Simple scripts for plotting and fitting data in the natural sciences
Designed for students of chemistry, biology, medicine, and related fields
who don't want to deal with Igor, Origin – or simply can't afford Windows.

These scripts are built to help students quickly visualize and analyze their lab data.
They focus on basic polynomial fitting – nothing fancy, just enough to get clean plots and reproducible fits for:

• first-year chemistry or physics labs
• reports and presentations
• basic regression tasks in introductory science courses

They are intentionally kept simple, to avoid distractions and reduce friction when plotting.

Because sometimes you just want to:

• Fit a curve without worrying about how polyfit() works
• Plot your data without clicking through five menus
• Avoid commercial software that runs only on expensive hardware or Windows
• Get your lab report done without fighting GUIs or licensing hell

If you're taking chemistry as a minor, or doing just a few physics or biostats labs – this might be all you need.

Many of us had to:

• draw plots by hand using graph paper and rulers in our first semester
• implement a linear least squares fit from scratch in the second semester
• use Mathematica (once) – then never again
• resort to Gnuplot, R, or whatever script worked on Linux

These scripts continue that spirit: lightweight, minimal, and focused.

• Polynomial least squares fits (default: linear)
• Output of fit parameters and covariance matrix
• Automatic plot generation and export (fit_plot.png)
• Easy to adapt for:
  • Logarithmic transformations (e.g. linearizing exponentials)
  • Parabolic fits (e.g. when doing Taylor expansions near a minimum)
  • Subsets of data (just slice your array)

dependencies:
  - numpy
  - scipy
  - matplotlib