{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import os\nimport numpy as np\nimport pandas as pd","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"PATH_TO_INPUT = \"../input/nfl-player-contact-detection\"\nPATH_TO_OUTPUT = \".\"\n\nTRAIN_LABELS = \"train_labels.csv\"\nTRAIN_PLAYER_TRACKING = \"train_player_tracking.csv\"\nTRAIN_BASELINE_HELMETS = \"train_baseline_helmets.csv\"\nTRAIN_VIDEO_METADATA = \"train_video_metadata.csv\"\n\nSAMPLE_SUBMISSION = \"sample_submission.csv\"\nTEST_PLAYER_TRACKING = \"test_player_tracking.csv\"\nTEST_BASELINE_HELMETS = \"test_baseline_helmets.csv\"\nTEST_VIDEO_METADATA = \"test_video_metadata.csv\"\n\nSUBMISSION = \"submission.csv\"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_labels = pd.read_csv(os.path.join(PATH_TO_INPUT, TRAIN_LABELS))\ntrain_player_tracking = pd.read_csv(os.path.join(PATH_TO_INPUT, TRAIN_PLAYER_TRACKING))\ntrain_baseline_helmets = pd.read_csv(\n    os.path.join(PATH_TO_INPUT, TRAIN_BASELINE_HELMETS)\n)\ntrain_video_metadata = pd.read_csv(os.path.join(PATH_TO_INPUT, TRAIN_VIDEO_METADATA))\n\nsample_submission = pd.read_csv(os.path.join(PATH_TO_INPUT, SAMPLE_SUBMISSION))\ntest_player_tracking = pd.read_csv(os.path.join(PATH_TO_INPUT, TEST_PLAYER_TRACKING))\ntest_baseline_helmets = pd.read_csv(os.path.join(PATH_TO_INPUT, TEST_BASELINE_HELMETS))\ntest_video_metadata = pd.read_csv(os.path.join(PATH_TO_INPUT, TEST_VIDEO_METADATA))\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# train_labels\n\n\n## 一言で. \n### コンタクトidに紐づくkeyが入っているテーブル.　\n### → いつ、誰と誰が、ぶつかった？\n\n## KEY\n### contact　以外全部\n","metadata":{}},{"cell_type":"code","source":"train_labels","metadata":{"execution":{"iopub.status.busy":"2023-01-08T09:14:27.756849Z","iopub.execute_input":"2023-01-08T09:14:27.757330Z","iopub.status.idle":"2023-01-08T09:14:27.776689Z","shell.execute_reply.started":"2023-01-08T09:14:27.757296Z","shell.execute_reply":"2023-01-08T09:14:27.775295Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## カラム説明(抜粋)\n\n- contact_id: 'id1_id2' if 対人間　else 'id1_G'\n- step:　ホイッスルを基点として, 0.1秒毎に増減する変数\n<br>\n   - フレームに変換できるらしい\n     <br> (参考)\n     https://www.kaggle.com/competitions/nfl-player-contact-detection/discussion/371638\n- contact: ぶつかったかどうかのboolean \n","metadata":{}},{"cell_type":"markdown","source":"---\n\n# train_player_tracking\n## 一言で\n### 選手, time_stamp 毎に動き（センサデータ）を格納したテーブル.\n### → いつ、どこで、誰が、何した？\n\n\n## KEY\n### game_key, play_id, nfl_player_id, datetime, step","metadata":{}},{"cell_type":"code","source":"train_player_tracking","metadata":{"execution":{"iopub.status.busy":"2023-01-08T09:14:21.315721Z","iopub.execute_input":"2023-01-08T09:14:21.316134Z","iopub.status.idle":"2023-01-08T09:14:21.348523Z","shell.execute_reply.started":"2023-01-08T09:14:21.316101Z","shell.execute_reply":"2023-01-08T09:14:21.347378Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## col　メモ\n- position: football position\n- x/y_positoin: フィールドのx/y軸\n- speed: speed in yards/second.\n- distance: 前地点からの距離（ヤード）\n- orientation: プレイヤーの向く方向\n- angle: プレイヤーの動く方向\n- acceleration:総加速度の大きさ（ヤード/秒）^2\n- sa: プレーヤーの進行方向の加速度yards/second^2\n","metadata":{}},{"cell_type":"markdown","source":"--- \n# train_baseline_helmets \n## 一言で\n\n### ビデオのframe毎に、選手がどこにいたかを格納したテーブル\n### → いつ、誰が、どこにいた？（画像から読み取られた位置）\n\n## Key\n### game_key, play_id, view, video, nfl_player_id\n","metadata":{}},{"cell_type":"code","source":"train_baseline_helmets","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n","metadata":{}},{"cell_type":"code","source":"%reset -f","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}