{"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":{},"cell_type":"markdown","source":"This code is based on https://www.kaggle.com/li325040229/the-game-of-life-display-of-cell-changes"},{"metadata":{},"cell_type":"markdown","source":"## Load dataset"},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import pandas as pd\n\ntrain_df = pd.read_csv('../input/conways-reverse-game-of-life-2020/train.csv')\ntest_df = pd.read_csv(\"../input/conways-reverse-game-of-life-2020/test.csv\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Display"},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport numpy as np","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def sampling(xs):\n  for x in xs:\n    #extraction pixel values for start and stop images\n    start_x = train_df.loc[x, train_df.columns.str.startswith('start')]\n    stop_x = train_df.loc[x, train_df.columns.str.startswith('stop')]\n    #reshap array en matrice 25x25\n    start_x = np.asarray(start_x).reshape(25, 25)\n    stop_x = np.asarray(stop_x).reshape(25, 25)\n    \n    #calculation\n    Invariant=start_x+stop_x\n    Invariant[(Invariant<2) & (Invariant>0)]=0\n    #print(Invariant)\n    change=start_x-stop_x\n    #print(change)\n    variation=Invariant+change\n\n    #iteration number extract\n    delta=train_df.loc[x,'delta']\n    \n    #Display\n    colors = ['red','white','blue','black']\n    cmap = mpl.colors.ListedColormap(colors)\n\n    fig, ax = plt.subplots(1, 3, figsize=(15, 5), dpi=300)\n    ax[0] = plt.subplot2grid((1,3), (0,0), colspan=1)\n    ax[1] = plt.subplot2grid((1,3), (0,1), colspan=1)\n    ax[2] = plt.subplot2grid((1,3), (0,2), colspan=1)\n    \n    clipped01=ax[0].imshow(2*(start_x),cmap=cmap,vmin=-1,vmax=2,interpolation='none')\n    ax[0].set_title(\"Start Board {0}\".format(x))\n\n    clipped00=ax[1].imshow(2*(stop_x),cmap=cmap,vmin=-1,vmax=2,interpolation='none')\n    ax[1].set_title(\"Board after {0} time(s) step\".format(delta))\n       \n    clipped02=ax[2].imshow(variation,cmap=cmap,vmin=-1,vmax=2,interpolation='none')\n    ax[2].set_title(\"variation\")\n    plt.colorbar(clipped02,ax=ax[2])\n    plt.show()\n    \n    print (\"RED:Add, BLUE:Remove, BLACK: No change\")\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sampling([1])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","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}