Measure

examples/measure/calc-final-results.py

@@ -22,12 +22,14 @@
 # 全ての index ディレクトリからのデータを1つに統合
 combined_df = pd.concat(all_data, ignore_index=True)
 
-# max-delay と multi-num ごとにグループ化し、elapse と stddev の平均と標準偏差を計算
+# max-delay と multi-num ごとにグループ化し、elapse, stddev, mean の平均と標準偏差を計算
 grouped = combined_df.groupby(['max-delay', 'multi-num']).agg(
     elapse_mean=('elapse', 'mean'),
     elapse_std=('elapse', 'std'),
     stddev_mean=('stddev', 'mean'),
-    stddev_std=('stddev', 'std')
+    stddev_std=('stddev', 'std'),
+    mean_mean=('mean', 'mean'),
+    mean_std=('mean', 'std')
 ).reset_index()
 
 # 結果を CSV ファイルに保存

examples/measure/calc-result.py

@@ -27,9 +27,10 @@
                 multi_num = parts[1][1:]  # 'm' の後の数値を取得
                 max_delay = parts[2][1:]  # 'd' の後の数値を取得
 
-                # elapse.txt と stddev.txt のファイルパス
+                # elapse.txt, stddev.txt, mean.txt のファイルパス
                 elapse_file = os.path.join(folder_path, 'elapse.txt')
                 stddev_file = os.path.join(folder_path, 'stddev.txt')
+                mean_file = os.path.join(folder_path, 'mean.txt')
 
                 # elapse.txt の内容を読み取る
                 if os.path.exists(elapse_file):
@@ -45,15 +46,22 @@
                 else:
                     stddev_content = 'N/A'
 
+                # mean.txt の内容を読み取る
+                if os.path.exists(mean_file):
+                    with open(mean_file, 'r') as mf:
+                        mean_content = mf.read().strip()
+                else:
+                    mean_content = 'N/A'
+
                 # 結果をリストに追加
-                results.append([max_delay, multi_num, elapse_content, stddev_content])
+                results.append([max_delay, multi_num, elapse_content, stddev_content, mean_content])
 
 # CSVファイルとして保存
 csv_file = f'results/{args.index}/results.csv'
 with open(csv_file, mode='w', newline='') as file:
     writer = csv.writer(file)
     # ヘッダーを書き込む
-    writer.writerow(['max-delay', 'multi-num', 'elapse', 'stddev'])
+    writer.writerow(['max-delay', 'multi-num', 'elapse', 'stddev', 'mean'])
     # データを書き込む
     writer.writerows(results)
 

examples/measure/final_results.csv

@@ -0,0 +1,26 @@
+max-delay,multi-num,elapse_mean,elapse_std,stddev_mean,stddev_std,mean_mean,mean_std
+10,1,1.345,0.061508806957486416,0.0,0.0,0.0,0.0
+10,2,1.455,0.1209453503768449,0.0,0.0,0.0,0.0
+10,4,1.498,0.09150591966279188,0.0,0.0,0.0,0.0
+10,8,3.217,0.11136027618101135,0.0,0.0,0.0,0.0
+10,10,6.348,0.5858668790774915,0.0,0.0,0.0,0.0
+20,1,1.288,0.05769652406245022,0.24,0.08393118874676113,0.0064,0.003747591819348052
+20,2,1.377,0.08028421735033324,0.157,0.05907622195096771,0.003,0.0016666666666666666
+20,4,1.405,0.0981212401969001,0.202,0.04638007234913622,0.0052,0.0018287822299126934
+20,8,2.347,0.14840260554765652,0.475,0.029533408577782253,0.027581,0.0028267431200352587
+20,10,3.7800000000000002,0.19002923751652306,0.531,0.04121757985239902,0.03305,0.0045789858168715815
+40,1,1.342,0.14763317904710857,0.341,0.10450412006763711,19.9974,0.002674987019699523
+40,2,1.323,0.10044899203078153,8.325,1.5585338551914032,12.334931999999998,0.577863608817475
+40,4,1.404,0.074416246732671,8.004000000000001,0.6033093917901678,8.546217,0.498103888852516
+40,8,2.0170000000000003,0.07409453421137087,8.234,0.03777124126457452,5.798415,0.05374506659530096
+40,10,2.915,0.16834158395621948,7.882,0.03614784456460247,5.119236,0.06796787986492844
+80,1,1.288,0.09863513009515877,0.683,0.11324015777688291,59.993801,0.007938402372156407
+80,2,1.392,0.08664102184685185,21.782,2.797442482776802,39.874624,0.9986372713653355
+80,4,1.542,0.3335266106324951,15.64,5.373403638911437,24.052989,4.327821830265325
+80,8,1.8579999999999999,0.12263767773404716,22.81,0.11333333333333223,19.938176,0.6972848400793212
+80,10,2.497,0.11372969904314544,22.02,0.08511430223202486,18.047281,0.28663555805819657
+100,1,1.264,0.062039413995369865,0.942,0.1875455500227434,79.988602,0.010922129218548057
+100,2,1.3130000000000002,0.06056218108504497,30.130000000000003,2.325510696599781,55.315787,2.192565356684822
+100,4,1.4200000000000002,0.11372481406154657,27.297000000000004,3.5979223943208605,39.112477999999996,5.449785273747132
+100,8,1.8510000000000002,0.08089087299153923,29.895999999999997,0.2630673762450312,27.283742999999998,0.475126317848445
+100,10,2.4210000000000003,0.2359119421403767,28.841,0.08504247304860137,24.99906,0.3809165732808172

