A vertical column visualization showing geological rock layers with standardized lithology patterns, formation names, ages, and thickness scales. Each layer is represented as a stacked rectangular block filled with a distinctive pattern (e.g., brick pattern for limestone, dots for sandstone, dashes for shale) following FGDC/USGS conventions. This plot is essential for communicating subsurface geology and sedimentary sequences in a compact, standardized format.
""" anyplot.ai
column-stratigraphic: Stratigraphic Column with Lithology Patterns
Library: altair 6.2.1 | Python 3.13.13
Quality: 87/100 | Updated: 2026-06-17
"""
import os
import altair as alt
import pandas as pd
from PIL import Image
# Theme-adaptive chrome (see prompts/default-style-guide.md "Theme-adaptive Chrome")
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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
# Imprint palette — theme-independent categorical hues. Lithologies are abstract
# rock-type categories, so positions are assigned 1→N in canonical order, but
# position 5 (matte red #AE3030) is reserved as the semantic anchor for the
# unconformity markers (a gap/loss in the geological record) — so lithologies
# use positions 1,2,3,4,6.
LITHO_GREEN = "#009E73" # Imprint 1 — brand, first categorical series
LITHO_LAV = "#C475FD" # Imprint 2
LITHO_BLUE = "#4467A3" # Imprint 3
LITHO_OCHRE = "#BD8233" # Imprint 4
LITHO_CYAN = "#2ABCCD" # Imprint 6
UNCONFORMITY = "#AE3030" # Imprint 5 — semantic red for missing-time / unconformity
# Data: Grand Canyon sedimentary section, 10 layers spanning Cambrian to Permian
# Dramatic thickness variation (10-35 m) to showcase the stratigraphic format
layers = pd.DataFrame(
{
"top": [0, 30, 45, 75, 85, 110, 120, 155, 170, 180],
"bottom": [30, 45, 75, 85, 110, 120, 155, 170, 180, 200],
"lithology": [
"Sandstone",
"Shale",
"Limestone",
"Siltstone",
"Sandstone",
"Conglomerate",
"Shale",
"Limestone",
"Siltstone",
"Sandstone",
],
"formation": [
"Cedar Mesa Fm",
"Organ Rock Fm",
"White Rim Fm",
"De Chelly Fm",
"Coconino Fm",
"Hermit Fm",
"Supai Group",
"Redwall Fm",
"Temple Butte Fm",
"Muav Fm",
],
"age": [
"Permian",
"Permian",
"Permian",
"Permian",
"Permian",
"Permian",
"Pennsylvanian",
"Mississippian",
"Devonian",
"Cambrian",
],
}
)
layers["thickness"] = layers["bottom"] - layers["top"]
layers["mid_depth"] = (layers["top"] + layers["bottom"]) / 2
# Lithology → Imprint palette (5 distinct rock types)
lithology_order = ["Sandstone", "Shale", "Limestone", "Siltstone", "Conglomerate"]
lithology_colors = {
"Sandstone": LITHO_GREEN,
"Shale": LITHO_LAV,
"Limestone": LITHO_BLUE,
"Siltstone": LITHO_OCHRE,
"Conglomerate": LITHO_CYAN,
}
# Lithology pattern glyphs approximating FGDC/USGS texture conventions. Single
# motif per rock type, tiled across the full column width (rows × columns) so each
# layer reads as a true fill texture rather than a centered band.
pattern_symbols = {"Sandstone": "·", "Shale": "—", "Limestone": "▤", "Siltstone": "╌", "Conglomerate": "◯"}
