There are a ton of apps, software, online resources, programming languages, libraries, etc. for taking data and visualizing it through plots and analyzing it through fitting. In my opinion, visualizing data should be applied to MOST/ALL problems in biophysical chemistry (biopchem). In fact, I’d go as far to say that visualization really needs to be an emphasis in any/all STEM related classes, labs, research, industries, etc…
Okay, enough preaching… This post is really meant to provide a list of apps, software, online resources, programming languages, libraries, etc. that I most commonly use:
Starting with the ones that are really polished, nice and unfortunately pricey (unless your company has site licenses, which I’m lucky enough to have):
- Matlab – If I could only have one piece of software it would definitely be Matlab, with all the toolboxes, it can really do anything and everything!! BUT I will say that using ipython and associated python libraries is open-source and getting better and better everyday!! I just haven’t been able to complete switch over because Matlab is so polished and easy to use with a clean interface and up-to-date features. There are other programs like Igor and
- Mathematica – For symbolic math, there is simply nothing better!! But again, open-source programs are getting better, and Maple is really making a push with its educational platform and similar symbolic platform.
- Excel, Numbers, Sheets, Calc (Libre), Calc (Open Office), Zoho Sheets – It is also very useful to have a basic spreadsheet package for making and manipulating data. All of these packages are getting better and better at plotting and fitting (data analysis). It is hard for them to compete on the extremely complex analysis and advanced visualization/plotting, BUT for 90% of standard x/y data plots and basic linear and non-linear fitting, these spreadsheet programs work great… and with a bit of finagling, you can even make the plots and fits look publication quality.
- R & Python – For real programming based data analysis, statistics, fitting and plotting, using R and/or Python is a must. There is a steep learning curve, but if your job is ‘data science’ driven, then this is really a must, in my opinion. Tons of great libraries/gui’s/etc. The ones I most use are: Jupyter (iPython), PyQtGraph, Rstudio, Plotly R, and most libraries I use in Python are described here: Guide to Python.
To throw a few more apps/programs in the mix… here are a few that I have used that range from free / open sources to minimal expense….
1) scilab: scilab.org/ ; ; ; ; ; ; ; ; ; ; (a matlab like package)
2) Octave: gnu.org/software/octave/ ; ; ; ; ; ; ; ; ; (another matlab like package)
3) Plotly, plot.ly (online plotting and fitting)
4) MagicPlot, magicplot.com/ ; ; ; ; ; ; ; (free student/educational version)
5) LabPlot, labplot.kde.org/ ; ; ; ; ; ; (free and works on linux, mac and pc)
6) CurveExpert, curveexpert.net (not free, but not too pricey, and works on linux, mac and pc)
7) MyCurveFit, mycurvefit.com/ ; ; (simple online resource for quick and easy fitting and plotting)
8) QuickFit, lelandstanfordjunior.com/qui.. ;(simple online resource for quick and easy fitting and plotting of data)
Windows/PC specific apps/software:
Linux and Freeware apps/software:
|Ctioga||Command-line plotting utility|
|LabPlot||Data plotting and function analysis tool for KDE|
|matplotlib||Python based plotting system in a style similar to Matlab|
|QtiPlot||Fully fledged plotting software|
|ROOT||Solves the data analysis challenges of high-energy physics|
|DataMelt||Produces professional graphics and has many libraries for data manipulation|
|GLE||Graphics Layout Engine|
|PLplot||Scientific plotting library|
|RLPlot||Generate publication quality graphs|
|Genius||Educational tool in mathematics and for research|