WebPlotDigitizer VS cheatsheets

Are you looking for a digitizer tool like https://automeris.io/WebPlotDigitizer/ ? With the new AI tools being developed, I could see a tool like this being more automated.

If you’re only doing the one graph it makes more sense to just use an existing graph-to-data tool like this one.

There are several, yes. Here is one, and here is anther, and here is a third. There is a detailed comparison here.

Theory of Wing Sections by Abbott and von Doenhoff. Use a plot digitizer to extract data from published graphs.

If you don’t require complete accuracy you can try this for an approximation. https://automeris.io/WebPlotDigitizer/

At least in the case of Linux Debian, the cross-link to the project's homepage appears no longer accessible. Is it because the developer reorganized his web site? An alternative to scan the phase diagram can be Ankit Rohatgi's webplotdigitizer (Windows/Linux/Mac, can be used from the web browser).

There may the occasion you actually need the data from a publication, and want to plot them altogether with data newly collected data in one diagram in common. An overlay, though possible, can become tricky (scaling, centering, alignment, etc.) and plotting all data in a diagram generated from scratch (gnuplot/octave, matplotlib, Origin, ...) exported as an illustration in the usual formats (.pdf/.png), or pstricks/tikz is easier/more consistent with the material already compiled in your document. Then you like to reach out for a program like WebPlotDigitizer* to extract .csv data.

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.