Tutorial 03 — Plot Types

maxplotlib exposes all common Matplotlib plot types through a unified API. This notebook demonstrates each type individually, then shows how to combine multiple types in a single subplot.

Plot types covered: plot · scatter · bar · fill_between · errorbar · axhline / axvline · hlines / vlines · combined plots · annotate / text

from maxplotlib import Canvas
import numpy as np

%matplotlib inline

rng = np.random.default_rng(42)
x = np.linspace(0, 2 * np.pi, 300)

1 Line plot — ax.plot()

canvas, ax = Canvas.subplots()

ax.plot(x, np.sin(x), label="sin(x)", color="royalblue", linestyle="solid", linewidth=2)
ax.plot(x, np.cos(x), label="cos(x)", color="tomato", linestyle="dashed", linewidth=2)

ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title("Line Plot")
ax.set_legend(True)
ax.set_grid(True)
canvas.show()

2 Scatter plot — ax.scatter()

Use c= to colour each point by a scalar value (requires a colormap-compatible array).

n = 200
sx = rng.standard_normal(n)
sy = rng.standard_normal(n)
values = np.sqrt(sx**2 + sy**2)  # colour by distance from origin

canvas, ax = Canvas.subplots()
ax.scatter(sx, sy, c=values, s=30, label="data points")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title("Scatter Plot — coloured by distance")
ax.set_aspect("equal")
canvas.show()

3 Bar chart — ax.bar()

categories = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]
values_bar = [12, 19, 15, 22, 30, 27]

canvas, ax = Canvas.subplots()
ax.bar(categories, values_bar, color="steelblue", width=0.6, label="monthly sales")
ax.set_xlabel("Month")
ax.set_ylabel("Sales (units)")
ax.set_title("Bar Chart")
ax.set_legend(True)
# canvas.show() # TODO: Fix this error

4 Fill between — ax.fill_between()

Useful for shading confidence bands or uncertainty regions.

t = np.linspace(0, 4 * np.pi, 300)
mean = np.sin(t) * np.exp(-t / 10)
upper = mean + 0.3 * (1 - t / (4 * np.pi))
lower = mean - 0.3 * (1 - t / (4 * np.pi))

canvas, ax = Canvas.subplots()
ax.plot(t, mean, color="royalblue", label="mean", linewidth=2)
ax.fill_between(t, lower, upper, alpha=0.25, color="royalblue", label="±1 std")
ax.set_xlabel("t")
ax.set_ylabel("value")
ax.set_title("Fill Between — Confidence Band")
ax.set_legend(True)
canvas.show()

5 Error bars — ax.errorbar()

xm = np.linspace(0, 2 * np.pi, 12)
ym = np.sin(xm) + rng.normal(0, 0.1, len(xm))
yerr = 0.1 + 0.05 * rng.random(len(xm))

canvas, ax = Canvas.subplots()
ax.errorbar(xm, ym, yerr=yerr, fmt="o", capsize=4, color="tomato", label="measurements")
ax.plot(x, np.sin(x), color="gray", linestyle="dashed", label="true sin(x)")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title("Error Bars")
ax.set_legend(True)
ax.set_grid(True)
canvas.show()

6 Reference lines — axhline and axvline

Span the entire axis to mark thresholds or zero-crossings.

canvas, ax = Canvas.subplots()
ax.plot(x, np.sin(x), color="royalblue", label="sin(x)")

ax.axhline(y=0, color="black", linestyle="solid", linewidth=0.8)
ax.axhline(y=0.5, color="green", linestyle="dashed", linewidth=1.2, label="y = 0.5")
ax.axhline(y=-0.5, color="green", linestyle="dashed", linewidth=1.2, label="y = -0.5")
ax.axvline(x=np.pi, color="red", linestyle="dotted", linewidth=1.5, label="x = π")

ax.set_xlabel("x")
ax.set_title("axhline / axvline")
ax.set_legend(True)
canvas.show()

7 Segment lines — hlines and vlines

Draw multiple horizontal or vertical line segments with explicit start/end coordinates.

canvas, ax = Canvas.subplots()
ax.plot(x, np.sin(x), color="lightgray", linewidth=1)

# Horizontal segments spanning half the x-range
ax.hlines(
    y=[0.5, -0.5],
    xmin=0,
    xmax=np.pi,
    colors="seagreen",
    linestyles="dashed",
    label="hlines at ±0.5",
)

# Vertical segments at specific x positions
ax.vlines(
    x=[np.pi / 2, 3 * np.pi / 2],
    ymin=-1,
    ymax=1,
    colors="tomato",
    linestyles="dotted",
    label="vlines at π/2, 3π/2",
)

ax.set_xlabel("x")
ax.set_title("hlines / vlines")
ax.set_legend(True)
canvas.show()

8 Combining plot types

A single subplot can hold many plot types simultaneously. Here we overlay a line, a shaded uncertainty band, and scatter points.

t = np.linspace(0, 2 * np.pi, 300)
signal = np.sin(t) * np.exp(-t / 8)
noise = rng.normal(0, 0.08, len(t))
band = 0.15 * np.exp(-t / 8)

# Sparse measurement points
tidx = np.arange(0, len(t), 20)
tx, ty = t[tidx], (signal + noise)[tidx]

canvas, ax = Canvas.subplots()

ax.fill_between(
    t, signal - band, signal + band, alpha=0.2, color="royalblue", label="uncertainty"
)
ax.plot(t, signal, color="royalblue", linewidth=2, label="model")
ax.scatter(tx, ty, color="tomato", s=25, marker="o", label="measurements")
ax.axhline(y=0, color="gray", linestyle="dashed", linewidth=0.8)

ax.set_xlabel("time")
ax.set_ylabel("amplitude")
ax.set_title("Combined: line + fill_between + scatter")
ax.set_legend(True)
ax.set_grid(True)
canvas.show()

9 Annotations — ax.annotate() and ax.text()

canvas, ax = Canvas.subplots()
ax.plot(x, np.sin(x), color="royalblue")

# Arrow annotation pointing to the peak
ax.annotate(
    "peak",
    xy=(np.pi / 2, 1.0),
    xytext=(np.pi / 2 + 0.8, 0.7),
    arrowprops=dict(arrowstyle="->"),
)

# Free-floating text label
ax.text(3 * np.pi / 2, 0.15, "zero\ncrossing", ha="center", fontsize=9)

ax.set_xlabel("x")
ax.set_ylabel("sin(x)")
ax.set_title("Annotate and Text")
canvas.show()

Summary

Plot type	Method	Key kwargs
Line	ax.plot(x, y)	color, linestyle, linewidth, label
Scatter	ax.scatter(x, y)	c, s, marker, label
Bar	ax.bar(x, height)	color, width, label
Filled band	ax.fill_between(x, y1, y2)	alpha, color, label
Error bars	ax.errorbar(x, y, yerr=...)	fmt, capsize, label
Full-span h/v line	ax.axhline(y=...) / ax.axvline(x=...)	color, linestyle
Segment lines	ax.hlines(y, xmin, xmax) / ax.vlines(x, ymin, ymax)	colors, linestyles
Arrow annotation	ax.annotate(text, xy=..., xytext=..., arrowprops=...)
Free text	ax.text(x, y, text)	ha, fontsize

You now know the full set of plot types available in maxplotlib. 🎉