feat(bokeh): implement line-styled (#2636)

## Implementation: `line-styled` - bokeh

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20594557446)*

---------

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-styled/implementations/bokeh.py

@@ -0,0 +1,116 @@
+""" pyplots.ai
+line-styled: Styled Line Plot
+Library: bokeh 3.8.1 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+from bokeh.io import export_png, output_file, save
+from bokeh.models import ColumnDataSource, Legend
+from bokeh.plotting import figure
+
+
+# Data - Monthly performance metrics over a year
+np.random.seed(42)
+months = np.arange(1, 13)
+
+# Generate realistic trending data for different metrics
+base = np.array([65, 68, 72, 75, 78, 82, 85, 84, 80, 77, 74, 70])
+cpu_usage = base + np.random.randn(12) * 3
+memory_usage = base * 0.85 + np.random.randn(12) * 4 + 10
+disk_io = base * 0.7 + np.random.randn(12) * 5 + 20
+network = base * 1.1 + np.random.randn(12) * 2 - 5
+
+# Create ColumnDataSource
+source = ColumnDataSource(
+    data={"month": months, "cpu": cpu_usage, "memory": memory_usage, "disk": disk_io, "network": network}
+)
+
+# Create figure (4800 × 2700 px)
+p = figure(
+    width=4800,
+    height=2700,
+    title="line-styled · bokeh · pyplots.ai",
+    x_axis_label="Month",
+    y_axis_label="Utilization (%)",
+)
+
+# Define line styles and colors
+# Using solid, dashed, dotted, and dash-dot patterns with pronounced differences
+line_styles = ["solid", [20, 10], [4, 8], [20, 8, 4, 8]]
+colors = ["#306998", "#FFD43B", "#4CAF50", "#FF5722"]
+series_names = ["CPU Usage", "Memory Usage", "Disk I/O", "Network Traffic"]
+y_columns = ["cpu", "memory", "disk", "network"]
+
+# Create legend items
+legend_items = []
+
+for col, style, color, name in zip(y_columns, line_styles, colors, series_names, strict=True):
+    # Add line with appropriate style
+    line = p.line(x="month", y=col, source=source, line_width=6, color=color, line_dash=style)
+
+    # Add scatter points for better visibility
+    scatter = p.scatter(x="month", y=col, source=source, size=25, color=color, alpha=0.9)
+
+    legend_items.append((name, [line, scatter]))
+
+# Create and configure legend - place inside plot area
+legend = Legend(items=legend_items, location="top_left")
+legend.label_text_font_size = "28pt"
+legend.glyph_height = 40
+legend.glyph_width = 80
+legend.spacing = 15
+legend.padding = 20
+legend.background_fill_alpha = 0.85
+legend.background_fill_color = "white"
+legend.border_line_color = "#cccccc"
+legend.border_line_width = 2
+p.add_layout(legend, "center")
+p.legend.location = "top_left"
+
+# Style configuration - larger fonts for 4800x2700 canvas
+p.title.text_font_size = "48pt"
+p.title.align = "center"
+p.xaxis.axis_label_text_font_size = "36pt"
+p.yaxis.axis_label_text_font_size = "36pt"
+p.xaxis.major_label_text_font_size = "28pt"
+p.yaxis.major_label_text_font_size = "28pt"
+
+# Grid styling - subtle
+p.grid.grid_line_alpha = 0.3
+p.grid.grid_line_dash = "dashed"
+
+# Axis styling
+p.xaxis.ticker = list(range(1, 13))
+p.xaxis.major_label_overrides = {
+    1: "Jan",
+    2: "Feb",
+    3: "Mar",
+    4: "Apr",
+    5: "May",
+    6: "Jun",
+    7: "Jul",
+    8: "Aug",
+    9: "Sep",
+    10: "Oct",
+    11: "Nov",
+    12: "Dec",
+}
+
+# Background and outline
+p.background_fill_color = "#fafafa"
+p.border_fill_color = "#ffffff"
+p.outline_line_color = "#333333"
+
+# Axis line styling
+p.xaxis.axis_line_width = 2
+p.yaxis.axis_line_width = 2
+p.xaxis.major_tick_line_width = 2
+p.yaxis.major_tick_line_width = 2
+
+# Save as PNG
+export_png(p, filename="plot.png")
+
+# Save as HTML for interactivity
+output_file("plot.html")
+save(p)

plots/line-styled/metadata/bokeh.yaml

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+library: bokeh
+specification_id: line-styled
+created: '2025-12-30T10:40:59Z'
+updated: '2025-12-30T10:50:35Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20594557446
+issue: 0
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/line-styled/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-styled/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/line-styled/bokeh/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent text sizing for the 4800×2700 canvas with 48pt title and 36pt axis labels
+  - All four line styles (solid, dashed, dotted, dash-dot) are clearly distinguishable
+    as required by spec
+  - Clean code structure with well-organized ColumnDataSource
+  - Good use of markers on data points to enhance visibility
+  - Legend is well-formatted with appropriate sizing and semi-transparent background
+  weaknesses:
+  - Grid uses dashed style which can visually compete with the dashed line series
+    - consider solid grid with lower alpha
+  - Could add HoverTool for interactivity to better leverage Bokeh's strengths
+  - Data scenario is somewhat generic - Monthly performance metrics could be more
+    specific