A Python library for creating ternary plots (triangular diagrams) using Matplotlib.
python-ternary is a Python library built on Matplotlib for creating ternary plots, which are triangular diagrams used to visualize data composed of three components that sum to a constant. It solves the problem of plotting three-dimensional proportional data in two dimensions, commonly needed in scientific fields like chemistry, geology, and population genetics. The library provides functions for lines, curves, scatter plots, and heatmaps within the ternary coordinate system.
Scientists, researchers, and data analysts working with three-component systems, such as chemists analyzing phase diagrams, geologists studying soil compositions, or geneticists visualizing allele frequencies.
Developers choose python-ternary because it offers a seamless Matplotlib-integrated API for ternary plotting, supports multiple plot types and heatmap styles, and is specifically designed for scientific visualization where ternary diagrams are standard. Its compatibility with Matplotlib allows for extensive customization and easy integration into existing Python data analysis workflows.
:small_red_triangle: Ternary plotting library for python with matplotlib
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Provides a TernaryAxesSubplot class that mimics Matplotlib's API, allowing familiar usage and full customization with standard keyword arguments, as shown in the wrapper examples.
Supports lines, curves, scatter plots, and heatmaps within the ternary coordinate system, demonstrated in the gallery and code snippets for diverse scientific visualization.
Offers triangular, dual-triangular, and hexagonal heatmap styles for different data representation needs, detailed in the heatmap section with visual examples.
Enables heatmaps with custom RGBA color tuples for advanced color mapping, as illustrated in the RGBA example with code for color point functions.
Has a list of citations and usage in publications, indicating peer adoption and reliability for academic research, as referenced in the citations section.
Known issues on Macs where axes labels may not render without manually calling tax._redraw_labels(), as admitted in the Known-Issues section, requiring workarounds.
Heatmaps with large scale parameters can consume significant RAM due to O(n^2) polygon rendering, as warned in the heatmap section, limiting fine-resolution plots.
Solely focused on ternary diagrams; for other plot types, users must integrate additional libraries, adding complexity to multi-plot projects.
Inherits Matplotlib's static nature and performance issues, making it unsuitable for interactive or real-time visualizations without external tools.