update(density-basic): plotly — comprehensive quality review (#4382)

## Summary

Updated **plotly** implementation for **density-basic**.

**Changes:** Comprehensive quality review

### Changes
- Replaced manual KDE with scipy.stats.gaussian_kde
- Changed data to SAT Math Scores with realistic range
- Added units to axis labels, refined grid styling
- Quality: 89/100 (local self-evaluation, after 2 iterations)

## Test Plan

- [x] Preview images uploaded to GCS staging
- [x] Implementation file passes ruff format/check
- [x] Metadata YAML updated with current versions
- [ ] Automated review triggered

---
Generated with [Claude Code](https://claude.com/claude-code) `/update`
command

---------

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

Author: MarkusNeusinger

plots/density-basic/implementations/plotly.py

@@ -1,97 +1,134 @@
 """ pyplots.ai
 density-basic: Basic Density Plot
-Library: plotly 6.5.0 | Python 3.13.11
-Quality: 92/100 | Created: 2025-12-23
+Library: plotly 6.5.2 | Python 3.14.3
+Quality: 93/100 | Updated: 2026-02-23
 """
 
 import numpy as np
 import plotly.graph_objects as go
+from scipy.stats import gaussian_kde
 
 
-# Data - Test scores with realistic bimodal distribution
+# Data - SAT Math scores with bimodal distribution
 np.random.seed(42)
-scores = np.concatenate(
+sat_scores = np.concatenate(
     [
-        np.random.normal(72, 10, 300),  # Main group around 72
-        np.random.normal(88, 5, 100),  # High achievers around 88
+        np.random.normal(540, 60, 350),  # Main group around 540
+        np.random.normal(680, 35, 150),  # High achievers around 680
     ]
 )
+sat_scores = np.clip(sat_scores, 200, 800)  # SAT range
 
-# Compute KDE using Silverman's rule of thumb for bandwidth
-n = len(scores)
-std = np.std(scores, ddof=1)
-iqr = np.percentile(scores, 75) - np.percentile(scores, 25)
-bandwidth = 0.9 * min(std, iqr / 1.34) * n ** (-0.2)
+# KDE using scipy
+kde = gaussian_kde(sat_scores)
+x_grid = np.linspace(350, 800, 500)
+density = kde(x_grid)
 
-# Evaluate density at each point on a grid
-x_range = np.linspace(scores.min() - 10, scores.max() + 10, 500)
-density = np.zeros_like(x_range)
-for xi in scores:
-    density += np.exp(-0.5 * ((x_range - xi) / bandwidth) ** 2)
-density /= n * bandwidth * np.sqrt(2 * np.pi)
+# Identify peaks for annotations (split at valley ~620 pts)
+split = int(500 * (620 - 350) / (800 - 350))
+peak1_idx = np.argmax(density[:split])
+peak2_idx = split + np.argmax(density[split:])
+peak1_x, peak1_y = x_grid[peak1_idx], density[peak1_idx]
+peak2_x, peak2_y = x_grid[peak2_idx], density[peak2_idx]
 
-# Create figure
+# Plot
 fig = go.Figure()
 
-# Density curve with fill
 fig.add_trace(
     go.Scatter(
-        x=x_range,
+        x=x_grid,
         y=density,
         mode="lines",
         fill="tozeroy",
-        fillcolor="rgba(48, 105, 152, 0.3)",
-        line={"color": "#306998", "width": 4},
+        fillcolor="rgba(48, 105, 152, 0.25)",
+        line={"color": "#306998", "width": 3.5},
         name="Density",
-        hovertemplate="Score: %{x:.1f}<br>Density: %{y:.4f}<extra></extra>",
+        hovertemplate="Score: %{x:.0f}<br>Density: %{y:.4f}<extra></extra>",
     )
 )
 
-# Rug plot showing individual observations
+# Rug plot
 fig.add_trace(
     go.Scatter(
-        x=scores,
-        y=[-0.001] * len(scores),
+        x=sat_scores,
+        y=np.zeros(len(sat_scores)),
         mode="markers",
-        marker={"symbol": "line-ns", "size": 12, "color": "#306998", "line": {"width": 1.5}},
+        marker={"symbol": "line-ns", "size": 14, "color": "#306998", "opacity": 0.5, "line": {"width": 1.5}},
         name="Observations",
-        hovertemplate="Score: %{x:.1f}<extra></extra>",
+        hovertemplate="Score: %{x:.0f}<extra></extra>",
     )
 )
 
+# Peak annotations to highlight bimodal structure
+fig.add_annotation(
+    x=peak1_x,
+    y=peak1_y,
+    text=f"<b>Primary Peak</b><br>~{peak1_x:.0f} pts",
+    showarrow=True,
+    arrowhead=2,
+    arrowsize=1.2,
+    arrowwidth=2,
+    arrowcolor="#306998",
+    font={"size": 18, "color": "#306998"},
+    ax=-80,
+    ay=-50,
+    bgcolor="rgba(255, 255, 255, 0.9)",
+    borderpad=6,
+)
+fig.add_annotation(
+    x=peak2_x,
+    y=peak2_y,
+    text=f"<b>High Achievers</b><br>~{peak2_x:.0f} pts",
+    showarrow=True,
+    arrowhead=2,
+    arrowsize=1.2,
+    arrowwidth=2,
+    arrowcolor="#306998",
+    font={"size": 18, "color": "#306998"},
+    ax=80,
+    ay=-40,
+    bgcolor="rgba(255, 255, 255, 0.9)",
+    borderpad=6,
+)
+
 # Layout
 fig.update_layout(
     title={"text": "density-basic · plotly · pyplots.ai", "font": {"size": 36}, "x": 0.5, "xanchor": "center"},
     xaxis={
-        "title": {"text": "Test Score", "font": {"size": 28}},
+        "title": {"text": "SAT Math Score (points)", "font": {"size": 28}},
         "tickfont": {"size": 22},
-        "showgrid": True,
-        "gridwidth": 1,
-        "gridcolor": "rgba(128, 128, 128, 0.2)",
+        "showgrid": False,
         "zeroline": False,
+        "showspikes": True,
+        "spikemode": "across",
+        "spikethickness": 1,
+        "spikecolor": "rgba(48, 105, 152, 0.3)",
+        "spikedash": "dot",
     },
     yaxis={
         "title": {"text": "Density", "font": {"size": 28}},
         "tickfont": {"size": 22},
-        "showgrid": True,
+        "gridcolor": "rgba(128, 128, 128, 0.15)",
         "gridwidth": 1,
-        "gridcolor": "rgba(128, 128, 128, 0.2)",
         "zeroline": False,
         "rangemode": "tozero",
     },
     template="plotly_white",
     showlegend=True,
     legend={
         "font": {"size": 20},
-        "x": 0.98,
-        "y": 0.98,
+        "x": 0.97,
+        "y": 0.95,
         "xanchor": "right",
         "yanchor": "top",
         "bgcolor": "rgba(255, 255, 255, 0.8)",
+        "borderwidth": 0,
     },
-    margin={"l": 100, "r": 60, "t": 100, "b": 100},
+    hovermode="x",
+    margin={"l": 90, "r": 40, "t": 90, "b": 90},
+    plot_bgcolor="white",
 )
 
-# Save as PNG and HTML
+# Save
 fig.write_image("plot.png", width=1600, height=900, scale=3)
-fig.write_html("plot.html", include_plotlyjs="cdn")
+fig.write_html("plot.html", include_plotlyjs="cdn", config={"displayModeBar": True, "scrollZoom": True})