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feat(altair): implement scatter-constellation-diagram (#4970)
## Implementation: `scatter-constellation-diagram` - altair Implements the **altair** version of `scatter-constellation-diagram`. **File:** `plots/scatter-constellation-diagram/implementations/altair.py` **Parent Issue:** #4562 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/23220948598)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/scatter-constellation-diagram/implementations/altair.py‎

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""" pyplots.ai
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scatter-constellation-diagram: Digital Modulation Constellation Diagram
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Library: altair 6.0.0 | Python 3.14.3
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Quality: 87/100 | Created: 2026-03-17
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"""
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import altair as alt
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import numpy as np
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import pandas as pd
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# Data
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np.random.seed(42)
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ideal_vals = [-3, -1, 1, 3]
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ideal_i, ideal_q = np.meshgrid(ideal_vals, ideal_vals)
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ideal_i = ideal_i.flatten()
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ideal_q = ideal_q.flatten()
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n_symbols = 1000
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symbol_indices = np.random.randint(0, 16, size=n_symbols)
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snr_db = 20
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snr_linear = 10 ** (snr_db / 10)
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signal_power = np.mean(ideal_i**2 + ideal_q**2)
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noise_std = np.sqrt(signal_power / snr_linear)
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received_i = ideal_i[symbol_indices] + np.random.normal(0, noise_std, n_symbols)
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received_q = ideal_q[symbol_indices] + np.random.normal(0, noise_std, n_symbols)
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error_vectors = np.sqrt((received_i - ideal_i[symbol_indices]) ** 2 + (received_q - ideal_q[symbol_indices]) ** 2)
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rms_signal = np.sqrt(signal_power)
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evm_pct = np.sqrt(np.mean(error_vectors**2)) / rms_signal * 100
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# Per-symbol error magnitude for color encoding
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df_received = pd.DataFrame(
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{
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"I": received_i,
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"Q": received_q,
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"Error Magnitude": error_vectors,
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"Nearest I": ideal_i[symbol_indices],
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"Nearest Q": ideal_q[symbol_indices],
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}
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)
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df_ideal = pd.DataFrame({"I": ideal_i, "Q": ideal_q, "label": "Ideal"})
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# Decision boundaries
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boundary_vals = [-4, -2, 0, 2, 4]
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boundary_h = pd.DataFrame([{"x": -5.2, "x2": 5.2, "y": v} for v in boundary_vals])
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boundary_v = pd.DataFrame([{"y": -5.2, "y2": 5.2, "x": v} for v in boundary_vals])
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# EVM annotation
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df_evm = pd.DataFrame({"I": [4.2], "Q": [4.8], "label": [f"EVM = {evm_pct:.1f}%"]})
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# Selection for interactive nearest-point highlighting
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nearest = alt.selection_point(on="pointerover", nearest=True, fields=["I", "Q"], empty=False)
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# Plot layers
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scale_x = alt.Scale(domain=[-5.5, 5.5], nice=False)
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scale_y = alt.Scale(domain=[-5.5, 5.5], nice=False)
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received_layer = (
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alt.Chart(df_received)
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.mark_circle(size=45)
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.encode(
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x=alt.X("I:Q", title="In-Phase (I)", scale=scale_x),
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y=alt.Y("Q:Q", title="Quadrature (Q)", scale=scale_y),
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color=alt.Color(
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"Error Magnitude:Q",
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scale=alt.Scale(scheme="viridis"),
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legend=alt.Legend(
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title="Error Mag.", titleFontSize=16, labelFontSize=14, orient="right", gradientLength=200
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),
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),
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opacity=alt.condition(nearest, alt.value(0.85), alt.value(0.3)),
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size=alt.condition(nearest, alt.value(120), alt.value(45)),
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tooltip=[
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alt.Tooltip("I:Q", format=".3f"),
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alt.Tooltip("Q:Q", format=".3f"),
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alt.Tooltip("Error Magnitude:Q", format=".3f", title="Error"),
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alt.Tooltip("Nearest I:Q", format=".0f", title="Ideal I"),
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alt.Tooltip("Nearest Q:Q", format=".0f", title="Ideal Q"),
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],
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)
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.add_params(nearest)
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)
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ideal_layer = (
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alt.Chart(df_ideal)
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.mark_point(size=400, filled=False, strokeWidth=3.5)
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.encode(
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x="I:Q",
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y="Q:Q",
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color=alt.value("#E45756"),
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shape=alt.value("cross"),
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tooltip=[alt.Tooltip("I:Q", format=".0f", title="Ideal I"), alt.Tooltip("Q:Q", format=".0f", title="Ideal Q")],
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)
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)
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h_rules = (
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alt.Chart(boundary_h)
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.mark_rule(strokeDash=[8, 5], strokeWidth=1, opacity=0.35)
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.encode(x=alt.X("x:Q", scale=scale_x), x2="x2:Q", y=alt.Y("y:Q", scale=scale_y), color=alt.value("#AAAAAA"))
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)
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v_rules = (
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alt.Chart(boundary_v)
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.mark_rule(strokeDash=[8, 5], strokeWidth=1, opacity=0.35)
