<filename>data_loading_functions.py
import numpy as np
import pandas as pd


def load_raw_volcano_data(separate_test_training = True):
    """ Loads the Magellanic Volcano data for the analysis.

    Use the arg separate_test_training if one wants to have it all together
    as a dataframe and not separated into test and training sets. """

    train_images = pd.read_csv(
        ("../data/volcano_data/train_images.csv"), header = None)
    train_labels = pd.read_csv(("../data/volcano_data/train_labels.csv"))
    test_images = pd.read_csv(
        ("../data/volcano_data/test_images.csv"), header = None)
    test_labels = pd.read_csv(("../data/volcano_data/test_labels.csv"))

    total_img = pd.concat([train_images, test_images], ignore_index=True)
    total_lbl = pd.concat([train_labels, test_labels], ignore_index=True)

    if separate_test_training == True:
        return train_images, train_labels, test_images, test_labels

    else:
        return total_img, total_lbl

def load_cleaned_volcano_data():
    """ Loads the cleaned Magellanic Data for the analysis and to produce the
    augmented data set. """

    img_data = pd.read_csv("../data/image_data_non_corrupted.csv")

    lbl_data = pd.read_csv("../data/train_labels_non_corrupted.csv")

    return img_data, lbl_data

def load_augmented_data():
    """ Gets all of the augmented data sets and loads them into a separate
    array, leaving the originals out. """

    img_data_aug = pd.DataFrame()
    img_labl_aug = pd.DataFrame()

    for i in range(1, 5):
        load_aug_data_img = (
            pd.read_csv("../data/volcano_type{}_augmented_img.csv".format(i))
        )

        load_aug_data_lbl = (
            pd.read_csv("../data/volcano_type{}_augmented_lbl.csv".format(i))
        )

        img_data_aug = pd.concat(
            [img_data_aug, load_aug_data_img], ignore_index=True
        )
        img_labl_aug = pd.concat(
            [img_labl_aug, load_aug_data_lbl], ignore_index=True
        )

    return img_data_aug, img_labl_aug

<filename>data_loading_functions.py
import numpy as np
import pandas as pd


def load_raw_volcano_data(separate_test_training = True):
    """ Loads the Magellanic Volcano data for the analysis.

    Use the arg separate_test_training if one wants to have it all together
    as a dataframe and not separated into test and training sets. """

    train_images = pd.read_csv(
        ("../data/volcano_data/train_images.csv"), header = None)
    train_labels = pd.read_csv(("../data/volcano_data/train_labels.csv"))
    test_images = pd.read_csv(
        ("../data/volcano_data/test_images.csv"), header = None)
    test_labels = pd.read_csv(("../data/volcano_data/test_labels.csv"))

    total_img = pd.concat([train_images, test_images], ignore_index=True)
    total_lbl = pd.concat([train_labels, test_labels], ignore_index=True)

    if separate_test_training == True:
        return train_images, train_labels, test_images, test_labels

    else:
        return total_img, total_lbl

def load_cleaned_volcano_data():
    """ Loads the cleaned Magellanic Data for the analysis and to produce the
    augmented data set. """

    img_data = pd.read_csv("../data/image_data_non_corrupted.csv")

    lbl_data = pd.read_csv("../data/train_labels_non_corrupted.csv")

    return img_data, lbl_data

def load_augmented_data():
    """ Gets all of the augmented data sets and loads them into a separate
    array, leaving the originals out. """

    img_data_aug = pd.DataFrame()
    img_labl_aug = pd.DataFrame()

    for i in range(1, 5):
        load_aug_data_img = (
            pd.read_csv("../data/volcano_type{}_augmented_img.csv".format(i))
        )

        load_aug_data_lbl = (
            pd.read_csv("../data/volcano_type{}_augmented_lbl.csv".format(i))
        )

        img_data_aug = pd.concat(
            [img_data_aug, load_aug_data_img], ignore_index=True
        )
        img_labl_aug = pd.concat(
            [img_labl_aug, load_aug_data_lbl], ignore_index=True
        )

    return img_data_aug, img_labl_aug

Loads the Magellanic Volcano data for the analysis.      Use the arg separate_test_training if one wants to have it all together     as a dataframe and not separated into test and training sets. Loads the cleaned Magellanic Data for the analysis and to produce the     augmented data set. Gets all of the augmented data sets and loads them into a separate     array, leaving the originals out.