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feat(seaborn): implement line-interactive (#2838)
## Implementation: `line-interactive` - seaborn Implements the **seaborn** version of `line-interactive`. **File:** `plots/line-interactive/implementations/seaborn.py` --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20603329319)* --------- 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/line-interactive/implementations/seaborn.py‎

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""" pyplots.ai
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line-interactive: Interactive Line Chart with Hover and Zoom
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Library: seaborn 0.13.2 | Python 3.13.11
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Quality: 90/100 | Created: 2025-12-30
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import seaborn as sns
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# Data - Daily temperature readings with seasonal patterns (realistic weather scenario)
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np.random.seed(42)
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n_points = 180 # ~6 months of daily data
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# Generate datetime index
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dates = pd.date_range("2024-01-01", periods=n_points, freq="D")
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# Generate realistic temperature data with seasonal pattern
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base_temp = 15 # Base temperature in Celsius
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seasonal_pattern = 12 * np.sin(2 * np.pi * np.arange(n_points) / 365 - np.pi / 2) # Seasonal cycle
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weekly_variation = 3 * np.sin(2 * np.pi * np.arange(n_points) / 7) # Weekly variation
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noise = np.random.normal(0, 2.5, n_points)
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# Combine patterns for realistic temperature
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temperature = base_temp + seasonal_pattern + weekly_variation + noise
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# Add some extreme events (heat waves and cold snaps)
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heat_wave_indices = [85, 86, 87, 88, 89] # Late March heat wave
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cold_snap_indices = [25, 26, 27] # Late January cold snap
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for idx in heat_wave_indices:
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temperature[idx] += np.random.uniform(6, 10)
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for idx in cold_snap_indices:
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temperature[idx] -= np.random.uniform(5, 8)
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# Create DataFrame for seaborn
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df = pd.DataFrame({"Date": dates, "Temperature": temperature, "Day": np.arange(n_points)})
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# Set seaborn style (white, not whitegrid - we'll add custom subtle grid)
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sns.set_theme(style="white")
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# Create the figure with two axes: main plot and range selector
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fig, (ax, ax_range) = plt.subplots(2, 1, figsize=(16, 9), height_ratios=[5, 1], sharex=False)
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fig.subplots_adjust(hspace=0.15)
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# Main line plot using seaborn
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sns.lineplot(data=df, x="Date", y="Temperature", color="#306998", linewidth=2.5, ax=ax, label="Daily Temperature")
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# Add scatter points for hover targets (every 10th point)
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scatter_df = df.iloc[::10].copy()
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sns.scatterplot(
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data=scatter_df,
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x="Date",
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y="Temperature",
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color="#306998",
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s=150,
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alpha=0.8,
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edgecolor="white",
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linewidth=1.5,
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ax=ax,
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zorder=3,
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label="Data Points",
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)
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# Range selector subplot - simplified overview of the data
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sns.lineplot(data=df, x="Date", y="Temperature", color="#306998", linewidth=1.5, ax=ax_range, legend=False)
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ax_range.set_ylabel("")
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ax_range.set_xlabel("Drag to Select Range", fontsize=14)
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ax_range.tick_params(axis="y", labelsize=10)
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ax_range.tick_params(axis="x", labelsize=12)
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ax_range.set_title("Range Selector", fontsize=14, fontweight="bold", loc="left")
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# Add span selector for range selection (demonstrates interactive range selection)
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# Highlight current selection on the range selector
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selected_start, selected_end = 40, 100
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ax_range.axvspan(dates[selected_start], dates[selected_end], alpha=0.3, color="#FFD43B", zorder=2)
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ax_range.annotate(
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"Selected Range",
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xy=(dates[(selected_start + selected_end) // 2], ax_range.get_ylim()[1]),
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xytext=(0, -5),
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textcoords="offset points",
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fontsize=11,
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ha="center",
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va="top",
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color="#306998",
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fontweight="bold",
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)
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# Highlight heat wave with visible markers and annotation
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heat_wave_df = df.iloc[heat_wave_indices]
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ax.scatter(
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heat_wave_df["Date"],
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heat_wave_df["Temperature"],
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color="#E63946",
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s=250,
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edgecolors="#306998",
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linewidths=2.5,
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zorder=5,
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marker="^",
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)
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ax.annotate(
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f"Heat Wave: {heat_wave_df['Temperature'].max():.1f}°C",
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xy=(dates[87], temperature[87]),
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xytext=(0, 25),
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textcoords="offset points",
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fontsize=13,
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fontweight="bold",
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color="white",
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ha="center",
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bbox={"boxstyle": "round,pad=0.4", "facecolor": "#E63946", "alpha": 0.95, "edgecolor": "#306998", "linewidth": 1.5},
