Visualization Guide¶

Learning Objectives

After completing this section, you will be able to:

Create publication-quality rheological plots
Apply three visualization templates (default, publication, presentation)
Customize plot styles and export for journals
Use BayesianPlotter for MCMC diagnostics

Prerequisites

Getting Started with Model Fitting — Basic fitting
Bayesian Inference (for Bayesian plots)

Basic Plotting¶

Automatic plot type detection:

from rheojax.visualization import plot_rheo_data
import matplotlib.pyplot as plt

fig, ax = plot_rheo_data(data, style='publication')
plt.savefig('figure.png', dpi=300)

Templates:

style='default': Standard matplotlib
style='publication': Journal-ready (larger fonts, clean)
style='presentation': Slides (extra-large fonts, bold)

Bayesian Diagnostics¶

from rheojax.visualization import BayesianPlotter

plotter = BayesianPlotter(bayesian_result)
plotter.plot_posterior()  # Posterior distributions
plotter.plot_trace()       # MCMC traces
plotter.plot_pair()        # Parameter correlations

Summary¶

RheoJAX provides automatic plot type selection and three templates for different use cases. BayesianPlotter offers comprehensive MCMC diagnostic visualizations.