A ternary plot displays three-component compositional data on an equilateral triangle where each vertex represents 100% of one component. Points inside the triangle show compositions that sum to a constant total (usually 100%), with position indicating relative proportions. This visualization is essential for data where three variables are interdependent and constrained to sum to a fixed value.
""" anyplot.ai
ternary-basic: Basic Ternary Plot
Library: altair 6.1.0 | Python 3.13.13
Quality: 93/100 | Updated: 2026-05-06
"""
import os
import sys
import numpy as np
import pandas as pd
# Clean sys.path early to avoid importing this file as 'altair' (file naming conflict)
_script_dir = os.path.dirname(os.path.abspath(__file__))
while _script_dir in sys.path:
sys.path.remove(_script_dir)
import altair as alt
# 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"
BRAND = "#009E73"
# Data - Soil composition samples (sand, silt, clay)
np.random.seed(42)
n_points = 50
# Generate random compositional data that sums to 100
raw = np.random.dirichlet([2, 2, 2], size=n_points) * 100
sand = raw[:, 0]
silt = raw[:, 1]
clay = raw[:, 2]
# Ternary to Cartesian conversion
# In a standard ternary plot with equilateral triangle:
# - Bottom-left vertex (0,0): 100% Sand
# - Bottom-right vertex (1,0): 100% Silt
# - Top vertex (0.5, sqrt(3)/2): 100% Clay
height = np.sqrt(3) / 2
total = sand + silt + clay
x = silt / total + 0.5 * clay / total
y = clay / total * height
df = pd.DataFrame({"x": x, "y": y, "Sand (%)": sand.round(1), "Silt (%)": silt.round(1), "Clay (%)": clay.round(1)})
# Create triangle outline
triangle_vertices = pd.DataFrame({"x": [0, 1, 0.5, 0], "y": [0, 0, height, 0], "order": [0, 1, 2, 3]})
# Create grid lines at 20% intervals
grid_lines = []
for pct in [20, 40, 60, 80]:
# Lines parallel to bottom edge (constant clay)
a1, b1, c1 = 100 - pct, 0, pct
a2, b2, c2 = 0, 100 - pct, pct
x1 = b1 / 100 + 0.5 * c1 / 100
y1 = c1 / 100 * height
x2 = b2 / 100 + 0.5 * c2 / 100
y2 = c2 / 100 * height
grid_lines.append({"x": x1, "y": y1, "x2": x2, "y2": y2})
# Lines parallel to left edge (constant silt)
a1, b1, c1 = 100 - pct, pct, 0
a2, b2, c2 = 0, pct, 100 - pct
x1 = b1 / 100 + 0.5 * c1 / 100
y1 = c1 / 100 * height
x2 = b2 / 100 + 0.5 * c2 / 100
y2 = c2 / 100 * height
grid_lines.append({"x": x1, "y": y1, "x2": x2, "y2": y2})
# Lines parallel to right edge (constant sand)
a1, b1, c1 = pct, 100 - pct, 0
a2, b2, c2 = pct, 0, 100 - pct
x1 = b1 / 100 + 0.5 * c1 / 100
y1 = c1 / 100 * height
x2 = b2 / 100 + 0.5 * c2 / 100
y2 = c2 / 100 * height
grid_lines.append({"x": x1, "y": y1, "x2": x2, "y2": y2})
grid_df = pd.DataFrame(grid_lines)
# Create tick marks along each edge (exclude 0 and 100 to avoid vertex overlap)
tick_data = []
tick_length = 0.03
for pct in [20, 40, 60, 80]:
# Bottom edge ticks (sand axis) - from left (100%) to right (0%)
tx = pct / 100
tick_data.append(
{"x": tx, "y": 0, "x2": tx, "y2": -tick_length, "label": str(100 - pct), "label_x": tx, "label_y": -0.06}
)
# Left edge ticks (clay axis) - from bottom (0%) to top (100%)
cx = 0.5 * pct / 100
cy = pct / 100 * height
dx = -tick_length * np.cos(np.pi / 6)
dy = -tick_length * np.sin(np.pi / 6)
tick_data.append(
{
"x": cx,
"y": cy,
"x2": cx + dx,
"y2": cy + dy,
"label": str(pct),
"label_x": cx + dx * 2.5,
"label_y": cy + dy * 2.5,
}
)
# Right edge ticks (silt axis) - from bottom (0%) to top (100%)
sx = 1 - 0.5 * pct / 100
sy = pct / 100 * height
dx = tick_length * np.cos(np.pi / 6)
dy = -tick_length * np.sin(np.pi / 6)
tick_data.append(
{
"x": sx,
"y": sy,
"x2": sx + dx,
"y2": sy + dy,
"label": str(pct),
"label_x": sx + dx * 2.5,
"label_y": sy + dy * 2.5,
}
)
tick_df = pd.DataFrame(tick_data)
# Vertex labels
vertex_labels = pd.DataFrame(
{"x": [0, 1, 0.5], "y": [-0.12, -0.12, height + 0.08], "label": ["Sand (100%)", "Silt (100%)", "Clay (100%)"]}
)
# Triangle outline
triangle = (
alt.Chart(triangle_vertices)
.mark_line(strokeWidth=3, color=INK_SOFT)
.encode(x=alt.X("x:Q"), y=alt.Y("y:Q"), order="order:O")
)
# Grid lines
grid = (
alt.Chart(grid_df)
.mark_rule(strokeWidth=1, opacity=0.15, color=INK_SOFT)
.encode(x="x:Q", y="y:Q", x2="x2:Q", y2="y2:Q")
)
# Tick marks
ticks = alt.Chart(tick_df).mark_rule(strokeWidth=2, color=INK_SOFT).encode(x="x:Q", y="y:Q", x2="x2:Q", y2="y2:Q")
# Tick labels
tick_labels = (
alt.Chart(tick_df).mark_text(fontSize=14, color=INK_SOFT).encode(x="label_x:Q", y="label_y:Q", text="label:N")
)
# Vertex labels
vertex_text = (
alt.Chart(vertex_labels)
.mark_text(fontSize=22, fontWeight="bold", color=INK)
.encode(x="x:Q", y="y:Q", text="label:N")
)
# Data points - Okabe-Ito brand green
points = (
alt.Chart(df)
.mark_point(filled=True, size=300, color=BRAND, opacity=0.8)
.encode(
x="x:Q",
y="y:Q",
tooltip=[
alt.Tooltip("Sand (%):Q", format=".1f"),
alt.Tooltip("Silt (%):Q", format=".1f"),
alt.Tooltip("Clay (%):Q", format=".1f"),
],
)
)
# Combine all layers and hide default axes
chart = (
alt.layer(grid, triangle, ticks, tick_labels, vertex_text, points)
.properties(
width=1600,
height=900,
background=PAGE_BG,
title=alt.Title(text="ternary-basic · altair · anyplot.ai", fontSize=28, color=INK),
)
.configure_axis(grid=False, domain=False, ticks=False, labels=False, title=None)
.configure_view(strokeWidth=0, fill=PAGE_BG)
)
# Save (1600 * 3 = 4800, 900 * 3 = 2700)
chart.save(f"plot-{THEME}.png", scale_factor=3.0)
chart.save(f"plot-{THEME}.html")
Part of Basic Ternary Plot on anyplot.ai.