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Merge branch 'main' into implementation/area-elevation-profile/pygal
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‎plots/area-elevation-profile/implementations/altair.py‎

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""" pyplots.ai
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area-elevation-profile: Terrain Elevation Profile Along Transect
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Library: altair 6.0.0 | Python 3.14.3
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Quality: 86/100 | Created: 2026-03-15
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"""
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import altair as alt
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import numpy as np
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import pandas as pd
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from PIL import Image
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# Data - Alpine hiking trail ~120 km with realistic terrain
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np.random.seed(42)
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num_points = 480
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distance = np.linspace(0, 120, num_points)
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# Build realistic elevation profile with multiple peaks and valleys
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elevation = 900 + np.zeros(num_points)
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elevation += 1000 * np.sin(distance * np.pi / 60) ** 2
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elevation += 500 * np.sin(distance * np.pi / 30 + 1.2) ** 2
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elevation += 250 * np.sin(distance * np.pi / 15 + 0.5)
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elevation += np.cumsum(np.random.randn(num_points) * 3)
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elevation += np.random.randn(num_points) * 15
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kernel = np.ones(5) / 5
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elevation = np.convolve(elevation, kernel, mode="same")
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elevation = np.clip(elevation, 600, 2800)
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df = pd.DataFrame({"distance": distance, "elevation": elevation})
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# Landmarks along the trail with type for visual differentiation
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landmarks = pd.DataFrame(
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{
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"name": [
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"Grindelwald (Start)",
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"Bachsee Lake",
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"Faulhorn Summit",
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"Schynige Platte",
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"Kleine Scheidegg",
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"Männlichen Summit",
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"Wengen (End)",
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],
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"distance": [0.0, 18.0, 35.0, 55.0, 75.0, 95.0, 120.0],
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"type": ["town", "lake", "summit", "plateau", "pass", "summit", "town"],
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}
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)
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landmarks["elevation"] = np.interp(landmarks["distance"], distance, elevation)
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# Combined label: name + elevation on two lines
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landmarks["label"] = landmarks.apply(lambda r: f"{r['name']}\n{r['elevation']:.0f} m", axis=1)
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# Tighter axis domains to eliminate wasted canvas space
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y_min = int(np.floor(elevation.min() / 100) * 100)
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# Area chart - terrain silhouette with gradient fill
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area = (
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alt.Chart(df)
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.mark_area(
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line={"color": "#306998", "strokeWidth": 2.5},
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color=alt.Gradient(
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gradient="linear",
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stops=[
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alt.GradientStop(color="rgba(48, 105, 152, 0.05)", offset=0),
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alt.GradientStop(color="rgba(48, 105, 152, 0.22)", offset=0.3),
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alt.GradientStop(color="rgba(48, 105, 152, 0.55)", offset=1),
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],
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x1=1,
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x2=1,
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y1=1,
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y2=0,
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),
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)
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.encode(
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x=alt.X("distance:Q", title="Distance (km)", scale=alt.Scale(domain=[0, 120])),
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y=alt.Y("elevation:Q", title="Elevation (m)", scale=alt.Scale(domain=[y_min, 2800])),
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tooltip=[
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alt.Tooltip("distance:Q", title="Distance (km)", format=".1f"),
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alt.Tooltip("elevation:Q", title="Elevation (m)", format=".0f"),
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],
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)
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)
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# Landmark vertical rules with color by type
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landmark_rules = (
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alt.Chart(landmarks)
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.mark_rule(strokeWidth=1, strokeDash=[5, 4], opacity=0.4)
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.encode(x="distance:Q", color=alt.condition(alt.datum.type == "summit", alt.value("#A0522D"), alt.value("#8B7355")))
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)
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# Landmark points with shape encoding by type
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landmark_points = (
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alt.Chart(landmarks)
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.mark_point(size=160, filled=True, stroke="white", strokeWidth=1.5)
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.encode(
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x="distance:Q",
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y="elevation:Q",
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shape=alt.Shape(
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"type:N",
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legend=None,
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scale=alt.Scale(
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domain=["summit", "lake", "pass", "plateau", "town"],
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range=["triangle-up", "diamond", "cross", "square", "circle"],
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),
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),
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color=alt.Color(
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"type:N",
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legend=None,
