scienceplots-toolkit 0.2.0

Publication-quality Matplotlib plotting utilities with SciencePlots styles and LaTeX typesetting

SciencePlots Toolkit

Publication-quality Matplotlib plotting utilities with SciencePlots styles and LaTeX typesetting.

Features

• Pre-configured SciencePlots styles with professional defaults
• LaTeX typesetting for mathematical expressions and units
• 24-hour time axis utilities for daily profiles
• Statistical annotations for average/peak values
• Quantile shading for uncertainty visualization
• Multi-panel grid generation for comparative plots

Example Plots

See what you can create with scienceplots-toolkit:

Basic Line Plot

from scienceplots_toolkit import configure_matplotlib_style, save_plot
import matplotlib.pyplot as plt
import numpy as np

configure_matplotlib_style(use_latex=False)

fig, ax = plt.subplots()
x = np.linspace(0, 10, 100)
ax.plot(x, np.sin(x), label=r'$\sin(x)$')
ax.plot(x, np.cos(x), label=r'$\cos(x)$')
ax.set_xlabel('Time (s)')
ax.set_ylabel('Amplitude')
ax.legend()
save_plot(fig, 'my_plot')

24h Load Profile with Statistics

from scienceplots_toolkit.utils import configure_24h_axis, add_stats_box

fig, ax = plt.subplots(figsize=(12, 6))
hours = np.arange(24)
load = 50 + 30 * np.sin(2 * np.pi * (hours - 6) / 24)
ax.plot(hours, load, marker='o')
configure_24h_axis(ax)
ax.set_ylabel('Power (kW)')
add_stats_box(ax, avg=50, peak=80, unit='kW')

Quantile Shading for Uncertainty

from scienceplots_toolkit import plot_profile_with_quantiles

fig, ax = plt.subplots()
x = np.linspace(0, 24, 100)
mean = 50 + 25 * np.sin(2 * np.pi * (x - 6) / 24)
q10, q90 = mean * 0.85, mean * 1.15
plot_profile_with_quantiles(ax, x, mean, q10, q90, label='Forecast')

Multi-Panel Comparison

from scienceplots_toolkit import generate_profile_grid

fig, axes = generate_profile_grid(n_rows=2, n_cols=2)
for i, ax in enumerate(axes):
    ax.plot(x, data[i])
    ax.set_title(f'Scenario {i+1}')

Measurements with Error Bars

x = np.arange(10)
y = 2 * x + 5 + np.random.normal(0, 2, 10)
yerr = np.random.uniform(1, 3, 10)
ax.errorbar(x, y, yerr=yerr, fmt='o', capsize=5)

💡 Tip: Run uv run python examples/example_readme.py to regenerate all example plots.

Installation

From PyPI

pip install scienceplots-toolkit

From source with uv

git clone https://github.com/jakobbuch/scienceplots-toolkit.git
cd scienceplots-toolkit
uv sync

From source with pip

git clone https://github.com/jakobbuch/scienceplots-toolkit.git
cd scienceplots-toolkit
pip install -e .

Quick Start

from scienceplots_toolkit import configure_matplotlib_style, save_plot
from scienceplots_toolkit.utils import configure_24h_axis, add_stats_box
import matplotlib.pyplot as plt
import numpy as np

# Configure the style
configure_matplotlib_style(use_latex=True)

# Create a simple plot
fig, ax = plt.subplots()
x = np.linspace(0, 10, 400)
ax.plot(x, np.sin(x), label=r"$\sin(x)$")
ax.set_xlabel(r"Time (s)")
ax.set_ylabel(r"Amplitude")
ax.legend()

# Save the plot
save_plot(fig, "my_first_plot")

Examples

Basic Plots

Run the basic examples to see all features in action:

# Without LaTeX (uses mathtext)
uv run examples/example_basic.py

# With LaTeX rendering (requires LaTeX installation)
uv run examples/example_basic.py --latex

Energy Profiles

Advanced examples with 24h load profiles and quantile shading:

uv run examples/example_energy.py
uv run examples/example_energy.py --latex

Generated plots are saved to the output/ directory in both PNG and PDF formats.

API Reference

Style Configuration

from scienceplots_toolkit import configure_matplotlib_style

configure_matplotlib_style(
    styles=["science", "ieee", "grid"],  # SciencePlots styles to use
    grid_linewidth=3,                     # Grid line width in points
    lines_linewidth=4,                    # Plot line width in points
    fontsize=26,                          # Base font size
    figsize=(16, 10),                     # Default figure size (inches)
    font="serif",                         # Font family
    sans_serif_math=False,                # Use sans-serif for math
    cmap_name="seaborn:tab10_new",       # Qualitative colormap
    use_latex=False,                      # Enable LaTeX rendering
    grid=True,                            # Enable axis gridlines
    legend_framealpha=1.0,                # Legend background transparency
    legend_shadow=True,                   # Legend shadow effect
)

Utilities

from scienceplots_toolkit.utils import (
    save_plot,              # Save figure to PNG and PDF
    configure_24h_axis,     # Set up 0-24h x-axis with 4h ticks
    add_stats_box,          # Add average/peak annotation box
)

# Save a figure
save_plot(fig, "my_plot", dpi=300)

# Configure 24-hour axis
configure_24h_axis(ax)

# Add statistics box
add_stats_box(ax, avg=5.2, peak=12.3, unit=r"\text{kW}")

Analysis Tools

from scienceplots_toolkit import (
    plot_profile_with_quantiles,  # Plot mean with shaded quantiles
    generate_profile_grid,        # Create multi-panel grid of plots
)

# Plot with uncertainty shading
plot_profile_with_quantiles(
    ax, x, mean, q10, q90,
    label="Load Profile",
    color="C0"
)

# Create a 2x2 grid of subplots
fig, axes = generate_profile_grid(n_rows=2, n_cols=2)

LaTeX Support

The package supports two modes:

1. Mathtext (default): Uses Matplotlib's built-in math renderer. No external dependencies.
2. LaTeX: Uses system LaTeX for professional typesetting. Requires:
   • TeX Live or MiKTeX installation
   • Packages: amsmath, amssymb, amsfonts, textcomp, gensymb, siunitx, graphicx

Enable LaTeX mode:

configure_matplotlib_style(use_latex=True)

Or via CLI:

uv run examples/example_basic.py --latex

Project Structure

scienceplots-toolkit/
├── src/scienceplots_toolkit/
│   ├── __init__.py          # Public API exports
│   ├── style.py             # Matplotlib style configuration
│   ├── utils.py             # Utility functions
│   └── analysis.py          # Analysis and visualization tools
├── examples/
│   ├── example_basic.py     # Basic plotting examples
│   └── example_energy.py    # Energy profile examples
├── tests/
├── pyproject.toml
├── README.md
└── LICENSE
## Development

### Setup

```bash
uv sync --group dev

Run Tests

uv run pytest tests/ -v

Linting and Formatting

uv run ruff format .
uv run ruff check .
uv run ty check .

Acknowledgments

This package builds upon the excellent SciencePlots library by John Garrett, which is also licensed under the MIT License.

SciencePlots provides Matplotlib styles for publication-quality plots. For more information, see: https://github.com/garrettj403/SciencePlots

License

Distributed under the MIT License. See LICENSE for details.

Contact

Jakob Buchmeier - [e-mail]

Project Link: https://github.com/jakobbuch/scienceplots-toolkit