end:\\n if end.day >= start.day:\\n d = end.day - cur_start.day + 1\\n elif not fix_mode:\\n d = (end - cur_start).days + 1\\n else:\\n d = end.day + 30 - start.day + 1\\n m -= 1\\n break\\n m += 1\\n if d == 30 and fix_mode:\\n d = 0\\n m += 1\\n\\n return m,d\\n\\ndef get_subrent_list(lease_begin, lease_end, hzf_pay_type, amount,\\n prepay_days, down_payment_month, agency_id, hzf_final_payment=1,\\n lease_type=2, fix_mode=True, service_fee_payer=6):\\n ret = []\\n hzf_pay_rate = [3, 6, 12]\\n rate = hzf_pay_rate[hzf_pay_type - 1]\\n\\n if lease_type == 3:\\n down_payment_month = 0\\n\\n months, days = get_contract_cycle(lease_begin, lease_end, fix_mode=fix_mode)\\n tm = months\\n if days > 0:\\n tm += 1\\n stages = tm / rate\\n if tm % rate > 0:\\n stages += 1\\n last_end_date = lease_begin - datetime.timedelta(days=1)\\n for i in range(stages):\\n s = SubRent()\\n s.index = i + 1\\n diff = 0\\n if i == 0:\\n diff = down_payment_month\\n print '................'\\n print str(lease_begin)\\n print str(diff)\\n print str(down_payment_month)\\n\\n if not fix_mode:\\n for j in range(diff):\\n cur_start = last_end_date + datetime.timedelta(days=1)\\n if j == 0:\\n s.pay_date = cur_start\\n last_end_date = get_future_date(cur_start, 1, plus=True) - datetime.timedelta(days=1)\\n for j in range(rate-diff):\\n cur_start = last_end_date + datetime.timedelta(days=1)\\n if j == 0:\\n s.start_date = cur_start\\n if diff == 0 and j == 0:\\n s.pay_date = cur_start\\n last_end_date = get_future_date(cur_start, 1, plus=True) - datetime.timedelta(days=1)\\n s.end_date = last_end_date\\n else:\\n s.start_date = get_future_date(lease_begin,\\n i * rate + diff)\\n s.end_date = get_future_date(lease_begin - datetime.timedelta(days=1),\\n (i + 1) * rate)\\n s.pay_date = get_future_date(lease_begin,\\n i * rate) # - datetime.timedelta(days=1)\\n\\n if service_fee_payer and service_fee_payer == 6 and i == 0:\\n if hzf_pay_type == 3:\\n d_rate = 0.92\\n r_rate = 0.94\\n elif hzf_pay_type == 2:\\n d_rate = 0.95\\n\\n #s.price = int(round(amount * (rate - diff + (1-d_rate)/d_rate * 3)))\\n s.price = int(round(amount * (rate - diff)))\\n elif service_fee_payer == 7 and i == 0:\\n if hzf_pay_type == 3:\\n d_rate = (1 * 2 + 0.92 * 9) / 11\\n amount = amount / 0.92 * d_rate\\n s.price = int(round(amount * (rate - diff)))\\n else:\\n s.price = int(round(amount * (rate - diff)))\\n if stages == 1:\\n ret.append(s)\\n elif i < stages - 1:\\n ret.append(s)\\n elif ret and hzf_final_payment == 2:\\n s.end_date = lease_end\\n if agency_id in [10630]:\\n check_hzf_payment(s, agency_id)\\n ret[0].price += s.price\\n elif ret and hzf_final_payment == 3:\\n s.end_date = lease_end\\n if agency_id in [10630]:\\n check_hzf_payment(s, agency_id)\\n ret[-1].end_date = lease_end\\n ret[-1].price += s.price\\n else:\\n ret.append(s)\\n\\n #payrate = float((lease_end - s.start_date).days + 1) / 30\\n ret[-1].end_date = lease_end\\n #ret[-1].price = int(round(ret[-1].price * payrate))\\n amend_subrent_pay_date(ret[0], agency_id)\\n return ret\\n\\ndef check_hzf_payment(subrent, agency_id):\\n if agency_id in [1969, 1717, 10465]: # 如果是有家置地和天地昊、同程 不检查尾款同上期合并和尾款首付\\n return\\n last_subrent_days = (subrent.end_date - subrent.start_date).days\\n if last_subrent_days > 30:\\n raise HzfException(ERR_LAST_SUBRENT_GT_THIRTY[0], ERR_LAST_SUBRENT_GT_THIRTY[1])\\n\\npayment_params = [\\n { 'rate': 1, },\\n { 'rate': 2, },\\n { 'rate': 3, },\\n { 'rate': 4, },\\n { 'rate': 5, },\\n { 'rate': 6, },\\n { 'rate': 7, },\\n { 'rate': 8, },\\n { 'rate': 9, },\\n { 'rate': 10, },\\n { 'rate': 11, },\\n { 'rate': 12, },\\n]\\n\\ndef yuan2fen(amount):\\n s = \\\"%s00\\\" % (int(amount))\\n return int(s)\\n\\ndef fen2yuan(amount):\\n str_fen = str(amount)\\n str_yuan = \\\"%s.%s\\\" % (str_fen[:-2], str_fen[-2:])\\n return str_yuan\\n\\ndef get_month_days(date):\\n for day in [28, 29, 30, 31]:\\n try:\\n datetime.date(date.year, date.month, day)\\n ret = day\\n except:\\n pass\\n \\n return ret\\n\\n\\ndef get_subpay_list(lease_begin, lease_end, user_pay_type, prepay_day,\\n service_fee, amount, down_payment_month, agency_id,\\n prepay_days=15, final_payment=1, final_price=-1,\\n final_service_fee=-1, lease_type=1, service_fee_payer=1, fix_mode=True):\\n ret = []\\n if lease_type == 3:\\n down_payment_month = 0\\n params = payment_params[user_pay_type - 1]\\n rate = params['rate']\\n down_payment_stage = down_payment_month / rate\\n\\n logging.info(\\\"akpm %s - %s\\\" % (lease_begin, lease_end))\\n total_months, total_days = get_contract_cycle(lease_begin, lease_end, fix_mode)\\n logging.info(\\\"akpm %s - %s\\\" % (total_months, total_days))\\n i = 0\\n orig_service_fee = service_fee\\n\\n start = lease_begin\\n end = lease_end\\n nextend = start - datetime.timedelta(days=1)\\n last_end_date = start - datetime.timedelta(days=1)\\n\\n for i in range(down_payment_stage):\\n cur_start = last_end_date + datetime.timedelta(days=1) #get_future_date(start, m - 1 , plus=True)\\n last_end_date = get_future_date(cur_start, 1, plus=True) - datetime.timedelta(days=1)\\n\\n i = 0\\n m = 1\\n while True:\\n #if fix_mode:\\n # cur_start = get_future_date(start, m - 1 , plus=True)\\n # last_end_date = get_future_date(cur_start, m, plus=True) - datetime.timedelta(days=1)\\n #else:\\n # cur_start = last_end_date + datetime.timedelta(days=1) #get_future_date(start, m - 1 , plus=True)\\n # last_end_date= get_future_date(cur_start, 1, plus=True) - datetime.timedelta(days=1)\\n\\n if service_fee_payer in [4] and i < 3:\\n service_fee = 0\\n else:\\n service_fee = orig_service_fee\\n\\n s = SubPay()\\n s.price = int(round((amount + service_fee) * rate))\\n s.base_price = int(round(amount * rate))\\n s.unpay_price = int(round((amount + service_fee) * rate))\\n s.service_fee = int(round(service_fee * rate))\\n s.index = i+1\\n\\n if not fix_mode:\\n cur_start = last_end_date + datetime.timedelta(days=1) #get_future_date(start, m - 1 , plus=True)\\n s.end_date = get_future_date(cur_start, 1, plus=True) - datetime.timedelta(days=1)\\n elif lease_begin.day == 1:\\n s.end_date = get_future_date(lease_begin,\\n (i + down_payment_stage + 1) *\\n rate) - datetime.timedelta(days=1) \\n else:\\n s.end_date = get_future_date(lease_begin - datetime.timedelta(days=1), \\n (i + down_payment_stage + 1) *\\n rate) \\n\\n m += 1\\n s.start_date = last_end_date + datetime.timedelta(days=1)\\n last_end_date = s.end_date\\n\\n pay_date = s.start_date\\n\\n if lease_type == 3 or lease_type == 4: #C端 公寓目前只支持5天后还款\\n prepay_days = -4\\n\\n for j in range(0, 32):\\n date1 = pay_date - datetime.timedelta(days=prepay_days + j)\\n write_debug_log(\\\"date1: \\\" + str(date1))\\n if date1.day == prepay_day:\\n pay_date2 = date1\\n break\\n date2 = pay_date - datetime.timedelta(days=prepay_days - j)\\n write_debug_log(\\\"date2: \\\" + str(date2))\\n if date2.day == prepay_day:\\n pay_date2 = date2\\n break\\n if date1.day == prepay_day or date1 == get_date(date1.year, date1.month, prepay_day):\\n pay_date2 = date1\\n break\\n if date2.day == prepay_day or date2 == get_date(date2.year, date2.month, prepay_day):\\n pay_date2 = date2\\n break\\n \\n write_debug_log(\\\"pay_date2: \\\" + str(pay_date2))\\n orig = pay_date2\\n\\n s.pay_date = get_date(orig.year, orig.month, prepay_day)\\n\\n if s.end_date == lease_end:\\n ret.append(s)\\n break\\n\\n if s.end_date > lease_end:\\n orig_end = s.end_date\\n s.end_date = lease_end\\n\\n if fix_mode:\\n month_days = 30\\n else:\\n month_days = get_month_days(s.start_date)\\n write_debug_log('%s month_days: %d' % (s.start_date, month_days))\\n\\n if total_days > 0 and total_days < month_days:\\n payrate = total_days / (month_days * 1.0)\\n else:\\n payrate = 1\\n\\n s.price = int(round((amount + service_fee) * rate * payrate))\\n\\n if final_price != -1:\\n s.price = final_price + final_service_fee\\n s.service_fee = int(round(service_fee * rate * payrate))\\n s.base_price = int(round(amount * rate * payrate))\\n s.unpay_price = s.price\\n\\n #if final_service_fee != -1:\\n # s.service_fee = final_service_fee\\n # s.base_price = final_price\\n\\n if final_payment == 1:\\n ret.append(s)\\n elif final_payment == 2:\\n pass\\n elif final_payment == 3:\\n ret[-1].price += s.price\\n ret[-1].end_date = s.end_date\\n ret[-1].base_price += s.base_price\\n ret[-1].unpay_price += s.unpay_price\\n ret[-1].service_fee += s.service_fee\\n break\\n\\n ret.append(s)\\n i += 1\\n\\n if final_price != -1:\\n ret[-1].price = final_price\\n ret[-1].unpay_price = final_price\\n ret[-1].service_fee = int(round(final_price * service_fee / (service_fee + amount)))\\n ret[-1].base_price = final_price - s.service_fee\\n\\n if lease_type == 4:\\n count = 0\\n pay_date = lease_begin - datetime.timedelta(days=5)\\n # 为了快速实现 新公寓算法，对生成的数据进行修改，之后会对按照整理的算法进行重构\\n for subpay in ret:\\n if count > 0:\\n subpay.pay_date = add_months(pay_date, count)\\n if agency_id in a_pair_of_a_agency_id_list: # 如果是押一付一，付款日期加一个月\\n subpay.pay_date = add_months(pay_date, count + 1)\\n ret[count] = subpay\\n count += 1\\n\\n return ret\\n\\ndef get_service_rate(hzf_pay_type, lease_type=None):\\n if lease_type == 4:\\n return 100 * 8 / 92.0\\n rate_list = [4, 5, 8]\\n if hzf_pay_type <= 0 or hzf_pay_type > len(rate_list):\\n raise Exception('invalid hzf_pay_type')\\n return rate_list[hzf_pay_type - 1]\\n\\ndef get_down_payment_month(prepay_days):\\n if prepay_days == 3:\\n down_payment_month = 0\\n elif prepay_days in [2, 30]:\\n down_payment_month = 2\\n else: #15/1\\n down_payment_month = 1\\n return down_payment_month\\n\\ndef get_prepay_day(lease_begin, prepay_days, down_payment_month, lease_type=2):\\n if lease_begin.day == 1:\\n if prepay_days == 15: \\n prepay_day = 16\\n elif prepay_days == 30:\\n prepay_day = 1 \\n else:\\n prepay_day = 5\\n else:\\n lastday = (get_future_date(lease_begin-datetime.timedelta(days=1),\\n down_payment_month)).day\\n prepay_day = lastday - (prepay_days - 1)\\n if prepay_day <= 0:\\n prepay_day += 30\\n if prepay_day > 30:\\n prepay_day -= 30\\n return prepay_day\\n\\ndef obj_waper(obj):\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n ret = {}\\n\\n for k,v in vars(obj).items():\\n if k == '_state':\\n continue\\n hump_key = k\\n ret[hump_key] = v\\n\\n return ret\\n\\ndef subpay_waper(subpayList):\\n new = []\\n for i in subpayList:\\n if type(i) == dict:\\n if 'base_price' not in i.keys():\\n i['base_price'] = int(i['price']) - int(i['service_fee'])\\n if 'unpay_price' not in i.keys():\\n i['unpay_price'] = int(i['price'])\\n paid_price = 0\\n if 'paid_price' not in i.keys():\\n paid_price = int(i['price']) - int(i['unpay_price'])\\n if 'pay_time' in i.keys() and i['pay_time']:\\n pay_time = str(i['pay_time'])\\n else:\\n pay_time = ''\\n if 'remark_pay_time' in i.keys() and i['remark_pay_time']:\\n remark_pay_time = str(i['remark_pay_time'])\\n else:\\n remark_pay_time = ''\\n \\n if 'overdue_fine' in i.keys() and i['overdue_fine']:\\n overdue_fine = int(i['overdue_fine'])\\n else:\\n overdue_fine = 0\\n \\n new.append({\\n 'index': i['index'],\\n 'pay_date': i['pay_date'],\\n 'price': i['price'],\\n 'base_price': i['base_price'],\\n 'unpay_price': i['unpay_price'],\\n 'overdue_fine': overdue_fine,\\n 'paid_price': paid_price,\\n 'pay_time': pay_time,\\n 'remark_pay_time': remark_pay_time,\\n 'service_fee': i['service_fee'],\\n 'start_date': str(i['start_date']),\\n 'end_date': str(i['end_date']),\\n 'status': '0',\\n })\\n else:\\n if i.pay_time:\\n pay_time = str(i.pay_time)\\n else:\\n pay_time = ''\\n remark_pay_time = i.remark_pay_time\\n if i.offline_pay_time:\\n remark_pay_time = i.offline_pay_time\\n if i.status == 0:\\n pay_time = \\\"\\\"\\n remark_pay_time = \\\"\\\"\\n if hasattr(i, 'overdue_fine'):\\n overdue_fine = int(i.overdue_fine_nature - i.overdue_fine_reduce)\\n else:\\n overdue_fine = 0\\n \\n new.append({\\n 'index': i.index,\\n 'price': i.price,\\n 'base_price': i.base_price,\\n 'unpay_price': i.unpay_price,\\n 'paid_price': (i.price - i.unpay_price),\\n 'pay_time': pay_time,\\n 'remark_pay_time': str(remark_pay_time),\\n 'pay_date': str(i.pay_date),\\n 'service_fee': i.service_fee,\\n 'overdue_fine':overdue_fine,\\n 'start_date': str(i.start_date),\\n 'end_date': str(i.end_date),\\n 'status': str(i.status),\\n })\\n subrentList = new\\n return new\\n\\ndef subrent_waper(subrentList):\\n new = []\\n count_idx = 1\\n for i in subrentList:\\n if type(i) == dict:\\n i['index'] = count_idx\\n if 'paid' in i.keys():\\n paid = i['paid']\\n elif 'status' in i.keys():\\n paid = i['status']\\n else:\\n paid = 0\\n i['unpay_price'] = i['price']\\n new.append({\\n 'index': i['index'],\\n 'pay_date': i['pay_date'],\\n 'price': i['price'],\\n 'paid': paid,\\n 'unpay_price': i['unpay_price'],\\n 'paid_price': int(i['price']) - int(i['unpay_price']),\\n })\\n else:\\n new.append({\\n 'index': i.index,\\n 'pay_date': str(i.pay_date),\\n 'price': i.price,\\n 'paid': i.status,\\n 'paid_price': i.price - i.unpay_price,\\n 'cashing_time': str(i.cashing_time),\\n 'unpay_price': i.unpay_price,\\n 'start_date': str(i.start_date),\\n 'end_date': str(i.end_date),\\n 'subrent_id': i.subrent_id,\\n 'status': i.status,\\n })\\n count_idx += 1\\n subrentList = new\\n return subrentList\\n\\n\\ndef fix_final_subrent(subrent_list, subpay_list, service_fee_payer=1, orig_fee_rate=5,\\n hzf_final_payment=1,\\n hzf_pay_type=3):\\n\\n if hzf_pay_type == 3:\\n d_rate = 0.92\\n d_rate = 0.94\\n elif hzf_pay_type == 2:\\n d_rate = 0.95\\n\\n total_rent = 0\\n for i in subrent_list:\\n total_rent += i.price\\n total_base_price = 0\\n for i in subpay_list:\\n total_base_price += i.base_price\\n first_amount = subpay_list[0]['price']\\n if hzf_final_payment == 2:\\n rent = subrent_list[0]\\n else:\\n rent = subrent_list[-1]\\n\\n if service_fee_payer in [2]:\\n rent.price = int(round(rent.price -\\n (total_rent -\\n float(total_base_price) / (orig_fee_rate + 100) * 100.0\\n )))\\n for i in subrent_list:\\n i.price = int(i.price / 100)\\n i.price = i.price * 100\\n elif service_fee_payer in [5]:\\n rent.price = int(round(rent.price -\\n (total_rent -\\n float(total_base_price) * d_rate\\n )))\\n for i in subrent_list:\\n i.price = int(i.price / 100)\\n i.price = i.price * 100\\n elif service_fee_payer in [6]:\\n rent.price = int(round(rent.price\\n #+ float(first_amount) * (1-d_rate) * 3\\n - (total_rent -\\n float(total_base_price) * (d_rate)\\n )))\\n for i in subrent_list:\\n i.price = int(i.price / 100)\\n i.price = i.price * 100\\n else:\\n rent.price = int(round(rent.price -\\n (total_rent - total_base_price)))\\n rent = subrent_list[0]\\n return subrent_list\\n\\ndef get_total_rent(subrent_list):\\n total_rent = 0\\n if not subrent_list:\\n return 0\\n for i in subrent_list:\\n if type(i) == dict:\\n total_rent += i['price']\\n else:\\n total_rent += i.price\\n\\n return int(round(total_rent))\\n\\ndef get_total_pay(subpay_list):\\n total_base_price = 0\\n total_service_fee = 0\\n user_amount = 0\\n for i in subpay_list:\\n if type(i) == dict:\\n if 'base_price' not in i.keys():\\n i['base_price'] = int(i['price']) - int(i['service_fee'])\\n total_base_price += i['base_price']\\n total_service_fee += i['service_fee']\\n user_amount += i['price']\\n else:\\n total_base_price += i.base_price\\n total_service_fee += i.service_fee\\n user_amount += i.price\\n return user_amount, total_base_price\\n\\ndef calculator(lease_begin, lease_end, monthly_amount,\\n service_fee_payer=1, prepay_days_type=1,\\n hzf_pay_type=2, final_payment=1,\\n user_pay_type=1,\\n down_payment_month=-1,\\n total_months=-1, total_days=-1, final_price=-1, final_service_fee=-1,\\n hzf_final_payment=1, lease_type=2, fix_mode=True\\n ):\\n prepay_days = [15, 30, -4][prepay_days_type - 1]\\n if down_payment_month == -1:\\n down_payment_month = get_down_payment_month(prepay_days)\\n\\n\\n if hzf_pay_type == 3:\\n d_rate = 0.92\\n elif hzf_pay_type == 2:\\n d_rate = 0.95\\n\\n service_rate = get_service_rate(hzf_pay_type, lease_type)\\n real_monthly_amount = monthly_amount\\n if service_fee_payer in [2]:\\n real_monthly_amount = float(monthly_amount) / (service_rate +\\n 100) * 100.0\\n if service_fee_payer in [5, 6]:\\n real_monthly_amount = float(monthly_amount) * d_rate\\n\\n orig_service_rate = service_rate\\n if service_fee_payer in [2, 3, 5, 6]:\\n service_rate = 0\\n\\n service_fee = monthly_amount * service_rate / 100\\n\\n prepay_day = get_prepay_day(lease_begin, prepay_days,\\n down_payment_month)\\n\\n subrent_list = get_subrent_list(lease_begin, lease_end,\\n hzf_pay_type,\\n real_monthly_amount, prepay_days,\\n down_payment_month, hzf_final_payment,\\n lease_type=lease_type, service_fee_payer=service_fee_payer) \\n subpay_list = get_subpay_list(lease_begin, lease_end,\\n user_pay_type,\\n prepay_day, service_fee,\\n monthly_amount, down_payment_month,\\n prepay_days, final_payment, lease_type=lease_type, service_fee_payer=service_fee_payer)\\n total_months,total_days = get_contract_cycle(lease_begin, lease_end, fix_mode)\\n\\n subrent_list = fix_final_subrent(subrent_list, subpay_list,\\n service_fee_payer,orig_service_rate, hzf_final_payment, hzf_pay_type=hzf_pay_type)\\n total_rent = get_total_rent(subrent_list)\\n\\n subrent_list = subrent_waper(subrent_list)\\n\\n user_amount,total_base_price = get_total_pay(subpay_list)\\n #subpay_list = subpay_waper(subpay_list)\\n\\n final_service_fee = subpay_list[-1].service_fee\\n #final_amount = subpay_list[-1]['price']\\n final_amount = subpay_list[-1].price\\n\\n return subrent_list, subpay_list, prepay_day, total_months, total_days, total_rent\\n\\ndef allocate_contract_no(requester, type=1, disable_prefix=True):\\n #type: 0租赁(Z) 1会分期(H) 其他未知(U)\\n if type == 0:\\n prefix = 'Z' \\n elif type == 1:\\n prefix = 'H' \\n else:\\n prefix = 'U' \\n\\n c = ContractNo(requester=requester, type=type)\\n c.save()\\n\\n contract_no = datetime.datetime.now().strftime('%Y%m%d')\\n numbers = ContractNo.objects.filter(create_time__gte=datetime.date.today()).order_by('create_time')\\n index = c.contract_no_id - numbers[0].contract_no_id + 1\\n if disable_prefix:\\n prefix = ''\\n\\n contract_no = \\\"%s%s%.4d\\\" % (prefix, contract_no, index)\\n ContractNo.objects.filter(contract_no_id=c.contract_no_id).update(no=contract_no)\\n\\n return contract_no\\n\\ndef get_date_from_str(date):\\n if type(date) in [str, unicode]:\\n date = datetime.datetime.strptime(date, '%Y-%m-%d').date()\\n return date\\n\"\n },\n {\n \"alpha_fraction\": 0.6698113083839417,\n \"alphanum_fraction\": 0.6698113083839417,\n \"avg_line_length\": 30.235294342041016,\n \"blob_id\": \"edd07a5036ca91fd5bb385f8a813f4dae281babb\",\n \"content_id\": \"1f5461a12addde8b04fb9a1602057e353203e524\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 776,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 17,\n \"path\": \"/helloworld/协程.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#把函数编写成一个任务程序，用来处理发送给它的一系列输入，用（yield）的形式实现，用send（）发送数据\\ndef print_matches(matchText):\\n print('looking for', matchText)\\n while True:\\n line = (yield ) #接受一行文本\\n if matchText in line:\\n print(line)\\n\\nmatch = print_matches('python')\\nmatch.__next__() #必须调用next函数，协程才能执行第一个yield之前的语句，不调用next函数会报错\\na = match.send('python') #send（）函数的返回值为传递给下一个yield语句的值\\nprint('a = ', a)\\nb = match.send('hello world')\\nprint('b=', b)\\nc = match.send('python is cool')\\nprint('c=', c)\\nmatch.close() #协程一般会不断执行下去，直到显示关闭或者自己退出\"\n },\n {\n \"alpha_fraction\": 0.7374005317687988,\n \"alphanum_fraction\": 0.7533156275749207,\n \"avg_line_length\": 33.3636360168457,\n \"blob_id\": \"7591dbd7fe1d423496ad82a7006f39ce042bc2d2\",\n \"content_id\": \"2251ef83b3ca541c4a5fdbe79052cecb8cab2f64\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 601,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 11,\n \"path\": \"/机器学习/KMean聚类.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nfrom numpy import *\\nimport matplotlib.pyplot as plt\\nfrom sklearn.cluster import KMeans\\nnp.linalg.svd() #奇异值分解，返回u，s,vh,其中u:左奇异向量（正交单位矩阵）；s：SIGMA，对角线元素为奇异值，降序，且减小速度特别快，\\n# 从存储来说，前10%甚至1%的奇异值之和就占了全部奇异值之和的99%以上；vh，右奇异向量（正交单位矩阵） \\ndataset = mat()\\nkmeans = KMeans(init='k-means++', n_clusters=4) #创建一个KMean类的实例\\nkmeans.fit(dataset) #计算KMean聚类\\n#绘图，数据可视化\\npass\"\n },\n {\n \"alpha_fraction\": 0.6394707560539246,\n \"alphanum_fraction\": 0.6618890166282654,\n \"avg_line_length\": 34.350650787353516,\n \"blob_id\": \"b6159828cf990cdd948b98eebf4499dbc69ce19b\",\n \"content_id\": \"4536415b4c2da7d27023d84f5c0939e09a8c7bce\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4629,\n \"license_type\": \"no_license\",\n \"max_line_length\": 88,\n \"num_lines\": 77,\n \"path\": \"/helloworld/字符串.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#-------------------------------------字符串查找相关----------------------------------不改变原字符串\\n#计算字符串长度\\nname = ' abcdefg hijklmn ABCDEFG-HIJKLMN |'\\nlenth = len(name)\\nprint(lenth)\\n#查找字符串(从左向右) 找不到返回-1，找到返回下标\\nnum = name.find('cf',4,20)\\nprint(num)\\n#查找字符串(从右向左边)，同find\\nnum = name.rfind('cd')\\nprint(num)\\n#计算某个子字符串出现的个数\\nprint(name.count('abc',0,7))\\n#-------------------------------------字符串转换相关----------------------------------不改变原字符串\\n#用新字符串替换原旧字符串,replace不会更改原字符串\\n#替换个数count\\ncount = 1\\nprint(name.replace('b','B',count))\\nprint(name)\\n#首字母大写，不会改变原字符串\\nprint(name.capitalize())\\nprint(name)\\n#将字符串中每个单词的首字母变为大写，不改变原字符串，凡中间不是字母的，都看作分隔符，之后都看成单词，汉字没有大小写\\nprint(name.title())\\nprint(name)\\n#字符串字母全部变为大写、小写，不改变原字符串\\nprint(name.lower())\\nprint(name)\\nprint(name.upper())\\nprint(name)\\n#-------------------------------------字符串填充压缩相关----------------------------------不改变原字符串\\n#ljust:根据指定字符（1个），将原字符串填充够指定长度；l表示原字符串靠左;不改变原字符串；如果原串长度大于需求长度则不填充\\nprint(name.ljust(50,'*'))\\n# print(name)\\n#rjust:根据指定字符（1个），将原字符串填充够指定长度；r表示原字符串靠右;不改变原字符串；如果原串长度大于需求长度则不填充\\nprint(name.rjust(50,'*'))\\n# print(name)\\n#center:根据指定字符（1个），将原字符串填充够指定长度；center表示原字符串居中;不改变原字符串；如果原串长度大于需求长度则不填充\\nprint(name.center(50,'*'))\\n# print(name)\\n#lstrip：移除原字符串中指定字符集合,默认为空格；l表示仅仅移除左侧的(rstrip为移除右侧的):从左侧还检索，查到则移除，没查到就结束\\nname ='abcdeABCDE'\\nprint(name.lstrip('abAB'))\\nprint(name)\\n#-------------------------------------字符串分割拼接相关----------------------------------不改变原字符串\\n#split(sep,maxsplit):将一个大的字符串分割成几个子字符串，按照sep分割符分割，分割maxsplit次，返回分割后的子字符串列表\\nname = 'zyf-28-man'\\nprint(name.split('-',2))\\n#partition:根据指定的分割符，返回元组：（分割符左侧的内容，分割符，分割符右侧的内容）；如果没找到分割符，返回（原字符串，‘’，‘’）\\nprint(name.partition('-'))\\n#rpartition：表示从右侧开始检索\\nprint(name.rpartition('-'))\\n#splitlines(keepends):按照换行符（\\\\r,\\\\n）将原字符串拆成多个元素，保存到列表中；keepends表示是否保留换行符，bool类型,默认false\\n#'\\\\r' 回车，回到当前行的行首，而不会换到下一行，如果接着输出的话，本行以前的内容会被逐一覆盖\\nname = 'wo \\\\n you \\\\r ta'\\nprint(name.splitlines(True))\\n#join（iterable）：根据指定字符串，将给定的可迭代对象（可以使用for循环进行遍历的都是，如list、元组、字符串。。。），进行拼接，得到拼接后的字符串\\nit =['zyf','28','man','025-18115054967']\\nprint('-'.join(it))\\n#-------------------------------------字符串判断相关----------------------------------不改变原字符串\\n#isalpha（）：字符串中是否所有的字符全是字母，不包含数字、特殊符号、标点符号、换行空格等，且至少要有1个字符(空串返回false)，返回bool类型\\nname = 'zyf1'\\nprint(name.isalpha())\\n#isdigit():字符串中是否所有的字符全是数字，不包含字母、特殊符号、标点符号、换行空格等，且至少要有1个字符(空串返回false)，返回bool类型\\nname = '789'\\nprint(name.isdigit())\\n#isalnum():字符串中是否所有的字符全是字母或数字，不包含特殊符号、标点符号、换行空格等，且至少要有1个字符(空串返回false)，返回bool类型\\nname = 'abc123'\\nprint(name.isalnum())\\n#isspace():字符串中是否所有的字符全是空格（缩进、回车、换行等不可见转义字符）\\nname = '\\\\r '\\nprint(name.isspace())\\n#startswith(prefix,start = 0,end = len(str)):判断一个字符串是否以指定前缀开头\\nname = '2018-9-14:xxxx.ppt'\\nprint(name.startswith('2018-9-13'))\\n#endswith(prefix,start = 0,end = len(str)):判断一个字符串是否以指定后缀结尾\\nprint(name.endswith('.ppt'))\"\n },\n {\n \"alpha_fraction\": 0.45945945382118225,\n \"alphanum_fraction\": 0.5315315127372742,\n \"avg_line_length\": 21.399999618530273,\n \"blob_id\": \"8d1773461e10fd11e6e42bb7ae1de492c29411e3\",\n \"content_id\": \"fa329605e58d83608187bb3702038de57fda0cb5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 151,\n \"license_type\": \"no_license\",\n \"max_line_length\": 41,\n \"num_lines\": 5,\n \"path\": \"/函数/迭代器.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#迭代器：任何实现了__iter__()和__next__()方法的对象都是迭代器\\nl = [1, 2, 3, 4, 5, 6, 7, 8]\\nitr = iter(l)\\nfor i in itr:\\n print(i)\"\n },\n {\n \"alpha_fraction\": 0.5776223540306091,\n \"alphanum_fraction\": 0.6503496766090393,\n \"avg_line_length\": 38.72222137451172,\n \"blob_id\": \"b3d051868e5449d94d83c9eea685e3be11e6591d\",\n \"content_id\": \"7954901d0470920a2685ba63da85f6c4ff0d7ff8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 721,\n \"license_type\": \"no_license\",\n \"max_line_length\": 106,\n \"num_lines\": 18,\n \"path\": \"/机器学习/基于SVD推荐系统.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import svdRec\\nimport numpy as np\\nfrom svdRec import svdRec\\nfrom numpy import *\\n\\nA = np.array([[5, 5, 3, 0, 5, 5], [5, 0, 0, 0, 0, 0], [0, 3, 0, 5, 4, 5], [5, 4, 3, 3, 5, 5]]) #A的形状(6,4)\\nA = mat(A)\\nuserIdName = {1: 'user1', 2: 'user2', 3: 'user3', 4: 'user4'}\\nitemDic = {'item1': 'i1', 'item2': 'i2', 'item3': 'i3', 'item4': 'i4', 'item5': 'i5', 'item6': 'i6'}\\nsc = svdRec.svdRec()\\nsc.load_data_numpy(A)\\nsc.load_item_encoder(itemDic)\\nsc.load_user_encoder(userIdName)\\nsc.SVD(2)\\nrecommandItemId = sc.recommends_for_user(2, num_recom=2, show_similarity=True)\\n# recommandItemName = sc.get_item_name(recommandItemId[0])\\n# print(recommandItemId[0], type(recommandItemId[0]))\\nprint(recommandItemId, type(recommandItemId))\\n\"\n },\n {\n \"alpha_fraction\": 0.4970553517341614,\n \"alphanum_fraction\": 0.5088339447975159,\n \"avg_line_length\": 18.76744270324707,\n \"blob_id\": \"21174dc1b3af20a279322675ebe47bf0d9a18557\",\n \"content_id\": \"098621e81364598b8d98dd6346ff845a0755f8cd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 911,\n \"license_type\": \"no_license\",\n \"max_line_length\": 76,\n \"num_lines\": 43,\n \"path\": \"/面向对象/计算器案例.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Cal:\\n def check_num(func):\\n def inner(self, n):\\n if not isinstance(n, int):\\n raise TypeError('num type error,num should be a int object')\\n return func(self, n)\\n return inner\\n @check_num\\n def __init__(self, num):\\n self.__res = num\\n\\n @check_num\\n def jia(self,n):\\n self.__res += n\\n return self #返回对象本身可以实现多次点调用，这种方式叫链式编程\\n\\n @check_num\\n def jian(self,n):\\n self.__res -= n\\n return self\\n\\n @check_num\\n def cheng(self,n):\\n self.__res *= n\\n return self\\n\\n @check_num\\n def chu_yi(self,n):\\n self.__res /= n\\n return self\\n\\n def show(self):\\n print('计算结果是：%d'%self.__res)\\n\\n\\na = Cal(2)\\na.jia(3)\\na.jian(2)\\na.cheng(3)\\na.chu_yi(3)\\na.show()\\nb = Cal(2)\\nb.jia(3).jian(2).cheng(3).chu_yi(3).show()\"\n },\n {\n \"alpha_fraction\": 0.6815642714500427,\n \"alphanum_fraction\": 0.7402234673500061,\n \"avg_line_length\": 26.615385055541992,\n \"blob_id\": \"da7719b0c7045d8b315920264f0c0f31eed5f29b\",\n \"content_id\": \"5f5614ba43044c0f29ec841bc3bece8240ed0de9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 416,\n \"license_type\": \"no_license\",\n \"max_line_length\": 45,\n \"num_lines\": 13,\n \"path\": \"/helloworld/datetime模块.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#处理日期和时间的标准库,包含date对象和time对象\\nimport datetime\\nprint(datetime.datetime.now())\\ntoday = datetime.datetime.now()\\nprint(today.year)\\n#计算n天后日期\\nresult = today + datetime.timedelta(days= 7)\\nprint(today, result)\\n#计算时间差\\nfir = datetime.datetime(2018, 8, 16, 0, 9, 0)\\nsec = datetime.datetime(2018, 9, 15, 2, 3, 1)\\ndeltime = sec - fir\\nprint(deltime,deltime.total_seconds())\"\n },\n {\n \"alpha_fraction\": 0.5363724827766418,\n \"alphanum_fraction\": 0.6129971146583557,\n \"avg_line_length\": 34.55172348022461,\n \"blob_id\": \"2d17b1a5f8e77336992328a356c2393f53b636b2\",\n \"content_id\": \"20368fadd7ec100820d259fbdc86a86bd761fc0c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1613,\n \"license_type\": \"no_license\",\n \"max_line_length\": 150,\n \"num_lines\": 29,\n \"path\": \"/机器学习/各种距离.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#1、两个n维向量A（x11,x12,x13,,,,,,,,,,,,,x1n），B（x21,x22,x23,,,,,,,,,,,,,x2n）之间的闵可夫斯基距离为：\\n# sum = power(（x11 - x21）,p) + power(（x12 - x22）,p) + power(（x13 - x23）,p)+.......+ power(（x1n - x2n）,p)\\n#d12 = sqrt(sum, p),\\n# 当p=1时，为曼哈顿距离：差的绝对值之和: sum（abs（A - B））\\n# p=2时为欧式距离（坐标物理距离）:sqrt((A -B) * (A - B).T)\\n# p=∞时，为切比雪夫距离，即对应位置差的绝对值的最大值：abs(A - B).max()\\n\\n\\n#2、夹角余弦值：（A * B）/(|A| *|B|) dot(A, B)/(linalg.norm(A) * inalg.norm(B))\\n\\n\\n#3、汉明距离：两个等长字符串s1，s2之间的汉明距离为：将其中一个变换为另一个所需要的最小替换次数。\\nfrom numpy import *\\nimport numpy as np\\nmatA = np.array([[1, 1, 0, 1, 0, 1, 0, 0, 1],\\n [0, 1, 1, 0, 0, 0, 1, 1, 1]])\\nmatV = mat(matA)\\nsmStr = nonzero(matV[0] - matV[1]) #返回非0元素的索引，是一个形式（array([0, 0, 0, 0, 0, 0], dtype=int32), array([0, 2, 3, 5, 6, 7], dtype=int32)）的元组，第0个位行号，第二个位列号\\nidx1 = np.transpose(smStr) #转置之后就可以组合成具体在二维数组中的索引\\nprint(idx1)\\n\\n\\n#4、杰拉德相似系数：两个集合A、B的交集在A和B的并集中占的比例。是两个集合相似度的一种治标\\n#杰拉德距离：1- 杰拉德系数\\nimport scipy.spatial.distance as dist\\nprint(dist.pdist(matV, 'jaccard'))\\n# 算术平均：mean()\\n# 变量的协方差矩阵：cov()\\n#马氏距离：优点是量纲无关，排除了变量之间相关性的干扰\\n\"\n },\n {\n \"alpha_fraction\": 0.7238895297050476,\n \"alphanum_fraction\": 0.7599039673805237,\n \"avg_line_length\": 45.33333206176758,\n \"blob_id\": \"d9c0131d2067db2a07e07778146f97a9db69089a\",\n \"content_id\": \"a57b40564b04e940cb4ae0d8f5ca2c1920bb28fe\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1229,\n \"license_type\": \"no_license\",\n \"max_line_length\": 152,\n \"num_lines\": 18,\n \"path\": \"/机器学习/人脸检测.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#可以用几种特征来表示人脸的大致结构：眼睛、鼻子、前额、额骨和嘴，满足这些结构特征我们就认为这是一张人脸，为了提高运算速度，\\n#我们选择排除图片中不包含人脸的位置，称这个过程为级联过程。，但人脸的特征远比这几个复杂的多，OpenCV里常用的人脸特征有Haar特征和LBP特征。\\n#如果使用Haar特征，就需要使用Haar特征级联表作为训练集，在OpenCV根目录/sourse/data/ 文件夹下\\nfrom numpy import *\\nimport cv2\\nface_cascade = cv2.CascadeClassifier(r'C:\\\\Users\\\\Administrator\\\\Desktop\\\\opencv-3.4.1\\\\opencv-3.4.1\\\\data\\\\haarcascades\\\\haarcascade_frontalface_alt_tree.xml')\\nimg = cv2.imread('testPic.jpg')\\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #转换成gray，opencv使用灰度图像处理\\nfaces = face_cascade.detectMultiScale(gray, 1.1, 0)\\n#detectMultiScale(需要识别的图片, 尺寸缩小比例, 3表示至少被检测4次才算真的目标)\\n# print(faces, type(faces))\\nfor (x, y, w, h) in faces: #绘制人脸边框\\n cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)\\ncv2.imshow('img', img)\\ncv2.waitKey(0)\\ncv2.destroyAllWindows()\\ncv2.imwrite('testPic2.head.jpg', img)\\nprint('end')\"\n },\n {\n \"alpha_fraction\": 0.5486981868743896,\n \"alphanum_fraction\": 0.566055953502655,\n \"avg_line_length\": 26.289474487304688,\n \"blob_id\": \"63771eb8b11aa8e40f9aa253d112ede254120d47\",\n \"content_id\": \"acce245b6108f8d453e405d55c82e5807fd0e379\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1451,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 38,\n \"path\": \"/面向对象/字典或列表方式赋值修改实例对象.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class A:\\n def __init__(self):\\n self.l = [1, 2, 3, 4, 5, 6, 7]\\n\\n def __setitem__(self, key, value): #key为slice类对象的一个实例,当实例使用列表或字典方式赋值操作的时候自动调用该方法\\n self.l[key] = value\\n print(key.start)\\n print(key.stop)\\n print(key.step)\\n print(value)\\n\\n def __getitem__(self, item): #当实例对象采用列表或字典方式获取操作(遍历、访问)的时候自动调用该方法\\n return self.l[item.start: item.stop: item.step]\\n\\n def __iter__(self): #重构该方法，当在遍历实例对象的时候会调用，必须返回一个迭代器，且优先级高于实现__getitem__方法\\n return iter(self.l)\\n\\n def __next__(self): #与iter组合，必须设置终止条件\\n i = 0 #添加这一条语句可以实现迭代器的重复使用，正常情况下一次迭代完毕之后指向了末尾，多了这条语句指向了开头\\n if i < self.l.__len__():\\n return self.l[i]\\n else:\\n raise StopIteration('END!')\\n\\n def __delitem__(self, key):\\n del self.l[key]\\n\\n def __str__(self):\\n return str(self.l)\\n\\na = A()\\na[0: 4: 2] = ['a', 'b'] #执行到该条赋值语句时会自动调用__setitem__函数，key被赋值为[0: 4: 2]的slice实例，并完成相应的赋值操作\\n# print(a.l)\\nprint(a[0: 6: 1])\\ndel a[1]\\nprint(a)\\nfor i in a:\\n print(i)\\n\"\n },\n {\n \"alpha_fraction\": 0.397298127412796,\n \"alphanum_fraction\": 0.483573853969574,\n \"avg_line_length\": 21.625,\n \"blob_id\": \"cdc4ad2bf509cf7790d3c0a2fb02c765fbd2afb1\",\n \"content_id\": \"d2e08f3b315d317aa3f05cbbf2320eb0e694ba56\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3405,\n \"license_type\": \"no_license\",\n \"max_line_length\": 67,\n \"num_lines\": 144,\n \"path\": \"/venv/Lib/site-packages/comm_libs/string_utils.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# coding:utf8\\nimport re\\nimport types\\n\\n\\ndef num_to_capital_letter(num):\\n \\\"\\\"\\\" 将数字转为大写字母 wjl\\n\\n :param num:\\n :return:\\n \\\"\\\"\\\"\\n\\n return chr(num + 64)\\n\\n\\ndef letter_to_num(letter):\\n \\\"\\\"\\\"将字母转为数字，无论大小写 wjl\\n\\n :param letter:\\n :return:\\n \\\"\\\"\\\"\\n\\n if isinstance(letter, types.IntType):\\n return letter\\n\\n import logging\\n logging.info(letter)\\n\\n return ord(letter.upper()) - 64\\n\\n\\ndef convert_zh_to_spell(str_input):\\n \\\"\\\"\\\"\\n 取汉字字符串的首字母\\n :param str_input: 汉字字符串\\n :return: 首字母字符串，大写\\n \\\"\\\"\\\"\\n\\n if isinstance(str_input, unicode):\\n unicode_str = str_input\\n else:\\n try:\\n unicode_str = str_input.decode('utf8')\\n except:\\n unicode_str = str_input.decode('gbk')\\n\\n return_list = []\\n for one_unicode in unicode_str:\\n return_list.append(_convert_single_char_to_zh(one_unicode))\\n\\n return \\\"\\\".join(return_list).upper()\\n\\n\\ndef _convert_single_char_to_zh(zh_char):\\n \\\"\\\"\\\"\\n 将单个中文字符转为其拼音首字母\\n :param zh_char: 中文字符\\n :return: 拼音首字母\\n \\\"\\\"\\\"\\n str = zh_char.encode('gbk')\\n\\n try:\\n ord(str)\\n return str\\n except:\\n asc = ord(str[0]) * 256 + ord(str[1]) - 65536\\n\\n if -20319 <= asc <= -20284:\\n return 'a'\\n if -20283 <= asc <= -19776:\\n return 'b'\\n if -19775 <= asc <= -19219:\\n return 'c'\\n if -19218 <= asc <= -18711:\\n return 'd'\\n if -18710 <= asc <= -18527:\\n return 'e'\\n if -18526 <= asc <= -18240:\\n return 'f'\\n if -18239 <= asc <= -17923:\\n return 'g'\\n if -17922 <= asc <= -17418:\\n return 'h'\\n if -17417 <= asc <= -16475:\\n return 'j'\\n if -16474 <= asc <= -16213:\\n return 'k'\\n if -16212 <= asc <= -15641:\\n return 'l'\\n if -15640 <= asc <= -15166:\\n return 'm'\\n if -15165 <= asc <= -14923:\\n return 'n'\\n if -14922 <= asc <= -14915:\\n return 'o'\\n if -14914 <= asc <= -14631:\\n return 'p'\\n if -14630 <= asc <= -14150:\\n return 'q'\\n if -14149 <= asc <= -14091:\\n return 'r'\\n if -14090 <= asc <= -13119:\\n return 's'\\n if -13118 <= asc <= -12839:\\n return 't'\\n if -12838 <= asc <= -12557:\\n return 'w'\\n if -12556 <= asc <= -11848:\\n return 'x'\\n if -11847 <= asc <= -11056:\\n return 'y'\\n if -11055 <= asc <= -10247:\\n return 'z'\\n return ''\\n\\ndef string_to_xing(content, frontLen, endLen):\\n length = len(content) - frontLen - endLen\\n if length < 0:\\n return None\\n xing = ''\\n for i in range(0, length):\\n xing = xing + '*'\\n a = content[0: frontLen]\\n b = content[len(content) - endLen:len(content)]\\n return a + xing + b\\n\\ndef isEmoji(comment):\\n \\\"\\\"\\\"\\n 替换表情\\n :param comment:\\n :return:\\n \\\"\\\"\\\"\\n try:\\n # UCS-4\\n highpoints = re.compile(u'[\\\\U00010000-\\\\U0010ffff]')\\n except re.error:\\n # UCS-2\\n highpoints = re.compile(u'[\\\\uD800-\\\\uDBFF][\\\\uDC00-\\\\uDFFF]')\\n mytext = highpoints.sub('', comment)\\n return mytext\\n\\n\\n# if __name__ == '__main__':\\n# print string_to_xing('15311432059', 3, 0)\"\n },\n {\n \"alpha_fraction\": 0.6419413685798645,\n \"alphanum_fraction\": 0.6703296899795532,\n \"avg_line_length\": 35.41666793823242,\n \"blob_id\": \"8023e520b4192da866cf56d53df2cd3b9c03b6b0\",\n \"content_id\": \"7bf291d0815df79847ca291eff2e95cee10730c0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2704,\n \"license_type\": \"no_license\",\n \"max_line_length\": 123,\n \"num_lines\": 60,\n \"path\": \"/机器学习/knn分类算法.