A heatmap specifically designed to display correlation coefficients between variables, using a diverging color scheme centered at zero. The symmetric matrix visualization makes it easy to identify positive correlations, negative correlations, and independent variables at a glance. Essential for exploratory data analysis, feature engineering, and multicollinearity detection in statistical and machine learning workflows.
""" anyplot.ai
heatmap-correlation: Correlation Matrix Heatmap
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 85/100 | Updated: 2026-05-08
"""
import os
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Rectangle
# Theme configuration
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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
EMPHASIS_COLOR = "#E67E22" if THEME == "light" else "#F39C12"
# Data - Realistic weather variables correlation matrix
np.random.seed(42)
# Variable names - weather metrics
variables = [
"Temperature",
"Humidity",
"Wind Speed",
"Precipitation",
"Air Pressure",
"Solar Radiation",
"UV Index",
"Cloud Cover",
]
n_vars = len(variables)
# Create a realistic correlation matrix with varied relationships
correlation_matrix = np.eye(n_vars)
# Define realistic correlations between weather variables
correlations = {
(0, 1): -0.82, # Temperature - Humidity (strong negative)
(0, 2): 0.15, # Temperature - Wind Speed (weak positive)
(0, 3): -0.48, # Temperature - Precipitation (negative)
(0, 4): -0.71, # Temperature - Air Pressure (strong negative)
(0, 5): 0.88, # Temperature - Solar Radiation (strong positive)
(0, 6): 0.79, # Temperature - UV Index (strong positive)
(0, 7): -0.65, # Temperature - Cloud Cover (negative)
(1, 2): 0.35, # Humidity - Wind Speed (weak positive)
(1, 3): 0.72, # Humidity - Precipitation (strong positive)
(1, 4): 0.58, # Humidity - Air Pressure (positive)
(1, 5): -0.84, # Humidity - Solar Radiation (strong negative)
(1, 6): -0.76, # Humidity - UV Index (strong negative)
(1, 7): 0.81, # Humidity - Cloud Cover (strong positive)
(2, 3): 0.42, # Wind Speed - Precipitation (positive)
(2, 4): -0.31, # Wind Speed - Air Pressure (weak negative)
(2, 5): 0.09, # Wind Speed - Solar Radiation (very weak)
(2, 6): -0.12, # Wind Speed - UV Index (very weak negative)
(2, 7): 0.38, # Wind Speed - Cloud Cover (weak positive)
(3, 4): -0.52, # Precipitation - Air Pressure (negative)
(3, 5): -0.68, # Precipitation - Solar Radiation (strong negative)
(3, 6): -0.61, # Precipitation - UV Index (strong negative)
(3, 7): 0.74, # Precipitation - Cloud Cover (strong positive)
(4, 5): 0.45, # Air Pressure - Solar Radiation (positive)
(4, 6): 0.39, # Air Pressure - UV Index (positive)
(4, 7): -0.43, # Air Pressure - Cloud Cover (negative)
(5, 6): 0.85, # Solar Radiation - UV Index (strong positive)
(5, 7): -0.79, # Solar Radiation - Cloud Cover (strong negative)
(6, 7): -0.71, # UV Index - Cloud Cover (strong negative)
}
# Fill in the correlation matrix (symmetric)
for (i, j), corr in correlations.items():
correlation_matrix[i, j] = corr
correlation_matrix[j, i] = corr
# Create mask for upper triangle
mask = np.triu(np.ones_like(correlation_matrix, dtype=bool), k=1)
# Apply mask - set upper triangle to nan for visualization
masked_corr = np.where(mask, np.nan, correlation_matrix)
# Create figure - square format for heatmap
fig, ax = plt.subplots(figsize=(12, 12), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
# Create heatmap with diverging colormap centered at zero
im = ax.imshow(masked_corr, cmap="BrBG", vmin=-1, vmax=1, aspect="equal")
# Add subtle grid lines for visual hierarchy
ax.set_xticks(np.arange(n_vars) - 0.5, minor=True)
ax.set_yticks(np.arange(n_vars) - 0.5, minor=True)
ax.grid(which="minor", color=INK_MUTED, linestyle="-", linewidth=0.5, alpha=0.3)
# Add colorbar with enhanced styling
cbar = ax.figure.colorbar(im, ax=ax, shrink=0.8, pad=0.02)
cbar.ax.set_ylabel("Correlation Coefficient", fontsize=18, labelpad=15, color=INK, fontweight="medium")
cbar.ax.tick_params(labelsize=14, colors=INK_SOFT, labelcolor=INK_SOFT)
cbar.ax.set_facecolor(PAGE_BG)
# Set ticks and labels with theme-adaptive coloring
ax.set_xticks(np.arange(n_vars))
ax.set_yticks(np.arange(n_vars))
ax.set_xticklabels(variables, fontsize=16, rotation=45, ha="right", rotation_mode="anchor", color=INK_SOFT)
ax.set_yticklabels(variables, fontsize=16, color=INK_SOFT)
# Add axis labels with theme-adaptive coloring
ax.set_xlabel("Weather Variables", fontsize=20, labelpad=15, color=INK, fontweight="medium")
ax.set_ylabel("Weather Variables", fontsize=20, labelpad=15, color=INK, fontweight="medium")
# Add visual emphasis to strong correlations via borders
for i in range(n_vars):
for j in range(n_vars):
if not mask[i, j]:
value = abs(correlation_matrix[i, j])
if value > 0.75 and i != j:
rect = Rectangle(
(j - 0.45, i - 0.45), 0.9, 0.9, linewidth=2, edgecolor=EMPHASIS_COLOR, facecolor="none", alpha=0.5
)
ax.add_patch(rect)
# Annotate cells with correlation values
for i in range(n_vars):
for j in range(n_vars):
if not mask[i, j]:
value = correlation_matrix[i, j]
text_color = "white" 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")
# Title with theme-adaptive coloring and enhanced styling
ax.set_title("heatmap-correlation · matplotlib · pyplots.ai", fontsize=24, pad=20, fontweight="bold", color=INK)
# Remove spines for clean look
for spine in ax.spines.values():
spine.set_visible(False)
# Adjust layout
plt.tight_layout()
# Save with theme-based filename and background
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Part of Correlation Matrix Heatmap on anyplot.ai.