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: bokeh 3.9.0 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-29
"""
import os
import time
from pathlib import Path
import numpy as np
from bokeh.io import output_file, save
from bokeh.models import ColumnDataSource
from bokeh.plotting import figure
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
# Theme tokens (Imprint palette — see prompts/default-style-guide.md)
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 = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]
# Data - Solar irradiance forecast with 95% confidence interval
np.random.seed(42)
hours = np.linspace(6, 20, 120) # 6 AM to 8 PM
peak_hour = 13.0
irradiance = 850 * np.exp(-0.5 * ((hours - peak_hour) / 2.8) ** 2) + 50
# Uncertainty tight at midday (clear sky), widens in afternoon (convective clouds)
base_uncertainty = 15 + 8 * np.abs(hours - peak_hour)
afternoon_spike = 45 * np.clip((hours - 15) / 2, 0, 1) ** 1.5
uncertainty = base_uncertainty + afternoon_spike
y_upper = irradiance + 1.96 * uncertainty
y_lower = np.maximum(irradiance - 1.96 * uncertainty, 0)
source = ColumnDataSource(data={"hours": hours, "irradiance": irradiance, "y_upper": y_upper, "y_lower": y_lower})
# Plot
title = "Solar Irradiance Forecast · band-basic · python · bokeh · anyplot.ai"
p = figure(
width=3200,
height=1800,
title=title,
x_axis_label="Hour of Day",
y_axis_label="Solar Irradiance (W/m²)",
toolbar_location=None,
min_border_bottom=160,
min_border_left=180,
min_border_top=110,
min_border_right=50,
)
# Confidence interval band — first series → Imprint position 1
# Higher alpha in dark theme: alpha=0.25 on #1A1A17 composites to near-invisible forest-green
band_alpha = 0.38 if THEME == "dark" else 0.25
p.varea(
x="hours",
y1="y_lower",
y2="y_upper",
source=source,
fill_color=IMPRINT_PALETTE[0],
fill_alpha=band_alpha,
legend_label="95% Confidence Interval",
)
# Forecast mean line — second series → Imprint position 2
p.line(
x="hours", y="irradiance", source=source, line_color=IMPRINT_PALETTE[1], line_width=6, legend_label="Forecast Mean"
)
# Theme-adaptive chrome
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.outline_line_color = None
p.title.text_font_size = "50pt"
p.title.text_font_style = "normal"
p.title.text_color = INK
p.xaxis.axis_label_text_font_size = "42pt"
p.yaxis.axis_label_text_font_size = "42pt"
p.xaxis.axis_label_text_color = INK
p.yaxis.axis_label_text_color = INK
p.xaxis.axis_label_text_font_style = "normal"
p.yaxis.axis_label_text_font_style = "normal"
p.xaxis.major_label_text_font_size = "34pt"
p.yaxis.major_label_text_font_size = "34pt"
p.xaxis.major_label_text_color = INK_SOFT
p.yaxis.major_label_text_color = INK_SOFT
p.xaxis.axis_line_color = INK_SOFT
p.yaxis.axis_line_color = INK_SOFT
p.xaxis.axis_line_width = 1
p.yaxis.axis_line_width = 1
p.xaxis.major_tick_line_color = None
p.yaxis.major_tick_line_color = None
p.xaxis.minor_tick_line_color = None
p.yaxis.minor_tick_line_color = None
# Y-grid only (style guide: y-axis grid for line charts)
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = INK
p.ygrid.grid_line_alpha = 0.15
# Legend
p.legend.label_text_font_size = "34pt"
p.legend.label_text_color = INK_SOFT
p.legend.location = "top_right"
p.legend.background_fill_color = ELEVATED_BG
p.legend.background_fill_alpha = 0.85
p.legend.border_line_color = INK_SOFT
p.legend.glyph_width = 60
p.legend.glyph_height = 40
p.legend.padding = 20
p.legend.spacing = 12
# Save HTML artifact
output_file(f"plot-{THEME}.html")
save(p)
# Screenshot with headless Selenium — export_png not used (snap driver incompatible)
W, H = 3200, 1800
opts = Options()
for arg in (
"--headless=new",
"--no-sandbox",
"--disable-dev-shm-usage",
"--disable-gpu",
f"--window-size={W},{H}",
"--hide-scrollbars",
):
opts.add_argument(arg)
driver = webdriver.Chrome(options=opts)
# CDP override pins the viewport to exactly W×H at DPR=1, avoiding
# headless-Chrome viewport-vs-window-size discrepancies
driver.execute_cdp_cmd(
"Emulation.setDeviceMetricsOverride", {"width": W, "height": H, "deviceScaleFactor": 1, "mobile": False}
)
driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
time.sleep(3)
driver.save_screenshot(f"plot-{THEME}.png")
driver.quit()
Part of Basic Band Plot on anyplot.ai.