This assessment was completed using an Anaconda distribution of Python 3.6.10 and SQLite version 3.13.0.
The solutions to Task 1 and Task 2 are provided in the file NeighboringNodes.py. Unit tests are provided in the file NeighboringNodesTests.py
The solution to Task 3 is provided in the file q3.sql. Sample data may be loaded with the file create_q3.sql.
This question required a class containing a method which would take in the parameters size: int and debug: bool to produce a grid of size x size instantiated with nodes containing their index, x coordinate, and y-coordinate. If debug is set to true, the build_grid() method will print the grid of nodes.
This requirement was satisfied with the build_grid method of the NeighboringNodes class.
Sample Usage:
from NeighboringNodes import *
size = 2
nn = NeighboringNodes(size, False)
grid = nn.build_grid()
'''
Grid will resemble:
[
[{'x': 0, 'y': 0, 'i': 0}, {'x': 1, 'y': 0, 'i': 1}],
[{'x': 0, 'y': 1, 'i': 2}, {'x': 1, 'y': 1, 'i': 3}]
]
'''Additionally, this question required a method which accepts the index of a node and returns the (x, y) coordinate of that node. This requirement was satisfied with the get_coords_from_index method of the NeighboringNodes class.
Sample Usage:
from NeighboringNodes import *
size = 3
nn = NeighboringNodes(size)
nn.get_coords_from_index(5)
'''
Output:
(2, 1)
'''This question required the addition of a method for the NeighboringNodes class, which found all the neighbors of a node within a distance of m using a neighborhood topology of type 'SQUARE', 'CROSS' or 'DIAMOND'. This method was implemented as find_neighboring_nodes. This method accepts the following parameters:
-
x: x-axis coordinate -
y: y-axis coordinate -
i: nodeindex -
m: neighborhood radius (int, 0 < m <= size/2) -
type: neighborhood type(enum:SQUARE,CROSS,DIAMOND)
Additionally, this method accepts either x AND y, or only i, but not both.
Sample Usage:
from NeighboringNodes import *
size = 7
nn = NeighboringNodes(size)
neighbors = nn.find_neighboring_nodes(x=3, y=3, m=2, type='SQUARE')
'''
neighbors will resemble:
[
(1, 1), (2, 1), (3, 1), (4, 1), (5, 1),
(1, 2), (2, 2), (3, 2), (4, 2), (5, 2),
(1, 3), (2, 3), (3, 3), (4, 3), (5, 3),
(1, 4), (2, 4), (3, 4), (4, 4), (5, 4),
(1, 5), (2, 5), (3, 5), (4, 5), (5, 5)
]
'''
size = 7
nn = NeighboringNodes(size)
neighbors = nn.find_neighboring_nodes(x=3, y=3, m=2, type='CROSS')
'''
neighbors will resemble:
[
(3, 1),
(3, 2),
(1, 3), (2, 3), (3, 3), (4, 3), (5, 3),
(3, 4),
(3, 5)
]
'''
size = 9
nn = NeighboringNodes(size)
neighbors = nn.find_neighboring_nodes(x=3, y=5, m=3, type='DIAMOND')
'''
neighbors will resemble:
[
(3, 2),
(2, 3), (3, 3), (4, 3),
(1, 4), (2, 4), (3, 4), (4, 4), (5, 4),
(0, 5), (1, 5), (2, 5), (3, 5), (4, 5), (5, 5), (6, 5),
(1, 6), (2, 6), (3, 6), (4, 6), (5, 6),
(2, 7), (3, 7), (4, 7),
(3, 8)
]
'''This question asked: Given a table having records of user visits in the following format: user_id, visit_date, url, find out the longest consecutive days each user has visited.
This was accomplished using a recursive CTE in SQLite found in the file q3.sql and uses test data which may be found in create_q3.sql. To load the data and run the query, run the following commands in a terminal:
sqlite3
.headers on
.read create_q3.sql
.read q3.sql
Output:
UserName|CONSEC
user1|4
user2|4
user3|2