Datum Plot

Scientific graphs, statistics and curve fitting: one app for Windows.

What it does

Datum Plot is part of Datum; see its page: https://edgar-mendonca.github.io/Datum/apps/plot

Datum Plot keeps a project as a chain: data tables → analyses → results → graphs → layouts. Everything stays linked, so changing a value redraws its graphs.

• Data tables that work like a spreadsheet: XY tables (an X column and data sets with replicates, or a mean with SD or SEM and N) and column tables (one group per column). Paste from a spreadsheet, fill series, exclude a value without deleting it. Worksheets with free columns (X, Y, Y error), units and comments, and column formulas such as log10(A) or (B - mean(B)) / sd(B) that update as the data change.
• Publication-quality graphs drawn with matplotlib: means with SD, SEM, 95% CI or range, linear or log axes, colour-blind-safe colours, sizes in millimetres for journal columns.
• Export as PNG or TIFF at any dpi, or as SVG, PDF and EPS with editable text; every graph as slides (.pptx) or a report with graphs, results and methods (.docx); posters and slides as page layouts. Copy into documents and slides. Print with headers, footers and captions.
• Import CSV, TXT and .xlsx files, the tables of .pzfx projects, 96- and 384-well plates, and many runs at once. Large data sets (hundreds of thousands of points) stay quick, and a region tool gives the statistics of any part of a curve.
• Statistics and regression on the built-in Python engine (numpy, SciPy, statsmodels, lmfit): descriptive statistics, normality, outliers, t-tests, one-way ANOVA with multiple comparisons, nonparametric tests, effect sizes, correlation, power; linear regression (fit ranges, Deming, comparing lines), curve fitting with a model library or your own equation, global fits and model comparison, area under the curve and smoothing; multiple, logistic and Poisson regression, principal components and clustering on tables of many variables; survival analysis (Kaplan-Meier curves, median survival, log-rank test, hazard ratios); nested t-tests and ANOVA for subgroups within groups. Results stay linked to the data, explain themselves and give methods text.
• Matrices and 3D: Z values on an X–Y grid as contour maps, heat maps or 3D surfaces; surface fits (plane, quadratic with its maximum or minimum, cubic, 2D Gaussian peak); polar graphs of XY data.
• Peaks and signals: find peaks above a straight or polynomial baseline (positions, heights, widths, areas), fit them as Gaussian, Lorentzian or Voigt peaks; Fourier spectra, low-, high- and band-pass filters, resampling.
• Materials testing: tensile tests (modulus, yield, UTS, elongation, toughness), Weibull strength, Arrhenius activation energies, S–N fatigue curves (Basquin, with run-outs), DSC peaks (onset, peak temperature, enthalpy) and TGA steps (onset, mass lost, residue).
• Column graphs (dots, bars, box plots, violins) with significance stars, journal styles and colour-blind-safe schemes. Export every graph at once, or the whole project to a spreadsheet (.xlsx). More arrives in releases: see the .

Everything stays on your computer. The only network call is the update check to GitHub.

Install

Download DatumPlot-x.y.z-setup.exe from Releases and run it. It installs for your user account, with its own Python, and updates itself from the Home page.

The Python console

Tools › Python Console runs your own Python on the project's data, inside the app (nothing else to install):

1. Write code in the box at the top. Your tables are ready as pandas DataFrames: tables lists them, table(0) is the first, tables['Name'] one by its name. Columns are named after the data sets (replicates as Control 1, Control 2), X first.
2. Run it with ▶ Run or Ctrl+Enter. What you print, the value of the last line and any plt figure appear underneath; an error names the line.
3. Keep going: variables last between runs (Reset clears them). np, pd, stats (scipy.stats), scipy and plt are ready.
4. Put results back: new_table('Name', data) adds a table to the project; result('Analysis name') gives an analysis's numbers.
5. Save code you reuse with Save As My Analysis, and run it again from Tools › My Analyses.

df = table(0)
fit = stats.linregress(df.iloc[:, 0], df.iloc[:, 1])
print(f"slope {fit.slope:.4g}, R² {fit.rvalue**2:.4f}")
new_table("Log values", {"X": df.iloc[:, 0], "log10 Y": np.log10(df.iloc[:, 1])})

The ⓘ How to use button in the console and the ? beside it in the ribbon show the same guide. Code runs only when you press Run; projects never contain code, so opening someone else's project never runs anything.

How to cite

If Datum Plot helped your work, please cite it with the version you used (File › Updates shows it):

Mendonca, E. (2026). Datum Plot (Version 0.3.2) [Computer software]. https://github.com/Edgar-Mendonca/Datum-Plot

@software{datum_plot,
  author  = {Mendonca, Edgar},
  title   = {Datum Plot},
  version = {0.3.2},
  year    = {2026},
  url     = {https://github.com/Edgar-Mendonca/Datum-Plot},
  license = {MIT}
}

GitHub's Cite this repository button (from CITATION.cff) gives the same in APA and BibTeX. Every result's methods text already names Datum Plot and the Python library behind the calculation (for example SciPy or statsmodels); cite those too if your journal asks for it.

Build

Rust (stable), Node 22 and Python 3.13 with pip install -r py/requirements.txt:

npm ci
npm run build:ui
cargo run -p datum-plot          # the app, using the Python on your PATH (or DATUM_PYTHON)
cargo test --workspace && npm run test:py && npm run test:ui

Datum Plot is the second app of the Datum family, after Datum Plan. MIT licence; third-party notices in NOTICE.