A heatmap with numeric values displayed inside each cell, combining color intensity with exact value labels. Essential for correlation matrices, confusion matrices, and any matrix visualization where both pattern recognition and precise values matter. Text color automatically contrasts with background for readability.
""" anyplot.ai
heatmap-annotated: Annotated Heatmap
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 86/100 | Updated: 2026-05-06
"""
import os
import matplotlib.pyplot as plt
import numpy as np
# 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"
# Data: Laboratory measurement correlations
np.random.seed(42)
measurements = ["Temperature", "pH", "Viscosity", "Density", "Turbidity", "Conductivity", "Salinity", "Pressure"]
n = len(measurements)
# Generate a realistic correlation matrix (symmetric, diagonal = 1)
base = np.random.randn(n, n) * 0.3
correlation = (base + base.T) / 2
np.fill_diagonal(correlation, 1.0)
correlation = np.clip(correlation, -1, 1)
# Add realistic scientific correlations
correlation[0, 1] = correlation[1, 0] = -0.68 # Temperature-pH: negative
correlation[0, 2] = correlation[2, 0] = 0.55 # Temperature-Viscosity: positive
correlation[3, 5] = correlation[5, 3] = 0.77 # Density-Conductivity: strong positive
correlation[4, 5] = correlation[5, 4] = -0.62 # Turbidity-Conductivity: negative
correlation[6, 7] = correlation[7, 6] = 0.81 # Salinity-Pressure: strong positive
correlation[1, 4] = correlation[4, 1] = 0.45 # pH-Turbidity: positive
# Create plot (square format for heatmap)
fig, ax = plt.subplots(figsize=(12, 12), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
# Create heatmap with diverging colormap
im = ax.imshow(correlation, cmap="BrBG", vmin=-1, vmax=1, aspect="equal")
# Add colorbar with theme-adaptive styling
cbar = ax.figure.colorbar(im, ax=ax, shrink=0.8, aspect=30)
cbar.ax.tick_params(labelsize=16, colors=INK_SOFT)
cbar.ax.set_facecolor(PAGE_BG)
cbar.set_label("Correlation Coefficient", fontsize=18, labelpad=15, color=INK)
cbar.outline.set_edgecolor(INK_SOFT)
cbar.outline.set_linewidth(1)
# Set ticks and labels
ax.set_xticks(np.arange(n))
ax.set_yticks(np.arange(n))
ax.set_xticklabels(measurements, fontsize=16, color=INK_SOFT)
ax.set_yticklabels(measurements, fontsize=16, color=INK_SOFT)
# Rotate x-axis labels for readability
plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor")
# Add text annotations in each cell
for i in range(n):
for j in range(n):
value = correlation[i, j]
# Choose text color based on background intensity
text_color = INK if abs(value) > 0.5 else INK_SOFT
ax.text(j, i, f"{value:.2f}", ha="center", va="center", color=text_color, fontsize=14, fontweight="bold")
# Styling
ax.set_title("heatmap-annotated · matplotlib · anyplot.ai", fontsize=24, pad=20, color=INK)
ax.set_xlabel("Laboratory Measurements", fontsize=20, labelpad=15, color=INK)
ax.set_ylabel("Laboratory Measurements", fontsize=20, labelpad=15, color=INK)
# Add subtle grid between cells
ax.set_xticks(np.arange(n + 1) - 0.5, minor=True)
ax.set_yticks(np.arange(n + 1) - 0.5, minor=True)
ax.grid(which="minor", color=INK_SOFT, linestyle="-", linewidth=1, alpha=0.3)
ax.tick_params(which="minor", bottom=False, left=False)
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Part of Annotated Heatmap on anyplot.ai.