# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in 

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory

import os
#print(os.listdir("../input/"))
#print(os.listdir())
# from os import listdir 
# from os.path import isfile, join 
# #mypath = "users/name/desktop/"
# mypath = "audio_train"
# files = [f for f in listdir(mypath) if isfile(join(mypath, f))]
#os.chdir("audio_train")

import glob
import os
import librosa
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf

from matplotlib.pyplot import specgram
#matplotlib inline

def load_sound_files(file_paths):
    raw_sounds = []
    for fp in file_paths:
        X,sr = librosa.load(fp)
        raw_sounds.append(X)
    return raw_sounds

def plot_waves(sound_names,raw_sounds):
    i = 1
    fig = plt.figure(figsize=(25,60), dpi = 900)
    for n,f in zip(sound_names,raw_sounds):
        plt.subplot(10,1,i)
        librosa.display.waveplot(np.array(f),sr=22050)
        plt.title(n.title())
        i += 1
    plt.suptitle("Figure 1: Waveplot",x=0.5, y=0.915,fontsize=18)
    plt.show()
    
def plot_specgram(sound_names,raw_sounds):
    i = 1
    fig = plt.figure(figsize=(25,60), dpi = 900)
    for n,f in zip(sound_names,raw_sounds):
        plt.subplot(10,1,i)
        specgram(np.array(f), Fs=22050)
        plt.title(n.title())
        i += 1
    plt.suptitle("Figure 2: Spectrogram",x=0.5, y=0.915,fontsize=18)
    plt.show()

def plot_log_power_specgram(sound_names,raw_sounds):
    i = 1
    fig = plt.figure(figsize=(25,60), dpi = 900)
    for n,f in zip(sound_names,raw_sounds):
        plt.subplot(10,1,i)
        D = librosa.logamplitude(np.abs(librosa.stft(f))**2, ref_power=np.max)
        librosa.display.specshow(D,x_axis='time' ,y_axis='log')
        plt.title(n.title())
        i += 1
    plt.suptitle("Figure 3: Log power spectrogram",x=0.5, y=0.915,fontsize=18)
    plt.show()
# Any results you write to the current directory are saved as output.

#Future catagories of sound
#"Acoustic_guitar", "Applause", "Bark", "Bass_drum", "Burping_or_eructation", "Bus", 
#"Cello", "Chime", "Clarinet", "Computer_keyboard", "Cough", "Cowbell", "Double_bass", 
#"Drawer_open_or_close", "Electric_piano", "Fart", "Finger_snapping", "Fireworks", "Flute", 
#"Glockenspiel", "Gong", "Gunshot_or_gunfire", "Harmonica", "Hi-hat", "Keys_jangling", "Knock", 
#"Laughter", "Meow", "Microwave_oven", "Oboe", "Saxophone", "Scissors", "Shatter", "Snare_drum", 
#"Squeak", "Tambourine", "Tearing", "Telephone", "Trumpet", "Violin_or_fiddle", "Writing"

# sound_file_paths = ["57320-0-0-7.wav","24074-1-0-3.wav","15564-2-0-1.wav","31323-3-0-1.wav",
# "46669-4-0-35.wav","89948-5-0-0.wav","40722-8-0-4.wav",
# "103074-7-3-2.wav","106905-8-0-0.wav","108041-9-0-4.wav"]
sound_file_paths = ["audio_train/cdc5fe8e.wav", "audio_train/34881c5f.wav",  "audio_train/6649f294.wav",  "audio_train/9ae5bbd7.wav",  "audio_train/cdc8f61d.wav"]
sound_names = ["air conditioner","car horn","children playing","dog bark","drilling","engine idling", "gun shot","jackhammer","siren","street music"]

#file_path_real = "audio_train/"+sound_file_paths
#"audio_train/" +
raw_sounds = load_sound_files(sound_file_paths)

plot_waves(sound_names,raw_sounds)
plot_specgram(sound_names,raw_sounds)
plot_log_power_specgram(sound_names,raw_sounds)