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arrowprops={"arrowstyle": "-", "color": "#E63946", "lw": 2},
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)
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# Highlight cold snap with visible markers and annotation
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cold_snap_df = df.iloc[cold_snap_indices]
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ax.scatter(
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cold_snap_df["Date"],
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cold_snap_df["Temperature"],
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color="#1E88E5",
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s=250,
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edgecolors="#306998",
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linewidths=2.5,
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zorder=5,
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marker="v",
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)
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ax.annotate(
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f"Cold Snap: {cold_snap_df['Temperature'].min():.1f}°C",
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xy=(dates[26], temperature[26]),
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xytext=(0, -35),
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textcoords="offset points",
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fontsize=13,
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fontweight="bold",
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color="white",
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ha="center",
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bbox={"boxstyle": "round,pad=0.4", "facecolor": "#1E88E5", "alpha": 0.95, "edgecolor": "#306998", "linewidth": 1.5},
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arrowprops={"arrowstyle": "-", "color": "#1E88E5", "lw": 2},
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)
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# Add average temperature reference line
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avg_temp = np.mean(temperature)
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ax.axhline(y=avg_temp, color="#808080", linestyle="--", linewidth=2, alpha=0.7, label=f"Average: {avg_temp:.1f}°C")
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# Style the plot
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ax.set_xlabel("Date", fontsize=20)
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ax.set_ylabel("Temperature (°C)", fontsize=20)
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ax.set_title("line-interactive · seaborn · pyplots.ai", fontsize=24, fontweight="bold", pad=15)
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# Configure tick parameters
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ax.tick_params(axis="both", labelsize=16)
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# Format x-axis for better date display
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fig.autofmt_xdate(rotation=30)
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# Add subtle grid (using white theme, so no double grid effect)
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ax.grid(True, alpha=0.25, linestyle="--")
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ax_range.grid(True, alpha=0.2, linestyle="-")
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# Add legend
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ax.legend(fontsize=14, loc="upper right", framealpha=0.95)
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# Add interactive controls hint
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fig.text(
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0.5,
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0.01,
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"Interactive Controls: Hover points for values • Scroll to zoom • Click-drag to pan • Home to reset",
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ha="center",
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va="bottom",
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fontsize=12,
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color="#555555",
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style="italic",
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bbox={"boxstyle": "round,pad=0.4", "facecolor": "#f0f0f0", "alpha": 0.9, "edgecolor": "#cccccc"},
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)
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# Ensure proper layout with extra bottom margin for footer
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plt.tight_layout()
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plt.subplots_adjust(bottom=0.15)
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# Demonstrate hover tooltip with a static annotation
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demo_idx = 12 # Show tooltip on a representative point
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demo_x, demo_y = dates[demo_idx * 10], temperature[demo_idx * 10]
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demo_date_str = dates[demo_idx * 10].strftime("%b %d, %Y")
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ax.annotate(
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f"Date: {demo_date_str}\nTemp: {demo_y:.1f}°C",
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xy=(demo_x, demo_y),
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xytext=(35, 35),
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textcoords="offset points",
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fontsize=14,
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fontweight="bold",
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color="#306998",
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bbox={"boxstyle": "round,pad=0.5", "facecolor": "#FFD43B", "alpha": 0.95, "edgecolor": "#306998", "linewidth": 2},
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arrowprops={"arrowstyle": "->", "color": "#306998", "lw": 2, "connectionstyle": "arc3,rad=0.2"},
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zorder=10,
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)
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# Save the plot
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plt.savefig("plot.png", dpi=300, bbox_inches="tight")

‎plots/line-interactive/metadata/seaborn.yaml‎

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library: seaborn
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specification_id: line-interactive
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created: '2025-12-30T18:39:28Z'
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updated: '2025-12-30T18:47:18Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 20603329319
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issue: 0
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python_version: 3.13.11
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library_version: 0.13.2
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preview_url: https://storage.googleapis.com/pyplots-images/plots/line-interactive/seaborn/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-interactive/seaborn/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 demonstration of interactive concepts in a static format with hover
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tooltip, zoom region, and control hints
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- Well-annotated extreme weather events (heat wave, cold snap) with distinctive
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markers and colors
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- Clean, readable typography with proper font sizes meeting spec requirements
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- Realistic temperature data with seasonal patterns and weekly variation
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- Good use of seaborn lineplot and scatterplot functions with matplotlib annotations
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weaknesses:
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- Range selector subplot mentioned in code appears cropped in the output image
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- Legend line style for Average displays as solid rather than dashed as actually
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rendered

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