For the purpose of visualizing chemical structures, I will now post a few method that can achive this goal.
For the purpose of visualizing chemical structures, I will now post a few method that can achive this goal.
Linear regression is a fundamental method in data analysis to understand the relationship between two variables. Here, I summarize four reusable Python functions for performing and visualizing linear fitting:
All methods:
In this tutorial, I will demonstrate how to create a simple, interactive web app that visualizes correlations between variables. We will also walk you through the necessary environment setup and deployment steps.
In this post, I document the full procedure for turning a personal Python project into a public package, available on both GitHub and the Python Package Index (PyPI).
As a demonstration, I create a small package named PalAniSh (Palette of Animation of Shanghai), which extracts and displays color palettes from classical Chinese animations produced by the Shanghai Animation Film Studio.
I’m writing today about downloading, handling, and plotting satellite derived air pollution maps with cartopy and fiona using Python. One key task in this post is to clip a raster-like (2-d array) dataset with a polygon in pure Python environment (i.e., no need for ArcGIS or QGIS GUI-based software).
The satellite sensor can offer critical supplementary data of several atmospheric species, e.g., SO2, NO2, PM2.5. Comparaing to ground-based monitoring which might be sparse in some areas (e.g., Africa, South America, oceans), the satellite observation offers a full picture for better understanding the spatiotemporal patterns of some air pollutants.
Below is an excerpt of a NO2 column maps within Chengyu urabn agglomeration in China.
Hillshade is the representation of the earth’s surface under the radiation of sun. A terrain raster data can be better visualized by adding the information of hillshades. This blog will introduce some procrdures to overlay the hillshade with terrain for a nice picture.
Welcome to my collection of Python scripts and code snippets for everyday use. This document contains a variety of scripts and code snippets that can be used to automate repetitive tasks, process data, and perform various other operations. Whether you are a beginner or an experienced Python programmer, this collection of scripts and code snippets will provide you with useful tools and shortcuts for your everyday work.
In this document, you will find code snippets and scripts for tasks such as:
This document is organized into sections for easy navigation, so you can quickly find the script or code snippet that you need. Many of them were collected from stackoverflow.
On 2022.03.31, I re-organize all the scripts into Python 3.x version and post again.
On 2023.02.12, I edited the whole content with help from ChatGPT
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