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Specification

This module defines an Array interface that represents a one-dimensional array. This file lists the stipulations and more information on the methods and their expected behavior. YOU SHOULD NOT MODIFY THIS FILE. Implement the Array class in the array.py file.

IArray

Bases: Sequence[T], Generic[T], ABC

Array representing a one-dimensional array that grows dynamically. Supports the bracket operator for getting and setting items in the array, the length operator, the equality and non-equality operators, the iterator and reversed iterator operators, the delete operator, the contains operator, the clear method, the string representation, and the resize method.

Source code in src/datastructures/iarray.py 
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class IArray(Sequence[T], Generic[T], ABC):
    """Array representing a one-dimensional array that grows dynamically. Supports the bracket operator for getting and 
        setting items in the array, the length operator, the equality and non-equality operators, the iterator and reversed iterator operators,
        the delete operator, the contains operator, the clear method, the string representation, and the resize method.
    """   

    @abstractmethod
    def __init__(self, starting_sequence: Sequence[T]=[], data_type: type=object) -> None:
        """ Array Constructor. Initializes the Array using a Sequence type (anything that supports the bracket operator like a Python list).
                The Sequence should contain elements of the same type specified by the second parameter data_type.
                The underlying structure in the Array must be a NumPy Array.
                Internally, the Array should also manage a physical size (the size of the internal numpy array) and a logical size (the number of 
                items in the Array).

        Examples:
            >>> array = Array[int](starting_sequence=[], data_type=int)
            >>> print(repr(array))
            Array(logical size: 0, items: [], physical size: 0, data type: <class 'int'>)
            >>> array = Array[int](starting_sequence=[1, 2, 3, 4, 5], data_type=int)
            >>> print(repr(array))
            Array(logical size: 5, items: [1, 2, 3, 4, 5], physical size: 5, data type: <class 'int'>)
            >>> array = Array[int]([num for num in range(15)], data_type=int)
            >>> print(repr(array))
            Array(logical size: 15, items: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], physical size: 15, data type: <class 'int'>)

        Args:
            sequence (Sequence[T]): the desired sequence type to initialize the Array with.
            data_type (type): the desired data type to initialize the Array with (default=object).

        Returns:
            None

        Raises:
            ValueError: if sequence is not a valid sequence type.
            ValueError: if data_type is not a valid data type.
        """
        pass

    @overload
    def __getitem__(self, index: int) -> T: ...
    @overload
    def __getitem__(self, index: slice) -> Sequence[T]: ...
    @abstractmethod
    def __getitem__(self, index: int | slice) -> T | Sequence[T]:
        """ Bracket operator for getting an item (via int) or items (via slice) in an Array. If index is an integer,
            return the item at the index. If index is a slice, return the items at the slice. Supports wrap-around
            indexing via negative indixes.

        Examples:
            >>> array = Array[str](starting_sequence=['zero', 'one' , 'two', 'three', 'four'], data_type=str)
            >>> print(repr(str(array[1]))) # invokes __getitem__ with an `int` for the index.
            'one'
            >>> print(array[1:3]) # invokes __getitem__ with a `slice` for the index.
            ['one', 'two']
            >>> array = Array[str](starting_sequence=['zero', 'one', 'two', 'three', 'four'], data_type=str)
            >>> print(repr(str(array[-1]))) # invokes __getitem__ with a negative `int` for the index.
            'four'
            >>> print(array[-3:]) # invokes __getitem__ with a `slice` for the index.
            ['two', 'three', 'four']

        Args:
            index (int | slice): the desired index or slice to get.

        Returns:
            item (T | Sequence[T]): the item or items at index or slice. Items are of type T and slices are returned as list[T].

        Raises:
            IndexError: if the index is out of bounds.
            TypeError: if the index is not an integer or slice.
        """
        pass

    @abstractmethod
    def __setitem__(self, index: int, item: T) -> None:
        """ Bracket operator for setting an item in an Array.

        Examples:
            >>> array = Array[int](starting_sequence=[50], data_type=int)
            >>> array[0] = 10 # invokes __setitem__ with 0 as the index and 10 as the item to store at that index.
            >>> print(array[0]) 
            10
            >>> print(array) 
            [10]

        Args:
            index (int): the desired index to set.
            item (T): the desired item to set at index.

        Returns:
            None

        Raises: 
            IndexError: if the index is out of bounds.
            TypeError: if the item is not the same type as the Array.
        """
        pass

    @abstractmethod
    def append(self, data: T) -> None:
        """ Append an item to the end of the Array. Internally, the Array should manage a physical size (the size of the internal numpy array) 
            and a logical size (the number of items in the Array). The algorithm should double the array physical size when the number of items in the array (logical size) 
            is equal to the physical size.

        Examples:
            >>> array = Array[int](starting_sequence=[], data_type=int)
            >>> print(repr(array))
            Array(logical size: 0, items: [], physical size: 0, data type: <class 'int'>)
            >>> for i in range(10): 
            ...     array.append(i)
            ...     print(repr(array))
            Array(logical size: 0, items: [], physical size: 0, data type: <class 'int'>)
            Array(logical size: 1, items: [0], physical size: 2, data type: <class 'int'>)
            Array(logical size: 2, items: [0, 1], physical size: 2, data type: <class 'int'>)
            Array(logical size: 3, items: [0, 1, 2], physical size: 4, data type: <class 'int'>)
            Array(logical size: 4, items: [0, 1, 2, 3], physical size: 4, data type: <class 'int'>)
            Array(logical size: 5, items: [0, 1, 2, 3, 4], physical size: 8, data type: <class 'int'>)
            Array(logical size: 6, items: [0, 1, 2, 3, 4, 5], physical size: 8, data type: <class 'int'>)
            Array(logical size: 7, items: [0, 1, 2, 3, 4, 5, 6], physical size: 8, data type: <class 'int'>)
            Array(logical size: 8, items: [0, 1, 2, 3, 4, 5, 6, 7], physical size: 8, data type: <class 'int'>)
            Array(logical size: 9, items: [0, 1, 2, 3, 4, 5, 6, 7, 8], physical size: 16, data type: <class 'int'>)
            Array(logical size: 10, items: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], physical size: 16, data type: <class 'int'>)
    Args:
        data (T): the desired data to append.