# Column horizontal span (data units within x_domain) — the rectangles and the
# tiled texture share these bounds.
COL_L, COL_R = 3.2, 10.7
# Texture grid: tile each layer with rows (depth) × columns (across the width) of
# the lithology glyph, so the pattern fills the whole rectangle.
n_cols = 11
col_x = [COL_L + 0.35 + i * ((COL_R - COL_L - 0.7) / (n_cols - 1)) for i in range(n_cols)]
pattern_rows = []
for _, row in layers.iterrows():
layer_height = row["bottom"] - row["top"]
n_rows = max(2, int(layer_height / 6))
spacing = layer_height / (n_rows + 1)
sym = pattern_symbols[row["lithology"]]
for i in range(n_rows):
depth = row["top"] + spacing * (i + 1)
for xp in col_x:
pattern_rows.append({"depth": depth, "pattern": sym, "x_mid": xp})
pattern_df = pd.DataFrame(pattern_rows)
# Age groups — one bracket per contiguous geological period
age_groups = []
current_age = None
for _, row in layers.iterrows():
if row["age"] != current_age:
current_age = row["age"]
group_rows = layers[layers["age"] == current_age]
age_groups.append(
{
"age": current_age,
"top": group_rows["top"].min(),
"bottom": group_rows["bottom"].max(),
"mid_depth": (group_rows["top"].min() + group_rows["bottom"].max()) / 2,
}
)
age_df = pd.DataFrame(age_groups)
# Unconformities at major age boundaries (missing time / erosional gaps)
unconformity_df = pd.DataFrame({"depth": [120, 170], "label": ["Unconformity", "Unconformity"]})
# Shared horizontal layout — generous x domain spreads side labels into clear lanes
x_domain = [0, 20]
# Layer rectangles — the stratigraphic column itself (x: 3.0 to 10.5)
rects = (
alt.Chart(layers)
.mark_rect(stroke=INK, strokeWidth=1.2)
.encode(
y=alt.Y(
"top:Q",
title="Depth (m)",
scale=alt.Scale(domain=[0, 200], reverse=True),
axis=alt.Axis(
labelFontSize=11,
titleFontSize=13,
tickCount=10,
gridColor=INK,
gridOpacity=0.15,
domainColor=INK_SOFT,
domainWidth=1.2,
tickColor=INK_SOFT,
labelColor=INK_SOFT,
titleColor=INK,
),
),
y2="bottom:Q",
x=alt.X("x:Q", scale=alt.Scale(domain=x_domain), axis=None),
x2="x2:Q",
color=alt.Color(
"lithology:N",
title="Lithology",
scale=alt.Scale(domain=lithology_order, range=[lithology_colors[k] for k in lithology_order]),
legend=alt.Legend(
titleFontSize=12,
labelFontSize=11,
symbolSize=320,
orient="bottom",
titlePadding=8,
direction="horizontal",
labelLimit=200,
symbolStrokeWidth=1.0,
symbolStrokeColor=INK_SOFT,
padding=10,
columns=5,
),
),
tooltip=[
alt.Tooltip("formation:N", title="Formation"),
alt.Tooltip("lithology:N", title="Lithology"),
alt.Tooltip("age:N", title="Age"),
alt.Tooltip("top:Q", title="Top (m)"),
alt.Tooltip("bottom:Q", title="Bottom (m)"),
alt.Tooltip("thickness:Q", title="Thickness (m)"),
],
)
.transform_calculate(x=f"{COL_L}", x2=f"{COL_R}")
)
# Tiled pattern texture overlay — dark ink reads on every Imprint fill in both themes
pattern_text = (
alt.Chart(pattern_df)
.mark_text(fontSize=13, color="#1A1A17", opacity=0.68, fontWeight="bold")
.encode(y=alt.Y("depth:Q"), x=alt.X("x_mid:Q", scale=alt.Scale(domain=x_domain)), text="pattern:N")
)
# Formation name labels — to the right of the column
formation_labels = (
alt.Chart(layers)
.mark_text(fontSize=12, fontWeight="bold", align="left", color=INK)
.encode(y=alt.Y("mid_depth:Q"), x=alt.X("x_pos:Q", scale=alt.Scale(domain=x_domain)), text="formation:N")
.transform_calculate(x_pos=f"{COL_R + 0.4}")
)
# Thickness annotations — far-right lane, tertiary text
thickness_labels = (
alt.Chart(layers)
.mark_text(fontSize=11, align="right", color=INK_MUTED, fontStyle="italic")
.encode(y=alt.Y("mid_depth:Q"), x=alt.X("x_pos:Q", scale=alt.Scale(domain=x_domain)), text="label:N")
.transform_calculate(x_pos="19.6", label="datum.thickness + ' m'")
)
# Age bracket vertical lines — far-left, just clear of the depth-axis numerals
age_brackets_v = (
alt.Chart(age_df)
.mark_rule(strokeWidth=2.0, color=INK_SOFT)
.encode(y=alt.Y("top:Q"), y2="bottom:Q", x=alt.X("x_pos:Q", scale=alt.Scale(domain=x_domain)))
.transform_calculate(x_pos="0.35")
)
