{"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":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\nimport glob\nfrom main_io_comp_vis_all import calibrate_magnetic_wifi_ibeacon_to_position, read_data_file","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_paths = glob.glob('../input/indoor-location-navigation/train/*/*/*')\ntest_paths = glob.glob('../input/indoor-location-navigation/test/*')\nmall_list = os.listdir('../input/indoor-location-navigation/train')\nfloor_txt_num = {'F1':1,'1F':1,'L1':1,'1L':1,'F2':3,'2F':3,'L2':3,'2L':3,'F3':4,'3F':4,'L3':4,'3L':4,'F4':5,'4F':5,'L4':5,'4L':5,\n                'F5':6,'5F':6,'L5':6,'5L':6,'F6':7,'6F':7,'L6':7,'6L':7,'F7':8,'7F':8,'L7':8,'7L':8,'F8':9,'8F':9,'L8':9,'8L':9,\n                'F9':10,'9F':10,'L9':10,'9L':10,'F10':11,'10F':11,'L10':11,'10L':11,'F11':12,'11F':12,'L11':12,'11L':12,\n                'B1':1,'1B':1,\"B\":1,'B2':2,'2B':2,'B3':3,'3B':3,'B4':4,'4B':4}","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 1) Find Max of f_x_y","metadata":{}},{"cell_type":"code","source":"temp=0\nnp_max_fxy = np.ones((len(mall_list),3))\nnp_max_fxy[:,0] = 1\nnp_max_fxy[:,1] = 3\nnp_max_fxy[:,2] = 3","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"floor_max_num = []\nfor mall in mall_list:\n    floor_num = []\n    for key in list(os.walk('../input/indoor-location-navigation/train/'+mall))[0][1]:\n        try:\n            floor_num.append(floor_txt_num[key])\n        except:\n            floor_num.append(1)\n    floor_max_num.append(max(floor_num))\nnp_max_fxy[:,0]=np.array(floor_max_num)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mall_max_x_y=[]\ntemp=0\nfor mall in mall_list:\n    mall_train_path = glob.glob('../input/indoor-location-navigation/train/'+mall+'/*/*')\n    max_x_y = [2,2]\n    for path in mall_train_path:\n        try:\n            mwi_data_xy = (read_data_file(path)).waypoint\n            if (mwi_data_xy != []) and  (max_x_y[0] < max(mwi_data_xy[:,1])):\n                max_x_y[0]= max(mwi_data_xy[:,1])\n            if (mwi_data_xy != []) and  (max_x_y[1] < max(mwi_data_xy[:,2])):\n                max_x_y[1]= max(mwi_data_xy[:,2])\n        except:\n            continue\n    mall_max_x_y.append(max_x_y)\n    print(temp,\" out of 204\")\n    temp+=1","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"np_max_fxy[:,1:3]=np.array(mall_max_x_y)\nnp_max_fxy = np_max_fxy.astype('int')\ndf_max_fxy = pd.DataFrame(np_max_fxy)\ndf_max_fxy.columns = [\"max_floor\",\"max_x\",\"max_y\"]\ndf_max_fxy[\"mall\"]=mall_list\ndf_max_fxy.to_csv('max_fxy.csv')","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}