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: letsplot 4.9.0 | Python 3.13.13
Quality: 93/100 | Updated: 2026-05-06
"""
import os
import numpy as np
import pandas as pd
from lets_plot import (
LetsPlot,
aes,
element_blank,
element_rect,
element_text,
geom_point,
geom_polygon,
geom_segment,
geom_text,
ggplot,
ggsave,
ggsize,
labs,
scale_color_manual,
theme,
)
LetsPlot.setup_html()
# 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"
# Okabe-Ito palette
IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
# Data: Soil composition samples (Sand, Silt, Clay)
np.random.seed(42)
# Generate realistic soil composition data in different soil type regions
samples = []
soil_types = []
# Sandy soils (high sand)
for _ in range(20):
sand = np.random.uniform(60, 90)
remaining = 100 - sand
silt = np.random.uniform(0, remaining)
clay = remaining - silt
samples.append((sand, silt, clay))
soil_types.append("Sandy")
# Silty soils (high silt)
for _ in range(20):
silt = np.random.uniform(50, 80)
remaining = 100 - silt
sand = np.random.uniform(0, remaining)
clay = remaining - sand
samples.append((sand, silt, clay))
soil_types.append("Silty")
# Clay soils (high clay)
for _ in range(20):
clay = np.random.uniform(40, 70)
remaining = 100 - clay
sand = np.random.uniform(0, remaining)
silt = remaining - sand
samples.append((sand, silt, clay))
soil_types.append("Clayey")
# Convert ternary to Cartesian coordinates
# Formula: x = 0.5 * (2*b + c) / (a+b+c), y = sqrt(3)/2 * c / (a+b+c)
# where a=Sand (bottom-left), b=Silt (bottom-right), c=Clay (top)
sqrt3_2 = np.sqrt(3) / 2
x_coords = []
y_coords = []
for sand, silt, clay in samples:
total = sand + silt + clay
silt_norm = silt / total
clay_norm = clay / total
x_coords.append(0.5 * (2 * silt_norm + clay_norm))
y_coords.append(sqrt3_2 * clay_norm)
df = pd.DataFrame({"x": x_coords, "y": y_coords, "soil_type": soil_types})
# Triangle vertices (Sand at bottom-left, Silt at bottom-right, Clay at top)
vertices = pd.DataFrame({"x": [0, 1, 0.5, 0], "y": [0, 0, sqrt3_2, 0]})
# Grid lines at 20% intervals
grid_segments = []
for pct in [0.2, 0.4, 0.6, 0.8]:
# Lines parallel to Sand-Silt edge (constant Clay)
x1 = 0.5 * (0 + pct)
y1 = sqrt3_2 * pct
x2 = 0.5 * (2 * (1 - pct) + pct)
y2 = sqrt3_2 * pct
grid_segments.append({"x": x1, "y": y1, "xend": x2, "yend": y2})
# Lines parallel to Sand-Clay edge (constant Silt)
x1 = 0.5 * (2 * pct + 0)
y1 = 0
x2 = 0.5 * (2 * pct + (1 - pct))
y2 = sqrt3_2 * (1 - pct)
grid_segments.append({"x": x1, "y": y1, "xend": x2, "yend": y2})
# Lines parallel to Silt-Clay edge (constant Sand)
x1 = 0.5 * (2 * (1 - pct) + 0)
y1 = 0
x2 = 0.5 * (0 + (1 - pct))
y2 = sqrt3_2 * (1 - pct)
grid_segments.append({"x": x1, "y": y1, "xend": x2, "yend": y2})
grid_df = pd.DataFrame(grid_segments)
# Vertex labels with offset for readability
label_offset = 0.06
labels_df = pd.DataFrame(
{
"x": [0 + label_offset, 1 - label_offset, 0.5],
"y": [-label_offset - 0.02, -label_offset - 0.02, sqrt3_2 + label_offset],
"label": ["Sand (%)", "Silt (%)", "Clay (%)"],
}
)
# Tick labels along edges (at 20%, 40%, 60%, 80%)
tick_labels = []
for pct in [20, 40, 60, 80]:
frac = pct / 100
# Along left edge
x = 0.5 * frac
y = sqrt3_2 * frac
tick_labels.append({"x": x - 0.04, "y": y + 0.02, "label": str(pct)})
# Along right edge
x = 0.5 * (2 * (1 - frac) + frac)
y = sqrt3_2 * frac
tick_labels.append({"x": x + 0.04, "y": y + 0.02, "label": str(pct)})
# Along bottom edge
x = 0.5 * (2 * frac)
y = 0
tick_labels.append({"x": x, "y": y - 0.04, "label": str(pct)})
tick_df = pd.DataFrame(tick_labels)
# Plot
plot = (
ggplot()
# Triangle outline
+ geom_polygon(data=vertices, mapping=aes(x="x", y="y"), fill=PAGE_BG, color=INK_SOFT, size=1.5, alpha=1)
# Grid lines
+ geom_segment(
data=grid_df, mapping=aes(x="x", y="y", xend="xend", yend="yend"), color=INK_SOFT, size=0.6, alpha=0.3
)
# Data points
+ geom_point(data=df, mapping=aes(x="x", y="y", color="soil_type"), size=6, alpha=0.8)
# Vertex labels
+ geom_text(data=labels_df, mapping=aes(x="x", y="y", label="label"), size=18, fontface="bold", color=INK)
# Tick labels
+ geom_text(data=tick_df, mapping=aes(x="x", y="y", label="label"), size=11, color=INK_SOFT)
# Color scale using Okabe-Ito palette
+ scale_color_manual(values=IMPRINT)
# Labels and title
+ labs(title="ternary-basic · letsplot · anyplot.ai", color="Soil Type")
# Theme
+ theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
plot_title=element_text(size=24, face="bold", color=INK),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_title=element_text(size=18, color=INK),
legend_text=element_text(size=16, color=INK_SOFT),
axis_title=element_blank(),
axis_text=element_blank(),
axis_ticks=element_blank(),
axis_line=element_blank(),
panel_grid=element_blank(),
legend_position="right",
)
+ ggsize(1600, 900)
)
# Save as PNG (scale 3x to get 4800 x 2700 px) and HTML
ggsave(plot, f"plot-{THEME}.png", path=".", scale=3)
ggsave(plot, f"plot-{THEME}.html", path=".")
Part of Basic Ternary Plot on anyplot.ai.