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.encode(y=alt.Y("y:Q", scale=scale_y), y2="y2:Q", x=alt.X("x:Q", scale=scale_x), color=alt.value("#AAAAAA"))
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)
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evm_label = (
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alt.Chart(df_evm)
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.mark_text(fontSize=22, fontWeight="bold", align="right", font="monospace")
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.encode(x="I:Q", y="Q:Q", text="label:N", color=alt.value("#222222"))
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)
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chart = (
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alt.layer(h_rules, v_rules, received_layer, ideal_layer, evm_label)
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.properties(
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width=1020,
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height=1100,
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title=alt.Title(
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"scatter-constellation-diagram \u00b7 altair \u00b7 pyplots.ai", fontSize=28, anchor="middle", offset=12
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),
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)
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.configure_axis(
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labelFontSize=18, titleFontSize=22, tickSize=8, domainColor="#666666", tickColor="#888888", grid=False
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)
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.configure_view(strokeWidth=0)
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)
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# Save
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chart.save("plot.png", scale_factor=3.0)
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chart.save("plot.html")

‎plots/scatter-constellation-diagram/metadata/altair.yaml‎

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library: altair
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specification_id: scatter-constellation-diagram
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created: '2026-03-17T23:22:50Z'
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updated: '2026-03-17T23:56:47Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 23220948598
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issue: 4562
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python_version: 3.14.3
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library_version: 6.0.0
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preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-constellation-diagram/altair/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-constellation-diagram/altair/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-constellation-diagram/altair/plot.html
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quality_score: 87
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review:
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strengths:
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- Perfect spec compliance with all required features correctly implemented (16-QAM,
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ideal crosses, received dots, decision boundaries, EVM, axis labels, title format)
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- Viridis color encoding of error magnitude adds a meaningful analytical dimension
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beyond spec requirements
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- Interactive features (selection highlighting, conditional encoding, tooltips)
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showcase Altair distinctive capabilities
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- Mathematically correct EVM calculation consistent with 20 dB SNR
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- Clean, well-structured code following KISS principles
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weaknesses: []
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image_description: The plot displays a 16-QAM constellation diagram on a near-square
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canvas (1020x1100). Sixteen ideal constellation points appear as red/salmon cross
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markers (#E45756) arranged in a 4x4 grid at I/Q coordinates +/-1 and +/-3. Approximately
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1000 received symbols are shown as semi-transparent circles color-encoded by error
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magnitude using the viridis colormap — dark purple clusters near ideal points
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(low error) transitioning to teal/green and yellow for higher-error outliers.
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Dashed gray decision boundary lines are drawn at I/Q values -4, -2, 0, 2, 4 on
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both axes. The title "scatter-constellation-diagram · altair · pyplots.ai" is
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displayed at top center in 28pt font. Axes are labeled "In-Phase (I)" (x) and
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"Quadrature (Q)" (y) with tick labels at 18pt. A bold monospace "EVM = 14.0%"
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annotation sits in the upper-right quadrant. A viridis color legend labeled "Error
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Mag." appears on the right side. The background is white with no default grid;
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view border stroke is removed. The overall layout is clean and approximately square
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with the constellation geometry well-preserved.
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criteria_checklist:
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visual_quality:
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score: 27
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max: 30
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items:
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- id: VQ-01
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name: Text Legibility
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score: 7
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max: 8
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passed: true
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comment: 'Font sizes explicitly set: title=28, axis titles=22, ticks=18, EVM=22,
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legend title=16, legend labels=14. All readable. Legend labels slightly
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small at 14pt.'
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- id: VQ-02
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name: No Overlap
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score: 6
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max: 6
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passed: true
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comment: No text or element overlap anywhere in the plot.
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- id: VQ-03
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name: Element Visibility
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score: 5
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max: 6
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passed: true
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comment: Size=45, opacity=0.3 for 1000 points is within guidelines. Ideal
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crosses at size=400 are prominent. Some peripheral points faint at base
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opacity.
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- id: VQ-04
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name: Color Accessibility
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score: 4
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max: 4
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passed: true
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comment: Viridis colormap is perceptually uniform and colorblind-safe. Red
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crosses provide strong luminance contrast.
