{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","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\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 read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"data = pd.read_csv(\"../input/osic-pulmonary-fibrosis-progression/train.csv\") \ndata.sort_values(by=['Patient','Weeks'], inplace=True)\n\nprint(data)\nprint(data[data['Patient'] == 'ID00007637202177411956430'])\nprint(data[data['Patient'] == 'ID00426637202313170790466'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import pydicom \nds1 = dicom.read_file(\"../input/osic-pulmonary-fibrosis-progression/test/ID00423637202312137826377/1.dcm\")\nds2 = dicom.read_file(\"../input/osic-pulmonary-fibrosis-progression/test/ID00423637202312137826377/2.dcm\")\nprint(ds1)\nprint(ds2)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def extract_DICOM_attributes(folder_path):\n\n    images = list(os.listdir(folder_path))\n    df = pd.DataFrame()\n    for image in images:\n        image_name = image.split(\".\")[0]\n        dicom_file_path = os.path.join(folder_path,image)\n        dicom_file_dataset = dicom.read_file(dicom_file_path)\n        #study_date = dicom_file_dataset.StudyDate\n        modality = dicom_file_dataset.Modality\n        #age = dicom_file_dataset.PatientAge\n        sex = dicom_file_dataset.PatientSex\n        body_part_examined = dicom_file_dataset.BodyPartExamined\n        patient_orientation = dicom_file_dataset.PatientOrientation\n        photometric_interpretation = dicom_file_dataset.PhotometricInterpretation\n        rows = dicom_file_dataset.Rows\n        columns = dicom_file_dataset.Columns\n\n        df = df.append(pd.DataFrame({'image_name': image_name, \n                        'dcm_modality': modality, 'dcm_sex': sex,\n                        'dcm_body_part_examined': body_part_examined,'dcm_patient_orientation': patient_orientation,\n                        'dcm_photometric_interpretation': photometric_interpretation,\n                        'dcm_rows': rows, 'dcm_columns': columns}, index=[0]))\n    return df","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"dicomfile = extract_DICOM_attributes(\"../input/osic-pulmonary-fibrosis-progression/test/ID00419637202311204720264/\")\nprint(dicomfile)","execution_count":null,"outputs":[]}],"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":4,"nbformat_minor":4}