examples/measure/graph.py

@@ -12,15 +12,17 @@
 parser.add_argument('--duration', type=int, required=False, default=-1, help='Duration for the data')
 parser.add_argument('--csv-dir', type=str, required=False, default='./', help='Directory containing the CSV files')
 parser.add_argument('--stddev-only', action='store_true', help='Output only the standard deviation mean')
+parser.add_argument('--mean-only', action='store_true', help='Output only the mean value of core time - asset time')
 
 args = parser.parse_args()
 
 # グラフの初期化（複数のアセットを同じグラフにプロットするため）
-if not args.stddev_only:
+if not args.stddev_only and not args.mean_only:
     plt.figure(figsize=(10, 6))
 
-# 標準偏差を記録するリスト
+# 標準偏差と平均を記録するリスト
 std_devs = []
+means = []
 
 for i in range(0, args.multi_num):
     print(f"index: {i}")
@@ -59,7 +61,7 @@
     asset_time = df['asset-time'] / 1000.0
     core_time = df['core-time'] / 1000.0
 
-    if args.type == 'phase' and not args.stddev_only:
+    if args.type == 'phase' and not args.stddev_only and not args.mean_only:
         x_value = asset_time
         y_value = core_time
         plt.plot(x_value, y_value, label=f'asset-{i}', marker='o')
@@ -71,13 +73,16 @@
     elif args.type == 'time':
         x_value = core_time
         y_value = core_time - asset_time
-        if not args.stddev_only:
+        if not args.stddev_only and not args.mean_only:
             plt.plot(x_value, y_value, label=f'asset-{i}', marker='o')
 
-        # アセットごとの標準偏差を計算して出力
+        # アセットごとの標準偏差と平均を計算して出力
         std_dev = np.std(y_value)
+        mean_value = np.mean(y_value)
         std_devs.append(std_dev)
+        means.append(mean_value)
         print(f"Asset-{i} の標準偏差: {std_dev:.4f}")
+        print(f"Asset-{i} の平均値: {mean_value:.4f}")
 
 # 全アセットの標準偏差の平均を計算して出力
 if std_devs:
@@ -87,18 +92,27 @@
     min_std_dev = np.min(std_devs)
     range_std_dev = max_std_dev - min_std_dev  # 最大値と最小値の差
 
-    if not args.stddev_only:
+    if not args.stddev_only and not args.mean_only:
         print(f"全アセットの標準偏差の平均: {mean_std_dev:.4f}")
         print(f"全アセットの標準偏差の分散: {variance_std_dev:.4f}")
         print(f"標準偏差の最大値: {max_std_dev:.4f}")
         print(f"標準偏差の最小値: {min_std_dev:.4f}")
         print(f"標準偏差の最大値と最小値の差: {range_std_dev:.4f}")
-    else:
+    elif args.stddev_only:
         with open(os.path.join(args.csv_dir, 'stddev.txt'), 'w') as f:
             f.write(f"{mean_std_dev:.2f}\n")
 
