# Solving the Logistic Equation with Python

The Logistic Equation serves as a ubiquitous model in several scientific disciplines to depict population growth within a resource-constrained environment. This article delves into how Python, aided by the SymPy, NumPy, and Matplotlib libraries, can efficiently tackle this equation both analytically and numerically.

### The Logistic Equation

The Logistic Equation is mathematically expressed as:

$\frac{dP}{dt}=rP\left(1-\frac{P}{K}\right)$

Where:

• P represents the population,
• t is the time,
• r is the growth rate,
• K is the carrying capacity of the environment.

## Analytical Solution with SymPy

To ascertain the analytical solution, one can leverage the symbolic computation capabilities of the SymPy library.

### Import Libraries and Define Variables

Firstly, SymPy is imported and the necessary variables are defined.

from sympy import symbols, Function, Eq, dsolve

t = symbols('t')
P = Function('P')(t)
r, K = symbols('r K')

logistic_eq = Eq(P.diff(t), r * P * (1 - P/K))


### Integration and Solution

The general solution of the logistic equation can be found via integration:

$\int \frac{dP}{P\left(1-P/K\right)}=\int rdt$

Using SymPy, the analytical solution can be obtained as:

analytical_solution = dsolve(logistic_eq)


The analytical solution is:

$P\left(t\right)=\frac{K\mathrm{exp}\left({C}_{1}K+rt\right)}{\mathrm{exp}\left({C}_{1}K+rt\right)-1}$

Where is an integration constant.

## Numerical Solution with NumPy and Matplotlib

For a numerical solution, the Euler method can be employed to approximate the solution to the differential equation. NumPy is used for the computations and Matplotlib for visualization.

### Code Implementation

import numpy as np
import matplotlib.pyplot as plt

r_value = 0.1
K_value = 1000
P0 = 100
T = 100
dt = 0.1
steps = int(T / dt)

P_numerical = np.zeros(steps)
P_numerical[0] = P0

for i in range(1, steps):
dP = r_value * P_numerical[i-1] * (1 - P_numerical[i-1] / K_value) * dt
P_numerical[i] = P_numerical[i-1] + dP

time = np.linspace(0, T, steps)
plt.plot(time, P_numerical, label='Numerical Solution')
plt.xlabel('Time')
plt.ylabel('Population')
plt.legend()
plt.grid(True)
plt.show()


## Comparing Analytical and Numerical Solutions

A comparison between the two solutions confirms the accuracy of the numerical model. To do this, the numerical solution can be overlaid onto the analytically calculated values.

# Code to calculate and plot the analytical solution for a subset of points
# ...
plt.plot(time_reduced, P_analytical_reduced_real, label='Analytical Solution', linestyle='dashed')
plt.scatter(time, P_numerical, color='red', s=10, label='Numerical Solution')
plt.legend()
plt.grid(True)
plt.show()


## Conclusion

We have observed that Python, with specialized libraries like SymPy, NumPy, and Matplotlib, can be a powerful tool for both analytical and numerical analysis of differential equations. Specifically, the logistic equation was solved using analytical and numerical methods, providing a complete understanding of population dynamics in a resource-limited setting.

This hybrid approach, blending analytical and numerical analysis, offers a robust strategy for understanding and solving complex problems across various applied science fields.