feat(bokeh): implement andrews-curves (#2894)

## Implementation: `andrews-curves` - bokeh

Implements the **bokeh** version of `andrews-curves`.

**File:** `plots/andrews-curves/implementations/bokeh.py`

**Parent Issue:** #2859

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20608492713)*

---------

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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/andrews-curves/implementations/bokeh.py

@@ -0,0 +1,98 @@
+""" pyplots.ai
+andrews-curves: Andrews Curves for Multivariate Data
+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, output_file, save
+from bokeh.models import ColumnDataSource, Legend
+from bokeh.plotting import figure
+from sklearn.datasets import load_iris
+from sklearn.preprocessing import StandardScaler
+
+
+# Load and prepare data
+iris = load_iris()
+X = iris.data
+y = iris.target
+species_names = ["Setosa", "Versicolor", "Virginica"]
+
+# Normalize variables to similar scales
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+
+
+# Andrews curve function: f(t) = x1/sqrt(2) + x2*sin(t) + x3*cos(t) + x4*sin(2t) + ...
+def andrews_curve(coeffs, t):
+    n = len(coeffs)
+    result = coeffs[0] / np.sqrt(2)
+    for i in range(1, n):
+        if i % 2 == 1:
+            result += coeffs[i] * np.sin((i // 2 + 1) * t)
+        else:
+            result += coeffs[i] * np.cos((i // 2) * t)
+    return result
+
+
+# Generate t values from -π to π
+t_values = np.linspace(-np.pi, np.pi, 200)
+
+# Colors for each species (Python Blue, Python Yellow, and a colorblind-safe teal)
+colors = ["#306998", "#FFD43B", "#2AA198"]
+
+# Create figure
+p = figure(
+    width=4800,
+    height=2700,
+    title="andrews-curves · bokeh · pyplots.ai",
+    x_axis_label="t (radians)",
+    y_axis_label="f(t)",
+)
+
+# Store legend items
+legend_items = []
+
+# Plot curves for each species
+for species_idx in range(3):
+    species_mask = y == species_idx
+    X_species = X_scaled[species_mask]
+
+    # Track first line for legend
+    first_line = None
+
+    for coeffs in X_species:
+        curve_values = andrews_curve(coeffs, t_values)
+
+        source = ColumnDataSource(data={"x": t_values, "y": curve_values})
+
+        line = p.line(x="x", y="y", source=source, line_color=colors[species_idx], line_alpha=0.4, line_width=2)
+
+        if first_line is None:
+            first_line = line
+
+    legend_items.append((species_names[species_idx], [first_line]))
+
+# Create and add legend
+legend = Legend(items=legend_items, location="top_right")
+legend.label_text_font_size = "20pt"
+legend.background_fill_alpha = 0.8
+p.add_layout(legend, "right")
+
+# Style the plot
+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 = "dashed"
+p.ygrid.grid_line_dash = "dashed"
+
+# Save as PNG and HTML
+export_png(p, filename="plot.png")
+output_file("plot.html", title="Andrews Curves - Bokeh")
+save(p)

plots/andrews-curves/metadata/bokeh.yaml

@@ -0,0 +1,30 @@
+library: bokeh
+specification_id: andrews-curves
+created: '2025-12-30T23:57:27Z'
+updated: '2025-12-31T00:04:51Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20608492713
+issue: 2859
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/andrews-curves/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/andrews-curves/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/andrews-curves/bokeh/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent implementation of the Andrews curves mathematical transformation with
+    correct Fourier expansion
+  - Perfect use of the Iris dataset as a canonical multivariate example demonstrating
+    clear cluster separation
+  - Good application of StandardScaler for normalization as recommended in the specification
+  - Appropriate transparency (alpha=0.4) reveals density patterns where curves overlap
+  - Colorblind-safe palette with good distinction between species
+  - Correct title format following pyplots.ai conventions
+  - Both PNG and HTML outputs generated, leveraging Bokeh dual output capability
+  weaknesses:
+  - Contains a helper function (andrews_curve) which deviates from strict KISS structure;
+    the Fourier computation could be vectorized inline
+  - Legend background could have slightly higher opacity for better readability
+  - Does not leverage Bokeh interactive features like hover tooltips which would enhance
+    the visualization