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feat(plotnine): implement smith-chart-basic (#3866)
## Implementation: `smith-chart-basic` - plotnine Implements the **plotnine** version of `smith-chart-basic`. **File:** `plots/smith-chart-basic/implementations/plotnine.py` **Parent Issue:** #3792 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/21047489075)* --------- 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/smith-chart-basic/implementations/plotnine.py‎

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""" pyplots.ai
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smith-chart-basic: Smith Chart for RF/Impedance
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Library: plotnine 0.15.2 | Python 3.13.11
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Quality: 90/100 | Created: 2026-01-15
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"""
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import numpy as np
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import pandas as pd
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from plotnine import (
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aes,
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annotate,
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coord_fixed,
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element_blank,
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element_rect,
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element_text,
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geom_path,
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geom_point,
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geom_text,
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ggplot,
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labs,
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scale_x_continuous,
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scale_y_continuous,
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theme,
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)
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# Reference impedance
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Z0 = 50
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# Generate Smith chart grid - constant resistance circles
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# For resistance r, circle center at (r/(r+1), 0) with radius 1/(r+1)
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r_values = [0, 0.2, 0.5, 1, 2, 5]
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theta = np.linspace(0, 2 * np.pi, 200)
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r_circle_data = []
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for r in r_values:
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cx = r / (r + 1)
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radius = 1 / (r + 1)
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x = cx + radius * np.cos(theta)
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y = radius * np.sin(theta)
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# Clip to unit circle
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mask = x**2 + y**2 <= 1.001
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r_circle_data.append(pd.DataFrame({"x": x[mask], "y": y[mask], "type": "r_circle", "value": f"r={r}"}))
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r_circles_df = pd.concat(r_circle_data, ignore_index=True)
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# Constant reactance arcs
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# For reactance x, circle center at (1, 1/x) with radius 1/|x|
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x_values = [0.2, 0.5, 1, 2, 5]
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reactance_data = []
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for xv in x_values:
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# Positive reactance (upper half)
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radius_x = 1 / abs(xv)
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cy_pos = 1 / xv
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t = np.linspace(0, 2 * np.pi, 500)
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arc_x = 1 + radius_x * np.cos(t)
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arc_y = cy_pos + radius_x * np.sin(t)
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# Keep only points inside unit circle
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mask = (arc_x**2 + arc_y**2 <= 1.001) & (arc_x >= -0.01)
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if np.any(mask):
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reactance_data.append(pd.DataFrame({"x": arc_x[mask], "y": arc_y[mask], "type": "x_arc", "value": f"x={xv}"}))
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# Negative reactance (lower half)
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cy_neg = -1 / xv
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arc_y_neg = cy_neg + radius_x * np.sin(t)
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mask_neg = (arc_x**2 + arc_y_neg**2 <= 1.001) & (arc_x >= -0.01)
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if np.any(mask_neg):
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reactance_data.append(
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pd.DataFrame({"x": arc_x[mask_neg], "y": arc_y_neg[mask_neg], "type": "x_arc", "value": f"x=-{xv}"})
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)
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reactance_df = pd.concat(reactance_data, ignore_index=True)
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# Outer boundary circle (|gamma| = 1)
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boundary_theta = np.linspace(0, 2 * np.pi, 200)
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boundary_df = pd.DataFrame(
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{"x": np.cos(boundary_theta), "y": np.sin(boundary_theta), "type": "boundary", "value": "boundary"}
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)
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# Horizontal axis (real axis)
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axis_df = pd.DataFrame({"x": [-1, 1], "y": [0, 0], "type": "axis", "value": "axis"})
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# Example impedance data: antenna S11 measurement from 1-6 GHz
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np.random.seed(42)
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n_points = 50
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freq_ghz = np.linspace(1, 6, n_points)
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# Simulate antenna impedance variation with frequency
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# Creates realistic spiral pattern on Smith chart
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z_real = 50 + 30 * np.sin(2 * np.pi * freq_ghz / 2.5) + np.random.randn(n_points) * 3
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z_imag = 20 * np.cos(2 * np.pi * freq_ghz / 3) + 15 * (freq_ghz - 3) + np.random.randn(n_points) * 2
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# Normalize impedance
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z_norm = (z_real + 1j * z_imag) / Z0
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# Convert to reflection coefficient (gamma) for Smith chart coordinates
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gamma = (z_norm - 1) / (z_norm + 1)
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gamma_real = np.real(gamma)
