{"spec_id":"spectrum-basic","library":"plotly","language":"python","code":"\"\"\" anyplot.ai\nspectrum-basic: Frequency Spectrum Plot\nLibrary: plotly 6.7.0 | Python 3.13.13\nQuality: 93/100 | Updated: 2026-05-14\n\"\"\"\n\nimport os\n\nimport numpy as np\nimport plotly.graph_objects as go\n\n\n# Theme tokens\nTHEME = os.getenv(\"ANYPLOT_THEME\", \"light\")\nPAGE_BG = \"#FAF8F1\" if THEME == \"light\" else \"#1A1A17\"\nELEVATED_BG = \"#FFFDF6\" if THEME == \"light\" else \"#242420\"\nINK = \"#1A1A17\" if THEME == \"light\" else \"#F0EFE8\"\nINK_SOFT = \"#4A4A44\" if THEME == \"light\" else \"#B8B7B0\"\nGRID = \"rgba(26,26,23,0.10)\" if THEME == \"light\" else \"rgba(240,239,232,0.10)\"\nBRAND = \"#009E73\"\n\n# Data - Generate synthetic signal with multiple frequency components\nnp.random.seed(42)\n\n# Signal parameters\nsample_rate = 1000  # Hz\nduration = 1.0  # seconds\nn_samples = int(sample_rate * duration)\nt = np.linspace(0, duration, n_samples, endpoint=False)\n\n# Create synthetic signal: sum of sinusoids at 50, 120, and 300 Hz\nsignal = (\n    1.0 * np.sin(2 * np.pi * 50 * t)  # 50 Hz fundamental\n    + 0.5 * np.sin(2 * np.pi * 120 * t)  # 120 Hz component\n    + 0.3 * np.sin(2 * np.pi * 300 * t)  # 300 Hz component\n    + 0.1 * np.random.randn(n_samples)  # Noise\n)\n\n# Compute FFT\nfft_result = np.fft.rfft(signal)\nfrequency = np.fft.rfftfreq(n_samples, 1 / sample_rate)\namplitude_db = 20 * np.log10(np.abs(fft_result) / n_samples + 1e-10)\n\n# Plot\nfig = go.Figure()\n\nfig.add_trace(\n    go.Scatter(\n        x=frequency,\n        y=amplitude_db,\n        mode=\"lines\",\n        line=dict(color=BRAND, width=3),\n        fill=\"tozeroy\",\n        fillcolor=\"rgba(0, 158, 115, 0.15)\",\n        name=\"Spectrum\",\n        hovertemplate=\"<b>Frequency:</b> %{x:.1f} Hz<br><b>Amplitude:</b> %{y:.1f} dB<extra></extra>\",\n    )\n)\n\n# Layout with theme-adaptive colors\nfig.update_layout(\n    title=dict(text=\"spectrum-basic · plotly · anyplot.ai\", font=dict(size=28, color=INK), x=0.5, xanchor=\"center\"),\n    xaxis=dict(\n        title=dict(text=\"Frequency (Hz)\", font=dict(size=22, color=INK)),\n        tickfont=dict(size=18, color=INK_SOFT),\n        range=[0, 500],\n        gridcolor=GRID,\n        gridwidth=0.8,\n        linecolor=INK_SOFT,\n        zerolinecolor=INK_SOFT,\n    ),\n    yaxis=dict(\n        title=dict(text=\"Amplitude (dB)\", font=dict(size=22, color=INK)),\n        tickfont=dict(size=18, color=INK_SOFT),\n        gridcolor=GRID,\n        gridwidth=0.8,\n        linecolor=INK_SOFT,\n        zerolinecolor=INK_SOFT,\n    ),\n    paper_bgcolor=PAGE_BG,\n    plot_bgcolor=PAGE_BG,\n    font=dict(color=INK),\n    showlegend=False,\n    hovermode=\"x unified\",\n    margin=dict(l=100, r=60, t=100, b=80),\n)\n\n# Add annotations for peak frequencies\npeak_freqs = [50, 120, 300]\nfor freq in peak_freqs:\n    idx = np.argmin(np.abs(frequency - freq))\n    fig.add_annotation(\n        x=frequency[idx],\n        y=amplitude_db[idx],\n        text=f\"{freq} Hz\",\n        showarrow=True,\n        arrowhead=2,\n        arrowsize=1.5,\n        arrowwidth=2,\n        arrowcolor=INK_SOFT,\n        font=dict(size=16, color=INK),\n        ax=0,\n        ay=-50,\n    )\n\n# Save\nfig.write_image(f\"plot-{THEME}.png\", width=1600, height=900, scale=3)\nfig.write_html(f\"plot-{THEME}.html\", include_plotlyjs=\"cdn\")\n"}