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feat(altair): implement column-stratigraphic (#4912)
## Implementation: `column-stratigraphic` - altair Implements the **altair** version of `column-stratigraphic`. **File:** `plots/column-stratigraphic/implementations/altair.py` **Parent Issue:** #4573 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/23121198787)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/column-stratigraphic/implementations/altair.py‎

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""" pyplots.ai
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column-stratigraphic: Stratigraphic Column with Lithology Patterns
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Library: altair 6.0.0 | Python 3.14.3
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Quality: 87/100 | Created: 2026-03-15
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"""
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import altair as alt
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import pandas as pd
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# Data: Grand Canyon sedimentary section with 10 layers spanning Cambrian to Permian
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# Dramatic thickness variation (10-35 m) to showcase the format
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layers = pd.DataFrame(
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{
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"top": [0, 30, 45, 75, 85, 110, 120, 155, 170, 180],
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"bottom": [30, 45, 75, 85, 110, 120, 155, 170, 180, 200],
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"lithology": [
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"Sandstone",
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"Shale",
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"Limestone",
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"Siltstone",
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"Sandstone",
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"Conglomerate",
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"Shale",
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"Limestone",
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"Siltstone",
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"Sandstone",
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],
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"formation": [
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"Cedar Mesa Fm",
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"Organ Rock Fm",
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"White Rim Fm",
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"De Chelly Fm",
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"Coconino Fm",
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"Hermit Fm",
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"Supai Group",
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"Redwall Fm",
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"Temple Butte Fm",
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"Muav Fm",
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],
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"age": [
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"Permian",
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"Permian",
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"Permian",
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"Permian",
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"Permian",
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"Permian",
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"Pennsylvanian",
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"Mississippian",
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"Devonian",
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"Cambrian",
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],
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}
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)
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layers["thickness"] = layers["bottom"] - layers["top"]
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layers["mid_depth"] = (layers["top"] + layers["bottom"]) / 2
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# Colorblind-safe geological palette
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lithology_colors = {
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"Sandstone": "#E8C547",
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"Shale": "#7B8894",
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"Limestone": "#4A90D9",
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"Siltstone": "#9B6DBF",
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"Conglomerate": "#D4772C",
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}
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lithology_order = ["Sandstone", "Shale", "Limestone", "Siltstone", "Conglomerate"]
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# Lithology pattern symbols — wider for better visibility
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pattern_symbols = {
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"Sandstone": "· · · · · · · ·",
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"Shale": "— — — — — —",
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"Limestone": "▤ ▤ ▤ ▤ ▤ ▤",
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"Siltstone": "╌ ╌ ╌ ╌ ╌ ╌",
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"Conglomerate": "◯ ◯ ◯ ◯ ◯ ◯",
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}
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# Create multiple pattern rows per layer for denser texture fill
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pattern_rows = []
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for _, row in layers.iterrows():
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layer_height = row["bottom"] - row["top"]
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n_rows = max(2, int(layer_height / 6))
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spacing = layer_height / (n_rows + 1)
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for i in range(n_rows):
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depth = row["top"] + spacing * (i + 1)
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pattern_rows.append({"depth": depth, "pattern": pattern_symbols[row["lithology"]]})
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pattern_df = pd.DataFrame(pattern_rows)
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# Identify unique age groups
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age_groups = []
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current_age = None
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for _, row in layers.iterrows():
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if row["age"] != current_age:
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current_age = row["age"]
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group_rows = layers[layers["age"] == current_age]
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age_groups.append(
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{
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"age": current_age,
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"top": group_rows["top"].min(),
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"bottom": group_rows["bottom"].max(),
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"mid_depth": (group_rows["top"].min() + group_rows["bottom"].max()) / 2,
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}
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)
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age_df = pd.DataFrame(age_groups)
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# Unconformity markers at major geological transitions
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unconformity_df = pd.DataFrame({"depth": [120, 170], "label": ["Unconformity", "Great Unconformity"]})
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# Shared scales — wider x domain for better canvas utilization
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x_domain = [0, 18]
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x_axis_none = alt.Axis(labels=False, ticks=False, domain=False, grid=False)
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# Layer rectangles — wider column (x: 2.5 to 10.5)
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rects = (
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alt.Chart(layers)
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.mark_rect(stroke="#2C3E50", strokeWidth=1.5)
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.encode(
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y=alt.Y(
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"top:Q",
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title="Depth (m)",
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scale=alt.Scale(domain=[0, 200], reverse=True),
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axis=alt.Axis(
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labelFontSize=18,
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titleFontSize=22,
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tickCount=10,
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gridColor="#D5D8DC",
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gridDash=[2, 4],
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domainColor="#2C3E50",
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domainWidth=1.5,
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),
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),
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y2="bottom:Q",
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x=alt.X("x:Q", scale=alt.Scale(domain=x_domain), axis=None),
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x2="x2:Q",
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color=alt.Color(
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"lithology:N",
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title="Lithology",
