feat(bokeh): implement scatter-regression-lowess

plots/scatter-regression-lowess/implementations/bokeh.py

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+""" pyplots.ai
+scatter-regression-lowess: Scatter Plot with LOWESS Regression
+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, save
+from bokeh.models import ColumnDataSource
+from bokeh.plotting import figure
+from bokeh.resources import CDN
+from statsmodels.nonparametric.smoothers_lowess import lowess
+
+
+# Data: Simulate a complex non-linear relationship (e.g., temperature vs enzyme activity)
+np.random.seed(42)
+n = 200
+
+# Create x values (temperature in Celsius)
+x = np.linspace(10, 50, n) + np.random.normal(0, 1, n)
+x = np.sort(x)
+
+# Create y with complex non-linear relationship (enzyme activity %)
+# Activity increases, peaks around 35°C, then decreases (typical enzyme behavior)
+y_true = 20 + 60 * np.exp(-0.5 * ((x - 35) / 8) ** 2)  # Gaussian peak
+y = y_true + np.random.normal(0, 5, n)  # Add noise
+
+# Calculate LOWESS regression
+lowess_result = lowess(y, x, frac=0.4)
+x_lowess = lowess_result[:, 0]
+y_lowess = lowess_result[:, 1]
+
+# Create figure
+p = figure(
+    width=4800,
+    height=2700,
+    title="scatter-regression-lowess · bokeh · pyplots.ai",
+    x_axis_label="Temperature (°C)",
+    y_axis_label="Enzyme Activity (%)",
+)
+
+# Scatter points
+source_scatter = ColumnDataSource(data={"x": x, "y": y})
+p.scatter(x="x", y="y", source=source_scatter, size=18, color="#306998", alpha=0.6, legend_label="Data Points")
+
+# LOWESS curve
+source_lowess = ColumnDataSource(data={"x": x_lowess, "y": y_lowess})
+p.line(x="x", y="y", source=source_lowess, line_width=5, color="#FFD43B", legend_label="LOWESS Fit")
+
+# Styling - larger text for 4800x2700 canvas
+p.title.text_font_size = "36pt"
+p.xaxis.axis_label_text_font_size = "28pt"
+p.yaxis.axis_label_text_font_size = "28pt"
+p.xaxis.major_label_text_font_size = "22pt"
+p.yaxis.major_label_text_font_size = "22pt"
+
+# Grid styling
+p.grid.grid_line_alpha = 0.3
+p.grid.grid_line_dash = "dashed"
+
+# Legend styling
+p.legend.label_text_font_size = "22pt"
+p.legend.location = "top_right"
+p.legend.background_fill_alpha = 0.8
+
+# Save outputs
+export_png(p, filename="plot.png")
+save(p, filename="plot.html", resources=CDN, title="scatter-regression-lowess")

plots/scatter-regression-lowess/metadata/bokeh.yaml

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+library: bokeh
+specification_id: scatter-regression-lowess
+created: '2025-12-30T23:53:54Z'
+updated: '2025-12-30T23:58:02Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20608462380
+issue: 2855
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-regression-lowess/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-regression-lowess/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-regression-lowess/bokeh/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent biological data example (enzyme activity curve) that perfectly demonstrates
+    LOWESS smoothing on non-linear data
+  - Clean color contrast between blue points and yellow LOWESS curve makes the relationship
+    immediately clear
+  - Proper use of ColumnDataSource pattern for data management
+  - Text sizing scaled appropriately for 4800x2700 canvas
+  - Correctly uses statsmodels LOWESS with appropriate smoothing fraction (0.4)
+  - Generates both static PNG and interactive HTML output
+  weaknesses:
+  - Legend text could be larger to match the canvas scale (currently 22pt vs 28pt
+    axis labels)
+  - Could add HoverTool to show individual point values in interactive version
+  - Grid dashed style may be unnecessary - solid subtle lines would be cleaner