feat(matplotlib): implement line-realtime (#3122)

## Implementation: `line-realtime` - matplotlib

Implements the **matplotlib** version of `line-realtime`.

**File:** `plots/line-realtime/implementations/matplotlib.py`

**Parent Issue:** #3073

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20620484861)*

---------

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>

Author: github-actions[bot]

plots/line-realtime/implementations/matplotlib.py

@@ -0,0 +1,112 @@
+""" pyplots.ai
+line-realtime: Real-Time Updating Line Chart
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-31
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.patches import FancyArrowPatch
+
+
+# Data - Simulated CPU usage monitoring with sliding window
+np.random.seed(42)
+n_points = 100
+visible_points = 60
+
+# Generate realistic CPU usage pattern (0-100%)
+base_usage = 45 + 15 * np.sin(np.linspace(0, 4 * np.pi, n_points))
+noise = np.random.normal(0, 5, n_points)
+spikes = np.zeros(n_points)
+spike_indices = [15, 35, 55, 78]
+for idx in spike_indices:
+    spikes[idx : idx + 3] = np.array([20, 15, 8])[: min(3, n_points - idx)]
+
+cpu_usage = np.clip(base_usage + noise + spikes, 0, 100)
+
+# Time axis (seconds ago, with most recent on right)
+time_seconds = np.arange(n_points) * 0.1
+
+# Visible window (last 60 points)
+visible_time = time_seconds[-visible_points:]
+visible_cpu = cpu_usage[-visible_points:]
+
+# Current value for prominent display
+current_value = visible_cpu[-1]
+
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Create gradient fill effect using multiple fills with decreasing alpha
+n_gradient = 10
+for i in range(n_gradient):
+    alpha_fill = 0.05 + 0.15 * (i / n_gradient)
+    y_offset = visible_cpu * (1 - 0.02 * (n_gradient - i))
+    ax.fill_between(visible_time, 0, y_offset, alpha=alpha_fill, color="#306998", linewidth=0)
+
+# Main line with gradient effect (darker at right/newer)
+colors = np.linspace(0.3, 1.0, visible_points)
+for i in range(len(visible_time) - 1):
+    ax.plot(
+        visible_time[i : i + 2],
+        visible_cpu[i : i + 2],
+        color="#306998",
+        alpha=colors[i],
+        linewidth=3,
+        solid_capstyle="round",
+    )
+
+# Fade effect on left edge (older data)
+fade_width = 8
+for i in range(fade_width):
+    fade_alpha = 1 - (fade_width - i) / fade_width
+    ax.axvspan(visible_time[i], visible_time[i + 1], alpha=0.3 * (1 - fade_alpha), color="white", zorder=2)
+
+# Arrow indicating scroll direction (data flowing left)
+arrow = FancyArrowPatch(
+    (visible_time[5], 85),
+    (visible_time[0], 85),
+    arrowstyle="->",
+    mutation_scale=25,
+    color="#666666",
+    linewidth=2,
+    alpha=0.7,
+)
+ax.add_patch(arrow)
+ax.text(visible_time[2], 90, "← older data", fontsize=14, color="#666666", ha="center")
+
+# Current value indicator
+ax.scatter([visible_time[-1]], [current_value], s=300, color="#FFD43B", zorder=5, edgecolor="#306998", linewidth=2)
+ax.annotate(
+    f"{current_value:.1f}%",
+    (visible_time[-1], current_value),
+    xytext=(15, 10),
+    textcoords="offset points",
+    fontsize=18,
+    fontweight="bold",
+    color="#306998",
+    bbox={"boxstyle": "round,pad=0.3", "facecolor": "#FFD43B", "edgecolor": "#306998", "alpha": 0.9},
+)
+
+# Current time label
+ax.text(visible_time[-1], -8, "NOW", fontsize=14, fontweight="bold", color="#306998", ha="center")
+
+# Styling
+ax.set_xlabel("Time (seconds)", fontsize=20)
+ax.set_ylabel("CPU Usage (%)", fontsize=20)
+ax.set_title("line-realtime · matplotlib · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+
+# Y-axis from 0 to 100 for CPU percentage
+ax.set_ylim(-12, 105)
+ax.set_xlim(visible_time[0] - 0.2, visible_time[-1] + 0.8)
+
+# Grid
+ax.grid(True, alpha=0.3, linestyle="--")
+
+# Add horizontal threshold line
+ax.axhline(y=80, color="#CC4444", linestyle="--", linewidth=2, alpha=0.6)
+ax.text(visible_time[-1] + 0.3, 80, "Warning", fontsize=12, color="#CC4444", va="center")
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/line-realtime/metadata/matplotlib.yaml

@@ -0,0 +1,26 @@
+library: matplotlib
+specification_id: line-realtime
+created: '2025-12-31T14:05:13Z'
+updated: '2025-12-31T14:15:39Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20620484861
+issue: 3073
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/line-realtime/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-realtime/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent implementation of sliding window concept with fade effect on older data
+  - Creative gradient fill technique using layered fill_between calls
+  - Prominent current value indicator with yellow badge is highly visible
+  - Clear scroll direction arrow with text annotation
+  - Warning threshold line adds practical monitoring context
+  - Realistic CPU usage data pattern with sinusoidal base and random spikes
+  - Perfect title format and axis labels with units
+  weaknesses:
+  - Missing legend entry for the warning threshold line (just has text label "Warning")
+  - The gradient effect on the line itself (segment-by-segment plotting) could be
+    smoother