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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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