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- id: VQ-05
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name: Layout & Canvas
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score: 3
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max: 4
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passed: true
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comment: Canvas 1020x1100 at scale 3.0 = 3060x3300 px. Improved from 2700x2700
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but below 3600x3600 target. Not perfectly square.
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- id: VQ-06
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name: Axis Labels & Title
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score: 2
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max: 2
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passed: true
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comment: In-Phase (I) and Quadrature (Q) are descriptive with standard domain
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notation.
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design_excellence:
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score: 13
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max: 20
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items:
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- id: DE-01
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name: Aesthetic Sophistication
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score: 5
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max: 8
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passed: true
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comment: Viridis error magnitude encoding is thoughtful. Red crosses contrast
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well. Monospace EVM annotation. Above defaults but not publication-level.
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- id: DE-02
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name: Visual Refinement
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score: 4
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max: 6
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passed: true
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comment: Grid disabled, view stroke removed, custom domain/tick colors, dashed
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decision boundaries as structural context.
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- id: DE-03
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name: Data Storytelling
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score: 4
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max: 6
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passed: true
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comment: Error magnitude color encoding creates visual hierarchy. EVM annotation
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quantifies modulation quality. Viewer immediately understands signal integrity.
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spec_compliance:
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score: 15
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max: 15
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items:
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- id: SC-01
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name: Plot Type
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score: 5
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max: 5
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passed: true
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comment: Correct I/Q scatter constellation diagram for 16-QAM.
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- id: SC-02
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name: Required Features
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score: 4
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max: 4
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passed: true
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comment: 'All spec features present: ideal crosses, received dots, decision
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boundaries, equal aspect, EVM annotation, axis labels.'
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- id: SC-03
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name: Data Mapping
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score: 3
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max: 3
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passed: true
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comment: I mapped to x-axis, Q to y-axis, symmetric limits centered at origin.
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- id: SC-04
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name: Title & Legend
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score: 3
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max: 3
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passed: true
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comment: Title follows exact format. Legend shows Error Mag. which is appropriate.
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data_quality:
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score: 14
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max: 15
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items:
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- id: DQ-01
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name: Feature Coverage
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score: 5
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max: 6
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passed: true
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comment: Shows all 16 ideal points, noisy received symbols, error magnitude
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via color, decision boundaries, EVM. Color adds analytical depth.
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- id: DQ-02
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name: Realistic Context
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score: 5
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max: 5
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passed: true
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comment: 16-QAM is a real digital modulation scheme used in Wi-Fi, 5G NR,
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DVB. Neutral technical domain.
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- id: DQ-03
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name: Appropriate Scale
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score: 4
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max: 4
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passed: true
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comment: Standard grid values for 16-QAM, 20 dB SNR realistic, 1000 symbols
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appropriate, EVM=14.0% consistent.
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code_quality:
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score: 10
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max: 10
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items:
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- id: CQ-01
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name: KISS Structure
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score: 3
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max: 3
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passed: true
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comment: 'Clean linear flow: imports, data generation, plot layers, composition,
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save.'
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- id: CQ-02
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name: Reproducibility
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score: 2
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max: 2
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passed: true
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comment: np.random.seed(42) set at start.
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- id: CQ-03
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name: Clean Imports
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score: 2
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max: 2
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passed: true
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comment: All three imports (altair, numpy, pandas) are used.
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- id: CQ-04
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name: Code Elegance
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score: 2
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max: 2
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passed: true
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comment: Clean, well-structured. Proper EVM calculation with correct signal
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processing math.
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- id: CQ-05
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name: Output & API
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score: 1
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max: 1
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passed: true
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comment: Saves as plot.png and plot.html. Current Altair 6.0 API.
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library_mastery:
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score: 8
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max: 10
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items:
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- id: LM-01
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name: Idiomatic Usage
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score: 4
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max: 5
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passed: true
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comment: 'Good declarative grammar: alt.layer(), proper encoding types, Scale/Legend/Title
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configs, configure_axis/configure_view.'
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- id: LM-02
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name: Distinctive Features
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score: 4
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max: 5
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passed: true
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comment: Uses alt.selection_point for nearest-point highlighting, alt.condition
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for dynamic opacity/size, rich formatted tooltips, HTML export. Distinctive
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Altair features.
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verdict: APPROVED
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impl_tags:
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dependencies: []
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techniques:
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- layer-composition
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- annotations
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- hover-tooltips
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- html-export
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- custom-legend
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patterns:
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- data-generation
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dataprep: []
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styling:
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- custom-colormap
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- alpha-blending

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