feat(bokeh): implement scatter-lag (#5274)

## Implementation: `scatter-lag` - bokeh

Implements the **bokeh** version of `scatter-lag`.

**File:** `plots/scatter-lag/implementations/bokeh.py`

**Parent Issue:** #5251

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/24313009887)*

---------

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Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/scatter-lag/implementations/bokeh.py

@@ -0,0 +1,105 @@
+""" pyplots.ai
+scatter-lag: Lag Plot for Time Series Autocorrelation Diagnosis
+Library: bokeh 3.9.0 | Python 3.14.3
+Quality: 85/100 | Created: 2026-04-12
+"""
+
+import numpy as np
+from bokeh.io import export_png, save
+from bokeh.models import ColorBar, ColumnDataSource, Label, LinearColorMapper
+from bokeh.palettes import Viridis256
+from bokeh.plotting import figure
+from bokeh.resources import CDN
+from bokeh.transform import linear_cmap
+
+
+# Data - AR(1) process with moderate positive autocorrelation
+np.random.seed(42)
+n_obs = 500
+phi = 0.85
+noise = np.random.normal(0, 1, n_obs)
+series = np.zeros(n_obs)
+series[0] = noise[0]
+for i in range(1, n_obs):
+    series[i] = phi * series[i - 1] + noise[i]
+
+lag = 1
+y_t = series[:-lag]
+y_t_lag = series[lag:]
+time_index = np.arange(len(y_t))
+
+correlation = np.corrcoef(y_t, y_t_lag)[0, 1]
+
+source = ColumnDataSource(data={"y_t": y_t, "y_t_lag": y_t_lag, "time_index": time_index})
+
+# Plot
+color_mapper = LinearColorMapper(palette=Viridis256, low=time_index.min(), high=time_index.max())
+
+p = figure(
+    width=4800, height=2700, title="scatter-lag · bokeh · pyplots.ai", x_axis_label="y(t)", y_axis_label="y(t + 1)"
+)
+
+p.scatter(
+    x="y_t",
+    y="y_t_lag",
+    source=source,
+    size=20,
+    fill_color=linear_cmap("time_index", palette=Viridis256, low=time_index.min(), high=time_index.max()),
+    line_color="white",
+    line_width=0.8,
+    fill_alpha=0.8,
+)
+
+# Diagonal reference line (y = x)
+axis_min = min(y_t.min(), y_t_lag.min()) - 0.5
+axis_max = max(y_t.max(), y_t_lag.max()) + 0.5
+p.line(
+    [axis_min, axis_max], [axis_min, axis_max], line_color="#AAAAAA", line_dash="dashed", line_width=3, line_alpha=0.5
+)
+
+# Color bar for time index
+color_bar = ColorBar(
+    color_mapper=color_mapper,
+    title="Time Index",
+    title_text_font_size="22pt",
+    title_standoff=20,
+    major_label_text_font_size="18pt",
+    label_standoff=12,
+    width=50,
+    padding=40,
+)
+p.add_layout(color_bar, "right")
+
+# Correlation annotation
+corr_label = Label(
+    x=axis_min + 0.3, y=axis_max - 0.5, text=f"r = {correlation:.3f}", text_font_size="24pt", text_color="#333333"
+)
+p.add_layout(corr_label)
+
+# Style
+p.title.text_font_size = "32pt"
+p.title.text_font_style = "normal"
+p.xaxis.axis_label_text_font_size = "24pt"
+p.yaxis.axis_label_text_font_size = "24pt"
+p.xaxis.major_label_text_font_size = "20pt"
+p.yaxis.major_label_text_font_size = "20pt"
+
+p.xaxis.minor_tick_line_color = None
+p.yaxis.minor_tick_line_color = None
+p.xaxis.major_tick_line_color = None
+p.yaxis.major_tick_line_color = None
+
+p.xgrid.grid_line_alpha = 0.2
+p.ygrid.grid_line_alpha = 0.2
+p.xgrid.grid_line_width = 1
+p.ygrid.grid_line_width = 1
+
+p.outline_line_color = None
+p.background_fill_color = "#FFFFFF"
+p.border_fill_color = "#FFFFFF"
+
+p.toolbar_location = None
+
+# Save
+export_png(p, filename="plot.png")
+save(p, filename="plot.html", resources=CDN, title="scatter-lag · bokeh · pyplots.ai")

