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: plotly 6.9.0 | Python 3.13.15
Quality: 94/100 | Updated: 2026-08-18
"""
import os
import numpy as np
import plotly.graph_objects as go
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
# Imprint diverging colormap — matte red (negative) -> theme midpoint -> blue (positive)
midpoint = PAGE_BG
imprint_div = [[0.0, "#AE3030"], [0.5, midpoint], [1.0, "#4467A3"]]
def hex_to_rgb(hex_color):
hex_color = hex_color.lstrip("#")
return tuple(int(hex_color[i : i + 2], 16) for i in (0, 2, 4))
def cell_text_color(r, mid_rgb):
# Re-derive the interpolated cell color to pick a legibly-contrasting text
# color — the midpoint stop is theme-adaptive, so a fixed threshold on |r|
# would pick the wrong tone for one of the two themes.
red_rgb, blue_rgb = hex_to_rgb("#AE3030"), hex_to_rgb("#4467A3")
position = (r + 1) / 2
lo, hi, t = (red_rgb, mid_rgb, position / 0.5) if position <= 0.5 else (mid_rgb, blue_rgb, (position - 0.5) / 0.5)
cell_rgb = [lo[k] + (hi[k] - lo[k]) * t for k in range(3)]
luminance = 0.299 * cell_rgb[0] + 0.587 * cell_rgb[1] + 0.114 * cell_rgb[2]
return "#1A1A17" if luminance > 140 else "#FAF8F1"
# Data - Healthcare metrics correlation matrix
np.random.seed(42)
variables = [
"Heart Rate",
"Blood Pressure",
"Cholesterol",
"BMI",
"Sleep Hours",
"Exercise (hrs)",
"Stress Level",
"Resting O2",
]
# Create realistic correlation matrix with meaningful health relationships
n_vars = len(variables)
base = np.random.randn(200, n_vars)
# Add realistic correlations based on health domain knowledge
base[:, 1] = base[:, 0] * 0.65 + np.random.randn(200) * 0.4 # BP ~ Heart Rate
base[:, 2] = base[:, 0] * 0.5 + base[:, 3] * 0.6 + np.random.randn(200) * 0.4 # Cholesterol
base[:, 3] = np.random.randn(200) # BMI (independent)
base[:, 4] = -base[:, 6] * 0.7 + np.random.randn(200) * 0.4 # Sleep ~ -Stress
base[:, 5] = (
-base[:, 0] * 0.5 - base[:, 6] * 0.4 + np.random.randn(200) * 0.5
) # Exercise inversely related to HR and Stress
base[:, 6] = np.random.randn(200) # Stress (independent)
base[:, 7] = base[:, 4] * 0.6 + np.random.randn(200) * 0.5 # O2 ~ Sleep
# Calculate correlation matrix
correlation_matrix = np.corrcoef(base.T)
# Mask the strict upper triangle so each unique pair is shown once
mask = np.triu(np.ones_like(correlation_matrix, dtype=bool), k=1)
masked_corr = np.where(mask, np.nan, correlation_matrix)
# Numeric axis positions keep shape placement simple (see strong-pair highlight below)
positions = list(range(n_vars))
mid_rgb = hex_to_rgb(midpoint)
