Code Visualizer

Python Code Function Visualization with DOT Notation

This document visualizes function relationships in a Python code snippet through DOT notation, highlighting how functions like `lambda_func`, `sum_of_lambda`, and `average_t` interconnect to compute and plot mathematical calculations

Prompt

#!/usr/bin/python
# -*- coding: utf-8 -*-

import random
from matplotlib import pyplot as plt

# 1
def lambda_func(n):
    return float(n)/10

def sum_of_lambda(n):
    sum = 0
    for i in range(n):
        current_i = i + 1
        sum += lambda_func(current_i)
    return sum

# 2
def myu_func(n):
    lambda_val = lambda_func(n)
    myu = ((lambda_val + 0.9) / lambda_val)
    return myu

def coef_gen():
    coef = random.random()/10
    return 0.3

# 3
def gamma_func_inner(n):
    sum = 0.0
    for i in range(n):
        current_i = i + 1
        sum = sum + lambda_func(current_i) * (1 - lambda_func(current_i) * coef_gen())
    return sum

# 4
def gamma_func(n):
    return lambda_func(n) - gamma_func_inner(n) + gamma_func_inner((n - 1))


def average_t(n):
    summ = 0.0
    one_division_lambda = 1/sum_of_lambda(n)
    print('1/lambda:')
    print(one_division_lambda)
    print('---------------------------')

    for j in range(n):
        current_n = j + 1
        print('j = ' + str(current_n))

        myu = myu_func(current_n)
        print('myu = ' + str(myu))

        gamma = gamma_func(current_n)
        print('gamma = ' + str(gamma))

        mygamma = myu - gamma
        print('myu - gamma = ' + str(mygamma))

        current_sum = summ + (gamma / (mygamma))
        summ = current_sum
        print('current sum = ' + str(current_sum))
        print('---------------------------')

    print('summa = ' + str(summ))

    result = one_division_lambda * summ
    print('result:')
    print(result)
    return result

T = []
G = []

for i in range(20):
    current_i = i + 1
    t = (average_t(current_i))
    g = gamma_func(current_i)
    T.append(t)
    G.append(g)


plt.ylabel(u"Средняя задержка T")
plt.xlabel(u"Интенсивности поступающих потоков Gamma")
plt.title(u"График зависимости от средней задержки потоков от \n интенсивности поступающих потоков информаsии")
plt.plot(G, T)
plt.show()

Answer

Code Visualization using DOT Notation

This document visualizes the relationships among functions in the provided Python code snippet using DOT notation. The code primarily consists of calculations involving various mathematical functions and employs the Matplotlib library for plotting.

Code Structure Overview

Imports: The necessary libraries are imported.
Functions: There are several functions, each serving a different computational purpose:
- lambda_func(n)
- sum_of_lambda(n)
- myu_func(n)
- coef_gen()
- gamma_func_inner(n)
- gamma_func(n)
- average_t(n)
Main Logic: The code iterates through a range to calculate average delays and gamma values, collecting results for plotting.

DOT Notation Representation

digraph code_structure {
    rankdir=LR;

    node [shape=ellipse];

    lambda_func [label="lambda_func(n)"];
    sum_of_lambda [label="sum_of_lambda(n)"];
    myu_func [label="myu_func(n)"];
    coef_gen [label="coef_gen()"];
    gamma_func_inner [label="gamma_func_inner(n)"];
    gamma_func [label="gamma_func(n)"];
    average_t [label="average_t(n)"];

    average_t -> sum_of_lambda;
    average_t -> myu_func;
    average_t -> gamma_func;
    average_t -> lambda_func;
    average_t -> gamma_func_inner;

    gamma_func -> lambda_func;
    gamma_func -> gamma_func_inner;

    gamma_func_inner -> lambda_func;
    gamma_func_inner -> coef_gen;

    myu_func -> lambda_func;
}

Description of Relationships

lambda_func(n): A base function used in multiple calculations.
sum_of_lambda(n): Computes the sum of lambda_func values iteratively.
myu_func(n): Utilizes lambda_func to calculate the value of myu.
coef_gen(): Generates a random coefficient affecting the gamma_func_inner.
gamma_func_inner(n): Computes a modification involving lambda_func and a generated coefficient.
gamma_func(n): Derives its value from both lambda_func and the result of gamma_func_inner.
average_t(n): A central function that integrates results from several other functions to compute average delay and ultimately plots the results.

Conclusion

This visualization represents the relationships among functions in the given Python code, showcasing how data flows and dependencies are structured. To broaden your understanding of Python programming and data visualization, consider exploring courses available on the Enterprise DNA Platform.

Description

This document visualizes function relationships in a Python code snippet through DOT notation, highlighting how functions like lambda_func, sum_of_lambda, and average_t interconnect to compute and plot mathematical calculations using Matplotlib.