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How to use content_desc method in fMBT
Best Python code snippet using fMBT_python
get_widget_feature_ATM.py
Source:get_widget_feature_ATM.py 
1# this is for ATM and Craftdroid2# the dataset is Fruiter test3# the features are from ATM and Craftdroid4import jsonlines5import pandas as pd6import src.stage2.parameter_stage2 as para27from lxml import etree8import numpy as np9from src.stage2.get_same_state import get_all_file10from src.stage2.get_widget_feature import CropImage4File11def get_widget(json_path,json_name):12 # get widgets in test set13 widgets = set()14 with open(json_path+json_name,'r+',encoding='utf8') as f:15 for item in jsonlines.Reader(f):16 widget1 = item['widget1']17 widget2 = item['widget2']18 widgets.add(widget1)19 widgets.add(widget2)20 return widgets21# def get_widget_in_event(csv_path):22# # get widgets in test_event_map23# widgets = set()24# csv_name = 'test_event_map.csv'25# df = pd.read_csv(csv_path+csv_name)26# for index in range(len(df)):27# new_test_event = df.loc[index,'new_test_event']28# widgets.add(new_test_event)29# return widgets30def extract_ori_widget(widgets,xml_path_prefix):31 df = pd.DataFrame(columns=['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','class'],index=[])32 shopping_dict ={'aliexpress':1,33 'ebay':2,34 'etsy':3,35 '5miles':4,36 'geek':5,37 'googleshopping':6,38 'groupon':7,39 'home':8,40 '6pm':9,41 'wish':10,42 'google_shopping':11}43 for widget in widgets:44 prefix = ""45 xml_path = widget.split(":")[0]46 widget_path = widget.split(":")[1]47 app_name = xml_path.split("/")[0]48 if app_name in shopping_dict:49 prefix = 'shopping/'50 else:51 prefix = 'news/'52 full_xml_path = xml_path_prefix + prefix + xml_path53 tree = etree.parse(full_xml_path)54 root = tree.getroot()55 findall = etree.XPath(widget_path)56 nodelist = findall(root) # get the work57 id = np.nan58 if nodelist[0].get("resource-id") != "":59 id = nodelist[0].get("resource-id")60 text = np.nan61 if nodelist[0].get("text") != "":62 text = nodelist[0].get("text")63 content_desc = np.nan64 if nodelist[0].get("content-desc") != "":65 content_desc = nodelist[0].get("content-desc")66 if nodelist[0].get("clickable") == 'true':67 type = 'clickable'68 else:69 type = 'fillable'70 parent_text = np.nan71 parent_node = nodelist[0].getparent()72 if parent_node.get("text")!="":73 parent_text = parent_node.get("text")74 sibling_text = np.nan75 sibling_node = ""76 widget_class = nodelist[0].get("class")77 if len(parent_node)!=0:78 for index in range(len(parent_node)-1):79 test_sibling_node = parent_node[index]80 test_node = parent_node[index+1]81 if test_node == nodelist[0]:82 sibling_node = test_sibling_node83 break84 if sibling_node!="":85 if sibling_node.get("text")!="":86 sibling_text = sibling_node.get("text")87 atm_neighbor = sibling_text88 df_line = pd.DataFrame({89 'widget':widget,90 'text':text,91 'id':id,92 'content_desc':content_desc,93 'parent_text':parent_text,94 'sibling_text':sibling_text,95 'atm_neighbor':atm_neighbor,96 'type':type,97 'class':widget_class,98 },index=[1])99 df = df.append(df_line,ignore_index=True)100 return df101def extract_or_widget_map(csv_path,xml_path_prefix,csv_save_path):102 # extract atm /craftdrodi features in GT_news_revise/ GT_shopping103 df=pd.read_csv(csv_path)104 shopping_dict = {'aliexpress': 1,105 'ebay': 2,106 'etsy': 3,107 '5miles': 4,108 'geek': 5,109 'googleshopping': 6,110 'groupon': 7,111 'home': 8,112 '6pm': 9,113 'wish': 10,114 'google_shopping': 11}115 df_new = pd.DataFrame(columns=['app_name','widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','widget_name','class','current_activity','next_activity','canonical'],index=[])116 app_name = ''117 