plottini 2026.2.3

A user-friendly graph builder with matplotlib backend for creating publication-quality plots from TSV data

Plottini

A user-friendly graph builder for creating publication-quality plots from TSV data

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Overview

Plottini is designed for researchers, scientists, and anyone who needs to create high-quality graphs from tabular data without writing code. With an intuitive UI powered by Streamlit (available as a desktop app via PyWebView or in the browser) and matplotlib as the rendering backend, Plottini makes it easy to:

• Load TSV data files with automatic validation
• Create various chart types (line, bar, scatter, histogram, and more)
• Apply mathematical transformations to your data
• Overlay multiple datasets on the same plot
• Export publication-ready figures in PNG, SVG, PDF, or EPS formats

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Features

Core Capabilities

• Multiple data sources: Load one or more TSV files with configurable headers and comment delimiters
• Rich chart types: Line, Bar, Pie, Scatter, Histogram, Polar, Box, Violin, Area, and more
• Data transformations: Apply preset functions (log, sqrt, power, trig functions) to your data
• Derived columns: Create computed columns using safe mathematical expressions
• Data filtering: Filter rows by value ranges before plotting
• Multi-file alignment: Align multiple datasets by a common column
• Secondary Y-axis: Display two different scales on the same plot
• Live preview: See your changes in real-time as you configure your plot
• Publication quality: Colorblind-safe palettes and professional styling by default

Export Options

• Formats: PNG, SVG, PDF, EPS
• Configurable DPI: High-resolution output for publications
• Vector formats: Scalable graphics for presentations and papers

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Installation

From PyPI

pip install plottini

From Source

git clone https://github.com/lanthoor/plottini.git
cd plottini
pip install -e .

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Quick Start

Start the UI

plottini

This will start the desktop app (using PyWebView) with the Streamlit-based interface.

Command-Line Options

# Start on a specific port
plottini --port 8080

# Show version
plottini version

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Usage

1. Load Your Data

• Click "+ Add Files" to load one or more TSV files
• Toggle "Has header row" if your files have column names
• Set comment characters (default: #)

2. Preview Your Data

• View a paginated table of your parsed data
• Verify that all values were correctly interpreted as numbers

3. Configure Series

• Select which columns to plot on X and Y axes
• Choose colors, line styles, and markers
• Apply transformations (log scale, square root, etc.)
• Use secondary Y-axis for different scales

4. Customize Plot Settings

• Select chart type
• Add title and axis labels
• Configure grid, legend, and figure size
• Adjust font sizes for publication

5. Advanced Options

• Derived Columns: Create new columns from expressions like col1 / col2
• Filters: Exclude data outside specified ranges
• Multi-file Alignment: Merge datasets by a common column
• Layout: Overlay series or create separate subplots

6. Export

• Choose format: PNG, SVG, PDF, or EPS
• Set DPI for raster formats
• Click "Export" to save your figure

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Supported Chart Types

Category	Chart Types
Basic	Line, Scatter, Bar (vertical/horizontal)
Statistical	Histogram, Box plot, Violin plot
Area	Area, Stacked area
Specialized	Stem, Step, Error bar, Pie, Polar

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Mathematical Transformations

Available preset transformations:

• Logarithmic: log, log10, log2
• Power: square, cube, sqrt, cbrt
• Trigonometric: sin, cos, tan, arcsin, arccos, arctan
• Other: abs, inverse (1/x), exp, negate

Derived Columns: Create custom expressions like:

• col1 / col2
• sqrt(col1**2 + col2**2)
• 0.5 * mass * velocity**2

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Requirements

• Python 3.10 or higher

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Contributing

Contributions are welcome! Please see CONTRIBUTING.md for:

• Development setup instructions
• Code style guidelines
• Testing requirements
• Pull request process

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License

MIT License

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Support

• Issues: GitHub Issues
• Author: Lallu Anthoor ([e-mail])

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Acknowledgments

Built with:

• matplotlib - The plotting backend
• Streamlit - The web UI framework
• PyWebView - Desktop app wrapper
• Click - CLI framework
• NumPy - Numerical computing

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Plottini - Making publication-quality graphs accessible to everyone.