# Age period labels — left-aligned immediately right of the bracket so the long
# italic period names sit in their own lane between the bracket and the column,
# never reaching back into the depth-axis tick numbers (140/160/180).
age_labels = (
alt.Chart(age_df)
.mark_text(fontSize=10, fontStyle="italic", fontWeight="bold", align="left", color=INK)
.encode(y=alt.Y("mid_depth:Q"), x=alt.X("x_pos:Q", scale=alt.Scale(domain=x_domain)), text="age:N")
.transform_calculate(x_pos="0.9")
)
# Age bracket horizontal ticks (top and bottom of each age group)
bracket_ticks_data = []
for _, row in age_df.iterrows():
bracket_ticks_data.append({"depth": row["top"]})
bracket_ticks_data.append({"depth": row["bottom"]})
bracket_ticks_df = pd.DataFrame(bracket_ticks_data)
age_bracket_ticks = (
alt.Chart(bracket_ticks_df)
.mark_rule(strokeWidth=2.0, color=INK_SOFT)
.encode(y=alt.Y("depth:Q"), x=alt.X("x1:Q", scale=alt.Scale(domain=x_domain)), x2="x2:Q")
.transform_calculate(x1="0.35", x2="0.7")
)
# Unconformity markers — red dashed lines crossing the column at age boundaries
unconformity_rules = (
alt.Chart(unconformity_df)
.mark_rule(strokeWidth=3.0, color=UNCONFORMITY, strokeDash=[8, 4])
.encode(y=alt.Y("depth:Q"), x=alt.X("x1:Q", scale=alt.Scale(domain=x_domain)), x2="x2:Q")
.transform_calculate(x1=f"{COL_L}", x2=f"{COL_R}")
)
# Unconformity labels — pinned just inside the column's left edge, lifted clear of
# the layer texture above the dashed rule
unconformity_labels_chart = (
alt.Chart(unconformity_df)
.mark_text(fontSize=12, color=UNCONFORMITY, fontWeight="bold", align="left", dy=-11)
.encode(y=alt.Y("depth:Q"), x=alt.X("x_pos:Q", scale=alt.Scale(domain=x_domain)), text="label:N")
.transform_calculate(x_pos=f"{COL_L + 0.15}")
)
# Compose all layers
title = "column-stratigraphic · python · altair · anyplot.ai"
chart = (
(
rects
+ pattern_text
+ formation_labels
+ thickness_labels
+ age_labels
+ age_brackets_v
+ age_bracket_ticks
+ unconformity_rules
+ unconformity_labels_chart
)
.properties(
width=700,
height=280,
title=alt.Title(
title,
fontSize=16,
anchor="middle",
offset=12,
color=INK,
subtitle="Grand Canyon Sedimentary Section — Cambrian to Permian",
subtitleFontSize=12,
subtitleColor=INK_SOFT,
subtitlePadding=6,
),
)
.configure_view(strokeWidth=0, fill=PAGE_BG)
.configure(background=PAGE_BG)
.configure_legend(fillColor=ELEVATED_BG, strokeColor=INK_SOFT, labelColor=INK_SOFT, titleColor=INK)
)
# Save PNG, then PAD-only up to the exact landscape target (never crop — see altair.md)
chart.save(f"plot-{THEME}.png", scale_factor=4.0)
TW, TH = 3200, 1800
_img = Image.open(f"plot-{THEME}.png").convert("RGB")
_w, _h = _img.size
if _w > TW or _h > TH:
raise SystemExit(
f"altair vl-convert produced {_w}×{_h}, exceeds target {TW}×{TH}. "
f"Shrink chart .properties(width=, height=) values and re-render."
)
if _w < TW or _h < TH:
_canvas = Image.new("RGB", (TW, TH), PAGE_BG)
_canvas.paste(_img, ((TW - _w) // 2, (TH - _h) // 2))
_canvas.save(f"plot-{THEME}.png")
chart.save(f"plot-{THEME}.html")
Part of Stratigraphic Column with Lithology Patterns on anyplot.ai.