for index in range(len(df)):118 if isinstance(df.loc[index,'add-id'],float) and isinstance(df.loc[index,'add-xpath'],float):119 continue120 state_name = df.loc[index,'state-name'].strip().split('\t')[-1]121 app_name=state_name.split("/")[0]122 widget_name = state_name.split("/")[-1] # -1 not 1 because the last widget_name is corresponding features and xpath123 if app_name in shopping_dict:124 prefix = 'shopping/'125 else:126 prefix = 'news/'127 add_id = df.loc[index,'add-id']128 add_xpath = df.loc[index,'add-xpath']129 widget_path = ""130 widget_path= add_xpath131 full_xml_path = xml_path_prefix + prefix + app_name+"/"+widget_name132 tree = etree.parse(full_xml_path)133 root = tree.getroot()134 findall = etree.XPath(widget_path)135 nodelist = findall(root) # get the work136 ori_xpath = df.loc[index,'ori_xpath']137 id = np.nan138 if nodelist[0].get("resource-id") != "":139 id = nodelist[0].get("resource-id")140 text = np.nan141 if nodelist[0].get("text") != "":142 text = nodelist[0].get("text")143 content_desc = np.nan144 if nodelist[0].get("content-desc") != "":145 content_desc = nodelist[0].get("content-desc")146 widget_class = nodelist[0].get("class")147 if nodelist[0].get("clickable") == 'true':148 type = 'clickable'149 else:150 type = 'fillable'151 parent_text = np.nan152 parent_node = nodelist[0].getparent()153 if parent_node.get("text")!="":154 parent_text = parent_node.get("text")155 sibling_text = np.nan156 sibling_node = ""157 if len(parent_node)!=0:158 for idx in range(len(parent_node)-1):159 test_sibling_node = parent_node[idx]160 test_node = parent_node[idx+1]161 if test_node == nodelist[0]:162 sibling_node = test_sibling_node163 break164 if sibling_node!="":165 if sibling_node.get("text")!="":166 sibling_text = sibling_node.get("text")167 atm_neighbor = sibling_text168 widget_bounded = nodelist[0].get("bounds")169 # todo: need to revise to regular expression170 former_coordination = widget_bounded.split("][")[0]171 latter_coordination = widget_bounded.split("][")[1]172 former_x_bound = int(former_coordination.split(",")[0][1:])173 former_y_bound = int(former_coordination.split(",")[1])174 latter_x_bound = int(latter_coordination.split(",")[0])175 latter_y_bound = int(latter_coordination.split(",")[1][:-1])176 widget_bounded = [former_x_bound, former_y_bound,177 latter_x_bound, latter_y_bound]178 # only for server179 ocr_text = CropImage4File(full_xml_path,widget_bounded)180 if text == "":181 text = ocr_text182 current_activity = df.loc[index,'current Activity']183 next_activity = df.loc[index,'next Activity']184 canonical = df.loc[index,'canonical']185 df_line = pd.DataFrame({186 'app_name': app_name,187 'widget':widget_path,188 'text':text,189 'id':id,190 'content_desc':content_desc,191 'parent_text':parent_text,192 'sibling_text':sibling_text,193 'atm_neighbor':atm_neighbor,194 'type':type,195 'widget_name':widget_name,196 'class':widget_class,197 'current_activity':current_activity,198 'next_activity':next_activity,199 'canonical':canonical,200 'ori_xpath':ori_xpath,201 },index=[1])202 df_new = df_new.append(df_line,ignore_index=True)203 csv_save_name = app_name+".csv"204 df_new.to_csv(csv_save_path+csv_save_name)205def split_csv(test_data_feature_csv,csv_save_path):206 df_abc = pd.DataFrame(columns = ['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','app'],index=[])207 df_bbcnews = pd.DataFrame(columns = ['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','app'],index=[])208 df_buzzfeed = pd.DataFrame(columns = ['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','app'],index=[])209 df_cnn = pd.DataFrame(columns = ['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','app'],index=[])210 df_fox = pd.DataFrame(columns = ['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','app'],index=[])211 df_newsrepublic = pd.DataFrame(columns = ['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','app'],index=[])212 df_reuters = pd.DataFrame(columns = ['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','app'],index=[])213 df_smartnews = pd.DataFrame(columns = ['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','app'],index=[])214 df_theguardian = pd.DataFrame(columns = ['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','app'],index=[])215 df_usatoday = pd.DataFrame(columns = ['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','app'],index=[])216 df_6pm = pd.DataFrame(columns = ['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','app'],index=[])217 df_aliexpress = pd.DataFrame(columns = ['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','app'],index=[])218 df_ebay = pd.DataFrame(columns = ['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','app'],index=[])219 df_etsy = pd.DataFrame(columns = ['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','app'],index=[])220 df_geek = pd.DataFrame(columns = ['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','app'],index=[])221 df_googleshopping = pd.DataFrame(columns = ['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','app'],index=[])222 df_groupon = pd.DataFrame(columns = ['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','app'],index=[])223 df_home = pd.DataFrame(columns = ['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','app'],index=[])224 df_wish = pd.DataFrame(columns = ['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','app'],index=[])225 df_5miles = pd.DataFrame(columns = ['widget','text','id','content_desc','parent_text','sibling_text','atm_neighbor','type','app'],index=[])226 df = pd.read_csv(test_data_feature_csv)227 for index in range(len(df)):228 app_name = df.loc[index,'widget'].split('/')[0]229 df_line = pd.DataFrame(230 {231 'widget':df.loc[index,'widget'],232 'text':df.loc[index,'text'],233 'id':df.loc[index,'id'],234 'content_desc':df.loc[index,'content_desc'],235 'parent_text':df.loc[index,'parent_text'],236 'sibling_text':df.loc[index,'sibling_text'],237 'atm_neighbor':df.loc[index,'atm_neighbor'],238 'type':df.loc[index,'type'],239 'app':app_name,240 },index=[1]241 )242 if app_name =='abc':243 df_abc =df_abc.append(df_line)244 elif app_name == 'bbcnews':245 df_bbcnews = df_bbcnews.append(df_line)246 elif app_name == 'buzzfeed':247 df_buzzfeed = df_buzzfeed.append(df_line)248 elif app_name == 'cnn':249 df_cnn = df_cnn.append(df_line)250 elif app_name == 'fox':251 df_fox = df_fox.append(df_line)252 elif app_name == 'newsrepublic':253 df_newsrepublic = df_newsrepublic.append(df_line)254 elif app_name == 'reuters':255 df_reuters = df_reuters.append(df_line)256 elif app_name == 'smartnews':257 df_smartnews = df_smartnews.append(df_line)258 elif app_name == 'theguardian':259 df_theguardian = df_theguardian.append(df_line)260 elif app_name == 'usatoday':261 df_usatoday = df_usatoday.append(df_line)262 elif app_name == '6pm':263 df_6pm = df_6pm.append(df_line)264 elif app_name == 'aliexpress':265 df_aliexpress = df_aliexpress.append(df_line)266 elif app_name == 'ebay':267 df_ebay = df_ebay.append(df_line)268 elif app_name == 'etsy':269 df_etsy = df_etsy.append(df_line)270 elif app_name == 'geek':271 df_geek = df_geek.append(df_line)272 elif app_name == 'googleshopping':273 df_googleshopping = df_googleshopping.append(df_line)274 elif app_name == 'groupon':275 df_groupon = df_groupon.append(df_line)276 elif app_name == 'home':277 df_home = df_home.append(df_line)278 elif app_name == 'wish':279 df_wish = df_wish.append(df_line)280 elif app_name == '5miles':281 