csv_files_times.py

import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import os

targetPath = os.path.abspath("../csv")

np.set_printoptions(threshold=np.inf)

mfccFiles = [os.path.join(path, name)
             for path, dirs, files in os.walk(targetPath)
             for name in files if name.endswith(("_mfcc.csv"))]

del1Files = [os.path.join(path, name)
             for path, dirs, files in os.walk(targetPath)
             for name in files if name.endswith(("_del1.csv"))]

del2Files = [os.path.join(path, name)
             for path, dirs, files in os.walk(targetPath)
             for name in files if name.endswith(("_del2.csv"))]

entropyFiles = [os.path.join(path, name)
                for path, dirs, files in os.walk(targetPath)
                for name in files if name.endswith(("_entropy.csv"))]

rmsFiles = [os.path.join(path, name)
            for path, dirs, files in os.walk(targetPath)
            for name in files if name.endswith(("_rms.csv"))]

zeroCrossingFiles = [os.path.join(path, name)
                     for path, dirs, files in os.walk(targetPath)
                     for name in files if name.endswith(("_zeroCrossing.csv"))]

powerFiles = [os.path.join(path, name)
              for path, dirs, files in os.walk(targetPath)
              for name in files if name.endswith(("_powerSpectrum.csv"))]

def printTimes(startIndex, endIndex, jump, localFile, writeToFile):
    mfcc = np.nan_to_num(np.array(pd.read_csv(localFile + '_mfcc.csv', header=None), dtype='float64'))
    power = np.nan_to_num(np.array(pd.read_csv(localFile + '_powerSpectrum.csv', header=None), dtype='float64'))
    count = 0

    for i in range(len(mfcc)):
        # print(startIndex, "ms ->", endIndex, "ms Mean Mfcc:", np.mean(mfcc[i]), "Mean Power:", np.mean(power[i]))
        print(startIndex, endIndex, file = writeToFile)
        startIndex += jump
        endIndex += jump
        count += 1

    return

def powerEntropyMfccZC(localFile):
    # print("Power file: " + str(powerFile.split("/")[-1]) + " Entropy file: " + str(entropyFile.split("/")[-1]))
    
    power = np.nan_to_num(np.array(pd.read_csv(localFile + '_powerSpectrum.csv', header=None), dtype='float64'))
    entropy = np.nan_to_num(np.array(pd.read_csv(localFile + '_entropy.csv', header=None), dtype='float64'))
    mfcc = np.nan_to_num(np.array(pd.read_csv(localFile + '_mfcc.csv', header=None), dtype='float64'))
    zeroCrossing = np.nan_to_num(np.array(pd.read_csv(localFile + '_zeroCrossing.csv', header=None), dtype='float64'))

    if power.shape[0] != entropy.shape[1]:
        print("Number of frames in power array is not equal to number of entropy values in entropy array.")
        return

    if (power.shape[0] != mfcc.shape[0]):
        print("Number of frames in power array is not equal to number of frames in mfcc array.")
        return

    avgPowerOfFrames = []
    avgPowerOfFrames = np.array(np.append(avgPowerOfFrames, np.mean(power, axis=1)))

    entropy = np.reshape(entropy, entropy.shape[1])

    ratioOfMfccs = []
    for frame in mfcc:
        avg1 = sum(frame[0:3]) / 3
        avg2 = sum(frame[10:13]) / 3
        ratioOfMfccs = np.append(ratioOfMfccs, avg1 / avg2)
    ratioOfMfccs = np.nan_to_num(ratioOfMfccs)

    zeroCrossing = np.reshape(zeroCrossing, zeroCrossing.shape[1])

    # print("avgPowerOfFrames: ", str(avgPowerOfFrames.shape), " entropy: ", str(entropy.shape))

    return avgPowerOfFrames, entropy, ratioOfMfccs, zeroCrossing

def main():
    menNames = np.ravel(np.array(pd.read_csv('Docs/filenames/men_filenames.csv', header = None, dtype = str)))
    # print(menNames)
    for localFileName in menNames:
        # if(localFileName.split('/')[-1][0] > 'k' or localFileName.split('/')[-1][0] > 'K'):
        localFile = open('25ms_times/' + localFileName.split('/')[-1] + '_times.csv', 'w')
        print(localFileName.split('/')[-1], file = localFile)
        printTimes(0, 25, 15, localFileName, localFile)

if __name__ == '__main__':
    main()