Scatter Plot with LOWESS Regression — Pygal

A scatter plot with a LOWESS (Locally Weighted Scatterplot Smoothing) regression curve overlaid. LOWESS is a non-parametric method that fits smooth curves by performing local weighted regressions at each point, adapting to local data patterns without assuming a specific functional form. This makes it ideal for exploring complex relationships where the underlying pattern is unknown or varies across the data range.

Scatter Plot with LOWESS Regression rendered with Pygal

Python source (Pygal)

""" anyplot.ai
scatter-regression-lowess: Scatter Plot with LOWESS Regression
Library: pygal 3.1.0 | Python 3.13.13
Quality: 86/100 | Updated: 2026-05-14
"""

import os

import numpy as np
import pygal
from pygal.style import Style
from statsmodels.nonparametric.smoothers_lowess import lowess


# 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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"

IMPRINT = ("#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477")

# Data - Drug dose-response relationship with non-linear effect
np.random.seed(42)
n_points = 150

# Drug concentration in mg/L (log-spaced for pharmacological realism)
concentration = np.linspace(0.1, 50, n_points)

# Enzyme activity response: sigmoidal with saturation and hormesis effect
base_response = 25 + 55 * (1 - np.exp(-concentration / 8)) - 10 * np.exp(-concentration / 3)
noise = np.random.normal(0, 4, n_points)
activity = base_response + noise

# Calculate LOWESS smoothed curve
lowess_result = lowess(activity, concentration, frac=0.35, return_sorted=True)
conc_smooth = lowess_result[:, 0]
activity_smooth = lowess_result[:, 1]

# Custom style with theme-adaptive tokens
custom_style = Style(
    background=PAGE_BG,
    plot_background=PAGE_BG,
    foreground=INK,
    foreground_strong=INK,
    foreground_subtle=INK_MUTED,
    colors=IMPRINT,
    title_font_size=28,
    label_font_size=22,
    major_label_font_size=18,
    legend_font_size=16,
    value_font_size=14,
    stroke_width=3,
    opacity=0.6,
    opacity_hover=0.9,
)

# Create XY chart for scatter plot
chart = pygal.XY(
    width=4800,
    height=2700,
    style=custom_style,
    title="scatter-regression-lowess · pygal · anyplot.ai",
    x_title="Drug Concentration (mg/L)",
    y_title="Enzyme Activity (%)",
    show_dots=True,
    dots_size=8,
    stroke=False,
    show_x_guides=True,
    show_y_guides=True,
)

# Add scatter points (brand green - Okabe-Ito position 1)
scatter_data = list(zip(concentration, activity, strict=True))
chart.add("Observed Response", scatter_data, stroke=False, dots_size=10)

# Add LOWESS curve (vermillion - Okabe-Ito position 2)
lowess_data = list(zip(conc_smooth, activity_smooth, strict=True))
chart.add("LOWESS Fit (frac=0.35)", lowess_data, stroke=True, show_dots=False, stroke_style={"width": 6})

# Save as PNG and HTML with theme suffix
chart.render_to_png(f"plot-{THEME}.png")
with open(f"plot-{THEME}.html", "wb") as f:
    f.write(chart.render())

Part of Scatter Plot with LOWESS Regression on anyplot.ai.

Scatter Plot with LOWESS Regression — Pygal

Python source (Pygal)

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