A panoramic mountain silhouette chart that renders the horizon as seen from a fixed vantage point, like a photograph of a ridgeline against the sky. A filled area under the skyline curve traces the ridgeline across a horizontal viewing range (in degrees of bearing or horizontal distance), and major summits are annotated with their name and elevation. The skyline is jagged and angular — sharp triangular peaks with steep, often asymmetric flanks meeting at pointed apexes, connected by rugged ridges with cols, sub-peaks and rocky notches — not a sequence of smooth bell-shaped bumps. Unlike an elevation-profile-along-a-trail, this plot is the angular view of the surrounding peaks from a single observer, making it ideal for summit-identification infographics, alpine panoramas, and travel guides.
""" anyplot.ai
area-mountain-panorama: Mountain Panorama Profile with Labeled Peaks
Library: plotnine 0.15.7 | Python 3.13.14
Quality: 87/100 | Updated: 2026-06-30
"""
import os
import numpy as np
import pandas as pd
from plotnine import (
aes,
coord_cartesian,
element_blank,
element_line,
element_rect,
element_text,
geom_line,
geom_point,
geom_ribbon,
geom_segment,
geom_text,
ggplot,
labs,
scale_x_continuous,
scale_y_continuous,
theme,
theme_minimal,
)
# Imprint palette / theme-adaptive chrome 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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
BRAND = "#009E73" # Imprint palette position 1 — peak markers (data elements)
MOUNTAIN = "#263040" # Dark slate — spec requires dark solid fill for dusk/photo-like feel
# Wallis (Valais) panorama from Gornergrat vantage
peaks = (
pd.DataFrame(
{
"name": [
"Weisshorn",
"Zinalrothorn",
"Ober Gabelhorn",
"Dent Blanche",
"Matterhorn",
"Liskamm",
"Castor",
"Pollux",
"Breithorn",
"Monte Rosa",
"Strahlhorn",
"Rimpfischhorn",
"Allalinhorn",
"Alphubel",
"Täschhorn",
"Dom",
],
"angle_deg": [4, 12, 19, 26, 38, 60, 67, 72, 78, 90, 105, 113, 122, 130, 137, 144],
"elevation_m": [
4506,
4221,
4063,
4358,
4478,
4527,
4223,
4092,
4164,
4634,
4190,
4199,
4027,
4206,
4491,
4545,
],
}
)
.sort_values("angle_deg")
.reset_index(drop=True)
)
FLOOR = 2500
# Piecewise-linear tent/triangle functions — spec forbids Gaussian bumps;
# each peak contributes a sharp triangular profile with asymmetric linear flanks
np.random.seed(42)
n_samples = 1600
angles = np.linspace(-3, 152, n_samples)
# Undulating base ridge (valley floors and connecting ridges between peaks)
base_ridge = 2750 + 80 * np.sin(angles / 14) + 55 * np.sin(angles / 4.5 + 0.7)
elevation = base_ridge.copy()
for _, p in peaks.iterrows():
# Per-peak seed for reproducible asymmetric flank widths independent of iteration order
rng = np.random.RandomState(int(p["angle_deg"] * 17 + 3))
left_w = rng.uniform(6, 14) # degrees from apex to left base
right_w = rng.uniform(5, 11) # degrees from apex to right base (asymmetric)
peak_h = p["elevation_m"] - FLOOR
dist = angles - p["angle_deg"]
tent = np.where(
dist < 0,
np.maximum(0.0, 1.0 + dist / left_w), # linear rise on left flank
np.maximum(0.0, 1.0 - dist / right_w), # linear fall on right flank
)
elevation = np.maximum(elevation, FLOOR + peak_h * tent)
# Window=3 only: preserves rocky jaggedness while removing single-point spikes
ridge_noise = np.random.normal(0, 28, n_samples)
elevation = elevation + ridge_noise