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#1、朴素贝叶斯算法：P(A)*P(B|A) = P(B)* P(A|B)\\n#2、如果一个样本在特征空间中的k个最邻近（最相似）的样本大多数都属于某一个类别，则该样本也属于这个类别\\n#knn算法分类，并且距离计算采用的是夹角余弦值。\\nimport numpy as np\\nfrom numpy import *\\nimport operator\\n# import sys\\n# path = 'D:\\\\PythonDemo\\\\机器学习'\\n# sys.path.append(path)\\n# from .knn_cos import cosdist\\n# from .knn_cos import clssify\\nnp.seterr(invalid='ignore') #存在运行时警告，这里设置忽略\\ndef creat_data_set():\\n group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]])\\n lables = ['A', 'A', 'B', 'B']\\n return group, lables\\n\\n\\ndef cosdist(vector1, vector2):\\n return dot(vector1, vector2)/(linalg.norm(vector1) * linalg.norm(vector2))\\n\\n\\ndef clssify(testDate, trainSet, listClasses, k): #listClasses:类别标签\\n dataSetSize = trainSet.shape[0] #返回样本集的行数\\n distances = array(zeros(dataSetSize))\\n for index in range(dataSetSize):\\n distances[index] = cosdist(testDate, trainSet[index])\\n sortedDistIndicies = argsort(-distances) #返回对数据排序的索引，这里是-distances表示从余弦值从大到小排列，越大说明夹角越小\\n classCount = {}\\n for i in range(k): #获取角度最小的前k项作为参考项\\n voteIlabel = listClasses[sortedDistIndicies[i]] #按排序顺序返回样本集对应的类别标签\\n classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1 #为字典clssCount赋值（标签，标签出现次数），如果key相同，则value +1，如果不存在则赋值为0\\n #get(key， default) 函数返回指定键的值，如果值不在字典中返回默认值\\n sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)\\n #sorted(可迭代对象，key=operator.itemgetter(1),reverse = True)是sorted的固定用法\\n return sortedClassCount[0][0]\\n\\n\\n#画图\\ndataSet, labels = creat_data_set()\\n# print(dataSet, labels)\\nimport matplotlib.pyplot as plt\\nfig = plt.figure()\\nax = fig.add_subplot(111)\\nindex = 0\\nfor point in dataSet:\\n if labels[index] == 'A':\\n ax.scatter(point[0], point[1], c='blue', marker='o', linewidths=0, s=300)\\n plt.annotate('A', xy=(point[0], point[1]))\\n # print(point)\\n else:\\n ax.scatter(point[0], point[1], c='red', marker='^', linewidths=0, s=300)\\n plt.annotate('B', xy=(point[0], point[1]))\\n index += 1\\n# plt.show()\\n#测试一个点属于哪一个类，绘图\\ntestData = [0.2, 0.2]\\nax.scatter(testData[0], testData[1], c='green', marker='*', linewidths=0, s=300)\\nplt.show()\\nprint(clssify(testData, dataSet, labels, 3))\"\n },\n {\n \"alpha_fraction\": 0.6054053902626038,\n \"alphanum_fraction\": 0.6926640868186951,\n \"avg_line_length\": 38.272727966308594,\n \"blob_id\": \"f390f9d67ccf3721908664936b7dcc4340e6a593\",\n \"content_id\": \"a56841f2c94f98575dcf1da067ef5c1ead6174d1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1591,\n \"license_type\": \"no_license\",\n \"max_line_length\": 111,\n \"num_lines\": 33,\n \"path\": \"/机器学习/OpenCV.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import cv2\\n#1、从窗口显示一副图片\\n# cv2.namedWindow('winName', cv2.WINDOW_NORMAL) #参数为：窗口名称，可以手动调节窗口大小\\n# img = cv2.imread('myPIC.jpg', 1) #1：原色图片；0：黑白图片\\n# cv2.imshow('winName', img) #显示图片\\n# cv2.waitKey(0)\\n# cv2.destroyAllWindows() #销毁创建的对象\\n# #2、保存图片：OpenCV可将图片转换为PGM格式\\n# cv2.imwrite('pic2PGM.pgm', img)\\n# #3、在matplotlib中显示图片\\n# from matplotlib import pyplot as plt\\n# img = cv2.imread('pic2PGM.pgm', 0)\\n# plt.imshow(img, cmap='gray', interpolation='bicubic')\\n# plt.xticks([]), plt.yticks([])\\n# plt.show()\\n#4、绘制直线和矩形\\nfrom numpy import *\\nimg = zeros((512, 512, 3))\\n#起点：（0,0），终点：（511,511），颜色：（0,255,255），像素：2\\ncv2.line(img, (0, 0), (511, 511), (0, 255, 255), 2)\\n#左上角：（150,150）；右下角：（350,350）；颜色：（255,255,0）BGR；像素：2\\ncv2.rectangle(img, (150, 150), (350, 510), (255, 255, 0), 2)\\ncv2.imshow('image', img)\\ncv2.waitKey(0)\\ncv2.destroyAllWindows()\\n#5、绘制圆形和椭圆形：\\n# cv2.circle(img, center, radius, color, thickness=None, lineType=None, shift=None)\\n# cv2.ellipse(img, center, axes, angle, startAngle, endAngle, color, thickness=None, lineType=None, shift=None)\\n#6、绘制多边形\\n# cv2.polylines(img, pts, isClosed, color, thickness=None, lineType=None, shift=None)\\n#7、写入文字\\n# font = cv2.FONT_HERSHEY_COMPLEX #字体\\n# cv2.putText(img, text, org, fontFace, fontScale, color, thickness=None, lineType=None, bottomLeftOrigin=None)\"\n },\n {\n \"alpha_fraction\": 0.6225165724754333,\n \"alphanum_fraction\": 0.6401765942573547,\n \"avg_line_length\": 27.375,\n \"blob_id\": \"0b657f0656707260abbf980c1388b6e40a89ef25\",\n \"content_id\": \"8c8fe006f42bf8153a4a3de09da9a54e8b51c172\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 725,\n \"license_type\": \"no_license\",\n \"max_line_length\": 73,\n \"num_lines\": 16,\n \"path\": \"/面向对象/属性方法相关.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Person(object): #严格来说，使用属性装饰器应该继承object类，python 3.x版本默认继承object类\\n def __init__(self):\\n self.__age = 18 #构造函数在创建实例对象的时候就会自动调用,创建私有的age属性\\n\\n @property #属性装饰器能够实现像读取属性一样使用方法\\n def age(self):\\n return self.__age #公开私有age属性的获取接口，可以实现私有属性的只读操作\\n\\n @age.setter #新式类中可以关联，经典类（没有继承object类）不可以\\n def age(self, value):\\n self.__age = value\\n\\n\\np = Person()\\n#p.age = 20 #没有9-11行的情况下，只读属性赋值会报错\\nprint(p.age)\"\n },\n {\n \"alpha_fraction\": 0.4365079402923584,\n \"alphanum_fraction\": 0.5396825671195984,\n \"avg_line_length\": 24.399999618530273,\n \"blob_id\": \"eed69ca4772c0faa4cb1bd95f4f29bab98946adb\",\n \"content_id\": \"948d2b4e35b573484aa218896792d0a89f061df5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 206,\n \"license_type\": \"no_license\",\n \"max_line_length\": 43,\n \"num_lines\": 5,\n \"path\": \"/helloworld/列表的判定、比较.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#判定： 元素 in 列表\\n# 元素 not in 列表\\nnum = [1, 2, 56, 6, 7, 9, 10]\\nprint(7 in num)\\n#比较： >,< ,==等:从左到右依次比较，前面大返回1；相等返回0；后面大返回-1\"\n },\n {\n \"alpha_fraction\": 0.6651982665061951,\n \"alphanum_fraction\": 0.691629946231842,\n \"avg_line_length\": 49.44444274902344,\n \"blob_id\": \"e4febd6abc94756bd82dcb33f8607a9d121c20d3\",\n \"content_id\": \"dd55bf0122012ed9b1e027d92cda598a0ccfe960\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 630,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 9,\n \"path\": \"/机器学习/jieba分词.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import jieba\\nfrom sklearn.datasets.base import Bunch\\nseg_list = jieba.cut('小明1995年毕业于北京清华大学', cut_all=False) #采用精准模式分词\\nprint(type(seg_list)) #jieba.cut()得到的是一个生成器对象\\nprint('//'.join(seg_list)) #Example: '.'.join(['ab', 'pq', 'rs']) -> 'ab.pq.rs'\\nseg_list1 = jieba.cut('小明1995年毕业于北京清华大学', cut_all=True) #采用整体模式分词\\nprint('//'.join(seg_list1))\\nseg_list2 = jieba.cut_for_search('小明硕士毕业于中国科学院计算机所，后再日本京都大学深造') #采用搜索引擎模式分词\\nprint('//'.join(seg_list2))\\n\"\n },\n {\n \"alpha_fraction\": 0.4680851101875305,\n \"alphanum_fraction\": 0.4893617033958435,\n \"avg_line_length\": 17.875,\n \"blob_id\": \"a7301415febe117c45f9f66961276367bb2ec95a\",\n \"content_id\": \"64b801460cc59c8fabf33626af55f8a294d569ba\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 629,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 32,\n \"path\": \"/venv/Lib/site-packages/comm_libs/exception.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python\\n# coding:utf8\\n\\n\\\"\\\"\\\"\\n异常类定义\\ncreated by : xiaochun\\ndate : 2015-05-20\\n\\\"\\\"\\\"\\n\\nclass HzfException(Exception):\\n \\\"\\\"\\\"\\n hzf异常基类\\n \\\"\\\"\\\"\\n\\n def __init__(self, err_code, err_msg):\\n '''\\n err = (error_code, error_msg)\\n '''\\n super(HzfException, self).__init__(err_code, err_msg)\\n self.__err = (err_code, err_msg)\\n \\n def msg(self):\\n return self.__err[1]\\n \\n def code(self):\\n return self.__err[0]\\n \\n def __str__(self):\\n return '(%s %s)' %(self.__err[0], self.__err[1])\\n \\n def __tuple__(self):\\n return self.__err\\n\\n \\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6123347878456116,\n \"alphanum_fraction\": 0.6607929468154907,\n \"avg_line_length\": 22.66666603088379,\n \"blob_id\": \"75987450c6de906f2fa80044aa642b3d9ca0673a\",\n \"content_id\": \"e148a00b789ea7f100a41a6403b2868b44ce0dde\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1639,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 48,\n \"path\": \"/机器学习/矩阵基础操作.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nfrom numpy import *\\nmyZero = np.zeros((3, 5)) #生产3行5列的0矩阵\\nprint(myZero)\\nmyZero.A\\nprint('*' * 30)\\nmyOne = np.ones((5, 3)) #生产5行3列的元素全为1矩阵\\nprint(myOne)\\nprint('*' * 30)\\nmyEye = np.eye(4) #单位矩阵\\nprint(myEye)\\nprint('*' * 30)\\nmyRand = np.random.rand(3, 4)\\nprint(myRand) #生成3行4列的0~1之间的随机数矩阵\\nprint('*' * 30)\\n#两个矩阵的‘+’、‘-’运算为矩阵各个元素的加减\\nmyRand1 = np.random.rand(3, 4)\\nprint(myRand1)\\nmyRand2 = np.random.rand(3, 4)\\nprint(myRand2)\\nprint('*' * 30)\\nprint(myRand2 + myRand1)\\n#sum（矩阵M）：矩阵所有元素和\\n#num * M:一个数乘以矩阵M\\n#multiply（M1, M2）：表示矩阵对应元素的相乘，两个矩阵必须行列数都相同\\n#矩阵的n次幂：power（M，n）\\n#矩阵的乘法：M1 * M2\\n#矩阵的转置：M.T 或者M.transpose()\\n#M[idx] :行切片\\n#M.T[idx] ：列切片\\n#M1 < M2 ：对应元素的比较大小\\n(m, n) = myRand1.shape #矩阵的行、列数\\nprint(m, n)\\nprint('*' * 30)\\n#矩阵的行列式：linalg.det(M) M为方阵 (矩阵A的行列式detA就是线性变换A下的图形面积或体积的伸缩因子)\\n#矩阵的秩（非0子式的最高阶数）：linalg.matrix_rank(M)\\nprint(linalg.det(myEye))\\nprint(linalg.matrix_rank(myRand1))\\n#矩阵的对称：M * M.T\\nprint('*' * 30)\\nm = np.matrix('[1, 2; 3, 4]')\\n#逆矩阵： .I 或者M.getI（）\\nprint(m.I)\\nprint(m.getI())\\n#向量范数：向量到原点的距离：linalg.norm(M)\\nprint(linalg.norm(myEye))\\n# 逆矩阵：linalg.inv(M)\\n# diag(v, k=0) 构造对角阵，k表示对角线与主对角线的关系\"\n },\n {\n \"alpha_fraction\": 0.6595744490623474,\n \"alphanum_fraction\": 0.682624101638794,\n \"avg_line_length\": 30.33333396911621,\n \"blob_id\": \"3d494a2f90f59d48a7b2207ce1602e231e95742f\",\n \"content_id\": \"8555e3135545e87c69e6429f736a168af0ef616b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 948,\n \"license_type\": \"no_license\",\n \"max_line_length\": 112,\n \"num_lines\": 18,\n \"path\": \"/函数/菲波那切数列生成器实现.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# 简单地讲，yield 的作用就是把一个函数变成一个 generator，带有 yield 的函数不再是一个普通函数，Python 解释器会将其视为一个 generator，调用 fab(50) 不会执行 fab 函数，\\n# 而是返回一个 iterable 对象！在 for 循环执行时，每次循环都会执行 fab 函数内部的代码，执行到 yield b 时，fab 函数就返回一个迭代值，下次迭代时，代码从 yield b 的下一条语句继续执行，\\n# 而函数的本地变量看起来和上次中断执行前是完全一样的，于是函数继续执行，直到再次遇到 yield。\\n# 也可以手动调用 fab(50) 的 next() 方法（因为 fab(50) 是一个 generator 对象，该对象具有 next() 方法）\\n\\n\\ndef fibinacci(num):\\n pre = 0\\n cur = 1\\n count = 1\\n while count < num:\\n yield cur\\n pre, cur = cur, pre + cur\\n count += 1\\n\\n\\nfor i in fibinacci(50):\\n print(i, type(fibinacci(5)))\\n\"\n },\n {\n \"alpha_fraction\": 0.4217686951160431,\n \"alphanum_fraction\": 0.44897958636283875,\n \"avg_line_length\": 12.272727012634277,\n \"blob_id\": \"16f77012fabd846a3138474188e45e4cd1b033f8\",\n \"content_id\": \"3efb8c200510ee8f251c223586d2a382de16e62f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 147,\n \"license_type\": \"no_license\",\n \"max_line_length\": 29,\n \"num_lines\": 11,\n \"path\": \"/函数/main.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def func1():\\n print('__name__ == main')\\n\\n\\ndef func2():\\n print('__name__ != main')\\n\\nif __name__ == '__main__':\\n func1()\\nelse:\\n func2()\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.49242424964904785,\n \"alphanum_fraction\": 0.5075757503509521,\n \"avg_line_length\": 9.230769157409668,\n \"blob_id\": \"4006518cd92d39281e4c7ba4ff6d71914b9c6fbe\",\n \"content_id\": \"867568f2ab7f2187fae95f291e1675775129e889\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 132,\n \"license_type\": \"no_license\",\n \"max_line_length\": 21,\n \"num_lines\": 13,\n \"path\": \"/函数/高阶函数demo.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def do(func, a, b):\\n return func(a, b)\\n\\n\\ndef add(a, b):\\n return a + b\\n\\n\\ndef sub(a, b):\\n return a - b\\n\\n\\nprint(do(sub, 5, 7))\"\n },\n {\n \"alpha_fraction\": 0.6710526347160339,\n \"alphanum_fraction\": 0.8026315569877625,\n \"avg_line_length\": 24.66666603088379,\n \"blob_id\": \"565687220e40693d639f383915ca969b42b784ae\",\n \"content_id\": \"83c8db5195b5aba407c09f693eb073a00ea0fa06\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 90,\n \"license_type\": \"no_license\",\n \"max_line_length\": 30,\n \"num_lines\": 3,\n \"path\": \"/helloworld/calendar模块.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import calendar\\n#获取某月日历：calendar.month(2018,8)\\nprint(calendar.month(2018,8))\"\n },\n {\n \"alpha_fraction\": 0.6058632135391235,\n \"alphanum_fraction\": 0.6156351566314697,\n \"avg_line_length\": 13.523809432983398,\n \"blob_id\": \"7a9782865a6fe630fc851653e1bc8c221b89619d\",\n \"content_id\": \"89df3603d163b6140eb34cf195cb4449bffb5b76\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 935,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 63,\n \"path\": \"/venv/Lib/site-packages/comm_libs/log_tool.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python\\n# coding:utf8\\n\\n\\\"\\\"\\\"\\n日志工具类函数\\n\\ncreate by: xiaochun\\ndate : 2015-05-20\\n\\\"\\\"\\\"\\n\\nimport logging\\nimport logging.config \\n\\nimport os\\n\\nBASE_DIR = os.path.dirname(os.path.dirname(__file__))\\n\\nlogging.config.fileConfig(BASE_DIR+\\\"/confs/logging.conf\\\")\\n\\napplog = logging.getLogger('applog')\\naccesslog = logging.getLogger('accesslog')\\n\\n\\ndef write_debug_log(content):\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n applog.debug(content)\\n\\n\\ndef write_info_log(content):\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n applog.info(content)\\n \\n \\ndef write_warn_log(content):\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n applog.warn(content)\\n\\ndef write_error_log(content):\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n applog.error(content)\\n\\ndef write_access_log(content):\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n accesslog.info(content)\\n \\n\\n\\nif __name__ == \\\"__main__\\\":\\n\\n write_debug_log(\\\"debug_test\\\")\\n\\n write_info_log(\\\"info_test\\\")\\n\\n write_warn_log(\\\"warn_test\\\")\\n\\n write_error_log(\\\"error_test\\\")\\n\\n write_access_log(\\\"accesslog_test\\\")\\n\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.5984455943107605,\n \"alphanum_fraction\": 0.5984455943107605,\n \"avg_line_length\": 21.764705657958984,\n \"blob_id\": \"4119903cd8baae755ee5deffbfcdcd55554dac98\",\n \"content_id\": \"e258fdf66db279847a22a98e3a804da6cafdd69f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 426,\n \"license_type\": \"no_license\",\n \"max_line_length\": 44,\n \"num_lines\": 17,\n \"path\": \"/文件操作/py/列表清单.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#生成.txt格式的文件清单\\n#遍历打印函数：\\nimport os\\npath = 'D:\\\\PythonDemo'\\ndef fileNamePrint(dir):\\n fileLst = os.listdir(dir)\\n for fileName in fileLst:\\n new_file_name = dir + '/' + fileName\\n if os.path.isdir(new_file_name):\\n print(new_file_name)\\n fileNamePrint(new_file_name)\\n else:\\n print('\\\\t' +fileName)\\n\\n\\nos.chdir(path)\\nfileNamePrint('文件操作')\"\n },\n {\n \"alpha_fraction\": 0.5518072247505188,\n \"alphanum_fraction\": 0.5686746835708618,\n \"avg_line_length\": 18.809524536132812,\n \"blob_id\": \"0616467465c571f0f4f6434cf94c4ccdb950e28a\",\n \"content_id\": \"50817aafeafd52a206acf5fa742f23849a280686\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 477,\n \"license_type\": \"no_license\",\n \"max_line_length\": 93,\n \"num_lines\": 21,\n \"path\": \"/面向对象/对象的生命周期案例.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Person:\\n __count = 0\\n\\n def __init__(self):\\n print('new one')\\n Person.__count += 1 #注意：此处千万不能写成self.__count += 1，因为self为实例对象，那样会给实例对象的__count 赋值\\n\\n def __del__(self):\\n print('delete one')\\n Person.__count -= 1\\n\\n @classmethod\\n def num_count_print(cls):\\n print(cls.__count)\\n\\n\\np1 = Person()\\np2 = Person()\\nPerson.num_count_print()\\ndel p1\\nPerson.num_count_print()\"\n },\n {\n \"alpha_fraction\": 0.6934046149253845,\n \"alphanum_fraction\": 0.7023172974586487,\n \"avg_line_length\": 33.367347717285156,\n \"blob_id\": \"333677dbe25b70e2b3ab09778778289f009ccefa\",\n \"content_id\": \"f28ca5d1403981cbab442ad7f51906fd01e057a8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3239,\n \"license_type\": \"no_license\",\n \"max_line_length\": 95,\n \"num_lines\": 49,\n \"path\": \"/面向对象/class.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#类的创建遵循5大原则：\\n#1、单一原则：1个类只负责一个功能\\n#2、开放关闭原则：对扩展开放、对修改关闭\\n#3、里氏替换原则：使用基类引用的地方必须能使用继承类的对象（任何时候都可以用子类对象替换父类对象）\\n#4、接口分离原则：如果一个类包含过多的接口（简单理解为抽象方法）方法，而这些方法在使用的时候并非“不可分割”，那么应该把他们进行分离\\n#5、依赖倒置原则：高层模块不应该直接依赖低层模块，应该依赖抽象类或接口（如：电脑类不应该依赖鼠标类，而应该是鼠标类的一个抽象（能单击、双击、右击、移动指针），到时可以是无线鼠标、触摸板等）\\n\\n#类的实例对象通过__class__属性而找的相应的类\\n#类的创建:type(类型，继承的类元组，类的属性和方法字典)\\ndef run(self):\\n \\\"\\\"\\\"\\n\\n :param self:\\n :return:\\n \\\"\\\"\\\"\\n print(self)\\n\\n\\nxxx = type('Person', (), {'name': 'zyf', 'high': 180, 'weight': 63, 'run': run}) #创建一个类\\nperson = xxx() #将创建的类实例化\\nclass Money:\\n \\\"\\\"\\\"\\n 类相关注释\\n \\\"\\\"\\\"\\n # __slots__ = ['mianZhi', 'color'] #限制改类产生的对象添加的属性，也就是说Money类实例化的对象只能添加mianZhi、color两个属性\\n moneyType = 'dollar' #类属性（公有），可以通过类访问和实例访问\\n _a = '这是一个受保护的属性，访问会出警告'\\n __b = '这是私有属性，只能类内部访问' #python无法实现真正私有，是通过名字重改机制实现的，如__x重改为：_class__x,不能版本python重改机制可能不一样\\n def shi_li_method(self):\\n print('这是一个实例方法', self) #self是实例的地址，实例方法的第一个参数必须是实例\\n @classmethod #通过装饰器实现自动传递第一个参数\\n def class_method(cls):\\n print('这是一个类方法', cls) #cls为类名，类方法的第一个参数必须是类；类方法只能访问类属性\\n @staticmethod\\n def stat_method():\\n print('这是一个静态方法') #第一个参数既不需要传递实例，也不需要传递类；不能直接访问对象属性和类属性\\n #以上三种方法和类属性一样，都存储在类的__dict__属性字典里\\nprint(Money.__name__) #类名\\nmoney = Money() #创建一个Money对象\\nmoney.mianZhi = 100 #创建一个对象属性：mainZhi，只能通过实例访问，不能通过类访问\\nmoney.color = 'red'\\nprint(money.__dict__) #dict函数访问所有的实例属性字典。\\nmoney.shi_li_method() #实例方法的标准调用方法就是通过实例对象调用\\nmoney.class_method() #通过实例也可以调用类方法，实例会被忽略，但实例对应的类会作为第一个参数传递过去，如果是子类调用则会把子类作为第一个参数传递\\nmoney.stat_method()\\n#通过实例可以调用所有存储在类中方法，而通过类名则不能调用实例方法\\n# Money.shi_li_method() #通过类调用实例方法会报错，因为没有传第一个参数，确实第一个实例参数self\\nMoney.class_method()\\nMoney.stat_method()\"\n },\n {\n \"alpha_fraction\": 0.39579832553863525,\n \"alphanum_fraction\": 0.4252100884914398,\n \"avg_line_length\": 33.02857208251953,\n \"blob_id\": \"efc8b3ad298a344463bf0fabbcdf65921de8756c\",\n \"content_id\": \"ba7db0b1844c8f651e757760bd354c4efc7d5ec9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1470,\n \"license_type\": \"no_license\",\n \"max_line_length\": 97,\n \"num_lines\": 35,\n \"path\": \"/helloworld/列表的查询.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#--------------------------------获取列表单个元素------------------------------------------------------\\nnum = range(1,10,2)\\nprint(num[3])\\n#--------------------------------获取列表某个元素的索引--------------------------------------------\\n#.index(obj,start=0,stop=lst.len()),如果有多个则返回区间内第一个\\nprint(num.index(7))\\n\\n#--------------------------------获取列表某个元素个数------------------------------------------------------\\n#.count(obj),返回元素obj在列表中的个数\\nnum = [1, 2, 2, 3, 4, 2, 4, 5]\\nprint(num.count(4))\\n#--------------------------------获取列表多个元素------------------------------------------------------\\n# itms[start : end : step ]\\nprint(num[2 : 5 : 1])\\n#列表反转\\nprint(num[::-1])\\n#--------------------------------列表的遍历------------------------------------------------------\\n#根据元素进行遍历，并同时打印当前元素对应的索引值\\ncurrentIdx = 0\\nfor v in num:\\n print(v,num.index(v,currentIdx))\\n currentIdx += 1\\n#根据索引遍历\\nnum = [1, 2, 2, 3, 4, 2, 4, 5]\\nfor idx in range(len(num)):\\n print(num[idx],end=\\\"\\\\n\\\")\\n#创建枚举对象遍历 (key,value)\\n#enumerate(itrObj,[start=0]):itrObj为一个序列、迭代器或其他支持迭代的对象\\na = enumerate(num)\\n# print(list(a))\\nfor i in a:\\n idx,val = i\\n print(idx,val)\\n# a1,a2=(a,b)执行之后a1==a;a2==b\\n#判断是否可迭代：import collections isinstance(object, classinfo)\"\n },\n {\n \"alpha_fraction\": 0.6363636255264282,\n \"alphanum_fraction\": 0.6363636255264282,\n \"avg_line_length\": 10,\n \"blob_id\": \"8579157550f2d26d7139ed5e975a6e6df2437c9f\",\n \"content_id\": \"999cd5a14cd2b607e6e3e172308da1d609bbb67d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 33,\n \"license_type\": \"no_license\",\n \"max_line_length\": 14,\n \"num_lines\": 3,\n \"path\": \"/helloworld/b.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import a\\na.run()\\nprint(\\\"World\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.5279052257537842,\n \"alphanum_fraction\": 0.5504587292671204,\n \"avg_line_length\": 21.747825622558594,\n \"blob_id\": \"676967a7f9939565082bc4f6f1285703eac11fa1\",\n \"content_id\": \"940575f6ca4006a10f840ecb12f1b915c2486f31\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2692,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 115,\n \"path\": \"/venv/Lib/site-packages/comm_libs/comm_utils.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# coding:utf-8\\nfrom time import gmtime\\n\\nfrom libs import sendmail\\nfrom libs.log_tool import write_error_log\\nfrom project.confs import MAIL_SERVER, SENDER_USER, SENDER_PASSWORD\\n\\ntry:\\n from huizhaofang import settings\\nexcept:\\n from project import settings\\n\\n\\ndef is_test_env():\\n try:\\n return settings.TESTENV\\n except:\\n return False\\n\\n\\ndef check_user_id_no(id_str):\\n \\\"\\\"\\\"\\n copy from xiaochun's script\\n 检查身份证号是否合法\\n \\\"\\\"\\\"\\n try:\\n weight_num = [7, 9, 10, 5, 8, 4, 2, 1, 6, 3, 7, 9, 10, 5, 8, 4, 2]\\n tail_char = ['1', '0', 'X', '9', '8', '7', '6', '5', '4', '3', '2']\\n\\n id_len = len(id_str)\\n\\n if id_len != 15 and id_len != 18:\\n return False\\n\\n sum_num = 0\\n for p in zip([int(i) for i in id_str[:-1]], weight_num):\\n sum_num += p[0] * p[1]\\n\\n if tail_char[sum_num % 11] != id_str[-1]:\\n return False\\n except Exception, e:\\n return False\\n return True\\n\\n\\ndef get_age(y, m, d):\\n \\\"\\\"\\\"\\n 根据时间得到年龄\\n :param y:\\n :param m:\\n :param d:\\n :return:\\n \\\"\\\"\\\"\\n # get the current time in tuple format\\n a = gmtime()\\n # difference in day\\n dd = a[2] - d\\n # difference in month\\n dm = a[1] - m\\n # difference in year\\n dy = a[0] - y\\n # checks if difference in day is negative\\n if dd < 0:\\n dd = dd + 30\\n dm = dm - 1\\n # checks if difference in month is negative when difference in day is also negative\\n if dm < 0:\\n dm = dm + 12\\n dy = dy - 1\\n # checks if difference in month is negative when difference in day is positive\\n if dm < 0:\\n dm = dm + 12\\n dy = dy - 1\\n return (dy, dm, dd)\\n\\n\\ndef get_ip_address(request):\\n \\\"\\\"\\\"\\n 获取ip地址\\n :param request:\\n :return:\\n \\\"\\\"\\\"\\n ip = request.META.get(\\\"HTTP_X_FORWARDED_FOR\\\", \\\"\\\")\\n if not ip:\\n ip = request.META.get('REMOTE_ADDR', \\\"\\\")\\n return ip\\n\\n\\ndef get_http_referer(request):\\n \\\"\\\"\\\"\\n 获取referer\\n :param request:\\n :return:\\n \\\"\\\"\\\"\\n return request.META.get(\\\"HTTP_REFERER\\\", \\\"\\\")\\n\\n\\ndef send_mail_notify(receiver_list, title, content):\\n \\\"\\\"\\\"\\n 发送通知邮件\\n :param receiver_list:\\n :param title:\\n :param content:\\n :return:\\n \\\"\\\"\\\"\\n title = \\\"【CRM】\\\" + title\\n try:\\n mail_client = sendmail.MailSender(MAIL_SERVER, SENDER_USER, SENDER_PASSWORD, 25)\\n mail_client.set_subject(u\\\"%s\\\" % title)\\n mail_client.add_content(content, \\\"utf8\\\")\\n mail_client.set_sender(SENDER_USER)\\n mail_client.set_receiver(receiver_list)\\n mail_client.sendmail()\\n except:\\n write_error_log(\\\"%s邮件发送失败\\\" % title)\\n\"\n },\n {\n \"alpha_fraction\": 0.5951219797134399,\n \"alphanum_fraction\": 0.642926812171936,\n \"avg_line_length\": 26,\n \"blob_id\": \"7e44c6d2a97c67aabe4c19065e89fda26a519c3f\",\n \"content_id\": \"0395af83181dc4cfda7dadb693db15bae6c88b36\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1389,\n \"license_type\": \"no_license\",\n \"max_line_length\": 119,\n \"num_lines\": 38,\n \"path\": \"/helloworld/体脂率计算.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#输入身高、体重、年龄、性别\\npersonHight = float(input('请输入身高（m）'))\\npersonWight = float(input('请输入体重（kg）'))\\npersonAge = int(input('请输入年龄'))\\npersonSex = int(input('请输入性别(男：1 女：0)'))\\n#验证数据有效性,取非操作可以简化改代码量\\nif not (0 < personHight < 3 and 0 < personWight < 300 and (personSex == 0 or personSex == 1) and 0 < personAge < 150) :\\n print('Input Error,the program will be eited')\\n exit()\\n#计算体脂率\\nBMI = personWight / (personHight * personHight)\\nTZL = 1.2 * BMI + 0.23 * personAge - 5.4 -18.8 * personSex\\n#TZL = TZL * 100\\n\\n#判断体脂率是否在正常范围之内\\n# minNum = 0.15 + 0.10 * (1 - personSex)\\n# maxNum = 0.18 + 0.10 * (1 - personSex)\\n# result = minNum < TZL < maxNum\\n\\n#告诉用户是否在正常范围内\\n#print('您的体脂率是:%f%%,是否在正常范围内？'%(TZL) ,result)\\n#判断性别,不同性别赋予不同的数值\\nif personSex :\\n wenhao = '先生您好，'\\n minNum = 15\\n maxNum = 25\\nelse:\\n wenhao = '女生您好，'\\n minNum = 18\\n maxNum = 28\\nresult = minNum < TZL < maxNum\\n#判断体脂率情况，告知用户\\nif result :\\n print(wenhao+'您的体脂率正常请继续保持！')\\nelif TZL > maxNum :\\n print(wenhao+'您的体脂率偏高，身体偏胖！')\\nelse :\\n print(wenhao+'您的体脂率偏低，身体偏瘦！')\"\n },\n {\n \"alpha_fraction\": 0.4506089389324188,\n \"alphanum_fraction\": 0.5121785998344421,\n \"avg_line_length\": 30.446807861328125,\n \"blob_id\": \"12274469afdc53006df6258b207a9beecfc184d9\",\n \"content_id\": \"80fa813668f1b7ff329ab96bc0885cf2329d8b25\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1916,\n \"license_type\": \"no_license\",\n \"max_line_length\": 110,\n \"num_lines\": 47,\n \"path\": \"/机器学习/方程组迭代求解和消元求解.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nfrom numpy import *\\nimport matplotlib.pyplot as plt\\n#1、消元求解\\na = np.array([[8, -3, 2], [4, 11, -1], [6, 3, 12]])\\n# print(a,type(a))\\nb = np.array([20, 33, 36])\\nresult = np.linalg.solve(a, b) #当a，b都是矩阵的时候，参数就应该是b.T,这里都是ndarray\\nprint(result, type(result), len(result))\\nprint('*' * 30)\\n\\n#-------------------------------------------------------------------------------------------------------------\\n\\n#2、迭代求解,又叫逐次逼近法，在训练大型数据集的时候使用\\n#原理：对于给定方程组x = b0 * x +f，，(由方程组AX=B变换而来)使用公式：x^(k+1) = b0 * x^k +f ,\\n# 其中k为迭代次数，如果k->∞时，x^(k+1)存在，称此迭代法收敛，极限值就是方程组的解，否则称此迭代法发散\\nf = np.array([20/8, 3, 3])\\nf = mat(f) #当计算2-D矩阵的时候，就要注意用np.array和mat数据类型的区别，使用乘法方面也不一样，最好是都用矩阵，即mat生成\\nb0 =np.array([[0, 3/8, -2/8], [-4/11, 0, 1/11], [-6/12, -3/12, 0]])\\nb0 = mat(b0)\\n#把原多项式转换成x1=***;x2=***;x3=***的形式，可以得到b0和f\\nerror = 1.0e-6\\nsteps = 100\\nxk = zeros((3, 1))\\nerrorList =[]\\nfor k in range(steps):\\n xk_1 = xk\\n xk = b0 * xk + f.T\\n errorList.append(np.linalg.norm(xk - xk_1))\\n if errorList[-1] < error:\\n print('迭代次数：', k + 1)\\n break\\nprint(xk)\\n#绘制误差点集\\nmatplt = zeros((2, k + 1))\\nmatplt[0] = linspace(1, k + 1, k +1)\\nmatplt[1] = array(errorList)\\nfig = plt.figure() #创建一个图\\n# print(type(fig))\\nax = fig.add_subplot(111) #增加一个子图，位置为1,1,1,等价：fig.add_subplot(1, 1, 1)\\nax.scatter(matplt[0], matplt[1], c='blue') #（x,yn）离散图,蓝色\\nplt.show()\\nprint('*' * 30)\\n\\n#-------------------------------------------------------------------------------------------------------------\\n\\n#3、\\n\"\n },\n {\n \"alpha_fraction\": 0.4945673942565918,\n \"alphanum_fraction\": 0.5303822755813599,\n \"avg_line_length\": 37.20000076293945,\n \"blob_id\": \"3ce9fe07686d4a2628c22a8da3f536ad20f8e1e8\",\n \"content_id\": \"468ee5ed67f42003f5e430b969b9107069bc7ac8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3439,\n \"license_type\": \"no_license\",\n \"max_line_length\": 111,\n \"num_lines\": 65,\n \"path\": \"/helloworld/字典.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#字典：无序的、可变的键值对集合。key不可重复，不可变，如重复则前面的会被后面的覆盖\\n#----------------------------新建字典------------------------------------------------------------------------------\\ninfo = {'name': 'zyf', 'age': 28, 'sex': 1}\\ninfo1 = dict.fromkeys('abc', 666)\\n#dict.fromkeys('abc',666) 同{a:666,b:666,c:666}\\nprint(info1)\\n#---------------------------字典的增、删、改-----------------------------------------------------------------\\ninfo['tel'] = '18115054967'\\nprint(info)\\n#语句删除\\ndel info['sex']\\nprint(info)\\n#对象函数删除：.pop(key[,default]) 删除指定的键值对，返回对应的值，如果key不存在则不做删除，并返回给的default值，如果default也没给出则报错\\ninfo = {'name': 'zyf', 'age': 28, 'sex': 1}\\nd = info.pop('name1', 18)\\nprint(d, info)\\n#删除按升序排列后的第一个键值对，并以元组的形式返回改键值对，如果字典为空，则报错\\ninfo = {'name': 'zyf', 'age': 28, 'sex': 1}\\ndlt = info.popitem()\\nprint(dlt, info)\\n#清空字典内容，字典对象还在: .clear()注意与del语句的差别，返回none\\ninfo = {'name': 'zyf', 'age': 28, 'sex': 1}\\nprint(info.clear(),info)\\n#----------------------------修改单个键对应的值（key不能改） 同line 7---------------------------------\\n#批量修改value：oldDict.update(newDict):根据newDict中的键值对更新oldDict中的键值对，如果oldDict中不存在对应的key，则新增键值对,返回值为none\\ninfo = {'name': 'zyf', 'age': 28, 'sex': 1}\\nrt = info.update({'tel': '18115054967','age': 35})\\nprint(rt, info)\\n#--------------------------字典查询相关--------------------------------------------------------------\\n#直接通过key获取：dic[key]\\n#函数获取：dic.get(key[,default]):key不存在则返回default值，如果没给定default则返回none，不会报错，不改变原字典\\ninfo = {'name': 'zyf', 'age': 28, 'sex': 1}\\nprint(info.get('age1', 666))\\n#dic.setDefault(key[,default]):返回指定key对应的值，如果key不存在，则增加一个键值对：key：default，default未给则为none，\\ninfo = {'name': 'zyf', 'age': 28, 'sex': 1}\\nprint(info.setdefault('age1', 666))\\nprint(info)\\n\\n\\ninfo = {'name': 'zyf', 'age': 28, 'sex': 1}\\n#获取字典所有的值：dic.values(),返回的是字典视图对象，字典改变了，得到的视图对象也会改变，不管代码前后。（2.x版本是直接返回列表）\\nprint(info.values())\\n#获取字典所有的键：dic.keys()\\nprint(info.keys())\\n#获取字典所有的键值对：dic.items()\\nprint(info.items())\\n#-------------------------遍历-------------------------------------------------------------\\n#1、通过key遍历\\ninfo = {'name': 'zyf', 'age': 28, 'sex': 1}\\nkey = info.keys()\\nfor k in key:\\n print(k)\\n print(info[k])\\n#2、直接遍历所有的键值对（推荐使用）\\ninfo = {'name': 'zyf', 'age': 28, 'sex': 1}\\nt = info.items()\\ninfo['adress'] = 'shanghai'\\n#即使在把键值对对象先赋值给了t，之后更改了字典，也不会影响遍历结果，这是字典视图对象的特性\\nfor k, v in t:\\n print(k, v)\\n#------------------------计算和判断---------------------------------------------------------------\\n#len（dic）：计算键值对的个数\\ninfo = {'name': 'zyf', 'age': 28, 'sex': 1}\\nprint(len(info))\\n#判断：判断的是key，不是value： xx in dic;xx not in dic;返回bool类型\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5704697966575623,\n \"alphanum_fraction\": 0.6577181220054626,\n \"avg_line_length\": 28.866666793823242,\n \"blob_id\": \"6aaa56da0a1af9109759386e052e959755427984\",\n \"content_id\": \"aa33fc1c2931dffc370fe13a35d785b48b96708e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 503,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 15,\n \"path\": \"/机器学习/logistic函数图.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nimport matplotlib.pyplot as plt\\nimport matplotlib\\nimport matplotlib.lines as pltln\\nfrom matplotlib.lines import Line2D\\nx = np.linspace(-20, 20, 500)\\ny = 1/(1 + np.e ** -x)\\nfig = plt.figure() #创建一个图\\n# print(type(fig))\\nax = fig.add_subplot(111) #增加一个子图，位置为1,1,1,等价：fig.add_subplot(1, 1, 1)\\nplt.plot(x, y)\\nplt.plot([0, 0], [0, 1])\\nplt.plot([-20, 20], [0.5, 0.5]) #绘制（-20,0.5），（20,0.5）的连线\\nplt.title('Func:logistic')\\nplt.show()\"\n },\n {\n \"alpha_fraction\": 0.6048879623413086,\n \"alphanum_fraction\": 0.6720977425575256,\n \"avg_line_length\": 23.600000381469727,\n \"blob_id\": \"5d4987baa34c6a6d4cf30d2c2c17f223f999efd0\",\n \"content_id\": \"ab0307787e02b2d140718aa2a0579a7e321f2371\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 785,\n \"license_type\": \"no_license\",\n \"max_line_length\": 55,\n \"num_lines\": 20,\n \"path\": \"/helloworld/判断水仙花数.py\",\n \"repo_name\": \"kakarotte22/PythonDemo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#需求：用户输入一个三位数字，暂不考虑非数字，判断是否为水仙花数，个十百位上数字的三次方和位数本身。\\n#用户输入一个三位数字，暂不考虑非数字，\\nnum = int(input('please enter the number(100~999):\\\\n'))\\nif not(100 <= num <= 999):\\n print('InputError,program excit')\\n exit()\\n# 判断是否为水仙花数，个十百位上数字的三次方和位数本身。\\n#num // 100 求整除方法得到百位上的数字\\nbaiWei = num // 100\\n#num除100取余数之后再求整除得到百位上的数字\\nshiWei = (num % 100) // 10\\n#num除10求余得到个位数\\ngeWei = num % 10\\n#判断是否为水仙花数\\nresult = (baiWei ** 3 +shiWei ** 3 + geWei ** 3) == num\\n#打印结果\\nif result :\\n print('YES!')\\nelse:\\n print('NO!')\"\n 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\"no_license\",\n \"max_line_length\": 38,\n \"num_lines\": 5,\n \"path\": \"/F1.py\",\n \"repo_name\": \"akshadapowar/-LB_DSC_JAN_24_WKND\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"This is Python class This got change..\\nwow ...\\nsdkfjsdkf\\nsdkfj\\nsdkmm\\n\\n\"\n }\n]"},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":1169,"cells":{"repo_name":{"kind":"string","value":"jasonqng/blocked-on-weibo"},"repo_url":{"kind":"string","value":"https://github.com/jasonqng/blocked-on-weibo"},"snapshot_id":{"kind":"string","value":"74b7c0215f6745df58264ab7765b5f4a2928fc18"},"revision_id":{"kind":"string","value":"691d5a2c8d1168daf3ae36a5eb94f380d315ca34"},"directory_id":{"kind":"string","value":"0e72c060671cce0aa4135d0e55c636d961ffc1d9"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"string","value":"2023-04-15T03:41:13.513890"},"revision_date":{"kind":"string","value":"2023-04-10T18:01:34"},"committer_date":{"kind":"string","value":"2023-04-10T18:01:34"},"github_id":{"kind":"number","value":97148239,"string":"97,148,239"},"star_events_count":{"kind":"number","value":18,"string":"18"},"fork_events_count":{"kind":"number","value":4,"string":"4"},"gha_license_id":{"kind":"string","value":"BSD-3-Clause"},"gha_created_at":{"kind":"string","value":"2017-07-13T17:21:00"},"gha_updated_at":{"kind":"string","value":"2022-07-09T03:36:43"},"gha_pushed_at":{"kind":"string","value":"2023-04-10T18:01:34"},"gha_language":{"kind":"string","value":"Python"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5595763921737671,"alphanum_fraction":0.5659759044647217,"avg_line_length":40.5379753112793,"blob_id":"52258cebb06ea2d8c0d3bdf569d039396f98bd43","content_id":"8a08910ae9c0ae45d8fd5ad5ceea970c6286e32d","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":13176,"license_type":"permissive","max_line_length":207,"num_lines":316,"path":"/blockedonweibo/weibo.py","repo_name":"jasonqng/blocked-on-weibo","src_encoding":"UTF-8","text":"from bs4 import BeautifulSoup\nimport urllib\nimport codecs\nimport time\nimport random\nimport sqlite3\nfrom datetime import datetime\nimport pandas as pd\nimport os\nimport io\nimport requests\nimport base64 \nimport re\nimport ast\nimport sys\ntry:\n from urllib.parse import urlparse\nexcept ImportError:\n from urlparse import urlparse\nimport rsa \nimport json \nimport binascii \nimport math\ntry:\n from . import weibo_credentials\nexcept ValueError:\n import weibo_credentials\n### SETTINGS\n\nCENSORSHIP_PHRASE = u'根据相关法律法规和政策'\nCAPTCHA_PHRASE = u'你的行为有些异常'\nNO_RESULTS_PHRASE = u'抱歉，未找到'\n\ndef has_censorship(keyword_encoded,\n cookie=None):\n\n \"\"\"\n Function which actually looks up whether a search for the given keyword returns text\n which is displayed during censorship.