{"cells":[
 {
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  "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\nimport sklearn\nimport glob, os\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\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output.\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\nsmjpegs = [f for f in glob.glob(\"../input/train_sm/*.jpeg\")]\nprint(smjpegs[:9])\nset175 = [smj for smj in smjpegs if \"set175\" in smj]\nset175.sort()\nprint(set175)\nfor imagePath in set175:\n    print(imagePath)\n    im = plt.imread(imagePath)\n    plt.figure(figsize=(4,8))\n    plt.imshow(im)\n    plt.show"
 }
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