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gamma_imag = np.imag(gamma)
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impedance_df = pd.DataFrame({"x": gamma_real, "y": gamma_imag, "freq": freq_ghz, "type": "impedance"})
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# Select frequency labels at key points
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label_indices = [0, 12, 24, 36, 49]
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labels_df = impedance_df.iloc[label_indices].copy()
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labels_df["label"] = [f"{f:.1f} GHz" for f in labels_df["freq"]]
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labels_df["y_offset"] = labels_df["y"] + 0.08
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# Combine grid data
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grid_df = pd.concat([r_circles_df, reactance_df, boundary_df, axis_df], ignore_index=True)
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# Create plot
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plot = (
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ggplot()
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# Grid lines
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+ geom_path(
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aes(x="x", y="y", group="value"),
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data=grid_df[grid_df["type"] == "r_circle"],
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color="#CCCCCC",
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size=0.5,
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alpha=0.8,
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)
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+ geom_path(
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aes(x="x", y="y", group="value"), data=grid_df[grid_df["type"] == "x_arc"], color="#CCCCCC", size=0.5, alpha=0.8
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)
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# Boundary circle
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+ geom_path(aes(x="x", y="y"), data=boundary_df, color="#333333", size=1)
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# Real axis
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+ geom_path(aes(x="x", y="y"), data=axis_df, color="#333333", size=0.5)
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# Impedance locus curve
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+ geom_path(aes(x="x", y="y"), data=impedance_df, color="#306998", size=1.5)
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# Data points
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+ geom_point(aes(x="x", y="y"), data=impedance_df, color="#306998", size=2, alpha=0.7)
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# Frequency labels
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+ geom_text(aes(x="x", y="y_offset", label="label"), data=labels_df, color="#FFD43B", size=12, fontweight="bold")
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# Center point marker (Z = Z0)
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+ annotate("point", x=0, y=0, color="#E74C3C", size=4)
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+ annotate("text", x=0.12, y=-0.08, label="Z=Z₀", color="#E74C3C", size=11)
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# Styling
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+ coord_fixed(ratio=1, xlim=(-1.3, 1.3), ylim=(-1.3, 1.3))
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+ scale_x_continuous(breaks=[])
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+ scale_y_continuous(breaks=[])
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+ labs(title="smith-chart-basic · plotnine · pyplots.ai", x="", y="")
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+ theme(
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figure_size=(12, 12),
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plot_title=element_text(size=24, ha="center", weight="bold"),
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panel_background=element_rect(fill="white"),
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panel_grid_major=element_blank(),
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panel_grid_minor=element_blank(),
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axis_text=element_blank(),
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axis_ticks=element_blank(),
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plot_background=element_rect(fill="white"),
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)
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)
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# Save
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plot.save("plot.png", dpi=300)

‎plots/smith-chart-basic/metadata/plotnine.yaml‎

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library: plotnine
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specification_id: smith-chart-basic
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created: '2026-01-15T21:51:40Z'
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updated: '2026-01-15T21:54:21Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 21047489075
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issue: 3792
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python_version: 3.13.11
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library_version: 0.15.2
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preview_url: https://storage.googleapis.com/pyplots-images/plots/smith-chart-basic/plotnine/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/smith-chart-basic/plotnine/plot_thumb.png
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preview_html: null
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quality_score: 90
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review:
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strengths:
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- Excellent implementation of Smith chart using plotnine grammar of graphics approach
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- Mathematically correct calculation of constant resistance circles and reactance
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arcs
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- Proper conversion from complex impedance to reflection coefficient coordinates
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- Clean layered composition with grid, boundary, axis, data locus, and annotations
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- Realistic RF engineering data scenario with antenna S11 measurements
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- Good use of coord_fixed for maintaining circular aspect ratio
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weaknesses:
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- Frequency labels in yellow may have lower contrast against white background than
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ideal
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- Grid line labels (r=0, r=0.2, x=0.5, etc.) not shown - would enhance educational
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value
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- Data point markers could be slightly larger for better visibility at full resolution
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image_description: The plot displays a Smith chart with the correct circular layout.
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The outer boundary is a black circle representing |gamma|=1 (total reflection).
