Utilities, Not Wrappers

Most visualization libraries aim to reduce boilerplate by providing high-level abstractions:

# Typical high-level library approach
chart = SomeLib.line_chart(
    data=df,
    x='date',
    y='value',
    theme='modern',
    title='My Chart',
    show_legend=True
)
chart.save('output.png')

While this looks convenient for simple cases, it creates several problems.

Additional Abstraction Layers

Every wrapper library introduces a new layer between you and matplotlib. When you need to customize something beyond the library’s API:

  1. You must understand both the wrapper’s API and matplotlib’s API.

  2. You need to find escape hatches to access underlying matplotlib objects.

  3. The wrapper’s design decisions may conflict with your requirements.

Diminishing Returns for Custom Visualizations

The more customized your visualization needs to be, the less value these wrappers provide:

# When you need fine-grained control, wrappers become obstacles
chart = SomeLib.create_chart(data, theme='custom')
# How do I adjust the spine thickness?
# How do I add a secondary y-axis with specific formatting?
# How do I position a legend outside the plot area precisely?

# You end up doing this:
ax = chart.get_underlying_axes()  # escape hatch
ax.spines['top'].set_visible(False)  # back to matplotlib
# ... mixing two APIs becomes confusing

You’re Still Writing matplotlib

With dartwork-mpl, your code remains fundamentally matplotlib code. We provide utilities that enhance matplotlib, not wrappers that replace it:

import matplotlib.pyplot as plt
import dartwork_mpl as dm
import numpy as np

# Apply style (sets rcParams, nothing more)
dm.style.use('scientific')

# Standard matplotlib figure creation
fig = plt.figure(figsize=(dm.cm2in(9), dm.cm2in(7)), dpi=200)
ax = fig.add_subplot(111)

# Standard matplotlib plotting
x = np.linspace(0, 10, 100)
ax.plot(x, np.sin(x), color='oc.red5', linewidth=0.7)  # only color is extended
ax.set_xlabel('Time [s]')
ax.set_ylabel('Amplitude')
ax.legend(fontsize=dm.fs(-1))  # utility for relative font size

# Utility for layout optimization
dm.simple_layout(fig)

# Standard matplotlib or utility for saving
fig.savefig('output.png')

Every line is recognizable matplotlib. The dartwork-mpl additions are clearly utilities.

Predictable Behavior

matplotlib’s API is well-documented and behaves consistently. When you write:

ax.plot(x, y, color='red', linewidth=2)
ax.set_xlim(0, 10)

You know exactly what will happen. There are no hidden transformations or automatic adjustments that might surprise you. You retain low-level control over every aspect of your figure, backed by extensive official documentation and decades of community knowledge.

Summary: We believe you shouldn’t have to learn a new plotting paradigm. matplotlib knowledge combined with minimal dartwork-mpl familiarity is all you need to create publication-quality visualizations.