elevation = pd.Series(elevation).rolling(window=3, center=True, min_periods=1).mean().values
elevation = np.maximum(elevation, FLOOR)
skyline = pd.DataFrame({"angle_deg": angles, "elevation_m": elevation, "y_floor": FLOOR})
# 3-row stagger — 3 vertical tiers break up the dense 0–78° cluster
row_map = {
"Weisshorn": 0,
"Zinalrothorn": 1,
"Ober Gabelhorn": 2,
"Dent Blanche": 0,
"Matterhorn": 1,
"Liskamm": 0,
"Castor": 2,
"Pollux": 1,
"Breithorn": 0,
"Monte Rosa": 2,
"Strahlhorn": 1,
"Rimpfischhorn": 0,
"Allalinhorn": 2,
"Alphubel": 1,
"Täschhorn": 0,
"Dom": 2,
}
row_y = {0: 5050, 1: 5250, 2: 5450}
peaks["row"] = peaks["name"].map(row_map)
peaks["label_y"] = peaks["row"].map(row_y)
peaks["leader_top"] = peaks["label_y"] - 80
peaks["label"] = peaks.apply(lambda r: f"{r['name']}\n{int(r['elevation_m']):,} m", axis=1)
peaks["is_anchor"] = peaks["name"] == "Matterhorn"
others = peaks[~peaks["is_anchor"]]
anchor = peaks[peaks["is_anchor"]]
plot = (
ggplot()
# Dark silhouette fill — photo-like, evening/dusk alpine feel
+ geom_ribbon(aes(x="angle_deg", ymin="y_floor", ymax="elevation_m"), data=skyline, fill=MOUNTAIN, alpha=1.0)
# Ridgeline outline for crispness at the sky-mountain boundary
+ geom_line(aes(x="angle_deg", y="elevation_m"), data=skyline, color=INK_SOFT, size=0.4, alpha=0.35)
# Leader lines from summit up to label tier
+ geom_segment(
aes(x="angle_deg", xend="angle_deg", y="elevation_m", yend="leader_top"),
data=others,
color=INK_MUTED,
size=0.35,
)
+ geom_segment(
aes(x="angle_deg", xend="angle_deg", y="elevation_m", yend="leader_top"), data=anchor, color=INK, size=0.65
)
# Summit markers — brand green data elements marking each labeled peak
+ geom_point(
aes(x="angle_deg", y="elevation_m"), data=others, size=2.0, color=PAGE_BG, fill=BRAND, stroke=0.5, shape="o"
)
+ geom_point(
aes(x="angle_deg", y="elevation_m"), data=anchor, size=3.2, color=PAGE_BG, fill=BRAND, stroke=0.9, shape="o"
)
# Peak labels — Matterhorn bold as focal summit
+ geom_text(
aes(x="angle_deg", y="label_y", label="label"), data=others, size=3.0, color=INK, ha="center", va="bottom"
)
+ geom_text(
aes(x="angle_deg", y="label_y", label="label"),
data=anchor,
size=3.8,
color=INK,
ha="center",
va="bottom",
fontweight="bold",
)
+ scale_x_continuous(expand=(0.005, 0))
+ scale_y_continuous(
breaks=[2500, 3000, 3500, 4000, 4500, 5000], labels=["2,500", "3,000", "3,500", "4,000", "4,500", "5,000"]
)
+ coord_cartesian(xlim=(-2, 151), ylim=(2500, 5700))
+ labs(
x="",
y="Elevation (m)",
title="Wallis from Gornergrat · area-mountain-panorama · python · plotnine · anyplot.ai",
)
+ theme_minimal()
+ theme(
figure_size=(8, 4.5),
text=element_text(size=7, color=INK_SOFT),
plot_title=element_text(size=11, color=INK, ha="left", margin={"b": 8}),
axis_title_y=element_text(size=10, color=INK, margin={"r": 8}),
axis_text_y=element_text(size=8, color=INK_SOFT),
axis_text_x=element_blank(),
axis_ticks=element_blank(),
axis_line=element_blank(),
panel_grid_major_x=element_blank(),
panel_grid_major_y=element_line(color=INK_SOFT, size=0.25, alpha=0.12),
panel_grid_minor=element_blank(),
panel_border=element_blank(),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
plot_margin=0.03,
)
)
plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)
Part of Mountain Panorama Profile with Labeled Peaks on anyplot.ai.