# Cell annotations — text color follows the actual interpolated cell color so it
# stays legible whether that cell renders light (near the theme midpoint) or a
# fully saturated red/blue (near the fixed scale endpoints).
annotations = []
for i in range(n_vars):
for j in range(n_vars):
if mask[i, j]:
continue
r = correlation_matrix[i, j]
annotations.append(
{
"x": j,
"y": i,
"text": f"{r:.2f}",
"showarrow": False,
"font": {"size": 11, "color": cell_text_color(r, mid_rgb)},
}
)
# Rich hover text interpreting correlation strength and direction
hover_text = []
for i in range(n_vars):
row = []
for j in range(n_vars):
if mask[i, j]:
row.append("")
else:
r = correlation_matrix[i, j]
if abs(r) >= 0.7:
strength = "Strong"
elif abs(r) >= 0.4:
strength = "Moderate"
else:
strength = "Weak"
direction = "positive" if r > 0 else "negative" if r < 0 else "none"
row.append(
f"<b>{variables[i]}</b> vs <b>{variables[j]}</b><br>"
f"Correlation: <b>{r:.3f}</b><br>"
f"Strength: {strength} {direction}"
)
hover_text.append(row)
# Heatmap — Imprint diverging scale, thin page-background gaps replace axis gridlines
fig = go.Figure(
data=go.Heatmap(
z=masked_corr,
x=positions,
y=positions,
colorscale=imprint_div,
zmin=-1,
zmax=1,
xgap=2,
ygap=2,
colorbar={
"title": {"text": "Pearson r", "font": {"size": 11, "color": INK}},
"tickfont": {"size": 9, "color": INK_SOFT},
"thickness": 15,
"len": 0.8,
"tickvals": [-1, -0.5, 0, 0.5, 1],
"outlinewidth": 0,
},
hoverongaps=False,
hovertemplate="%{customdata}<extra></extra>",
customdata=hover_text,
)
)
# Highlight strong pairs (|r| >= 0.7, off-diagonal) with an outlined cell border —
# a real data-driven emphasis, not a simulated interaction. The stroke color
# contrasts against PAGE_BG (the xgap/ygap color the border sits on top of), not
# against the cell fill — otherwise a fill that interpolates near PAGE_BG makes
# the border blend into the surrounding gap and disappear.
for i in range(n_vars):
for j in range(i):
r = correlation_matrix[i, j]
if abs(r) >= 0.7:
fig.add_shape(
type="rect",
x0=j - 0.5,
x1=j + 0.5,
y0=i - 0.5,
y1=i + 0.5,
line={"color": INK, "width": 2.5},
fillcolor="rgba(0,0,0,0)",
layer="above",
)
# Layout for 2400x2400 px (square — symmetric matrix)
fig.update_layout(
autosize=False,
title={
"text": "heatmap-correlation · python · plotly · anyplot.ai",
"font": {"size": 18, "color": INK},
"x": 0.5,
"xanchor": "center",
},
xaxis={
"title": {"text": "Health Metrics", "font": {"size": 13, "color": INK}},
"tickfont": {"size": 10, "color": INK_SOFT},
"tickmode": "array",
"tickvals": positions,
"ticktext": variables,
"side": "bottom",
"tickangle": 45,
"showgrid": False,
"zeroline": False,
"showline": False,
},
yaxis={
"title": {"text": "Health Metrics", "font": {"size": 13, "color": INK}},
"tickfont": {"size": 10, "color": INK_SOFT},
"tickmode": "array",
"tickvals": positions,
"ticktext": variables,
"autorange": "reversed",
"showgrid": False,
"zeroline": False,
"showline": False,
},
annotations=annotations,
paper_bgcolor=PAGE_BG,
plot_bgcolor=PAGE_BG,
font={"color": INK},
margin={"l": 70, "r": 30, "t": 60, "b": 90},
width=600,
height=600,
)
# Save as PNG and HTML with theme-suffixed filenames
fig.write_image(f"plot-{THEME}.png", width=600, height=600, scale=4)
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/heatmap-correlation/plotly/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "heatmap-correlation",
"language": "python",
"library": "plotly",
"page": "https://anyplot.ai/heatmap-correlation/python/plotly",
"hub": "https://anyplot.ai/heatmap-correlation",
"code_json": "https://api.anyplot.ai/specs/heatmap-correlation/plotly/code",
"spec_json": "https://api.anyplot.ai/specs/heatmap-correlation",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/heatmap-correlation/python/plotly/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/heatmap-correlation/python/plotly/plot-dark.png",
"interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/heatmap-correlation/python/plotly/plot-light.html",
"interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/heatmap-correlation/python/plotly/plot-dark.html",
"quality_score": 94.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Correlation Matrix Heatmap on anyplot.ai.