feat(plotnine): implement line-interactive (#2839)

## Implementation: `line-interactive` - plotnine

Implements the **plotnine** version of `line-interactive`.

**File:** `plots/line-interactive/implementations/plotnine.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20603327438)*

---------

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>

Author: github-actions[bot]

plots/line-interactive/implementations/plotnine.py

@@ -0,0 +1,172 @@
+""" pyplots.ai
+line-interactive: Interactive Line Chart with Hover and Zoom
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 90/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import (
+    aes,
+    annotate,
+    element_line,
+    element_rect,
+    element_text,
+    geom_line,
+    geom_point,
+    geom_rect,
+    ggplot,
+    guide_legend,
+    guides,
+    labs,
+    scale_color_manual,
+    scale_x_datetime,
+    theme,
+    theme_minimal,
+)
+
+
+# Data - Server response time metrics (realistic monitoring scenario)
+np.random.seed(42)
+n_points = 200
+
+# Generate datetime index for one week of hourly data
+dates = pd.date_range("2024-01-01", periods=n_points, freq="h")
+
+# Generate realistic server response times with patterns
+base_response = 120  # Base response time in ms
+daily_pattern = 30 * np.sin(2 * np.pi * np.arange(n_points) / 24)
+weekly_pattern = 15 * np.sin(2 * np.pi * np.arange(n_points) / 168)
+noise = np.random.normal(0, 10, n_points)
+trend = np.linspace(0, 20, n_points)
+
+# Add spike anomalies
+response_times = base_response + daily_pattern + weekly_pattern + noise + trend
+spike_indices = [45, 120, 175]
+for idx in spike_indices:
+    response_times[idx] += np.random.uniform(50, 100)
+
+# Calculate bounds
+avg_response = np.mean(response_times)
+y_min = np.min(response_times) - 10
+y_max = np.max(response_times) + 50
+
+# Create unified DataFrame with series type for legend
+df = pd.DataFrame({"datetime": dates, "response_time": response_times, "series": "Response Time"})
+
+# Add anomaly points
+anomaly_df = pd.DataFrame(
+    {"datetime": dates[spike_indices], "response_time": response_times[spike_indices], "series": "Anomaly Spike"}
+)
+
+# Add average line
+avg_df = pd.DataFrame(
+    {"datetime": [dates[0], dates[-1]], "response_time": [avg_response, avg_response], "series": "Average"}
+)
+
+# Combine for legend
+combined_df = pd.concat([df, anomaly_df, avg_df], ignore_index=True)
+
+# Demo hover tooltip
+demo_idx = 75
+demo_x = dates[demo_idx]
+demo_y = response_times[demo_idx]
+demo_date_str = demo_x.strftime("%Y-%m-%d %H:%M")
+
+# Zoom region bounds
+zoom_start = dates[70]
+zoom_end = dates[100]
+
+# Build plot
+plot = (
+    ggplot(combined_df, aes(x="datetime", y="response_time", color="series"))
+    # Zoom region highlight
+    + geom_rect(
+        aes(xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax"),
+        data=pd.DataFrame({"xmin": [zoom_start], "xmax": [zoom_end], "ymin": [y_min], "ymax": [y_max]}),
+        inherit_aes=False,
+        fill="#2A9D8F",
+        alpha=0.15,
+    )
+    # Main time series line
+    + geom_line(data=df, size=1.8)
+    # Scatter points for hover targets (larger for visibility)
+    + geom_point(data=df.iloc[::5], size=5, alpha=0.9)
+    # Average reference line
+    + geom_line(data=avg_df, linetype="dashed", size=1.5)
+    # Anomaly markers (large triangles)
+    + geom_point(data=anomaly_df, size=8, shape="^")
+    # Color scale with explicit legend
+    + scale_color_manual(
+        name="Data Series",
+        values={"Response Time": "#306998", "Anomaly Spike": "#E63946", "Average": "#808080"},
+        breaks=["Response Time", "Average", "Anomaly Spike"],
+    )
+    + guides(color=guide_legend(override_aes={"size": 6}))
+    # Demo tooltip box
+    + annotate(
+        "rect",
+        xmin=demo_x - pd.Timedelta(hours=8),
+        xmax=demo_x + pd.Timedelta(hours=8),
+        ymin=demo_y + 18,
+        ymax=demo_y + 55,
+        fill="#FFD43B",
+        alpha=0.95,
+    )
+    + annotate(
+        "text",
+        x=demo_x,
+        y=demo_y + 36,
+        label=f"Time: {demo_date_str}\nResponse: {demo_y:.1f} ms",
+        size=11,
+        fontweight="bold",
+        color="#306998",
+    )
+    + annotate("segment", x=demo_x, xend=demo_x, y=demo_y + 18, yend=demo_y + 4, color="#306998", size=1.2)
+    # Zoom region label
+    + annotate(
+        "text",
+        x=dates[85],
+        y=y_min + 12,
+        label="Zoom Region\n(range selection)",
+        size=10,
+        color="#2A9D8F",
+        fontweight="bold",
+    )
+    # Subtitle (positioned prominently below title area)
+    + annotate(
+        "text",
+        x=dates[50],
+        y=y_max - 8,
+        label="Static demonstration of interactive concepts: tooltips, zoom regions, anomaly markers",
+        size=12,
+        color="#444444",
+        fontstyle="italic",
+        ha="left",
+    )
+    # Labels
+    + labs(x="Time", y="Response Time (ms)", title="line-interactive · plotnine · pyplots.ai")
+    + scale_x_datetime(date_labels="%b %d\n%H:%M")
+    + theme_minimal()
+    + theme(
+        figure_size=(16, 9),
+        plot_title=element_text(size=24, weight="bold"),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=14),
+        axis_text_x=element_text(rotation=30),
+        panel_grid_major=element_line(color="#CCCCCC", size=0.5, alpha=0.3),
+        panel_grid_minor=element_line(color="#EEEEEE", size=0.3, alpha=0.2),
+        plot_background=element_rect(fill="white"),
+        panel_background=element_rect(fill="white"),
+        legend_title=element_text(size=16, weight="bold"),
+        legend_text=element_text(size=14),
+        legend_position=(0.85, 0.75),
+        legend_direction="vertical",
+        legend_key_size=20,
+        legend_background=element_rect(fill="white", color="#CCCCCC", alpha=0.95),
+        legend_box_margin=10,
+    )
+)
+
+# Save
+plot.save("plot.png", dpi=300, verbose=False)

plots/line-interactive/metadata/plotnine.yaml

@@ -0,0 +1,27 @@
+library: plotnine
+specification_id: line-interactive
+created: '2025-12-30T18:40:02Z'
+updated: '2025-12-30T19:02:16Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20603327438
+issue: 0
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/line-interactive/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-interactive/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 90
+review:
+  strengths:
+  - Excellent creative approach to demonstrating interactive concepts in a static
+    library
+  - Realistic server monitoring scenario with meaningful patterns (daily cycles, anomaly
+    spikes)
+  - Clean demonstration of tooltip, zoom region, and control instructions via annotations
+  - Proper use of plotnine grammar with well-structured theming
+  - Good color scheme and visual hierarchy
+  - Appropriate text sizing for 4800x2700 output
+  weaknesses:
+  - Legend not rendering in output despite being configured (legend_position may need
+    adjustment)
+  - The size parameter in geom_line is deprecated in newer plotnine versions