feat(matplotlib): implement line-interactive

plots/line-interactive/implementations/matplotlib.py

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+""" pyplots.ai
+line-interactive: Interactive Line Chart with Hover and Zoom
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 88/100 | Created: 2025-12-30
+"""
+
+import matplotlib.pyplot as plt
+import mplcursors
+import numpy as np
+import pandas as pd
+
+
+# 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)  # Daily cycle
+weekly_pattern = 15 * np.sin(2 * np.pi * np.arange(n_points) / 168)  # Weekly cycle
+noise = np.random.normal(0, 10, n_points)
+trend = np.linspace(0, 20, n_points)  # Slight upward trend
+
+# Add some 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)
+
+# Create the figure with interactive backend capabilities
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Main line plot
+(line,) = ax.plot(dates, response_times, color="#306998", linewidth=2.5, label="Response Time", zorder=2)
+
+# Add scatter points for hover targets (every 5th point for better interactivity)
+scatter = ax.scatter(
+    dates[::5],
+    response_times[::5],
+    color="#306998",
+    s=100,
+    alpha=0.8,
+    edgecolors="white",
+    linewidths=1.5,
+    zorder=3,
+    label="Data Points",
+)
+
+# Setup mplcursors for interactive hover tooltips on scatter points
+cursor = mplcursors.cursor(scatter, hover=True)
+
+
+@cursor.connect("add")
+def on_add(sel):
+    """Format hover tooltip with datetime and value."""
+    idx = sel.index * 5  # Map back to original index
+    date_str = dates[idx].strftime("%Y-%m-%d %H:%M")
+    val = response_times[idx]
+    sel.annotation.set_text(f"Time: {date_str}\nResponse: {val:.1f} ms")
+    sel.annotation.get_bbox_patch().set(facecolor="#FFD43B", alpha=0.95)
+    sel.annotation.set_fontsize(14)
+    sel.annotation.set_fontweight("bold")
+
+
+# Highlight anomaly spikes with visible annotations
+for i, idx in enumerate(spike_indices):
+    ax.scatter(
+        dates[idx],
+        response_times[idx],
+        color="#E63946",
+        s=250,
+        edgecolors="#306998",
+        linewidths=2.5,
+        zorder=5,
+        marker="^",
+    )
+    offset_y = 20 if i % 2 == 0 else -35
+    ax.annotate(
+        f"Anomaly: {response_times[idx]:.0f} ms",
+        xy=(dates[idx], response_times[idx]),
+        xytext=(0, offset_y),
+        textcoords="offset points",
+        fontsize=13,
+        fontweight="bold",
+        color="white",
+        ha="center",
+        bbox=dict(boxstyle="round,pad=0.4", facecolor="#E63946", alpha=0.95, edgecolor="#306998", linewidth=1.5),
+        arrowprops=dict(arrowstyle="-", color="#E63946", lw=2),
+    )
+
+# Add a reference line for average response time
+avg_response = np.mean(response_times)
+ax.axhline(
+    y=avg_response, color="#808080", linestyle="--", linewidth=2, alpha=0.7, label=f"Average: {avg_response:.0f} ms"
+)
+
+# Highlight zoom region to demonstrate range selection capability
+zoom_start, zoom_end = 70, 100
+ax.axvspan(dates[zoom_start], dates[zoom_end], alpha=0.2, color="#2A9D8F", zorder=1)
+ax.annotate(
+    "Zoom Region\n(use scroll/drag)",
+    xy=(dates[85], ax.get_ylim()[0] + 10),
+    fontsize=12,
+    ha="center",
+    va="bottom",
+    color="#2A9D8F",
+    fontweight="bold",
+)
+
+# Style the plot
+ax.set_xlabel("Time", fontsize=20)
+ax.set_ylabel("Response Time (ms)", fontsize=20)
+ax.set_title("line-interactive · matplotlib · pyplots.ai", fontsize=24, fontweight="bold", pad=15)
+
+# Configure tick parameters
+ax.tick_params(axis="both", labelsize=16)
+
+# Format x-axis for better date display
+fig.autofmt_xdate(rotation=30)
+
+# Add subtle grid (reduced alpha for busy chart)
+ax.grid(True, alpha=0.2, linestyle="--", zorder=1)
+
+# Add compact legend
+ax.legend(fontsize=14, loc="upper left", framealpha=0.95)
+
+# Add toolbar hint for navigation
+fig.text(
+    0.5,
+    0.01,
+    "Interactive Controls: Hover points for values • Scroll to zoom • Click-drag to pan • Home to reset",
+    ha="center",
+    va="bottom",
+    fontsize=12,
+    color="#555555",
+    style="italic",
+    bbox=dict(boxstyle="round,pad=0.4", facecolor="#f0f0f0", alpha=0.9, edgecolor="#cccccc"),
+)
+
+# Ensure proper layout with extra bottom margin for footer
+plt.tight_layout()
+plt.subplots_adjust(bottom=0.15)
+
+# Simulate a hover state by programmatically adding an annotation at one point
+# This demonstrates the hover tooltip that mplcursors provides
+demo_idx = 15  # Show tooltip on a representative point
+demo_x, demo_y = dates[demo_idx * 5], response_times[demo_idx * 5]
+demo_date_str = dates[demo_idx * 5].strftime("%Y-%m-%d %H:%M")
+ax.annotate(
+    f"Time: {demo_date_str}\nResponse: {demo_y:.1f} ms",
+    xy=(demo_x, demo_y),
+    xytext=(30, 30),
+    textcoords="offset points",
+    fontsize=14,
+    fontweight="bold",
+    color="#306998",
+    bbox=dict(boxstyle="round,pad=0.5", facecolor="#FFD43B", alpha=0.95, edgecolor="#306998", linewidth=2),
+    arrowprops=dict(arrowstyle="->", color="#306998", lw=2, connectionstyle="arc3,rad=0.2"),
+    zorder=10,
+)
+
+# Save the plot
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/line-interactive/metadata/matplotlib.yaml

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+library: matplotlib
+specification_id: line-interactive
+created: '2025-12-30T17:49:35Z'
+updated: '2025-12-30T18:28:44Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20602449294
+issue: 0
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/line-interactive/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-interactive/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 88
+review:
+  strengths: []
+  weaknesses: []