    Returns:
        None
    """
        pass

    @abstractmethod
    def append_front(self, data: T) -> None:
        """ Append an item to the front of the Array. Internally, the Array should manage a physical size (the size of the internal numpy array) 
        and a logical size (the number of items in the Array). The algorithm should double the array physical size when the number of items in the array (logical size) 
        is equal to the physical size. 

        Examples:
            >>> array = Array[int](starting_sequence=[], data_type=int)
            >>> print(repr(array))
            Array(logical size: 0, items: [], physical size: 0, data type: <class 'int'>)
            >>> for num in range(10, 0, -1): 
            ...     array.append_front(i)
            ...     print(repr(array))
            Array(logical size: 1, items: [10], physical size: 2, data type: <class 'int'>)
            Array(logical size: 2, items: [9, 10], physical size: 2, data type: <class 'int'>)
            Array(logical size: 3, items: [8, 9, 10], physical size: 4, data type: <class 'int'>)
            Array(logical size: 4, items: [7, 8, 9, 10], physical size: 4, data type: <class 'int'>)
            Array(logical size: 5, items: [6, 7, 8, 9, 10], physical size: 8, data type: <class 'int'>)
            Array(logical size: 6, items: [5, 6, 7, 8, 9, 10], physical size: 8, data type: <class 'int'>)
            Array(logical size: 7, items: [4, 5, 6, 7, 8, 9, 10], physical size: 8, data type: <class 'int'>)
            Array(logical size: 8, items: [3, 4, 5, 6, 7, 8, 9, 10], physical size: 8, data type: <class 'int'>)
            Array(logical size: 9, items: [2, 3, 4, 5, 6, 7, 8, 9, 10], physical size: 16, data type: <class 'int'>)
            Array(logical size: 10, items: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], physical size: 16, data type: <class 'int'>)

        Args:
            data (T): the desired data to append.

        Returns:
            None
        """
        pass

    @abstractmethod
    def pop(self) -> None:
        """ 
        Pop an item from the end of the Array. Internally, the Array should manage a physical size (the size of the internal numpy array)
            and a logical size (the number of items in the Array). The algorithm should shrink the array physical size by half when the logical size is less than or equal to 1/4 of the physical size.

        Examples:
            >>> array = Array[int](starting_sequence=[num for num in range(10)], data_type=int)
            >>> print(repr(array))
            Array(logical size: 9 items: [0, 1, 2, 3, 4, 5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
            >>> for i in range(10):
            ...     array.pop()
            ...     print(repr(array))
            Array(logical size: 8 items: [0, 1, 2, 3, 4, 5, 6, 7] physical size: 10, data type: <class 'int'>)
            Array(logical size: 7 items: [0, 1, 2, 3, 4, 5, 6] physical size: 10, data type: <class 'int'>)
            Array(logical size: 6 items: [0, 1, 2, 3, 4, 5] physical size: 10, data type: <class 'int'>)
            Array(logical size: 5 items: [0, 1, 2, 3, 4] physical size: 10, data type: <class 'int'>)
            Array(logical size: 4 items: [0, 1, 2, 3] physical size: 10, data type: <class 'int'>)
            Array(logical size: 3 items: [0, 1, 2] physical size: 10, data type: <class 'int'>)
            Array(logical size: 2 items: [0, 1] physical size: 5, data type: <class 'int'>)
            Array(logical size: 1 items: [0] physical size: 2, data type: <class 'int'>)
            Array(logical size: 0 items: [] physical size: 1, data type: <class 'int'>)

        Returns:
            None
        """
        pass

    @abstractmethod
    def pop_front(self) -> None:
        """ 
        Pop an item from the front of the Array. Internally, the Array should manage a physical size (the size of the internal numpy array)
        and a logical size (the number of items in the Array). The algorithm should shrink the array physical size by half when the logical size is less than or equal to 1/4 of the physical size.

        Examples:
            >>> array = Array[int](starting_squence[num for num in range(10)], data_type=int)
            >>> print(repr(array))
            Array(logical size: 9 items: [0, 1, 2, 3, 4, 5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
            >>> for i in range(10):
            ...     array.pop_front()
            ...     print(repr(array))
            Array(logical size: 8 items: [1, 2, 3, 4, 5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
            Array(logical size: 7 items: [2, 3, 4, 5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
            Array(logical size: 6 items: [3, 4, 5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
            Array(logical size: 5 items: [4, 5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
            Array(logical size: 4 items: [5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
            Array(logical size: 3 items: [6, 7, 8] physical size: 10, data type: <class 'int'>)
            Array(logical size: 2 items: [7, 8] physical size: 5, data type: <class 'int'>)
            Array(logical size: 1 items: [8] physical size: 2, data type: <class 'int'>)
            Array(logical size: 0 items: [] physical size: 1, data type: <class
        Returns:
            None

        Raises:
            IndexError: if the Array is empty.
        """
        pass

    @abstractmethod
    def __len__(self) -> int:
        """ Length operator for getting the logical length (size) of the Array (number of items in the Array).

        Examples:
            >>> array = Array[int](starting_sequence=[num for num in range(10)], data_type=int)
            >>> print(len(array))
            10

        Returns:
            length (int): the length of the Array.
        """
        pass

    @abstractmethod
    def __eq__(self, other: object) -> bool:
        """ Equality operator == to check if two Arrays are equal (deep check).

