def prep_whole_dividable(path_load, path_save, skipcount, option, firstdim, secdim):

    import string
    import os
    import numpy as np
    import scipy.io
    import math
    from tqdm import tqdm

    # --------------------------------------------------------------

    # Get files from path

    path_save_one = path_save

    for p in range(1,2):
        if p == 0:
            path_load_real = path_load + '/Train'
            path_save_real = path_save_one + '/Train'
            if not os.path.exists(path_save_real):
                os.makedirs(path_save_real)
        elif p == 1:
            path_load_real = path_load + '/Validation'
            path_save_real = path_save_one + '/Validation'
            if not os.path.exists(path_save_real):
                os.makedirs(path_save_real)
        elif p == 2:
            path_load_real = path_load + '/Test'
            path_save_real = path_save_one + '/Test'
            if not os.path.exists(path_save_real):
                os.makedirs(path_save_real)

        files_all = os.listdir(path_load_real)
        files_all = sorted(files_all)

        for files in range(0,len(files_all),skipcount):
            fourierspec2 = []
            labelpsd2 = []

            for file in tqdm(files_all[files:files+skipcount]):
                path_loadcontents = path_load_real + '/' + file
                contents = np.load(path_loadcontents)

                fourierspec = []
                labelpsd = []

                fourier = contents['dbmixed']
                fourier = fourier[:firstdim,:secdim]

                fourierspec2.append(fourier)

                if option == 'music':
                    label = contents['dbmusic']
                elif option == 'speech':
                    label = contents['dbspeech']
                else:
                    print('option not right!')

                label = label[:firstdim,:secdim]

                labelpsd2.append(label)

            train_data = np.array(fourierspec2)
            train_data = np.reshape(train_data, (train_data.shape[0], train_data.shape[1], train_data.shape[2], 1))

            train_labels = np.array(labelpsd2)
            train_labels = np.reshape(train_labels, (train_labels.shape[0], train_labels.shape[1], train_labels.shape[2], 1))

            path_savecontents = path_save_real + '/Processed_' + str(files) + '_' + str(train_data.shape[0]) + '_whole_dividable.npz'
            np.savez(path_savecontents, x=train_data, y=train_labels)

    return path_save_one


def prep_flatten_1weiter(path_load, path_save, skipcount, framelength, option):

    import os
    import numpy as np
    import scipy.io
    import math
    from tqdm import tqdm

    # -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

    path_save_one = path_save

    for p in range(0,3):
        if p == 0:
            path_load_real = path_load + '/Train'
            path_save_real = path_save_one + '/Train'
            if not os.path.exists(path_save_real):
                os.makedirs(path_save_real)
        elif p == 1:
            path_load_real = path_load + '/Validation'
            path_save_real = path_save_one + '/Validation'
            if not os.path.exists(path_save_real):
                os.makedirs(path_save_real)
        elif p == 2:
            path_load_real = path_load + '/Test'
            path_save_real = path_save_one + '/Test'
            if not os.path.exists(path_save_real):
                os.makedirs(path_save_real)

        # Get files from path
        files_all = os.listdir(path_load_real)
        files_all = sorted(files_all)


        for files in range(0,len(files_all),skipcount):

            fourierspec2 = []
            labelpsd2 = []

            for file in tqdm(files_all[files:files+skipcount]):

                path_loadcontents = path_load_real + '/' + file
                contents = np.load(path_loadcontents)

                fourierspec = []
                labelpsd = []

                fourier = contents['dbmixed']

                for n in list(range(0, (math.floor(fourier.shape[1]/framelength)-1)*framelength+1)):
                    fourierlil = fourier[:,n:n+framelength]
                    fourierlil = np.transpose(fourierlil)
                    fourierlil = np.reshape(fourierlil, (fourier.shape[0]*framelength))
                    fourierspec.append(fourierlil)

                fourierspec2.extend(fourierspec)

                if option == 'music':
                    label = contents['dbmusic']
                elif option == 'speech':
                    label = contents['dbspeech']
                else:
                    print('option not right!')

                for n in list(range(0, (math.floor(fourier.shape[1]/framelength)-1)*framelength+1)):
                    labellil = label[:,n+framelength-1]
                    labelpsd.append(labellil)

                labelpsd2.extend(labelpsd)

            train_data = np.array(fourierspec2)
            train_labels = np.array(labelpsd2)

            path_savecontents = path_save_real + '/Processed_' + str(files) + '_' + str(train_data.shape[0]) + '_flatten_1weiter.npz'
            np.savez(path_savecontents, x=train_data, y=train_labels)

        diffcount = (math.floor(fourier.shape[1]/framelength)-1)*framelength+1
    return diffcount


def prep_flatten_4weiter(path_load, path_save, skipcount, framelength, option):

    import os
    import numpy as np
    import scipy.io
    import math
    from tqdm import tqdm

    # -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    path_save_one = path_save

    for p in range(0,3):
        if p == 0:
            path_load_real = path_load + '/Train'
            path_save_real = path_save_one + '/Train'
            if not os.path.exists(path_save_real):
                os.makedirs(path_save_real)
        elif p == 1:
            path_load_real = path_load + '/Validation'
            path_save_real = path_save_one + '/Validation'
            if not os.path.exists(path_save_real):
                os.makedirs(path_save_real)
        elif p == 2:
            path_load_real = path_load + '/Test'
            path_save_real = path_save_one + '/Test'
            if not os.path.exists(path_save_real):
                os.makedirs(path_save_real)

        # Get files from path
        files_all = os.listdir(path_load_real)
        files_all = sorted(files_all)

        for files in tqdm(range(0,len(files_all),skipcount)):

            fourierspec2 = []
            labelpsd2 = []

            for file in tqdm(files_all[files:files+skipcount]):

                path_loadcontents = path_load_real + '/' + file
                contents = np.load(path_loadcontents)

                fourierspec = []
                labelpsd = []

                fourier = contents['dbmixed']
                print(fourier.shape)
                # if p == 0 & files == 0:
                #     diffcount = math.floor(fourier.shape[1]/framelength)
                #     return diffcount

                for n in list(range(0, math.floor(fourier.shape[1]/framelength))):
                    fourierlil = fourier[:,framelength*n:framelength*n+framelength]
                    fourierlil = np.transpose(fourierlil)
                    fourierlil = np.reshape(fourierlil, (fourier.shape[0]*framelength))
                    print(np.size(fourierlil))
                    fourierspec.append(fourierlil)
                    print(np.size(fourierspec))

                fourierspec2.extend(fourierspec)

                if option == 'music':
                    label = contents['dbmusic']
                elif option == 'speech':
                    label = contents['dbspeech']
                else:
                    print('option not right!')

                for n in list(range(0, math.floor(label.shape[1]/framelength))):
                    labellil = label[:,framelength*n+framelength-1]
                    print(np.size(labellil))
                    labelpsd.append(labellil)
                    print(np.size(labelpsd))

                labelpsd2.extend(labelpsd)

            train_data = np.array(fourierspec2)
            train_labels = np.array(labelpsd2)

            path_savecontents = path_save_real + '/Processed_' + str(files) + '_' + str(train_data.shape[0]) + '_' + str(framelength) + '_flatten.npz'
            np.savez(path_savecontents, x=train_data, y=train_labels)