A time-domain visualization of audio amplitude that displays the raw waveform shape as seen in digital audio workstations (DAWs). The plot shows positive and negative amplitude symmetrically around a zero baseline, with time on the x-axis and normalized amplitude (-1 to +1) on the y-axis. This is the fundamental representation for inspecting audio signals, revealing dynamics, clipping, silence, and transient characteristics at a glance.
""" anyplot.ai
waveform-audio: Audio Waveform Plot
Library: letsplot 4.10.1 | Python 3.13.13
Quality: 90/100 | Updated: 2026-06-03
"""
import os
import numpy as np
import pandas as pd
from lets_plot import *
from lets_plot.export import ggsave
LetsPlot.setup_html()
# Theme tokens — Imprint palette, theme-adaptive chrome
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"
GRID_COLOR = "rgba(26,26,23,0.15)" if THEME == "light" else "rgba(240,239,232,0.15)"
# Data — synthetic audio waveform: 220 Hz tone with harmonics and ASR amplitude envelope
np.random.seed(42)
sample_rate = 22050
duration = 1.5
n_samples = int(sample_rate * duration)
time = np.linspace(0, duration, n_samples)
fundamental = 220
signal = (
0.6 * np.sin(2 * np.pi * fundamental * time)
+ 0.25 * np.sin(2 * np.pi * fundamental * 2 * time)
+ 0.1 * np.sin(2 * np.pi * fundamental * 3 * time)
+ 0.05 * np.sin(2 * np.pi * fundamental * 5 * time)
)
# Attack-sustain-release amplitude envelope
envelope = np.ones(n_samples)
attack_samples = int(0.05 * sample_rate)
release_samples = int(0.3 * sample_rate)
envelope[:attack_samples] = np.linspace(0, 1, attack_samples)
envelope[-release_samples:] = np.linspace(1, 0, release_samples)
envelope[int(0.4 * sample_rate) : int(0.7 * sample_rate)] *= 0.5
signal = signal * envelope
signal = signal / np.max(np.abs(signal))
# Downsample via min/max envelope binning to avoid aliasing at display resolution
n_bins = 800
bin_edges = np.linspace(0, n_samples, n_bins + 1, dtype=int)
time_env = np.array([time[(bin_edges[i] + bin_edges[i + 1]) // 2] for i in range(n_bins)])
amp_max = np.array([signal[bin_edges[i] : bin_edges[i + 1]].max() for i in range(n_bins)])
amp_min = np.array([signal[bin_edges[i] : bin_edges[i + 1]].min() for i in range(n_bins)])
amp_range = amp_max - amp_min
df = pd.DataFrame({"time": time_env, "ymin": amp_min, "ymax": amp_max, "intensity": amp_range})
ann_data = pd.DataFrame(
{
"time": [0.025, 0.225, 0.55, 0.95, 1.35],
"y": [1.07, 1.07, 1.07, 1.07, 1.07],
"label": ["Attack", "Sustain", "Dip", "Sustain", "Release"],
}
)
section_df = pd.DataFrame({"x": [0.05, 0.4, 0.7, 1.2]})
subtitle = "220 Hz fundamental + harmonics · ASR envelope with amplitude dip at 0.4–0.7 s"
# Theme-adaptive chrome applied after theme_minimal()
anyplot_chrome = theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
panel_grid_major_y=element_line(color=GRID_COLOR, size=0.3),
panel_grid_major_x=element_blank(),
panel_grid_minor=element_blank(),
axis_title=element_text(color=INK, size=12),
axis_text=element_text(color=INK_SOFT, size=10),
axis_line=element_line(color=INK_SOFT),
plot_title=element_text(color=INK, size=16),
plot_subtitle=element_text(color=INK_SOFT, size=10),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_text=element_text(color=INK_SOFT, size=10),
legend_title=element_text(color=INK, size=12),
legend_position="right",
plot_margin=[40, 20, 20, 20],
)
plot = (
ggplot(df)
# DAW-style vertical bars coloured by local amplitude range (Imprint sequential cmap)
+ geom_segment(
mapping=aes(x="time", y="ymin", xend="time", yend="ymax", color="intensity"),
size=1.5,
alpha=0.85,
tooltips=layer_tooltips()
.format("ymax", ".2f")
.format("ymin", ".2f")
.format("time", ".3f")
.line("Time: @time s")
.line("Max: @ymax")
.line("Min: @ymin"),
)
# Imprint sequential: brand green (#009E73) → blue (#4467A3) for single-polarity data
+ scale_color_gradient(low="#009E73", high="#4467A3", name="Amplitude\nRange")
# Zero reference line
+ geom_hline(yintercept=0, color=INK_MUTED, size=0.5, linetype="dashed")
# Envelope section boundary markers
+ geom_vline(
data=section_df,
mapping=aes(xintercept="x"),
color=INK_MUTED,
size=0.4,
linetype="dotted",
)
# Section labels — geom_text size is in mm (~2.845 mm = 1 pt)
+ geom_text(
data=ann_data,
mapping=aes(x="time", y="y", label="label"),
size=4,
color=INK,
fontface="italic",
)
+ scale_x_continuous(name="Time (seconds)", limits=[0, duration])
+ scale_y_continuous(
name="Amplitude", limits=[-1.15, 1.18], breaks=[-1.0, -0.5, 0.0, 0.5, 1.0]
)
+ labs(title="waveform-audio · python · letsplot · anyplot.ai", subtitle=subtitle)
# Canvas: 800×450 × scale=4 → 3200×1800 px (landscape 16:9)
+ ggsize(800, 450)
+ theme_minimal()
+ anyplot_chrome
)
# Save PNG (3200×1800 px) and interactive HTML — path="." keeps files in the current dir
ggsave(plot, filename=f"plot-{THEME}.png", path=".", scale=4)
ggsave(plot, filename=f"plot-{THEME}.html", path=".")
Part of Audio Waveform Plot on anyplot.ai.