A New Project API References

data_processing.sort module

data_processing.sort.insertion_sort.sortInsertionAsc(alist: list) → None

Apply insertion sort in ascending order on a list

Parameters:: alist (list) – The list to be sorted

data_processing.sort.insertion_sort.sortInsertionDesc(alist: list) → None

Apply insertion sort in decending order on a list

Parameters:: alist (list) – The list to be sorted

data_processing.sort.pigeonhole_sort.pigeonHoleSortAsc(alist: list, maxvalue: int = 160000)

Apply pigeon-hole sort in ascending order on a list

Parameters:

alist (list) – The list to be sorted
maxvalue (int, optional) – The maximum value in the list, defaults to 160000

data_processing.sort.selection_sort.sortSelectionAsc(alist: list) → None

Apply selection sort in ascending order on a list

Parameters:: alist (list) – The list to be sorted

data_processing.sort.selection_sort.sortSelectionDesc(alist: list) → None

Apply selection sort in decending order on a list

Parameters:: alist (list) – The list to be sorted

data_processing.structures module

class data_processing.structures.binary_tree_adt.BSTree

Bases: object

Models a binary search tree (BST)

countNodes() → int

Returns the number of nodes in a binary search tree

Returns:: The number of nodes
Return type:: int

insert(key, data=None)

Insert a data as a node in this BST

Parameters:

key (any immutable type) – The key for the binary search tree
data (any, optional) – The payload, defaults to None

printInOrder(): Print the data in a binary search tree in in-order traveral

printInOrderReverse(): Print the data in a binary search tree in reverse in-order traveral

printPostOrder(): Print the data in a binary search tree in post-order traveral

printPreOrder(): Print the data in a binary search tree in pre-order traveral

searchKey(key)

Search for the node that contains the key in this BST

Parameters:: key (any immutable type) – The key for the binary search tree
Returns:: The node or None if not found
Return type:: any

treeHeight() → int

Returns the height of a binary search tree

Returns:: The tree height
Return type:: int

class data_processing.structures.binary_tree_adt.Node(key, data=None)

Bases: object

Models a node in a binary search tree

countNodes() → int

Returns the number of nodes in a binary search tree

Returns:: The number of nodes
Return type:: int

insert(key, data=None)

Insert data as a nodein the binary search tree

Parameters:

key (any immutable type) – The key for the binary search tree
data (any, optional) – The payload, defaults to None

printInOrder(): Print the data in a binary search tree in in-order traveral

printInOrderReverse(): Print the data in a binary search tree in reverse in-order traveral

printPostOrder(): Print the data in a binary search tree in post-order traveral

printPreOrder(): Print the data in a binary search tree in pre-order traveral

searchKey(key)

Search for data using the query key

Parameters:: key (any immutable type) – The key for the binary search tree
Returns:: The data or None if the key is not found
Return type:: any

treeHeight() → int

Returns the height of a binary search tree

Returns:: The tree height
Return type:: int

class data_processing.structures.linked_list_adt.LinkedList

Bases: object

Models a linkdd list

delete(index: int)

Delete the node at a given index

Parameters:: index (int) – The index of the node to be deleted
Raises:: IndexError – The index is out of range

deleteNodeOfData(targetdata)

Delete the node containing the target data

Parameters:: targetdata (any) – The target data of which the node is to be deleted

insert(data)

Insert new data at the front of linked list

Parameters:: data (any) – The data to be inserted

insertEnd(data)

Insert new data at the end of linked list

Parameters:: data (any) – The data to be inserted

print(): Printing all nodes by traversal

class data_processing.structures.linked_list_adt.Node(data=None)

Bases: object

Models the node of a linked list

class data_processing.structures.queue.Queue

Bases: object

Models a FIFO queue

deQueue()

Remove the value at the front of the queue

Raises:: IndexError – The queue is empty
Returns:: The data removed at the front
Return type:: any

enQueue(value)

Add a value at the end of the queue

Parameters:: value (any) – The value to be added

isEmpty() → bool

Returns True if the queue is empty

Returns:: True if the queue is empty
Return type:: bool

isFull() → bool

Returns True if the queue is full

Returns:: True if the queue is full
Return type:: bool

print(sep=',')

Print the content of the queue

Parameters:: sep (str, optional) – The separating character between the values in the queue, defaults to ‘,’

class data_processing.structures.stack.Stack

Bases: object

Models a FILO stack

isEmpty() → bool

Returns True if the queue is empty

Returns:: True if the queue is empty
Return type:: bool

isFull() → bool

Returns True if the queue is full

Returns:: True if the queue is full
Return type:: bool

pop()

Remove the value on the top of the stack

Raises:: IndexError – The stack is empty
Returns:: The value at the top of the stack
Return type:: any

print(sep=',')

Print the content of the queue

Parameters:: sep (str, optional) – The separating character between the values in the queue, defaults to ‘,’

push(value)

Push the value on top of the stack

Parameters:: value (any) – The value to be pushed to the stack

tools module

tools.opencv_tools.compare_masks(mask1, mask2) → float

Find the similarity of two marks

Parameters:

mask1 (np.array) – The first mark image for comparison
mask2 (np.array) – The second mark image for comparison

Returns:

The similarity score

Return type:

float

tools.opencv_tools.copy_and_pad(mask, row: int, col: int) → numpy.array

Create a new image of shape (row, col) and copy the mask referenced at the center

Parameters:

mask (np.array) – The source mask image
row (int) – The number of rows in the new image
col (int) – The number of columns in the new image

Returns:

The new image

Return type:

np.array

tools.opencv_tools.extract_masked_points(image_mask, mask_value=255) → set

Extracts masked location as a set

Args:: image_mask (nparray or cvimage): 2d nparray of integers mask_value (bool): the value representing the mask
Returns:: list: a list of points (tuples of (row, col)) of the mask

tools.opencv_tools.extract_region(seed, points)

Gather a connected region from the points of mask, starting from the seed

Args:: image_mask (nparray or cvimage): 2d nparray of integers mask_value (bool): the value representing the mask
Returns:: list: a list of points (tuples of (row, col)) of the mask

tools.opencv_tools.generate_neighbours(point)

Private: