = audio[pin:selection] 
        song.window()     
        song.M = 2048            
        song.Spectrum()
        e_m = energy(song.magnitude_spectrum)
        SNR = 10 * np.log10(e_m / e_n)
        ft = song.fft(song.windowed_x)[:song.H+1]
        power_spectral_density = np.abs(ft) ** 2
        song.Envelope()
        song.AttackTime()
        rel_time = rel(song.attack_time)
        rel_coef = to_coef(rel_time, 44100)
        at_coef = to_coef(song.attack_time, 44100)
        ca = ca + song.attack_time
        cr = cr + rel_time 
        if SNR > 0:                
            np.add.at(output, range(pin, selection), audio[pin:selection])                                
        else:                    
            if np.any(power_spectral_density < noise_floor):                                    

# 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

    noise_floor = np.exp(2.0 * noise_power.mean())                                    
    mn = song.magnitude_spectrum
    e_n = energy(mn)   
    pin = 0                
    output = np.zeros(len(audio))
    hold_time = 0
    ca = 0
    cr = 0
    amp = audio.max()
    while pin < pend:
        selection = pin+2048
        song.frame = audio[pin:selection] 
        song.window()     
        song.M = 2048            
        song.Spectrum()
        e_m = energy(song.magnitude_spectrum)
        SNR = 10 * np.log10(e_m / e_n)
        ft = song.fft(song.windowed_x)[:song.H+1]
        power_spectral_density = np.abs(ft) ** 2
        song.Envelope()
        song.AttackTime()
        rel_time = rel(song.attack_time)
        rel_coef = to_coef(rel_time, 44100)
        at_coef = to_coef(song.attack_time, 44100)
        ca = ca + song.attack_time
        cr = cr + rel_time 
        if SNR > 0:                
            np.add.at(output, range(pin, selection), audio[pin:selection])                                
        else:                    
            if np.any(power_spectral_density < noise_floor):                                    

                gc = dyn_constraint_satis(ft, [power_spectral_density, noise_floor], 0.12589254117941673) 