A stacked area chart that displays multiple data series as cumulative areas, with each series surrounded by uncertainty or confidence bands. This visualization combines the composition insight of stacked areas with the statistical rigor of confidence intervals, making it ideal for showing how parts contribute to a whole while simultaneously communicating uncertainty in each component. The bands reveal where estimates are precise versus uncertain across the stacked series.
""" anyplot.ai
area-stacked-confidence: Stacked Area Chart with Confidence Bands
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-18
"""
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# 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"
# Okabe-Ito palette
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
# Data: quarterly energy consumption by source with measurement uncertainty
np.random.seed(42)
# Time axis: 8 years of quarterly data (32 quarters)
quarters = pd.date_range(start="2016-01-01", periods=32, freq="QS")
# Base consumption patterns for each energy source (in TWh)
# Solar: growing trend with seasonal variation
solar_base = 20 + np.linspace(0, 40, 32) + 8 * np.sin(np.linspace(0, 8 * np.pi, 32))
solar_uncertainty = 3 + np.linspace(0, 5, 32)
# Wind: moderate growth with higher seasonal variation
wind_base = 35 + np.linspace(0, 30, 32) + 12 * np.sin(np.linspace(0, 8 * np.pi, 32) + np.pi / 2)
wind_uncertainty = 5 + np.linspace(0, 6, 32)
# Hydro: stable with seasonal peaks
hydro_base = 50 + 15 * np.sin(np.linspace(0, 8 * np.pi, 32) - np.pi / 4)
hydro_uncertainty = 4 + 2 * np.abs(np.sin(np.linspace(0, 8 * np.pi, 32)))
# Natural Gas: declining trend
gas_base = 80 - np.linspace(0, 25, 32) + 5 * np.random.randn(32)
gas_uncertainty = 6 + np.linspace(0, 4, 32)
# Create stacked values (cumulative)
solar_stack = solar_base
wind_stack = solar_base + wind_base
hydro_stack = wind_stack + hydro_base
gas_stack = hydro_stack + gas_base
# Confidence bands (stacked appropriately)
# Solar bands
solar_lower = solar_base - solar_uncertainty
solar_upper = solar_base + solar_uncertainty
# Wind bands (stacked on solar)
wind_lower = solar_stack + (wind_base - wind_uncertainty)
wind_upper = solar_stack + (wind_base + wind_uncertainty)
# Hydro bands (stacked on wind)
hydro_lower = wind_stack + (hydro_base - hydro_uncertainty)
hydro_upper = wind_stack + (hydro_base + hydro_uncertainty)
# Gas bands (stacked on hydro)
gas_lower = hydro_stack + (gas_base - gas_uncertainty)
gas_upper = hydro_stack + (gas_base + gas_uncertainty)
# Create figure
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
# Plot stacked areas from bottom to top with enhanced styling
# Solar (bottom layer)
ax.fill_between(quarters, 0, solar_stack, color=IMPRINT[0], alpha=0.85, label="Solar", zorder=3)
ax.fill_between(quarters, solar_lower, solar_upper, color=IMPRINT[0], alpha=0.25, linewidth=0, zorder=2)
ax.plot(quarters, solar_lower, color=IMPRINT[0], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
ax.plot(quarters, solar_upper, color=IMPRINT[0], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
# Wind (second layer)
ax.fill_between(quarters, solar_stack, wind_stack, color=IMPRINT[1], alpha=0.85, label="Wind", zorder=3)
ax.fill_between(quarters, wind_lower, wind_upper, color=IMPRINT[1], alpha=0.25, linewidth=0, zorder=2)
ax.plot(quarters, wind_lower, color=IMPRINT[1], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
ax.plot(quarters, wind_upper, color=IMPRINT[1], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
# Hydro (third layer)
ax.fill_between(quarters, wind_stack, hydro_stack, color=IMPRINT[2], alpha=0.85, label="Hydro", zorder=3)
ax.fill_between(quarters, hydro_lower, hydro_upper, color=IMPRINT[2], alpha=0.25, linewidth=0, zorder=2)
ax.plot(quarters, hydro_lower, color=IMPRINT[2], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
ax.plot(quarters, hydro_upper, color=IMPRINT[2], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
# Natural Gas (top layer)
ax.fill_between(quarters, hydro_stack, gas_stack, color=IMPRINT[3], alpha=0.85, label="Natural Gas", zorder=3)
ax.fill_between(quarters, gas_lower, gas_upper, color=IMPRINT[3], alpha=0.25, linewidth=0, zorder=2)
ax.plot(quarters, gas_lower, color=IMPRINT[3], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
ax.plot(quarters, gas_upper, color=IMPRINT[3], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
# Add prominent center lines for clarity and visual hierarchy
ax.plot(quarters, solar_stack, color=IMPRINT[0], linewidth=3, alpha=1.0, zorder=4, solid_capstyle="round")
ax.plot(quarters, wind_stack, color=IMPRINT[1], linewidth=3, alpha=1.0, zorder=4, solid_capstyle="round")
ax.plot(quarters, hydro_stack, color=IMPRINT[2], linewidth=3, alpha=1.0, zorder=4, solid_capstyle="round")
ax.plot(quarters, gas_stack, color=IMPRINT[3], linewidth=3, alpha=1.0, zorder=4, solid_capstyle="round")
# Styling
ax.set_xlabel("Quarter", fontsize=20, color=INK)
ax.set_ylabel("Energy Consumption (TWh)", fontsize=20, color=INK)
ax.set_title("area-stacked-confidence · Python · matplotlib · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)
# Remove top and right spines
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for s in ("left", "bottom"):
ax.spines[s].set_color(INK_SOFT)
# Grid on both axes (subtle)
ax.grid(True, alpha=0.1, linewidth=0.8, color=INK)
ax.set_axisbelow(True)
# Format x-axis dates
fig.autofmt_xdate(rotation=45)
# Legend with confidence band note
legend = ax.legend(
loc="upper left", fontsize=16, framealpha=0.95, title="Energy Source (shaded: 90% CI bands)", title_fontsize=16
)
legend.get_frame().set_facecolor(ELEVATED_BG)
legend.get_frame().set_edgecolor(INK_SOFT)
legend.get_frame().set_linewidth(1)
for text in legend.get_texts():
text.set_color(INK_SOFT)
legend.get_title().set_color(INK)
# Set y-axis to start at 0
ax.set_ylim(bottom=0)
# Add subtle interpretive annotations highlighting key trends
solar_trend_idx = -1
solar_trend_value = solar_stack[solar_trend_idx]
gas_trend_idx = -1
gas_trend_value = gas_stack[gas_trend_idx] - hydro_stack[gas_trend_idx]
mid_idx = 15
ax.annotate(
"Renewable expansion",
xy=(quarters[mid_idx], solar_stack[mid_idx] * 0.35),
xytext=(quarters[mid_idx + 4], solar_stack[mid_idx + 4] * 0.25),
fontsize=12,
color=INK_SOFT,
weight="500",
bbox={
"boxstyle": "round,pad=0.4",
"facecolor": ELEVATED_BG,
"edgecolor": INK_SOFT,
"alpha": 0.75,
"linewidth": 0.8,
},
arrowprops={"arrowstyle": "->", "connectionstyle": "arc3,rad=0.2", "color": INK_SOFT, "lw": 1.2, "alpha": 0.7},
)
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Part of Stacked Area Chart with Confidence Bands on anyplot.ai.