Tutorial 04 – Styling

maxplotlib passes matplotlib kwargs directly to the underlying renderer, so every colour, linestyle, marker, and alpha option you know from matplotlib works here too.

from maxplotlib import Canvas
import numpy as np

1 · Colors

Named colors, hex strings, and RGB tuples are all accepted.

x = np.linspace(0, 2 * np.pi, 200)

canvas, ax = Canvas.subplots(width="10cm", ratio=0.6)

ax.plot(x, np.sin(x), color="steelblue", label="named: steelblue")
ax.plot(x, np.sin(x - 0.5), color="#e74c3c", label="hex: #e74c3c")
ax.plot(x, np.sin(x - 1.0), color=(0.2, 0.7, 0.3), label="RGB tuple")

ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title("Color options")
ax.set_legend(True)
canvas.show()

2 · Linestyles

Use long names ('solid', 'dashed', 'dotted', 'dashdot') or short aliases ('-', '--', ':', '-.').

x = np.linspace(0, 4 * np.pi, 300)
styles = [("solid", "-"), ("dashed", "--"), ("dotted", ":"), ("dashdot", "-.")]

canvas, ax = Canvas.subplots(width="10cm", ratio=0.6)

for i, (name, ls) in enumerate(styles):
    ax.plot(
        x,
        np.sin(x) + i * 0.4,
        linestyle=ls,
        linewidth=2,
        color="steelblue",
        label=f"{name!r} / {ls!r}",
    )

ax.set_xlabel("x")
ax.set_title("Linestyle comparison")
ax.set_legend(True)
canvas.show()

3 · Markers

Pass marker= to ax.plot or ax.scatter.

x = np.linspace(0, 2 * np.pi, 10)
markers = ["o", "s", "^", "D", "*", "x", "+"]

canvas, ax = Canvas.subplots(width="10cm", ratio=0.65)

for i, m in enumerate(markers):
    ax.plot(
        x,
        np.sin(x) + i * 0.5,
        marker=m,
        linestyle="-",
        markersize=7,
        label=f"marker={m!r}",
    )

ax.set_xlabel("x")
ax.set_title("Marker comparison")
ax.set_legend(True)
canvas.show()

4 · Linewidth and markersize

Control visual weight with linewidth= and markersize=.

x = np.linspace(0, 2 * np.pi, 40)

canvas, ax = Canvas.subplots(width="10cm", ratio=0.55)

ax.plot(x, np.sin(x), linewidth=0.8, marker="o", markersize=3, label="thin / small")
ax.plot(x, np.sin(x) - 0.6, linewidth=2.5, marker="o", markersize=7, label="medium")
ax.plot(
    x, np.sin(x) - 1.2, linewidth=4.5, marker="o", markersize=12, label="thick / large"
)

ax.set_xlabel("x")
ax.set_title("Linewidth and markersize")
ax.set_legend(True)
canvas.show()

5 · Alpha (transparency)

alpha= ranges from 0 (invisible) to 1 (opaque). Useful for overlapping fill_between regions.

x = np.linspace(0, 2 * np.pi, 200)

canvas, ax = Canvas.subplots(width="10cm", ratio=0.55)

ax.fill_between(x, np.sin(x), 0, alpha=0.7, color="steelblue", label="alpha=0.7")
ax.fill_between(x, np.sin(2 * x), 0, alpha=0.4, color="tomato", label="alpha=0.4")
ax.fill_between(x, np.sin(3 * x), 0, alpha=0.2, color="seagreen", label="alpha=0.2")

ax.set_xlabel("x")
ax.set_title("Alpha transparency")
ax.set_legend(True)
canvas.show()

6 · Combining styles – a publication-ready plot

Combine color, linestyle, marker, linewidth, and alpha in one plot.

x = np.linspace(0, 2 * np.pi, 80)
noise = np.random.default_rng(0).normal(0, 0.05, len(x))

canvas, ax = Canvas.subplots(width="10cm", ratio=0.6)

# Shaded uncertainty band
ax.fill_between(x, np.sin(x) - 0.15, np.sin(x) + 0.15, alpha=0.15, color="steelblue")

# Noisy data
ax.scatter(
    x, np.sin(x) + noise, color="steelblue", marker="o", s=18, alpha=0.6, label="data"
)

# Clean model
ax.plot(
    x,
    np.sin(x),
    color="#e74c3c",
    linestyle="dashed",
    linewidth=2.0,
    alpha=0.9,
    label="model",
)

ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title("Publication-style plot")
ax.set_legend(True)
canvas.show()

Summary

Option	Example
Named color	color='steelblue'
Hex color	color='#e74c3c'
RGB tuple	color=(0.2, 0.7, 0.3)
Linestyle	linestyle='dashed' or '--'
Marker	marker='o'
Linewidth	linewidth=2.0
Markersize	markersize=7
Transparency	alpha=0.5