Numerical simulations play a pivotal role in understanding complex systems governed by differential equations. Python, with its extensive libraries like SciPy, NumPy, and Matplotlib, provides a robust environment for simulating and analyzing ordinary and partial differential equations. This guide covers the essentials of setting up and conducting numerical simulations for ODEs and PDEs using Python.
Scipy
SciPy is a wonderful Python module. Enter here to see SciPy power!
Solving Differential Equations with SciPy
Differential equations are at the heart of many engineering, physics, and mathematics problems. Python’s SciPy library offers powerful tools to solve these equations. This guide will walk you through solving differential equations using SciPy, covering both ordinary and partial differential equations.
How to Use scipy.optimize.least_squares
In this blog post, I will show you how to use scipy.optimize.least_squares to solve nonlinear least squares problems in Python. Nonlinear least squares problems are optimization problems where the objective function is a sum of squared residuals, and the residuals depend on some nonlinear function of the parameters. For example, suppose we have some data Continue reading
Using scipy curve_fit to fit a function with multiple independent variables
In this blog post, I will show you how to use scipy curve_fit to fit a function with multiple independent variables. Curve fitting is a technique to find the best parameters for a model function that describes the relationship between a dependent variable and one or more independent variables. Scipy curve_fit is a function in Continue reading
How to Calculate the Factorial of an Array in Numpy
You will learn how to calculate the factorial of an array in Numpy.
How to calculate definite integral in Python
Definite integrals are fundamental in mathematics, physics, and engineering. Python offers multiple libraries for exact and numerical integration. This guide covers four primary methods: symbolic integration with SymPy, numerical quadrature with SciPy, arbitrary-precision integration with mpmath, and discrete approximation with NumPy.
How to perform Chi-Squared test using SciPy?
Learn how to perform a Chi-Squared test in Python using SciPy to analyze categorical data distributions and test statistical hypotheses with code examples.
How to generate distribution plot the easiest way in Python?
Creating a normal distribution plot is a common task in statistics and data analysis. See how to generate a normal distribution plot in Python using the simplest method.
How to Calculate Geometric Mean in Python (statistics.geometric_mean, NumPy, and SciPy Examples)
This guide shows how to calculate geometric mean in Python using the statistics module, NumPy logarithmic method, or SciPy’s gmean function for data analysis tasks. The geometric mean is a measure of central tendency that is useful for dealing with data that has a wide range of values and is often used in finance, biology, Continue reading