        Examples:
            >>> array1 = Array[int](starting_sequence=[0, 1, 2, 3, 4], data_type=int)
            >>> array2 = Array[int](starting_sequence=[0, 1, 2, 3, 4], data_type=int)
            >>> print(array1 == array2)
            True
            array3 = Array[int](starting_sequence=[0, 1, 2, 3, 5], data_type=int)
            >>> print(array1 == array3)
            False

        Args:
            other (object): the instance to compare self to.

        Returns:
            is_equal (bool): true if the arrays are equal (deep check).
        """
        pass

    @abstractmethod
    def __iter__(self) -> Iterator[T]:
        """ Iterator operator. Allows for iteration over the Array.

        Examples:
            >>> array = Array[str](starting_sequence=['one', 'two', 'three', 'four', 'five'], data_type=str)
            >>> for item in array: # invokes __iter__
            ...     print(repr(str(item)), end= ' ') 
            'one' 'two' 'three' 'four' 'five'

        Yields:
            item (T): yields the item at index
        """
        pass

    @abstractmethod
    def __reversed__(self) -> Iterator[T]:
        """ Reversed iterator operator. Allows for iteration over the Array in reverse.

        Examples:
            >>> array = Array[str](starting_sequence=['one', 'two', 'three', 'four', 'five'], data_type=str)
            >>> for item in reversed(array):
            ...     print(repr(str(item)), end= ' ')
            'five' 'four' 'three' 'two' 'one'

        Yields:
            item (T): yields the item at index starting at the end
        """
        pass

    @abstractmethod
    def __delitem__(self, index: int) -> None:
        """ Delete an item in the array. Copies the array contents from index + 1 down
            to fill the gap caused by deleting the item and shrinks the array size down by one.
            The algorithm should shrink the array physical size when the number of items in the array (logical size) is less than or 
            equal to 1/4 of the physical size.

        Examples:
            >>> array = Array[str](starting_sequence=['zero', 'one', 'two', 'three', 'four'], data_type=str)
            >>> print(repr(array))
            Array(logical size: 5, items: ['zero', 'one', 'two', 'three', 'four'], physical size: 5, data type: <class 'str'>)
            >>> del array[2]
            >>> print(repr(array))
            Array(logical size: 4, items: ['zero', 'one', 'three', 'four'], physical size: 5, data type: <class 'str'>)

        Args:
            index (int): the desired index to delete.

        Returns:
            None
        """
        pass

    @abstractmethod
    def __contains__(self, item: Any) -> bool:
        """ Contains operator (in). Checks if the array contains the item.

        Examples:
            >>> array = Array[str](starting_sequence=['zero', 'one', 'two', 'three', 'four'], data_type=str)
            >>> print('three' in array)
            True
            >>> print('five' in array)
            False

        Args:
            item (Any): the desired item to check whether it's in the array.

        Returns:
            contains_item (bool): true if the array contains the item.
        """
        pass

    @abstractmethod
    def clear(self) -> None:
        """ Clear the Array

        Examples:
            >>> array = Array[int](starting_sequence=[num for num in range(10)], data_type=int)
            >>> print(repr(array))
            Array(logical size: 10, items: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], physical size: 10, data type: <class 'int'>)
            >>> array.clear()
            >>> print(repr(array))
            Array(logical size: 0, items: [], physical size: 0, data type: <class 'int'>)

        Returns:
            None
        """
        pass

    @abstractmethod
    def __str__(self) -> str:
        """ Return a string representation of the data and structure. 

        Examples:
            >>> array = Array[int](starting_sequence=[num for num in range(10)], data_type=int)
            >>> print(str(array))
            [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
            >>> array = Array[str](starting_sequence=['zero', 'one', 'two', 'three', 'four'], data_type=str)
            >>> print(str(array)))
            [zero, one, two, three, four]

        Returns:
            string (str): the string representation of the data and structure.
        """
        pass

    @abstractmethod
    def __repr__(self) -> str:
        """ Return a programmer's representation of the data and structure.

        Examples:
            >>> array = Array[int](starting_sequence=[num for num in range(10)], data_type=int)
            >>> print(repr(array))
            Array(logical size: 10, items: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], physical size: 10, data type: <class 'int'>)
            >>> array = Array[str](starting_sequence=['zero', 'one', 'two', 'three', 'four'], data_type=str)
            >>> print(repr(array))
            Array(logical size: 5, items: ['zero', 'one', 'two', 'three', 'four'], physical size: 5, data type: <class 'str'>)

        Returns:
            string (str): the programmer's representation of the data and structure.
        """
        pass

__contains__(item) abstractmethod

Contains operator (in). Checks if the array contains the item.

Examples:

>>> array = Array[str](starting_sequence=['zero', 'one', 'two', 'three', 'four'], data_type=str)
>>> print('three' in array)
True
>>> print('five' in array)
False

Parameters:

Name Type Description Default
item Any

the desired item to check whether it's in the array.

 required

Returns:

Name Type Description
contains_item bool

true if the array contains the item.
Source code in src/datastructures/iarray.py 
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@abstractmethod
def __contains__(self, item: Any) -> bool:
    """ Contains operator (in). Checks if the array contains the item.

    Examples:
        >>> array = Array[str](starting_sequence=['zero', 'one', 'two', 'three', 'four'], data_type=str)
        >>> print('three' in array)
        True
        >>> print('five' in array)
        False

    Args:
        item (Any): the desired item to check whether it's in the array.

    Returns:
        contains_item (bool): true if the array contains the item.
    """
    pass

__delitem__(index) abstractmethod

Delete an item in the array. Copies the array contents from index + 1 down to fill the gap caused by deleting the item and shrinks the array size down by one. The algorithm should shrink the array physical size when the number of items in the array (logical size) is less than or equal to 1/4 of the physical size.