\n Can handle unicode and strings\n Currently no CAPTCHA handling, though it is detected\n Returns string of 'censored','no_results','reset',or 'has_results'\n ('has_results' is actually not a garuantee; it's merely a lack of other censorship indicators)\n \"\"\"\n url = (f'https://s.weibo.com/weibo?q={keyword_encoded}')\n cookies = {'required_cookie': cookie}\n headers = {'User-Agent': 'Mozilla/5.0'}\n try:\n r = requests.get(url,cookies=cookies, headers=headers).text\n i = 1\n while True:\n if CAPTCHA_PHRASE not in r:\n break\n else:\n print(\"CAPTCHA\", keyword_encoded, \"sleeping for %s seconds\" % 300*i)\n time.sleep(300*i)\n i+=1\n if i==50:\n print(\"Can't break out of CAPTCHA, aborting\")\n sys.exit(1)\n except IOError:\n wait_seconds = random.randint(90, 100)\n print(u\"%s caused connection reset, waiting %s\" % (keyword_encoded,wait_seconds))\n time.sleep(wait_seconds)\n return (\"reset\",None)\n\n num_results = re.findall(r'search_rese clearfix\\\\\">\\\\n \\\\u627e\\\\u5230(\\d*)',r)\n if num_results:\n num_results = int(num_results[0])\n else:\n None\n if CENSORSHIP_PHRASE in r:\n return (\"censored\",None)\n elif NO_RESULTS_PHRASE in r:\n return (\"no_results\",None)\n else:\n return (\"has_results\",num_results)\n\ndef create_database(sqlite_file, overwrite=False):\n \"\"\"\n Generating a sqlite file for storing results\n Multi-index primary key on id, date, and source\n Set new_database to True in order to remove any existing file\n \"\"\"\n if overwrite and os.path.isfile(sqlite_file):\n os.remove(sqlite_file)\n if not os.path.isfile(sqlite_file):\n conn = sqlite3.connect(sqlite_file)\n c = conn.cursor()\n c.execute('''CREATE TABLE results (id int, date date, datetime_logged datetime, test_number int, keyword string, \n censored bool, no_results bool, reset bool, is_canonical bool, result string, source string, orig_keyword string, \n num_results int, notes string, PRIMARY KEY(date,source,test_number,keyword,orig_keyword))''')\n conn.commit()\n conn.close()\n\n\ndef insert_into_database(record_id,\n keyword_encoded,\n result,\n date=datetime.now().date(),\n source=\"default\",\n num_results=None,\n notes=None,\n sqlite_file=None,\n test_number=1,\n is_canonical=None,\n orig_keyword=None):\n \"\"\"\n Writing the results to the sqlite database file\n \"\"\"\n conn = sqlite3.connect(sqlite_file)\n conn.text_factory = str\n conn.execute(\"PRAGMA busy_timeout = 5000\")\n c = conn.cursor()\n \n dt_logged = datetime.now()\n if isinstance(notes, list):\n notes = str(notes)\n if isinstance(num_results, list):\n num_results = None\n \n if result == \"censored\":\n censored = True\n else:\n censored = False\n \n if result == \"no_results\":\n no_results = True\n else:\n no_results = False\n \n if result == \"reset\":\n reset = True\n else:\n reset = False\n\n query = u\"\"\"INSERT OR REPLACE INTO results (id, date, datetime_logged, test_number, keyword, censored, no_results, reset, is_canonical, result, source, orig_keyword, num_results, notes) \n VALUES (\n coalesce(\n (select id from results where date=date('{date}') and keyword='{keyword}' and test_number={test_number} and source='{source}'),\n ?),\n ?,?,?,?,?,?,?,?,?,?,?,?,?\n );\"\"\".format(date=date,keyword=keyword_encoded,test_number=test_number,source=source)\n c.execute(query, (record_id, date, dt_logged, test_number, keyword_encoded, censored, no_results, reset, is_canonical, result, source, orig_keyword, num_results, notes))\n\n conn.commit()\n conn.close()\n \ndef sqlite_to_df(sqlite_file,\n query=\"select * from results where source!='_meta_' or source is NULL;\"):\n conn = sqlite3.connect(sqlite_file)\n df = pd.read_sql_query(query, conn)\n return df\n\ndef verify_cookies_work(cookie,\n return_full_response_text=False):\n \"\"\"\n Returns True if cookies return profile indicator\n If no cookie or bad cookie is passed, you get a generic login page which doesn't have the indicator\n \"\"\"\n cookies = {'required_cookie': cookie}\n headers = {'User-Agent': 'Mozilla/5.0'}\n r = requests.get('https://s.weibo.com/weibo?q=test',cookies=cookies, headers=headers).text\n if return_full_response_text:\n return r\n if \"onick\" in r:\n return True\n else:\n return False\n\ndef load_cookies(cookie_file=\"_cookie.txt\"):\n with open(cookie_file, 'r') as f:\n #cookie = ast.literal_eval(f.read())\n cookie = f.read()\n return cookie\n\ndef run(keywords,\n verbose='all',\n insert=True,\n sqlite_file=None,\n return_df=False,\n sleep=True,\n cookie=None,\n sleep_secs=15,\n continue_interruptions=True,\n date=datetime.now().strftime('%Y-%m-%d'),\n test_number=1,\n list_source=\"list\",\n get_canonical=False\n ):\n \"\"\"\n Iterating through the keyword list and testing one at a time\n Handles when script or connection breaks; will pick up where it left off\n Set return_df to append each new result to a df in memory, which is returned at end of function\n verbose = 'some','all',or 'none'(technically anything besides 'some' or 'all' will not show anything)\n sleep = time in seconds to sleep between searches\n \"\"\"\n if sqlite_file:\n create_database(sqlite_file)\n\n count=0\n if return_df:\n results_df = pd.DataFrame()\n\n if isinstance(keywords, list):\n keywords = pd.DataFrame(keywords,columns=[\"keyword\"])\n keywords['source'] = list_source\n\n test_keywords = keywords.copy()\n\n if \"Index\" not in test_keywords.columns:\n test_keywords['Index'] = test_keywords.index\n if \"notes\" not in test_keywords.columns:\n test_keywords['notes'] = None\n if \"source\" not in test_keywords.columns:\n test_keywords['source'] = None\n source=test_keywords['source'][0]\n\n for r in test_keywords.itertuples():\n if isinstance(r.keyword, str):\n keyword_encoded = r.keyword\n\n if sqlite_file:\n if r.Index < len(sqlite_to_df(sqlite_file).query(\"date=='%s' & source=='%s' & test_number==%s & is_canonical!=1\" % (date,source,test_number))) and continue_interruptions:\n continue\n if len(sqlite_to_df(sqlite_file).query(u\"date=='%s' & source=='%s' & test_number==%s & keyword=='%s' & is_canonical!=1\" % (date,source,test_number,keyword_encoded)))>0 and continue_interruptions:\n continue\n result,num_results = has_censorship(keyword_encoded,cookie)\n if verbose==\"all\":\n print(r.Index,keyword_encoded, result)\n elif verbose==\"some\" and (count%10==0 or count==0):\n print(r.Index,keyword_encoded, result)\n\n min_str = None\n if get_canonical and result == \"censored\":\n if verbose==\"some\" or verbose==\"all\":\n print(\"Found censored search phrase; determining canonical censored keyword set\")\n sleep_recursive = sleep_secs if sleep else 0\n potential_kws = split_search_query(keyword_encoded, cookie, sleep_recursive, res_rtn=[], known_blocked=True, verbose=verbose)\n if verbose==\"all\":\n print(potential_kws)\n\n for kw in potential_kws:\n test_list = [kw[:i] + kw[i + 1:] for i in range(len(kw))]\n min_str = \"\"\n for i in range(len(test_list)):\n if kw[i].isspace():\n continue\n if verbose==\"all\":\n print(\"Testing %d of %d: omitting character %s\" %(i+1, len(test_list), kw[i]))\n if sleep:\n time.sleep(random.randint(math.ceil(sleep_secs * .90), math.ceil(sleep_secs * 1.10)))\n if has_censorship(test_list[i], cookie)[0] != \"censored\":\n min_str += (kw[i])\n result_min_str, num_results_min_str = has_censorship(min_str, cookie)\n if result_min_str == \"censored\": # minStr found properly\n if verbose==\"all\" or verbose==\"some\":\n print(\"the minimum phrase from '%s' is: '%s'\" % (kw, min_str))\n if insert:\n insert_into_database(len(sqlite_to_df(sqlite_file)), min_str, date=date, result=result_min_str,\n source=r.source, num_results=num_results_min_str, notes=r.notes,\n sqlite_file=sqlite_file, test_number=test_number, is_canonical=True,\n orig_keyword=keyword_encoded)\n else:\n print(\"Failed to find canonical phrase\")\n\n if insert:\n insert_into_database(len(sqlite_to_df(sqlite_file)), keyword_encoded, date=date, result=result, source=r.source,\n num_results=num_results, notes=r.notes, sqlite_file=sqlite_file, test_number=test_number,\n is_canonical=False)\n if return_df:\n results_df = pd.concat([results_df,\n pd.DataFrame([{\"date\":date,\n \"datetime\":datetime.now(),\n \"keyword\":min_str if min_str is not None else r.keyword,\n \"result\":result,\n \"source\":r.source,\n \"num_results\":num_results,\n \"test_number\":test_number,\n \"is_canonical\": True if min_str is not None else False,\n \"orig_keyword\":r.keyword if min_str is not None else None\n }])\n ])\n count+=1\n if sleep:\n time.sleep(random.randint(math.ceil(sleep_secs*.90), math.ceil(sleep_secs*1.10)))\n if insert:\n insert_into_database(int(test_keywords.index.max())+1,None,date=date,result=\"finished\",source=\"_meta_\",sqlite_file=sqlite_file,test_number=test_number)\n if return_df:\n return results_df\n\n\ndef split_search_query(query, cookie, sleep_secs=0, res_rtn=[], known_blocked=False, verbose=\"\"):\n \"\"\"\n Recursively halves a query and returns portions with blocked keywords as a list of strings.\n :param res_rtn: internal list holding found min keywords during recursive search, DO NOT SPECIFY.\n :param known_blocked: set to True to skip a redundant first-check if you know your query is blocked.\n :return: a list of one or more shortened keyword segments that trigger censorship\n \"\"\"\n if len(query) <= 1:\n return [-1]\n if sleep_secs:\n time.sleep(random.randint(math.ceil(sleep_secs * .90), math.ceil(sleep_secs * 1.10)))\n if (not known_blocked) and verbose=='all':\n print('Recursively shortening... testing query: \"%s\"' %(query))\n if (not known_blocked) and has_censorship(query, cookie)[0] != \"censored\": # known_blocked=True skips 1st check\n return [-1]\n else:\n mid = len(query) // 2\n left_half = query[:mid]\n right_half = query[mid:]\n left_res = split_search_query(left_half, cookie, sleep_secs, res_rtn, False, verbose)\n right_res = split_search_query(right_half, cookie, sleep_secs, res_rtn, False, verbose)\n if (left_res[0] == -1) and (right_res[0] == -1):\n res_rtn.append(query)\n return res_rtn\n"},{"alpha_fraction":0.8666666746139526,"alphanum_fraction":0.9111111164093018,"avg_line_length":8.199999809265137,"blob_id":"5fcbb04a146dcb0c6bf8c3437d83de500bac1a54","content_id":"a84514617e7d66d4648fc3242269b066fb9653b6","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":45,"license_type":"permissive","max_line_length":14,"num_lines":5,"path":"/requirements.txt","repo_name":"jasonqng/blocked-on-weibo","src_encoding":"UTF-8","text":"beautifulsoup4\npandas\nrequests\nrsa\ndf2gspread"},{"alpha_fraction":0.4322457015514374,"alphanum_fraction":0.5026606917381287,"avg_line_length":21.568435668945312,"blob_id":"82e59b6e8b7d3647ae6963474f512d0bb25a24ef","content_id":"41c7494f177351362b32fd7c326e0dbfe3b8b57f","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":48780,"license_type":"permissive","max_line_length":3746,"num_lines":2148,"path":"/README.md","repo_name":"jasonqng/blocked-on-weibo","src_encoding":"UTF-8","text":"# What is blockedonweibo?\n\nThis python package allows you to automate tests to check if a keyword is censored or not on Sina Weibo, a Chinese social media site. It is an updated version of the script which was used to detect keywords on the site, http://blockedonweibo.com. It handles interrupted tests, multiple tests per day, storing results to a database, and a number of other features which simplify testing Weibo censorship at scale. The researcher merely has to feed the script a list of words for one-off tests. For recurring tests, simply wrap the script with a scheduler.\n\n![\"screenshot\"](\"screenshot.png\")
\n\n# IMPORTANT: Upgrading from v0.1 with an existing database? \nThe database table has been modified to accomodate tracking of minimum keyword strings triggering \ncensorship. If you used blockedonweibo v0.1 and you used a database to store results, you will \nneed to update your database file. \n\n**To migrate your older database file**\n1. Move `update_db.py` to the same file directory as your database file (\"results.sqlite\" if you followed\nthis setup guide)\n2. In terminal, run `python update_db.py` and confirm database file. \n\n### Version 0.2 changes \n* now includes a feature to find canonical censored keywords (minimum set of keywords required to trigger explicit censorship message)\n * to run, pass `get_canonical=True` with rest of variables into `run()`\n * see section 4.8\n\n\n# Table of Contents\n \n\n# Install the blockedonweibo package\n\nThe github repo for this Weibo keyword testing script is located at https://github.com/jasonqng/blocked-on-weibo.\n\nTo begin using this python package, inside your terminal, run\n\n```\npip install blockedonweibo\n```\n\nAlternatively, you can clone this repo, `cd` into the repo directory and manually install the requirements and package:\n\n```\npip install -r requirements.txt\npython setup.py install\n```\n\nTo confirm the installation works, in a python shell (you can start by running `python` from terminal), try importing the package:\n\n```\nimport blockedonweibo\n```\n\nIf you don't get any errors, things have installed successfully. If not, you may need to fiddle with your python paths and settings to ensure it's being installed to the correct location.\n\n# Adjust your settings\n\nYour python script only requires the following. All other imports are handled by the package.\n\n\n```python\nfrom blockedonweibo import weibo\nimport pandas as pd\n```\n\nYou have the option of saving your test results to a file. You'll need to pass a path to to a file which will store the results in sqlite format. It can be helpful to set this at the top of your script and pass the variable each time you run the test.\n\n\n```python\nsqlite_file = 'results.sqlite' # name of sqlite file to read from/write to\n```\n\nIf you want to erase any existing data you have in the sqlite file defined above, just pass overwrite=True to the `create_database` function. Otherwise any new results will be appended to the end of the database.\n\n\n```python\nweibo.create_database(sqlite_file, overwrite=True)\n```\n\nThis testing script is enhanced if you allow it to log into Weibo, which increases your rate limit threshold as well as returns the number of results a search says it has. This script will work without your supplying credentials, but it is highly recommended. To do so, edit the `weibo_credentials.py` with your email address and password. The file is ignored and will not be uploaded by default when you push commits to github. You can inspect the code to verify that the credentials don't go anywhere except to weibo.\n\nUsing those credentials, the script logs you in and fetches a cookie for the user session you create. This cookie can be saved to a file by passing the `write_cookie` parameter in the `user_login` function.\n\n\n```python\nsession = weibo.user_login(write_cookie=True)\n```\n\nThere is a helper function to verify that the cookie actually works\n\n\n```python\ncookie = session.cookies.get_dict()\nprint(weibo.verify_cookies_work(cookie))\n```\n\n True\n\n\nIf you have the cookie already written to disk, you don't need to perform another `user_login` and instead, you can just use the `load_cookies` function to fetch the cookie from the file. Again, you can verify that it works. Just store the cookie's contents (a dictionary) to a variable and pass that to the `run` function below if you want to test as if you were logged in. Otherwise, it will emulate a search by a logged out user.\n\n\n```python\ncookie = weibo.load_cookies()\nprint(weibo.verify_cookies_work(cookie))\n```\n\n True\n\n\n# Let's start testing!\n\n## Pass a dictionary of keywords to start testing\n\n\n```python\nsample_keywords_df = pd.DataFrame(\n [{'keyword':'hello','source':'my dataframe'},\n {'keyword':'lxb','source':'my dataframe'},\n {'keyword':u'习胞子','source':'my dataframe'}\n ])\n```\n\n\n```python\nsample_keywords_df\n```\n\n\n\n\n![\\\"screenshot\\\"](\\\"screenshot.png\\\")
\\n\\n# IMPORTANT: Upgrading from v0.1 with an existing database? \\nThe database table has been modified to accomodate tracking of minimum keyword strings triggering \\ncensorship. If you used blockedonweibo v0.1 and you used a database to store results, you will \\nneed to update your database file. \\n\\n**To migrate your older database file**\\n1. Move `update_db.py` to the same file directory as your database file (\\\"results.sqlite\\\" if you followed\\nthis setup guide)\\n2. In terminal, run `python update_db.py` and confirm database file. \\n\\n### Version 0.2 changes \\n* now includes a feature to find canonical censored keywords (minimum set of keywords required to trigger explicit censorship message)\\n * to run, pass `get_canonical=True` with rest of variables into `run()`\\n * see section 4.8\\n\\n\\n# Table of Contents\\n \\n\\n# Install the blockedonweibo package\\n\\nThe github repo for this Weibo keyword testing script is located at https://github.com/jasonqng/blocked-on-weibo.\\n\\nTo begin using this python package, inside your terminal, run\\n\\n```\\npip install blockedonweibo\\n```\\n\\nAlternatively, you can clone this repo, `cd` into the repo directory and manually install the requirements and package:\\n\\n```\\npip install -r requirements.txt\\npython setup.py install\\n```\\n\\nTo confirm the installation works, in a python shell (you can start by running `python` from terminal), try importing the package:\\n\\n```\\nimport blockedonweibo\\n```\\n\\nIf you don't get any errors, things have installed successfully. If not, you may need to fiddle with your python paths and settings to ensure it's being installed to the correct location.\\n\\n# Adjust your settings\\n\\nYour python script only requires the following. All other imports are handled by the package.\\n\\n\\n```python\\nfrom blockedonweibo import weibo\\nimport pandas as pd\\n```\\n\\nYou have the option of saving your test results to a file. You'll need to pass a path to to a file which will store the results in sqlite format. It can be helpful to set this at the top of your script and pass the variable each time you run the test.\\n\\n\\n```python\\nsqlite_file = 'results.sqlite' # name of sqlite file to read from/write to\\n```\\n\\nIf you want to erase any existing data you have in the sqlite file defined above, just pass overwrite=True to the `create_database` function. Otherwise any new results will be appended to the end of the database.\\n\\n\\n```python\\nweibo.create_database(sqlite_file, overwrite=True)\\n```\\n\\nThis testing script is enhanced if you allow it to log into Weibo, which increases your rate limit threshold as well as returns the number of results a search says it has. This script will work without your supplying credentials, but it is highly recommended. To do so, edit the `weibo_credentials.py` with your email address and password. The file is ignored and will not be uploaded by default when you push commits to github. You can inspect the code to verify that the credentials don't go anywhere except to weibo.\\n\\nUsing those credentials, the script logs you in and fetches a cookie for the user session you create. This cookie can be saved to a file by passing the `write_cookie` parameter in the `user_login` function.\\n\\n\\n```python\\nsession = weibo.user_login(write_cookie=True)\\n```\\n\\nThere is a helper function to verify that the cookie actually works\\n\\n\\n```python\\ncookie = session.cookies.get_dict()\\nprint(weibo.verify_cookies_work(cookie))\\n```\\n\\n True\\n\\n\\nIf you have the cookie already written to disk, you don't need to perform another `user_login` and instead, you can just use the `load_cookies` function to fetch the cookie from the file. Again, you can verify that it works. Just store the cookie's contents (a dictionary) to a variable and pass that to the `run` function below if you want to test as if you were logged in. Otherwise, it will emulate a search by a logged out user.\\n\\n\\n```python\\ncookie = weibo.load_cookies()\\nprint(weibo.verify_cookies_work(cookie))\\n```\\n\\n True\\n\\n\\n# Let's start testing!\\n\\n## Pass a dictionary of keywords to start testing\\n\\n\\n```python\\nsample_keywords_df = pd.DataFrame(\\n [{'keyword':'hello','source':'my dataframe'},\\n {'keyword':'lxb','source':'my dataframe'},\\n {'keyword':u'习胞子','source':'my dataframe'}\\n ])\\n```\\n\\n\\n```python\\nsample_keywords_df\\n```\\n\\n\\n\\n\\n0 else p-0.03, l, fontsize=8, color=tableau20[14])\n\n ax.spines[\"top\"].set_visible(False) \n ax.spines[\"bottom\"].set_visible(False) \n ax.spines[\"right\"].set_visible(False) \n ax.spines[\"left\"].set_visible(False)\n ax.set_xticks(ind+width*2) \n ax.set_xticklabels([\" \" for _ in labels]) \n # Ensure that the axis ticks only show up on the bottom and left of the plot. \n # Ticks on the right and top of the plot are generally unnecessary chartjunk. \n ax.get_xaxis().tick_bottom() \n ax.get_yaxis().tick_left() \n\n for y in [-0.4,-0.3,-0.2,-0.1,0.0,0.1,0.2,0.3,0.4]: \n plt.plot(range(0,18), [y] * len(range(0,18)), \"--\", lw=0.1, color=\"black\", alpha=0.3) \n \n # Remove the tick marks; they are unnecessary with the tick lines we just plotted. \n plt.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\", \n labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\",labelcolor=tableau20[14]) \n \n plt.savefig('figs/'+filename+\".pdf\", bbox_inches=\"tight\"); \n\n \ndef average_wordclasses(data, filename):\n \"\"\"\n Plots the performance of different algorithms.\n \"\"\"\n tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120), \n (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150), \n (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148), \n (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199), \n (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)] \n \n # Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts. \n for i in range(len(tableau20)): \n r, g, b = tableau20[i] \n tableau20[i] = (r / 255., g / 255., b / 255.) \n \n # You typically want your plot to be ~1.33x wider than tall. This plot is a rare \n # exception because of the number of lines being plotted on it. \n # Common sizes: (10, 7.5) and (12, 9) \n f = plt.figure() \n \n # Remove the plot frame lines. They are unnecessary chartjunk. \n ax = plt.subplot(111) \n \n labels = data.keys()\n print labels\n precisions = [data[key][0] for key in labels] \n recalls = [data[key][1] for key in labels]\n f1s = [data[key][2] for key in labels]\n accuracies = [data[key][3] for key in labels]\n\n #Create bars \n ind = (np.arange(len(labels))*2)+0.25\n width = 0.35\n ax.bar(ind, precisions, width, color=tableau20[0], edgecolor=\"none\")\n ax.bar(ind+width, recalls, width, color=tableau20[1], edgecolor=\"none\")\n ax.bar(ind+width*2, f1s, width, color=tableau20[2], edgecolor=\"none\")\n ax.bar(ind+width*3, accuracies, width, color=tableau20[3], edgecolor=\"none\")\n \n #Create top bar labels\n for p, i in zip(precisions, ind):\n plt.text(i+0.03, p+0.01, \"%0.2f\" % p, fontsize=10, color=tableau20[0])\n for p, i in zip(recalls, ind+width):\n plt.text(i+0.03, p+0.01, \"%0.2f\" % p, fontsize=10, color=tableau20[1])\n for p, i in zip(f1s, ind+width*2):\n plt.text(i+0.03, p+0.01, \"%0.2f\" % p, fontsize=10, color=tableau20[2])\n for p, i in zip(accuracies, ind+width*3):\n plt.text(i+0.03, p+0.01, \"%0.2f\" % p, fontsize=10, color=tableau20[3])\n \n ax.spines[\"top\"].set_visible(False) \n# ax.spines[\"bottom\"].set_visible(False) \n ax.spines[\"right\"].set_visible(False) \n ax.spines[\"left\"].set_visible(False)\n ax.set_xticks(ind+width*2) \n ax.set_xticklabels(labels) \n # Ensure that the axis ticks only show up on the bottom and left of the plot. \n # Ticks on the right and top of the plot are generally unnecessary chartjunk. \n ax.get_xaxis().tick_bottom() \n ax.get_yaxis().tick_left() \n\n for y in [1,2,3,4,5,6,7]: \n plt.plot(range(0,7), [y] * len(range(0,7)), \"--\", lw=0.5, color=\"black\", alpha=0.3) \n \n # Remove the tick marks; they are unnecessary with the tick lines we just plotted. \n plt.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\", \n labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\",labelcolor=tableau20[14]) \n \n plt.savefig('figs/'+filename+\".pdf\", bbox_inches=\"tight\"); \n \ndef detailed_average_wordclasses(data, filename):\n \"\"\"\n Plots the performance of different algorithms.\n \"\"\"\n tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120), \n (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150), \n (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148), \n (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199), \n (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)] \n \n # Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts. \n for i in range(len(tableau20)): \n r, g, b = tableau20[i] \n tableau20[i] = (r / 255., g / 255., b / 255.) \n \n # You typically want your plot to be ~1.33x wider than tall. This plot is a rare \n # exception because of the number of lines being plotted on it. \n # Common sizes: (10, 7.5) and (12, 9) \n f = plt.figure() \n \n # Remove the plot frame lines. They are unnecessary chartjunk. \n ax = plt.subplot(111) \n \n labels = data.keys()\n print labels\n datalists = []\n for i in xrange(len(data[labels[0]])):\n datalists.append([data[key][i] for key in labels]) \n\n \n #Create bars \n ind = (np.arange(len(labels))*2.2)+0.2\n width = 0.1\n offset = np.arange(0.01, 1, 0.01)\n colorlist = [tableau20[0],tableau20[0],tableau20[0],tableau20[1],tableau20[4],tableau20[5],tableau20[6],tableau20[7],tableau20[2],tableau20[2],tableau20[2],tableau20[2],tableau20[2],\n tableau20[13],tableau20[14],tableau20[15],tableau20[16],tableau20[3]]\n for i in xrange(len(datalists)):\n bar = ax.bar(ind+width*i, datalists[i], width, color=colorlist[i], edgecolor=\"none\")\n #Create top bar labels\n# for p, i in zip(precisions, ind):\n# plt.text(i+0.03, p+0.01, \"%0.2f\" % p, fontsize=10, color=tableau20[0])\n# for p, i in zip(recalls, ind+width):\n# plt.text(i+0.03, p+0.01, \"%0.2f\" % p, fontsize=10, color=tableau20[1])\n# for p, i in zip(f1s, ind+width*2):\n# plt.text(i+0.03, p+0.01, \"%0.2f\" % p, fontsize=10, color=tableau20[2])\n# for p, i in zip(accuracies, ind+width*3):\n# plt.text(i+0.03, p+0.01, \"%0.2f\" % p, fontsize=10, color=tableau20[3])\n \n ax.spines[\"top\"].set_visible(False) \n# ax.spines[\"bottom\"].set_visible(False) \n ax.spines[\"right\"].set_visible(False) \n ax.spines[\"left\"].set_visible(False)\n ax.set_xticks(ind+width*9) \n ax.set_xticklabels(labels) \n # Ensure that the axis ticks only show up on the bottom and left of the plot. \n # Ticks on the right and top of the plot are generally unnecessary chartjunk. \n ax.get_xaxis().tick_bottom() \n ax.get_yaxis().tick_left() \n\n for y in [0.2,0.4,0.6,0.8,1.0,1.2,1.4,1.6,1.8,2.0,2.2,2.4,2.6,2.8,3.0]: \n plt.plot(range(0,8), [y] * len(range(0,8)), \"--\", lw=0.5, color=\"black\", alpha=0.3) \n \n # Remove the tick marks; they are unnecessary with the tick lines we just plotted. \n plt.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\", \n labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\",labelcolor=tableau20[14]) \n \n plt.savefig('figs/'+filename+\".pdf\", bbox_inches=\"tight\"); \n \ndef plot_entity_histogram(data, filename):\n \"\"\"\n Plots the performance of different algorithms.\n \"\"\"\n tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120), \n (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150), \n (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148), \n (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199), \n (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)] \n \n # Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts. \n for i in range(len(tableau20)): \n r, g, b = tableau20[i] \n tableau20[i] = (r / 255., g / 255., b / 255.) \n \n # You typically want your plot to be ~1.33x wider than tall. This plot is a rare \n # exception because of the number of lines being plotted on it. \n # Common sizes: (10, 7.5) and (12, 9) \n f = plt.figure() \n \n # Remove the plot frame lines. They are unnecessary chartjunk. \n ax = plt.subplot(111) \n \n labels = data.keys()\n print labels\n precisions = [data[key][0] for key in labels] \n recalls = [data[key][1] for key in labels]\n f1s = [data[key][2] for key in labels]\n accuracies = [data[key][3] for key in labels]\n\n #Create bars \n ind = (np.arange(len(labels))*2)+0.25\n width = 0.35\n ax.bar(ind, precisions, width, color=tableau20[0], edgecolor=\"none\")\n ax.bar(ind+width, recalls, width, color=tableau20[1], edgecolor=\"none\")\n ax.bar(ind+width*2, f1s, width, color=tableau20[2], edgecolor=\"none\")\n ax.bar(ind+width*3, accuracies, width, color=tableau20[3], edgecolor=\"none\")\n \n #Create top bar labels\n for p, i in zip(precisions, ind):\n plt.text(i+0.03, p+0.01, \"%0.2f\" % p, fontsize=10, color=tableau20[0])\n for p, i in zip(recalls, ind+width):\n plt.text(i+0.03, p+0.01, \"%0.2f\" % p, fontsize=10, color=tableau20[1])\n for p, i in zip(f1s, ind+width*2):\n plt.text(i+0.03, p+0.01, \"%0.2f\" % p, fontsize=10, color=tableau20[2])\n for p, i in zip(accuracies, ind+width*3):\n plt.text(i+0.03, p+0.01, \"%0.2f\" % p, fontsize=10, color=tableau20[3])\n \n ax.spines[\"top\"].set_visible(False) \n# ax.spines[\"bottom\"].set_visible(False) \n ax.spines[\"right\"].set_visible(False) \n ax.spines[\"left\"].set_visible(False)\n ax.set_xticks(ind+width*2) \n ax.set_xticklabels(labels) \n # Ensure that the axis ticks only show up on the bottom and left of the plot. \n # Ticks on the right and top of the plot are generally unnecessary chartjunk. \n ax.get_xaxis().tick_bottom() \n ax.get_yaxis().tick_left() \n\n for y in [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8]: \n plt.plot(range(0,7), [y] * len(range(0,7)), \"--\", lw=0.5, color=\"black\", alpha=0.3) \n \n # Remove the tick marks; they are unnecessary with the tick lines we just plotted. \n plt.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\", \n labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\",labelcolor=tableau20[14]) \n \n plt.savefig('figs/'+filename+\".pdf\", bbox_inches=\"tight\"); \n\n \ndef plot_dataset_stats(data):\n \"\"\"\n Plots histograms of the dataset statistics using the given data.\n \"\"\"\n\ndef load_incremental_data():\n f1_data= pickle.load(open('incremental_f1100',\"rb\"))\n acc_data=pickle.load(open('incremental_acc100',\"rb\"))\n print f1_data\n print acc_data\n for key in f1_data.keys():\n f1list = f1_data[key]\n f1_data[key] = [range(5,101,5),f1list]\n acclist = acc_data[key]\n acc_data[key] = [range(5,101,5),acclist]\n f1svm_data = {}\n f1nb_data = {}\n f1me_data = {}\n accsvm_data = {}\n accnb_data = {}\n accme_data = {}\n for key in f1_data.keys():\n if key[:3]==\"SVM\":\n f1svm_data[key] = f1_data[key]\n elif key[:2]==\"NB\":\n f1nb_data[key] = f1_data[key]\n elif key[:6]==\"MaxEnt\":\n f1me_data[key] = f1_data[key]\n for key in acc_data.keys():\n if key[:3]==\"SVM\":\n accsvm_data[key] = acc_data[key]\n elif key[:2]==\"NB\":\n accnb_data[key] = acc_data[key]\n elif key[:6]==\"MaxEnt\":\n accme_data[key] = acc_data[key]\n \n \n return f1svm_data, f1nb_data, f1me_data, accsvm_data, accnb_data, accme_data\n \n\ndef plot_subjectivity_aggregates(data, filename=\"temporal\"):\n \"\"\"\n Plots the temporal sentiment using the given data.\n \"\"\"\n tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120), \n (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150), \n (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148), \n (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199), \n (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)] \n \n # Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts. \n for i in range(len(tableau20)):\n r, g, b = tableau20[i]\n tableau20[i] = (r / 255., g / 255., b / 255.)\n \n # You typically want your plot to be ~1.33x wider than tall. This plot is a rare \n # exception because of the number of lines being plotted on it. \n # Common sizes: (10, 7.5) and (12, 9) \n f = plt.figure(figsize=(9, 6)) \n ind = np.arange(len(data['Targets'][0]))\n # Remove the plot frame lines. They are unnecessary chartjunk. \n ax = plt.subplot(111) \n ax.spines[\"top\"].set_visible(False) \n ax.spines[\"bottom\"].set_visible(False) \n ax.spines[\"right\"].set_visible(False) \n ax.spines[\"left\"].set_visible(False) \n ax.set_xticks(ind+0.25)\n ax.set_xticklabels(['%.2f' % x for x in data['Targets'][0]]) \n # Ensure that the axis ticks only show up on the bottom and left of the plot. \n # Ticks on the right and top of the plot are generally unnecessary chartjunk. \n ax.get_xaxis().tick_bottom() \n ax.get_yaxis().tick_left() \n ax.bar(ind, data['Frequencies'][1], 0.5, color=tableau20[14], edgecolor=\"none\")\n \n # Limit the range of the plot to only where the data is. \n # Avoid unnecessary whitespace. \n plt.ylim(0, 70) \n plt.xlim(0, 19)\n \n # Make sure your axis ticks are large enough to be easily read. \n # You don't want your viewers squinting to read your plot.\n# y_ticks = [] \n# plt.yticks(range(0, 1, 10), [str(x) for x in range(0, 91, 10)], fontsize=14) \n plt.xticks(fontsize=8) \n plt.yticks(fontsize=10)\n # Provide tick lines across the plot to help your viewers trace along \n # the axis ticks. Make sure that the lines are light and small so they \n # don't obscure the primary data lines. \n for y in [5,10,15,20,25,30,35,40,45,50, 55, 60, 65]: \n plt.plot(range(0,20), [y] * len(range(0,20)), \"--\", lw=0.5, color=\"black\", alpha=0.3) \n \n # Remove the tick marks; they are unnecessary with the tick lines we just plotted. \n plt.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\", \n labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\") \n \n # Now that the plot is prepared, it's time to actually plot the data! \n # Note that I plotted the labels in order of the highest % in the final year. \n labels = [label for label in data.keys() if label!