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Inside, light gray constant resistance circles are drawn (centered along the horizontal
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axis) and constant reactance arcs curve from the right edge. A horizontal real
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axis line passes through the center. The impedance locus is drawn as a connected
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blue (#306998) path with small markers showing an antenna S11 measurement sweep
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from 1-6 GHz. Five frequency labels (1.0 GHz, 2.2 GHz, 3.4 GHz, 4.7 GHz, 6.0 GHz)
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appear in yellow/gold text positioned above their corresponding data points. A
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red dot marks the center point (Z=Z₀) with a red label. The chart uses a clean
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white background with no axis labels or tick marks (appropriate for a Smith chart).
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The title "smith-chart-basic · plotnine · pyplots.ai" appears centered at the
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top in bold black text.
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criteria_checklist:
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visual_quality:
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score: 36
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max: 40
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items:
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- id: VQ-01
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name: Text Legibility
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score: 9
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max: 10
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passed: true
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comment: Title is large and bold, frequency labels are readable though could
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be slightly larger
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- id: VQ-02
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name: No Overlap
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score: 8
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max: 8
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passed: true
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comment: No overlapping text elements
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- id: VQ-03
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name: Element Visibility
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score: 7
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max: 8
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passed: true
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comment: Grid lines visible with appropriate alpha, data points could be slightly
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larger
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- id: VQ-04
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name: Color Accessibility
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score: 5
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max: 5
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passed: true
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comment: Blue/yellow/red color scheme is colorblind-safe with good contrast
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- id: VQ-05
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name: Layout Balance
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score: 5
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max: 5
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passed: true
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comment: Chart is well-centered with balanced margins, uses square 1:1 aspect
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ratio appropriately
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- id: VQ-06
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name: Axis Labels
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score: 0
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max: 2
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passed: true
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comment: N/A for Smith chart (no conventional axes), but spec-appropriate
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- id: VQ-07
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name: Grid & Legend
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score: 2
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max: 2
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passed: true
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comment: Grid lines are subtle with appropriate alpha, no legend needed
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spec_compliance:
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score: 24
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max: 25
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items:
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- id: SC-01
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name: Plot Type
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score: 8
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max: 8
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passed: true
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comment: Correct Smith chart with resistance circles and reactance arcs
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- id: SC-02
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name: Data Mapping
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score: 5
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max: 5
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passed: true
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comment: Complex impedance correctly converted to reflection coefficient (gamma)
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coordinates
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- id: SC-03
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name: Required Features
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score: 5
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max: 5
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passed: true
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comment: 'All required features present: grid, normalized impedance, connected
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locus curve, frequency labels, center marker'
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- id: SC-04
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name: Data Range
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score: 3
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max: 3
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passed: true
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comment: All data visible within unit circle boundary
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- id: SC-05
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name: Legend Accuracy
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score: 1
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max: 2
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passed: true
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comment: No legend needed, but labels could better explain the grid values
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- id: SC-06
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name: Title Format
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score: 2
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max: 2
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passed: true
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comment: 'Correct format: smith-chart-basic · plotnine · pyplots.ai'
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data_quality:
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score: 18
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max: 20
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items:
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- id: DQ-01
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name: Feature Coverage
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score: 7
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max: 8
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passed: true
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comment: Shows impedance variation with frequency creating realistic spiral
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pattern, visits different regions of Smith chart
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- id: DQ-02
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name: Realistic Context
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score: 7
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max: 7
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passed: true
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comment: Antenna S11 measurement from 1-6 GHz is a realistic RF engineering
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scenario
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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: 5
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passed: true
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comment: Impedance values around 50 ohms reference are realistic, frequency
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range is typical for antenna design
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code_quality:
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score: 9
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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: 'Flat structure: imports, data generation, plot construction, save'
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- id: CQ-02
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name: Reproducibility
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score: 3
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max: 3
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passed: true
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comment: Uses np.random.seed(42)
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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 imports are used
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- id: CQ-04
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name: No Deprecated API
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score: 1
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max: 1
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passed: true
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comment: Uses current plotnine API
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- id: CQ-05
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name: Output Correct
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score: 0
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max: 1
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passed: false
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comment: 'Minor: could use higher dpi for 4800x2700 target'
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library_features:
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score: 3
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max: 5
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items:
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- id: LF-01
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name: Distinctive Features
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score: 3
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max: 5
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passed: true
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comment: Uses plotnine grammar of graphics with layered geom_path and geom_point,
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coord_fixed for aspect ratio, proper theming
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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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- annotations
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- layer-composition
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- patches
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patterns:
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- data-generation
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- iteration-over-groups
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- matrix-construction
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dataprep:
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- normalization
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styling:
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- minimal-chrome
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- alpha-blending

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