Examples:

>>> array = Array[str](starting_sequence=['zero', 'one', 'two', 'three', 'four'], data_type=str)
>>> print(repr(array))
Array(logical size: 5, items: ['zero', 'one', 'two', 'three', 'four'], physical size: 5, data type: <class 'str'>)
>>> del array[2]
>>> print(repr(array))
Array(logical size: 4, items: ['zero', 'one', 'three', 'four'], physical size: 5, data type: <class 'str'>)

Parameters:

Name Type Description Default
index int

the desired index to delete.

 required

Returns:

Type Description
None

None
Source code in src/datastructures/iarray.py 
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@abstractmethod
def __delitem__(self, index: int) -> None:
    """ Delete an item in the array. Copies the array contents from index + 1 down
        to fill the gap caused by deleting the item and shrinks the array size down by one.
        The algorithm should shrink the array physical size when the number of items in the array (logical size) is less than or 
        equal to 1/4 of the physical size.

    Examples:
        >>> array = Array[str](starting_sequence=['zero', 'one', 'two', 'three', 'four'], data_type=str)
        >>> print(repr(array))
        Array(logical size: 5, items: ['zero', 'one', 'two', 'three', 'four'], physical size: 5, data type: <class 'str'>)
        >>> del array[2]
        >>> print(repr(array))
        Array(logical size: 4, items: ['zero', 'one', 'three', 'four'], physical size: 5, data type: <class 'str'>)

    Args:
        index (int): the desired index to delete.

    Returns:
        None
    """
    pass

__eq__(other) abstractmethod

Equality operator == to check if two Arrays are equal (deep check).

Examples:

>>> array1 = Array[int](starting_sequence=[0, 1, 2, 3, 4], data_type=int)
>>> array2 = Array[int](starting_sequence=[0, 1, 2, 3, 4], data_type=int)
>>> print(array1 == array2)
True
array3 = Array[int](starting_sequence=[0, 1, 2, 3, 5], data_type=int)
>>> print(array1 == array3)
False

Parameters:

Name Type Description Default
other object

the instance to compare self to.

 required

Returns:

Name Type Description
is_equal bool

true if the arrays are equal (deep check).
Source code in src/datastructures/iarray.py 
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@abstractmethod
def __eq__(self, other: object) -> bool:
    """ Equality operator == to check if two Arrays are equal (deep check).

    Examples:
        >>> array1 = Array[int](starting_sequence=[0, 1, 2, 3, 4], data_type=int)
        >>> array2 = Array[int](starting_sequence=[0, 1, 2, 3, 4], data_type=int)
        >>> print(array1 == array2)
        True
        array3 = Array[int](starting_sequence=[0, 1, 2, 3, 5], data_type=int)
        >>> print(array1 == array3)
        False

    Args:
        other (object): the instance to compare self to.

    Returns:
        is_equal (bool): true if the arrays are equal (deep check).
    """
    pass

__getitem__(index) abstractmethod 

__getitem__(index: int) -> T

__getitem__(index: slice) -> Sequence[T]

Bracket operator for getting an item (via int) or items (via slice) in an Array. If index is an integer, return the item at the index. If index is a slice, return the items at the slice. Supports wrap-around indexing via negative indixes.

Examples:

>>> array = Array[str](starting_sequence=['zero', 'one' , 'two', 'three', 'four'], data_type=str)
>>> print(repr(str(array[1]))) # invokes __getitem__ with an `int` for the index.
'one'
>>> print(array[1:3]) # invokes __getitem__ with a `slice` for the index.
['one', 'two']
>>> array = Array[str](starting_sequence=['zero', 'one', 'two', 'three', 'four'], data_type=str)
>>> print(repr(str(array[-1]))) # invokes __getitem__ with a negative `int` for the index.
'four'
>>> print(array[-3:]) # invokes __getitem__ with a `slice` for the index.
['two', 'three', 'four']

Parameters:

Name Type Description Default
index int | slice

the desired index or slice to get.

 required

Returns:

Name Type Description
item T | Sequence[T]

the item or items at index or slice. Items are of type T and slices are returned as list[T].

Raises:

Type Description
IndexError

if the index is out of bounds.
TypeError

if the index is not an integer or slice.
Source code in src/datastructures/iarray.py 
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@abstractmethod
def __getitem__(self, index: int | slice) -> T | Sequence[T]:
    """ Bracket operator for getting an item (via int) or items (via slice) in an Array. If index is an integer,
        return the item at the index. If index is a slice, return the items at the slice. Supports wrap-around
        indexing via negative indixes.

    Examples:
        >>> array = Array[str](starting_sequence=['zero', 'one' , 'two', 'three', 'four'], data_type=str)
        >>> print(repr(str(array[1]))) # invokes __getitem__ with an `int` for the index.
        'one'
        >>> print(array[1:3]) # invokes __getitem__ with a `slice` for the index.
        ['one', 'two']
        >>> array = Array[str](starting_sequence=['zero', 'one', 'two', 'three', 'four'], data_type=str)
        >>> print(repr(str(array[-1]))) # invokes __getitem__ with a negative `int` for the index.
        'four'
        >>> print(array[-3:]) # invokes __getitem__ with a `slice` for the index.
        ['two', 'three', 'four']

    Args:
        index (int | slice): the desired index or slice to get.

    Returns:
        item (T | Sequence[T]): the item or items at index or slice. Items are of type T and slices are returned as list[T].

