{"cells":[{"metadata":{"_uuid":"410bd90db0dfa35c046d26f6c97dd7c08b27cd8b"},"cell_type":"markdown","source":"# Some test plots"},{"metadata":{"_uuid":"7d93b0f69b90cfe528f974b5f5009de8b7c49719"},"cell_type":"markdown","source":"## Load the data\n\nFor this notebook, we'll only need the metadata."},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport scipy.interpolate as itp","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"07771416a764cb921cf9fca1ae5d38053714a6f3"},"cell_type":"code","source":"%matplotlib inline","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"082fa9b8f15ffdf5090cf470260ea331e09a2778"},"cell_type":"markdown","source":"Set seasborn style"},{"metadata":{"trusted":true,"_uuid":"3293e1a1c20e2537f709970980070a3a46c526b9"},"cell_type":"code","source":"sns.set()","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"meta_data = pd.read_csv('../input/training_set_metadata.csv')\ndata = pd.read_csv('../input/training_set.csv')\n\ntarget = meta_data['target']\nobject_id = meta_data['object_id']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"scrolled":true,"_uuid":"0098965a475104351142a62b71b5ff10e825de5b"},"cell_type":"code","source":"plt.scatter(meta_data['gal_l'], meta_data['gal_b'], s=1)\nplt.xlabel('l')\nplt.ylabel('b')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"adde23a6e20cccbd83aa470adb0a21325dd33960"},"cell_type":"code","source":"colours = np.array(('purple', 'blue', 'green', 'orange', 'red', 'brown'))\nfor i in range(10):\n    plt.figure(i, figsize=(20, 15))\n    mask_u = (data['object_id']==object_id[i]) & (data['passband']==0)\n    mask_g = (data['object_id']==object_id[i]) & (data['passband']==1)\n    mask_r = (data['object_id']==object_id[i]) & (data['passband']==2)\n    mask_i = (data['object_id']==object_id[i]) & (data['passband']==3)\n    mask_z = (data['object_id']==object_id[i]) & (data['passband']==4)\n    mask_y = (data['object_id']==object_id[i]) & (data['passband']==5)\n    plt.scatter(data['mjd'][mask_u], data['flux'][mask_u], s=1, color=colours[0])\n    plt.scatter(data['mjd'][mask_g], data['flux'][mask_g], s=1, color=colours[1])\n    plt.scatter(data['mjd'][mask_r], data['flux'][mask_r], s=1, color=colours[2])\n    plt.scatter(data['mjd'][mask_i], data['flux'][mask_i], s=1, color=colours[3])\n    plt.scatter(data['mjd'][mask_z], data['flux'][mask_z], s=1, color=colours[4])\n    plt.scatter(data['mjd'][mask_y], data['flux'][mask_y], s=1, color=colours[5])\n    plt.plot(data['mjd'][mask_u],itp.UnivariateSpline(data['mjd'][mask_u], data['flux'][mask_u], s=1000.*np.mean(data['flux_err'][mask_u]))(data['mjd'][mask_u]),lw=1, color=colours[0])\n    plt.plot(data['mjd'][mask_g],itp.UnivariateSpline(data['mjd'][mask_g], data['flux'][mask_g], s=1000.*np.mean(data['flux_err'][mask_g]))(data['mjd'][mask_g]),lw=1, color=colours[1])\n    plt.plot(data['mjd'][mask_r],itp.UnivariateSpline(data['mjd'][mask_r], data['flux'][mask_r], s=1000.*np.mean(data['flux_err'][mask_r]))(data['mjd'][mask_r]),lw=1, color=colours[2])\n    plt.plot(data['mjd'][mask_i],itp.UnivariateSpline(data['mjd'][mask_i], data['flux'][mask_i], s=1000.*np.mean(data['flux_err'][mask_i]))(data['mjd'][mask_i]),lw=1, color=colours[3])\n    plt.plot(data['mjd'][mask_z],itp.UnivariateSpline(data['mjd'][mask_z], data['flux'][mask_z], s=1000.*np.mean(data['flux_err'][mask_z]))(data['mjd'][mask_z]),lw=1, color=colours[4])\n    plt.plot(data['mjd'][mask_y],itp.UnivariateSpline(data['mjd'][mask_y], data['flux'][mask_y], s=1000.*np.mean(data['flux_err'][mask_y]))(data['mjd'][mask_y]),lw=1, color=colours[5])\n    plt.title(\"Class = \" + str(target[i]))\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"5365fcb95b6ff007cf708acf54351593adf45eb1"},"cell_type":"code","source":"colours = np.array(('purple', 'blue', 'green', 'orange', 'red', 'brown'))\n\nfor i, class_id in enumerate(set(target)):\n    plt.figure(i, figsize=(20, 15))\n    mask_u = (class_id == target) & (data['passband']==0)\n    mask_g = (class_id == target) & (data['passband']==1)\n    mask_r = (class_id == target) & (data['passband']==2)\n    mask_i = (class_id == target) & (data['passband']==3)\n    mask_z = (class_id == target) & (data['passband']==4)\n    mask_y = (class_id == target) & (data['passband']==5)\n    plt.scatter(data['mjd'][mask_u], data['flux'][mask_u], s=1, color=colours[0])\n    plt.scatter(data['mjd'][mask_g], data['flux'][mask_g], s=1, color=colours[1])\n    plt.scatter(data['mjd'][mask_r], data['flux'][mask_r], s=1, color=colours[2])\n    plt.scatter(data['mjd'][mask_i], data['flux'][mask_i], s=1, color=colours[3])\n    plt.scatter(data['mjd'][mask_z], data['flux'][mask_z], s=1, color=colours[4])\n    plt.scatter(data['mjd'][mask_y], data['flux'][mask_y], s=1, color=colours[5])\n    #plt.plot(data['mjd'][mask_u],itp.UnivariateSpline(data['mjd'][mask_u], data['flux'][mask_u], s=1000.*np.mean(data['flux_err'][mask_u]))(data['mjd'][mask_u]),lw=1, color=colours[0])\n    #plt.plot(data['mjd'][mask_g],itp.UnivariateSpline(data['mjd'][mask_g], data['flux'][mask_g], s=1000.*np.mean(data['flux_err'][mask_g]))(data['mjd'][mask_g]),lw=1, color=colours[1])\n    #plt.plot(data['mjd'][mask_r],itp.UnivariateSpline(data['mjd'][mask_r], data['flux'][mask_r], s=1000.*np.mean(data['flux_err'][mask_r]))(data['mjd'][mask_r]),lw=1, color=colours[2])\n    #plt.plot(data['mjd'][mask_i],itp.UnivariateSpline(data['mjd'][mask_i], data['flux'][mask_i], s=1000.*np.mean(data['flux_err'][mask_i]))(data['mjd'][mask_i]),lw=1, color=colours[3])\n    #plt.plot(data['mjd'][mask_z],itp.UnivariateSpline(data['mjd'][mask_z], data['flux'][mask_z], s=1000.*np.mean(data['flux_err'][mask_z]))(data['mjd'][mask_z]),lw=1, color=colours[4])\n    #plt.plot(data['mjd'][mask_y],itp.UnivariateSpline(data['mjd'][mask_y], data['flux'][mask_y], s=1000.*np.mean(data['flux_err'][mask_y]))(data['mjd'][mask_y]),lw=1, color=colours[5])\n    plt.title(\"Class = \" + str(class_id))\n","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}