""" HPCI 4軸 レーダーチャート生成。 軸配置 (本体プロンプト §11 固定): 12時方向: C (Concentration) 3時方向: R (Top-5 concentration, 旧称 Repeat) 6時方向: D (Density) 9時方向: S (Self-involvement) 時計回り C → R → D → S 描画には normalized 値 (0-100) を使用する。元値ではない。 """ from __future__ import annotations import argparse import base64 import json import sys from pathlib import Path import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt import numpy as np # このスクリプトを直接実行できるよう sys.path を追加 sys.path.insert(0, str(Path(__file__).resolve().parent)) from compute_hpci import ALBUM_REGISTRY, compute_hpci, format_value, write_json_atomic # noqa: E402 def generate_radar_chart( result: dict, out_png: Path, out_base64: Path | None = None, ) -> None: axes_norm = result["axes_normalized"] # 12時C / 3時R / 6時D / 9時S を実現するため、matplotlib の極座標系で: # theta = pi/2 (12時) を起点に、時計回り (-direction) で配置 labels = ["C\nConcentration", "R\nTop-5 concentration", "D\nDensity", "S\nSelf-involvement"] values = [ axes_norm["C_axis_normalized"], axes_norm["R_axis_normalized"], axes_norm["D_axis_normalized"], axes_norm["S_axis_normalized"], ] # 元値も表示用に取得 raw = result["axes"] raw_values = [raw["C_axis"], raw["R_axis"], raw["D_axis"], raw["S_axis"]] raw_units = ["(0-10000)", "(%)", "(persons/track)", "(%)"] n = len(labels) # set_theta_zero_location("N") で 12時 = 0、set_theta_direction(-1) で時計回り。 # この設定下では theta = 2π*i/n をそのまま使えば: # i=0 → 12時(C), i=1 → 3時(R), i=2 → 6時(D), i=3 → 9時(S) theta = [2*np.pi*i/n for i in range(n)] theta_closed = theta + [theta[0]] values_closed = values + [values[0]] fig, ax = plt.subplots( figsize=(12, 12), dpi=100, subplot_kw={"projection": "polar"}, ) ax.set_theta_zero_location("N") # 12時=0度 ax.set_theta_direction(-1) # 時計回り ax.spines["polar"].set_visible(False) # 外側のラベル用余白を目盛りと見せない # 軸の目盛り (0-100 を 20刻みで) ax.set_ylim(0, 100) ax.set_yticks([20, 40, 60, 80, 100]) ax.set_yticklabels(["20", "40", "60", "80", "100"], fontsize=9, color="#666") ax.set_rlabel_position(45) # データ描画 ax.plot(theta_closed, values_closed, linewidth=2.5, color="#1a1a1a") ax.fill(theta_closed, values_closed, alpha=0.20, color="#1a1a1a") # 各軸の数値ラベル (実値) for angle, label, raw_v, unit in zip(theta, labels, raw_values, raw_units): # 左右の軸も名称と原値を縦に並べ、同じ水平線上で重ならないようにする。 alignment = "left" if label.startswith("R") else "right" if label.startswith("S") else "center" ax.text( angle, 117, f"{label}\n{format_value(raw_v)} {unit}", ha=alignment, va="center", fontsize=11, fontweight="bold", linespacing=1.5, ) # デフォルト軸ラベルは消す (上で手動配置したので) ax.set_xticks(theta) ax.set_xticklabels([""] * n) ax.set_ylim(0, 130) # ラベルが入るよう外側に余裕 # タイトル title = result.get("chart_title") or f"{result['artist']} — {result['title']} ({result['release_year']})" subtitle = "HPCI Production Constellation Index (4-axis)" fig.suptitle(title, fontsize=18, fontweight="bold", y=0.97) ax.set_title(subtitle, fontsize=12, color="#555", pad=30) # 凡例的なフッター footer = ( f"total_tracks={result['total_tracks']} " f"collaborators={result['intermediate_values']['collaborators_total_count']} " f"mentions={result['intermediate_values']['total_credit_mentions']}" ) fig.text(0.5, 0.03, footer, ha="center", fontsize=10, color="#666") fig.text(0.5, 0.01, "HIPHOPCs / hpci_constellation", ha="center", fontsize=8, color="#999") out_png.parent.mkdir(parents=True, exist_ok=True) fig.savefig(out_png, dpi=100, bbox_inches="tight", facecolor="white") plt.close(fig) if out_base64: with out_png.open("rb") as f: b64 = base64.b64encode(f.read()).decode("ascii") out_base64.write_text(f"data:image/png;base64,{b64}\n", encoding="utf-8") def generate_overview(out_png: Path, out_json: Path) -> None: """固定5人×5曲の合成CSVを通常の算出関数へ渡して概要図を描く。""" import csv import tempfile with tempfile.TemporaryDirectory() as