{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.0"}},"nbformat":4,"nbformat_minor":0,"cells":[{"metadata":{"_cell_guid":"4633f9d6-cb57-0a2f-eadf-0192576d37ed","_active":false,"collapsed":false},"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)\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\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input/train\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"cell_type":"code","outputs":[],"execution_state":"idle"},{"metadata":{"_cell_guid":"01b91e81-0897-3776-b0c5-952a331f0d20","_active":true,"collapsed":false},"source":"for patient in patients[:1]:\n    label = labels_df.get_value(patient, 'cancer')\n    path = data_dir + patient\n    \n    # a couple great 1-liners from: https://www.kaggle.com/gzuidhof/data-science-bowl-2017/full-preprocessing-tutorial\n    slices = [dicom.read_file(path + '/' + s) for s in os.listdir(path)]\n    slices.sort(key = lambda x: int(x.ImagePositionPatient[2]))\n    print(len(slices),label)\n    print(slices[0])","execution_count":null,"cell_type":"code","outputs":[],"execution_state":"idle"}]}