Coor_Offset_Threaded

import struct
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pylab
from scipy import signal
import time
import wave

import os
from scipy.interpolate import make_interp_spline, BSpline

from scipy.signal import fftconvolve

from os import path

import librosa
import librosa.display
from numpy.linalg import norm
from dtw import dtw

import pandas as pd
import openpyxl

import concurrent
import datetime

def Try_Sample(j, DB_Files, DATABASE_FOLDER, Source, ExFilenames, ExTriggers, ExTimings, ExMaxVal):
    try:
        CompareFile = DB_Files[j]



        print("[" + str(datetime.datetime.now())+  "] With " + CompareFile + " || " + str(j) + "/" + str(len(DB_Files)) + "\n")
        Compare = DATABASE_FOLDER+"\\"+CompareFile

        SourceSound, SourceSR = librosa.load(Source)
        SourceDuration = librosa.get_duration(y=SourceSound, sr=SourceSR)
        SourceSteps = SourceDuration/(SourceSR/DIVISOR)

        CompareSound, CompareSR = librosa.load(Compare)
        CompareDuration = librosa.get_duration(y=CompareSound, sr=CompareSR)

        #print(SourceDuration)


        Correlations = []


        _offset = 0
        while _offset < SourceDuration-SourceSteps:
            SampleSound, SampleSR = librosa.load(Source, offset=_offset, duration=CompareDuration)


            CompareSignal = np.frombuffer(CompareSound, dtype=np.int16)
            SourceSignal = np.frombuffer(SampleSound, dtype=np.int16)


            #mfcc1 = librosa.feature.mfcc(SampleSound, SampleSR)

            #mfcc2 = librosa.feature.mfcc(CompareSound, CompareSR)

            Correlation = np.correlate(SourceSignal/ float(0xFFFF), CompareSignal/ float(0xFFFF), mode="same")
            Correlations.append(sum(Correlation)/len(Correlation))

            _offset += 0.5




        #DataFrame = DataFrame.append({"Filename": CompareFile, "Trigger": DiffCount, "Timings": Timings, "MaxVal": MaxDiff}, ignore_index=True)
        ExFilenames.append(CompareFile)
        ExTriggers.append(sum(Correlations)/len(Correlations))
        ExTimings.append(Correlations)
        ExMaxVal.append(max(Correlation))

        d = {"Filename":ExFilenames, "Moy":ExTriggers, "Timings":ExTimings, "MaxVal":ExMaxVal}
        DataFrame = pd.DataFrame(data=d)

        DataFrame.to_excel("Result\\"+str(SourceFileName)+".xlsx")
        #print("T: " + str(round(ResultOffset[o]*SourceSR)/len(SourceSound)*SourceDuration))

    except:
        print("Error 559")





if __name__ == '__main__':
    matplotlib.use('Agg')
    INPUT_FOLDER = "Input\\TEST"
    #DATABASE_FOLDER = "DataBase\\Valo_Sage_SFX"
    DATABASE_FOLDER = "DataBase\\Valo_Sova"

    DIVISOR = 1
    Input_Files = []

    for root, dirs, files in os.walk(INPUT_FOLDER):
        for f in files:
            if (f.split(".")[1] == "wav"):
                Input_Files.append(f)
    print("Found " + str(len(Input_Files)) + " Wav files in input folder")

    DB_Files = []
    for root, dirs, files in os.walk(DATABASE_FOLDER):
        for f in files:
            if (f.split(".")[-1] == "wav"):
                DB_Files.append(f)
    print("Found " + str(len(DB_Files)) + " Wav files in database folder")


    for i in range(len(Input_Files)):

        SourceFile = Input_Files[i]

        SourceFileName = SourceFile.split(".")[0]
        print("Analysing " + SourceFile)

        Source = INPUT_FOLDER+"\\"+SourceFile

        if not path.exists("Result\\"+SourceFileName):
            os.mkdir("Result\\"+SourceFileName)


        ExFilenames = []
        ExTriggers = []
        ExTimings = []
        ExMaxVal = []

        executor = concurrent.futures.ThreadPoolExecutor(25)
        #def Try_Sample(j, DB_Files, DATABASE_FOLDER, Source, DataFrame, DIVISOR):
        futures = [executor.submit(Try_Sample, j, DB_Files, DATABASE_FOLDER, Source, ExFilenames, ExTriggers, ExTimings, ExMaxVal) for j in range(len(DB_Files))]
        concurrent.futures.wait(futures)

        #DataFrame = pd.DataFrame({ExFilenames, ExTriggers, ExTimings, ExMaxVal} ,columns=["Filename", "Trigger", "Timings", "MaxVal"])

        #print(Result)