    Raises:
        IndexError: if the index is out of bounds.
        TypeError: if the index is not an integer or slice.
    """
    pass

__init__(starting_sequence=[], data_type=object) abstractmethod

Array Constructor. Initializes the Array using a Sequence type (anything that supports the bracket operator like a Python list). The Sequence should contain elements of the same type specified by the second parameter data_type. The underlying structure in the Array must be a NumPy Array. Internally, the Array should also manage a physical size (the size of the internal numpy array) and a logical size (the number of items in the Array).

Examples:

>>> array = Array[int](starting_sequence=[], data_type=int)
>>> print(repr(array))
Array(logical size: 0, items: [], physical size: 0, data type: <class 'int'>)
>>> array = Array[int](starting_sequence=[1, 2, 3, 4, 5], data_type=int)
>>> print(repr(array))
Array(logical size: 5, items: [1, 2, 3, 4, 5], physical size: 5, data type: <class 'int'>)
>>> array = Array[int]([num for num in range(15)], data_type=int)
>>> print(repr(array))
Array(logical size: 15, items: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], physical size: 15, data type: <class 'int'>)

Parameters:

Name Type Description Default
sequence Sequence[T]

the desired sequence type to initialize the Array with.

 required
data_type type

the desired data type to initialize the Array with (default=object).

 object

Returns:

Type Description
None

None

Raises:

Type Description
ValueError

if sequence is not a valid sequence type.
ValueError

if data_type is not a valid data type.
Source code in src/datastructures/iarray.py 
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@abstractmethod
def __init__(self, starting_sequence: Sequence[T]=[], data_type: type=object) -> None:
    """ Array Constructor. Initializes the Array using a Sequence type (anything that supports the bracket operator like a Python list).
            The Sequence should contain elements of the same type specified by the second parameter data_type.
            The underlying structure in the Array must be a NumPy Array.
            Internally, the Array should also manage a physical size (the size of the internal numpy array) and a logical size (the number of 
            items in the Array).

    Examples:
        >>> array = Array[int](starting_sequence=[], data_type=int)
        >>> print(repr(array))
        Array(logical size: 0, items: [], physical size: 0, data type: <class 'int'>)
        >>> array = Array[int](starting_sequence=[1, 2, 3, 4, 5], data_type=int)
        >>> print(repr(array))
        Array(logical size: 5, items: [1, 2, 3, 4, 5], physical size: 5, data type: <class 'int'>)
        >>> array = Array[int]([num for num in range(15)], data_type=int)
        >>> print(repr(array))
        Array(logical size: 15, items: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], physical size: 15, data type: <class 'int'>)

    Args:
        sequence (Sequence[T]): the desired sequence type to initialize the Array with.
        data_type (type): the desired data type to initialize the Array with (default=object).

    Returns:
        None

    Raises:
        ValueError: if sequence is not a valid sequence type.
        ValueError: if data_type is not a valid data type.
    """
    pass

__iter__() abstractmethod

Iterator operator. Allows for iteration over the Array.

Examples:

>>> array = Array[str](starting_sequence=['one', 'two', 'three', 'four', 'five'], data_type=str)
>>> for item in array: # invokes __iter__
...     print(repr(str(item)), end= ' ') 
'one' 'two' 'three' 'four' 'five'

Yields:

Name Type Description
item T

yields the item at index
Source code in src/datastructures/iarray.py 
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@abstractmethod
def __iter__(self) -> Iterator[T]:
    """ Iterator operator. Allows for iteration over the Array.

    Examples:
        >>> array = Array[str](starting_sequence=['one', 'two', 'three', 'four', 'five'], data_type=str)
        >>> for item in array: # invokes __iter__
        ...     print(repr(str(item)), end= ' ') 
        'one' 'two' 'three' 'four' 'five'

    Yields:
        item (T): yields the item at index
    """
    pass

__len__() abstractmethod

Length operator for getting the logical length (size) of the Array (number of items in the Array).

Examples:

>>> array = Array[int](starting_sequence=[num for num in range(10)], data_type=int)
>>> print(len(array))
10

Returns:

Name Type Description
length int

the length of the Array.
Source code in src/datastructures/iarray.py 
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@abstractmethod
def __len__(self) -> int:
    """ Length operator for getting the logical length (size) of the Array (number of items in the Array).

    Examples:
        >>> array = Array[int](starting_sequence=[num for num in range(10)], data_type=int)
        >>> print(len(array))
        10

    Returns:
        length (int): the length of the Array.
    """
    pass

__repr__() abstractmethod

Return a programmer's representation of the data and structure.

Examples:

>>> array = Array[int](starting_sequence=[num for num in range(10)], data_type=int)
>>> print(repr(array))
Array(logical size: 10, items: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], physical size: 10, data type: <class 'int'>)
>>> array = Array[str](starting_sequence=['zero', 'one', 'two', 'three', 'four'], data_type=str)
>>> print(repr(array))
Array(logical size: 5, items: ['zero', 'one', 'two', 'three', 'four'], physical size: 5, data type: <class 'str'>)

Returns:

Name Type Description
string str

the programmer's representation of the data and structure.
Source code in src/datastructures/iarray.py 
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@abstractmethod
def __repr__(self) -> str:
    """ Return a programmer's representation of the data and structure.

    Examples:
        >>> array = Array[int](starting_sequence=[num for num in range(10)], data_type=int)
        >>> print(repr(array))
        Array(logical size: 10, items: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], physical size: 10, data type: <class 'int'>)
        >>> array = Array[str](starting_sequence=['zero', 'one', 'two', 'three', 'four'], data_type=str)
        >>> print(repr(array))
        Array(logical size: 5, items: ['zero', 'one', 'two', 'three', 'four'], physical size: 5, data type: <class 'str'>)

    Returns:
        string (str): the programmer's representation of the data and structure.
    """
    pass

__reversed__() abstractmethod

Reversed iterator operator. Allows for iteration over the Array in reverse.