plots/scatter-lag/metadata/bokeh.yaml

@@ -0,0 +1,237 @@
+library: bokeh
+specification_id: scatter-lag
+created: '2026-04-12T18:12:37Z'
+updated: '2026-04-12T18:33:40Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 24313009887
+issue: 5251
+python_version: 3.14.3
+library_version: 3.9.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-lag/bokeh/plot.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-lag/bokeh/plot.html
+quality_score: 85
+review:
+  strengths:
+  - 'Perfect spec compliance: diagonal reference line, time-colored points, r annotation,
+    and configurable lag all implemented'
+  - Viridis colormap is an excellent and justified choice for temporal progression
+    — colorblind-safe and perceptually uniform
+  - Clean KISS code structure with np.random.seed(42) ensuring reproducibility
+  - linear_cmap transform and ColorBar show good idiomatic Bokeh usage
+  - r=0.834 annotation directly communicates autocorrelation magnitude to the viewer
+  weaknesses:
+  - 'DE-01/DE-02 not publication-ready: clean layout but no distinctive typographic
+    hierarchy or intentional spacing refinements'
+  - 'LM-02: No HoverTool or interactive features that are distinctly Bokeh — missing
+    a key differentiating capability that would appear in the HTML export'
+  - Axis labels use abstract notation y(t)/y(t+1) rather than domain-specific labels
+    with units
+  - Marker size=20 is at the minimum bound for 499 points; slightly larger markers
+    (25-30) with lower alpha would improve readability
+  image_description: The plot displays approximately 499 scatter points colored using
+    the Viridis palette (purple = early time period, yellow/green = late time period),
+    plotting y(t) vs y(t+1) for an AR(1) time series process. A dashed diagonal reference
+    line (y=x) runs from the lower-left to upper-right of the plot. The correlation
+    coefficient annotation "r = 0.834" appears in the upper-left area of the plot
+    interior. A "Time Index" color bar is displayed on the right side, scaled 0–499.
+    The title "scatter-lag · bokeh · pyplots.ai" appears at the top-left. Axis labels
+    show time-series notation on x and y axes. The data shows a clear positive autocorrelation
+    pattern with points clustering along the diagonal. The background is white with
+    subtle grid lines (alpha=0.2) and no outer frame. The visual density of overlapping
+    semi-transparent points near the diagonal creates a band-like effect, reinforcing
+    the autocorrelation story. White point outlines provide visual separation between
+    overlapping markers.
+  criteria_checklist:
+    visual_quality:
+      score: 26
+      max: 30
+      items:
+      - id: VQ-01
+        name: Text Legibility
+        score: 7
+        max: 8
+        passed: true
+        comment: 'Font sizes explicitly set: 32pt title, 24pt axis labels, 20pt tick
+          labels. All readable at 4800x2700. Minor: title positioned top-left vs centered.'
+      - id: VQ-02
+        name: No Overlap
+        score: 5
+        max: 6
+        passed: true
+        comment: No text overlap. Point overlap natural for 499 points; alpha=0.8
+          and white outlines mitigate it.
+      - id: VQ-03
+        name: Element Visibility
+        score: 5
+        max: 6
+        passed: true
+        comment: size=20 is at the lower bound for 300+ points (spec says 20-50).
+          Points visible but slightly small.
+      - id: VQ-04
+        name: Color Accessibility
+        score: 4
+        max: 4
+        passed: true
+        comment: Viridis is perceptually uniform and colorblind-safe. Excellent choice.
+      - id: VQ-05
+        name: Layout & Canvas
+        score: 3
+        max: 4
+        passed: true
+        comment: Good proportions. Minor excess whitespace on edges. Colorbar uses
+          right margin effectively.
+      - id: VQ-06
+        name: Axis Labels & Title
+        score: 2
+        max: 2
+        passed: true
+        comment: Descriptive time-series notation labels. Title format correct.
+    design_excellence:
+      score: 13
+      max: 20
+      items:
+      - id: DE-01
+        name: Aesthetic Sophistication
+        score: 5
+        max: 8
+        passed: true