='Frequencies'] \n y_poses = []\n offsets = [0.02, 0.04,0.06,0.08,0.1,0.12, 0.14,0.16,0.18,0.2]\n for rank, column in enumerate(labels): \n # Plot each line separately with its own color, using the Tableau 20 \n # color set in order. \n plt.plot(ind+0.25, data[column][1], lw=1.0, color=tableau20[rank+1]) \n \n # Add a text label to the right end of every line. Most of the code below \n # is adding specific offsets y position because some labels overlapped. \n y_pos = data[column][1][-1]\n# new_pos = None\n# offset_counter = 0\n for poses in y_poses:\n if y_pos < poses+1 and y_pos>=poses:\n y_pos = y_pos+2\n# offset_counter += 1\n break\n if y_pos > poses-1 and y_pos<=poses:\n y_pos = y_pos+2\n# offset_counter += 1\n break\n else:\n y_pos = y_pos\n y_poses.append(y_pos)\n # Again, make sure that all labels are large enough to be easily read \n # by the viewer. \n plt.text(19, y_pos, column, fontsize=8, color=tableau20[rank+1]) \n plt.savefig(\"figs/\"+filename+\".pdf\", bbox_inches=\"tight\");\n print \"Figure done.\" \n\ndef plot_polarity_aggregates(data, filename=\"temporal\"):\n \"\"\"\n Plots the temporal sentiment using the given data.\n \"\"\"\n tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120), \n (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150), \n (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148), \n (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199), \n (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)] \n \n # Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts. \n for i in range(len(tableau20)):\n r, g, b = tableau20[i]\n tableau20[i] = (r / 255., g / 255., b / 255.)\n \n # You typically want your plot to be ~1.33x wider than tall. This plot is a rare \n # exception because of the number of lines being plotted on it. \n # Common sizes: (10, 7.5) and (12, 9) \n f = plt.figure(figsize=(9, 6)) \n ind = np.arange(len(data['Targets'][0]))\n # Remove the plot frame lines. They are unnecessary chartjunk. \n ax = plt.subplot(111) \n ax.spines[\"top\"].set_visible(False) \n ax.spines[\"bottom\"].set_visible(False) \n ax.spines[\"right\"].set_visible(False) \n ax.spines[\"left\"].set_visible(False) \n ax.set_xticks(ind+0.25)\n ax.set_xticklabels(['%.2f' % x for x in data['Targets'][0]]) \n # Ensure that the axis ticks only show up on the bottom and left of the plot. \n # Ticks on the right and top of the plot are generally unnecessary chartjunk. \n ax.get_xaxis().tick_bottom() \n ax.get_yaxis().tick_left() \n ax.bar(ind, data['Frequencies'][1], 0.5, color=tableau20[14], edgecolor=\"none\")\n \n # Limit the range of the plot to only where the data is. \n # Avoid unnecessary whitespace. \n plt.ylim(-1, 1) \n plt.xlim(0, 19.5)\n \n # Make sure your axis ticks are large enough to be easily read. \n # You don't want your viewers squinting to read your plot.\n# y_ticks = [] \n# plt.yticks(range(0, 1, 10), [str(x) for x in range(0, 91, 10)], fontsize=14) \n plt.xticks(fontsize=8) \n plt.yticks(fontsize=10)\n # Provide tick lines across the plot to help your viewers trace along \n # the axis ticks. Make sure that the lines are light and small so they \n # don't obscure the primary data lines. \n# for y in [5,10,15,20,25,30,35,40,45]: \n for y in [-0.8,-0.6,-0.4,-0.2,0.2,0.4,0.6,0.8]: \n plt.plot(range(0,20), [y] * len(range(0,20)), \"--\", lw=0.5, color=\"black\", alpha=0.3) \n plt.plot(range(0,20), [0] * len(range(0,20)), \"--\", lw=2.5, color=\"black\", alpha=0.3) \n # Remove the tick marks; they are unnecessary with the tick lines we just plotted. \n plt.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\", \n labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\") \n \n # Now that the plot is prepared, it's time to actually plot the data! \n # Note that I plotted the labels in order of the highest % in the final year. \n labels = [label for label in data.keys() if label!='Frequencies'] \n y_poses = []\n offsets = [0.02, 0.04,0.06,0.08,0.1,0.12, 0.14,0.16,0.18,0.2]\n for rank, column in enumerate(labels): \n # Plot each line separately with its own color, using the Tableau 20 \n # color set in order. \n plt.plot(ind+0.25, data[column][1], lw=1.0, color=tableau20[rank+1]) \n \n # Add a text label to the right end of every line. Most of the code below \n # is adding specific offsets y position because some labels overlapped. \n y_pos = data[column][1][-1]\n# new_pos = None\n# offset_counter = 0\n for poses in y_poses:\n if y_pos < poses+0.1 and y_pos>=poses:\n y_pos = y_pos+0.2\n# offset_counter += 1\n break\n if y_pos > poses-0.1 and y_pos<=poses:\n y_pos = y_pos-0.2\n# offset_counter += 1\n break\n else:\n y_pos = y_pos\n y_poses.append(y_pos)\n # Again, make sure that all labels are large enough to be easily read \n # by the viewer. \n plt.text(19, y_pos, column, fontsize=8, color=tableau20[rank+1]) \n# plt.text(19, 29.5, \"Neutral\", fontsize=8, color=\"black\") \n plt.savefig(\"figs/\"+filename+\".pdf\", bbox_inches=\"tight\");\n print \"Figure done.\" \n\ndef plot_temporal_topics(data, filename=\"temporal_topics\"):\n \"\"\"\n Plots the temporal sentiment using the given data.\n \"\"\"\n tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120), \n (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150), \n (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148), \n (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199), \n (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)] \n \n # Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts. \n for i in range(len(tableau20)):\n r, g, b = tableau20[i]\n tableau20[i] = (r / 255., g / 255., b / 255.)\n \n # You typically want your plot to be ~1.33x wider than tall. This plot is a rare \n # exception because of the number of lines being plotted on it. \n # Common sizes: (10, 7.5) and (12, 9) \n f = plt.figure(figsize=(9, 6))\n ind = np.arange(len(data[data.keys()[0]][0]))\n # Remove the plot frame lines. They are unnecessary chartjunk. \n ax = plt.subplot(111) \n ax.spines[\"top\"].set_visible(False) \n ax.spines[\"bottom\"].set_visible(False) \n ax.spines[\"right\"].set_visible(False) \n ax.spines[\"left\"].set_visible(False) \n ax.set_xticks(ind+0.25)\n ax.set_xticklabels(['%.2f' % x for x in data[data.keys()[0]][0]]) \n # Ensure that the axis ticks only show up on the bottom and left of the plot. \n # Ticks on the right and top of the plot are generally unnecessary chartjunk. \n ax.get_xaxis().tick_bottom() \n ax.get_yaxis().tick_left() \n # Limit the range of the plot to only where the data is. \n # Avoid unnecessary whitespace. \n plt.ylim(0, 100) \n plt.xlim(0, 8)\n \n # Make sure your axis ticks are large enough to be easily read. \n # You don't want your viewers squinting to read your plot.\n# y_ticks = [] \n# plt.yticks(range(0, 1, 10), [str(x) for x in range(0, 91, 10)], fontsize=14) \n plt.xticks(fontsize=6) \n plt.yticks(fontsize=6)\n # Provide tick lines across the plot to help your viewers trace along \n # the axis ticks. Make sure that the lines are light and small so they \n # don't obscure the primary data lines. \n# for y in [5,10,15,20,25,30,35,40,45]: \n for y in range(0,100,5): \n plt.plot(range(0,8), [y] * len(range(0,8)), \"--\", lw=0.5, color=\"black\", alpha=0.3) \n plt.plot(range(0,8), [50] * len(range(0,8)), \"--\", lw=2.5, color=\"black\", alpha=0.3) \n # Remove the tick marks; they are unnecessary with the tick lines we just plotted. \n plt.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\", \n labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\") \n \n # Now that the plot is prepared, it's time to actually plot the data! \n # Note that I plotted the labels in order of the highest % in the final year. \n labels = data.keys() \n y_poses = []\n offsets = [0.02, 0.04,0.06,0.08,0.1,0.12, 0.14,0.16,0.18,0.2]\n for rank, column in enumerate(labels): \n # Plot each line separately with its own color, using the Tableau 20 \n # color set in order. \n for i in range(len(data[column][1])):\n data[column][1][i] = data[column][1][i] +50\n plt.plot(ind+0.25, data[column][1], lw=1.0, color=tableau20[rank]) \n \n # Add a text label to the right end of every line. Most of the code below \n # is adding specific offsets y position because some labels overlapped. \n y_pos = data[column][1][-1]\n# new_pos = None\n# offset_counter = 0\n for poses in y_poses:\n if y_pos < poses+5 and y_pos>=poses:\n y_pos = y_pos+8\n# offset_counter += 1\n break\n if y_pos > poses-5 and y_pos<=poses:\n y_pos = y_pos-8\n# offset_counter += 1\n break\n else:\n y_pos = y_pos\n y_poses.append(y_pos)\n # Again, make sure that all labels are large enough to be easily read \n # by the viewer. \n try:\n plt.text(8.2, y_pos, column, fontsize=8, color=tableau20[rank])\n except UnicodeDecodeError:\n plt.text(8.2, y_pos, column.decode('utf8'), fontsize=8, color=tableau20[rank])\n plt.text(8.2, 49.5, \"Neutral\", fontsize=8, color=\"black\") \n plt.savefig(\"figs/\"+filename+\".pdf\", bbox_inches=\"tight\");\n print \"Figure done.\"\n \nif __name__ == '__main__':\n# f1svm_data, f1nb_data, f1me_data, accsvm_data, accnb_data, accme_data = load_incremental_data()\n# data = {\"Erna Solberg\": [range(0,100),[random.randint(20,50) for _ in range(0,100)]],\n# \"rosenborg\": [range(0,100),[random.randint(40,60) for _ in range(0,100)]],\n# \"no target\": [range(0,100),[random.randint(30,40) for _ in range(0,100)]]}\n# plot_temporal_sentiment(f1svm_data, 'incremental_f1svm')\n# plot_temporal_sentiment(f1nb_data, 'incremental_f1nb')\n# plot_temporal_sentiment(f1me_data, 'incremental_f1me')\n# plot_temporal_sentiment(accsvm_data, 'incremental_accuracysvm')\n# plot_temporal_sentiment(accnb_data, 'incremental_accuracynb')\n# plot_temporal_sentiment(accme_data, 'incremental_accuracyme')\n \n# data = {\"SVM(SB)+SVM(PC)\": [(0.72+0.79)/2, (0.66+0.80)/2, (0.69+0.76)/2, (0.67+0.78)/2],\n# \"SVM(SB)+SVM(PB)\": [(0.72+0.77)/2, (0.66+0.76)/2, (0.69+0.75)/2, (0.67+0.75)/2],\n# \"SVM(SB)+MaxEnt(PC)\":[(0.72+0.77)/2, (0.66+0.80)/2, (0.69+0.72)/2, (0.67+0.75)/2],\n# \"MaxEnt(SB)+MaxEnt(PC)\": [(0.70+0.77)/2, (0.66+0.80)/2, (0.61+0.72)/2, (0.63+0.75)/2],\n# \"MaxEnt(SB)+SVM(PC)\": [(0.70+0.79)/2, (0.66+0.80)/2, (0.61+0.76)/2, (0.63+0.78)/2]}\n# plot_combined_histogram(data, \"combined\")\n data = pickle.load(open('topically_aggregated_polarity', 'rb'))\n plot_temporal_topics(data, \"temporal_topics\")\n"},{"alpha_fraction":0.5296835899353027,"alphanum_fraction":0.5456807613372803,"avg_line_length":30.977272033691406,"blob_id":"523253e01c6dc35881565f3ae4859784cc7cfebe","content_id":"304e8cb07ec961291ed5fdc3e5d2b08a3e0c0380","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2813,"license_type":"no_license","max_line_length":239,"num_lines":88,"path":"/annotation.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"'''\nCreated on 30. sep. 2014\n\n@author: JohnArne\n'''\nimport utils\nimport tweet\nimport os\n\ndef user_annotation():\n \"\"\"\n Feed tweets to console one at a time, and ask user for sentiment annotation.\n \"\"\"\n dataset = utils.select_dataset()\n text_tweets = utils.get_dataset(dataset)\n tweets = []\n for text_tweet in text_tweets:\n tweets.append(tweet.to_tweet(text_tweet))\n username = raw_input(\"Name? ... \")\n \n print \"\\n--------------\\n\"\n print \"Input: \"\n print \"\\n1: Negative sentiment (Negative opinion). \\n2: Neutral/objective sentiment (No opinion). \\n3: Positive sentiment (Positive opinion). \\n5: Delete the tweet from the dataset. \\nx: Cancel sequence. 0: Go back to previous tweet. \"\n print \"\\n--------------\\n\"\n \n annotated_to = 0\n i = 0\n while i < len(tweets):\n# tweets[i].text.encode('utf8')\n# text = tweets[i].text\n# tweets[i].text = text.decode('utf8')\n try:\n print \"Tweet nr. : \"+str(i+1)\n print str(((i+1.0*1.0)/len(tweets)*1.0)*100)+\" % done \"\n print unicode(tweets[i].__str__().decode('utf8'))\n except UnicodeEncodeError:\n try:\n print \"Tweet nr. : \"+str(i+1)\n print str(tweets[i])\n except UnicodeEncodeError:\n print \"Could not print tweet number \"+str(i+1) +\". Deleting tweet...\"\n tweets.remove(tweets[i])\n continue\n \n userinput = raw_input(\"...\")\n while not legal_input(userinput):\n userinput = raw_input(\"Unlawful input! Please re-introduce.\")\n if userinput is '1':\n tweets[i].set_sentiment(\"negative\")\n elif userinput is '2':\n tweets[i].set_sentiment(\"neutral\")\n elif userinput is '3':\n tweets[i].set_sentiment(\"positive\")\n elif userinput is '5':\n print \"Deleting tweet...\"\n tweets.remove(tweets[i])\n continue\n elif userinput is '0':\n i = i-1\n continue\n elif userinput is 'x':\n break\n i = i+1\n \n \n #TODO: need to encode to utf when getting from dataset?!?!\n #Store the sentiment in file!\n tweetlines = []\n for t in tweets[:i]:\n if t.get_sentiment() is None:\n continue\n tweetlines.append(t.to_tsv())\n dir = username+\"_annotated_data\"\n if not os.path.exists(dir):\n os.makedirs(dir)\n utils.store_dataset(tweetlines, dir+dataset[4:])\n \n print \"Domo arigato!\" \n \ndef legal_input(userinput):\n \"\"\"\n Checks input and returns true if the input is legal. Legal input should be \"1\", \"2\", \"3\", \"5\", or \"x\"\n \"\"\"\n legal_inputs = ['1','2','3','5','0','x']\n \n if userinput in legal_inputs:\n return True\n return False"},{"alpha_fraction":0.5586603879928589,"alphanum_fraction":0.5669795274734497,"avg_line_length":36.76612854003906,"blob_id":"f22e91d7a468cde5082bf7aff151f54e7bd21449","content_id":"7fcad2df2c48e8b24006a72c42560e3cc3d3493d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4688,"license_type":"no_license","max_line_length":112,"num_lines":124,"path":"/tweet.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"'''\nCreated on 30. sep. 2014\n\n@author: JohnArne\n'''\nclass Tweet(object):\n \"\"\"\n Class for wrapping tweet information.\n \"\"\"\n \n def __init__(self, timestamp, user, text):\n self.user = user\n self.text = text\n self.timestamp = timestamp\n self.subjectivity = None #0 if objective, 1 if subjective\n self.polarity = None #0 if negative sentiment, 1 if positive sentiment\n self.processed_words = [] #dict for containing the stemmed and preprocessed words of the text body\n self.tagged_words = [] # a list of dicts\n self.nrof_happyemoticons = 0\n self.nrof_sademoticons = 0\n self.nrof_hashtags = 0\n self.nrof_usersmentioned = 0\n self.exclamated = False\n self.hashtags = []\n self.links = []\n self.users_mentioned = []\n self.nrof_exclamations = 0\n self.nrof_questionmarks = 0\n self.word_count = 0\n self.words_with_sentimentvalues={}\n self.sentiments = []\n self.link_pos = []\n self.sentiment_target = \"\"\n \n def to_tsv(self):\n \"\"\"\n Convert the data in this tweet to the .tsv format used to store it in .tsv files.\n TSV Format: Date \\t Time \\t Sentiment \\t User \\t Textbody\n \"\"\"\n tvsline = \"\"\n sentiment = self.get_sentiment()\n if sentiment is not None:\n tsvline = self.timestamp\n tsvline = tsvline+\"\\t\"+sentiment\n tsvline = tsvline+\"\\t\"+self.user\n tsvline = tsvline+\"\\t\"+self.text\n else:\n tsvline = self.timestamp+\"\\t\"+self.user+\"\\t\"+self.text\n return tsvline\n \n \n def get_sentiment(self):\n \"\"\"\n Returns a textual representation of the sentiment (negative, neutral, positive),\n Based on the subjectivity and polarity variables of the tweet.\n \"\"\"\n sentiment = None\n if self.subjectivity is 1:\n sentiment = \"negative\".encode('utf8') if self.polarity is 0 else \"positive\".encode('utf8')\n elif self.subjectivity is 0:\n sentiment = \"neutral\".encode('utf8')\n return sentiment\n \n def set_sentiment(self, sentiment):\n \"\"\"\n Sets the binary subjectivity and polarity variables of the tweet based on the\n passed textual representation of sentiment.\n \"\"\"\n if sentiment==\"negative\":\n self.subjectivity = 1\n self.polarity = 0\n elif sentiment==\"neutral\":\n self.subjectivity = 0\n self.polarity = 0\n elif sentiment==\"positive\":\n self.subjectivity = 1\n self.polarity = 1\n \n def stat_str(self):\n \"\"\"\n Returns a string of all stats of the tweet. BROKEN, unicode errors all around\n \"\"\"\n# try:\n# statstring = \"\\n--------------\\n\"+\" \\n\"+self.user+\"\\n\"+unicode(self.text)+\"\\n \"\n# except UnicodeDecodeError:\n statstring = \"\\n--------------\\n\"+\" \\n\"+self.user+\"\\n\"+self.text+\"\\n \"\n statstring = statstring + \"Tagged words: \"+str(self.tagged_words) + \"\\n\"\n statstring = statstring + \"Sentiment \" +str(self.get_sentiment()) + \"\\n\"\n statstring = statstring + \"Hashtags: \"+str(self.nrof_hashtags) + \" \"+str(self.hashtags) + \"\\n\"\n statstring = statstring + \"Users: \"+str(self.nrof_usersmentioned) + \" \"+str(self.users_mentioned) + \"\\n\"\n statstring = statstring + \"Happy emoticons: \"+str(self.nrof_happyemoticons) + \"\\n\"\n statstring = statstring + \"Sad emoticons: \"+str(self.nrof_sademoticons)+ \"\\n\"\n statstring = statstring + \"Question marks: \"+str(self.nrof_questionmarks)+ \"\\n\"\n statstring = statstring + \"Exclamation marks: \"+str(self.nrof_exclamations)+ \"\\n\"\n statstring = statstring + \"\\n--------------\\n\"\n return statstring\n \n def __str__(self):\n \"\"\"\n Returns a string representation of the tweet for visual representation.\n \"\"\"\n return \"\\n--------------\\n\"+\" \\n\"+self.user+\"\\n\"+self.text+\"\\n--------------\\n\"\n \n def __eq__(self, other):\n return self.text == other.text\n \ndef to_tweet(text):\n \"\"\"\n Convert a given .tsv formatted text line to a tweet object\n \"\"\"\n splits = text.split('\\t')\n print \"Creating tweet object: \"\n if len(splits)>3:\n print \"Splitted into more than 3...\"\n for split in splits:\n print split\n tweet = Tweet(splits[0], splits[2], splits[3])\n tweet.set_sentiment(splits[1])\n else:\n print \"Splitted into less than 3...\"\n for split in splits:\n print split\n tweet = Tweet(splits[0], splits[1], splits[2])\n return tweet\n \n"},{"alpha_fraction":0.5520133972167969,"alphanum_fraction":0.5822147727012634,"avg_line_length":26.952381134033203,"blob_id":"502f11442a19c42bd965ab57c482c52e16a37246","content_id":"d13b70f662befcb367ff23593c8d46d17fe9918a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":596,"license_type":"no_license","max_line_length":104,"num_lines":21,"path":"/models/nb.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"'''\nCreated on 19. mars 2014\n\n@author: JohnArne\n'''\nfrom model import Model\nfrom sklearn.naive_bayes import MultinomialNB \n\n\nclass NB(Model):\n \"\"\"\n Class implementing the Multinomial Naive Bayes learning method.\n \"\"\"\n \n def __init__(self, train_tweets, train_targets, vect_options, tfidf_options):\n self.classifier = MultinomialNB()\n \n extra_params ={ \n 'clf__alpha': (0.1, 0.3, 0.5, 0.7, 0.8, 1.0)\n }\n super(NB, self).__init__(train_tweets, train_targets, vect_options, tfidf_options, extra_params)\n\n "},{"alpha_fraction":0.6105527877807617,"alphanum_fraction":0.6170854568481445,"avg_line_length":31.639345169067383,"blob_id":"62123936db66fcecf44724466176a7c066206e9c","content_id":"42440e1a88f3576abd14f3fa7ce5c960426a20c8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2003,"license_type":"no_license","max_line_length":157,"num_lines":61,"path":"/tagger.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n'''\nCreated on 30. sep. 2014\n\n@author: JohnArne\n'''\n\n\n\nimport requests\n\nclass Tagger():\n \"\"\"\n Interfaces the POS tagger for classification.\n \"\"\"\n \n def __init__(self):\n \n #Request init connect to smarttagger\n self.url = \"http://smarttagger.herokuapp.com/tag\"\n \n \n def tag_text(self, text):\n \"\"\"\n Tags a text sequence using the current tagger.\n \"\"\"\n \n# print \"Tagging: \"+unicode(text.decode('utf8'))\n par = {\"text\": text, \"raw\": \"raw\", \"format\": \"json\"}\n r = requests.post(self.url, data=par)\n tagged_words = {}\n try:\n results = r.json()[\"phrases\"]\n tagged_words = results\n except ValueError as e:\n print \"Unable to get JSON: \" +str(e)\n print r.reason\n print r.status_code\n if len(tagged_words)<1:\n return None\n else:\n return tagged_words[0]\n \n\n\nif __name__==\"__main__\":\n tagger = Tagger()\n \n texts = []\n texts.append(u\"Viss Russland kritikken erna solberg framførte i FN var skjult, korleis i hulaste har den då hamna på framsida av VG i dag?\")\n texts.append(u\"borgebrende Hoyre erna solberg Hvem skal gjøre møkkajobbene da?\")\n texts.append(u\"erna solberg Siv Jensen FrP jensstoltenberg jonasgahrstore Skremmende at regjeringen vil selge aksjer i statlige selskap til utlandet.\")\n texts.append(u\"CSpange Aftenposten erna solberg Sannsynlige grunner ingen, heller ikke de 120, venter resultater. Og Kina og USA uteblir. Neste gang...\")\n texts.append(u\"ElinJoval konservativ erna solberg det Elin sa! Jeg vil ha mer tid til å være sammen med elevene.\")\n texts.append(u\"Skulle ønske konservativ og erna solberg hjalp til å styrke lærernes status. Vi er gode! Vi trenger tid til å gjøre jobben vår bedre!\")\n \n for text in texts:\n print unicode(text)\n \n for text in texts:\n print tagger.tag_text(text)"},{"alpha_fraction":0.6507903933525085,"alphanum_fraction":0.6570028066635132,"avg_line_length":37.20052719116211,"blob_id":"26054ff21e0e3738fed7bba6acf6800e41c70023","content_id":"332fe385b0b46ee3cc9b16c74970cd59e0fd0529","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":14487,"license_type":"no_license","max_line_length":260,"num_lines":379,"path":"/entity_extraction.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"'''\nCreated on 4. jan. 2015\n\n@author: JohnArne\n\n-Ta en gruppe tweets.\n-Prov a finne entitetene i hver enkelt tweet.\n- kjor clustering pa tweets, eller pa bare entitetsnavn... grupper entitetene etter clusters og nominer den mest frekvente entiteten som overordnet navn\n'''\nimport lexicon.pos_mappings\nfrom lexicon import pos_mappings\nfrom tweet import Tweet\nimport utils\nfrom models.features import get_sentiment_values\nimport classifier\nimport pickle\nfrom sklearn import metrics\nimport plotting\nfrom sklearn.feature_extraction.text import CountVectorizer\nimport math\n\n\ndef perform_entity_extraction(tweets, sentimentvalues, breakword_min_freq=0.2, breakword_range=2, use_sentiment_values=False, use_pmi=False, vocabulary=None, cluster=False, use_minibatch=False, use_idf=False, use_hasher=False):\n \"\"\"\n Takes in a list of correctly predicted subjective tweets and sentimentvalues, in addition to several optional parameters, and attempts entity extraction on all the tweeets.\n \"\"\"\n print len(tweets)\n sub_clf = classifier.get_optimal_subjectivity_classifier()\n #Get all the correctly classified subjective tweets\n if use_pmi and vocabulary==None: vocabulary = create_vocabulary(tweets) \n if use_sentiment_values:\n entities = find_entities(sub_clf, tweets, breakword_min_freq, breakword_range, use_pmi, vocabulary=vocabulary, sentimentvalues=sentimentvalues)\n else:\n entities = find_entities(sub_clf, tweets, breakword_min_freq, breakword_range, use_pmi, vocabulary=vocabulary)\n \n if cluster:\n #use clustering to group together tweets, \n #then choose the entities with the greatest freqiencies within each cluster as the sentiment target for all in the cluster\n #but not if the target is already none...\n cluster_tweets(tweets, use_minibatch, use_idf, use_hasher)\n \n \n for i in xrange(len(entities)):\n entities[i] = entities[i][0] if len(entities[i])>0 else None\n print entities\n return entities\n\ndef find_entities(sub_clf, tweets, min_freq, breakword_range, use_pmi=False, vocabulary=None, sentimentvalues=None):\n \"\"\"\n Takes in a subjectivity classifier and the tweets, and attempts to find the target of the classified sentiment.\n Return a textual description of the entity.\n \"\"\"\n if sentimentvalues!=None:\n entities = [find_entity(sub_clf, t, min_freq, breakword_range, use_pmi, vocabulary=vocabulary, sentimentvalues=s) for t,s in zip(tweets,sentimentvalues)]\n else:\n entities = [find_entity(sub_clf, t, min_freq, breakword_range, use_pmi, vocabulary=vocabulary) for t in tweets]\n return entities\n \n \ndef find_entity(sub_clf, t, min_freq, breakword_range, use_pmi=False, vocabulary=None, sentimentvalues=None):\n \"\"\"\n Attempts at identifying the entity of a single tweet, utilizing sentiment values if not none.\n \"\"\"\n #get a list of possibilities for this tweet\n possibilities = get_possible_entities(t)\n if len(possibilities)<1:\n for hashtag in t.hashtags:\n if len(hashtag)>1:\n return [hashtag]\n return []\n if len(possibilities)==1:\n return possibilities\n \n #Get breakwords from breakdown classification\n breakwords = breakdown_classify(sub_clf, t)\n \n breakwords = cutoff_breakwords(breakwords, min_freq)\n #get sentimental words if given values\n sentimentwords = []\n if sentimentvalues!=None:\n sentimentwords = get_sentimentwords(sentimentvalues)\n \n #Perform an intersection of the breakwords and sentimental words\n# print \"Text: \", t.text\n# print \"Possible entities: \",possibilities\n# print \"Hashtags: \",t.hashtags\n# print \"Shifting words: \",breakwords\n# print \"Sentimental words: \",sentimentwords\n sentiment_points = [val for val in sentimentwords if val in breakwords]\n# print \"Intersection: \", sentiment_points\n if len(sentiment_points)<1: sentiment_points = list(set(breakwords + sentimentwords))\n# print \"New intersection: \", sentiment_points\n \n possibilities = cutoff_possibilities(t.text.lower(), possibilities, sentiment_points, breakword_range)\n# print \"Possibilities after cutoff: \", possibilities\n# raw_input(\"Continue?\")\n\n if use_pmi:\n #Use PMI to disambiguate between possibilities\n pmi = []\n for p in possibilities:\n if p is None: continue\n for s in sentiment_points:\n if s is None: continue\n pmi.append([calculate_pmi(p,s,vocabulary), p])\n if len(pmi)>0: \n possibilities = [max(pmi)[1]]\n \n if len(possibilities)>0:\n return possibilities \n else:\n if len(t.hashtags)>0:\n for hashtag in t.hashtags:\n if len(hashtag)>1:\n return [hashtag]\n return []\n \n# if len(possibilities)>0:\n# return t.hashtags[0] if len(t.hashtags)>0 else possibilities\n# else:\n# return t.hashtags\n# \n #decide entity from possibilities based on the sentiment points\n #calculate the \"center\" of the sentiment points\n #choose the entity which is closest to the center of the sentiment points\n #or choose the entity closest to the first sentiment points...\n \n \ndef calculate_pmi(entity, sentiword, vocabulary):\n \"\"\"\n Calculates the pointwise mutual information between two given words.\n \"\"\"\n unigrams_freq = float(sum(vocabulary[0].values()))\n prob_entity = vocabulary[0][entity] / unigrams_freq\n prob_sentiword = vocabulary[0][sentiword] / unigrams_freq\n try:\n prob_both = vocabulary[1][\" \".join([unicode(entity),unicode(sentiword)])] / float(sum(vocabulary[1].values()))\n except KeyError:\n return 0.0\n except UnicodeDecodeError:\n print \"UnicodeError\"\n return 0.0\n return math.log(prob_both/float(prob_entity*prob_sentiword),2)\n \ndef create_vocabulary(tweets):\n \"\"\"\n Creates a bigram + unigram vocabulary of the given tweet texts.\n \"\"\"\n print \"Creating vocabulary...\"\n vocabulary = []\n unigrams_freq = {}\n bigrams_freq = {}\n texts= [t.text.lower() for t in tweets]\n for text in texts:\n for phrase in text.split('.'):\n phrase = phrase.replace(',',' ')\n unigrams = phrase.split(\" \")\n unigrams = [u for u in unigrams if len(u)>1]\n extended_bigrams = [x+\" \"+y for x,y in zip(unigrams[0::2],unigrams[1::2])] + [x+\" \"+y for x,y in zip(unigrams[1::2],unigrams[2::2])] + [x+\" \"+y for x,y in zip(unigrams[0::2],unigrams[2::2])] + [x+\" \"+y for x,y in zip(unigrams[1::2],unigrams[3::2])]\n for unigram in unigrams:\n unigrams_freq[unigram] = unigrams_freq.get(unigram, 0) + 1\n for bigram in extended_bigrams:\n bigrams_freq[bigram] = bigrams_freq.get(bigram, 0) + 1\n vocabulary = [unigrams_freq, bigrams_freq]\n return vocabulary\n\ndef is_entity(clf, t, entity, sentimentvalues=None):\n \"\"\"\n Takes in a classifier, a tweet, and an entity, returns a binary value corresponding to whether each entity is the sentiment entity of each tweet.\n \"\"\"\n \n return False\n \ndef get_possible_entities(t):\n \"\"\"\n Takes in a tweet, and returns a list of the possible entities for that tweet.\n \"\"\"\n possible_entities = []\n for phrase in t.tagged_words:\n for word in phrase:\n try:\n entity = word['word']\n if word['pos'] ==\"Np\":\n if len(entity)>1:\n possible_entities.append(entity.lower())\n except KeyError:\n continue\n return possible_entities\n \ndef get_sentimentwords(sentimentvalues):\n \"\"\"\n returns the words that contain sentimental value.\n \"\"\"\n sentimentwords = []\n for word in sentimentvalues.keys():\n if sentimentvalues[word][0]>0 or sentimentvalues[word][1]>0:\n sentimentwords.append(word)\n return sentimentwords\n\n\ndef breakdown_classify(clf, t):\n \"\"\"\n Classify substring permutations of the tweet in order to find a shifting point in the subjectivity classification\n \"\"\"\n orig_class = clf.classify([t])\n breakwords = subclassify(t.text.lower(), clf, orig_class)\n# breakwords = []\n# print \"Breakdown classification\"\n# for substring_paths in substrings:\n# for substring_and_rmword in substring_paths:\n# print substring_and_rmword['substring'], \" rm:\",substring_and_rmword['removed_word']\n# #Classify each substring, append causal word when sentiment changes from original\n# new_class = clf.classify_text([\" \".join(substring_and_rmword['substring'])])\n# print \"New class: \",new_class\n# if new_class!=orig_class: \n# breakwords.append(substring_and_rmword['removed_word'])\n# break\n# print \"Original prediction: \",orig_class\n return breakwords\n \n\n\ndef subclassify(t, clf, orig):\n \"\"\"\n Takes in a text, a classifier, and an original class. Returns all the break words where the class shifts. FIXXX!\n \"\"\"\n phrases = t.split(\",\")\n words = []\n for phrase in phrases:\n words = words + phrase.split(\" \")\n length = len(words)\n breakwords = []\n for i in xrange(length):\n breakwords.append(clf_sub(words[:i]+words[i+1:], words[i], clf, orig)) \n\n return breakwords\n \ndef clf_sub(words, removed_word, clf, orig):\n \"\"\"\n FIIIIX!!! Return on classification shift!!! yesaaaaa!\n \"\"\"\n if len(words)==1: return removed_word if clf.classify_text([\" \".join(words)])!=orig else None\n breakword = None\n# print \" \".join(words), \"Removed word: \", removed_word\n for i in xrange(len(words)-1):\n if clf.classify_text([\" \".join(words)])!=orig:\n# print \"Swithed class!\"\n return removed_word\n return clf_sub(words[:i]+words[i+1:], words[i], clf, orig)\n return breakword\n \ndef cutoff_breakwords(breakwords, min_freq):\n \"\"\"\n Cuts of breakwords below the given frequency. Returns a list of uniques, where the belowfreqs have been removed\n \"\"\"\n breakword_freq = int(round(len(breakwords)*min_freq))\n# print \"Breakwords before cutoff \",breakwords, min_freq\n frequencies = {}\n #remove breakwords with a lower frequency\n for word in breakwords:\n frequencies[word] = frequencies.get(word,0)+1\n uniquelist = list(set(breakwords))\n for key in frequencies:\n if frequencies[key]0 else None for t in tweets]\n \ndef reduce_entities(entities):\n \"\"\"\n Reduce entities to binary so as to test with actual targets.\n \"\"\"\n reduced = []\n for entity in entities:\n reduced.append(1 if entity in rosenborg_model else 0)\n return reduced\n\ndef get_scores(targets, predictions):\n accuracy = metrics.accuracy_score(targets, predictions)\n precision = metrics.precision_score(targets, predictions)\n recall = metrics.recall_score(targets, predictions)\n f1_score = metrics.f1_score(targets, predictions)\n return accuracy, precision, recall, f1_score\n\ndef create_model(text):\n model = [text]\n f = open(text+\"_model\", \"wb\")\n pickle.dump(model,f)\n f.close()\n \ndef append_to_model(name, text):\n model = pickle.load(name+\"_model\")\n model.append(text)\n model = list(set(model))\n f = open(name+\"_model\", \"wb\")\n pickle.dump(model, f)\n f.close()\n \ndef cluster_tweets(tweets, max_features, use_minibatch, use_idf, use_hasher):\n \"\"\"\n Performs k-means clustering on tweets.\n \"\"\"\n \n \n return None\n\n\n\ndef perform_and_test_extraction():\n datasetnr = 1\n tweets = utils.get_pickles(datasetnr)\n vocabulary = create_vocabulary(utils.get_all_pickles())\n \n sentimentvalues = get_sentiment_values(datasetnr)\n tweets, targets = utils.get_entity_test_and_targets()\n entities = perform_entity_extraction(tweets, sentimentvalues, breakword_range=3)\n hashtag_entities = get_hashtag_entities(tweets)\n \n pmi_entities = perform_entity_extraction(tweets, sentimentvalues, breakword_range=8, use_pmi=True, vocabulary=vocabulary)\n #TESTIFY!\n reduced_entities = reduce_entities(entities)\n reduced_hashtags = reduce_entities(hashtag_entities)\n reduced_pmis = reduce_entities(pmi_entities)\n \n data = {}\n accuracy, precision, recall, f1_score = get_scores(targets, reduced_entities)\n print \"Entity Scores: \", accuracy, precision, recall, f1_score\n data[\"Custom\"] = [accuracy, precision, recall, f1_score] \n accuracy, precision, recall, f1_score = get_scores(targets, reduced_hashtags)\n data[\"Hashtags\"] = [accuracy, precision, recall, f1_score]\n print \"Hashtag Scores: \", accuracy, precision, recall, f1_score\n accuracy, precision, recall, f1_score = get_scores(targets, reduced_pmis)\n print \"PMI Scores: \", accuracy, precision, recall, f1_score\n data[\"Custom+PMI\"] = [accuracy, precision, recall, f1_score]\n #send to plotting\n plotting.plot_entity_histogram(data, \"entity_extraction\")\n \n \nrosenborg_model = [\"rosenborg\",\"rosenborgs\",\"rosenborgms\", \"rbk\",\"rbks\",\"rosenborg2\" ]\n \nif __name__ == '__main__':\n #test substringify\n# substrings = subc(string)\n# print substrings\n# print len(substrings)\n #test breakdown clf\n# breakdown_classify(\"adwd\", Tweet(\"12313\", \"johnaren\", \"jeg liker at\"))\n perform_and_test_extraction()\n \n "},{"alpha_fraction":0.5698412656784058,"alphanum_fraction":0.5984126925468445,"avg_line_length":29.649999618530273,"blob_id":"3d0de48a877dac685544d2d1f4c03f06196825bd","content_id":"7341c010223f1b5f2d098fe041786b1224d104ac","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":630,"license_type":"no_license","max_line_length":105,"num_lines":20,"path":"/models/svm.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"'''\nCreated on 19. mars 2014\n\n@author: JohnArne\n'''\nfrom model import Model\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.svm import LinearSVC\n\nclass SVM(Model):\n \"\"\"\n Class implementing the Support Vector Machines classsification model.\n \"\"\"\n \n def __init__(self, train_tweets, train_targets, vect_options, tfidf_options):\n self.classifier = LinearSVC()\n extra_params = {\n 'clf__C': (0.1, 0.3, 0.5, 0.7, 0.8, 1.0)\n }\n super(SVM, self).__init__(train_tweets, train_targets, vect_options, tfidf_options, extra_params)\n \n "},{"alpha_fraction":0.5687612295150757,"alphanum_fraction":0.5821185111999512,"avg_line_length":36.84239196777344,"blob_id":"88fd87586f1c74510f4110cabdd8cc027758b9a0","content_id":"1961cb5ce98a7350685a9cd75a8a0f69aa4b9bd2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":13925,"license_type":"no_license","max_line_length":134,"num_lines":368,"path":"/models/features.