Examples:

>>> array = Array[str](starting_sequence=['one', 'two', 'three', 'four', 'five'], data_type=str)
>>> for item in reversed(array):
...     print(repr(str(item)), end= ' ')
'five' 'four' 'three' 'two' 'one'

Yields:

Name Type Description
item T

yields the item at index starting at the end
Source code in src/datastructures/iarray.py 
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@abstractmethod
def __reversed__(self) -> Iterator[T]:
    """ Reversed iterator operator. Allows for iteration over the Array in reverse.

    Examples:
        >>> array = Array[str](starting_sequence=['one', 'two', 'three', 'four', 'five'], data_type=str)
        >>> for item in reversed(array):
        ...     print(repr(str(item)), end= ' ')
        'five' 'four' 'three' 'two' 'one'

    Yields:
        item (T): yields the item at index starting at the end
    """
    pass

__setitem__(index, item) abstractmethod

Bracket operator for setting an item in an Array.

Examples:

>>> array = Array[int](starting_sequence=[50], data_type=int)
>>> array[0] = 10 # invokes __setitem__ with 0 as the index and 10 as the item to store at that index.
>>> print(array[0]) 
10
>>> print(array) 
[10]

Parameters:

Name Type Description Default
index int

the desired index to set.

 required
item T

the desired item to set at index.

 required

Returns:

Type Description
None

None

Raises:

Type Description
IndexError

if the index is out of bounds.
TypeError

if the item is not the same type as the Array.
Source code in src/datastructures/iarray.py 
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@abstractmethod
def __setitem__(self, index: int, item: T) -> None:
    """ Bracket operator for setting an item in an Array.

    Examples:
        >>> array = Array[int](starting_sequence=[50], data_type=int)
        >>> array[0] = 10 # invokes __setitem__ with 0 as the index and 10 as the item to store at that index.
        >>> print(array[0]) 
        10
        >>> print(array) 
        [10]

    Args:
        index (int): the desired index to set.
        item (T): the desired item to set at index.

    Returns:
        None

    Raises: 
        IndexError: if the index is out of bounds.
        TypeError: if the item is not the same type as the Array.
    """
    pass

__str__() abstractmethod

Return a string representation of the data and structure.

Examples:

>>> array = Array[int](starting_sequence=[num for num in range(10)], data_type=int)
>>> print(str(array))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> array = Array[str](starting_sequence=['zero', 'one', 'two', 'three', 'four'], data_type=str)
>>> print(str(array)))
[zero, one, two, three, four]

Returns:

Name Type Description
string str

the string representation of the data and structure.
Source code in src/datastructures/iarray.py 
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@abstractmethod
def __str__(self) -> str:
    """ Return a string representation of the data and structure. 

    Examples:
        >>> array = Array[int](starting_sequence=[num for num in range(10)], data_type=int)
        >>> print(str(array))
        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
        >>> array = Array[str](starting_sequence=['zero', 'one', 'two', 'three', 'four'], data_type=str)
        >>> print(str(array)))
        [zero, one, two, three, four]

    Returns:
        string (str): the string representation of the data and structure.
    """
    pass

append(data) abstractmethod

Append an item to the end of the Array. Internally, the Array should manage a physical size (the size of the internal numpy array) and a logical size (the number of items in the Array). The algorithm should double the array physical size when the number of items in the array (logical size) is equal to the physical size.

Examples:
    >>> array = Array[int](starting_sequence=[], data_type=int)
    >>> print(repr(array))
    Array(logical size: 0, items: [], physical size: 0, data type: <class 'int'>)
    >>> for i in range(10): 
    ...     array.append(i)
    ...     print(repr(array))
    Array(logical size: 0, items: [], physical size: 0, data type: <class 'int'>)
    Array(logical size: 1, items: [0], physical size: 2, data type: <class 'int'>)
    Array(logical size: 2, items: [0, 1], physical size: 2, data type: <class 'int'>)
    Array(logical size: 3, items: [0, 1, 2], physical size: 4, data type: <class 'int'>)
    Array(logical size: 4, items: [0, 1, 2, 3], physical size: 4, data type: <class 'int'>)
    Array(logical size: 5, items: [0, 1, 2, 3, 4], physical size: 8, data type: <class 'int'>)
    Array(logical size: 6, items: [0, 1, 2, 3, 4, 5], physical size: 8, data type: <class 'int'>)
    Array(logical size: 7, items: [0, 1, 2, 3, 4, 5, 6], physical size: 8, data type: <class 'int'>)
    Array(logical size: 8, items: [0, 1, 2, 3, 4, 5, 6, 7], physical size: 8, data type: <class 'int'>)
    Array(logical size: 9, items: [0, 1, 2, 3, 4, 5, 6, 7, 8], physical size: 16, data type: <class 'int'>)
    Array(logical size: 10, items: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], physical size: 16, data type: <class 'int'>)

Args: data (T): the desired data to append.

Returns:

Type Description
None

None
Source code in src/datastructures/iarray.py 
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@abstractmethod
def append(self, data: T) -> None:
    """ Append an item to the end of the Array. Internally, the Array should manage a physical size (the size of the internal numpy array) 
        and a logical size (the number of items in the Array). The algorithm should double the array physical size when the number of items in the array (logical size) 
        is equal to the physical size.

