A filled contour plot displays colored regions between level curves of a 2D scalar field, creating a smooth gradient visualization of continuous data. Unlike basic contour plots that show only isolines, filled contours use color bands to represent value ranges, making it easier to perceive gradual changes and identify regions of similar magnitude. This visualization is particularly effective for showing how a quantity varies continuously across a 2D surface.
""" anyplot.ai
contour-filled: Filled Contour Plot
Library: altair 6.1.0 | Python 3.13.13
Quality: 92/100 | Updated: 2026-05-11
"""
import os
import sys
# Remove script directory from sys.path to avoid importing local altair.py
script_dir = os.path.dirname(os.path.abspath(__file__))
if script_dir in sys.path:
sys.path.remove(script_dir)
import altair as alt
import numpy as np
import pandas as pd
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
# Data - 2D Gaussian peaks for filled contour visualization
np.random.seed(42)
n_points = 80
x = np.linspace(-3, 3, n_points)
y = np.linspace(-3, 3, n_points)
X, Y = np.meshgrid(x, y)
# Create surface with multiple peaks and valleys
Z = (
1.5 * np.exp(-((X - 1) ** 2 + (Y - 1) ** 2) / 0.8)
+ 1.2 * np.exp(-((X + 1) ** 2 + (Y + 0.5) ** 2) / 1.0)
- 0.6 * np.exp(-((X) ** 2 + (Y - 1.5) ** 2) / 0.6)
+ 0.2 * np.sin(X * 2) * np.cos(Y * 2)
)
# Create contour levels
n_levels = 12
z_min, z_max = Z.min(), Z.max()
levels = np.linspace(z_min, z_max, n_levels + 1)
# Bin z-values and map to level centers for color mapping
Z_binned = np.digitize(Z, levels) - 1
Z_binned = np.clip(Z_binned, 0, n_levels - 1)
level_centers = (levels[:-1] + levels[1:]) / 2
Z_discrete = level_centers[Z_binned]
# Create rectangle grid for filled contours
step = x[1] - x[0]
half_step = step / 2
df = pd.DataFrame(
{
"x": X.ravel() - half_step,
"x2": X.ravel() + half_step,
"y": Y.ravel() - half_step,
"y2": Y.ravel() + half_step,
"z": Z_discrete.ravel(),
}
)
# Filled contour using mark_rect
filled_contour = (
alt.Chart(df)
.mark_rect(stroke="none")
.encode(
x=alt.X(
"x:Q",
title="X Coordinate",
scale=alt.Scale(domain=[-3.1, 3.1]),
axis=alt.Axis(labelFontSize=18, titleFontSize=22, tickCount=7),
),
x2="x2:Q",
y=alt.Y(
"y:Q",
title="Y Coordinate",
scale=alt.Scale(domain=[-3.1, 3.1]),
axis=alt.Axis(labelFontSize=18, titleFontSize=22, tickCount=7),
),
y2="y2:Q",
color=alt.Color(
"z:Q",
title="Intensity",
scale=alt.Scale(scheme="viridis"),
legend=alt.Legend(
titleFontSize=20,
labelFontSize=16,
gradientLength=400,
gradientThickness=25,
fillColor=ELEVATED_BG,
strokeColor=INK_SOFT,
),
),
)
)
# Create contour line overlay
def find_contours(Z, level, x_coords, y_coords):
"""Extract contour lines using marching squares algorithm."""
rows, cols = Z.shape
segments = []
for i in range(rows - 1):
for j in range(cols - 1):
z00, z01, z10, z11 = Z[i, j], Z[i, j + 1], Z[i + 1, j], Z[i + 1, j + 1]
cell = [z00, z01, z10, z11]
case = sum([1 << k for k, v in enumerate(cell) if v >= level])
if case in (0, 15):
continue
x0, x1 = x_coords[j], x_coords[j + 1]
y0, y1 = y_coords[i], y_coords[i + 1]
def interp(v1, v2, c1, c2):
if abs(v2 - v1) < 1e-10:
return (c1 + c2) / 2
t = (level - v1) / (v2 - v1)
return c1 + t * (c2 - c1)
edges = {
"top": (interp(z00, z01, x0, x1), y0),
"bottom": (interp(z10, z11, x0, x1), y1),
"left": (x0, interp(z00, z10, y0, y1)),
"right": (x1, interp(z01, z11, y0, y1)),
}
cases = {
1: [("left", "top")],
2: [("top", "right")],
3: [("left", "right")],
4: [("bottom", "left")],
5: [("top", "bottom")],
6: [("top", "left"), ("bottom", "right")]
if (z00 + z11) / 2 >= level
else [("top", "right"), ("bottom", "left")],
7: [("bottom", "right")],
8: [("right", "bottom")],
9: [("left", "bottom"), ("right", "top")]
if (z00 + z11) / 2 >= level
else [("left", "top"), ("right", "bottom")],
10: [("top", "bottom")],
11: [("left", "bottom")],
12: [("left", "right")],
13: [("top", "right")],
14: [("left", "top")],
}
for e1, e2 in cases.get(case, []):
segments.append((edges[e1], edges[e2]))
return segments
contour_lines_data = []
contour_levels_subset = levels[2:-2:2]
for idx, level_val in enumerate(contour_levels_subset):
segments = find_contours(Z, level_val, x, y)
for seg_idx, (p1, p2) in enumerate(segments):
contour_id = f"L{idx}_S{seg_idx}"
contour_lines_data.append({"x": p1[0], "y": p1[1], "order": 0, "contour_id": contour_id})
contour_lines_data.append({"x": p2[0], "y": p2[1], "order": 1, "contour_id": contour_id})
contour_df = pd.DataFrame(contour_lines_data)
# Contour line overlay with theme-adaptive color
contour_color = "#2A2A25" if THEME == "light" else "#D5D4CC"
contour_overlay = (
alt.Chart(contour_df)
.mark_line(strokeWidth=1.2, opacity=0.4)
.encode(
x=alt.X("x:Q", scale=alt.Scale(domain=[-3.1, 3.1])),
y=alt.Y("y:Q", scale=alt.Scale(domain=[-3.1, 3.1])),
order="order:O",
detail="contour_id:N",
color=alt.value(contour_color),
)
)
# Combine layers with theme-adaptive styling
chart = (
alt.layer(filled_contour, contour_overlay)
.properties(
width=1600,
height=900,
title=alt.Title(text="contour-filled · altair · anyplot.ai", fontSize=28, anchor="middle"),
background=PAGE_BG,
)
.configure_view(fill=PAGE_BG, stroke=INK_SOFT, strokeWidth=0)
.configure_axis(domainColor=INK_SOFT, tickColor=INK_SOFT, labelColor=INK_SOFT, titleColor=INK)
.configure_title(color=INK)
)
# Save PNG and HTML with theme suffix
chart.save(f"plot-{THEME}.png", scale_factor=3.0)
chart.save(f"plot-{THEME}.html")
Part of Filled Contour Plot on anyplot.ai.