+# 全アセットの平均値の平均を計算して出力
+if means:
+    mean_of_means = np.mean(means)
+    if not args.stddev_only and not args.mean_only:
+        print(f"全アセットの平均値の平均: {mean_of_means:.4f}")
+    elif args.mean_only:
+        with open(os.path.join(args.csv_dir, 'mean.txt'), 'w') as f:
+            f.write(f"{mean_of_means:.5f}\n")
+
 # すべてのアセットをプロットした後にグラフを表示
-if not args.stddev_only:
+if not args.stddev_only and not args.mean_only:
     plt.title('hako-time graph')
     plt.legend()
     plt.grid(True)

examples/measure/mean.bash

@@ -0,0 +1,27 @@
+#!/bin/bash
+SAMPLE_NUM=10
+
+function do_test()
+{
+	index=${1}
+	# Ensure results directory exists
+	mkdir -p results/${index}
+
+	for multi in 1 2 4 8 10
+	do
+		for mdelay in 10 20 40 80 100
+		do
+			echo "DO TEST: MULTI_NUM=${multi} MAX_DELAY=${mdelay}"
+			DIR_NAME=results/${index}/result-m${multi}-d${mdelay}
+			python graph.py --multi-num ${multi} --type time --csv-dir ${DIR_NAME} --mean-only
+		done
+	done
+
+	python calc-result.py --index ${index}
+}
+
+# ループの回数を SAMPLE_NUM で制御
+for index in $(seq 0 $(($SAMPLE_NUM - 1)))
+do
+    do_test ${index}
+done

examples/measure/plot-final-results.py

@@ -5,7 +5,7 @@
 # コマンドライン引数を設定
 parser = argparse.ArgumentParser(description='Plot final results data')
 parser.add_argument('--csv-file', type=str, required=True, help='Path to the CSV file containing the final results data')
-parser.add_argument('--plot-type', choices=['elapse-vs-multi', 'stddev-vs-multi', 'stddev-vs-elapse'], required=True, help='Type of plot to generate')
+parser.add_argument('--plot-type', choices=['elapse-vs-multi', 'stddev-vs-multi', 'stddev-vs-elapse', 'mean-vs-multi'], required=True, help='Type of plot to generate')
 parser.add_argument('--multi-num', type=int, help='Specify the multi-num (only for stddev-vs-elapse plot)')
 args = parser.parse_args()
 
@@ -19,6 +19,8 @@
 df['elapse_std'] = df['elapse_std'].astype(float)
 df['stddev_mean'] = df['stddev_mean'].astype(float)
 df['stddev_std'] = df['stddev_std'].astype(float)
+df['mean_mean'] = df['mean_mean'].astype(float)
+df['mean_std'] = df['mean_std'].astype(float)
 
 # 1. 横軸：multi-num、縦軸：elapse_meanとして、max-delay毎に重ねて表示
 if args.plot_type == 'elapse-vs-multi':
@@ -65,5 +67,18 @@
     plt.grid(True)
     plt.tight_layout()
 
+# 4. 横軸：multi-num、縦軸：mean_meanとして、max-delay毎に重ねて表示
+elif args.plot_type == 'mean-vs-multi':
+    plt.figure(figsize=(10, 6))
+    for delay in sorted(df['max-delay'].unique()):
+        subset = df[df['max-delay'] == delay].sort_values(by='multi-num')  # multi-numでソート
+        plt.errorbar(subset['multi-num'], subset['mean_mean'], yerr=subset['mean_std'], marker='o', label=f'max-delay={delay}')
+    plt.xlabel('multi-num')
+    plt.ylabel('mean (± std)')
+    plt.title('Mean vs Multi-num with Stddev for different max-delay values')
+    plt.legend()
+    plt.grid(True)
+    plt.tight_layout()
+
 # グラフを表示
 plt.show()

math/README.md

@@ -1,5 +1,8 @@
 # 変更履歴
 
+**最新版はこちらの main ブランチとします。**
+https://github.com/toppers/hakoniwa-core-cpp-client/blob/main/math/README.md
+
 - (Version 3.1) https://github.com/toppers/hakoniwa-px4sim/issues/340 , issue #69 箱庭時刻同期の数学的証明より移動
 - (Version 3.2) https://github.com/toppers/hakoniwa-core-cpp-client/issues/69#issue-2541993494 より移動
 - (Version 4.0) https://github.com/toppers/hakoniwa-paper/tree/main/robosym2025 高瀬先生により論文化される（そこからのフィードバック戻し）