    Examples:
        >>> array = Array[int](starting_sequence=[], data_type=int)
        >>> print(repr(array))
        Array(logical size: 0, items: [], physical size: 0, data type: <class 'int'>)
        >>> for i in range(10): 
        ...     array.append(i)
        ...     print(repr(array))
        Array(logical size: 0, items: [], physical size: 0, data type: <class 'int'>)
        Array(logical size: 1, items: [0], physical size: 2, data type: <class 'int'>)
        Array(logical size: 2, items: [0, 1], physical size: 2, data type: <class 'int'>)
        Array(logical size: 3, items: [0, 1, 2], physical size: 4, data type: <class 'int'>)
        Array(logical size: 4, items: [0, 1, 2, 3], physical size: 4, data type: <class 'int'>)
        Array(logical size: 5, items: [0, 1, 2, 3, 4], physical size: 8, data type: <class 'int'>)
        Array(logical size: 6, items: [0, 1, 2, 3, 4, 5], physical size: 8, data type: <class 'int'>)
        Array(logical size: 7, items: [0, 1, 2, 3, 4, 5, 6], physical size: 8, data type: <class 'int'>)
        Array(logical size: 8, items: [0, 1, 2, 3, 4, 5, 6, 7], physical size: 8, data type: <class 'int'>)
        Array(logical size: 9, items: [0, 1, 2, 3, 4, 5, 6, 7, 8], physical size: 16, data type: <class 'int'>)
        Array(logical size: 10, items: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], physical size: 16, data type: <class 'int'>)
Args:
    data (T): the desired data to append.

Returns:
    None
"""
    pass

append_front(data) abstractmethod

Append an item to the front of the Array. Internally, the Array should manage a physical size (the size of the internal numpy array) and a logical size (the number of items in the Array). The algorithm should double the array physical size when the number of items in the array (logical size) is equal to the physical size.

Examples:

>>> array = Array[int](starting_sequence=[], data_type=int)
>>> print(repr(array))
Array(logical size: 0, items: [], physical size: 0, data type: <class 'int'>)
>>> for num in range(10, 0, -1): 
...     array.append_front(i)
...     print(repr(array))
Array(logical size: 1, items: [10], physical size: 2, data type: <class 'int'>)
Array(logical size: 2, items: [9, 10], physical size: 2, data type: <class 'int'>)
Array(logical size: 3, items: [8, 9, 10], physical size: 4, data type: <class 'int'>)
Array(logical size: 4, items: [7, 8, 9, 10], physical size: 4, data type: <class 'int'>)
Array(logical size: 5, items: [6, 7, 8, 9, 10], physical size: 8, data type: <class 'int'>)
Array(logical size: 6, items: [5, 6, 7, 8, 9, 10], physical size: 8, data type: <class 'int'>)
Array(logical size: 7, items: [4, 5, 6, 7, 8, 9, 10], physical size: 8, data type: <class 'int'>)
Array(logical size: 8, items: [3, 4, 5, 6, 7, 8, 9, 10], physical size: 8, data type: <class 'int'>)
Array(logical size: 9, items: [2, 3, 4, 5, 6, 7, 8, 9, 10], physical size: 16, data type: <class 'int'>)
Array(logical size: 10, items: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], physical size: 16, data type: <class 'int'>)

Parameters:

Name Type Description Default
data T

the desired data to append.

 required

Returns:

Type Description
None

None
Source code in src/datastructures/iarray.py 
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@abstractmethod
def append_front(self, data: T) -> None:
    """ Append an item to the front of the Array. Internally, the Array should manage a physical size (the size of the internal numpy array) 
    and a logical size (the number of items in the Array). The algorithm should double the array physical size when the number of items in the array (logical size) 
    is equal to the physical size. 

    Examples:
        >>> array = Array[int](starting_sequence=[], data_type=int)
        >>> print(repr(array))
        Array(logical size: 0, items: [], physical size: 0, data type: <class 'int'>)
        >>> for num in range(10, 0, -1): 
        ...     array.append_front(i)
        ...     print(repr(array))
        Array(logical size: 1, items: [10], physical size: 2, data type: <class 'int'>)
        Array(logical size: 2, items: [9, 10], physical size: 2, data type: <class 'int'>)
        Array(logical size: 3, items: [8, 9, 10], physical size: 4, data type: <class 'int'>)
        Array(logical size: 4, items: [7, 8, 9, 10], physical size: 4, data type: <class 'int'>)
        Array(logical size: 5, items: [6, 7, 8, 9, 10], physical size: 8, data type: <class 'int'>)
        Array(logical size: 6, items: [5, 6, 7, 8, 9, 10], physical size: 8, data type: <class 'int'>)
        Array(logical size: 7, items: [4, 5, 6, 7, 8, 9, 10], physical size: 8, data type: <class 'int'>)
        Array(logical size: 8, items: [3, 4, 5, 6, 7, 8, 9, 10], physical size: 8, data type: <class 'int'>)
        Array(logical size: 9, items: [2, 3, 4, 5, 6, 7, 8, 9, 10], physical size: 16, data type: <class 'int'>)
        Array(logical size: 10, items: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], physical size: 16, data type: <class 'int'>)

    Args:
        data (T): the desired data to append.

    Returns:
        None
    """
    pass

clear() abstractmethod

Clear the Array

Examples:

>>> array = Array[int](starting_sequence=[num for num in range(10)], data_type=int)
>>> print(repr(array))
Array(logical size: 10, items: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], physical size: 10, data type: <class 'int'>)
>>> array.clear()
>>> print(repr(array))
Array(logical size: 0, items: [], physical size: 0, data type: <class 'int'>)

Returns:

Type Description
None

None
Source code in src/datastructures/iarray.py 
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@abstractmethod
def clear(self) -> None:
    """ Clear the Array

    Examples:
        >>> array = Array[int](starting_sequence=[num for num in range(10)], data_type=int)
        >>> print(repr(array))
        Array(logical size: 10, items: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], physical size: 10, data type: <class 'int'>)
        >>> array.clear()
        >>> print(repr(array))
        Array(logical size: 0, items: [], physical size: 0, data type: <class 'int'>)

    Returns:
        None
    """
    pass

pop() abstractmethod

Pop an item from the end of the Array. Internally, the Array should manage a physical size (the size of the internal numpy array) and a logical size (the number of items in the Array). The algorithm should shrink the array physical size by half when the logical size is less than or equal to 1/4 of the physical size.

