Scientific Utility · Version 1.0

Jhatpat Plot™

Publication-Ready Scientific Plotting in Your Browser

No installation required. Upload CSV or Excel data, provide a public dataset URL, or explore a built-in scientific dataset. Choose what you want to compare, create publication-ready Matplotlib and Seaborn-style figures, explore data interactively, and export high-quality graphics.

No installationCSV / ExcelMatplotlibSeabornInteractiveSVG / PDF / PNG / JPEGPython codeGantt charts
Jhatpat Plot™ · Scientific Figure Builder

1. Load your data

Start with your own file, a public URL, or one of the built-in datasets.

QUICK START
Choose a scientific dataset
Version 1.0 supports CSV, TSV, TXT, XLSX and XLS.
Advanced Settings — multiple Y-axes, palettes, labels, filters, regression, figure size and publication controls

Variables and multiple Y-axes

Add up to three additional Y variables. Use secondary/right-offset axes or separate panels for different units.

For contour and 3D plots.

Labels, title and legend

Use automatic, custom or no labels. Matplotlib math text such as $\Delta G$ / eV is accepted in publication mode.

Publication style and colour

Use automatic publication styling or choose Seaborn-inspired palettes and visual details manually.

Figure size and publication preset

Aspect ratio and physical width are separate. Journal presets are dimension starting points, not guarantees of publisher compliance.

Axes, scales and grid

X and Y axes can use independent linear, logarithmic or symmetric-log scaling.

Data preparation and filters

Transformations apply only to the browser working copy; the source file is not changed.

Trend lines, references and annotations

Fitted curves are visual aids; the user must decide whether a model is scientifically appropriate.

Python engine will load in the background.

Plot Preview

Explore interactively or generate a publication-ready static figure from the same selections.

Your publication figure will appear here.Load a dataset, choose X and Y, then select Generate Plot.

Export

Publication formats are produced by Matplotlib. Interactive HTML preserves Plotly zoom, hover, pan and selection.

View reproducible Python code
Generate a plot to create Python code.
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Gantt Chart Maker

Build proposal-ready project timelines with work packages, tasks, milestones, deliverables, dependencies, activities, captions and publication-quality export.

1. Project

Start with only the essential project information. Detailed presentation and project-management controls are under Advanced Gantt Settings.

2. Work packages

Add only as many work packages and tasks as you need. Each package can contain tasks, milestones and deliverables.

3. Project-wide activities

Optional rows for publications, conferences, training, mentoring, reporting, management, outreach, secondments or custom activities.

Advanced Gantt Settings

Caption and headings

Choose whether a figure caption is generated and how the timeline is labelled.

Timeline and appearance

Control year headings, month numbering, task-bar appearance, grid density and dimensions.

Highlighted periods and timeline markers

Use these for secondments, visits, field campaigns, reviews, beamtime or other project-wide periods.

SVG is the recommended vector format for proposals and manuscripts.

Gantt Preview

Vector SVG preview; scroll horizontally for long project timelines.

Create or load a project plan, then select Generate Gantt Chart.

Official Documentation

The interface draws on Matplotlib's gallery, Seaborn's statistical styling and Plotly's interactive browser graphics.

Matplotlib 3.5.3 →Matplotlib Gallery →Seaborn →Plotly.js →Pyodide →HIGGS · UCI →PubChem Corpus →

Version 1.0 Notes

Most common plots work in publication and interactive modes. Pair and joint plots are publication-mode features. Matplotlib uses scattered-data triangulation for contour plots, while interactive contour exploration uses colour-mapped points unless the data are already gridded.

Large Dataset Mode samples remote HIGGS and PubChem sources rather than attempting to load millions of rows into browser memory. Dense samples are best viewed with Hexbin, 2D Histogram, density plots or aggregation.

Multiple Y-axes are available, but separate panels are often clearer for unrelated units.

The integrated Gantt Chart Maker supports work packages, tasks, milestones, deliverables, dependencies, progress, baselines, project-wide activities, highlighted periods, captions, CSV/Excel import, saved JSON projects and SVG/PDF/PNG/JPEG export.

Disclaimer: Jhatpat Plot™ is provided for academic, educational, research and general data-visualisation purposes. Generated plots, transformed data, fitted relationships, colour mappings, axis settings, labels, exported figures, interactive views and generated Python code are starting outputs that must be independently reviewed and validated by the user.

The user is solely responsible for verifying data accuracy, units, data preparation, statistical appropriateness, accessibility, interpretation, publication requirements and scientific validity. Jhatpat Plot™ does not determine whether a visualisation, regression, transformation, multi-axis representation or statistical treatment is scientifically appropriate for a dataset.

Use this utility at your own discretion and risk. The author accepts no responsibility for incorrect figures, misleading representations, data loss, publication outcomes, research conclusions or other consequences arising from use of generated outputs.