geogebra VS cheatsheets

https://github.com/geogebra/geogebra How can I obtain the source code for the Android platform and embed it into our own app with only the core layer code, DeskTop, and web platform source code? Thank you for your reply.

I don't think their codebases are particularly connected: AFAIK Desmos is a client-side browser app, so the software is distributed to the user for using in that way, which seems incompatible with the GPL3+ licensing of the GeoGebra codebase.

https://github.com/geogebra/geogebra

Edit: while learning derivatives you should definitely play around with a graphing calculator like https://www.geogebra.org/ . It will make understanding what's going on a lot more intuitive.

There are a lot of different cases, it's quite tricky. Here's the numerical algorithm in full https://github.com/geogebra/geogebra/blob/master/common/src/main/java/org/geogebra/common/kernel/algos/AlgoIntersectConics.java

Access to XMLHttpRequest at 'https://example.com/files' from origin 'https://www.geogebra.org' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource.

It's very simple and should stay that way - changing it would probably add bugs https://github.com/geogebra/geogebra/blob/master/common/src/main/java/org/geogebra/common/kernel/advanced/AlgoDynamicCoordinates.java

Can you redraw this on something like Geogebra and post a screenshot with the relevant points? Can you also explain where the circles come from and/or why you can just use trig?

There are some decent physics examples built out in Geogebra that you might be able to use directly or modify. https://www.geogebra.org/

Matplotlib - A Python 2D plotting library.

Data visualization: utilizing Python's Matplotlib for visualizing order book information.

Name: matplotlib Version: 3.7.1 Summary: Python plotting package Home-page: https://matplotlib.org Author: John D. Hunter, Michael Droettboom Author-email: [email protected] License: PSFLocation: /home/huinker/.local/lib/python3.10/site-packages

Python's pandas, NumPy, and SciPy libraries offer powerful functionality for data manipulation, while matplotlib, seaborn, and plotly provide versatile tools for creating visualizations. Similarly, in R, you can use dplyr, tidyverse, and data.table for data manipulation, and ggplot2, lattice, and shiny for visualization. These packages enable you to create insightful visualizations and perform statistical analyses without relying on expensive or proprietary software.

programming language: basic python, pandas, matplotlib -- you'll probably do these in school, but if not https://cs50.harvard.edu/python/2022/ https://matplotlib.org/

Analysis was done in Jupyter Notebook with Python 3.10, Pandas, Matplotlib, wordcloud and Mercury framework.

Analysis was done in Jupyter Notebook with Python 3.10 kernel, Pandas, Matplotlib, wordcloud and Mercury framework to share notebook as a web application with widgets and code hidden. Gif created in Canva.