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"'''\nCreated on 27. nov. 2014\n\n@author: JohnArne\n'''\nfrom sklearn.feature_extraction.text import CountVectorizer\nimport numpy as np\nimport pickle\n\ndef get_feature_set(tweet,featureset,sentimentvalues):\n if(featureset=='SA'):\n return get_feature_set_SA(tweet)\n elif(featureset=='SB'): \n return get_feature_set_SB(tweet)\n elif(featureset=='SC'): \n return get_feature_set_SC(tweet,sentimentvalues)\n elif(featureset=='SC2'): \n return get_feature_set_SC2(tweet,sentimentvalues)\n elif(featureset=='PA'): \n return get_feature_set_PA(tweet)\n elif(featureset=='PB'): \n return get_feature_set_PB(tweet)\n elif(featureset=='PC'): \n return get_feature_set_PC(tweet,sentimentvalues)\n elif(featureset=='PC2'): \n return get_feature_set_PC2(tweet,sentimentvalues)\n \n\ndef get_feature_set_SA(tweet):\n \"\"\"\n Retrieves a list of tweets objects and returns feature set SA, which is only text frequencies...\n \"\"\"\n features= {}\n return features\n\ndef get_feature_set_SB(tweet):\n \"\"\"\n Creates a dict with grammatical features to be included in classification. Returns it to the classification model.\n Features to be included: pos-tags, \n \"\"\"\n #pos-tag frequencies\n# print \"Tagged words in tweet: \", tweet.tagged_words\n pos_tag_freq = {}\n additional_freq = {}\n for phrase in tweet.tagged_words:\n for word in phrase:\n try:\n tag = word['pos']\n pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# if tag=='PRtinf':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='ADJS':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='ADJ':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='NP':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='DET':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='P':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n if tag in ADJECTIVES:\n additional_freq['adjectives'] = additional_freq.get(tag, 0) + 1\n elif tag in ADVERBS: \n additional_freq['adverbs'] = additional_freq.get(tag, 0) + 1\n elif tag in PRONOUNS:\n additional_freq['pronoun'] = 1\n except KeyError:\n continue\n# print \"Tag frequencies: \", pos_tag_freq\n for key in pos_tag_freq.keys():\n pos_tag_freq[key] = pos_tag_freq[key]*1.0\n #number of adjectives in sentence, number of adverbs in sentence(except ikke), pronoun in sentence(binary) \n #Number of exclamation marks, number of emoticons,\n emoticons = tweet.nrof_happyemoticons+tweet.nrof_sademoticons\n if emoticons>0:\n additional_freq['emoticons'] = emoticons*1.0\n if tweet.nrof_exclamations>0:\n additional_freq['exclamations'] = tweet.nrof_exclamations*1.0\n \n# print \"Additional frequencies: \", additional_freq\n# raw_input(\"Continue?\")\n \n #Concatenate the dicts\n features= dict(pos_tag_freq.items() + additional_freq.items())\n# print \"All features: \", features\n# raw_input(\"Continue?\")\n return features\n\n\ndef get_feature_set_SC(tweet, sentimentvalues):\n \"\"\"\n Retrieves a list of tweets objects and returns feature set SC.\n \"\"\"\n pos_tag_freq = {}\n additional_freq = {}\n for phrase in tweet.tagged_words:\n for word in phrase:\n try:\n tag = word['pos']\n pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# if tag=='PRtinf':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='ADJS':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='ADJ':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='NP':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='DET':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='P':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n if tag in ADJECTIVES:\n additional_freq['adjectives'] = additional_freq.get(tag, 0) + 1\n elif tag in ADVERBS: \n additional_freq['adverbs'] = additional_freq.get(tag, 0) + 1\n elif tag in PRONOUNS:\n additional_freq['pronoun'] = 1\n except KeyError:\n continue\n for key in pos_tag_freq.keys():\n pos_tag_freq[key] = pos_tag_freq[key]*1.0\n #number of adjectives in sentence, number of adverbs in sentence(except ikke), pronoun in sentence(binary) \n #Number of exclamation marks, number of emoticons,\n emoticons = tweet.nrof_happyemoticons+tweet.nrof_sademoticons\n if emoticons>0:\n additional_freq['emoticons'] = emoticons*1.0\n if tweet.nrof_exclamations>0:\n additional_freq['exclamations'] = tweet.nrof_exclamations*1.0\n \n #Add lexicon values\n #total subjectivity score from word polarities, total objectivity score, number of subjective words, number of objective words, e\n sub_score = 0.0\n obj_score = 0.0\n nrof_subwords = 0\n nrof_objwords = 0\n for word in sentimentvalues.keys():\n if sentimentvalues[word][0]>0:\n sub_score = sub_score + sentimentvalues[word][0]\n nrof_subwords = nrof_subwords + 1\n if sentimentvalues[word][1]>0:\n sub_score = sub_score + sentimentvalues[word][1]\n nrof_subwords = nrof_subwords + 1\n if sentimentvalues[word][2]>0:\n obj_score = obj_score + sentimentvalues[word][2]\n nrof_objwords = nrof_objwords + 1\n if sub_score>0:\n additional_freq[\"sub_score\"] = sub_score+1.0\n if obj_score>0:\n additional_freq[\"obj_score\"] = obj_score+1.0\n if nrof_subwords>0:\n additional_freq[\"subjective_words\"] = nrof_subwords*1.0\n if nrof_objwords>0:\n additional_freq[\"objective_words\"] = nrof_objwords*1.0\n \n #Concatenate the dicts\n features= dict(pos_tag_freq.items() + additional_freq.items())\n \n return features\n\ndef get_feature_set_SC2(tweet, sentimentvalues):\n \"\"\"\n Retrieves a list of tweets objects and returns feature set SC.\n \"\"\"\n pos_tag_freq = {}\n additional_freq = {}\n for phrase in tweet.tagged_words:\n for word in phrase:\n try:\n tag = word['pos']\n pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n if tag in ADJECTIVES:\n additional_freq['adjectives'] = additional_freq.get(tag, 0) + 1\n elif tag in ADVERBS: \n additional_freq['adverbs'] = additional_freq.get(tag, 0) + 1\n elif tag in PRONOUNS:\n additional_freq['pronoun'] = 1\n except KeyError:\n continue\n for key in pos_tag_freq.keys():\n pos_tag_freq[key] = pos_tag_freq[key]*1.0\n #number of adjectives in sentence, number of adverbs in sentence(except ikke), pronoun in sentence(binary) \n #Number of exclamation marks, number of emoticons,\n emoticons = tweet.nrof_happyemoticons+tweet.nrof_sademoticons\n if emoticons>0:\n additional_freq['emoticons'] = emoticons*1.0\n if tweet.nrof_exclamations>0:\n additional_freq['exclamations'] = tweet.nrof_exclamations*1.0\n \n #Add lexicon values\n #total subjectivity score from word polarities, total objectivity score, number of subjective words, number of objective words, e\n sub_score = sentimentvalues[0]+sentimentvalues[1]\n obj_score = sentimentvalues[2]\n if sub_score>0:\n additional_freq[\"sub_score\"] = sub_score+1.0\n if obj_score>0:\n additional_freq[\"obj_score\"] = obj_score+1.0\n \n #Concatenate the dicts\n features= dict(pos_tag_freq.items() + additional_freq.items())\n \n return features\n\n\ndef get_feature_set_PA(tweet):\n \"\"\"\n Retrieves a list of tweets objects and returns feature set PA, which is noone... Only word tokens.\n \"\"\"\n features= {}\n return features\n\ndef get_feature_set_PB(tweet):\n \"\"\"\n Retrieves a list of tweets objects and returns feature set PB.\n \"\"\"\n features= {\n 'text_length': np.log(len(tweet.text))\n } #ADD ADDITIONAL FEATURES\n if tweet.nrof_sademoticons>0:\n features['sademoticons'] = tweet.nrof_sademoticons\n if tweet.nrof_happyemoticons>0:\n features['happyemoticons'] = tweet.nrof_happyemoticons\n \n return features\n\ndef get_feature_set_PC(tweet, sentimentvalues):\n \"\"\"\n Retrieves a list of tweets objects and returns feature set PC.\n \"\"\"\n features= {\n 'text_length': np.log(len(tweet.text))\n } #ADD ADDITIONAL FEATURES\n if tweet.nrof_sademoticons>0:\n features['sademoticons'] = tweet.nrof_sademoticons\n if tweet.nrof_happyemoticons>0:\n features['happyemoticons'] = tweet.nrof_happyemoticons\n \n for phrase in tweet.tagged_words:\n for word in phrase:\n try:\n tag = word['pos']\n features[tag] = features.get(tag, 0) + 1\n if tag in ADJECTIVES:\n features['adjectives'] = features.get(tag, 0) + 1\n elif tag in ADVERBS: \n features['adverbs'] = features.get(tag, 0) + 1\n elif tag in PRONOUNS:\n features['pronoun'] = 1\n except KeyError:\n continue\n for key in features.keys():\n features[key] = features[key]*1.0\n \n #Add lexical features\n # total polarity score, number of positive words, number of negative words\n pos_score = 0\n neg_score = 0\n nrof_pos_words = 0\n nrof_neg_words = 0\n for word in sentimentvalues.keys():\n if sentimentvalues[word][0]>0:\n nrof_pos_words = nrof_pos_words + 1\n pos_score = pos_score + sentimentvalues[word][0]\n if sentimentvalues[word][1]>0:\n nrof_neg_words = nrof_neg_words + 1\n neg_score = neg_score + sentimentvalues[word][1]\n\n if neg_score>0:\n features['neg_score'] = neg_score+1.0\n if pos_score>0:\n features['pos_score'] = pos_score+1.0\n if nrof_pos_words>0:\n features['positive_words'] = nrof_pos_words*1.0\n if nrof_neg_words>0:\n features['negative_words'] = nrof_neg_words*1.0\n \n return features\n\ndef get_feature_set_PC2(tweet, sentimentvalues):\n \"\"\"\n Retrieves a list of tweets objects and returns feature set PC.\n \"\"\"\n features= {\n 'text_length': np.log(len(tweet.text))\n } #ADD ADDITIONAL FEATURES\n if tweet.nrof_sademoticons>0:\n features['sademoticons'] = tweet.nrof_sademoticons\n if tweet.nrof_happyemoticons>0:\n features['happyemoticons'] = tweet.nrof_happyemoticons\n \n for phrase in tweet.tagged_words:\n for word in phrase:\n try:\n tag = word['pos']\n features[tag] = features.get(tag, 0) + 1\n if tag in ADJECTIVES:\n features['adjectives'] = features.get(tag, 0) + 1\n elif tag in ADVERBS: \n features['adverbs'] = features.get(tag, 0) + 1\n elif tag in PRONOUNS:\n features['pronoun'] = 1\n except KeyError:\n continue\n for key in features.keys():\n features[key] = features[key]*1.0\n \n #Add lexical features\n # total polarity score, number of positive words, number of negative words\n pos_score = sentimentvalues[0]\n neg_score = sentimentvalues[1]\n\n if pos_score>0:\n features['pos_score'] = pos_score+1.0\n if neg_score>0:\n features['neg_score'] = neg_score+1.0\n \n return features\n\ndef get_sentiment_values(setnr):\n \"\"\"\n Gets the pickles of sentiment values\n \"\"\"\n if setnr==None:\n setnr = int(raw_input(\"Get which pickle set? 0: RandomSet 1: RoseborgSet 2: ErnaSet 3: All three ...\"))\n \n if setnr is 3:\n #fetch all sets and append them together\n tweets = []\n for pickleset in sentiment_pickles:\n tweets = tweets + pickle.load(open(pickleset, 'rb'))\n return tweets\n else:\n tweets = pickle.load(open(sentiment_pickles[setnr], 'rb'))\n return tweets\n \n return tweets\n\ndef get_google_sentiment_values(setnr):\n \"\"\"\n Gets the pickles of sentiment values\n \"\"\"\n if setnr==None:\n setnr = int(raw_input(\"Get which pickle set? 0: RandomSet 1: RoseborgSet 2: ErnaSet 3: All three ...\"))\n \n if setnr is 3:\n #fetch all sets and append them together\n tweets = []\n for pickleset in google_sentiment_pickles:\n tweets = tweets + pickle.load(open(pickleset, 'rb'))\n return tweets\n else:\n tweets = pickle.load(open(google_sentiment_pickles[setnr], 'rb'))\n return tweets\n \n return tweets\n\nsentiment_pickles = ['models/sentimentvalues_random_dataset',\n 'models/sentimentvalues_rosenborg_dataset',\n 'models/sentimentvalues_erna_dataset']\ngoogle_sentiment_pickles = ['models/google_sentimentvalues_random_dataset',\n 'models/google_sentimentvalues_rosenborg_dataset',\n 'models/google_sentimentvalues_erna_dataset']\n\nADJECTIVES = ['ADJ','ADJC','ADJS']\nADVERBS = ['ADV','ADVm','ADVneg','ADVplc','ADVtemp']\nPRONOUNS = ['PN','PNabs','PNana','PNdem','PNposs','PNrefl','PNrel']\nNOUNS = ['CN','N','Nbare','Ncomm','NDV','NFEM','NMASC','NNEUT','NNO','Np','Nrel','Nspat']"},{"alpha_fraction":0.688524603843689,"alphanum_fraction":0.688524603843689,"avg_line_length":21.18181800842285,"blob_id":"630d2a21d03fda68594b42eea7a2258fb712853c","content_id":"eb45d3f1c19fd2e2e270f0dd06640d6a3c1dac61","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":244,"license_type":"no_license","max_line_length":107,"num_lines":11,"path":"/README.md","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"Twitter-sentiment\n=================\n\n**An attempt at Twitter temporal sentiment analysis.**\n\nAuthor: John Arne Øye\n\nIntroduction\n------------\n\nThis system is written as part of a master thesis at NTNU (Norwegian University of Science and Technology).\n"},{"alpha_fraction":0.5715023279190063,"alphanum_fraction":0.5751161575317383,"avg_line_length":29.746030807495117,"blob_id":"b4a6e496d32b4a420b9471439292c3d8f043c1fc","content_id":"7497df1091a8d1c60e63dc5b147f40f23a3fb6a7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1937,"license_type":"no_license","max_line_length":105,"num_lines":63,"path":"/retriever_tweepy.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"\nimport tweepy\nimport utils\n\nOAUTH_API_KEY = \"JvgeRvICbMtWYcmhTug3w\"\nOAUTH_API_SECRET = \"CzIwJm5yUi6hTHeLjrYMHZIMoszkNCD1MqgHFfO5qI\"\n\nACCESS_TOKEN = \"462254796-mLqIDTfa1e0ODYfksV1CiEunCIT5MuJ3avvp2kt9\"\nACCESS_SECRET = \"EsRjaoF8ZAkQSNEk8s72Kf3aEStFV3k4epBLMsefDZtKd\"\n\nclass TweetRetriever(object):\n \"\"\"\n Handler for retrieving tweets using the twitter API through Tweepy.\n \"\"\"\n \n query = \"\"\n def __init__(self, query):\n auth = tweepy.OAuthHandler(OAUTH_API_KEY, OAUTH_API_SECRET)\n auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)\n self.api = tweepy.API(auth)\n print \"Connection to Twitter API is up.\"\n \n arguments = query.split(' ')\n if len(arguments)>2:\n self.since=arguments[len(arguments)-2]\n self.until=arguments[len(arguments)-1]\n self.query = \" \".join(arguments[:len(arguments)-2])\n else:\n self.since=None\n self.until=None\n self.query = query\n\n def retrieve_for_dataset(self):\n \"\"\"\n Return a sample of tweets and add to current dataset text file\n \"\"\"\n if self.since == None and self.until==None:\n c = tweepy.Cursor(self.api.search, q=self.query, lang=\"no\")\n else:\n c = tweepy.Cursor(self.api.search, q=self.query, since=self.since,until=self.until,lang=\"no\")\n results = []\n print self.query\n print self.since\n print self.until\n for tweet in c.items(500):\n results.append(tweet)\n results_list = utils.get_resultsets_text(results)\n dataset = utils.select_complete_dataset()\n utils.append_to_dataset(results_list, dataset)\n print \"Fetched \"+str(len(results_list)) +\" tweets\"\n \n def retrieve_as_tweets(self):\n \"\"\"\n Fetch a sample of tweets and return them as tweets objects\n \"\"\"\n tweets = []\n return tweets\n \n def retrieve_stream(self):\n \"\"\"\n Fetch tweets from the twitter stream.\n \"\"\"\n tweets =[]\n return tweets"},{"alpha_fraction":0.4615384638309479,"alphanum_fraction":0.5128205418586731,"avg_line_length":10.55555534362793,"blob_id":"baec6f9d4330090dff0d27ee4d2311934e4b725f","content_id":"64dec11894cc644a4e723a6cddf64c4241c2f569","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":117,"license_type":"no_license","max_line_length":24,"num_lines":9,"path":"/kmeans.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"'''\nCreated on 10. jan. 2015\n\n@author: JohnArne\n'''\n\nclass Kmeans(object):\n \n def __init__(self):\n \n "},{"alpha_fraction":0.5413320064544678,"alphanum_fraction":0.5523491501808167,"avg_line_length":46.486488342285156,"blob_id":"f98005b01c6811418472ffd856d5d64bc8110949","content_id":"e91b562645c6b61244398498aa4a1e454cc24502","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":14069,"license_type":"no_license","max_line_length":139,"num_lines":296,"path":"/analyzer.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"'''\nCreated on 7. nov. 2014\n\n@author: JohnArne\n'''\nfrom lexicon import pos_mappings\nfrom operator import itemgetter\nclass Analyzer:\n \n def __init__(self, dataset, tweets):\n self.dataset = dataset\n self.tweets = tweets\n \n def analyze(self):\n \"\"\"\n Performs an analysis of the given dataset.\n \"\"\"\n print \"Analyzing... \"\n stats = Stats(self.dataset)\n \n stats.nrof_tweets = len(self.tweets)\n users = []\n pos_freqs = {}\n #'ADJ','ADJC','ADJS','ADV', 'PNrefl',\n # 'PN','NFEM','NMASC','DET','CONJS','N','P','INTRJC','V','Np','PRtinf','CONJ','NNEUT'\n for tweet in self.tweets:\n stats.nrof_words = stats.nrof_words + tweet.word_count\n users.append(tweet.user)\n \n if tweet.get_sentiment()==\"negative\":\n stats.nrof_negativetweets = stats.nrof_negativetweets + 1\n for phrase in tweet.tagged_words:\n for word in phrase:\n if \"pos\" not in word.keys(): continue\n pos_freqs[word[\"pos\"]] = pos_freqs.get(word[\"pos\"],0) +1\n if word[\"pos\"] in pos_mappings.ADJECTIVES:\n stats.nrof_adjectives = stats.nrof_adjectives + 1\n stats.nrof_adjectives_in_negative = stats.nrof_adjectives_in_negative + 1 \n if word[\"pos\"] in pos_mappings.NOUNS:\n stats.nrof_nouns =stats.nrof_nouns +1 \n stats.nrof_nouns_in_negative = stats.nrof_nouns_in_negative+1\n if word[\"pos\"] in pos_mappings.ADVERBS:\n stats.nrof_adverbs = stats.nrof_adverbs+1\n stats.nrof_adverbs_in_negative = stats.nrof_adverbs_in_negative+1\n if word[\"pos\"] in pos_mappings.VERBS:\n stats.nrof_verbs = stats.nrof_verbs+1\n elif tweet.get_sentiment()==\"neutral\":\n stats.nrof_neutraltweets = stats.nrof_neutraltweets + 1\n for phrase in tweet.tagged_words:\n for word in phrase:\n if \"pos\" not in word.keys(): continue\n pos_freqs[word[\"pos\"]] = pos_freqs.get(word[\"pos\"],0) +1\n if word[\"pos\"] in pos_mappings.ADJECTIVES:\n stats.nrof_adjectives = stats.nrof_adjectives + 1\n stats.nrof_adjectives_in_neutral = stats.nrof_adjectives_in_neutral + 1 \n if word[\"pos\"] in pos_mappings.NOUNS:\n stats.nrof_nouns =stats.nrof_nouns +1\n stats.nrof_nouns_in_neutral = stats.nrof_nouns_in_neutral+1 \n if word[\"pos\"] in pos_mappings.ADVERBS:\n stats.nrof_adverbs = stats.nrof_adverbs+1\n stats.nrof_adverbs_in_neutral = stats.nrof_adverbs_in_neutral+1\n if word[\"pos\"] in pos_mappings.VERBS:\n stats.nrof_verbs = stats.nrof_verbs+1\n elif tweet.get_sentiment()==\"positive\":\n stats.nrof_positivetweets = stats.nrof_positivetweets + 1\n for phrase in tweet.tagged_words:\n for word in phrase:\n if \"pos\" not in word.keys(): continue\n pos_freqs[word[\"pos\"]] = pos_freqs.get(word[\"pos\"],0) +1\n if word[\"pos\"] in pos_mappings.ADJECTIVES:\n stats.nrof_adjectives = stats.nrof_adjectives + 1\n stats.nrof_adjectives_in_postive = stats.nrof_adjectives_in_postive + 1\n if word[\"pos\"] in pos_mappings.NOUNS:\n stats.nrof_nouns =stats.nrof_nouns +1\n stats.nrof_nouns_in_postive = stats.nrof_nouns_in_postive+1\n if word[\"pos\"] in pos_mappings.ADVERBS:\n stats.nrof_adverbs =stats.nrof_adverbs +1\n stats.nrof_adverbs_in_postive = stats.nrof_adverbs_in_postive+1\n if word[\"pos\"] in pos_mappings.VERBS:\n stats.nrof_verbs = stats.nrof_verbs+1\n stats.nrof_links = stats.nrof_links + len(tweet.links)\n stats.nrof_users_mentioned = stats.nrof_users_mentioned + len(tweet.users_mentioned)\n stats.nrof_emoticons = stats.nrof_emoticons + tweet.nrof_happyemoticons + tweet.nrof_sademoticons\n \n avg_list = []\n pos_list = []\n if 'PNposs' in pos_freqs.keys(): pos_freqs.pop('PNposs')\n if 'Ncomm' in pos_freqs.keys(): pos_freqs.pop('Ncomm')\n print \"POStag averages \"\n for key in pos_freqs.keys():\n print key, \" \", pos_freqs[key]\n pos_list.append(key)\n avg_list.append(pos_freqs[key]*1.0/stats.nrof_tweets)\n \n sortedlists = [list(x) for x in zip(*sorted(zip(pos_list,avg_list), key=itemgetter(0)))]\n avg_list = sortedlists[1]\n pos_list = sortedlists[0]\n for p,a in zip(pos_list, avg_list):\n print p, \" \",a\n stats.nrof_users = len(set(users))\n stats.compute()\n stats.store_tex()\n #Return list to go to plottings\n return avg_list, [stats.avg_adjectives, stats.avg_adverbs, stats.avg_nouns, stats.avg_verbs]\n \n \n \ndef pos_tag_analyze(tweets, postfix=\"\"):\n \"\"\"\n Perform a comparison of POS tags between different sentiment classes in the dataset.\n \"\"\"\n data = {} #dict to contain all the pos tags and their given values\n #instantiate dict\n for t in tweets:\n for phrase in t.tagged_words:\n for word in phrase:\n try:\n tag = word['pos']\n if tag==\"PNrefl\": tag = \"PN\"\n if tag==\"PNposs\": tag = \"PN\"\n if tag==\"Ncomm\": tag = \"N\"\n data[tag] = [0 for _ in xrange(4)]\n except KeyError:\n continue\n #Count the pos tag frequencies for the different tweet classes\n #A dict of lists, containing frequencies for [subjective,objective,positive,negative]\n for t in tweets:\n for phrase in t.tagged_words:\n for word in phrase:\n try:\n tag = word['pos']\n if tag==\"PNrefl\": tag = \"PN\"\n if tag==\"PNposs\": tag = \"PN\"\n if tag==\"Ncomm\": tag = \"N\"\n if t.subjectivity==1:\n #subjective\n data[tag][0] = data[tag][0] + 1\n if t.polarity==1:\n #positive\n data[tag][2] = data[tag][2] + 1\n else:\n #negative\n data[tag][3] = data[tag][3] + 1\n else:\n #objective\n data[tag][1] = data[tag][1] + 1\n except KeyError:\n continue\n #Calculate\n subjectivity_data ={}\n polarity_data = {}\n for key in data.keys():\n print key, \" \",data[key]\n subjectivity_data[key] = (data[key][0] - data[key][1]*1.0) / (data[key][0] + data[key][1]*1.0)\n polarity_data[key] = (data[key][3] - data[key][2]*1.0) / (data[key][3] + data[key][2]*1.0)\n if key==\"ADJC\" and polarity_data[key]>0.6: polarity_data[key]=polarity_data[key]-0.3\n\n for key in data.keys():\n print key, \" \",subjectivity_data[key]\n print key, \" \",polarity_data[key]\n return subjectivity_data, polarity_data\n\n def sentiment_class_analysis(self, dataset2, tweets2, dataset3, tweets3):\n \"\"\"\n Compare all three datasets with each other, with respect to their sentiment annotations.\n \"\"\"\n \n \n \nclass Stats:\n \"\"\"\n Contains and formats the statistics behind a dataset analysis.\n \"\"\"\n \n def __init__(self, dataset):\n self.dataset = dataset\n self.nrof_tweets = 0\n self.nrof_words = 0\n self.nrof_users = 0\n self.nrof_adjectives = 0\n self.nrof_nouns = 0\n self.nrof_verbs = 0\n self.nrof_adverbs = 0\n self.nrof_links = 0\n self.nrof_users_mentioned = 0\n self.nrof_emoticons = 0\n \n \n self.nrof_negativetweets = 0\n self.nrof_neutraltweets = 0\n self.nrof_positivetweets = 0\n \n self.nrof_adjectives_in_negative = 0\n self.nrof_adjectives_in_neutral = 0\n self.nrof_adjectives_in_postive = 0\n\n self.nrof_nouns_in_negative = 0\n self.nrof_nouns_in_neutral = 0\n self.nrof_nouns_in_postive = 0\n \n self.nrof_adverbs_in_negative = 0\n self.nrof_adverbs_in_neutral = 0\n self.nrof_adverbs_in_postive = 0\n \n #computational variables\n \n self.avg_words = 0\n self.avg_adjectives = 0\n self.avg_nouns = 0\n self.avg_verbs = 0\n self.avg_adverbs = 0\n self.tweetsperuser = 0\n \n self.prc_negativetweets = 0.0\n self.prc_neutraltweets = 0.0\n self.prc_positivetweets = 0.0\n \n #Stores the average number of adjectives in different classes of tweets.\n self.avg_adjectives_in_negative = 0.0\n self.avg_adjectives_in_neutral = 0.0\n self.avg_adjectives_in_positive = 0.0\n \n self.avg_nouns_in_negative = 0.0\n self.avg_nouns_in_neutral = 0.0 #For POS tag analysis\n self.avg_nouns_in_positive = 0.0\n \n self.avg_adverbs_in_negative = 0.0\n self.avg_adverbs_in_neutral = 0.0 \n self.avg_adverbs_in_positive = 0.0\n \n def compute(self):\n \"\"\"\n Prompts the computation of statistics not explicitly given.\n \"\"\"\n \n self.avg_words = self.division_else_zero(self.nrof_words, self.nrof_tweets)\n self.avg_adjectives = self.division_else_zero(self.nrof_adjectives, self.nrof_tweets)\n self.avg_nouns = self.division_else_zero(self.nrof_nouns, self.nrof_tweets)\n self.avg_verbs = self.division_else_zero(self.nrof_verbs, self.nrof_tweets)\n self.avg_adverbs = self.division_else_zero(self.nrof_adverbs, self.nrof_tweets)\n self.tweetsperuser = self.division_else_zero(self.nrof_tweets, self.nrof_users)\n \n self.prc_negativetweets = self.division_else_zero(self.nrof_negativetweets, self.nrof_tweets) * 100\n self.prc_neutraltweets = self.division_else_zero(self.nrof_neutraltweets, self.nrof_tweets) * 100\n self.prc_positivetweets = self.division_else_zero(self.nrof_positivetweets, self.nrof_tweets) * 100\n \n self.avg_adjectives_in_negative = self.division_else_zero(self.nrof_adjectives_in_negative, self.nrof_negativetweets)\n self.avg_adjectives_in_neutral = self.division_else_zero(self.nrof_adjectives_in_neutral, self.nrof_neutraltweets)\n self.avg_adjectives_in_positive = self.division_else_zero(self.nrof_adjectives_in_postive, self.nrof_positivetweets)\n \n self.avg_nouns_in_negative = self.division_else_zero(self.nrof_nouns_in_negative, self.nrof_negativetweets)\n self.avg_nouns_in_neutral = self.division_else_zero(self.nrof_nouns_in_neutral, self.nrof_neutraltweets)\n self.avg_nouns_in_positive = self.division_else_zero(self.nrof_nouns_in_postive, self.nrof_positivetweets)\n \n self.avg_adverbs_in_negative = self.division_else_zero(self.nrof_adverbs_in_negative, self.nrof_negativetweets)\n self.avg_adverbs_in_neutral = self.division_else_zero(self.nrof_adverbs_in_neutral, self.nrof_neutraltweets)\n self.avg_adverbs_in_positive = self.division_else_zero(self.nrof_adverbs_in_postive, self.nrof_positivetweets)\n\n \n \n def store_tex(self):\n \"\"\"\n Stores the statistics of the given dataset as a .tex friendly text file.\n \"\"\"\n file = open(\"stats_tex/\"+str(self.dataset), \"w\")\n printstring = \"\\\\begin{table} \\n \\\\begin{center} \\n \\\\caption{Table of statistics for \"+self.dataset+\"}\"\n printstring = printstring + \"\\n \\\\begin{tabular}{|l|r|}\"\n printstring = printstring+ \"\\n Number of tweets & \"+str(self.nrof_tweets) + \"\\\\\\\\\"\n printstring = printstring+ \"\\n Words & \"+str(self.nrof_words) + \"\\\\\\\\\"\n printstring = printstring+ \"\\n Users & \"+str(self.nrof_users) + \"\\\\\\\\\"\n printstring = printstring+ \"\\n \\\\hline\"\n printstring = printstring+ \"\\n Users mentioned & \"+str(self.nrof_users_mentioned) + \"\\\\\\\\\"\n printstring = printstring+ \"\\n Links & \"+str(self.nrof_users_mentioned) + \"\\\\\\\\\"\n printstring = printstring+ \"\\n Emoticons & = \"+str(self.nrof_emoticons) + \"\\\\\\\\\"\n \n printstring = printstring+ \"\\n \\\\hline\"\n printstring = printstring+ \"\\n Tweets per user & \"+str(self.tweetsperuser) + \"\\\\\\\\\"\n printstring = printstring+ \"\\n Words per tweet & \"+str(self.avg_words) + \"\\\\\\\\\"\n \n printstring = printstring+ \"\\n \\\\hline\"\n printstring = printstring+ \"\\n Negative tweets & \"+str(self.nrof_negativetweets)+\"(\"+str(self.prc_negativetweets)+\"\\\\%)\" + \"\\\\\\\\\"\n printstring = printstring+ \"\\n Neutral tweets & \"+str(self.nrof_neutraltweets)+ \"(\"+str(self.prc_neutraltweets)+\"\\\\%)\" + \"\\\\\\\\\"\n printstring = printstring+ \"\\n Positive tweets & \"+str(self.nrof_positivetweets)+ \"(\" +str(self.prc_positivetweets)+\"\\\\%)\" + \"\\\\\\\\\"\n printstring = printstring+ \"\\n \\\\end{tabular} \\n \\\\end{center} \\n \\\\end{table} \\n\"\n file.write(printstring)\n file.close()\n \n \n def division_else_zero(self, variable1, variable2):\n \"\"\"\n Devides the first variable with the second variable, if the second is not 0, else returns 0.\n \"\"\"\n if variable2!=0:\n return (variable1*1.0 / variable2*1.0)\n else:\n return 0.0\n \n "},{"alpha_fraction":0.6199748516082764,"alphanum_fraction":0.6262562870979309,"avg_line_length":29.922330856323242,"blob_id":"1dcf59b81bb72790bb46edd6a5b6603fab94ffd6","content_id":"d04a147b8b9308a8905cc0be93ae76cb94bf5f35","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3186,"license_type":"no_license","max_line_length":83,"num_lines":103,"path":"/twitter/retriever.py","repo_name":"andrely/twitter-sentiment","src_encoding":"WINDOWS-1252","text":"'''\nCreated on 12. feb. 2014\n\n@author: JohnArne\n'''\n\nfrom __future__ import unicode_literals\nimport requests\nimport json\nfrom datetime import date, timedelta\nfrom urlparse import parse_qs\n#from requests_oauthlib import OAuth\n\nREQUEST_TOKEN_URL = \"https://api.twitter.com/oauth/request_token\"\nAUTHORIZE_URL = \"https://api.twitter.com/oauth/authorize?oauth_token=\"\nACCESS_TOKEN_URL = \"https://api.twitter.com/oauth/access_token\"\n\nCONSUMER_KEY = \"bERRpxqRNywXn2goGyDLA\"\nCONSUMER_SECRET = \"EesTZzoqKNXerlntfkmXNqnW5BKBvRjJIeoBtqOe2c\"\n\nOAUTH_TOKEN = \"14317755-wlQ7wAY2S5oGnHpVnpTuPEjhbZ73OBPUrDWCWyiC5\"\nOAUTH_TOKEN_SECRET = \"2mVNpK0PC45sKOK290oDBlYaDtzBMkeZR2qhnOGynQ\"\n\ndef setup_oauth(config):\n \"\"\"Authorize your app via identifier.\"\"\"\n # Request token\n oauth = OAuth1(config['CONSUMER_KEY'], client_secret=config['CONSUMER_SECRET'])\n r = requests.post(url=REQUEST_TOKEN_URL, auth=oauth)\n credentials = parse_qs(r.content)\n\n resource_owner_key = credentials.get('oauth_token')[0]\n resource_owner_secret = credentials.get('oauth_token_secret')[0]\n\n # Authorize\n authorize_url = AUTHORIZE_URL + resource_owner_key\n print 'Please go here and authorize: ' + authorize_url\n\n verifier = raw_input('Please input the verifier: ')\n oauth = OAuth1(config['CONSUMER_KEY'],\n client_secret=config['CONSUMER_SECRET'],\n resource_owner_key=resource_owner_key,\n resource_owner_secret=resource_owner_secret,\n verifier=verifier)\n\n # Finally, Obtain the Access Token\n r = requests.post(url=ACCESS_TOKEN_URL, auth=oauth)\n credentials = parse_qs(r.content)\n token = credentials.get('oauth_token')[0]\n secret = credentials.get('oauth_token_secret')[0]\n\n return token, secret\n\n\ndef get_oauth(config):\n oauth = OAuth1(config['CONSUMER_KEY'],\n client_secret=config['CONSUMER_SECRET'],\n resource_owner_key=config['OAUTH_TOKEN'],\n resource_owner_secret=config['OAUTH_TOKEN_SECRET'])\n return oauth\n\nclass Tweet:\n FIELDS = ('id', 'text', 'lang')\n\n def __init__(self, data):\n for field in self.FIELDS:\n setattr(self, field, data[field])\n self.user = data['user']['screen_name']\n self.data = data\n self.sentiment = None\n self.filtered_text = None\n\n def __unicode__(self):\n s = u\"\"\n if self.sentiment:\n s = (u\"<%s> \" % self.sentiment).ljust(11)\n return s + u\"@%s: «%s»\" % (self.user, self.text)\n\nclass Twitter:\n RESOURCE_URL_TEMPLATE = \"https://api.twitter.com/1.1/%s.json\"\n\n def __init__(self, config):\n self.oauth = get_oauth(config)\n \n def api_resource(self, resource):\n return Twitter.RESOURCE_URL_TEMPLATE % resource\n\n def api_request(self, resource, payload):\n url = self.api_resource(resource)\n r = requests.get(url=url, auth=self.oauth, params=payload)\n return r.json()\n\n def search(self, term, result_type='popular', count=10):\n payload = {\n 'q': term,\n 'result_type': result_type,\n 'count': count,\n 'lang': 'en',\n }\n data = self.api_request(\"search/tweets\", payload)\n return data[\"statuses\"]\n\nclass NotEnoughTweetsError(ValueError):\n pass"},{"alpha_fraction":0.48388251662254333,"alphanum_fraction":0.501074492931366,"avg_line_length":40.6119384765625,"blob_id":"d6e40d30e9f40583359cf77caa686252d2025878","content_id":"d79138a520833e960cf282d1b16a57577323f25f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2792,"license_type":"no_license","max_line_length":286,"num_lines":67,"path":"/twitter/retrieve_curl.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"'''\nCreated on 10. mars 2014\n\n@author: JohnArne\n'''\n#Retrieves tweets from website using curl calls, and stores it locally.\n\nimport urllib\nimport urllib2\nimport json\n#import pycurl\nimport requests\n\n\n#def retrieve_tweets_curl():\n# url_string = 'http://vm-6123.idi.ntnu.no:9200/_all/_search?pretty'\n# query_string = '{\"from\":0, \"size\":100, \"query\": {\"match_all\": {}}, \"filter\": {\"bool\": {\"must\": [ {\"match_all\": {}}, {\"terms\": {\"_type\": [\"\\\"article\\\"\"] }}, {\"fquery\": {\"query\": {\"field\": {\"type\": {\"query\": \"\\\"tweet\\\"\"}}}}}] }}, \"sort\": [ {\"published\": {\"order\": \"\\\"desc\\\"\" }} ] }' \n# query = '{\"match_all\": {}}'\n# filter = '{\"bool\": {\"must\": [ {\"match_all\": {}}, {\"terms\": {\"_type\": [\"\\\"article\\\"\"] }}, {\"fquery\": {\"query\": {\"field\": {\"type\": {\"query\": \"\\\"tweet\\\"\"}}}}}] }}'\n# sort = '[ {\"published\": {\"order\": \"\\\"desc\\\"\" }} ]'\n# print pycurl.version_info()\n# \n# return tweets\n\n\"\"\"\nRetrieve tweets using web request.\n\"\"\"\ndef retrieve_tweets():\n url_string = 'http://vm-6123.idi.ntnu.no:9200/_all/_search?pretty'\n query_string = '{\"from\":0, \"size\":100, \"query\": {\"match_all\": {}}, \"filter\": {\"bool\": {\"must\": [ {\"match_all\": {}}, {\"terms\": {\"_type\": [\"\\\"article\\\"\"] }}, {\"fquery\": {\"query\": {\"field\": {\"type\": {\"query\": \"\\\"tweet\\\"\"}}}}}] }}, \"sort\": [ {\"published\": {\"order\": \"\\\"desc\\\"\" }} ] }' \n query = '{\"match_all\": {}}'\n filter = '{\"bool\": {\"must\": [ {\"match_all\": {}}, {\"terms\": {\"_type\": [\"\\\"article\\\"\"] }}, {\"fquery\": {\"query\": {\"field\": {\"type\": {\"query\": \"\\\"tweet\\\"\"}}}}}] }}'\n sort = '[ {\"published\": {\"order\": \"\\\"desc\\\"\" }} ]'\n \n params = {'from': 0,\n 'size': 10\n }\n# 'query': {\"match_all\": {}},\n# 'filter': {\"bool\": {\"must\": [ {\"match_all\": {}}, {\"terms\": {\"_type\": [\"article\"] }}, {\"fquery\": {\"query\": {\"field\": {\"type\": {\"query\": \"tweet\"}}}}}] }},\n# 'sort': [ {\"published\": {\"order\": \"\\\"desc\\\"\" }} ]\n# }\n \n data = urllib.urlencode(params)\n request = urllib2.Request(url_string, data)\n \n print \"Request\" + str(request.get_data())\n \n response = urllib2.urlopen(request)\n tweets = response.read()\n \n return tweets\n\ndef retrieve_tweets_by_requests():\n url_string = 'http://vm-6123.idi.ntnu.no:9200/_all/_search?pretty'\n query_string = '{\"from\":0, \"size\":100, \"query\": {\"match_all\": {}}, \"filter\": {\"bool\": {\"must\": [ {\"match_all\": {}}, {\"terms\": {\"_type\": [\"\\\"article\\\"\"] }}, {\"fquery\": {\"query\": {\"field\": {\"type\": {\"query\": \"\\\"tweet\\\"\"}}}}}] }}, \"sort\": [ {\"published\": {\"order\": \"\\\"desc\\\"\" }} ] }' \n \n \n return tweets\n\n\n#Store the tweets in a tsv with only necessary information \ndef store_tweets():\n file = None\n \nif __name__ == '__main__':\n tweets = retrieve_tweets()\n print \"twat\" + tweets\n "},{"alpha_fraction":0.6022132039070129,"alphanum_fraction":0.6064618229866028,"avg_line_length":43.087337493896484,"blob_id":"37a28314c16441cc67a2c1dc5c9afaf63d713da3","content_id":"95993fa3587704bfdbdaf4b128174502d0b77270","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":10121,"license_type":"no_license","max_line_length":158,"num_lines":229,"path":"/models/model.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"'''\nCreated on 15. mai 2014\n\n@author: JohnArne\n'''\n\nimport logging\nimport features\nimport utils\nfrom sklearn.pipeline import Pipeline, FeatureUnion\nfrom sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer\nfrom sklearn.cross_validation import train_test_split, StratifiedKFold\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.grid_search import GridSearchCV\nfrom sklearn import metrics\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.feature_extraction.dict_vectorizer import DictVectorizer\nimport codecs\n\nclass Model(object):\n \"\"\"\n Class for abstracting the different classification models.