{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"mimetype":"text/x-python","pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.5","file_extension":".py","codemirror_mode":{"version":3,"name":"ipython"},"name":"python"}},"nbformat_minor":1,"nbformat":4,"cells":[{"source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.","outputs":[],"execution_count":null,"cell_type":"code","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19"}},{"source":"import trackml","outputs":[],"execution_count":null,"cell_type":"code","metadata":{"collapsed":true,"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0"}},{"source":"from trackml.dataset import load_event","outputs":[],"execution_count":null,"cell_type":"code","metadata":{"collapsed":true}},{"source":"!ls","outputs":[],"execution_count":null,"cell_type":"code","metadata":{}},{"source":"!ls /kaggle/input/train_1","outputs":[],"execution_count":null,"cell_type":"code","metadata":{}},{"source":"hits, cells, particles, truth = load_event('/kaggle/input/train_1/event000001896')","outputs":[],"execution_count":null,"cell_type":"code","metadata":{"collapsed":true}},{"source":"truth.head()","outputs":[],"execution_count":null,"cell_type":"code","metadata":{}},{"source":"ptruth = [truth[truth.particle_id == truth['particle_id'][i]] for i in range(5)  ]","outputs":[],"execution_count":null,"cell_type":"code","metadata":{"collapsed":true}},{"source":"ptruth[2]","outputs":[],"execution_count":null,"cell_type":"code","metadata":{}},{"source":"\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom numpy import ma\nfig = plt.figure(figsize = (20, 100))\nfor i in range(5):\n    ax = fig.add_subplot(5, 1, i + 1, projection = '3d')\n    pt = ptruth[i]\n    plt.quiver(pt['tx'], pt['ty'], pt['tz'], pt['tpx'], pt['tpy'], pt['tpz'])","outputs":[],"execution_count":null,"cell_type":"code","metadata":{}},{"source":"\n    ","outputs":[],"execution_count":null,"cell_type":"code","metadata":{}},{"source":"","outputs":[],"execution_count":null,"cell_type":"code","metadata":{"collapsed":true}}]}