A band plot displays a filled region between two boundary lines, commonly used to show confidence intervals, prediction intervals, or ranges around a central trend line. The semi-transparent band provides visual representation of uncertainty or variability while maintaining visibility of underlying data or overlapping elements.
""" anyplot.ai
band-basic: Basic Band Plot
Library: altair 6.1.0 | Python 3.13.13
Quality: 91/100 | Updated: 2026-05-29
"""
import importlib
import os
import sys
# Drop script directory from sys.path so the `altair` package resolves, not this file
sys.path[:] = [p for p in sys.path if os.path.abspath(p or ".") != os.path.dirname(os.path.abspath(__file__))]
alt = importlib.import_module("altair")
np = importlib.import_module("numpy")
pd = importlib.import_module("pandas")
Image = importlib.import_module("PIL.Image")
# 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"
# Imprint palette
BRAND = "#009E73" # Imprint palette position 1 — ALWAYS first series
IMPRINT_BLUE = "#4467A3" # Imprint palette position 3 — band fills
IMPRINT_RED = "#AE3030" # Imprint palette position 5 — semantic callout anchor
# Data - oscilloscope voltage measurement with growing uncertainty
np.random.seed(42)
x = np.linspace(0, 10, 100)
y_center = 2 * np.sin(x) + 0.5 * x # Sinusoidal signal with linear drift
# Confidence band widens over time (realistic sensor drift uncertainty)
uncertainty = 0.5 + 0.15 * x
y_lower = y_center - 1.96 * uncertainty
y_upper = y_center + 1.96 * uncertainty
y_inner_lower = y_center - 0.674 * uncertainty # 50% CI inner band
y_inner_upper = y_center + 0.674 * uncertainty
df = pd.DataFrame(
{
"x": x,
"y_center": y_center,
"y_lower": y_lower,
"y_upper": y_upper,
"y_inner_lower": y_inner_lower,
"y_inner_upper": y_inner_upper,
}
)
# Annotation data — callout at x≈9.0 s where uncertainty is wide and clearly visible
ann_row = df.iloc[90]
ann_df = pd.DataFrame(
{
"x": [ann_row["x"]],
"y_upper": [ann_row["y_upper"]],
"y_lower": [ann_row["y_lower"]],
"y_mid": [(ann_row["y_upper"] + ann_row["y_lower"]) / 2],
"label": [f"95% CI: ±{(ann_row['y_upper'] - ann_row['y_lower']) / 2:.1f} mV"],
}
)
# Nearest-point selection for interactive HTML export
nearest = alt.selection_point(nearest=True, on="pointerover", fields=["x"], empty=False)
# 95% confidence band (outer) — Imprint blue at low opacity for depth
band_outer = (
alt.Chart(df)
.mark_area(opacity=0.15, color=IMPRINT_BLUE, interpolate="monotone")
.encode(
x=alt.X("x:Q", title="Time (s)"), y=alt.Y("y_lower:Q", title="Oscilloscope Signal (mV)"), y2=alt.Y2("y_upper:Q")
)
)
# 50% confidence band (inner) — Imprint blue at higher opacity for layered contrast
band_inner = (
alt.Chart(df)
.mark_area(opacity=0.30, color=IMPRINT_BLUE, interpolate="monotone")
.encode(x="x:Q", y="y_inner_lower:Q", y2="y_inner_upper:Q")
)
# Central trend line — Imprint brand green (first/primary series)
line = alt.Chart(df).mark_line(strokeWidth=2.5, color=BRAND, interpolate="monotone").encode(x="x:Q", y="y_center:Q")
# Annotation: dashed vertical bracket showing uncertainty span
ann_rule = (
alt.Chart(ann_df)
.mark_rule(color=IMPRINT_RED, strokeWidth=1.5, strokeDash=[6, 3])
.encode(x="x:Q", y="y_lower:Q", y2="y_upper:Q")
)
ann_text = (
alt.Chart(ann_df)
.mark_text(align="left", dx=10, fontSize=12, fontWeight="bold", color=IMPRINT_RED)
.encode(x="x:Q", y="y_mid:Q", text="label:N")
)
# Interactive tooltip points — visible on hover in HTML, hidden in static PNG
tooltip_points = (
alt.Chart(df)
.mark_point(color=BRAND, size=80)
.encode(
x="x:Q",
y="y_center:Q",
opacity=alt.condition(nearest, alt.value(1), alt.value(0)),
tooltip=[
alt.Tooltip("x:Q", title="Time (s)", format=".1f"),
alt.Tooltip("y_center:Q", title="Signal (mV)", format=".2f"),
alt.Tooltip("y_lower:Q", title="95% CI Lower", format=".2f"),
alt.Tooltip("y_upper:Q", title="95% CI Upper", format=".2f"),
],
)
.add_params(nearest)
)
# Vertical guide rule — visible on hover in HTML only
guide_rule = (
alt.Chart(df)
.mark_rule(color=INK_SOFT, strokeDash=[4, 4])
.encode(x="x:Q", opacity=alt.condition(nearest, alt.value(0.5), alt.value(0)))
)
title = "band-basic · python · altair · anyplot.ai"
n = len(title)
ratio = 67 / n if n > 67 else 1.0
title_fs = max(11, round(16 * ratio))
# Combine layers with theme-adaptive chrome
chart = (
(band_outer + band_inner + line + ann_rule + ann_text + tooltip_points + guide_rule)
.properties(width=620, height=320, background=PAGE_BG, title=alt.Title(title, fontSize=title_fs, color=INK))
.configure_view(fill=PAGE_BG, stroke=None)
.configure_axis(
labelFontSize=10,
titleFontSize=12,
labelColor=INK_SOFT,
titleColor=INK,
domainColor=INK_SOFT,
tickColor=INK_SOFT,
gridColor=INK,
gridOpacity=0.12,
)
.configure_axisX(grid=False)
)
# Save PNG
chart.save(f"plot-{THEME}.png", scale_factor=4.0)
# Pad PNG to exact 3200×1800 target (vl-convert may land slightly short)
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")
# Save HTML
chart.save(f"plot-{THEME}.html")
Part of Basic Band Plot on anyplot.ai.