Examples:

>>> array = Array[int](starting_sequence=[num for num in range(10)], data_type=int)
>>> print(repr(array))
Array(logical size: 9 items: [0, 1, 2, 3, 4, 5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
>>> for i in range(10):
...     array.pop()
...     print(repr(array))
Array(logical size: 8 items: [0, 1, 2, 3, 4, 5, 6, 7] physical size: 10, data type: <class 'int'>)
Array(logical size: 7 items: [0, 1, 2, 3, 4, 5, 6] physical size: 10, data type: <class 'int'>)
Array(logical size: 6 items: [0, 1, 2, 3, 4, 5] physical size: 10, data type: <class 'int'>)
Array(logical size: 5 items: [0, 1, 2, 3, 4] physical size: 10, data type: <class 'int'>)
Array(logical size: 4 items: [0, 1, 2, 3] physical size: 10, data type: <class 'int'>)
Array(logical size: 3 items: [0, 1, 2] physical size: 10, data type: <class 'int'>)
Array(logical size: 2 items: [0, 1] physical size: 5, data type: <class 'int'>)
Array(logical size: 1 items: [0] physical size: 2, data type: <class 'int'>)
Array(logical size: 0 items: [] physical size: 1, data type: <class 'int'>)

Returns:

Type Description
None

None
Source code in src/datastructures/iarray.py 
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@abstractmethod
def pop(self) -> None:
    """ 
    Pop an item from the end of the Array. Internally, the Array should manage a physical size (the size of the internal numpy array)
        and a logical size (the number of items in the Array). The algorithm should shrink the array physical size by half when the logical size is less than or equal to 1/4 of the physical size.

    Examples:
        >>> array = Array[int](starting_sequence=[num for num in range(10)], data_type=int)
        >>> print(repr(array))
        Array(logical size: 9 items: [0, 1, 2, 3, 4, 5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
        >>> for i in range(10):
        ...     array.pop()
        ...     print(repr(array))
        Array(logical size: 8 items: [0, 1, 2, 3, 4, 5, 6, 7] physical size: 10, data type: <class 'int'>)
        Array(logical size: 7 items: [0, 1, 2, 3, 4, 5, 6] physical size: 10, data type: <class 'int'>)
        Array(logical size: 6 items: [0, 1, 2, 3, 4, 5] physical size: 10, data type: <class 'int'>)
        Array(logical size: 5 items: [0, 1, 2, 3, 4] physical size: 10, data type: <class 'int'>)
        Array(logical size: 4 items: [0, 1, 2, 3] physical size: 10, data type: <class 'int'>)
        Array(logical size: 3 items: [0, 1, 2] physical size: 10, data type: <class 'int'>)
        Array(logical size: 2 items: [0, 1] physical size: 5, data type: <class 'int'>)
        Array(logical size: 1 items: [0] physical size: 2, data type: <class 'int'>)
        Array(logical size: 0 items: [] physical size: 1, data type: <class 'int'>)

    Returns:
        None
    """
    pass

pop_front() abstractmethod

Pop an item from the front of the Array. Internally, the Array should manage a physical size (the size of the internal numpy array) and a logical size (the number of items in the Array). The algorithm should shrink the array physical size by half when the logical size is less than or equal to 1/4 of the physical size.

Examples:

>>> array = Array[int](starting_squence[num for num in range(10)], data_type=int)
>>> print(repr(array))
Array(logical size: 9 items: [0, 1, 2, 3, 4, 5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
>>> for i in range(10):
...     array.pop_front()
...     print(repr(array))
Array(logical size: 8 items: [1, 2, 3, 4, 5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
Array(logical size: 7 items: [2, 3, 4, 5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
Array(logical size: 6 items: [3, 4, 5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
Array(logical size: 5 items: [4, 5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
Array(logical size: 4 items: [5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
Array(logical size: 3 items: [6, 7, 8] physical size: 10, data type: <class 'int'>)
Array(logical size: 2 items: [7, 8] physical size: 5, data type: <class 'int'>)
Array(logical size: 1 items: [8] physical size: 2, data type: <class 'int'>)
Array(logical size: 0 items: [] physical size: 1, data type: <class

Returns: None

Raises:

Type Description
IndexError

if the Array is empty.
Source code in src/datastructures/iarray.py 
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@abstractmethod
def pop_front(self) -> None:
    """ 
    Pop an item from the front of the Array. Internally, the Array should manage a physical size (the size of the internal numpy array)
    and a logical size (the number of items in the Array). The algorithm should shrink the array physical size by half when the logical size is less than or equal to 1/4 of the physical size.

    Examples:
        >>> array = Array[int](starting_squence[num for num in range(10)], data_type=int)
        >>> print(repr(array))
        Array(logical size: 9 items: [0, 1, 2, 3, 4, 5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
        >>> for i in range(10):
        ...     array.pop_front()
        ...     print(repr(array))
        Array(logical size: 8 items: [1, 2, 3, 4, 5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
        Array(logical size: 7 items: [2, 3, 4, 5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
        Array(logical size: 6 items: [3, 4, 5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
        Array(logical size: 5 items: [4, 5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
        Array(logical size: 4 items: [5, 6, 7, 8] physical size: 10, data type: <class 'int'>)
        Array(logical size: 3 items: [6, 7, 8] physical size: 10, data type: <class 'int'>)
        Array(logical size: 2 items: [7, 8] physical size: 5, data type: <class 'int'>)
        Array(logical size: 1 items: [8] physical size: 2, data type: <class 'int'>)
        Array(logical size: 0 items: [] physical size: 1, data type: <class
    Returns:
        None

    Raises:
        IndexError: if the Array is empty.
    """
    pass