feat(bokeh): implement line-filled

plots/line-filled/implementations/bokeh.py

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+""" pyplots.ai
+line-filled: Filled Line Plot
+Library: bokeh 3.8.1 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+from bokeh.io import export_png, save
+from bokeh.models import ColumnDataSource, HoverTool
+from bokeh.plotting import figure
+from bokeh.resources import CDN
+
+
+# Data - Monthly website traffic over a year
+np.random.seed(42)
+months = np.arange(1, 13)
+# Simulate traffic with seasonal trend (higher in summer/winter holidays)
+base_traffic = 50000
+seasonal = 15000 * np.sin(2 * np.pi * (months - 3) / 12)  # Peak in summer
+trend = 2000 * months  # Gradual growth
+noise = np.random.normal(0, 3000, 12)
+traffic = base_traffic + seasonal + trend + noise
+traffic = np.maximum(traffic, 0)  # Ensure positive values
+
+# Create ColumnDataSource
+source = ColumnDataSource(data={"month": months, "traffic": traffic, "traffic_zero": np.zeros(len(months))})
+
+# Create figure
+p = figure(
+    width=4800,
+    height=2700,
+    title="line-filled · bokeh · pyplots.ai",
+    x_axis_label="Month",
+    y_axis_label="Website Visitors",
+)
+
+# Filled area using varea
+p.varea(x="month", y1="traffic_zero", y2="traffic", source=source, fill_color="#306998", fill_alpha=0.4)
+
+# Line on top of the fill
+p.line(x="month", y="traffic", source=source, line_color="#306998", line_width=4)
+
+# Add points for visual emphasis
+p.scatter(x="month", y="traffic", source=source, size=12, color="#306998", fill_alpha=0.8)
+
+# Add hover tool
+hover = HoverTool(tooltips=[("Month", "@month"), ("Visitors", "@traffic{0,0}")])
+p.add_tools(hover)
+
+# Styling for large canvas
+p.title.text_font_size = "28pt"
+p.xaxis.axis_label_text_font_size = "22pt"
+p.yaxis.axis_label_text_font_size = "22pt"
+p.xaxis.major_label_text_font_size = "18pt"
+p.yaxis.major_label_text_font_size = "18pt"
+
+# Grid styling
+p.xgrid.grid_line_alpha = 0.3
+p.ygrid.grid_line_alpha = 0.3
+p.xgrid.grid_line_dash = [6, 4]
+p.ygrid.grid_line_dash = [6, 4]
+
+# Background
+p.background_fill_color = "#fafafa"
+p.border_fill_color = "#ffffff"
+
+# X-axis ticks for each month
+p.xaxis.ticker = list(range(1, 13))
+
+# Save as PNG and HTML
+export_png(p, filename="plot.png")
+
+# Also save interactive HTML
+save(p, filename="plot.html", resources=CDN, title="line-filled · bokeh · pyplots.ai")

plots/line-filled/metadata/bokeh.yaml

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+library: bokeh
+specification_id: line-filled
+created: '2025-12-30T11:23:14Z'
+updated: '2025-12-30T11:35:51Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20595334170
+issue: 0
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/line-filled/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-filled/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/line-filled/bokeh/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent use of Bokeh's varea glyph for the filled area effect
+  - Clean implementation with ColumnDataSource for data management
+  - Interactive HTML output with HoverTool showing formatted visitor counts
+  - Proper font sizing for the 4800x2700 canvas
+  - Subtle grid styling with dashed lines and low alpha
+  - Realistic website traffic scenario with seasonal variation
+  weaknesses:
+  - Axis labels lack units (e.g., "Website Visitors (count)" or "Month of Year")
+  - Could use more distinctive Bokeh features like CustomJS, band annotations, or
+    span annotations to highlight peaks