feat(matplotlib): implement line-win-probability

plots/line-win-probability/implementations/matplotlib.py

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+""" pyplots.ai
+line-win-probability: Win Probability Chart
+Library: matplotlib 3.10.8 | Python 3.14.3
+Quality: 92/100 | Created: 2026-03-20
+"""
+
+import matplotlib.patches as mpatches
+import matplotlib.patheffects as pe
+import matplotlib.pyplot as plt
+import matplotlib.ticker as mticker
+import numpy as np
+
+
+# Data - simulated NFL game: Eagles vs Cowboys
+np.random.seed(42)
+
+# Game plays (0 to ~120 plays)
+n_plays = 120
+plays = np.arange(n_plays + 1)
+
+# Build win probability with realistic scoring events
+win_prob = np.full(n_plays + 1, 0.50)
+
+# Scoring events: (play_number, probability_shift, label)
+scoring_events = [
+    (8, 0.12, "PHI Field Goal (3-0)"),
+    (22, -0.10, "DAL Touchdown (7-3)"),
+    (35, 0.15, "PHI Touchdown (10-7)"),
+    (48, 0.08, "PHI Field Goal (13-7)"),
+    (58, -0.18, "DAL Touchdown (14-13)"),
+    (72, 0.14, "PHI Touchdown (20-14)"),
+    (85, -0.06, "DAL Field Goal (20-17)"),
+    (95, 0.12, "PHI Touchdown (27-17)"),
+    (110, -0.05, "DAL Field Goal (27-20)"),
+]
+
+# Generate smooth probability curve with scoring jumps
+prob = 0.50
+noise = np.random.normal(0, 0.012, n_plays + 1)
+event_indices = {e[0]: (e[1], e[2]) for e in scoring_events}
+
+for i in range(1, n_plays + 1):
+    if i in event_indices:
+        prob += event_indices[i][0]
+    prob += noise[i]
+    # Mean reversion toward current level
+    prob = np.clip(prob, 0.02, 0.98)
+    win_prob[i] = prob
+
+# Force convergence to final outcome: Eagles win
+for i in range(105, n_plays + 1):
+    t = (i - 105) / (n_plays - 105)
+    win_prob[i] = win_prob[105] * (1 - t**2) + 1.0 * t**2
+
+# Quarter boundaries (roughly 30 plays each)
+quarter_boundaries = [0, 30, 60, 90, n_plays]
+quarter_labels = ["Q1", "Q2", "Q3", "Q4"]
+
+# Colors
+eagles_green = "#004C54"
+cowboys_navy = "#003594"
+baseline_color = "#444444"
+
+# Plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Fill above/below 50%
+ax.fill_between(plays, win_prob, 0.5, where=(win_prob >= 0.5), color=eagles_green, alpha=0.3, interpolate=True)
+ax.fill_between(plays, win_prob, 0.5, where=(win_prob < 0.5), color=cowboys_navy, alpha=0.45, interpolate=True)
+
+# Win probability line - color changes based on which team leads
+for i in range(len(plays) - 1):
+    color = eagles_green if win_prob[i] >= 0.5 else cowboys_navy
+    ax.plot(plays[i : i + 2], win_prob[i : i + 2], color=color, linewidth=3, zorder=3, solid_capstyle="round")
+
+# 50% baseline
+ax.axhline(y=0.5, color=baseline_color, linewidth=1.5, linestyle="--", alpha=0.5, zorder=2)
+
+# Quarter dividers
+for qb in quarter_boundaries[1:-1]:
+    ax.axvline(x=qb, color="#999999", linewidth=1, linestyle=":", alpha=0.4)
+
+# Quarter labels
+for i, label in enumerate(quarter_labels):
+    mid = (quarter_boundaries[i] + quarter_boundaries[i + 1]) / 2
+    ax.text(mid, 0.03, label, ha="center", va="center", fontsize=16, color="#888888", fontweight="medium")
+
+# Annotate key scoring events
+annotation_events = [
+    (8, "FG 3-0"),
+    (22, "TD 7-3"),
+    (35, "TD 10-7"),
+    (58, "TD 14-13"),
+    (72, "TD 20-14"),
+    (95, "TD 27-17"),
+]
+
+for play_idx, label in annotation_events:
+    wp = win_prob[play_idx]
+    offset_y = 0.06 if wp >= 0.5 else -0.06
+    txt = ax.annotate(
+        label,
+        xy=(play_idx, wp),
+        xytext=(play_idx, wp + offset_y),
+        fontsize=12,
+        fontweight="bold",
+        ha="center",
+        va="center",
+        color="#222222",
