wisent-visuals 0.1.0

Create beautiful plots in Wisent brand styling

Wisent Plots

Create beautiful, brand-styled plots with ease. This package provides ready-to-use chart types that follow Wisent's visual identity guidelines.

Features

• 5 Pre-configured Styles: Choose from 5 professionally designed styles
• Easy to Use: Simple API that works with familiar data structures
• Customizable: Override colors and settings as needed
• High Quality: Publication-ready output with customizable DPI
• Type Hints: Full type annotation support

Installation

pip install wisent-plots

For development:

git clone https://github.com/wisent/wisent-plots
cd wisent-plots
pip install -e ".[dev]"

Quick Start

from wisent_plots import AreaChart

# Create data
x = [1, 2, 3, 4, 5]
y = [2, 4, 3, 5, 4]

# Create chart with style 1 and edge enabled
chart = AreaChart(style=1, edge=True)

# Plot
fig, ax = chart.plot(
    x=x,
    y=y,
    title="My Chart",
    xlabel="Time",
    ylabel="Value"
)

# Save or show
chart.save(fig, "my_chart.png")
# Or: plt.show()

Available Styles

The package includes 5 distinct styles, each with unique color palettes and typography:

1. Corporate (style=1): Professional deep blue theme
2. Minimal (style=2): Clean, understated design
3. Bold (style=3): High-contrast, vibrant colors
4. Academic (style=4): Classic, publication-ready style
5. Modern (style=5): Contemporary gradient colors

Customizing Styles with Figma

To match your exact Figma design specifications:

1. Open wisent_plots/styles/style_config.py
2. Update the color values, fonts, and spacing for each style
3. The comments marked with # UPDATE with Figma show where to add your brand colors

Example of what to update:

"colors": {
    "primary": "#1E3A8A",  # UPDATE: Replace with your primary brand color
    "secondary": "#3B82F6",  # UPDATE: Replace with your secondary color
    # ... etc
},
"font": {
    "family": "Arial",  # UPDATE: Replace with your brand font
    # ...
}

Usage Examples

Single Series Area Chart

import numpy as np
from wisent_plots import AreaChart

x = np.linspace(0, 10, 50)
y = np.sin(x) * 3 + 5

chart = AreaChart(style=1, edge=True)
fig, ax = chart.plot(
    x=x,
    y=y,
    title="Sales Growth",
    xlabel="Month",
    ylabel="Revenue ($1000s)",
    label="Q1 Sales"
)
chart.save(fig, "sales_chart.png", dpi=300)

Multiple Series

chart = AreaChart(style=2, edge=False)

y1 = np.sin(x) * 3 + 5
y2 = np.cos(x) * 2 + 5
y3 = np.sin(x * 0.5) * 2 + 3

fig, ax = chart.plot_multiple(
    x=x,
    y_series=[y1, y2, y3],
    labels=["Product A", "Product B", "Product C"],
    title="Product Comparison",
    xlabel="Time (weeks)",
    ylabel="Units Sold"
)
chart.save(fig, "comparison.png")

Custom Colors

chart = AreaChart(style=1, edge=True)
fig, ax = chart.plot(
    x=x,
    y=y,
    title="Custom Colored Chart",
    color="#FF6B6B",  # Custom coral color
    label="Custom Series"
)

Using with Existing Matplotlib Figures

import matplotlib.pyplot as plt

fig, axes = plt.subplots(2, 2, figsize=(12, 10))

chart1 = AreaChart(style=1)
chart1.plot(x, y1, title="Chart 1", fig=fig, ax=axes[0, 0])

chart2 = AreaChart(style=2)
chart2.plot(x, y2, title="Chart 2", fig=fig, ax=axes[0, 1])

plt.tight_layout()
plt.savefig("grid.png")

API Reference

AreaChart

Constructor Parameters:

• style (int, default=1): Style number (1-5)
• edge (bool, default=False): Whether to draw edge around filled area
• figsize (tuple, default=(10, 6)): Figure size as (width, height) in inches
• dpi (int, default=100): Resolution in dots per inch

Methods:

plot(x, y, title=None, xlabel=None, ylabel=None, color=None, label=None, fig=None, ax=None)

Create a single-series area chart.

Parameters:

• x: X-axis data (list or numpy array)
• y: Y-axis data (list or numpy array)
• title: Chart title (optional)
• xlabel: X-axis label (optional)
• ylabel: Y-axis label (optional)
• color: Custom color as hex string (optional)
• label: Legend label (optional)
• fig: Existing matplotlib Figure (optional)
• ax: Existing matplotlib Axes (optional)

Returns: (fig, ax) tuple

plot_multiple(x, y_series, labels=None, colors=None, title=None, xlabel=None, ylabel=None, fig=None, ax=None)

Create a multi-series area chart.

Parameters:

• x: X-axis data (list or numpy array)
• y_series: List of Y-axis data arrays
• labels: List of legend labels (optional)
• colors: List of colors for each series (optional)
• title: Chart title (optional)
• xlabel: X-axis label (optional)
• ylabel: Y-axis label (optional)
• fig: Existing matplotlib Figure (optional)
• ax: Existing matplotlib Axes (optional)

Returns: (fig, ax) tuple

save(fig, filename, dpi=None, transparent=False)

Save the figure to a file.

Parameters:

• fig: Figure object to save
• filename: Output filename with extension (e.g., 'chart.png')
• dpi: Resolution in dots per inch (optional, uses figure dpi if not specified)
• transparent: Whether to save with transparent background (default=False)

Development

Running Examples

cd examples
python quick_start.py
python area_chart_demo.py

Running Tests

pytest tests/

Code Formatting

black wisent_plots/
isort wisent_plots/

Building and Publishing

Build the package

pip install build twine
python -m build

This creates distribution files in the dist/ directory.

Test with TestPyPI (recommended first)

# Upload to TestPyPI
python -m twine upload --repository testpypi dist/*

# Install from TestPyPI to test
pip install --index-url https://test.pypi.org/simple/ wisent-plots

Publish to PyPI

python -m twine upload dist/*

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Support

For issues and questions, please open an issue on the GitHub repository.