{"spec_id":"waveform-audio","library":"plotnine","language":"python","code":"\"\"\" anyplot.ai\nwaveform-audio: Audio Waveform Plot\nLibrary: plotnine 0.15.5 | Python 3.13.13\nQuality: 86/100 | Updated: 2026-06-03\n\"\"\"\n\nimport os\n\nimport numpy as np\nimport pandas as pd\nfrom plotnine import (\n    aes,\n    annotate,\n    element_blank,\n    element_line,\n    element_rect,\n    element_text,\n    geom_hline,\n    geom_ribbon,\n    geom_vline,\n    ggplot,\n    labs,\n    scale_alpha_manual,\n    scale_fill_manual,\n    scale_x_continuous,\n    scale_y_continuous,\n    theme,\n    theme_minimal,\n)\n\n\n# Theme tokens\nTHEME = os.getenv(\"ANYPLOT_THEME\", \"light\")\nPAGE_BG = \"#FAF8F1\" if THEME == \"light\" else \"#1A1A17\"\nINK = \"#1A1A17\" if THEME == \"light\" else \"#F0EFE8\"\nINK_SOFT = \"#4A4A44\" if THEME == \"light\" else \"#B8B7B0\"\nINK_MUTED = \"#6B6A63\" if THEME == \"light\" else \"#A8A79F\"\n\n# Imprint palette — positions 1, 2, 3 for Attack, Sustain, Release phases\nIMPRINT_PALETTE = [\"#009E73\", \"#C475FD\", \"#4467A3\", \"#BD8233\", \"#AE3030\", \"#2ABCCD\", \"#954477\", \"#99B314\"]\n\n# Data - synthetic audio waveform: tone with harmonics and amplitude envelope\nnp.random.seed(42)\nsample_rate = 22050\nduration = 1.5\nnum_samples = int(sample_rate * duration)\ntime = np.linspace(0, duration, num_samples)\n\nfundamental = 220\nsignal = (\n    0.6 * np.sin(2 * np.pi * fundamental * time)\n    + 0.25 * np.sin(2 * np.pi * fundamental * 2 * time)\n    + 0.1 * np.sin(2 * np.pi * fundamental * 3 * time)\n    + 0.05 * np.sin(2 * np.pi * fundamental * 5 * time)\n)\n\nenvelope = np.ones_like(time)\nattack_end = int(0.05 * sample_rate)\nsustain_end = int(1.1 * sample_rate)\nenvelope[:attack_end] = np.linspace(0, 1, attack_end)\nenvelope[sustain_end:] = np.linspace(1, 0, num_samples - sustain_end)\n\nvibrato = 1.0 + 0.03 * np.sin(2 * np.pi * 5 * time)\nnoise = np.random.normal(0, 0.02, num_samples)\namplitude = np.clip((signal * envelope * vibrato) + noise, -1.0, 1.0)\n\n# Downsample using min/max envelope to avoid aliasing\nchunk_size = 64\nnum_chunks = num_samples // chunk_size\ntime_chunks = np.array([time[i * chunk_size] for i in range(num_chunks)])\namp_min = np.array([amplitude[i * chunk_size : (i + 1) * chunk_size].min() for i in range(num_chunks)])\namp_max = np.array([amplitude[i * chunk_size : (i + 1) * chunk_size].max() for i in range(num_chunks)])\n\nattack_time = 0.05\nsustain_time = 1.1\nphase = []\nfor t in time_chunks:\n    if t < attack_time:\n        phase.append(\"Attack\")\n    elif t < sustain_time:\n        phase.append(\"Sustain\")\n    else:\n        phase.append(\"Release\")\n\ndf = pd.DataFrame(\n    {\n        \"time\": time_chunks,\n        \"amp_min\": amp_min,\n        \"amp_max\": amp_max,\n        \"phase\": pd.Categorical(phase, categories=[\"Attack\", \"Sustain\", \"Release\"], ordered=True),\n    }\n)\n\n# Imprint palette: Attack=green (pos 1), Sustain=lavender (pos 2), Release=blue (pos 3)\nphase_colors = {\"Attack\": IMPRINT_PALETTE[0], \"Sustain\": IMPRINT_PALETTE[1], \"Release\": IMPRINT_PALETTE[2]}\nphase_alphas = {\"Attack\": 0.85, \"Sustain\": 0.65, \"Release\": 0.55}\n\ntitle = \"waveform-audio · python · plotnine · anyplot.ai\"\n\n# Plot\nplot = (\n    ggplot(df, aes(x=\"time\"))\n    + geom_ribbon(aes(ymin=\"amp_min\", ymax=\"amp_max\", fill=\"phase\", alpha=\"phase\"), show_legend=False)\n    + scale_fill_manual(values=phase_colors)\n    + scale_alpha_manual(values=phase_alphas)\n    + geom_hline(yintercept=0, color=INK_MUTED, size=0.4, linetype=\"solid\")\n    + geom_vline(xintercept=attack_time, color=INK_SOFT, size=0.3, linetype=\"dashed\", alpha=0.5)\n    + geom_vline(xintercept=sustain_time, color=INK_SOFT, size=0.3, linetype=\"dashed\", alpha=0.5)\n    + annotate(\"text\", x=0.025, y=0.90, label=\"Attack\", size=3.5, color=IMPRINT_PALETTE[0], fontstyle=\"italic\")\n    + annotate(\"text\", x=0.575, y=0.90, label=\"Sustain\", size=3.5, color=IMPRINT_PALETTE[1], fontstyle=\"italic\")\n    + annotate(\"text\", x=1.30, y=0.90, label=\"Release\", size=3.5, color=IMPRINT_PALETTE[2], fontstyle=\"italic\")\n    + labs(x=\"Time (seconds)\", y=\"Amplitude\", title=title)\n    + scale_x_continuous(\n        breaks=np.arange(0, duration + 0.1, 0.25), labels=lambda lst: [f\"{v:.2f}\" for v in lst], expand=(0.01, 0.01)\n    )\n    + scale_y_continuous(limits=(-1.0, 1.0), breaks=np.arange(-1.0, 1.1, 0.25))\n    + theme_minimal()\n    + theme(\n        figure_size=(8, 4.5),\n        text=element_text(size=7, color=INK),\n        axis_title=element_text(size=10, color=INK),\n        axis_text=element_text(size=8, color=INK_SOFT),\n        plot_title=element_text(size=12, color=INK),\n        legend_text=element_text(size=8, color=INK_SOFT),\n        panel_background=element_rect(fill=PAGE_BG, color=\"none\"),\n        plot_background=element_rect(fill=PAGE_BG, color=\"none\"),\n        panel_grid_major_y=element_line(color=INK, size=0.3, alpha=0.15),\n        panel_grid_major_x=element_blank(),\n        panel_grid_minor=element_blank(),\n        axis_line_x=element_line(color=INK_SOFT, size=0.5),\n        axis_line_y=element_line(color=INK_SOFT, size=0.5),\n        axis_ticks_major_x=element_line(color=INK_SOFT, size=0.4),\n        axis_ticks_major_y=element_blank(),\n    )\n)\n\nplot.save(f\"plot-{THEME}.png\", dpi=400, width=8, height=4.5, units=\"in\", verbose=False)\n"}