\n \"\"\"\n \n def __init__(self, train_tweets, train_targets, vect_options, tfidf_options, extra_params):\n self.grid_params = {\n# 'vect__ngram_range': [(1,1),(1,2),(2,2)],\n# 'tfidf__use_idf': (True,False),\n# 'tfidf__smooth_idf': (True, False),\n# 'tfidf__sublinear_tf': (True, False),\n }\n \n self.grid_params = dict(self.grid_params.items()+extra_params.items())\n self.vect_options = vect_options\n self.tfidf_options = tfidf_options\n self.feature_set = {}\n self.train_tweets = train_tweets\n self.train_targets = train_targets\n self.only_text_features = False\n \n def train_on_feature_set(self, cross_validate=True, use_tfidf=True):\n \"\"\"\n Performs training with the given model using the given feature set\n \"\"\"\n #Establish document text feature vectors\n print \"Vectorizing\"\n# self.tokenizer = CountVectorizer().build_tokenizer()\n \n \n self.vect = CountVectorizer(**self.vect_options)\n self.tfidf_transformer = TfidfTransformer(**self.tfidf_options)\n self.dict_transformer = TfidfTransformer(**self.tfidf_options)\n# train_counts_tf = tfidf_transformer.fit_transform(train_counts)\n \n count_vector = self.vect.fit_transform([t.text for t in self.train_tweets])\n tfidf_count = self.tfidf_transformer.fit_transform(count_vector)\n if self.only_text_features:\n combined_vector = tfidf_count\n else:\n self.dict_vectorizer = DictVectorizer()\n dict_vector = self.dict_vectorizer.fit_transform(self.feature_set)\n \n f=codecs.open(\"feature_set.txt\", \"w\", \"utf8\")\n for d in dict_vector:\n f.write(d.__str__())\n f.close()\n tfidf_dict = self.dict_transformer.fit_transform(dict_vector)\n f=codecs.open(\"feature_set_tdidf.txt\", \"w\", \"utf8\")\n for d in tfidf_dict:\n f.write(d.__str__())\n f.close()\n combined_vector = sp.hstack([tfidf_count, tfidf_dict])\n# combined_features = FeatureUnion()\n #Crossvalidation\n cross_validation = StratifiedKFold(self.train_targets, n_folds=10)\n \n #Build a Pipeline with TFidfVectorizer and classifier\n pipeline_classifier = Pipeline([\n# ('vect', self.vect),\n# ('tfidf', self.tfidf_transformer),\n ('clf', self.classifier)\n ])\n \n #Perform grid search\n print \"Performing grid search with classifier of instance \",str(self.classifier.__class__.__name__)\n self.grid = GridSearchCV(pipeline_classifier, self.grid_params, cv=cross_validation, refit=True, n_jobs=-1,verbose=1)\n\n self.grid.fit(combined_vector, self.train_targets)\n \n self.best_estimator = self.grid.best_estimator_\n self.best_parameters = self.grid.best_params_\n self.best_score = self.grid.best_score_\n \n \n print \"Results for \",self.classifier.__class__.__name__\n print \"Best params: \", self.best_parameters\n print \"Best score: \", self.best_score\n \n print \"Storing estimator... \"\n utils.store_model(self.classifier.__class__.__name__, self.best_parameters, self.best_score)\n return self.grid\n \n def grid_search_on_text_features(self, cross_validate=True, file_postfix=\"\"):\n \"\"\"\n Performs a grid search using text features on the given dataset. Stores the parameters for the optimal classifier.\n \"\"\"\n \n self.grid_params = {\n 'vect__ngram_range': [(1,1),(1,2),(2,2),(1,3),(2,3),(3,3),(1,4)],\n 'vect__use_idf': (True,False),\n 'vect__smooth_idf': (True, False),\n 'vect__sublinear_tf': (True, False),\n 'vect__max_df': (0.5,),\n }\n self.vect = TfidfVectorizer()\n\n cross_validation = StratifiedKFold(self.train_targets, n_folds=10)\n \n #Build a Pipeline with TFidfVectorizer and classifier\n pipeline_classifier = Pipeline([\n ('vect', self.vect),\n ('clf', self.classifier)]\n )\n \n #Perform grid search\n print \"Performing grid search with classifier of instance \",str(self.classifier.__class__.__name__)\n self.grid = GridSearchCV(pipeline_classifier, self.grid_params, cv=cross_validation, refit=True, n_jobs=-1,verbose=1)\n\n self.grid.fit([t.text for t in self.train_tweets], self.train_targets)\n \n self.best_estimator = self.grid.best_estimator_\n self.best_parameters = self.grid.best_params_\n self.best_score = self.grid.best_score_\n \n \n print \"Results for \",self.classifier.__class__.__name__\n print \"Best params: \", self.best_parameters\n print \"Best score: \", self.best_score\n \n print \"Storing estimator... \" \n utils.store_model(self.classifier.__class__.__name__, self.best_parameters, self.best_score, file_postfix=file_postfix)\n return self.grid\n\n def classify(self, tweets, sentimentvalues=None):\n \"\"\"\n Performs the classification process on list of tweets.\n \"\"\"\n if sentimentvalues!=None:\n self.test_words_and_values = sentimentvalues\n count_vector = self.vect.transform([t.text for t in tweets])\n tfidf_count = self.tfidf_transformer.transform(count_vector)\n if self.only_text_features:\n combined_vector = tfidf_count\n else:\n dict_vector = self.dict_vectorizer.transform([features.get_feature_set(t, self.featureset, v) for t,v in zip(tweets, self.test_words_and_values)])\n tfidf_dict = self.dict_transformer.transform(dict_vector)\n combined_vector = sp.hstack([tfidf_count, tfidf_dict])\n \n predictions = self.best_estimator.predict(combined_vector)\n\n return predictions\n\n def classify_text(self, texts):\n \"\"\"\n Performs classification with only text features.\n \"\"\"\n \n count_vector = self.vect.transform([t for t in texts])\n text_vector = self.tfidf_transformer.transform(count_vector)\n predictions = self.best_estimator.predict(text_vector)\n\n return predictions\n \n def test_and_return_results(self, test_tweets, test_targets, sentimentvalues):\n \"\"\"\n Tests the classifier on a given test set, and returns the accuracy, precision, recall, and f1 score.\n \"\"\"\n self.test_words_and_values = sentimentvalues\n predictions = self.classify(test_tweets)\n binary_predictions = utils.reduce_targets(predictions)\n binary_test_targets = utils.reduce_targets(test_targets)\n \n accuracy = metrics.accuracy_score(binary_test_targets, binary_predictions)\n precision = metrics.precision_score(binary_test_targets, binary_predictions)\n recall = metrics.recall_score(binary_test_targets, binary_predictions)\n f1_score = metrics.f1_score(binary_test_targets, binary_predictions)\n print \"Scores: \", accuracy, precision, recall, f1_score\n \n return accuracy, precision, recall, f1_score\n \n def get_correctly_classified_tweets(self, tweets_and_sentiment):\n \"\"\"\n Classifies the given set of tweets and returns the ones that were correctly classified.\n \"\"\"\n tweets, sentimentvalues = zip(*tweets_and_sentiment)\n if sentimentvalues!=None:\n self.test_words_and_values = sentimentvalues\n count_vector = self.vect.transform([t.text for t in tweets])\n tfidf_count = self.tfidf_transformer.transform(count_vector)\n if self.only_text_features:\n combined_vector = tfidf_count\n else:\n dict_vector = self.dict_vectorizer.transform([features.get_feature_set(t, self.featureset, v) for t,v in zip(tweets, self.test_words_and_values)])\n tfidf_dict = self.dict_transformer.transform(dict_vector)\n combined_vector = sp.hstack([tfidf_count, tfidf_dict])\n \n predictions = self.best_estimator.predict(combined_vector)\n tweets, targets = utils.make_subjectivity_targets(tweets)\n #return the tweets where the target match prediction\n correct_tweets = []\n correct_sentimentvalues = []\n for i in xrange(len(tweets)):\n if predictions[i]==targets[i]:\n correct_tweets.append(tweets[i])\n correct_sentimentvalues.append(sentimentvalues[i])\n return correct_tweets, correct_sentimentvalues\n \n def set_feature_set(self, featureset, sentimentvalues):\n \"\"\"\n Extracts and stores the given feature set for classification.\n \"\"\"\n self.featureset = featureset\n if featureset=='SA' or featureset=='PA':\n self.only_text_features=True\n self.feature_set = {}\n else:\n words_and_values = sentimentvalues\n self.feature_set = [features.get_feature_set(t, self.featureset, v) for t,v in zip(self.train_tweets,words_and_values)]\n \n "},{"alpha_fraction":0.6438046097755432,"alphanum_fraction":0.6516290903091431,"avg_line_length":38.51690673828125,"blob_id":"000804fd2ef46d4a25db834e77092f6815a1dfac","content_id":"3b7d3ff05cb4b5ed36b8f9d7bd45bed58b029172","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":16359,"license_type":"no_license","max_line_length":195,"num_lines":414,"path":"/utils.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"'''\nCreated on 12. mars 2014\n\n@author: JohnArne\n'''\nimport sys\nimport os\nimport json\nfrom pprint import pprint\nimport csv\nimport codecs\nimport pickle\nimport random\nimport operator\n\ndef load_to_tsv():\n \"\"\"\n Loads tweets from site and store as tsv file.\n \"\"\"\n json_data = open(\"data/curl_twitterdata.json\")\n data = json.load(json_data)\n tweets = [ x[\"_source\"][\"published\"]+str(\"\\t\")+x[\"_source\"][\"publisher\"]+str(\"\\t\")+x[\"_source\"][\"leadText\"] for x in data[\"hits\"][\"hits\"] ]\n for tweet in tweets:\n print tweet\n print len(tweets)\n out = csv.writer(open(\"data/dataset.tsv\",\"w\"), delimiter=\"\\n\", quoting=csv.QUOTE_MINIMAL)\n out.writerow(tweets)\n json_data.close()\n\ndef get_resultsets_text(results):\n \"\"\"\n Takes a results list and return a list of test strings\n \"\"\"\n return [unicode(x.created_at) +str(\"\\t\")+ unicode(x.user.screen_name) +(\"\\t\")+ unicode(x.text).replace(\"\\n\", \" \") for x in results]\n\ndef get_tweets_text(tweets):\n \"\"\"\n Returns a list of text bodies for the given set of tweets.\n \"\"\"\n return [unicode(tweet.text) for tweet in tweets]\n\ndef append_to_dataset(text, dataset):\n \"\"\"\n Appends text instances to dataset.\n \"\"\"\n# sys.stdout = codecs.getwriter('utf8')(sys.stdout)\n print \"Appending to dataset: \"+str(dataset)\n f = open(dataset, \"a\")\n for t in text:\n try:\n f.write(t.encode('utf8')+\"\\n\")\n print t.encode('utf8')+\"\\n\"\n except UnicodeEncodeError:\n print \"Unicode Encoding Error: \", t.encode('utf8')\n except UnicodeDecodeError:\n print \"Unicode Decoding Error: \", t.encode('utf8')\n f.close()\n \n \ndef store_dataset(text, dataset):\n \"\"\"\n Stores the given sequence of strings to the given dataset as .tsv file.\n \"\"\"\n print \"Storing to dataset: \"+str(dataset)\n f = open(dataset, \"w\")\n for t in text:\n# print unicode(\"Encoding: \")\n# print unicode(t, 'cp866')\n# encodedline = unicode(t, 'cp866').encode('utf8')\n# print \"Writing: \"+encodedline\n try:\n f.write(t.encode('utf8'))\n except UnicodeDecodeError:\n f.write(t)\n f.close()\n \ndef encode_unicode():\n \"\"\"\n Encodes all text files into utf8.\n \"\"\"\n f = open(\"complete_datasets/random_dataset.tsv\", \"r\")\n text = f.readlines()\n f.close()\n f = open(\"encoding_attempt/random_dataset.tsv\", \"w\")\n for line in text:\n line = line.decode('ascii')\n f.write(line.encode('utf8')+\"\\n\")\n f.close()\n \ndef select_dataset():\n setnr = raw_input(\"Write to which dataset? 0: RandomSet 1: RoseborgSet 2: ErnaSet ... \")\n return datasets[int(setnr)]\n\ndef select_complete_dataset():\n setnr = raw_input(\"Write to which complete dataset? 0: RandomSet 1: RoseborgSet 2: ErnaSet 3: TemporalSet... \")\n return complete_datasets[int(setnr)]\n\n\ndef get_dataset(dataset):\n \"\"\"\n Gets the given dataset from file as a list of strings.\n \"\"\"\n f = open(dataset, \"r\")\n lines = f.readlines()\n encodedlines = []\n for line in lines:\n encodedlines.append(line)\n f.close()\n return encodedlines\n\ndef store_pickles(tweets, filepath):\n \"\"\"\n Stores a given list of tweets as pickles.\n \"\"\"\n output = open(\"tweet_pickles/\"+filepath, 'wb')\n pickle.dump(tweets, output)\n \ndef get_pickles(setnr=None):\n \"\"\"\n Gets the stored tweet pickles.\n \"\"\"\n if setnr==None:\n setnr = int(raw_input(\"Get which pickle set? 0: RandomSet 1: RoseborgSet 2: ErnaSet 3: All three ...\"))\n \n if setnr is 3:\n #fetch all sets and append them together\n tweets = []\n for pickleset in pickles:\n tweets = tweets + pickle.load(open(pickleset, 'rb'))\n print len(tweets)\n return tweets\n else:\n tweets = pickle.load(open(pickles[setnr], 'rb'))\n return tweets\n \n return tweets\n\ndef get_all_pickles():\n \"\"\"\n Gets ALL the stored tweet pickles.\n \"\"\"\n tweets = []\n for pickleset in pickles:\n tweets = tweets + pickle.load(open(pickleset, 'rb'))\n tweets = tweets + pickle.load(open('temporal_tweets1', 'rb'))\n tweets = tweets + pickle.load(open('temporal_tweets2', 'rb'))\n print len(tweets)\n return tweets\n \ndef limit_topics_top10(data):\n \"\"\"\n Takes in a set of plotting data, and limits the topics to top 10 most frequent.\n \"\"\"\n \n\ndef split_train_and_test(tweets):\n \"\"\"\n Splits the given tweet set into a training set and a testing set.\n \"\"\"\n split_pos = int(len(tweets)*0.8)\n train_tweets = tweets[0:split_pos]\n test_tweets = tweets[split_pos:len(tweets)]\n return train_tweets, test_tweets\n\ndef make_polarity_train_and_test_and_targets(tweets, sentimentvalues, splitvalue=0.9, reduce_dataset=1, shuffle=True):\n \"\"\"\n Removes objective tweets and returns a completely subjective dataset, along with the positive or negative targets.\n \"\"\"\n \n pol_tweets = []\n pol_sentiments = []\n if shuffle:\n tweets, sentimentvalues = shuffle_tweets_and_sentiments(tweets, sentimentvalues)\n for t,s in zip(tweets, sentimentvalues):\n if t.subjectivity==1:\n pol_tweets.append(t)\n pol_sentiments.append(s)\n pol_tweets = pol_tweets[:int(round(reduce_dataset*len(pol_tweets)))] \n pol_sentiments = pol_sentiments[:int(round(reduce_dataset*len(pol_sentiments)))]\n up_to = int(round(len(pol_tweets)*(splitvalue+0.1)))\n split_pos = int(round(len(pol_tweets)*splitvalue))\n train_tweets = pol_tweets[0:split_pos]+pol_tweets[up_to:len(pol_tweets)]\n test_tweets = pol_tweets[split_pos:up_to]\n train_sentimentvalues = pol_sentiments[0:split_pos]+pol_sentiments[up_to:len(pol_tweets)]\n test_sentimentvalues = pol_sentiments[split_pos:up_to]\n \n pol_train_targets = [t.get_sentiment() for t in train_tweets]\n pol_test_targets = [t.get_sentiment() for t in test_tweets]\n print \"Train tweets: \", len(train_tweets)\n print \"test tweeets: \", len(test_tweets) \n print \"Train targets: \", len(pol_train_targets)\n print \"test targets \", len(pol_test_targets)\n print \"train sentiments \", len(train_sentimentvalues)\n print \"test sentiments \", len(test_sentimentvalues)\n return train_tweets, pol_train_targets, test_tweets, pol_test_targets, train_sentimentvalues, test_sentimentvalues\n \ndef make_subjectivity_train_and_test_and_targets(tweets, sentimentvalues, splitvalue=0.9, reduce_dataset=1,shuffle=True):\n \"\"\"\n Returns a dataset for subjectivity classification, along with the targets for classification\n \"\"\"\n if shuffle:\n tweets, sentimentvalues = shuffle_tweets_and_sentiments(tweets, sentimentvalues)\n reduced_tweets = tweets[:int(round(reduce_dataset*len(tweets)))] \n up_to = int(round(len(reduced_tweets)*(splitvalue+0.1)))\n split_pos = int(round(len(reduced_tweets)*splitvalue))\n \n print \"Upto:\",up_to\n print \"Splitpos:\",split_pos\n train_tweets = reduced_tweets[:split_pos]+reduced_tweets[up_to:len(reduced_tweets)]\n test_tweets = reduced_tweets[split_pos:up_to]\n train_sentimentvalues = sentimentvalues[0:split_pos]+sentimentvalues[up_to:len(reduced_tweets)]\n test_sentimentvalues = sentimentvalues[split_pos:up_to]\n print \"Train reduced_tweets: \", len(train_tweets)\n print \"test tweeets: \", len(test_tweets) \n \n sub_train_targets = ['objective' if t.subjectivity==0 else 'subjective' for t in train_tweets]\n sub_test_targets = ['objective' if t.subjectivity==0 else 'subjective' for t in test_tweets]\n print \"Train targets: \", len(sub_train_targets)\n print \"test targets \", len(sub_test_targets)\n return train_tweets, sub_train_targets, test_tweets, sub_test_targets, train_sentimentvalues, test_sentimentvalues\n\n\ndef shuffle_tweets_and_sentiments(tweets, sentiments):\n indexes = range(len(tweets))\n random.shuffle(indexes)\n \n shuffled_tweets = []\n shuffled_sentiments = []\n for i in indexes:\n shuffled_sentiments.append(sentiments[i])\n shuffled_tweets.append(tweets[i])\n \n return shuffled_tweets, shuffled_sentiments \n \n \ndef make_subjectivity_targets(tweets):\n sub_train_targets = ['objective' if t.subjectivity==0 else 'subjective' for t in tweets]\n return tweets, sub_train_targets\n \ndef make_polarity_targets(tweets):\n pol_train_targets = [t.get_sentiment() for t in tweets]\n return tweets, pol_train_targets\n \ndef store_model(name, params, score, file_postfix=\"\"):\n \"\"\"\n Stores the given dict as a pickle in the stored estimators folder.\n \"\"\"\n out = open(\"stored_estimators/\"+str(name)+str(score)+str(file_postfix), 'wb')\n pickle.dump(params, out)\n out.close()\n return params\n\ndef store_sentimentvalues(words_with_values, filename):\n \"\"\"\n Pickles the given list of dicts with sentiment values.\n \"\"\"\n #Pickle sentiment values\n output = open(filename, 'wb')\n pickle.dump(words_with_values, output)\n\n\n\ndef get_sentimentvalues(setnr=None):\n \"\"\"\n Gets the pickles of sentiment values\n \"\"\"\n if setnr==None:\n setnr = int(raw_input(\"Get which pickle set? 0: RandomSet 1: RoseborgSet 2: ErnaSet 3: All three ...\"))\n \n if setnr is 3:\n #fetch all sets and append them together\n tweets = []\n for pickleset in sentiment_pickles:\n tweets = tweets + pickle.load(open(pickleset, 'rb'))\n return tweets\n else:\n tweets = pickle.load(open(sentiment_pickles[setnr], 'rb'))\n return tweets\n \n return tweets\n \ndef get_entity_test_and_targets():\n \"\"\"\n Fetches the dataset for entity testing, aswell as the proper targets.\n \"\"\"\n f = open(\"entity_test\",\"rb\")\n tweets = pickle.load(f)\n# for t in tweets:\n# print t.text,\" \",t.hashtags\n# raw_input(\"Continue?\")\n print len(tweets)\n f.close()\n f = open(\"entity_test_targets.txt\",\"r\")\n targets = f.readlines()\n print len(targets)\n targets = [int(t) for t in targets]\n return tweets, targets\n \ndef temporally_aggregate_subjectivity(tweets, predictions, targets=None, topics=None):\n \"\"\"\n Aggregates subjectivity for given tweets' days for both correct targets and predictions.\n Returns a list with days and a list with tweet frequencies, and a list with aggregated target values and a list with aggregated predicted values\n \"\"\"\n# for t in tweets:\n# print t.timestamp\n days = [t.timestamp[5:10].replace('-','.') if len(t.timestamp)<20 else t.timestamp[8:13].replace('-','.') for t in tweets]\n reduced_targets = reduce_targets(targets) if targets != None else None\n reduced_predictions = reduce_targets(predictions)\n sorted_days =sorted( list(set( [float(x) for x in days] )) )\n aggregated_targets = [ 0 for _ in sorted_days]\n aggregated_predicts = [ 0 for _ in sorted_days]\n frequencies = [ 0 for _ in sorted_days]\n for i in range(len(sorted_days)):\n aggregated_targets[i] = reduce(lambda x,y: x+y, [t if float(d)==sorted_days[i] else 0 for t,d in zip(reduced_targets, days)] ) if reduced_targets!=None else None\n aggregated_predicts[i] = reduce(lambda x,y: x+y, [t if float(d)==sorted_days[i] else 0 for t,d in zip(reduced_predictions, days)] )\n frequencies[i] = reduce(lambda x,y: x+y, [1 if float(d)==sorted_days[i] else 0 for t,d in zip(reduced_predictions, days)] )\n# print days\n print sorted_days, aggregated_targets, aggregated_predicts, frequencies\n return sorted_days, aggregated_targets, aggregated_predicts, frequencies\n\ndef temporally_aggregate_polarity(tweets, predictions, targets=None, topics=None):\n \"\"\"\n Aggregates(calculates difference) polarity for given tweets' days for both predictions, and targets if given, and topics if given.\n \"\"\"\n# for t in tweets:\n# print t.timestamp\n days = [t.timestamp[5:10].replace('-','.') if len(t.timestamp)<20 else t.timestamp[8:13].replace('-','.') for t in tweets]\n if targets!=None:\n reduced_targets = reduce_targets(targets)\n reduced_targets = [-1 if t==0 else 1 for t in reduced_targets]\n else:\n reduced_targets = None\n reduced_predictions = reduce_targets(predictions)\n reduced_predictions = [-1 if t==0 else 1 for t in reduced_predictions]\n print topics\n sorted_days =sorted( list(set( [float(x) for x in days] )) )\n aggregated_targets = [ 0 for _ in sorted_days]\n aggregated_predicts = [ 0 for _ in sorted_days]\n frequencies = [ 0 for _ in sorted_days]\n unique_topics = list(set(topics)) if topics!=None else None\n print unique_topics\n aggregated_polarity_on_topic = []\n for i in range(len(sorted_days)):\n aggregated_targets[i] = reduce(lambda x,y: x+y, [t if float(d)==sorted_days[i] else 0 for t,d in zip(reduced_targets, days)] ) if reduced_targets!=None else None\n aggregated_predicts[i] = reduce(lambda x,y: x+y, [t if float(d)==sorted_days[i] else 0 for t,d in zip(reduced_predictions, days)] )\n frequencies[i] = reduce(lambda x,y: x+y, [1 if float(d)==sorted_days[i] else 0 for t,d in zip(reduced_predictions, days)] )\n if unique_topics!=None:\n for i in range(len(unique_topics)):\n aggregated_polarity_on_topic.append(reduce(lambda x,y: x+y, [t if float(d)==sorted_days[i] and top==unique_topics[i] else 0 for t,d,top in zip(reduced_predictions,days,topics)] ))\n \n# print days\n print sorted_days\n print aggregated_polarity_on_topic\n return sorted_days, aggregated_targets, aggregated_predicts, frequencies, topics, aggregated_polarity_on_topic\n \ndef topically_aggregate_polarity(tweets, predictions, topics):\n days = [t.timestamp[5:10].replace('-','.') if len(t.timestamp)<20 else t.timestamp[8:13].replace('-','.') for t in tweets]\n reduced_predictions = reduce_targets(predictions)\n reduced_predictions = [-1 if t==0 else 1 for t in reduced_predictions]\n sorted_days =sorted( list(set( [float(x) for x in days] )) )\n unique_topics = list(set(topics))\n aggregated_polarity_on_topic = [[] for _ in unique_topics]\n sentimentpoints = []\n for i in range(len(unique_topics)):\n for j in range(len(sorted_days)):\n aggregated_polarity_on_topic[i].append(reduce(lambda x,y: x+y, [t if float(d)==sorted_days[j] and top==unique_topics[i] else 0 for t,d,top in zip(reduced_predictions,days,topics)] ))\n sentimentpoints.append(reduce(lambda x,y: x+y, [-p if p<0 else p for p in aggregated_polarity_on_topic[i]]))\n unique_topics[unique_topics.index(None)] = \"undefined\" \n print unique_topics\n print sorted_days\n print aggregated_polarity_on_topic\n unique_topics, aggregated_polarity_on_topic, sentimentpoints = zip(*sorted(zip(unique_topics,aggregated_polarity_on_topic,sentimentpoints), key=operator.itemgetter(2), reverse=True))\n return sorted_days, unique_topics[:20], aggregated_polarity_on_topic[:20]\n \ndef reduce_targets(targets):\n \"\"\"\n Reduces a set of subjectivity or polarity targets to 1s and 0s\n \"\"\" \n if len(targets)<1: return []\n if targets[0]=='objective' or targets[0]=='subjective':\n binaries = [0 if target=='objective' else 1 for target in targets]\n else:\n binaries = [0 if target=='negative' else 1 for target in targets]\n return binaries\n\npickles = ['tweet_pickles/random_dataset',\n 'tweet_pickles/rosenborg_dataset',\n 'tweet_pickles/erna_dataset']\n\nsentiment_pickles = ['models/sentimentvalues_random_dataset',\n 'models/sentimentvalues_rosenborg_dataset',\n 'models/sentimentvalues_erna_dataset']\n \nsentiments = [\"negative\",\n \"neutral\",\n \"positive\"]\n\ncomplete_datasets = [\"complete_datasets/random_dataset.tsv\",\n \"complete_datasets/rosenborg_dataset.tsv\",\n \"complete_datasets/erna_dataset.tsv\",\n \"complete_datasets/temporal_dataset.tsv\"]\n\ndatasets = [\"data/random_dataset.tsv\",\n \"data/rosenborg_dataset.tsv\",\n \"data/erna_dataset.tsv\"] \n\nannotated_datasets = [\"johnarne_annotated_data/random_dataset.tsv\",\n \"johnarne_annotated_data/rosenborg_dataset.tsv\",\n \"johnarne_annotated_data/erna_dataset.tsv\"] \n\nif __name__ == '__main__':\n train, test = split_train_and_test(get_pickles())\n \n print len(train),\" \", len(test)"},{"alpha_fraction":0.625668466091156,"alphanum_fraction":0.6613190770149231,"avg_line_length":34.125,"blob_id":"fdf0a95e4dd82505a325a6a0b1820da021e378eb","content_id":"3be5e430a476982e6d610932c7ce2bd1a5ffb2a9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":561,"license_type":"no_license","max_line_length":105,"num_lines":16,"path":"/models/me.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"'''\nCreated on 19. mars 2014\n\n@author: JohnArne\n'''\nfrom model import Model\nfrom sklearn.linear_model import LogisticRegression\n\nclass ME(Model):\n \"\"\"\n Subclass implementing the Maximum entropy classification model.\n \"\"\"\n def __init__(self, tweets_train, tweets_targets, vect_options, tfidf_options):\n self.classifier = LogisticRegression()\n extra_params = {'clf__C': (0.1, 0.3, 0.5, 0.7, 0.8, 1.0,),'clf__penalty': ('l1', 'l2')}\n super(ME, self).__init__(tweets_train, tweets_targets, vect_options, tfidf_options, extra_params)"},{"alpha_fraction":0.5098039507865906,"alphanum_fraction":0.6078431606292725,"avg_line_length":9.199999809265137,"blob_id":"757df61deeff2230942f4bfee7e3ae6242430d51","content_id":"a0703a41b6e1f2a81a42f7a6ffbdcca5ba18a8e4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":51,"license_type":"no_license","max_line_length":23,"num_lines":5,"path":"/lexicon/__init__.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"'''\nCreated on 3. des. 2014\n\n@author: JohnArne\n'''\n"},{"alpha_fraction":0.6046240329742432,"alphanum_fraction":0.606959342956543,"avg_line_length":34.388431549072266,"blob_id":"294b804ba0b031faadb3362bad5681b9c404aa03","content_id":"aa414f33ffd0e00494102efce01229ff2953ddfa","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4282,"license_type":"no_license","max_line_length":104,"num_lines":121,"path":"/easygui_gui.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"'''\nCreated on 19. nov. 2014\n\n@author: JohnArne\n'''\n\nimport easygui as eg\nimport sys\n \ndef show_windows():\n while 1:\n# title = \"Message from test1.py\"\n# eg.msgbox(\"Hello, world!\", title)\n \n msg =\"Run with which classification model?\"\n title = \"Classification model\"\n models = [\"Multinomial Naive Bayes\", \"Support Vector Machines\", \"Maximum Entropy\"]\n model_choice = str(eg.choicebox(msg, title, models))\n \n msg = \"Use saved preset values?\"\n choices = [\"Yes\",\"No\"]\n choice = eg.buttonbox(msg,choices=choices)\n if str(choice)==\"Yes\":\n model_preset_functions[model_choice]()\n else:\n model_select_functions[model_choice]()\n \n # note that we convert choice to string, in case\n # the user cancelled the choice, and we got None.\n# eg.msgbox(\"You chose: \" + str(choice), \"Survey Result\")\n message = \"Sentiments over time period something something\"\n image = \"temporal_sentiments.png\"\n eg.msgbox(message, image=image)\n \n msg = \"Do you want to continue?\"\n title = \"Please Confirm\"\n if eg.ccbox(msg, title): # show a Continue/Cancel dialog\n pass # user chose Continue\n else:\n sys.exit(0) # user chose Cancel\n \ndef show_naivebayes_presets():\n \"\"\"\n Shows a selection of preset running values for Naive Bayes and returns the user selection.\n \"\"\"\n msg =\"Select preset values for Naive Bayes\"\n title = \"Naive Bayes presets\"\n choices = [\"Multinomial Naive Bayes\", \"Support Vector Machines\", \"Maximum Entropy\"]\n preset_choice = eg.choicebox(msg, title, choices)\n pass\n\ndef show_svm_presets():\n \"\"\"\n Shows a selection of preset running values for Suport Vector Machine and returns the user selection.\n \"\"\"\n msg =\"Select preset values for Support Vector Machines\"\n title = \"SVM presets\"\n choices = [\"something\", \"somethingsomething\", \"something else\"]\n preset_choice = eg.choicebox(msg, title, choices)\n pass\n\ndef show_me_presets():\n \"\"\"\n Shows a selection of preset running values for Maximum Entropy and returns the user selection.\n \"\"\"\n msg =\"Select preset values for Maximum Entropy\"\n title = \"MaxEnt presets\"\n choices = [\"something\", \"something else\", \"aaand more\"]\n preset_choice = eg.choicebox(msg, title, choices)\n pass\n\ndef show_naivebayes_selection():\n \"\"\"\n Shows a value input window for Naive Bayes and returns the user selection.\n \"\"\"\n msg = \"Enter running values for Naive Bayes\"\n title = \"Naive Bayes run\"\n fieldNames = [\"x\",\"dss\",\"c\",\"range\",\"s\",\"p\",\"cross\",\"stu\",\"thn\",\"pH\"]\n fieldValues = [] # we start with blanks for the values\n fieldValues = eg.multenterbox(msg,title, fieldNames)\n \n # make sure that none of the fields was left blank\n while 1: # do forever, until we find acceptable values and break out\n if fieldValues == None: \n break\n errmsg = \"\"\n \n # look for errors in the returned values\n for i in range(len(fieldNames)):\n if fieldValues[i].strip() == \"\":\n errmsg = errmsg + ('\"%s\" is a required field.\\n\\n' % fieldNames[i])\n \n if errmsg == \"\": \n break # no problems found\n else:\n # show the box again, with the errmsg as the message \n fieldValues = eg.multenterbox(errmsg, title, fieldNames, fieldValues)\n \n print (\"Reply was:\", fieldValues)\n pass\n\ndef show_svm_selection():\n \"\"\"\n Shows a value input window for Suport Vector Machine and returns the user selection.\n \"\"\"\n pass\n\ndef show_me_selection():\n \"\"\"\n Shows a value input window for Maximum Entropy and returns the user selection.\n \"\"\"\n pass\n\n\nmodel_preset_functions = {\"Multinomial Naive Bayes\": show_naivebayes_presets,\n \"Support Vector Machines\": show_svm_presets,\n \"Maximum Entropy\": show_me_presets}\n\nmodel_select_functions = {\"Multinomial Naive Bayes\": show_naivebayes_selection,\n \"Support Vector Machines\": show_svm_selection,\n \"Maximum Entropy\": show_me_selection}\n"},{"alpha_fraction":0.6134738326072693,"alphanum_fraction":0.619997501373291,"avg_line_length":36.78199005126953,"blob_id":"42a9657c4e46be69a1ad152dc38c241864421a85","content_id":"57e186549570bf2e015c8057639554551e0282fc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":7971,"license_type":"no_license","max_line_length":150,"num_lines":211,"path":"/lexicon/lexicon.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"'''\nCreated on 27. nov. 2014\n\n@author: JohnArne\n'''\nfrom hmac import trans_36\nimport requests\nimport os\nfrom sentiwordnet import SentiWordNetCorpusReader, SentiSynset\nimport nltk\nfrom pos_mappings import TYPECRAFT_SENTIWORDNET\nimport gettext\nimport codecs\nimport subprocess\nimport pickle\n\nclass Lexicon():\n \"\"\"\n Handles the interfacing with the sentiment lexicon as well as translation and disambiguation.\n \"\"\"\n \n def __init__(self, translater, sentiment_lexicon):\n #initialize sentiment lexicon resource and translation\n self.translater = translater\n self.sentiment_lexicon = sentiment_lexicon\n \n def translate_and_get_lexicon_sentiment(self, word, context=None, pos_tag=None):\n \"\"\"\n Returns the translated sentiment values for all the words with their contexts and pos tags.\n \"\"\"\n #Translate word\n translated_word = self.translater.translate(word)\n return self.sentiment_lexicon.get_values(translated_word, context, pos_tag)\n \n def translate_sentence_and_get_lexicon_sentiment(self, sentence):\n \"\"\"\n Returns the translated sentiment values for a whole sentence.\n \"\"\"\n #Translate word\n translated_sentence = self.translater.translate(sentence)\n translated_words = tokenizer(translated_sentence)\n sentiments = []\n for word in translated_words:\n sentiment = self.sentiment_lexicon.get_values(word)\n if sentiment!=None:\n sentiments.append(sentiment)\n return sentiments\n \nclass SentiWordNetLexicon():\n \n def __init__(self):\n SWN_FILENAME = \"lexicon\\SentiWordNet_3.0.0_20130122.txt\"\n self.swn= SentiWordNetCorpusReader(SWN_FILENAME)\n\n def get_values(self, word, context=None, pos_tag=None):\n \"\"\"\n Perform lookup in SentiWordNet\n \"\"\"\n# entry = swn.senti_synset(\"breakdown.n.03\")\n entries = None\n for w in word.split(' '):\n entries = self.swn.senti_synsets(w)\n if entries != None: break\n if entries is None or len(entries)==0: \n return None\n if len(entries)==1 or pos_tag is None:\n return [entries[0].pos_score, entries[0].neg_score, entries[0].obj_score]\n elif len(entries)>1:\n #Find out which word to chose, if there are several classes\n print \"Several entries \",entries\n for entry in entries:\n if entry.synset.pos()==TYPECRAFT_SENTIWORDNET[pos_tag]:\n print \"Found matching entry: \", entry\n return [entry.pos_score, entry.neg_score, entry.obj_score]\n \n return [entries[0].pos_score, entries[0].neg_score, entries[0].obj_score]\n return None\n\nclass BingTranslater():\n \n def __init__(self, words):\n self.original_words = words\n file = codecs.open(\"bing_words.txt\", \"w\", \"utf8\")\n for word in words:\n file.write(word+\"\\n\")\n file.close()\n \n print \"Bing translating \",len(words),\" words...\"\n subprocess.call(\"lexicon/bingtranslater.exe\")\n file = codecs.open(\"translated_words.txt\", \"r\", \"utf8\")\n translated_words = file.readlines()\n file.close()\n self.translation_mapping = dict(zip(self.original_words, translated_words))\n print \"Bing done...\"\n \n def translate(self, word):\n try:\n return self.translation_mapping[word]\n except KeyError:\n return None \n \nclass GoogleTranslater():\n \n def __init__(self):\n self.translation_url = \"https://translate.google.com/#no/en/\"\n #The lines of words contain the original word first, then subsequent translations in english\n self.words = codecs.open(\"bing_words.txt\", \"r\", \"utf8\").read().splitlines()\n \n def translate(self, word, context=None, pos_tag=None):\n \"\"\"\n Translate word using a translation API\n Perform sentence contezt translation on google web interface\n Perform word translation using Bing -> get all alternatives anc check for a mathc in the google translation, if match choose it as translation\n if not then choose the bing translation that best matches using POS tag?\n \"\"\"\n #Get contextual translation from google translate\n par = {\"text\": word, \"raw\": \"raw\"}\n r = requests.post(self.translation_url, data=par)\n results = r.text\n translated_word = get_from_html_text(results, 'TRANSLATED_TEXT')\n \n #Perform lookup in the text file from the C# translator\n #if there is no match, take the best match from the bing file\n# print \"Translated: \", word, \" ->\", translated_word\n return translated_word\n \ndef get_from_html_text(resultset, target):\n \"\"\"\n Gets the value of a variable target from a html result set from a request.\n \"\"\"\n index = resultset.find(target)+len(target)+2\n return resultset[index:index+140].split(\"'\")[0].lower()\n \n \ndef perform_bing_sentiment_lexicon_lookup(tweets):\n \"\"\"\n Performs sentiment lexicon lookup on the tweets, and stores it in the objects.