feat(plotly): implement scatter-regression-lowess

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

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+""" pyplots.ai
+scatter-regression-lowess: Scatter Plot with LOWESS Regression
+Library: plotly 6.5.0 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import plotly.graph_objects as go
+
+
+# Data - Generate non-linear relationship with varying patterns
+np.random.seed(42)
+n_points = 200
+x = np.linspace(0, 10, n_points)
+# Complex non-linear relationship: combination of sinusoidal and polynomial
+y = 2 * np.sin(x) + 0.3 * x**2 - x + np.random.normal(0, 1.5, n_points)
+
+# LOWESS smoothing (Locally Weighted Scatterplot Smoothing)
+frac = 0.3
+n = len(x)
+k = int(np.ceil(frac * n))  # Number of neighbors to use
+
+# Sort data by x for processing
+sorted_idx = np.argsort(x)
+x_sorted = x[sorted_idx]
+y_sorted = y[sorted_idx]
+y_smooth = np.zeros(n)
+
+# Calculate smoothed values for each point
+for i in range(n):
+    # Distances from current point to all points
+    distances = np.abs(x_sorted - x_sorted[i])
+
+    # Find k nearest neighbors
+    neighbor_idx = np.argsort(distances)[:k]
+    max_dist = distances[neighbor_idx[-1]]
+
+    # Tricube weight function: w = (1 - (d/max_d)^3)^3
+    if max_dist > 0:
+        u = distances[neighbor_idx] / max_dist
+        weights = (1 - u**3) ** 3
+    else:
+        weights = np.ones(k)
+
+    # Weighted least squares regression
+    x_neighbors = x_sorted[neighbor_idx]
+    y_neighbors = y_sorted[neighbor_idx]
+
+    sum_w = np.sum(weights)
+    sum_wx = np.sum(weights * x_neighbors)
+    sum_wy = np.sum(weights * y_neighbors)
+    sum_wxx = np.sum(weights * x_neighbors**2)
+    sum_wxy = np.sum(weights * x_neighbors * y_neighbors)
+
+    denom = sum_w * sum_wxx - sum_wx**2
+    if np.abs(denom) > 1e-10:
+        b = (sum_w * sum_wxy - sum_wx * sum_wy) / denom
+        a = (sum_wy - b * sum_wx) / sum_w
+        y_smooth[i] = a + b * x_sorted[i]
+    else:
+        y_smooth[i] = sum_wy / sum_w if sum_w > 0 else y_sorted[i]
+
+x_lowess = x_sorted
+y_lowess = y_smooth
+
+# Create figure
+fig = go.Figure()
+
+# Add scatter points
+fig.add_trace(
+    go.Scatter(
+        x=x,
+        y=y,
+        mode="markers",
+        name="Data Points",
+        marker=dict(
+            size=10,
+            color="#306998",  # Python Blue
+            opacity=0.6,
+        ),
+    )
+)
+
+# Add LOWESS curve
+fig.add_trace(
+    go.Scatter(
+        x=x_lowess,
+        y=y_lowess,
+        mode="lines",
+        name="LOWESS Curve",
+        line=dict(
+            color="#FFD43B",  # Python Yellow
+            width=4,
+        ),
+    )
+)
+
+# Update layout for large canvas
+fig.update_layout(
+    title=dict(text="scatter-regression-lowess · plotly · pyplots.ai", font=dict(size=28), x=0.5, xanchor="center"),
+    xaxis=dict(
+        title=dict(text="X Value", font=dict(size=22)), tickfont=dict(size=18), gridcolor="rgba(0,0,0,0.1)", gridwidth=1
+    ),
+    yaxis=dict(
+        title=dict(text="Y Value", font=dict(size=22)), tickfont=dict(size=18), gridcolor="rgba(0,0,0,0.1)", gridwidth=1
+    ),
+    template="plotly_white",
+    legend=dict(font=dict(size=18), x=0.02, y=0.98, xanchor="left", yanchor="top", bgcolor="rgba(255,255,255,0.8)"),
+    margin=dict(l=80, r=40, t=80, b=80),
+)
+
+# Save as PNG (4800 x 2700 px)
+fig.write_image("plot.png", width=1600, height=900, scale=3)
+
+# Save as HTML for interactivity
+fig.write_html("plot.html")

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

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+library: plotly
+specification_id: scatter-regression-lowess
+created: '2025-12-30T23:54:51Z'
+updated: '2025-12-31T00:01:00Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20608460845
+issue: 2855
+python_version: 3.13.11
+library_version: 6.5.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-regression-lowess/plotly/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-regression-lowess/plotly/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-regression-lowess/plotly/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent implementation of LOWESS algorithm from scratch with proper tricube
+    weighting
+  - Clear visual distinction between scatter points (blue, transparent) and LOWESS
+    curve (yellow, solid line)
+  - Perfect title format and professional plotly_white template
+  - Good font sizes throughout ensuring readability at 4800x2700 resolution
+  - Well-chosen smoothing fraction (0.3) that captures the trend without overfitting
+  weaknesses:
+  - Axis labels lack units or more descriptive context (e.g., could use domain-specific
+    labels)
+  - Manual LOWESS implementation is educational but verbose; could leverage statsmodels
+    for production code