Basic Streamline Plot — Matplotlib

A streamline plot visualizes vector fields using smooth curves that are tangent to the field at every point. Unlike quiver plots that show discrete arrows, streamlines trace continuous paths through the field, revealing flow patterns, circulation, and field topology. This visualization is ideal for understanding fluid dynamics, electromagnetic fields, or gradient fields where the continuous nature of the flow is important.

Basic Streamline Plot rendered with Matplotlib

Python source (Matplotlib)

""" anyplot.ai
streamline-basic: Basic Streamline Plot
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 94/100 | Updated: 2026-05-14
"""

import os

import matplotlib.pyplot as plt
import numpy as np


# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"

# Data - Create a more complex flow field showing vortex and source features
np.random.seed(42)

# Grid setup (40x40 for smooth streamlines)
x = np.linspace(-3, 3, 40)
y = np.linspace(-3, 3, 40)
X, Y = np.meshgrid(x, y)

# Create a more interesting flow field combining vortex and source patterns
# Vortex: u = -y, v = x (circular flow)
# Add a source/sink at (0, 0): radial outflow
# Add secondary vortex at (1.5, 0): counterclockwise rotation
U = -0.8 * Y + 0.3 * X / (X**2 + Y**2 + 0.1)
V = 0.8 * X + 0.3 * Y / (X**2 + Y**2 + 0.1)

# Secondary vortex contribution
dx, dy = X - 1.5, Y
U += -0.4 * dy / ((dx**2 + dy**2 + 0.5) ** 0.5)
V += 0.4 * dx / ((dx**2 + dy**2 + 0.5) ** 0.5)

# Calculate velocity magnitude for color and linewidth encoding
speed = np.sqrt(U**2 + V**2)
speed_norm = (speed - speed.min()) / (speed.max() - speed.min() + 1e-6)

# Plot
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)

# Create streamlines with color based on velocity magnitude
strm = ax.streamplot(
    X,
    Y,
    U,
    V,
    color=speed,
    cmap="viridis",
    linewidth=1.5 + 2.5 * speed_norm,  # Linewidth varies with speed
    density=1.5,
    arrowsize=2,
    arrowstyle="->",
)

# Colorbar for velocity magnitude
cbar = fig.colorbar(strm.lines, ax=ax, shrink=0.8, pad=0.02)
cbar.set_label("Velocity Magnitude", fontsize=20, color=INK)
cbar.ax.tick_params(labelsize=16, colors=INK_SOFT)
cbar.outline.set_color(INK_SOFT)
cbar.outline.set_linewidth(0.5)

# Styling
ax.set_xlabel("X Position (m)", fontsize=20, color=INK)
ax.set_ylabel("Y Position (m)", fontsize=20, color=INK)
ax.set_title("streamline-basic · matplotlib · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)
ax.set_aspect("equal")

# Grid styling - subtle solid lines
ax.grid(True, alpha=0.10, linestyle="-", linewidth=0.8, color=INK)

# Spine styling
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for s in ("left", "bottom"):
    ax.spines[s].set_color(INK_SOFT)
    ax.spines[s].set_linewidth(0.5)

plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)

Part of Basic Streamline Plot on anyplot.ai.

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