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feat(bokeh): implement spectrogram-basic (#2969)
## Implementation: `spectrogram-basic` - bokeh Implements the **bokeh** version of `spectrogram-basic`. **File:** `plots/spectrogram-basic/implementations/bokeh.py` **Parent Issue:** #2927 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20612805934)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/spectrogram-basic/implementations/bokeh.py‎

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""" pyplots.ai
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spectrogram-basic: Spectrogram Time-Frequency Heatmap
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Library: bokeh 3.8.1 | Python 3.13.11
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Quality: 92/100 | Created: 2025-12-31
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"""
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import numpy as np
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from bokeh.io import export_png, output_file, save
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from bokeh.models import BasicTicker, ColorBar, LinearColorMapper
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from bokeh.plotting import figure
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from scipy import signal
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# Data - Generate chirp signal with increasing frequency
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np.random.seed(42)
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sample_rate = 8000 # 8 kHz sampling rate
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duration = 2.0 # 2 seconds
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t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False)
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# Create a chirp signal: frequency increases from 200 Hz to 2000 Hz
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f0 = 200 # Start frequency
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f1 = 2000 # End frequency
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chirp_signal = signal.chirp(t, f0, duration, f1, method="linear")
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# Add some noise for realism
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chirp_signal += 0.1 * np.random.randn(len(chirp_signal))
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# Compute spectrogram using scipy.signal
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nperseg = 256 # Window size
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noverlap = 192 # Overlap (75%)
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frequencies, times, Sxx = signal.spectrogram(
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chirp_signal, fs=sample_rate, nperseg=nperseg, noverlap=noverlap, scaling="density"
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)
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# Convert to dB scale for better visualization
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Sxx_db = 10 * np.log10(Sxx + 1e-10) # Add small value to avoid log(0)
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# Viridis palette (256 colors) - perceptually uniform
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viridis = [
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"#440154",
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"#440256",
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"#450457",
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"#450559",
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"#46075a",
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"#46085c",
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"#460a5d",
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"#460b5e",
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"#470d60",
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"#470e61",
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"#471063",
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"#471164",
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"#471365",
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"#481467",
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"#481668",
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"#481769",
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"#48186a",
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"#481a6c",
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"#481b6d",
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"#481c6e",
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"#481d6f",
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"#481f70",
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"#482071",
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"#482173",
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"#482374",
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"#482475",
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"#482576",
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"#482677",
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"#482878",
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"#482979",
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"#472a79",
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"#472c7a",
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"#472d7b",
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"#472e7c",
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"#472f7d",
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"#46307e",
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"#46327e",
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"#46337f",
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"#463480",
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"#453581",
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"#453681",
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"#453882",
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"#443983",
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"#443a83",
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"#443b84",
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"#433d84",
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"#433e85",
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"#423f85",
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"#424086",
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"#424186",
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"#414287",
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"#414487",
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"#404588",
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"#404688",
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"#3f4788",
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"#3f4889",
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"#3e4989",
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"#3e4a89",
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"#3d4b89",
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"#3d4c89",
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"#3c4d8a",
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"#3c4e8a",
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"#3b508a",
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"#3b518a",
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"#3a528b",
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"#3a538b",
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"#39548b",
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"#39558b",
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"#38568b",
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"#38578c",
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"#37588c",
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"#37598c",
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"#365a8c",
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"#365b8c",
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"#355c8c",
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"#355d8c",
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"#345e8d",
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"#345f8d",
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"#33608d",
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"#33618d",
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"#32628d",
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"#32638d",
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"#31648d",
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"#31658d",
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"#31668d",
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"#30678d",
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"#30688d",
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"#2f698d",
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"#2f6a8d",
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"#2e6b8e",
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"#2e6c8e",
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"#2e6d8e",
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"#2d6e8e",
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"#2d6f8e",
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"#2c708e",
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"#2c718e",
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"#2c728e",
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"#2b738e",
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"#2b748e",
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"#2a758e",
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"#2a768e",
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"#2a778e",
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"#29788e",
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"#29798e",
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"#297a8e",
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"#287b8e",
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"#287c8e",
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"#277d8e",
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"#277e8e",
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"#277f8e",
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"#26808e",
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"#26818e",
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"#26828e",
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"#25838e",
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"#25848e",
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"#25858e",
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"#24868e",
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"#24878e",
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"#23888e",
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"#23898e",
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"#238a8d",
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"#228b8d",
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"#228c8d",
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"#228d8d",
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"#218e8d",
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"#218f8d",
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"#21908d",
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"#21918c",
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"#20928c",
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"#20938c",
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"#20948c",
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"#1f958b",
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"#1f968b",
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"#1f978b",
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"#1f988a",
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"#1f998a",
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"#1f9a8a",
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"#1e9b89",
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"#1e9c89",
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"#1e9d88",
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"#1f9e88",
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"#1f9f88",
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"#1fa087",
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"#1fa187",
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"#1fa286",
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"#20a386",
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"#20a485",
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"#21a585",
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"#21a684",
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"#22a784",
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"#22a883",
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"#23a982",
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"#24aa82",
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"#24ab81",
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"#25ac81",
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"#26ad80",
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"#27ae80",
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"#27af7f",
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"#28b07e",
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"#29b17e",
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"#2ab27d",