+        arrowprops={"arrowstyle": "-", "color": "#999999", "linewidth": 0.8},
+        zorder=4,
+    )
+    txt.set_path_effects([pe.withStroke(linewidth=3, foreground="white")])
+
+# Scatter dots on scoring events for visibility
+for play_idx, _ in annotation_events:
+    ax.plot(
+        play_idx,
+        win_prob[play_idx],
+        "o",
+        color=eagles_green if win_prob[play_idx] >= 0.5 else cowboys_navy,
+        markersize=7,
+        zorder=5,
+        markeredgecolor="white",
+        markeredgewidth=1,
+    )
+
+# Style
+ax.set_xlim(0, n_plays)
+ax.set_ylim(0, 1)
+ax.set_yticks([0, 0.25, 0.5, 0.75, 1.0])
+ax.yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f"{x:.0%}"))
+
+# Subtle y-axis gridlines for easier probability reading
+ax.yaxis.grid(True, alpha=0.15, linewidth=0.8, color="#888888")
+ax.set_axisbelow(True)
+ax.set_xlabel("Play Number", fontsize=20)
+ax.set_ylabel("Win Probability", fontsize=20)
+ax.set_title(
+    "Eagles 27 – Cowboys 20 · line-win-probability · matplotlib · pyplots.ai", fontsize=24, fontweight="medium"
+)
+ax.tick_params(axis="both", labelsize=16)
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+
+# Legend
+eagles_patch = mpatches.Patch(color=eagles_green, alpha=0.4, label="Eagles")
+cowboys_patch = mpatches.Patch(color=cowboys_navy, alpha=0.5, label="Cowboys")
+ax.legend(handles=[eagles_patch, cowboys_patch], fontsize=16, loc="upper left", framealpha=0.8, edgecolor="none")
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/line-win-probability/metadata/matplotlib.yaml

@@ -0,0 +1,223 @@
+library: matplotlib
+specification_id: line-win-probability
+created: '2026-03-20T12:30:49Z'
+updated: '2026-03-20T12:44:26Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 23342814407
+issue: 4418
+python_version: 3.14.3
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/line-win-probability/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-win-probability/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent spec compliance — all required features (50% baseline, team color fills,
+    scoring annotations, quarter markers, final score) present and well-implemented
+  - Strong data storytelling through color fills, annotated scoring events, and natural
+    probability convergence
+  - Professional visual polish with path effects, custom team colors, and refined
+    typography
+  - Clean, reproducible code with appropriate complexity
+  weaknesses:
+  - Segment-by-segment line plotting loop is less efficient than using LineCollection
+  - Fill area visibility slightly reduced in narrow Cowboys-lead regions
+  image_description: The plot displays a win probability chart for an NFL game between
+    the Eagles and Cowboys. The x-axis shows "Play Number" (0–120) and the y-axis
+    shows "Win Probability" (0%–100%). A dashed horizontal line marks the 50% baseline.
+    The area above 50% is filled with a muted teal-green (Eagles) and below 50% with
+    navy blue (Cowboys). The main probability line changes color based on which team
+    leads. Six key scoring events are annotated with bold labels (FG 3-0, TD 7-3,
+    TD 10-7, TD 14-13, TD 20-14, TD 27-17) connected by thin lines to small dots on
+    the curve, each with a white stroke for readability. Quarter boundaries (Q1–Q4)
+    are shown as dotted vertical lines with labels at the bottom. The title reads
+    "Eagles 27 – Cowboys 20 · line-win-probability · matplotlib · pyplots.ai". A legend
+    in the upper left identifies the team color fills. The probability converges sharply
+    to 100% at game end. Top and right spines are removed; a subtle y-axis grid aids
+    reading.