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scale=alt.Scale(
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domain=["summit", "lake", "pass", "plateau", "town"],
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range=["#A0522D", "#4682B4", "#8B7355", "#6B8E23", "#555555"],
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),
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),
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)
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)
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# Combined label layers (name + elevation) - 3 layers by alignment
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lm_start = landmarks[landmarks["distance"] == 0.0]
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lm_end = landmarks[landmarks["distance"] == 120.0]
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lm_mid = landmarks[(landmarks["distance"] > 0) & (landmarks["distance"] < 120)]
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label_start = (
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alt.Chart(lm_start)
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.mark_text(
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align="left", dx=10, dy=-60, fontSize=16, fontWeight="bold", color="#3A3A3A", lineBreak="\n", lineHeight=20
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)
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.encode(x="distance:Q", y="elevation:Q", text="label:N")
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)
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label_end = (
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alt.Chart(lm_end)
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.mark_text(
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align="right", dx=-10, dy=-60, fontSize=16, fontWeight="bold", color="#3A3A3A", lineBreak="\n", lineHeight=20
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)
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.encode(x="distance:Q", y="elevation:Q", text="label:N")
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)
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# Split mid landmarks by elevation to apply different dy offsets
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lm_mid_low = lm_mid[lm_mid["elevation"] < 1500].copy()
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lm_mid_high = lm_mid[lm_mid["elevation"] >= 1500].copy()
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label_mid_low = (
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alt.Chart(lm_mid_low)
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.mark_text(align="center", dy=-60, fontSize=16, fontWeight="bold", lineBreak="\n", lineHeight=20)
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.encode(
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x="distance:Q",
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y="elevation:Q",
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text="label:N",
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color=alt.Color(
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"type:N",
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legend=None,
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scale=alt.Scale(
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domain=["summit", "lake", "pass", "plateau", "town"],
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range=["#6B3A1F", "#2E5E7E", "#5C4E3C", "#4A6317", "#3A3A3A"],
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),
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),
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)
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)
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label_mid_high = (
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alt.Chart(lm_mid_high)
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.mark_text(align="center", dy=-35, fontSize=16, fontWeight="bold", lineBreak="\n", lineHeight=20)
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.encode(
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x="distance:Q",
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y="elevation:Q",
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text="label:N",
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color=alt.Color(
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"type:N",
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legend=None,
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scale=alt.Scale(
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domain=["summit", "lake", "pass", "plateau", "town"],
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range=["#6B3A1F", "#2E5E7E", "#5C4E3C", "#4A6317", "#3A3A3A"],
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),
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),
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)
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)
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# Compose layered chart
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chart = (
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alt.layer(area, landmark_rules, landmark_points, label_start, label_mid_low, label_mid_high, label_end)
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.properties(
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width=1600,
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height=900,
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title=alt.Title(
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"Bernese Oberland Trail · area-elevation-profile · altair · pyplots.ai",
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fontSize=28,
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subtitle="120 km hiking transect from Grindelwald to Wengen · Vertical exaggeration ~10×",
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subtitleFontSize=16,
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subtitleColor="#777777",
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anchor="start",
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offset=12,
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),
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)
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.configure(background="white")
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.configure_axis(
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labelFontSize=18, titleFontSize=22, gridOpacity=0.12, grid=True, domainColor="#999999", tickColor="#999999"
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)
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.configure_view(strokeWidth=0)
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)
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# Save - render, trim bottom whitespace, resize to 4800x2700
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chart.save("plot.png", scale_factor=3.0)
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img = Image.open("plot.png").convert("RGB")
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# Trim empty whitespace from bottom by scanning rows
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as_array = np.array(img)
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# Find last row that's not all-white (threshold 250 for anti-aliasing)
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row_has_content = np.any(as_array < 250, axis=(1, 2))
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last_content_row = np.max(np.where(row_has_content)) + 40 # 40px padding
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last_content_row = min(last_content_row, img.height)
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img = img.crop((0, 0, img.width, last_content_row))
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# Resize to target 4800x2700
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img = img.resize((4800, 2700), Image.LANCZOS)
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img.save("plot.png")
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chart.interactive().save("plot.html")

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