{"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":"# 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 20GB 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","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import csv\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ntraining_set_file = '/kaggle/input/PLAsTiCC-2018/training_set.csv'\ntraining_set = pd.read_csv(training_set_file)  \n\ntraining_set_meta_file = '/kaggle/input/PLAsTiCC-2018/training_set_metadata.csv'\ntraining_set_meta = pd.read_csv(training_set_meta_file)  \n\n ","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def display_object_id(object_id,training_set,training_set_meta) :\n    print(\"+------------+ display object : {} \".format(object_id))\n    print(training_set[training_set['object_id']==object_id])\n\n    objdf = training_set[(training_set['object_id']==object_id)]\n    meta = training_set_meta[training_set_meta['object_id']==object_id]\n\n    fig, axs = plt.subplots(6)\n    fig.suptitle('Object id:{} - target: {}'.format(object_id,meta['target']))\n    \n    for passband in [0,1,2,3,4,5] :\n        objdf_passband = objdf[objdf['passband']==passband]\n        x1 = objdf_passband['mjd']\n        y1 = objdf_passband['flux']\n\n        axs[passband].plot(x1, y1, label = \"pb: {}\".format(passband))\n    plt.show(block = False)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"  \n# Extract 10 object with same target\ntarget = 90\nsub_training_set_meta = training_set_meta[training_set_meta['target'] == target]\n\nsubset = 5\n# sub_meta = sub_training_set_meta[0:subset] \nsub_meta = sub_training_set_meta.sample(n=subset)\n# print(sub_meta)\n\nfor (indice, meta) in sub_meta.iterrows():\n    #     print(\"meta : \",meta)\n    display_object_id(meta['object_id'],training_set,training_set_meta)","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}