Class

type.newbyteorder(new_order='S', /)

import numpy as np

# [offset] [type]          [value]          [description]
# 0000     32 bit integer  0x00000801(2049) magic number (MSB first)
# 0004     32 bit integer  60000            number of items
# 0008     unsigned byte   ??               label
# 0009     unsigned byte   ??               label
# ........
# xxxx     unsigned byte   ??               label
# The labels values are 0 to 9.


class ReadLabel:
    """Class for reading the labels from an MNIST label file"""

    def __init__(self, filename: str) -> None:
        self.filename: str = filename
        self.data = self.read_from_file(filename)

    def read_from_file(self, filename: str) -> np.ndarray:

        int_32bit_data = np.dtype(np.uint32)
        int_32bit_data = int_32bit_data.newbyteorder(">")

        with open(filename, "rb") as file:

            magic_flag: np.uint32 = np.frombuffer(file.read(4), int_32bit_data)[0]

            if magic_flag != 2049:
                data: np.ndarray = np.zeros(0)
                number_of_elements: int = 0
            else:
                number_of_elements = np.frombuffer(file.read(4), int_32bit_data)[0]

            if number_of_elements < 1:
                data = np.zeros(0)
            else:
                data = np.frombuffer(file.read(number_of_elements), dtype=np.uint8)

        return data


# [offset] [type]          [value]          [description]
# 0000     32 bit integer  0x00000803(2051) magic number
# 0004     32 bit integer  60000            number of images
# 0008     32 bit integer  28               number of rows
# 0012     32 bit integer  28               number of columns
# 0016     unsigned byte   ??               pixel
# 0017     unsigned byte   ??               pixel
# ........
# xxxx     unsigned byte   ??               pixel
# Pixels are organized row-wise.
# Pixel values are 0 to 255. 0 means background (white),
# 255 means foreground (black).


class ReadPicture:
    """Class for reading the images from an MNIST image file"""

    def __init__(self, filename: str) -> None:
        self.filename: str = filename
        self.Data = self.read_from_file(filename)

    def read_from_file(self, filename: str) -> np.ndarray:

        int_32bit_data = np.dtype(np.uint32)
        int_32bit_data = int_32bit_data.newbyteorder(">")

        with open(filename, "rb") as file:

            magic_flag = np.frombuffer(file.read(4), int_32bit_data)[0]

            if magic_flag != 2051:
                data = np.zeros(0)
                number_of_elements: int = 0
            else:
                number_of_elements = np.frombuffer(file.read(4), int_32bit_data)[0]

            if number_of_elements < 1:
                data = np.zeros(0)
                number_of_rows: int = 0
            else:
                number_of_rows = np.frombuffer(file.read(4), int_32bit_data)[0]

            if number_of_rows != 28:
                data = np.zeros(0)
                number_of_columns: int = 0
            else:
                number_of_columns = np.frombuffer(file.read(4), int_32bit_data)[0]

            if number_of_columns != 28:
                data = np.zeros(0)
            else:
                data = np.frombuffer(
                    file.read(number_of_elements * number_of_rows * number_of_columns),
                    dtype=np.uint8,
                )
                data = data.reshape(
                    number_of_elements, number_of_columns, number_of_rows
                )

        return data


def proprocess_dataset(testdata_mode: bool) -> None:

    if testdata_mode is True:
        filename_out_pattern: str = "test_pattern_storage.npy"
        filename_out_label: str = "test_label_storage.npy"
        filename_in_image: str = "t10k-images.idx3-ubyte"
        filename_in_label: str = "t10k-labels.idx1-ubyte"
    else:
        filename_out_pattern = "train_pattern_storage.npy"
        filename_out_label = "train_label_storage.npy"
        filename_in_image = "train-images.idx3-ubyte"
        filename_in_label = "train-labels.idx1-ubyte"

    pictures = ReadPicture(filename_in_image)
    labels = ReadLabel(filename_in_label)

    # Down to 0 ... 1.0
    max_value = np.max(pictures.Data.astype(np.float32))
    pattern_storage = np.float32(pictures.Data.astype(np.float32) / max_value).astype(
        np.float32
    )

    label_storage = np.uint64(labels.data)

    np.save(filename_out_pattern, pattern_storage)
    np.save(filename_out_label, label_storage)


proprocess_dataset(testdata_mode=True)
proprocess_dataset(testdata_mode=False)