🌱 1. 実験計画
📊 2. データ分析
# --- 植え付けフェーズ --- import numpy as np for y in range(8): for x in range(8): # 戦略を記述 farm[y][x].variety = np.random.choice(['A', 'B', 'C'])
# --- 分析フェーズ --- import matplotlib.pyplot as plt import numpy as np data = { v: [p.yield_val for row in farm for p in row if p.variety == v] for v in ['A', 'B', 'C'] } print("【統計レポート】") for v in ['A', 'B', 'C']: if data[v]: print(f"品種{v} 平均収量: {np.mean(data[v]):.2f}") plt.figure(figsize=(6, 4)) plt.boxplot([data[v] for v in ['A', 'B', 'C'] if data[v]], labels=[v for v in ['A', 'B', 'C'] if data[v]]) plt.ylabel("Yield") plt.title("Yield Analysis") plt.show()
植え付け実行
収穫シミュレーション
CSV保存
分析実行
Farm Field Map
Loading System...
Data Visualization