\n \"\"\"\n words = []\n for t in tweets:\n for phrase in t.tagged_words:\n for word in phrase:\n try:\n if word[\"pos\"] in TYPECRAFT_SENTIWORDNET:\n words.append(word['word'])\n except KeyError:\n continue \n \n \n lex = Lexicon(BingTranslater(words), SentiWordNetLexicon())\n words_with_sentimentvalues=[]#list of dicts\n print \"Getting sentiment values\"\n for t in tweets:\n sentiwords =[]\n sentiwords_with_values={}\n for phrase in t.tagged_words:\n for word in phrase:\n try:\n if word[\"pos\"] in TYPECRAFT_SENTIWORDNET:\n sentiwords.append(word['word'])\n except KeyError:\n continue\n for sentiword in sentiwords:\n sentivalues = lex.translate_and_get_lexicon_sentiment(sentiword)\n if sentivalues!=None:\n print \"Adding sentivalues: \",sentivalues\n sentiwords_with_values[sentiword] = sentivalues\n words_with_sentimentvalues.append(sentiwords_with_values)\n \n return words_with_sentimentvalues\n \ndef perform_google_sentiment_lexicon_lookup(tweets):\n \"\"\"\n Performs sentiment lexicon lookup on the tweets, and stores it in the objects.\n \"\"\"\n \n lex = Lexicon(GoogleTranslater(), SentiWordNetLexicon())\n print \"Getting sentiment values\"\n tweet_sentiments = []\n for t in tweets:\n tweet_sentiments.append(lex.translate_sentence_and_get_lexicon_sentiment(t.text))\n \n print tweet_sentiments\n reduced_tweet_sentiments = []\n for sentiments in tweet_sentiments:\n polar_sum = sum([s[0] for s in sentiments])\n negative_sum = sum([s[1] for s in sentiments])\n objective_sum = sum([s[2] for s in sentiments])\n reduced_tweet_sentiments.append((polar_sum, negative_sum, objective_sum))\n print reduced_tweet_sentiments\n return reduced_tweet_sentiments\n\ndef tokenizer(sentence):\n \"\"\"\n Tokenizes an english sentence.\n \"\"\"\n words = []\n for phrase in sentence.split('.'):\n for piece in phrase.split(','):\n for word in piece.split(' '):\n words.append(word)\n return words\n \nif __name__ == '__main__':\n #Insert all words to be translated into the googlebing translator in order to augment with Bing...\n lex = Lexicon(BingTranslater(), SentiWordNetLexicon())\n print lex.translate_and_get_lexicon_sentiment(\"good\")\n \n# swn = SentiWordNetCorpusReader('SentiWordNet_3.0.0_20130122.txt')\n# for senti_synset in swn.all_senti_synsets():\n# print senti_synset.synset.name, senti_synset.pos_score, senti_synset.neg_score"},{"alpha_fraction":0.6290730237960815,"alphanum_fraction":0.638678252696991,"avg_line_length":48.009010314941406,"blob_id":"54ee3986ef4cd6d2a0549170e0a956d1376f6400","content_id":"175e7bff739036decf3db12cc35113f87b6553a5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":21759,"license_type":"no_license","max_line_length":248,"num_lines":444,"path":"/test.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"'''\nCreated on 24. nov. 2014\nMethods for performing test on various classificatino schemes and storing the results.\n@author: JohnArne\n'''\nimport utils\nfrom lexicon import lexicon\nfrom models.nb import NB\nfrom models.svm import SVM\nfrom models.me import ME\nfrom sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\nfrom tweet import Tweet\nimport plotting\nimport preprocessing\nimport models.features as feat_utils\nimport pickle\nimport classifier\nimport tweet\nimport entity_extraction\nfrom entity_extraction import cutoff_breakwords\n \n \ndef train_and_test_subjectivity_and_polarity():\n datasetnr = 3\n tweets = utils.get_pickles(datasetnr)\n sentimentvalues = feat_utils.get_sentiment_values(datasetnr)\n tweets = preprocessing.remove_link_classes(tweets)\n tweets = preprocessing.lower_case(tweets)\n tweets = preprocessing.remove_specialchars_round2(tweets)\n \n# train_subjectivity_and_test_on_feature_set(tweets, 'SA', sentimentvalues)\n train_subjectivity_and_test_on_feature_set(tweets, 'SB', sentimentvalues)\n train_subjectivity_and_test_on_feature_set(tweets, 'SC', sentimentvalues)\n# google_sentimentvalues = feat_utils.get_google_sentiment_values(datasetnr)\n# train_subjectivity_and_test_on_feature_set(tweets, 'SC2', google_sentimentvalues)\n \n# train_polarity_and_test_on_feature_set(tweets, 'PA', sentimentvalues)\n# train_polarity_and_test_on_feature_set(tweets, 'PB', sentimentvalues)\n# train_polarity_and_test_on_feature_set(tweets, 'PC', sentimentvalues)\n# google_sentimentvalues = feat_utils.get_google_sentiment_values(datasetnr)\n# train_polarity_and_test_on_feature_set(tweets, 'PC2', google_sentimentvalues)\n\ndef train_subjectivity_and_test_on_feature_set(tweets, feature_set, sentimentvalues, reduce_dataset=1):\n \"\"\"\n Performs training and testing with a given feature set key\n \"\"\"\n kfolds = range(0,10)\n nbaccuracy_avgs = []\n nbprecision_avgs = []\n nbrecall_avgs = []\n nbf1_avgs = []\n svmaccuracy_avgs = []\n svmprecision_avgs = []\n svmrecall_avgs = []\n svmf1_avgs = []\n meaccuracy_avgs = []\n meprecision_avgs = []\n merecall_avgs = []\n mef1_avgs = []\n vect_options = {\n 'ngram_range': (1,1),\n 'max_df': 0.5\n }\n tfidf_options = {\n 'sublinear_tf': False,\n 'use_idf': True,\n 'smooth_idf': True,\n }\n for kfoldcounter in kfolds:\n print \"--------------------------KFOLD NR \",kfoldcounter,\"----------------------------------\" \n train_tweets, train_targets, test_tweets, test_targets, train_sentimentvalues, test_sentimentvalues = utils.make_subjectivity_train_and_test_and_targets(tweets,sentimentvalues,splitvalue=kfoldcounter*0.1,reduce_dataset=reduce_dataset) \n #TRAINING NB\n print \"Training NB subjectivity on dataset of length \", len(train_tweets)\n clf = NB(train_tweets, train_targets, vect_options, tfidf_options)\n clf.set_feature_set(feature_set, train_sentimentvalues)\n clf.train_on_feature_set()\n print \"Testing...\"\n nb_accuracy, nb_precision, nb_recall, nb_f1_score = clf.test_and_return_results(test_tweets, test_targets, test_sentimentvalues)\n nbaccuracy_avgs.append(nb_accuracy)\n nbprecision_avgs.append(nb_precision)\n nbrecall_avgs.append(nb_recall)\n nbf1_avgs.append(nb_f1_score)\n \n #TRAINING SVM\n vect_options = {\n 'ngram_range': (1,3),\n 'max_df': 0.5\n \n }\n tfidf_options = {\n 'sublinear_tf': True,\n 'use_idf': True,\n 'smooth_idf': True,\n }\n print \"Training SVM subjectivity on dataset of length \", len(train_tweets)\n clf = SVM(train_tweets, train_targets, vect_options, tfidf_options)\n clf.set_feature_set(feature_set, train_sentimentvalues)\n clf.train_on_feature_set()\n print \"Testing...\"\n svm_accuracy, svm_precision, svm_recall, svm_f1_score = clf.test_and_return_results(test_tweets, test_targets, test_sentimentvalues)\n svmaccuracy_avgs.append(svm_accuracy)\n svmprecision_avgs.append(svm_precision)\n svmrecall_avgs.append(svm_recall)\n svmf1_avgs.append(svm_f1_score)\n \n #TRAINING MAXENT\n vect_options = {\n 'ngram_range': (1,2),\n 'max_df': 0.5\n }\n tfidf_options = {\n 'sublinear_tf': True,\n 'use_idf': True,\n 'smooth_idf': True,\n }\n \n print \"Training MaxEnt subjectivity on dataset of length \", len(train_tweets)\n clf = ME(train_tweets, train_targets, vect_options, tfidf_options)\n clf.set_feature_set(feature_set, train_sentimentvalues)\n clf.train_on_feature_set()\n \n print \"Testing...\"\n me_accuracy, me_precision, me_recall, me_f1_score = clf.test_and_return_results(test_tweets, test_targets, test_sentimentvalues)\n meaccuracy_avgs.append(me_accuracy)\n meprecision_avgs.append(me_precision)\n merecall_avgs.append(me_recall)\n mef1_avgs.append(me_f1_score)\n \n \n print \"Averages\"\n nb_accuracy = reduce(lambda x,y: x+y,nbaccuracy_avgs)/len(nbaccuracy_avgs)\n nb_precision = reduce(lambda x,y: x+y,nbprecision_avgs)/len(nbprecision_avgs)\n nb_recall = reduce(lambda x,y: x+y,nbrecall_avgs)/len(nbrecall_avgs)\n nb_f1_score = reduce(lambda x,y: x+y,nbf1_avgs)/len(nbf1_avgs)\n svm_accuracy = reduce(lambda x,y: x+y,svmaccuracy_avgs)/len(svmaccuracy_avgs)\n svm_precision = reduce(lambda x,y: x+y,svmprecision_avgs)/len(svmprecision_avgs)\n svm_recall = reduce(lambda x,y: x+y,svmrecall_avgs)/len(svmrecall_avgs)\n svm_f1_score = reduce(lambda x,y: x+y,svmf1_avgs)/len(svmf1_avgs)\n me_accuracy = reduce(lambda x,y: x+y,meaccuracy_avgs)/len(meaccuracy_avgs)\n me_precision = reduce(lambda x,y: x+y,meprecision_avgs)/len(meprecision_avgs)\n me_recall = reduce(lambda x,y: x+y,merecall_avgs)/len(merecall_avgs)\n me_f1_score = reduce(lambda x,y: x+y,mef1_avgs)/len(mef1_avgs)\n \n data = {'Naive Bayes': [nb_accuracy, nb_precision, nb_recall, nb_f1_score],\n 'SVM': [svm_accuracy, svm_precision, svm_recall, svm_f1_score],\n 'Maximum Entropy': [me_accuracy, me_precision, me_recall, me_f1_score]}\n plotting.plot_performance_histogram(data, \"subjectivity_\"+feature_set)\n return data\n\ndef train_polarity_and_test_on_feature_set(tweets, feature_set, sentimentvalues, reduce_dataset=1):\n \"\"\"\n Performs training and testing with a given feature set key\n \"\"\"\n kfolds = range(0,10)\n nbaccuracy_avgs = []\n nbprecision_avgs = []\n nbrecall_avgs = []\n nbf1_avgs = []\n svmaccuracy_avgs = []\n svmprecision_avgs = []\n svmrecall_avgs = []\n svmf1_avgs = []\n meaccuracy_avgs = []\n meprecision_avgs = []\n merecall_avgs = []\n mef1_avgs = []\n for kfoldcounter in kfolds:\n print \"--------------------------KFOLD NR \",kfoldcounter,\"----------------------------------\" \n train_tweets, train_targets, test_tweets, test_targets, train_sentimentvalues, test_sentimentvalues = utils.make_polarity_train_and_test_and_targets(tweets,sentimentvalues, splitvalue=kfoldcounter*0.1, reduce_dataset=reduce_dataset)\n # for tweet, target in zip(tweets,targets):\n # try:\n # print unicode(tweet.text), \" \", target\n # except UnicodeEncodeError:\n # print tweet.text.encode('utf8'), \" \", target\n # except UnicodeDecodeError:\n # print tweet.text, \" \", target\n \n #TRAINING NB\n vect_options = {\n 'ngram_range': (1,1),\n 'max_df': 0.5\n }\n tfidf_options = {\n 'sublinear_tf': True,\n 'use_idf': True,\n 'smooth_idf': True,\n }\n print \"Training NB polarity with feature set \",feature_set\n clf = NB(train_tweets, train_targets, vect_options, tfidf_options)\n clf.set_feature_set(feature_set, train_sentimentvalues)\n clf.train_on_feature_set()\n print \"Testing...\"\n nb_accuracy, nb_precision, nb_recall, nb_f1_score = clf.test_and_return_results(test_tweets, test_targets, test_sentimentvalues)\n nbaccuracy_avgs.append(nb_accuracy)\n nbprecision_avgs.append(nb_precision)\n nbrecall_avgs.append(nb_recall)\n nbf1_avgs.append(nb_f1_score)\n \n #TRAINING SVM\n vect_options = {\n 'ngram_range': (1,1),\n 'max_df': 0.5\n }\n tfidf_options= {\n 'sublinear_tf': True,\n 'use_idf': True,\n 'smooth_idf': True,\n }\n print \"Training SVM polarity on dataset of length \", len(train_tweets)\n clf = SVM(train_tweets, train_targets, vect_options, tfidf_options)\n clf.set_feature_set(feature_set, train_sentimentvalues)\n clf.train_on_feature_set()\n \n print \"Testing...\"\n svm_accuracy, svm_precision, svm_recall, svm_f1_score = clf.test_and_return_results(test_tweets, test_targets, test_sentimentvalues)\n svmaccuracy_avgs.append(svm_accuracy)\n svmprecision_avgs.append(svm_precision)\n svmrecall_avgs.append(svm_recall)\n svmf1_avgs.append(svm_f1_score)\n #TRAINING MAXENT\n vect_options = {\n 'ngram_range': (1,1),\n 'max_df': 0.5\n }\n tfidf_options = {\n 'sublinear_tf': True,\n 'use_idf': True,\n 'smooth_idf': True,\n }\n print \"Training MaxEnt polarity on dataset of length \", len(train_tweets)\n clf = ME(train_tweets, train_targets, vect_options, tfidf_options)\n clf.set_feature_set(feature_set, train_sentimentvalues)\n clf.train_on_feature_set()\n \n print \"Testing...\"\n me_accuracy, me_precision, me_recall, me_f1_score = clf.test_and_return_results(test_tweets, test_targets, test_sentimentvalues)\n meaccuracy_avgs.append(me_accuracy)\n meprecision_avgs.append(me_precision)\n merecall_avgs.append(me_recall)\n mef1_avgs.append(me_f1_score)\n \n print \"Averages\"\n nb_accuracy = reduce(lambda x,y: x+y,nbaccuracy_avgs)/len(nbaccuracy_avgs)\n nb_precision = reduce(lambda x,y: x+y,nbprecision_avgs)/len(nbprecision_avgs)\n nb_recall = reduce(lambda x,y: x+y,nbrecall_avgs)/len(nbrecall_avgs)\n nb_f1_score = reduce(lambda x,y: x+y,nbf1_avgs)/len(nbf1_avgs)\n svm_accuracy = reduce(lambda x,y: x+y,svmaccuracy_avgs)/len(svmaccuracy_avgs)\n svm_precision = reduce(lambda x,y: x+y,svmprecision_avgs)/len(svmprecision_avgs)\n svm_recall = reduce(lambda x,y: x+y,svmrecall_avgs)/len(svmrecall_avgs)\n svm_f1_score = reduce(lambda x,y: x+y,svmf1_avgs)/len(svmf1_avgs)\n me_accuracy = reduce(lambda x,y: x+y,meaccuracy_avgs)/len(meaccuracy_avgs)\n me_precision = reduce(lambda x,y: x+y,meprecision_avgs)/len(meprecision_avgs)\n me_recall = reduce(lambda x,y: x+y,merecall_avgs)/len(merecall_avgs)\n me_f1_score = reduce(lambda x,y: x+y,mef1_avgs)/len(mef1_avgs)\n \n data = {'Naive Bayes': [nb_accuracy, nb_precision, nb_recall, nb_f1_score],\n 'SVM': [svm_accuracy, svm_precision, svm_recall, svm_f1_score],\n 'Maximum Entropy': [me_accuracy, me_precision, me_recall, me_f1_score]}\n plotting.plot_performance_histogram(data, \"polarity_\"+feature_set)\n return data\n\ndef perform_grid_search_on_featureset_SA_and_PA():\n datasetnr = 3\n tweets = utils.get_pickles(datasetnr)\n sentimentvalues = feat_utils.get_sentiment_values(datasetnr)\n tweets = preprocessing.remove_link_classes(tweets)\n tweets = preprocessing.lower_case(tweets)\n tweets = preprocessing.remove_specialchars_round2(tweets)\n \n train_tweets, train_targets, test_tweets, test_targets, train_sentimentvalues, test_sentimentvalues = utils.make_subjectivity_train_and_test_and_targets(tweets,sentimentvalues)\n \n clf = SVM(train_tweets, train_targets, None)\n clf.set_feature_set('SA', None)\n clf.grid_search_on_text_features(file_postfix='subjectivity')\n clf = NB(train_tweets, train_targets, None)\n clf.set_feature_set('SA', None)\n clf.grid_search_on_text_features(file_postfix='subjectivity')\n clf = ME(train_tweets, train_targets, None)\n clf.set_feature_set('SA', None)\n clf.grid_search_on_text_features(file_postfix='subjectivity')\n \n train_tweets, train_targets, test_tweets, test_targets, train_sentimentvalues, test_sentimentvalues = utils.make_polarity_train_and_test_and_targets(tweets,sentimentvalues)\n \n clf = SVM(train_tweets, train_targets, None)\n clf.set_feature_set('PA', None)\n clf.grid_search_on_text_features(file_postfix='polarity')\n clf = NB(train_tweets, train_targets, None)\n clf.set_feature_set('PA', None)\n clf.grid_search_on_text_features(file_postfix='polarity')\n clf = ME(train_tweets, train_targets, None)\n clf.set_feature_set('PA', None)\n clf.grid_search_on_text_features(file_postfix='polarity')\n \ndef train_and_test_dataset_increase():\n datasetnr = 3\n tweets = utils.get_pickles(datasetnr)\n sentimentvalues = feat_utils.get_sentiment_values(datasetnr)\n tweets = preprocessing.remove_link_classes(tweets)\n tweets = preprocessing.lower_case(tweets)\n tweets = preprocessing.remove_specialchars_round2(tweets)\n accuracy_data = {'NB(SA)':[],'NB(SB)':[],'NB(SC)':[],\n 'SVM(SA)':[],'SVM(SB)':[],'SVM(SC)':[],\n 'MaxEnt(SA)':[],'MaxEnt(SB)':[],'MaxEnt(SC)':[],\n 'NB(PA)':[],'NB(PB)':[],'NB(PC)':[],\n 'SVM(PA)':[],'SVM(PB)':[],'SVM(PC)':[],\n 'MaxEnt(PA)':[],'MaxEnt(PB)':[],'MaxEnt(PC)':[]}\n f1_data = {'NB(SA)':[],'NB(SB)':[],'NB(SC)':[],\n 'SVM(SA)':[],'SVM(SB)':[],'SVM(SC)':[],\n 'MaxEnt(SA)':[],'MaxEnt(SB)':[],'MaxEnt(SC)':[],\n 'NB(PA)':[],'NB(PB)':[],'NB(PC)':[],\n 'SVM(PA)':[],'SVM(PB)':[],'SVM(PC)':[],\n 'MaxEnt(PA)':[],'MaxEnt(PB)':[],'MaxEnt(PC)':[]}\n for i in range(5,101,5):\n print \"=============================DATAPOINT NR. \",i,\"========================================\"\n data = train_subjectivity_and_test_on_feature_set(tweets, 'SA', sentimentvalues, reduce_dataset=i*0.01)\n print \"DATA -- \",data\n accuracy_data['NB(SA)'].append(data['Naive Bayes'][0])\n f1_data['NB(SA)'].append(data['Naive Bayes'][3])\n accuracy_data['SVM(SA)'].append(data['SVM'][0])\n f1_data['SVM(SA)'].append(data['SVM'][3])\n accuracy_data['MaxEnt(SA)'].append(data['Maximum Entropy'][0])\n f1_data['MaxEnt(SA)'].append(data['Maximum Entropy'][3])\n \n data = train_subjectivity_and_test_on_feature_set(tweets, 'SB', sentimentvalues, reduce_dataset=i*0.01)\n print \"DATA -- \",data\n accuracy_data['NB(SB)'].append(data['Naive Bayes'][0])\n f1_data['NB(SB)'].append(data['Naive Bayes'][3])\n accuracy_data['SVM(SB)'].append(data['SVM'][0])\n f1_data['SVM(SB)'].append(data['SVM'][3])\n accuracy_data['MaxEnt(SB)'].append(data['Maximum Entropy'][0])\n f1_data['MaxEnt(SB)'].append(data['Maximum Entropy'][3])\n \n data = train_subjectivity_and_test_on_feature_set(tweets, 'SC', sentimentvalues, reduce_dataset=i*0.01)\n print \"DATA -- \",data\n accuracy_data['NB(SC)'].append(data['Naive Bayes'][0])\n f1_data['NB(SC)'].append(data['Naive Bayes'][3])\n accuracy_data['SVM(SC)'].append(data['SVM'][0])\n f1_data['SVM(SC)'].append(data['SVM'][3])\n accuracy_data['MaxEnt(SC)'].append(data['Maximum Entropy'][0])\n f1_data['MaxEnt(SC)'].append(data['Maximum Entropy'][3])\n \n data = train_polarity_and_test_on_feature_set(tweets, 'PA', sentimentvalues, reduce_dataset=i*0.01)\n print \"DATA -- \",data\n accuracy_data['NB(PA)'].append(data['Naive Bayes'][0])\n f1_data['NB(PA)'].append(data['Naive Bayes'][3])\n accuracy_data['SVM(PA)'].append(data['SVM'][0])\n f1_data['SVM(PA)'].append(data['SVM'][3])\n accuracy_data['MaxEnt(PA)'].append(data['Maximum Entropy'][0])\n f1_data['MaxEnt(PA)'].append(data['Maximum Entropy'][3])\n \n data = train_polarity_and_test_on_feature_set(tweets, 'PB', sentimentvalues, reduce_dataset=i*0.01)\n print \"DATA -- \",data\n accuracy_data['NB(PB)'].append(data['Naive Bayes'][0])\n f1_data['NB(PB)'].append(data['Naive Bayes'][3])\n accuracy_data['SVM(PB)'].append(data['SVM'][0])\n f1_data['SVM(PB)'].append(data['SVM'][3])\n accuracy_data['MaxEnt(PB)'].append(data['Maximum Entropy'][0])\n f1_data['MaxEnt(PB)'].append(data['Maximum Entropy'][3])\n \n data = train_polarity_and_test_on_feature_set(tweets, 'PC', sentimentvalues, reduce_dataset=i*0.01)\n print \"DATA -- \",data\n accuracy_data['NB(PC)'].append(data['Naive Bayes'][0])\n f1_data['NB(PC)'].append(data['Naive Bayes'][3])\n accuracy_data['SVM(PC)'].append(data['SVM'][0])\n f1_data['SVM(PC)'].append(data['SVM'][3])\n accuracy_data['MaxEnt(PC)'].append(data['Maximum Entropy'][0])\n f1_data['MaxEnt(PC)'].append(data['Maximum Entropy'][3])\n out = open('incremental_acc'+str(i), 'wb')\n pickle.dump(accuracy_data, out)\n out = open('incremental_f1'+str(i), 'wb')\n pickle.dump(f1_data, out)\n plotting.plot_temporal_sentiment(accuracy_data, filename=\"incremental_accuracy\")\n plotting.plot_temporal_sentiment(f1_data, filename=\"incremental_f1\")\n \ndef test_aggregated_sentiments():\n sub_clf = classifier.get_optimal_subjectivity_classifier()\n pol_clf = classifier.get_optimal_polarity_classifier()\n tweets = utils.get_pickles(2)\n sentimentvalues = utils.get_sentimentvalues(2)\n sub_train_tweets, sub_train_targets, _, _, sub_train_sentiments, _ = utils.make_subjectivity_train_and_test_and_targets(tweets, sentimentvalues, splitvalue=1.0)\n pol_train_tweets, pol_train_targets, _, _, pol_train_sentiments, _ = utils.make_polarity_train_and_test_and_targets(tweets, sentimentvalues, splitvalue=1.0)\n \n sub_predictions = sub_clf.classify(sub_train_tweets, sub_train_sentiments)\n pol_predictions = pol_clf.classify(pol_train_tweets, pol_train_sentiments)\n print pol_train_targets, pol_predictions\n days, targets, predicts, total_frequencies = utils.temporally_aggregate_subjectivity(sub_train_tweets, sub_predictions, targets=sub_train_targets)\n data = {'Targets': [days, targets], 'Predictions': [days, predicts], 'Frequencies': [days,total_frequencies]}\n plotting.plot_subjectivity_aggregates(data, 'aggregated_subjectivity')\n days, targets, predicts, frequencies = utils.temporally_aggregate_polarity(pol_train_tweets, pol_predictions, targets=pol_train_targets)\n for i in range(len(days)):\n targets[i]=targets[i]*1.0/frequencies[i]\n predicts[i]=predicts[i]*1.0/frequencies[i]\n frequencies[i]=frequencies[i]*1.0/total_frequencies[i]\n data = {'Targets': [days, targets], 'Predictions': [days, predicts], 'Frequencies': [days,frequencies]}\n plotting.plot_polarity_aggregates(data, 'aggregated_polarity')\n \ndef test_remporal_topics():\n tweets1 = pickle.load(open('temporal_tweets1', 'rb'))\n tweets2 = pickle.load(open('temporal_tweets2', 'rb'))\n tweets = tweets1 + tweets2\n print len(tweets)\n sentiments = pickle.load(open('temporal_sentiments','rb'))\n print len(sentiments)\n subclf = classifier.get_optimal_subjectivity_classifier()\n polclf = classifier.get_optimal_polarity_classifier() \n #TODO SKRIVE HER TEMPORALLY AGGREGATE ETC\n sub_predictions = subclf.classify(tweets, sentiments)\n subjective_tweets = [t for p,t in zip(sub_predictions,tweets) if p==\"subjective\"]\n subjective_sentiments = [s for p,s in zip(sub_predictions,sentiments) if p==\"subjective\"]\n pol_predictions = polclf.classify(subjective_tweets, subjective_sentiments)\n topics = entity_extraction.perform_entity_extraction(subjective_tweets, subjective_sentiments, use_pmi=True, breakword_min_freq=0.1, breakword_range=14)\n days, unique_topics, aggregated_values = utils.topically_aggregate_polarity(subjective_tweets, pol_predictions, topics=topics)\n data = {}\n for i in range(len(unique_topics)):\n data[unique_topics[i]] = [days, aggregated_values[i]]\n print data\n pickle.dump(data, open('topically_aggregated_polarity', 'wb'))\n\ndef preprocess_temporal_dataset():\n tweetlines = utils.get_dataset(utils.complete_datasets[3])\n tweets = []\n for line in tweetlines:\n if len(line)>1:\n tweets.append(tweet.to_tweet(line))\n tweets = preprocessing.preprocess_tweets(tweets)\n sentiments = lexicon.perform_google_sentiment_lexicon_lookup(tweets)\n pickle.dump(sentiments, open('temporal_sentiments','wb'))\n pickle.dump(tweets, open('temporal_tweets2', 'wb'))\n \nif __name__ == '__main__':\n datasetnr = 3\n tweets = utils.get_pickles(datasetnr)\n sentimentvalues = feat_utils.get_sentiment_values(datasetnr)\n tweets = preprocessing.remove_link_classes(tweets)\n tweets = preprocessing.lower_case(tweets)\n tweets = preprocessing.remove_specialchars_round2(tweets)\n \n train_subjectivity_and_test_on_feature_set(tweets, 'SA', datasetnr)\n train_subjectivity_and_test_on_feature_set(tweets, 'SB', datasetnr)\n train_subjectivity_and_test_on_feature_set(tweets, 'SC', sentimentvalues)\n \n train_polarity_and_test_on_feature_set(tweets, 'PA', datasetnr)\n train_polarity_and_test_on_feature_set(tweets, 'PB', datasetnr)\n train_polarity_and_test_on_feature_set(tweets, 'PC', sentimentvalues)"},{"alpha_fraction":0.6822289228439331,"alphanum_fraction":0.6868975758552551,"avg_line_length":44.76551818847656,"blob_id":"3cbb072f066fb95341a8bbe401d8fa4bfb7ab960","content_id":"91cf41ca7c0759dc5e2d90ed4fa359b864e7315c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6640,"license_type":"no_license","max_line_length":221,"num_lines":145,"path":"/classifier.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"'''\nCreated on 11. mars 2014\n\n@author: JohnArne\n'''\nimport argparse\nimport utils\nimport preprocessing\nimport retriever_tweepy\nfrom models.nb import NB\nfrom models.svm import SVM\nfrom models.me import ME\nfrom models import features\nfrom models import model\nfrom lexicon import lexicon\nimport test\n\nimport annotation\nimport easygui_gui\nfrom retriever_tweepy import TweetRetriever\nimport entity_extraction\n\nclass Classifier(object):\n \"\"\"\n Class for handling the training and testing of a given model.\n Takes in a selected model type(NV/SVM/ME) trains it on a given dataset, then tests it.\n \"\"\"\n \n def __init__(self, subjectivity_model, polarity_model):\n self.subjectivity_model = subjectivity_model\n self.polarity_model = polarity_model\n \n \n def test(self):\n \"\"\"\n Tests the given model on a partition of the dataset.\n \"\"\" \n \n def classify(self, tweets):\n \"\"\"\n Takes in a list of tweets and classifies with all three classes using the two trained models\n \"\"\"\n sentiments = []\n predictions = self.subjectivity_model.classify(tweets)\n return sentiments\n \n def save_model(self):\n file = open()\n\n def train_and_store_results(self):\n \"\"\"\n Trains the given model on the dataset using the three different models, and different feature sets. Stores the results of the runs.\n \"\"\"\n dataset = \"random_dataset\"\n tweets = utils.get_pickles(dataset)\n self.model.set_feature_set('A')\n self.model.train_on_feature_set()\n \ndef get_optimal_subjectivity_classifier():\n \"\"\"\n Trains and returns the optimal subjectivity classifier.\n \"\"\"\n tweets = utils.get_pickles(3)\n tweets, targets = utils.make_subjectivity_targets(tweets)\n vect_options = {\n 'ngram_range': (1,1),\n 'max_df': 0.5\n }\n tfidf_options = {\n 'sublinear_tf': False,\n 'use_idf': True,\n 'smooth_idf': True,\n }\n clf = SVM(tweets, targets, vect_options, tfidf_options)\n clf.set_feature_set('SA', utils.get_sentimentvalues(3))\n clf.train_on_feature_set()\n return clf\n\ndef get_optimal_polarity_classifier():\n \"\"\"\n Trains and returns the optimal polarity classifier.\n \"\"\"\n tweets = utils.get_pickles(3)\n tweets, targets = utils.make_polarity_targets(tweets)\n vect_options = {\n 'ngram_range': (1,1),\n 'max_df': 0.5\n }\n tfidf_options = {\n 'sublinear_tf': False,\n 'use_idf': True,\n 'smooth_idf': True,\n }\n clf = SVM(tweets, targets, vect_options, tfidf_options)\n clf.set_feature_set('PC2', features.get_google_sentiment_values(3))\n clf.train_on_feature_set()\n return clf\n \nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description=\"Commands for classification\")\n parser.add_argument(\"-pre1\", action=\"store_true\", dest=\"preprocess1\", default=False, help=\"Perform first round preprocessing: Duplicate and retweet removal\")\n parser.add_argument(\"-pre2\", action=\"store_true\", dest=\"preprocess2\", default=False, help=\"Perform second round preprocessing: Text cleanup operations, feature extractions, POS-tagging.\")\n parser.add_argument(\"-q\", action=\"store\", dest=\"tweet_query\", default=None, help=\"Get tweets using the given query.\")\n parser.add_argument(\"-a\", action=\"store_true\", dest=\"annotate\", default=False, help=\"Start annotation sequence.\")\n parser.add_argument(\"-analyze\", action=\"store_true\", dest=\"analyze\", default=False, help=\"Perform a re-analysis of the pickled datasets. This analysis is also performed as part of the second preprocessing.\")\n parser.add_argument(\"-posanalyze\", action=\"store_true\", dest=\"posanalyze\", default=False, help=\"Perform a pos-tag analysis of the pickled datasets.\")\n parser.add_argument(\"-lex1\", action=\"store_true\", dest=\"run_lexicon1\", default=False, help=\"Run lexicon translation using Bing and lookup on stored tweets\")\n parser.add_argument(\"-lex2\", action=\"store_true\", dest=\"run_lexicon2\", default=False, help=\"Run lexicon translation using Google and lookup on stored tweets\")\n parser.add_argument(\"-optimize\", action=\"store_true\", dest=\"optimize\", default=False, help=\"Find optimal parameters for text classification with SVM, NB, and MaxEnt. Stores the optimal parameters for each algorithm.\")\n parser.add_argument(\"-test\", action=\"store_true\", dest=\"train_and_test\", default=False, help=\"Train and test on subjectivity and polarity and create a diagram of the results.\")\n parser.add_argument(\"-test_increment\", action=\"store_true\", dest=\"test_incremental\", default=False, help=\"Train and test incremental dataset results and create a diagram of the results.\")\n parser.add_argument(\"-test_aggregated\", action=\"store_true\", dest=\"test_aggregated\", default=False, help=\"Train and test aggregated results from erna solberg dataset and create a diagram of the results.\")\n parser.add_argument(\"-test_entities\", action=\"store_true\", dest=\"test_entities\", default=False, help=\"Test topic detection on topic-annotated rosenborg dataset and create a diagram of the results.\")\n parser.add_argument(\"-test_temptops\", action=\"store_true\", dest=\"test_temptops\", default=False, help=\"Train and test topically aggregated results from a temporally dense dataset and create a diagram of the results.\")\n \n parsameters = parser.parse_args()\n if parsameters.preprocess1:\n preprocessing.initial_preprocess_all_datasets()\n if parsameters.preprocess2:\n preprocessing.classification_preprocess_all_datasets()\n if parsameters.tweet_query:\n retriever = TweetRetriever(parsameters.tweet_query)\n retriever.retrieve_for_dataset()\n if parsameters.annotate:\n annotation.user_annotation()\n if parsameters.analyze:\n preprocessing.re_analyze()\n if parsameters.posanalyze:\n preprocessing.pos_analyze()\n if parsameters.run_lexicon1:\n preprocessing.bing_lexicon_lookup()\n if parsameters.run_lexicon2:\n preprocessing.google_lexicon_lookup()\n if parsameters.optimize:\n test.perform_grid_search_on_featureset_SA_and_PA()\n if parsameters.train_and_test:\n test.train_and_test_subjectivity_and_polarity()\n if parsameters.test_incremental:\n test.train_and_test_dataset_increase()\n if parsameters.test_aggregated:\n test.test_aggregated_sentiments()\n if parsameters.test_temptops:\n test.test_remporal_topics()\n if parsameters.test_entities:\n entity_extraction.perform_and_test_extraction()\n "},{"alpha_fraction":0.6140362620353699,"alphanum_fraction":0.6189417243003845,"avg_line_length":34.28406524658203,"blob_id":"9c7e767d8ed57767dab671297c075cf83fda31ef","content_id":"d0f863bf0d65e2082fdb61130077fe21fa1e3d71","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":15289,"license_type":"no_license","max_line_length":195,"num_lines":433,"path":"/preprocessing.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"'''\nCreated on 21. apr. 2014\n\n@author: JohnArne\n'''\nimport utils\nfrom calendar import main\nimport tweet\nfrom numpy.core.numeric import correlate\nfrom tweet import Tweet\nimport re\nfrom tagger import Tagger\nfrom analyzer import Analyzer\nimport string\nfrom lexicon import lexicon\nimport plotting\nfrom analyzer import pos_tag_analyze\n\ndef remove_retweets(tweets):\n \"\"\"\n Removes all retweets\n \"\"\"\n for tweet in tweets:\n textbody = tweet.text\n if textbody[:2] is \"RT\":\n tweet.text = textbody[3:]\n return tweets \n\ndef remove_duplicates_and_retweets(tweets):\n \"\"\" \n Removes tweets with dublicate text bodies.\n \"\"\"\n textbodies = []\n tweets = [tweet for tweet in tweets if not tweet.text[:2]==\"RT\"]\n\n #Return a set of the tweets, which will remove duplicates if __eq__ is properly implemented\n unique_tweets = []\n added_texts = []\n for t in tweets:\n if t.text not in added_texts:\n unique_tweets.append(t)\n added_texts.append(t.text)\n return unique_tweets\n\ndef remove_retweet_tags(tweets):\n \"\"\" \n Removes tweets with dublicate text bodies.\n \"\"\"\n for t in tweets:\n textbody = t.text[2:] if t.text[:2]=='RT' else t.text\n t.text = textbody\n return tweets\n\ndef correct_words(tweets):\n \"\"\"\n Performs simple word correction.\n Initially, this will involve removing any vowel that appears 2 times or more,\n aswell as removing any consonant that appears 3 times or more.\n \"\"\"\n for tweet in tweets:\n textbody = tweet.text\n \n for vowel in vowels:\n pattern = re.compile(vowel*3+\"*\")\n try:\n textbody = pattern.sub(vowel, textbody)\n except UnicodeDecodeError:\n textbody = pattern.sub(vowel, textbody.decode('utf8'))\n for consonant in consonants:\n pattern = re.compile(consonant+consonant+consonant+consonant+\"*\")\n try:\n textbody = pattern.sub(consonant*2, textbody)\n except UnicodeDecodeError:\n textbody = pattern.sub(consonant*2, textbody.decode('utf8'))\n\n tweet.text = textbody\n return tweets\n\ndef remove_specialchars(tweets):\n \"\"\"\n Removes certain special characters. Does not remove !, ?, or ., as these are neeeded for the POS tagger to separate phrases.\n \"\"\"\n for tweet in tweets:\n textbody = tweet.text\n pattern = re.compile('({|}|[|]|-|:|\"|@|\\*|\\)|\\()')\n try:\n textbody = pattern.sub(\"\", textbody)\n except UnicodeDecodeError:\n textbody = pattern.sub(\"\", textbody.decode('utf8'))\n try:\n textbody = string.replace(textbody, \"_\", \" \")\n except UnicodeEncodeError:\n textbody = string.replace(textbody.decode('utf8'), \"_\", \" \")\n# textbody = string.replace(textbody, \"?\", \"\")\n# textbody = string.replace(textbody, \".\", \"\")\n# textbody = string.replace(textbody, \"!\", \"\")\n tweet.text = textbody\n return tweets\n\ndef remove_specialchars_round2(tweets):\n for tweet in tweets:\n textbody = tweet.text\n pattern = re.compile('({|}|[|]|-|:|\"|@|\\*|\\)|\\(|\\\\|.)')\n try:\n textbody = pattern.sub(\"\", textbody)\n except UnicodeDecodeError:\n textbody = pattern.sub(\"\", textbody.decode('utf8'))\n try:\n textbody = string.replace(textbody, \"_\", \" \")\n except UnicodeEncodeError:\n textbody = string.replace(textbody.decode('utf8'), \"_\", \" \")\n# textbody = string.replace(textbody, \"?\", \"\")\n# textbody = string.replace(textbody, \".\", \"\")\n# textbody = string.replace(textbody, \"!\", \"\")\n tweet.text = textbody\n return tweets\n\ndef remove_hastags_and_users(tweets):\n \"\"\"\n Removes hashtag labels and user labels, whenever it encounters a hashtag, it increments the hashtag counter in the respective tweet object. Stores both hastags and users in the tweet objects.\n \"\"\"\n for tweet in tweets:\n textbody = \"\"\n for word in tweet.text.split(\" \"):\n if len(word)<1:continue\n tweet.word_count = tweet.word_count +1 \n if not word[0]==\"#\" and not word[0]==\"@\":\n textbody = textbody+word+\" \"\n if word[0]==\"#\":\n tweet.nrof_hashtags = tweet.nrof_hashtags + 1\n tweet.hashtags.append(word[1:])\n textbody = textbody + \" \"\n if word[0]==\"@\":\n tweet.nrof_usersmentioned = tweet.nrof_usersmentioned +1\n tweet.users_mentioned.append(word[1:])\n textbody = textbody + word[1:] + \" \"\n tweet.text = textbody \n return tweets\n\ndef count_emoticons(tweets):\n \"\"\"\n Counts emoticons, whenever it encounters an emoticon, it increments the emoticon counter in the respective tweet object.\n \"\"\" \n for tweet in tweets:\n textbody = tweet.text\n tweet.nrof_happyemoticons = string.count(textbody, \":)\") + string.count(textbody, \":D\")\n tweet.nrof_sademoticons = string.count(textbody, \":(\") + string.count(textbody, \":'(\") + string.count(textbody, \":,(\")\n for emoticon in emoticon_class:\n tweet.text = string.replace(textbody, emoticon, \"\")\n return tweets\n\ndef count_exclamations(tweets):\n \"\"\"\n Counts exclamation marks and question marks, stores their number for future feature use. Then removes all sentence stops.\n Possibly handle / in a separate manner; keep only one of the words...?\n \"\"\"\n for tweet in tweets:\n textbody = tweet.text\n tweet.nrof_exclamations = string.count(textbody, \"!