feat(matplotlib): implement line-styled (#2613)

## Implementation: `line-styled` - matplotlib

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20594555861)*

---------

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-styled/implementations/matplotlib.py

@@ -0,0 +1,47 @@
+""" pyplots.ai
+line-styled: Styled Line Plot
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 93/100 | Created: 2025-12-30
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# Data - CPU performance benchmarks over time
+np.random.seed(42)
+months = np.arange(1, 13)
+month_labels = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
+
+# Simulated performance scores for different processors
+base_score = 100
+processor_a = base_score + np.cumsum(np.random.randn(12) * 3 + 2)  # Steady improvement
+processor_b = base_score + np.cumsum(np.random.randn(12) * 4 + 1.5)  # Variable improvement
+processor_c = base_score + np.cumsum(np.random.randn(12) * 2 + 2.5)  # Faster improvement
+processor_d = base_score + np.cumsum(np.random.randn(12) * 3 + 0.5)  # Slower improvement
+
+# Plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Plot lines with different styles
+ax.plot(months, processor_a, linestyle="-", linewidth=3, color="#306998", label="Processor A", marker="o", markersize=8)
+ax.plot(
+    months, processor_b, linestyle="--", linewidth=3, color="#FFD43B", label="Processor B", marker="s", markersize=8
+)
+ax.plot(months, processor_c, linestyle=":", linewidth=3, color="#4B8BBE", label="Processor C", marker="^", markersize=8)
+ax.plot(
+    months, processor_d, linestyle="-.", linewidth=3, color="#646464", label="Processor D", marker="D", markersize=8
+)
+
+# Labels and styling
+ax.set_xlabel("Month", fontsize=20)
+ax.set_ylabel("Performance Score", fontsize=20)
+ax.set_title("line-styled · matplotlib · pyplots.ai", fontsize=24)
+ax.set_xticks(months)
+ax.set_xticklabels(month_labels)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, alpha=0.3, linestyle="--")
+ax.legend(fontsize=16, loc="upper left")
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/line-styled/metadata/matplotlib.yaml

@@ -0,0 +1,27 @@
+library: matplotlib
+specification_id: line-styled
+created: '2025-12-30T10:37:53Z'
+updated: '2025-12-30T10:45:03Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20594555861
+issue: 0
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/line-styled/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-styled/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 93
+review:
+  strengths:
+  - All four standard line styles (solid, dashed, dotted, dash-dot) are clearly visible
+    and easily distinguishable
+  - Added markers provide additional visual differentiation beyond line styles alone
+  - Consistent line width across all series maintains visual harmony
+  - Legend clearly maps styles to series names as required by the specification
+  - The plot is well-suited for black-and-white printing - the different line styles
+    would remain distinguishable without color
+  weaknesses:
+  - Axis labels could include units (e.g., Performance Score (pts) or Relative Performance)
+  - 'Blue color palette has two similar shades (#306998 and #4B8BBE) - while line
+    styles make them distinguishable, more contrasting colors would improve color
+    accessibility further'