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Backends & Themes

Configure visualization backends and styling for NeoPKPD plots.

Overview

NeoPKPD supports dual visualization backends:

  • Matplotlib: Static, publication-quality figures
  • Plotly: Interactive, web-embeddable plots
from neopkpd import viz

# Set backend globally
viz.set_backend("matplotlib")  # or "plotly"

# Check current backend
print(viz.get_backend())

# List available backends
print(viz.available_backends())  # ["matplotlib", "plotly"]

Backend Selection

Matplotlib (Default)

Best for publication-quality static figures:

viz.set_backend("matplotlib")

fig = viz.plot_conc_time(result)
fig.savefig("plot.pdf", bbox_inches="tight")  # Vector format
fig.savefig("plot.png", dpi=300)              # Raster format

Advantages: - Publication-quality output - Vector format support (PDF, SVG, EPS) - Fine-grained customization - Familiar API for scientists

Plotly

Best for interactive exploration:

viz.set_backend("plotly")

fig = viz.plot_conc_time(result)
fig.write_html("plot.html")           # Interactive HTML
fig.write_image("plot.png", scale=2)  # Static image (requires kaleido)

Advantages: - Interactive zoom/pan - Hover tooltips - Web embedding - Animation support

Themes

Available Themes

# Set theme
viz.set_theme("neopkpd")      # Default professional theme
viz.set_theme("publication")    # Minimal for publications
viz.set_theme("presentation")   # Bold for slides

# List available themes
print(viz.available_themes())

Theme Properties

Theme Use Case Font Size Line Width
neopkpd General use Medium Medium
publication Journal figures Small Thin
presentation Slides Large Thick

Color Palette

# Access color palette
colors = viz.NEOPKPD_COLORS

print(colors)
# {
#     "primary": "#3498DB",
#     "secondary": "#2ECC71",
#     "accent": "#E74C3C",
#     "neutral": "#95A5A6",
#     "dark": "#2C3E50",
#     "light": "#ECF0F1"
# }

# Use in custom plots
import matplotlib.pyplot as plt
plt.plot(x, y, color=colors["primary"])

Per-Function Backend Override

Override backend for individual function calls:

# Global backend is matplotlib
viz.set_backend("matplotlib")

# But use plotly for this specific plot
fig = viz.plot_conc_time(result, backend="plotly")

Saving Figures

Matplotlib

viz.set_backend("matplotlib")
fig = viz.plot_conc_time(result)

# PNG (raster)
fig.savefig("plot.png", dpi=300, bbox_inches="tight")

# PDF (vector)
fig.savefig("plot.pdf", bbox_inches="tight")

# SVG (vector, web-friendly)
fig.savefig("plot.svg", bbox_inches="tight")

# Using save_path parameter
fig = viz.plot_conc_time(result, save_path="plot.png")

Plotly

viz.set_backend("plotly")
fig = viz.plot_conc_time(result)

# Interactive HTML
fig.write_html("plot.html")

# Static image (requires kaleido: pip install kaleido)
fig.write_image("plot.png", scale=2)
fig.write_image("plot.pdf")
fig.write_image("plot.svg")

Custom Styling

Matplotlib Customization

import matplotlib.pyplot as plt

# Custom rcParams
plt.rcParams.update({
    'font.family': 'Arial',
    'font.size': 12,
    'axes.linewidth': 1.5,
    'axes.labelsize': 14,
    'xtick.labelsize': 11,
    'ytick.labelsize': 11,
    'legend.fontsize': 11,
})

# Apply to plot
fig = viz.plot_conc_time(result)

Plotly Customization

fig = viz.plot_conc_time(result, backend="plotly")

# Update layout
fig.update_layout(
    font=dict(family="Arial", size=14),
    plot_bgcolor="white",
    paper_bgcolor="white"
)

# Update traces
fig.update_traces(line=dict(width=2))

See Also