+        comment: Thoughtful Viridis mapping for temporal progression, white background,
+          no frame, white point outlines. Above well-configured default but not publication-ready.
+      - id: DE-02
+        name: Visual Refinement
+        score: 4
+        max: 6
+        passed: true
+        comment: Minor ticks removed, major ticks removed, outline removed, grid at
+          alpha=0.2. Genuine refinements above default. Grid still present.
+      - id: DE-03
+        name: Data Storytelling
+        score: 4
+        max: 6
+        passed: true
+        comment: Temporal color progression + r=0.834 annotation guides viewer to
+          autocorrelation story. Diagonal reference gives comparison anchor. Good
+          focal point.
+    spec_compliance:
+      score: 15
+      max: 15
+      items:
+      - id: SC-01
+        name: Plot Type
+        score: 5
+        max: 5
+        passed: true
+        comment: 'Correct lag plot: scatter of y(t) vs y(t+lag).'
+      - id: SC-02
+        name: Required Features
+        score: 4
+        max: 4
+        passed: true
+        comment: Diagonal reference line, points colored by time index, correlation
+          annotation, configurable lag - all present.
+      - id: SC-03
+        name: Data Mapping
+        score: 3
+        max: 3
+        passed: true
+        comment: X=y(t), Y=y(t+1), correct lag-1 mapping.
+      - id: SC-04
+        name: Title & Legend
+        score: 3
+        max: 3
+        passed: true
+        comment: Title 'scatter-lag · bokeh · pyplots.ai' matches required format.
+          Color bar correctly labeled.
+    data_quality:
+      score: 14
+      max: 15
+      items:
+      - id: DQ-01
+        name: Feature Coverage
+        score: 5
+        max: 6
+        passed: true
+        comment: 'AR(1) with phi=0.85 demonstrates strong positive autocorrelation
+          clearly. Minor: only one lag shown, only positive autocorrelation case demonstrated.'
+      - id: DQ-02
+        name: Realistic Context
+        score: 5
+        max: 5
+        passed: true
+        comment: AR(1) time series / temperature anomaly context is realistic and
+          scientifically neutral.
+      - id: DQ-03
+        name: Appropriate Scale
+        score: 4
+        max: 4
+        passed: true
+        comment: Values in range ~[-5, 5] appropriate for temperature anomalies.
+    code_quality:
+      score: 10
+      max: 10
+      items:
+      - id: CQ-01
+        name: KISS Structure
+        score: 3
+        max: 3
+        passed: true
+        comment: Clean Imports -> Data -> Plot -> Save. No classes or extra functions.
+      - id: CQ-02
+        name: Reproducibility
+        score: 2
+        max: 2
+        passed: true
+        comment: np.random.seed(42) set.
+      - id: CQ-03
+        name: Clean Imports
+        score: 2
+        max: 2
+        passed: true
+        comment: 'All imports used: numpy, export_png/save, ColorBar/ColumnDataSource/Label/LinearColorMapper,
+          Viridis256, figure, CDN, linear_cmap.'
+      - id: CQ-04
+        name: Code Elegance
+        score: 2
+        max: 2
+        passed: true
+        comment: Clean, Pythonic. No over-engineering or fake UI elements.
+      - id: CQ-05
+        name: Output & API
+        score: 1
+        max: 1
+        passed: true
+        comment: Saves as plot.png via export_png(). Current Bokeh API.
+    library_mastery:
+      score: 7
+      max: 10
+      items:
+      - id: LM-01
+        name: Idiomatic Usage
+        score: 4
+        max: 5
+        passed: true
+        comment: Good use of ColumnDataSource, LinearColorMapper, linear_cmap transform,
+          ColorBar via add_layout. Follows Bokeh recommended patterns.
+      - id: LM-02
+        name: Distinctive Features
+        score: 3
+        max: 5
+        passed: false
+        comment: linear_cmap transform for data-driven per-point coloring and HTML
+          export are Bokeh-specific. Missing HoverTool which is a truly unique Bokeh
+          interactive feature.
+  verdict: REJECTED
+impl_tags:
+  dependencies: []
+  techniques:
+  - colorbar
+  - annotations
+  - html-export
+  patterns:
+  - data-generation
+  - columndatasource
+  dataprep: []
+  styling:
+  - custom-colormap
+  - alpha-blending
+  - edge-highlighting