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scale=alt.Scale(domain=lithology_order, range=[lithology_colors[k] for k in lithology_order]),
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legend=alt.Legend(
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titleFontSize=20,
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labelFontSize=18,
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symbolSize=600,
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orient="bottom",
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titlePadding=12,
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direction="horizontal",
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labelLimit=200,
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symbolStrokeWidth=1.5,
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symbolStrokeColor="#2C3E50",
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padding=20,
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columns=5,
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),
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),
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tooltip=[
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alt.Tooltip("formation:N", title="Formation"),
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alt.Tooltip("lithology:N", title="Lithology"),
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alt.Tooltip("age:N", title="Age"),
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alt.Tooltip("top:Q", title="Top (m)"),
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alt.Tooltip("bottom:Q", title="Bottom (m)"),
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alt.Tooltip("thickness:Q", title="Thickness (m)"),
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],
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)
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.transform_calculate(x="2.5", x2="10.5")
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)
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# Dense pattern texture overlay — bolder and more prominent
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pattern_text = (
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alt.Chart(pattern_df)
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.mark_text(fontSize=17, color="#2C3E50", opacity=0.65, fontWeight="bold")
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.encode(y=alt.Y("depth:Q"), x=alt.X("x_mid:Q", scale=alt.Scale(domain=x_domain)), text="pattern:N")
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.transform_calculate(x_mid="6.5")
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)
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# Formation name labels to the right
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formation_labels = (
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alt.Chart(layers)
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.mark_text(fontSize=17, fontWeight="bold", align="left", color="#1B2631")
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.encode(y=alt.Y("mid_depth:Q"), x=alt.X("x_pos:Q", scale=alt.Scale(domain=x_domain)), text="formation:N")
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.transform_calculate(x_pos="11.0")
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)
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# Thickness annotations — larger font for readability
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thickness_labels = (
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alt.Chart(layers)
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.mark_text(fontSize=16, align="right", color="#7F8C8D", fontStyle="italic")
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.encode(y=alt.Y("mid_depth:Q"), x=alt.X("x_pos:Q", scale=alt.Scale(domain=x_domain)), text="label:N")
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.transform_calculate(x_pos="17.8", label="datum.thickness + ' m'")
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)
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# Age period labels to the left
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age_labels = (
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alt.Chart(age_df)
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.mark_text(fontSize=18, fontStyle="italic", fontWeight="bold", align="right", color="#2C3E50")
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.encode(y=alt.Y("mid_depth:Q"), x=alt.X("x_pos:Q", scale=alt.Scale(domain=x_domain)), text="age:N")
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.transform_calculate(x_pos="1.8")
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)
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# Age bracket vertical lines
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age_brackets_v = (
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alt.Chart(age_df)
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.mark_rule(strokeWidth=2.5, color="#2C3E50")
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.encode(y=alt.Y("top:Q"), y2="bottom:Q", x=alt.X("x_pos:Q", scale=alt.Scale(domain=x_domain)))
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.transform_calculate(x_pos="2.2")
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)
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# Age bracket horizontal ticks (top and bottom of each age group)
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bracket_ticks_data = []
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for _, row in age_df.iterrows():
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bracket_ticks_data.append({"depth": row["top"]})
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bracket_ticks_data.append({"depth": row["bottom"]})
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bracket_ticks_df = pd.DataFrame(bracket_ticks_data)
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age_bracket_ticks = (
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alt.Chart(bracket_ticks_df)
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.mark_rule(strokeWidth=2.5, color="#2C3E50")
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.encode(y=alt.Y("depth:Q"), x=alt.X("x1:Q", scale=alt.Scale(domain=x_domain)), x2="x2:Q")
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.transform_calculate(x1="2.2", x2="2.5")
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)
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# Unconformity markers — red dashed lines at key geological transitions
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unconformity_rules = (
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alt.Chart(unconformity_df)
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.mark_rule(strokeWidth=4, color="#C0392B", strokeDash=[8, 4])
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.encode(y=alt.Y("depth:Q"), x=alt.X("x1:Q", scale=alt.Scale(domain=x_domain)), x2="x2:Q")
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.transform_calculate(x1="2.5", x2="10.5")
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)
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# Unconformity labels — positioned to the right of column to avoid overlapping patterns
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unconformity_labels_chart = (
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alt.Chart(unconformity_df)
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.mark_text(fontSize=13, color="#C0392B", fontWeight="bold", align="left", dy=-10)
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.encode(y=alt.Y("depth:Q"), x=alt.X("x_mid:Q", scale=alt.Scale(domain=x_domain)), text="label:N")
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.transform_calculate(x_mid="11.0")
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)
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# Combine all layers
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chart = (
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(
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rects
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+ pattern_text
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+ formation_labels
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+ thickness_labels
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+ age_labels
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+ age_brackets_v
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+ age_bracket_ticks
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+ unconformity_rules
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+ unconformity_labels_chart
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)
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.properties(
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width=1400,
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height=900,
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title=alt.Title(
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"column-stratigraphic · altair · pyplots.ai",
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fontSize=28,
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anchor="middle",
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offset=20,
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color="#1B2631",
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subtitle="Grand Canyon Sedimentary Section — Cambrian to Permian",
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subtitleFontSize=18,
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subtitleColor="#566573",
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subtitlePadding=8,
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),
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)
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.configure_view(strokeWidth=0)
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.configure(background="#FAFBFC")
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)
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# Save
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chart.save("plot.png", scale_factor=3.0)
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chart.save("plot.html")

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