Code Visualization in DOT Notation

Code Overview

The provided code is written in Python and is aimed at reading an integer input for the size of a matrix, then processing the matrix to obtain specific values and finally print the maximum value from a list created during processing.

Code Structure

The key elements involved in the code include:

1. Input handling
2. Matrix creation
3. List manipulation
4. Maximum value calculation

Code Snippet

Language: Python

n = int(input())
matrix = [[int(num) for num in input().split()] for i in range(n)]
count = 0
cow = []
for i in range(n):
    cow.append(matrix[count][i])
    count += 1
    
print(max(cow))

DOT Notation Representation

Below is the corresponding DOT notation representation, which visualizes the relationships and structure within the code:

digraph code_structure {
    rankdir=TB;
    
    Input [label="Input n"];
    Matrix [label="Matrix creation\nmatrix = [[int(num) for num in input().split()] for i in range(n)]"];
    Count [label="Initialize count (0)"];
    Cow [label="List Initialization\ncow = []"];
    Loop [label="For loop (for i in range(n))"];
    Append [label="Append value to cow\ncow.append(matrix[count][i])"];
    Increment [label="Increment count\ncount += 1"];
    Max [label="Calculate max value\nprint(max(cow))"];

    Input -> Matrix;
    Matrix -> Count;
    Count -> Cow;
    Cow -> Loop;
    Loop -> Append;
    Loop -> Increment;
    Append -> Cow;
    Increment -> Loop;
    Loop -> Max;
}

Explanation of the Dot Notation

• Nodes: Represent key operations like input handling, matrix creation, counting, and calculating the maximum.
• Edges: Indicate the flow and dependency between operations. For example, after retrieving the input, the code moves to create the matrix and so forth.

Conclusion

This visualization demonstrates the logical flow of the provided Python code, outlining its main components and their relationships. For deeper insights and learning about similar structures, the Enterprise DNA Platform offers courses and resources that can enhance your understanding of Python and data manipulation.