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"#2cb37c",
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"#2db47c",
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"#2eb57b",
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"#2fb67a",
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"#30b77a",
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"#32b879",
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"#33b978",
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"#35ba78",
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"#36bb77",
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"#37bc76",
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"#39bd75",
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"#3abe75",
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"#3cbf74",
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"#3ec073",
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"#3fc172",
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"#41c272",
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"#43c371",
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"#44c470",
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"#46c56f",
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"#48c66e",
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"#4ac76d",
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"#4cc86d",
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"#4ec96c",
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"#50ca6b",
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"#51cb6a",
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"#53cc69",
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"#55cd68",
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"#57ce67",
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"#59cf66",
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"#5bd066",
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"#5dd165",
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"#5fd264",
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"#61d363",
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"#63d462",
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"#65d561",
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"#67d660",
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"#69d75f",
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"#6cd85e",
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"#6ed95d",
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"#70da5c",
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"#72db5b",
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"#74dc5a",
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"#76dd59",
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"#78de58",
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"#7bdf57",
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"#7de056",
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"#7fe155",
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"#81e254",
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"#83e353",
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"#86e452",
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"#88e551",
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"#8ae64f",
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"#8ce74e",
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"#8fe84d",
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"#91e94c",
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"#93ea4b",
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"#96eb4a",
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"#98ec49",
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"#9aed48",
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"#9dee47",
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"#9fef46",
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"#a1f045",
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"#a4f144",
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"#a6f243",
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"#a8f342",
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"#abf341",
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"#adf440",
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"#b0f540",
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"#b2f63f",
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"#b5f73e",
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"#b7f83d",
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"#baf93c",
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"#bcfa3c",
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"#bffb3b",
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"#c1fc3b",
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"#c4fd3a",
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"#c7fd3a",
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"#c9fe39",
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"#ccfe39",
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"#cfff38",
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"#d1ff38",
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"#d4ff38",
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"#d7ff37",
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"#d9ff37",
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"#dcff37",
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"#dfff36",
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"#e1ff36",
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"#e4ff36",
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"#e7ff36",
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"#e9ff36",
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"#ecff36",
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"#efff36",
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"#f1ff36",
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"#f4ff36",
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"#f6ff36",
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]
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# Create figure with appropriate size
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p = figure(
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width=4800,
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height=2700,
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title="spectrogram-basic · bokeh · pyplots.ai",
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x_axis_label="Time (seconds)",
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y_axis_label="Frequency (Hz)",
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x_range=(times.min(), times.max()),
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y_range=(frequencies.min(), frequencies.max()),
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tools="",
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toolbar_location=None,
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)
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# Create color mapper
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color_mapper = LinearColorMapper(palette=viridis, low=Sxx_db.min(), high=Sxx_db.max())
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# Render spectrogram as image
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p.image(
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image=[Sxx_db],
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x=times.min(),
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y=frequencies.min(),
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dw=times.max() - times.min(),
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dh=frequencies.max() - frequencies.min(),
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color_mapper=color_mapper,
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level="image",
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)
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# Add colorbar with larger text
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color_bar = ColorBar(
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color_mapper=color_mapper,
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ticker=BasicTicker(),
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label_standoff=20,
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border_line_color=None,
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location=(0, 0),
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title="Power (dB)",
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title_text_font_size="36pt",
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major_label_text_font_size="28pt",
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width=80,
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padding=40,
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)
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p.add_layout(color_bar, "right")
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# Style text sizes for 4800x2700 canvas - enlarged for visibility
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p.title.text_font_size = "48pt"
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p.xaxis.axis_label_text_font_size = "36pt"
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p.yaxis.axis_label_text_font_size = "36pt"
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p.xaxis.major_label_text_font_size = "28pt"
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p.yaxis.major_label_text_font_size = "28pt"
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# Axis styling
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p.xaxis.axis_line_width = 3
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p.yaxis.axis_line_width = 3
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p.xaxis.major_tick_line_width = 3
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p.yaxis.major_tick_line_width = 3
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# Grid styling - subtle
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p.xgrid.grid_line_alpha = 0.3
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p.ygrid.grid_line_alpha = 0.3
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p.xgrid.grid_line_dash = "dashed"
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p.ygrid.grid_line_dash = "dashed"
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# Background
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p.background_fill_color = None
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p.border_fill_color = None
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# Outline
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p.outline_line_color = "#333333"
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p.outline_line_width = 2
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# Save PNG
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export_png(p, filename="plot.png")
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# Save HTML for interactive viewing
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output_file("plot.html", title="spectrogram-basic · bokeh · pyplots.ai")
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save(p)

‎plots/spectrogram-basic/metadata/bokeh.yaml‎

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library: bokeh
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specification_id: spectrogram-basic
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created: '2025-12-31T05:36:31Z'
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updated: '2025-12-31T05:49:59Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 20612805934
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issue: 2927
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python_version: 3.13.11
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library_version: 3.8.1
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preview_url: https://storage.googleapis.com/pyplots-images/plots/spectrogram-basic/bokeh/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/spectrogram-basic/bokeh/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/spectrogram-basic/bokeh/plot.html
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quality_score: 92
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review:
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strengths:
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- Excellent use of viridis colormap for perceptually uniform and colorblind-accessible
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visualization
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- Clean implementation with proper dB scale conversion for power spectral density
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- Clear chirp signal demonstrates time-frequency analysis effectively
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- Proper scipy.signal integration for spectrogram computation
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- Well-sized text for the 4800x2700 canvas
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weaknesses:
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- Grid lines (dashed) are somewhat distracting over the heatmap image; consider
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disabling or reducing opacity
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- Could leverage Bokeh interactive hover tools to show frequency/time/power values

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