+  criteria_checklist:
+    visual_quality:
+      score: 29
+      max: 30
+      items:
+      - id: VQ-01
+        name: Text Legibility
+        score: 8
+        max: 8
+        passed: true
+        comment: 'All font sizes explicitly set: title 24pt, labels 20pt, ticks 16pt,
+          annotations 12pt, quarter labels 16pt'
+      - id: VQ-02
+        name: No Overlap
+        score: 6
+        max: 6
+        passed: true
+        comment: All annotations well-spaced with white path effects ensuring readability
+      - id: VQ-03
+        name: Element Visibility
+        score: 5
+        max: 6
+        passed: true
+        comment: Line and markers clear; fill areas thin in narrow Cowboys-lead regions
+      - id: VQ-04
+        name: Color Accessibility
+        score: 4
+        max: 4
+        passed: true
+        comment: Teal green vs navy blue fully distinguishable for all color vision
+          types
+      - id: VQ-05
+        name: Layout & Canvas
+        score: 4
+        max: 4
+        passed: true
+        comment: 16:9 figure with tight_layout, plot fills canvas well
+      - id: VQ-06
+        name: Axis Labels & Title
+        score: 2
+        max: 2
+        passed: true
+        comment: Descriptive labels with percentage formatting on y-axis
+    design_excellence:
+      score: 16
+      max: 20
+      items:
+      - id: DE-01
+        name: Aesthetic Sophistication
+        score: 6
+        max: 8
+        passed: true
+        comment: Team-specific palette, color-changing line, path effects, approaching
+          broadcast quality
+      - id: DE-02
+        name: Visual Refinement
+        score: 5
+        max: 6
+        passed: true
+        comment: Spines removed, subtle grid, dotted quarter dividers, white stroke
+          on annotations
+      - id: DE-03
+        name: Data Storytelling
+        score: 5
+        max: 6
+        passed: true
+        comment: Clear game narrative through fills, annotations, and convergence
+    spec_compliance:
+      score: 15
+      max: 15
+      items:
+      - id: SC-01
+        name: Plot Type
+        score: 5
+        max: 5
+        passed: true
+        comment: Correct win probability line chart
+      - id: SC-02
+        name: Required Features
+        score: 4
+        max: 4
+        passed: true
+        comment: 'All spec features present: baseline, fills, annotations, score,
+          quarters'
+      - id: SC-03
+        name: Data Mapping
+        score: 3
+        max: 3
+        passed: true
+        comment: X=play number, Y=win probability, full range shown
+      - id: SC-04
+        name: Title & Legend
+        score: 3
+        max: 3
+        passed: true
+        comment: Title includes score and follows spec-id format; legend correct
+    data_quality:
+      score: 15
+      max: 15
+      items:
+      - id: DQ-01
+        name: Feature Coverage
+        score: 6
+        max: 6
+        passed: true
+        comment: Shows FG/TD events, lead changes, momentum swings, convergence
+      - id: DQ-02
+        name: Realistic Context
+        score: 5
+        max: 5
+        passed: true
+        comment: Eagles vs Cowboys NFL game with realistic 27-20 final score
+      - id: DQ-03
+        name: Appropriate Scale
+        score: 4
+        max: 4
+        passed: true
+        comment: Realistic probability shifts and natural convergence
+    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 flow, no functions or classes
+      - 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
+      - id: CQ-04
+        name: Code Elegance
+        score: 2
+        max: 2
+        passed: true
+        comment: Appropriate complexity, no fake UI
+      - id: CQ-05
+        name: Output & API
+        score: 1
+        max: 1
+        passed: true
+        comment: Saves as plot.png with dpi=300
+    library_mastery:
+      score: 7
+      max: 10
+      items:
+      - id: LM-01
+        name: Idiomatic Usage
+        score: 4
+        max: 5
+        passed: true
+        comment: Good ax-based usage; segment loop less idiomatic than LineCollection
+      - id: LM-02
+        name: Distinctive Features
+        score: 3
+        max: 5
+        passed: true
+        comment: fill_between with interpolate, patheffects, FuncFormatter, mpatches
+          legend
+  verdict: APPROVED
+impl_tags:
+  dependencies: []
+  techniques:
+  - annotations
+  - custom-legend
+  - manual-ticks
+  patterns:
+  - data-generation
+  - iteration-over-groups
+  dataprep:
+  - cumulative-sum
+  styling:
+  - alpha-blending
+  - grid-styling
+  - edge-highlighting