Matplotlib Tutorials

Hi there, welcome to LabEx! In this first lab, you'll learn the classic 'Hello, World!' program in Matplotlib.

In this project, we will develop a gravitational simulation using Python, showcasing the interaction between Earth and a hypothetical 'Super Jupiter,' a planet with 500 times the mass of Jupiter. This simulation aims to demonstrate the impact of such a massive body on Earth's motion, considering the immense gravitational forces at play. This project suits students and hobbyists passionate about physics, astronomy, and computational simulations. To achieve this, we will employ Python libraries like NumPy for numerical calculations and Matplotlib for visualizing the dynamic movements of the planets.

In this project, you will learn how to perform linear regression on a set of data points and visualize the results using Matplotlib. Linear regression is a fundamental machine learning technique used to model the relationship between a dependent variable (y) and one or more independent variables (x).

In this lab, we will explore the lifecycle of a plot using Matplotlib. We will start with raw data and end by saving a customized visualization. We will learn how to create a plot, control its style, customize its appearance, combine multiple visualizations, and save the plot to disk.

This lab will guide you through the process of customizing Matplotlib using style sheets and rcParams. Matplotlib is a powerful library for creating visualizations in Python. By customizing the properties and default styles of Matplotlib, you can create unique and visually appealing plots.

In this lab, you will learn how to plot and manipulate images using the Matplotlib library in Python. You will learn how to import image data into NumPy arrays, plot numpy arrays as images, apply pseudocolor schemes, add color scale references, examine specific data ranges, and explore different interpolation schemes.

In this tutorial, we will learn about the drawing order of artists in Matplotlib and how to adjust the order using the zorder attribute. We will also explore how to change the order for individual artists and the default value of zorder for different types of artists.

This tutorial provides a step-by-step guide to using the pyplot interface in Matplotlib. The pyplot module is a collection of functions that make Matplotlib work like MATLAB, allowing you to easily create and customize plots. This tutorial assumes you have a basic understanding of Matplotlib and its concepts.

This lab is a step-by-step tutorial on how to connect events in one window, for example, a mouse press, to another figure window in Python Matplotlib.

In this lab, we will learn how to create a line plot using Matplotlib. Line plots are a basic visualization that can be used to represent data points connected by straight line segments. We will use the Matplotlib library in Python to create a line plot.

The purpose of this lab is to demonstrate the use of cross-correlation and auto-correlation plots using the Python Matplotlib library. Cross-correlation and auto-correlation are mathematical tools used to measure the similarity between two signals. Cross-correlation measures the similarity between two different signals, while auto-correlation measures the similarity between a signal and a time-delayed version of itself. These tools are commonly used in signal processing, image analysis, and time series analysis.

This lab is designed to show you how to create a 3D wireframe plot in Python using Matplotlib. A wireframe plot is a visual representation of a three-dimensional surface that displays the structure of the surface using lines. In this lab, we will show how to set the rstride and cstride parameters to control the density of the lines in the plot.

This tutorial will guide you through the process of adding a cursor in WX to report data coordinates. We will be using Matplotlib, a plotting library for Python that provides tools to create a variety of plots, charts, and graphs.

Matplotlib is a data visualization library that provides various tools to create 2D and 3D plots. It is built on top of the NumPy library and its main purpose is to visualize data in a simple and effective way. In this tutorial, we will learn how to create two different types of plots using the xkcd style from the XKCD webcomic.

This lab will guide you through creating a 3D wireframe plot using Python's Matplotlib library. A wireframe plot is a visual representation of a 3D surface where lines are drawn between each point on the surface. This type of plot is useful for visualizing complex 3D data and can be customized to create impressive visualizations.

Matplotlib is a Python library used for creating static, animated, and interactive visualizations in Python. It is a powerful tool for data visualization, and it is widely used in the scientific community. In this lab, we will learn how to create a zoomed-in region in a Matplotlib plot.

This lab will guide you through the process of creating a 3D voxel plot using the Matplotlib library in Python. Voxel plots are useful for visualizing 3D data in a way that is both clear and aesthetically pleasing. In this lab, we will be using the Axes3D.voxels function to create a voxel plot of two cubes and a link between them.

In this lab, we will learn how to animate a 3D wireframe plot using Matplotlib, a popular data visualization library in Python.