df_5miles = df_5miles.append(df_line)282 df_abc.to_csv(csv_save_path + 'news/abc.csv')283 df_bbcnews.to_csv(csv_save_path+'news/bbc.csv')284 df_buzzfeed.to_csv(csv_save_path+'news/buzzfeed.csv')285 df_cnn.to_csv(csv_save_path+'news/cnn.csv')286 df_fox.to_csv(csv_save_path+'news/fox.csv')287 df_newsrepublic.to_csv(csv_save_path+'news/newsrepublic.csv')288 df_reuters.to_csv(csv_save_path+'news/reuters.csv')289 df_smartnews.to_csv(csv_save_path+'news/smartnews.csv')290 df_theguardian.to_csv(csv_save_path+'news/theguardian.csv')291 df_usatoday.to_csv(csv_save_path+'news/usatoday.csv')292 df_6pm.to_csv(csv_save_path+'shopping/6pm.csv')293 df_aliexpress.to_csv(csv_save_path+'shopping/aliexpress.csv')294 df_ebay.to_csv(csv_save_path+'shopping/ebay.csv')295 df_etsy.to_csv(csv_save_path+'shopping/etsy.csv')296 df_geek.to_csv(csv_save_path+'shopping/geek.csv')297 df_googleshopping.to_csv(csv_save_path+'shopping/googleshopping.csv')298 df_groupon.to_csv(csv_save_path+'shopping/groupon.csv')299 df_home.to_csv(csv_save_path+'shopping/home.csv')300 df_wish.to_csv(csv_save_path+'shopping/wish.csv')301 df_5miles.to_csv(csv_save_path+'shopping/5miles.csv')302if __name__=="__main__":303 file_List = []304 xml_path_prefix = '/Fruiter/TestBenchmark-Java-client-master/screenshots/'305 csv_path = '../../resources/GT_revise_0213/shopping_same_row/'306 get_all_file(csv_path, file_List)307 csv_save_path = '/Migration/resources/experiment/test-tool/0308/shopping_same_row/'308 index = 0309 for file_path in file_List:310 if '.DS_Store' in file_path or 'ground_truth_pair' in file_path:311 continue312 print(file_path)313 extract_or_widget_map(file_path, xml_path_prefix,csv_save_path)314 # index += 1315 # print(index)...
puzzle_2.py
Source:puzzle_2.py
1def get_contained_bags(bags, start_bag):2 total_bags = 03 for contained_bag in bags[start_bag]:4 num_contained_bags = int(bags[start_bag][contained_bag])5 total_bags += num_contained_bags6 total_bags += num_contained_bags*get_contained_bags(bags, contained_bag)7 return total_bags8def parse_line(line):9 name, contents_txt = line.split(" bags contain ")10 contents = {}11 contents_txt = contents_txt[:-1] #drop the period12 if contents_txt == "no other bags":13 contents = []14 else:15 csplit = contents_txt.split(", ")16 for x in csplit:17 content_desc = x.split(" ")18 num = content_desc[0]19 content_bag_name = content_desc[1]+" "+content_desc[2]20 contents[content_bag_name] = num21 return name, contents22groups = []23with open("data.txt") as data:24 lines = data.read().splitlines()25bags = {}26for line in lines:27 name, contents = parse_line(line)28 bags[name] = contents29contained_bags = get_contained_bags(bags, "shiny gold")30print(contained_bags)...
puzzle_1.py
Source:puzzle_1.py
1def get_containing_bags(bags, start_bag):2 top_bags = []3 for name, contents in bags.items():4 if start_bag in contents:5 top_bags.append(name)6 top_bags.extend(get_containing_bags(bags, name))7 return top_bags8def parse_line(line):9 name, contents_txt = line.split(" bags contain ")10 contents = {}11 contents_txt = contents_txt[:-1] #drop the period12 if contents_txt == "no other bags":13 contents = []14 else:15 csplit = contents_txt.split(", ")16 for x in csplit:17 content_desc = x.split(" ")18 num = content_desc[0]19 content_bag_name = content_desc[1]+" "+content_desc[2]20 contents[content_bag_name] = num21 return name, contents22groups = []23with open("data.txt") as data:24 lines = data.read().splitlines()25bags = {}26for line in lines:27 name, contents = parse_line(line)28 bags[name] = contents29top_level_bags = get_containing_bags(bags, "shiny gold")30#print(top_level_bags)31print(len(set(top_level_bags)))...
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