A heatmap displaying values in a matrix format using color intensity. Each cell's color represents the magnitude of the value, making it easy to identify patterns, clusters, and outliers in two-dimensional data. Essential for visualizing correlations, frequencies, and relationships between variables.
""" anyplot.ai
heatmap-basic: Basic Heatmap
Library: bokeh 3.9.0 | Python 3.13.13
Quality: 88/100 | Updated: 2026-05-28
"""
import base64
import io
import os
import sys
import time
from pathlib import Path
# Remove this script's directory from sys.path so "bokeh.py" doesn't shadow the bokeh package
_this_dir = os.path.dirname(os.path.abspath(__file__))
sys.path = [p for p in sys.path if os.path.abspath(p) != _this_dir]
import numpy as np
import pandas as pd
from bokeh.io import output_file, save
from bokeh.models import BasicTicker, ColumnDataSource, HoverTool, LabelSet
from bokeh.plotting import figure
from bokeh.transform import linear_cmap
from PIL import Image
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
# 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"
# Imprint diverging colormap — 256-stop ramp, theme-adaptive midpoint (red → neutral → blue)
def _lerp_hex(c0, c1, t):
r0, g0, b0 = (int(c0[i : i + 2], 16) for i in (1, 3, 5))
r1, g1, b1 = (int(c1[i : i + 2], 16) for i in (1, 3, 5))
r, g, b = (int(round(a + (b - a) * t)) for a, b in ((r0, r1), (g0, g1), (b0, b1)))
return f"#{r:02X}{g:02X}{b:02X}"
_mid = "#FAF8F1" if THEME == "light" else "#1A1A17"
IMPRINT_DIV256 = [_lerp_hex("#AE3030", _mid, t / 127.0) for t in range(128)] + [
_lerp_hex(_mid, "#4467A3", t / 127.0) for t in range(128)
]
# Data — monthly temperature anomalies (°C) for 7 cities across all 12 months
np.random.seed(42)
months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
cities = ["Oslo", "Berlin", "Madrid", "Cairo", "Mumbai", "Tokyo", "Sydney"]
base_anomalies = np.random.randn(len(cities), len(months)) * 0.6
for i, city in enumerate(cities):
seasonal = np.sin(np.linspace(-np.pi / 2, 3 * np.pi / 2, len(months)))
if city in ("Oslo", "Berlin"):
base_anomalies[i] += seasonal * 1.5 - 0.3
elif city in ("Madrid", "Cairo"):
base_anomalies[i] += seasonal * 1.2 + 0.4
elif city == "Mumbai":
base_anomalies[i] += 0.8
elif city == "Sydney":
base_anomalies[i] -= seasonal * 0.9
elif city == "Tokyo":
base_anomalies[i] += seasonal * 0.7
values = np.round(base_anomalies, 1)
records = []
for i, city in enumerate(cities):
for j, month in enumerate(months):
val = values[i, j]
# Light theme: dark text on near-zero (cream) cells, light text on deep red/blue cells
tc = ("#F0EFE8" if abs(val) > 1.0 else "#1A1A17") if THEME == "light" else "#F0EFE8"
records.append({"month": month, "city": city, "anomaly": val, "label": f"{val:+.1f}", "text_color": tc})
source = ColumnDataSource(pd.DataFrame(records))
# Color mapping — Imprint diverging palette
cmap = linear_cmap("anomaly", IMPRINT_DIV256, low=-2.5, high=2.5)
# Figure — square canvas for symmetric grid
title = "heatmap-basic · python · bokeh · anyplot.ai"
p = figure(
width=2400,
height=2400,
x_range=months,
y_range=list(reversed(cities)),
title=title,
x_axis_label="Month (2024)",
y_axis_label="City",
toolbar_location=None,
tools="",
min_border_bottom=160,
min_border_left=180,
min_border_top=110,
min_border_right=300,
)
# Heatmap rectangles
r = p.rect(x="month", y="city", width=1, height=1, source=source, fill_color=cmap, line_color="white", line_width=2)
# Cell value annotations
labels = LabelSet(
x="month",
y="city",
text="label",
text_color="text_color",
source=source,
text_align="center",
text_baseline="middle",
text_font_size="26pt",
)
p.add_layout(labels)
# Storytelling callout: highlight the most extreme anomaly cell with a bold border + label
extreme_idx = np.unravel_index(np.argmax(np.abs(values)), values.shape)
extreme_city = cities[extreme_idx[0]]
extreme_month = months[extreme_idx[1]]
extreme_val = values[extreme_idx[0], extreme_idx[1]]
p.rect(
x=[extreme_month], y=[extreme_city], width=1, height=1, fill_color=None, fill_alpha=0, line_color=INK, line_width=8
)
# Color bar (construct from renderer — idiomatic Bokeh)
color_bar = r.construct_color_bar(
width=50,
ticker=BasicTicker(desired_num_ticks=10),
label_standoff=16,
major_label_text_font_size="34pt",
major_label_text_color=INK_SOFT,
border_line_color=None,
padding=20,
title="Anomaly (°C)",
title_text_font_size="34pt",
title_text_color=INK,
title_standoff=20,
background_fill_color=PAGE_BG,
)
p.add_layout(color_bar, "right")
# HoverTool for interactive HTML
hover = HoverTool(tooltips=[("City", "@city"), ("Month", "@month"), ("Anomaly", "@anomaly{+0.0} °C")], renderers=[r])
p.add_tools(hover)
# 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_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.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 = None
p.yaxis.axis_line_color = None
p.xaxis.major_tick_line_color = None
p.yaxis.major_tick_line_color = None
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
# Save interactive HTML
output_file(f"plot-{THEME}.html")
save(p)
# Save PNG via headless Chrome (Selenium — export_png unavailable in this env)
W, H = 2400, 2400
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)
driver.set_window_size(W, H)
driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
time.sleep(3)
screenshot = driver.execute_cdp_cmd("Page.captureScreenshot", {"format": "png", "captureBeyondViewport": True})
driver.quit()
Image.open(io.BytesIO(base64.b64decode(screenshot["data"]))).crop((0, 0, W, H)).save(f"plot-{THEME}.png")
Part of Basic Heatmap on anyplot.ai.