folder: input_path = Path(folder) / "fixed_five_input.csv" with input_path.open("w", encoding="utf-8", newline="") as stream: writer = csv.writer(stream) writer.writerow(["album_slug", "track_no", "title", "writers", "producers", "sampling_rights_holders", "notes"]) for number in range(1, 6): writer.writerow(["fixed_five", number, f"Track {number}", "Drake; Person B; Person C; Person D; Person E", "", "", "SYNTHETIC"]) result = compute_hpci("fixed_five", "Drake", "Synthetic fixed five", 2026, input_path) assert result["axes"] == {"C_axis": 2000.0, "R_axis": 100.0, "D_axis": 5.0, "S_axis": 100.0} result["artist"] = "Synthetic example" result["title"] = "Same 5 people on all 5 tracks (one is the artist)" result["chart_title"] = "Synthetic example: same 5 people on all 5 tracks" write_json_atomic(out_json, result) generate_radar_chart(result, out_png) def generate_reading_guide(out_png: Path) -> None: """指標の用途を示す図。作品評価や固定メンバーを集約値から推定しない。""" fig, ax = plt.subplots(figsize=(12, 7), dpi=140) ax.set_axis_off() fig.suptitle("Reading HPCI", fontsize=24, fontweight="bold", y=0.95) items = [ ("1 C / Concentration", "Distribution of mentions across all credited people"), ("2 R / Top-5 concentration", "Share of all mentions held by the album's top five people"), ("3 D / Density", "Average number of credited people per track"), ("4 S / Self-involvement", "Share of tracks crediting the artist as writer or producer"), ] for index, (heading, description) in enumerate(items): y = 0.86 - index * 0.20 ax.text(0.06, y, heading, transform=ax.transAxes, fontsize=17, fontweight="bold") ax.text(0.06, y - 0.065, description, transform=ax.transAxes, fontsize=13) fig.text(0.08, 0.10, "RP and mean Jaccard are supporting measures of overlap.", fontsize=12) fig.text(0.08, 0.055, "To identify recurring members, inspect the credits for each track.", fontsize=12) out_png.parent.mkdir(parents=True, exist_ok=True) fig.savefig(out_png, facecolor="white", bbox_inches="tight") plt.close(fig) def main() -> int: parser = argparse.ArgumentParser() parser.add_argument("--album") parser.add_argument("--overview", action="store_true", help="固定5人×5曲の合成例を生成") parser.add_argument("--reading-guide", action="store_true", help="読み方の図を生成") parser.add_argument("--json", default=None, help="入力JSONパス") parser.add_argument("--out-png", default=None) parser.add_argument("--out-base64", default=None) args = parser.parse_args() if args.reading_guide: out_png = Path(args.out_png) if args.out_png else Path("hpci-reading-guide-v5.png") generate_reading_guide(out_png) print(f"Chart: {out_png}") return 0 if args.overview: out_png = Path(args.out_png) if args.out_png else Path("hpci-overview-v5.png") generate_overview(out_png, out_png.with_suffix(".json")) print(f"Chart: {out_png}") return 0 if args.album not in ALBUM_REGISTRY: print( f"ERROR: album '{args.album}' is not in ALBUM_REGISTRY.\n" f"Available albums: {sorted(ALBUM_REGISTRY.keys())}", file=sys.stderr, ) return 2 project_root = Path(__file__).resolve().parent.parent json_path = Path(args.json) if args.json else project_root / "data" / "credits" / f"{args.album}.json" out_png = Path(args.out_png) if args.out_png else project_root / "output" / f"{args.album}_radar.png" out_base64 = Path(args.out_base64) if args.out_base64 else project_root / "output" / f"{args.album}_radar_base64.txt" if not json_path.exists(): print(f"ERROR: JSON not found: {json_path}", file=sys.stderr) return 2 result = json.loads(json_path.read_text(encoding="utf-8")) generate_radar_chart(result, out_png, out_base64) print(f"✓ Chart: {out_png}") print(f"✓ Base64: {out_base64}") return 0 if __name__ == "__main__": sys.exit(main())