{"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 in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nimport seaborn as sns\nsns.set()\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\nimport os\nprint(os.listdir(\"../input\"))\n\nimport plotly.offline as py\npy.init_notebook_mode(connected=True)\nimport plotly.graph_objs as go\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"struct = pd.read_csv(\"../input/structures.csv\")\nstruct.head(10)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(atoms)\nprint(atoms[0])\nprint(atoms)\nprint(struct.loc[struct.atom==atoms[n]].x.values[0:M])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"M = 10000\nfig, ax = plt.subplots(1,3,figsize=(20,5))\n\ncolors = [\"blue\", \"red\", \"green\", \"yellow\", \"purple\"]\natoms = struct.atom.unique()\n\nfor n in range(len(atoms)):\n\n    ax[0].scatter(struct.loc[struct.atom==atoms[n]].x.values[0:M],\n                  struct.loc[struct.atom==atoms[n]].y.values[0:M],\n                  color=colors[n], s=2, alpha=0.5, label=atoms[n])\n    ax[0].legend()\n    ax[0].set_xlabel(\"x\")\n    ax[0].set_ylabel(\"y\")\n    \n    ax[1].scatter(struct.loc[struct.atom==atoms[n]].x.values[0:M],\n                  struct.loc[struct.atom==atoms[n]].z.values[0:M],\n                  color=colors[n], s=2, alpha=0.5, label=atoms[n])\n    ax[1].legend()\n    ax[1].set_xlabel(\"x\")\n    ax[1].set_ylabel(\"z\")\n    \n    ax[2].scatter(struct.loc[struct.atom==atoms[n]].y.values[0:M],\n                  struct.loc[struct.atom==atoms[n]].z.values[0:M],\n                  color=colors[n], s=2, alpha=0.5, label=atoms[n])\n    ax[2].legend()\n    ax[2].set_xlabel(\"y\")\n    ax[2].set_ylabel(\"z\")","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}