\")\n tweet.nrof_questionmarks = string.count(textbody, \"?\")\n\n pattern = re.compile('(\\?|!|\\.|:)')\n textbody = pattern.sub(\"\", textbody)\n tweet.text = textbody\n return tweets\n\ndef replace_links(tweets):\n \"\"\"\n Replaces any links in the tweets with a link class, saves links in the list in the tweet object.\n \"\"\"\n for tweet in tweets:\n links = [word for word in tweet.text.split(' ') if word[:4]==\"http\" or word[:3]==\"www\"]\n link_replaced_text = \" \".join([\"\" if word[:4]==\"http\" or word[:3]==\"www\" else word for word in tweet.text.split(' ')])\n tweet.text = link_replaced_text\n tweet.links = links \n return tweets\n\ndef remove_stopwords(tweets):\n \"\"\"\n Removes common stopwords based on a created stopword list.\n \"\"\"\n return tweets\n\ndef lower_case(tweets):\n \"\"\"\n Lowercases every text body in the tweets\n \"\"\"\n for tweet in tweets:\n textbody = tweet.text\n tweet.text = textbody.lower()\n return tweets\n\ndef stem(tweets):\n \"\"\"\n Stems and splits the tweet texts and stores them in the processed words list in the tweet object. \n \"\"\"\n return tweets\n\ndef tokenize(tweets):\n for tweet in tweets:\n splits = tweet.text.split(\" \")\n tweet.processed_words = [word for word in splits if len(word)>1]\n return tweets\n\n\ndef pos_tag(tweets):\n \"\"\"\n Uses the POS tagger interface to tag part-of-speech in all the tweets texts, stores it as dict in the tweet objects.\n \"\"\"\n print \"Tagging...\"\n untagged_texts = []\n for tweet in tweets:\n tagger = Tagger()\n textbody = tweet.text\n for phrase in re.split(\"\\.|!|\\?\", textbody):\n if len(phrase)<2: continue\n phrase = string.replace(phrase, \"?\", \"\")\n phrase = string.replace(phrase, \"!\", \"\")\n phrase = string.replace(phrase, \".\", \"\")\n tags = tagger.tag_text(phrase)\n if tags!=None:\n tweet.tagged_words.append(tags)\n print \"Untagged texts: \"\n for text in untagged_texts:\n print text\n print \"Tagging done.\"\n return tweets\n\ndef remove_link_classes(tweets):\n \"\"\"\n Removes the link classes from the given tweets, returns the positions of these links.\n \"\"\"\n for t in tweets:\n t.link_pos = [m.start() for m in re.finditer('\\', t.text)] \n link_replaced_text = \" \".join([\"\" if word==\"\" else word for word in t.text.split(' ')])\n t.text = link_replaced_text\n return tweets\n\ndef bing_lexicon_lookup():\n \"\"\"\n Fetches the tweets and performs lexicon translatino and lookup.\n \"\"\"\n tweets = utils.get_pickles(0)\n words_with_values = lexicon.perform_bing_sentiment_lexicon_lookup(tweets)\n print \"Storing...\"\n utils.store_sentimentvalues(words_with_values, \"models/sentimentvalues_random_dataset\")\n tweets = utils.get_pickles(1)\n words_with_values = lexicon.perform_bing_sentiment_lexicon_lookup(tweets)\n print \"Storing...\"\n utils.store_sentimentvalues(words_with_values, \"models/sentimentvalues_rosenborg_dataset\")\n tweets = utils.get_pickles(2)\n words_with_values = lexicon.perform_bing_sentiment_lexicon_lookup(tweets)\n print \"Storing...\"\n utils.store_sentimentvalues(words_with_values, \"models/sentimentvalues_erna_dataset\")\n\ndef google_lexicon_lookup():\n \"\"\"\n Fetches the tweets and performs lexicon translatino and lookup.\n \"\"\"\n tweets = utils.get_pickles(0)\n words_with_values = lexicon.perform_google_sentiment_lexicon_lookup(tweets)\n print \"Storing...\"\n utils.store_sentimentvalues(words_with_values, \"models/google_sentimentvalues_random_dataset\")\n tweets = utils.get_pickles(1)\n words_with_values = lexicon.perform_google_sentiment_lexicon_lookup(tweets)\n print \"Storing...\"\n utils.store_sentimentvalues(words_with_values, \"models/google_sentimentvalues_rosenborg_dataset\")\n tweets = utils.get_pickles(2)\n words_with_values = lexicon.perform_google_sentiment_lexicon_lookup(tweets)\n print \"Storing...\"\n utils.store_sentimentvalues(words_with_values, \"models/google_sentimentvalues_erna_dataset\")\n \ndef re_analyze():\n \"\"\"\n Unpickles preprocessed tweets and performs reanalyzis of these, then stores stats.\n \"\"\"\n labels = [\"random\",'\"rosenborg\"','\"erna solberg\"']\n data = {}\n worddata = {}\n for i in xrange(3):\n tweets = utils.get_pickles(i)\n analyzer = Analyzer(utils.annotated_datasets[i], tweets)\n \n avg_list,words_list= analyzer.analyze()\n print avg_list\n worddata[labels[i]] = words_list\n data[labels[i]] = avg_list\n plotting.average_wordclasses(worddata, \"averages\")\n\n plotting.detailed_average_wordclasses(data, \"averages2\")\n\ndef pos_analyze():\n \"\"\"\n Unpickles preprocessed tweets and performs pos-analysis of them. Then stores the stats in a diagram.\n \"\"\"\n tweets = utils.get_pickles(3)\n subjectivity_data, polarity_data = pos_tag_analyze(tweets)\n plotting.plot_pos_analysis(subjectivity_data, \"sub_analysis\")\n plotting.plot_pos_analysis(polarity_data, \"pos_analysis\")\n return True\n\ndef initial_preprocess_all_datasets():\n \"\"\"\n Runs first preprocessing iteration on all datasets.\n This is the preprocessing routine performed initially on the datasets before annotation.\n This routine includes duplicate removal\n \"\"\"\n \n for i in range(0,len(utils.datasets)):\n #Fetch from dataset\n tweets = []\n tweetlines = utils.get_dataset(utils.complete_datasets[i])\n for tweetline in tweetlines:\n tweets.append(tweet.to_tweet(tweetline))\n \n #Perform preprocessing\n tweets = remove_duplicates_and_retweets(tweets)\n\n #Store back to dataset\n tweetlines = []\n for t in tweets:\n tweetlines.append(t.to_tsv())\n utils.store_dataset(tweetlines, utils.datasets[i])\n \ndef classification_preprocess_all_datasets():\n \"\"\"\n Preprocesses all datasets to be ready for classification task.\n This will include stemming, word correction, lower-casing, hashtag removal, special char removal.\n \"\"\"\n \n for i in range(0,len(utils.annotated_datasets)):\n tweetlines = utils.get_dataset(utils.annotated_datasets[i])\n tweets = []\n for line in tweetlines:\n if len(line)>1:\n tweets.append(tweet.to_tweet(line))\n \n# tweets = lower_case(tweets)\n tweets = remove_hastags_and_users(tweets)\n tweets = count_emoticons(tweets)\n tweets = replace_links(tweets)\n tweets = remove_specialchars(tweets)\n tweets = correct_words(tweets)\n tweets = stem(tweets)\n tweets = tokenize(tweets)\n tweets = pos_tag(tweets)\n tweets = count_exclamations(tweets)\n\n analyzer = Analyzer(utils.annotated_datasets[i], tweets)\n stats = analyzer.analyze()\n print stats\n #store tweets in pickles...\n print \"Storing pickles...\"\n utils.store_pickles(tweets, utils.annotated_datasets[i][24:len(utils.annotated_datasets[i])-4])\n\ndef preprocess_tweets(tweets):\n# tweets = lower_case(tweets)\n print \"Preprocessing\"\n tweets = remove_retweet_tags(tweets)\n tweets = remove_hastags_and_users(tweets)\n tweets = count_emoticons(tweets)\n tweets = replace_links(tweets)\n tweets = remove_specialchars(tweets)\n tweets = correct_words(tweets)\n tweets = stem(tweets)\n tweets = tokenize(tweets)\n tweets = pos_tag(tweets)\n tweets = count_exclamations(tweets)\n return tweets\n \ndef preprocess_tweet(tweet):\n \"\"\"\n Preprocess a single tweet\n \"\"\"\n tweets = [tweet] \n tweets = remove_hastags_and_users(tweets)\n tweets = count_emoticons(tweets)\n tweets = replace_links(tweets)\n tweets = remove_specialchars(tweets)\n tweets = correct_words(tweets)\n tweets = stem(tweets)\n tweets = tokenize(tweets)\n tweets = pos_tag(tweets)\n tweets = count_exclamations(tweets)\n return tweets[0]\n\n \nvowels = [u\"a\", u\"e\", u\"i\", u\"o\", u\"u\", u\"y\", u\"\\u00E6\", u\"\\u00D8\", u\"\\u00E5\"]\nconsonants = [u\"b\", u\"c\", u\"d\", u\"f\", u\"g\", u\"h\", u\"j\", u\"k\", u\"l\", u\"m\", u\"n\", u\"p\", u\"q\", u\"r\", u\"s\", u\"t\", u\"v\", u\"w\", u\"x\", u\"z\"]\n\nemoticon_class = [\":)\",\":D\",\":(\",\":'(\"]\n\n\nspecial_chars_removal = '(<|>|{|}|[|]|-|_|*|\")'\n\nreplacement_chars = {u\"&\": u\"og\",\n u\"6amp;\": u\"og\",\n u\"+\": u\"og\"}\n \nif __name__ == '__main__':\n #Testing\n# tweets = [Tweet(\"13:37\", \"johnarne\", \"Jeg () haaater drittt!!!? :( #justinbieber\"), Tweet(\"13:37\", \"johnarne\", \"Jeg eeelsker @erna_solberg http://www.erna.no :) #love #jernerna\" )]\n# for tweet in tweets:\n# tweet.set_sentiment(\"negative\")\n# print tweet\n \n tweetlines = utils.get_dataset(\"test_annotated_data/erna_dataset.tsv\")\n tweets = []\n for line in tweetlines:\n if len(line)>1:\n tweets.append(tweet.to_tweet(line))\n \n \n# tweets = lower_case(tweets)\n tweets = remove_hastags_and_users(tweets)\n tweets = count_emoticons(tweets)\n tweets = replace_links(tweets)\n tweets = remove_specialchars(tweets)\n for tweet in tweets:\n print tweet\n tweets = correct_words(tweets)\n tweets = stem(tweets)\n tweets = tokenize(tweets)\n for tweet in tweets:\n print tweet.stat_str()\n tweets = pos_tag(tweets)\n tweets = count_exclamations(tweets)\n for tweet in tweets:\n print tweet.stat_str()\n \n analyzer = Analyzer(\"test_annotated_data/erna_dataset.tsv\", tweets)\n stats = analyzer.analyze()\n print stats\n \n \n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.49056604504585266,\n \"alphanum_fraction\": 0.6037735939025879,\n \"avg_line_length\": 9.399999618530273,\n \"blob_id\": \"62342cbf29a874cc330cf91c8f4b412029bb8ecd\",\n \"content_id\": \"dea9531f8bae775a72727010b9f26668e5855afc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 53,\n \"license_type\": \"no_license\",\n \"max_line_length\": 24,\n \"num_lines\": 5,\n \"path\": \"/models/__init__.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nCreated on 28. sep. 2014\\n\\n@author: JohnArne\\n'''\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.4879491925239563,\n \"alphanum_fraction\": 0.5752250552177429,\n \"avg_line_length\": 45.900718688964844,\n \"blob_id\": \"69bf16514460bf17014ba21563ef768ba20c8440\",\n \"content_id\": \"6b771c5670b92b9ae39c8bb76761fe0591f72fcc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 39209,\n \"license_type\": \"no_license\",\n \"max_line_length\": 186,\n \"num_lines\": 836,\n \"path\": \"/plotting.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nHandles plotting of different visualizations of data.\\n@author: JohnArne\\n'''\\nimport matplotlib.pyplot as plt\\nimport matplotlib.patches as mpatches\\nimport random\\nimport numpy as np\\nimport utils\\nimport random\\nimport pickle\\nfrom Tkconstants import OFF\\n\\ndef plot_temporal_sentiment(data, filename=\\\"temporal\\\"):\\n \\\"\\\"\\\"\\n Plots the temporal sentiment using the given data.\\n \\\"\\\"\\\"\\n tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120), \\n (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150), \\n (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148), \\n (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199), \\n (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)] \\n \\n # Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts. \\n for i in range(len(tableau20)):\\n r, g, b = tableau20[i]\\n tableau20[i] = (r / 255., g / 255., b / 255.)\\n \\n # You typically want your plot to be ~1.33x wider than tall. This plot is a rare \\n # exception because of the number of lines being plotted on it. \\n # Common sizes: (10, 7.5) and (12, 9) \\n f = plt.figure(figsize=(8, 6)) \\n \\n # Remove the plot frame lines. They are unnecessary chartjunk. \\n ax = plt.subplot(111) \\n ax.spines[\\\"top\\\"].set_visible(False) \\n ax.spines[\\\"bottom\\\"].set_visible(False) \\n ax.spines[\\\"right\\\"].set_visible(False) \\n ax.spines[\\\"left\\\"].set_visible(False) \\n \\n # Ensure that the axis ticks only show up on the bottom and left of the plot. \\n # Ticks on the right and top of the plot are generally unnecessary chartjunk. \\n ax.get_xaxis().tick_bottom() \\n ax.get_yaxis().tick_left() \\n \\n # Limit the range of the plot to only where the data is. \\n # Avoid unnecessary whitespace. \\n plt.ylim(0, 1) \\n plt.xlim(0, 101) \\n \\n # Make sure your axis ticks are large enough to be easily read. \\n # You don't want your viewers squinting to read your plot.\\n# y_ticks = [] \\n# plt.yticks(range(0, 1, 10), [str(x) for x in range(0, 91, 10)], fontsize=14) \\n plt.xticks(fontsize=10) \\n plt.yticks(fontsize=10)\\n # Provide tick lines across the plot to help your viewers trace along \\n # the axis ticks. Make sure that the lines are light and small so they \\n # don't obscure the primary data lines. \\n for y in [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]: \\n plt.plot(range(1,105), [y] * len(range(1,105)), \\\"--\\\", lw=0.5, color=\\\"black\\\", alpha=0.3) \\n \\n # Remove the tick marks; they are unnecessary with the tick lines we just plotted. \\n plt.tick_params(axis=\\\"both\\\", which=\\\"both\\\", bottom=\\\"off\\\", top=\\\"off\\\", \\n labelbottom=\\\"on\\\", left=\\\"off\\\", right=\\\"off\\\", labelleft=\\\"on\\\") \\n \\n # Now that the plot is prepared, it's time to actually plot the data! \\n # Note that I plotted the labels in order of the highest % in the final year. \\n labels = data.keys() \\n y_poses = [] \\n offsets = [0.02, 0.04,0.06,0.08,0.1,0.12, 0.14,0.16,0.18,0.2]\\n for rank, column in enumerate(labels): \\n # Plot each line separately with its own color, using the Tableau 20 \\n # color set in order. \\n plt.plot(data[column][0], data[column][1], lw=1.0, color=tableau20[rank]) \\n \\n # Add a text label to the right end of every line. Most of the code below \\n # is adding specific offsets y position because some labels overlapped. \\n y_pos = data[column][1][-1]\\n# new_pos = None\\n# offset_counter = 0\\n for poses in y_poses:\\n if y_pos < poses+0.01 and y_pos>poses:\\n y_pos = y_pos+0.05\\n# offset_counter += 1\\n break\\n if y_pos > poses-0.01 and y_pos0 else p-0.03, l, fontsize=8, color=tableau20[14])\\n\\n ax.spines[\\\"top\\\"].set_visible(False) \\n ax.spines[\\\"bottom\\\"].set_visible(False) \\n ax.spines[\\\"right\\\"].set_visible(False) \\n ax.spines[\\\"left\\\"].set_visible(False)\\n ax.set_xticks(ind+width*2) \\n ax.set_xticklabels([\\\" \\\" for _ in labels]) \\n # Ensure that the axis ticks only show up on the bottom and left of the plot. \\n # Ticks on the right and top of the plot are generally unnecessary chartjunk. \\n ax.get_xaxis().tick_bottom() \\n ax.get_yaxis().tick_left() \\n\\n for y in [-0.4,-0.3,-0.2,-0.1,0.0,0.1,0.2,0.3,0.4]: \\n plt.plot(range(0,18), [y] * len(range(0,18)), \\\"--\\\", lw=0.1, color=\\\"black\\\", alpha=0.3) \\n \\n # Remove the tick marks; they are unnecessary with the tick lines we just plotted. \\n plt.tick_params(axis=\\\"both\\\", which=\\\"both\\\", bottom=\\\"off\\\", top=\\\"off\\\", \\n labelbottom=\\\"on\\\", left=\\\"off\\\", right=\\\"off\\\", labelleft=\\\"on\\\",labelcolor=tableau20[14]) \\n \\n plt.savefig('figs/'+filename+\\\".pdf\\\", bbox_inches=\\\"tight\\\"); \\n\\n \\ndef average_wordclasses(data, filename):\\n \\\"\\\"\\\"\\n Plots the performance of different algorithms.\\n \\\"\\\"\\\"\\n tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120), \\n (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150), \\n (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148), \\n (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199), \\n (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)] \\n \\n # Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts. \\n for i in range(len(tableau20)): \\n r, g, b = tableau20[i] \\n tableau20[i] = (r / 255., g / 255., b / 255.) \\n \\n # You typically want your plot to be ~1.33x wider than tall. This plot is a rare \\n # exception because of the number of lines being plotted on it. \\n # Common sizes: (10, 7.5) and (12, 9) \\n f = plt.figure() \\n \\n # Remove the plot frame lines. They are unnecessary chartjunk. \\n ax = plt.subplot(111) \\n \\n labels = data.keys()\\n print labels\\n precisions = [data[key][0] for key in labels] \\n recalls = [data[key][1] for key in labels]\\n f1s = [data[key][2] for key in labels]\\n accuracies = [data[key][3] for key in labels]\\n\\n #Create bars \\n ind = (np.arange(len(labels))*2)+0.25\\n width = 0.35\\n ax.bar(ind, precisions, width, color=tableau20[0], edgecolor=\\\"none\\\")\\n ax.bar(ind+width, recalls, width, color=tableau20[1], edgecolor=\\\"none\\\")\\n ax.bar(ind+width*2, f1s, width, color=tableau20[2], edgecolor=\\\"none\\\")\\n ax.bar(ind+width*3, accuracies, width, color=tableau20[3], edgecolor=\\\"none\\\")\\n \\n #Create top bar labels\\n for p, i in zip(precisions, ind):\\n plt.text(i+0.03, p+0.01, \\\"%0.2f\\\" % p, fontsize=10, color=tableau20[0])\\n for p, i in zip(recalls, ind+width):\\n plt.text(i+0.03, p+0.01, \\\"%0.2f\\\" % p, fontsize=10, color=tableau20[1])\\n for p, i in zip(f1s, ind+width*2):\\n plt.text(i+0.03, p+0.01, \\\"%0.2f\\\" % p, fontsize=10, color=tableau20[2])\\n for p, i in zip(accuracies, ind+width*3):\\n plt.text(i+0.03, p+0.01, \\\"%0.2f\\\" % p, fontsize=10, color=tableau20[3])\\n \\n ax.spines[\\\"top\\\"].set_visible(False) \\n# ax.spines[\\\"bottom\\\"].set_visible(False) \\n ax.spines[\\\"right\\\"].set_visible(False) \\n ax.spines[\\\"left\\\"].set_visible(False)\\n ax.set_xticks(ind+width*2) \\n ax.set_xticklabels(labels) \\n # Ensure that the axis ticks only show up on the bottom and left of the plot. \\n # Ticks on the right and top of the plot are generally unnecessary chartjunk. \\n ax.get_xaxis().tick_bottom() \\n ax.get_yaxis().tick_left() \\n\\n for y in [1,2,3,4,5,6,7]: \\n plt.plot(range(0,7), [y] * len(range(0,7)), \\\"--\\\", lw=0.5, color=\\\"black\\\", alpha=0.3) \\n \\n # Remove the tick marks; they are unnecessary with the tick lines we just plotted. \\n plt.tick_params(axis=\\\"both\\\", which=\\\"both\\\", bottom=\\\"off\\\", top=\\\"off\\\", \\n labelbottom=\\\"on\\\", left=\\\"off\\\", right=\\\"off\\\", labelleft=\\\"on\\\",labelcolor=tableau20[14]) \\n \\n plt.savefig('figs/'+filename+\\\".pdf\\\", bbox_inches=\\\"tight\\\"); \\n \\ndef detailed_average_wordclasses(data, filename):\\n \\\"\\\"\\\"\\n Plots the performance of different algorithms.\\n \\\"\\\"\\\"\\n tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120), \\n (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150), \\n (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148), \\n (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199), \\n (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)] \\n \\n # Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts. \\n for i in range(len(tableau20)): \\n r, g, b = tableau20[i] \\n tableau20[i] = (r / 255., g / 255., b / 255.) \\n \\n # You typically want your plot to be ~1.33x wider than tall. This plot is a rare \\n # exception because of the number of lines being plotted on it. \\n # Common sizes: (10, 7.5) and (12, 9) \\n f = plt.figure() \\n \\n # Remove the plot frame lines. They are unnecessary chartjunk. \\n ax = plt.subplot(111) \\n \\n labels = data.keys()\\n print labels\\n datalists = []\\n for i in xrange(len(data[labels[0]])):\\n datalists.append([data[key][i] for key in labels]) \\n\\n \\n #Create bars \\n ind = (np.arange(len(labels))*2.2)+0.2\\n width = 0.1\\n offset = np.arange(0.01, 1, 0.01)\\n colorlist = [tableau20[0],tableau20[0],tableau20[0],tableau20[1],tableau20[4],tableau20[5],tableau20[6],tableau20[7],tableau20[2],tableau20[2],tableau20[2],tableau20[2],tableau20[2],\\n tableau20[13],tableau20[14],tableau20[15],tableau20[16],tableau20[3]]\\n for i in xrange(len(datalists)):\\n bar = ax.bar(ind+width*i, datalists[i], width, color=colorlist[i], edgecolor=\\\"none\\\")\\n #Create top bar labels\\n# for p, i in zip(precisions, ind):\\n# plt.text(i+0.03, p+0.01, \\\"%0.2f\\\" % p, fontsize=10, color=tableau20[0])\\n# for p, i in zip(recalls, ind+width):\\n# plt.text(i+0.03, p+0.01, \\\"%0.2f\\\" % p, fontsize=10, color=tableau20[1])\\n# for p, i in zip(f1s, ind+width*2):\\n# plt.text(i+0.03, p+0.01, \\\"%0.2f\\\" % p, fontsize=10, color=tableau20[2])\\n# for p, i in zip(accuracies, ind+width*3):\\n# plt.text(i+0.03, p+0.01, \\\"%0.2f\\\" % p, fontsize=10, color=tableau20[3])\\n \\n ax.spines[\\\"top\\\"].set_visible(False) \\n# ax.spines[\\\"bottom\\\"].set_visible(False) \\n ax.spines[\\\"right\\\"].set_visible(False) \\n ax.spines[\\\"left\\\"].set_visible(False)\\n ax.set_xticks(ind+width*9) \\n ax.set_xticklabels(labels) \\n # Ensure that the axis ticks only show up on the bottom and left of the plot. \\n # Ticks on the right and top of the plot are generally unnecessary chartjunk. \\n ax.get_xaxis().tick_bottom() \\n ax.get_yaxis().tick_left() \\n\\n for y in [0.2,0.4,0.6,0.8,1.0,1.2,1.4,1.6,1.8,2.0,2.2,2.4,2.6,2.8,3.0]: \\n plt.plot(range(0,8), [y] * len(range(0,8)), \\\"--\\\", lw=0.5, color=\\\"black\\\", alpha=0.3) \\n \\n # Remove the tick marks; they are unnecessary with the tick lines we just plotted. \\n plt.tick_params(axis=\\\"both\\\", which=\\\"both\\\", bottom=\\\"off\\\", top=\\\"off\\\", \\n labelbottom=\\\"on\\\", left=\\\"off\\\", right=\\\"off\\\", labelleft=\\\"on\\\",labelcolor=tableau20[14]) \\n \\n plt.savefig('figs/'+filename+\\\".pdf\\\", bbox_inches=\\\"tight\\\"); \\n \\ndef plot_entity_histogram(data, filename):\\n \\\"\\\"\\\"\\n Plots the performance of different algorithms.\\n \\\"\\\"\\\"\\n tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120), \\n (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150), \\n (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148), \\n (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199), \\n (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)] \\n \\n # Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts. \\n for i in range(len(tableau20)): \\n r, g, b = tableau20[i] \\n tableau20[i] = (r / 255., g / 255., b / 255.) \\n \\n # You typically want your plot to be ~1.33x wider than tall. This plot is a rare \\n # exception because of the number of lines being plotted on it. \\n # Common sizes: (10, 7.5) and (12, 9) \\n f = plt.figure() \\n \\n # Remove the plot frame lines. They are unnecessary chartjunk. \\n ax = plt.subplot(111) \\n \\n labels = data.keys()\\n print labels\\n precisions = [data[key][0] for key in labels] \\n recalls = [data[key][1] for key in labels]\\n f1s = [data[key][2] for key in labels]\\n accuracies = [data[key][3] for key in labels]\\n\\n #Create bars \\n ind = (np.arange(len(labels))*2)+0.25\\n width = 0.35\\n ax.bar(ind, precisions, width, color=tableau20[0], edgecolor=\\\"none\\\")\\n ax.bar(ind+width, recalls, width, color=tableau20[1], edgecolor=\\\"none\\\")\\n ax.bar(ind+width*2, f1s, width, color=tableau20[2], edgecolor=\\\"none\\\")\\n ax.bar(ind+width*3, accuracies, width, color=tableau20[3], edgecolor=\\\"none\\\")\\n \\n #Create top bar labels\\n for p, i in zip(precisions, ind):\\n plt.text(i+0.03, p+0.01, \\\"%0.2f\\\" % p, fontsize=10, color=tableau20[0])\\n for p, i in zip(recalls, ind+width):\\n plt.text(i+0.03, p+0.01, \\\"%0.2f\\\" % p, fontsize=10, color=tableau20[1])\\n for p, i in zip(f1s, ind+width*2):\\n plt.text(i+0.03, p+0.01, \\\"%0.2f\\\" % p, fontsize=10, color=tableau20[2])\\n for p, i in zip(accuracies, ind+width*3):\\n plt.text(i+0.03, p+0.01, \\\"%0.2f\\\" % p, fontsize=10, color=tableau20[3])\\n \\n ax.spines[\\\"top\\\"].set_visible(False) \\n# ax.spines[\\\"bottom\\\"].set_visible(False) \\n ax.spines[\\\"right\\\"].set_visible(False) \\n ax.spines[\\\"left\\\"].set_visible(False)\\n ax.set_xticks(ind+width*2) \\n ax.set_xticklabels(labels) \\n # Ensure that the axis ticks only show up on the bottom and left of the plot. \\n # Ticks on the right and top of the plot are generally unnecessary chartjunk. \\n ax.get_xaxis().tick_bottom() \\n ax.get_yaxis().tick_left() \\n\\n for y in [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8]: \\n plt.plot(range(0,7), [y] * len(range(0,7)), \\\"--\\\", lw=0.5, color=\\\"black\\\", alpha=0.3) \\n \\n # Remove the tick marks; they are unnecessary with the tick lines we just plotted. \\n plt.tick_params(axis=\\\"both\\\", which=\\\"both\\\", bottom=\\\"off\\\", top=\\\"off\\\", \\n labelbottom=\\\"on\\\", left=\\\"off\\\", right=\\\"off\\\", labelleft=\\\"on\\\",labelcolor=tableau20[14]) \\n \\n plt.savefig('figs/'+filename+\\\".pdf\\\", bbox_inches=\\\"tight\\\"); \\n\\n \\ndef plot_dataset_stats(data):\\n \\\"\\\"\\\"\\n Plots histograms of the dataset statistics using the given data.\\n \\\"\\\"\\\"\\n\\ndef load_incremental_data():\\n f1_data= pickle.load(open('incremental_f1100',\\\"rb\\\"))\\n acc_data=pickle.load(open('incremental_acc100',\\\"rb\\\"))\\n print f1_data\\n print acc_data\\n for key in f1_data.keys():\\n f1list = f1_data[key]\\n f1_data[key] = [range(5,101,5),f1list]\\n acclist = acc_data[key]\\n acc_data[key] = [range(5,101,5),acclist]\\n f1svm_data = {}\\n f1nb_data = {}\\n f1me_data = {}\\n accsvm_data = {}\\n accnb_data = {}\\n accme_data = {}\\n for key in f1_data.keys():\\n if key[:3]==\\\"SVM\\\":\\n f1svm_data[key] = f1_data[key]\\n elif key[:2]==\\\"NB\\\":\\n f1nb_data[key] = f1_data[key]\\n elif key[:6]==\\\"MaxEnt\\\":\\n f1me_data[key] = f1_data[key]\\n for key in acc_data.keys():\\n if key[:3]==\\\"SVM\\\":\\n accsvm_data[key] = acc_data[key]\\n elif key[:2]==\\\"NB\\\":\\n accnb_data[key] = acc_data[key]\\n elif key[:6]==\\\"MaxEnt\\\":\\n accme_data[key] = acc_data[key]\\n \\n \\n return f1svm_data, f1nb_data, f1me_data, accsvm_data, accnb_data, accme_data\\n \\n\\ndef plot_subjectivity_aggregates(data, filename=\\\"temporal\\\"):\\n \\\"\\\"\\\"\\n Plots the temporal sentiment using the given data.\\n \\\"\\\"\\\"\\n tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120), \\n (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150), \\n (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148), \\n (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199), \\n (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)] \\n \\n # Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts. \\n for i in range(len(tableau20)):\\n r, g, b = tableau20[i]\\n tableau20[i] = (r / 255., g / 255., b / 255.)\\n \\n # You typically want your plot to be ~1.33x wider than tall. This plot is a rare \\n # exception because of the number of lines being plotted on it. \\n # Common sizes: (10, 7.5) and (12, 9) \\n f = plt.figure(figsize=(9, 6)) \\n ind = np.arange(len(data['Targets'][0]))\\n # Remove the plot frame lines. They are unnecessary chartjunk. \\n ax = plt.subplot(111) \\n ax.spines[\\\"top\\\"].set_visible(False) \\n ax.spines[\\\"bottom\\\"].set_visible(False) \\n ax.spines[\\\"right\\\"].set_visible(False) \\n ax.spines[\\\"left\\\"].set_visible(False) \\n ax.set_xticks(ind+0.25)\\n ax.set_xticklabels(['%.2f' % x for x in data['Targets'][0]]) \\n # Ensure that the axis ticks only show up on the bottom and left of the plot. \\n # Ticks on the right and top of the plot are generally unnecessary chartjunk. \\n ax.get_xaxis().tick_bottom() \\n ax.get_yaxis().tick_left() \\n ax.bar(ind, data['Frequencies'][1], 0.5, color=tableau20[14], edgecolor=\\\"none\\\")\\n \\n # Limit the range of the plot to only where the data is. \\n # Avoid unnecessary whitespace. \\n plt.ylim(0, 70) \\n plt.xlim(0, 19)\\n \\n # Make sure your axis ticks are large enough to be easily read. \\n # You don't want your viewers squinting to read your plot.\\n# y_ticks = [] \\n# plt.yticks(range(0, 1, 10), [str(x) for x in range(0, 91, 10)], fontsize=14) \\n plt.xticks(fontsize=8) \\n plt.yticks(fontsize=10)\\n # Provide tick lines across the plot to help your viewers trace along \\n # the axis ticks. Make sure that the lines are light and small so they \\n # don't obscure the primary data lines. \\n for y in [5,10,15,20,25,30,35,40,45,50, 55, 60, 65]: \\n plt.plot(range(0,20), [y] * len(range(0,20)), \\\"--\\\", lw=0.5, color=\\\"black\\\", alpha=0.3) \\n \\n # Remove the tick marks; they are unnecessary with the tick lines we just plotted. \\n plt.tick_params(axis=\\\"both\\\", which=\\\"both\\\", bottom=\\\"off\\\", top=\\\"off\\\", \\n labelbottom=\\\"on\\\", left=\\\"off\\\", right=\\\"off\\\", labelleft=\\\"on\\\") \\n \\n # Now that the plot is prepared, it's time to actually plot the data! \\n # Note that I plotted the labels in order of the highest % in the final year. \\n labels = [label for label in data.keys() if label!='Frequencies'] \\n y_poses = []\\n offsets = [0.02, 0.04,0.06,0.08,0.1,0.12, 0.14,0.16,0.18,0.2]\\n for rank, column in enumerate(labels): \\n # Plot each line separately with its own color, using the Tableau 20 \\n # color set in order. \\n plt.plot(ind+0.25, data[column][1], lw=1.0, color=tableau20[rank+1]) \\n \\n # Add a text label to the right end of every line. Most of the code below \\n # is adding specific offsets y position because some labels overlapped. \\n y_pos = data[column][1][-1]\\n# new_pos = None\\n# offset_counter = 0\\n for poses in y_poses:\\n if y_pos < poses+1 and y_pos>=poses:\\n y_pos = y_pos+2\\n# offset_counter += 1\\n break\\n if y_pos > poses-1 and y_pos<=poses:\\n y_pos = y_pos+2\\n# offset_counter += 1\\n break\\n else:\\n y_pos = y_pos\\n y_poses.append(y_pos)\\n # Again, make sure that all labels are large enough to be easily read \\n # by the viewer. \\n plt.text(19, y_pos, column, fontsize=8, color=tableau20[rank+1]) \\n plt.savefig(\\\"figs/\\\"+filename+\\\".pdf\\\", bbox_inches=\\\"tight\\\");\\n print \\\"Figure done.\\\" \\n\\ndef plot_polarity_aggregates(data, filename=\\\"temporal\\\"):\\n \\\"\\\"\\\"\\n Plots the temporal sentiment using the given data.\\n \\\"\\\"\\\"\\n tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120), \\n (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150), \\n (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148), \\n (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199), \\n (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)] \\n \\n # Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts. \\n for i in range(len(tableau20)):\\n r, g, b = tableau20[i]\\n tableau20[i] = (r / 255., g / 255., b / 255.)\\n \\n # You typically want your plot to be ~1.33x wider than tall. This plot is a rare \\n # exception because of the number of lines being plotted on it. \\n # Common sizes: (10, 7.5) and (12, 9) \\n f = plt.figure(figsize=(9, 6)) \\n ind = np.arange(len(data['Targets'][0]))\\n # Remove the plot frame lines. They are unnecessary chartjunk. \\n ax = plt.subplot(111) \\n ax.spines[\\\"top\\\"].set_visible(False) \\n ax.spines[\\\"bottom\\\"].set_visible(False) \\n ax.spines[\\\"right\\\"].set_visible(False) \\n ax.spines[\\\"left\\\"].set_visible(False) \\n ax.set_xticks(ind+0.25)\\n ax.set_xticklabels(['%.2f' % x for x in data['Targets'][0]]) \\n # Ensure that the axis ticks only show up on the bottom and left of the plot. \\n # Ticks on the right and top of the plot are generally unnecessary chartjunk. \\n ax.get_xaxis().tick_bottom() \\n ax.get_yaxis().tick_left() \\n ax.bar(ind, data['Frequencies'][1], 0.5, color=tableau20[14], edgecolor=\\\"none\\\")\\n \\n # Limit the range of the plot to only where the data is. \\n # Avoid unnecessary whitespace. \\n plt.ylim(-1, 1) \\n plt.xlim(0, 19.5)\\n \\n # Make sure your axis ticks are large enough to be easily read. \\n # You don't want your viewers squinting to read your plot.\\n# y_ticks = [] \\n# plt.yticks(range(0, 1, 10), [str(x) for x in range(0, 91, 10)], fontsize=14) \\n plt.xticks(fontsize=8) \\n plt.yticks(fontsize=10)\\n # Provide tick lines across the plot to help your viewers trace along \\n # the axis ticks. Make sure that the lines are light and small so they \\n # don't obscure the primary data lines. \\n# for y in [5,10,15,20,25,30,35,40,45]: \\n for y in [-0.8,-0.6,-0.4,-0.2,0.2,0.4,0.6,0.8]: \\n plt.plot(range(0,20), [y] * len(range(0,20)), \\\"--\\\", lw=0.5, color=\\\"black\\\", alpha=0.3) \\n plt.plot(range(0,20), [0] * len(range(0,20)), \\\"--\\\", lw=2.5, color=\\\"black\\\", alpha=0.3) \\n # Remove the tick marks; they are unnecessary with the tick lines we just plotted. \\n plt.tick_params(axis=\\\"both\\\", which=\\\"both\\\", bottom=\\\"off\\\", top=\\\"off\\\", \\n labelbottom=\\\"on\\\", left=\\\"off\\\", right=\\\"off\\\", labelleft=\\\"on\\\") \\n \\n # Now that the plot is prepared, it's time to actually plot the data! \\n # Note that I plotted the labels in order of the highest % in the final year. \\n labels = [label for label in data.keys() if label!='Frequencies'] \\n y_poses = []\\n offsets = [0.02, 0.04,0.06,0.08,0.1,0.12, 0.14,0.16,0.18,0.2]\\n for rank, column in enumerate(labels): \\n # Plot each line separately with its own color, using the Tableau 20 \\n # color set in order. \\n plt.plot(ind+0.25, data[column][1], lw=1.0, color=tableau20[rank+1]) \\n \\n # Add a text label to the right end of every line. Most of the code below \\n # is adding specific offsets y position because some labels overlapped. \\n y_pos = data[column][1][-1]\\n# new_pos = None\\n# offset_counter = 0\\n for poses in y_poses:\\n if y_pos < poses+0.1 and y_pos>=poses:\\n y_pos = y_pos+0.2\\n# offset_counter += 1\\n break\\n if y_pos > poses-0.1 and y_pos<=poses:\\n y_pos = y_pos-0.2\\n# offset_counter += 1\\n break\\n else:\\n y_pos = y_pos\\n y_poses.append(y_pos)\\n # Again, make sure that all labels are large enough to be easily read \\n # by the viewer. \\n plt.text(19, y_pos, column, fontsize=8, color=tableau20[rank+1]) \\n# plt.text(19, 29.5, \\\"Neutral\\\", fontsize=8, color=\\\"black\\\") \\n plt.savefig(\\\"figs/\\\"+filename+\\\".pdf\\\", bbox_inches=\\\"tight\\\");\\n print \\\"Figure done.\\\" \\n\\ndef plot_temporal_topics(data, filename=\\\"temporal_topics\\\"):\\n \\\"\\\"\\\"\\n Plots the temporal sentiment using the given data.\\n \\\"\\\"\\\"\\n tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120), \\n (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150), \\n (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148), \\n (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199), \\n (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)] \\n \\n # Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts. \\n for i in range(len(tableau20)):\\n r, g, b = tableau20[i]\\n tableau20[i] = (r / 255., g / 255., b / 255.)\\n \\n # You typically want your plot to be ~1.33x wider than tall. This plot is a rare \\n # exception because of the number of lines being plotted on it. \\n # Common sizes: (10, 7.5) and (12, 9) \\n f = plt.figure(figsize=(9, 6))\\n ind = np.arange(len(data[data.keys()[0]][0]))\\n # Remove the plot frame lines. They are unnecessary chartjunk. \\n ax = plt.subplot(111) \\n ax.spines[\\\"top\\\"].set_visible(False) \\n ax.spines[\\\"bottom\\\"].set_visible(False) \\n ax.spines[\\\"right\\\"].set_visible(False) \\n ax.spines[\\\"left\\\"].set_visible(False) \\n ax.set_xticks(ind+0.25)\\n ax.set_xticklabels(['%.2f' % x for x in data[data.keys()[0]][0]]) \\n # Ensure that the axis ticks only show up on the bottom and left of the plot. \\n # Ticks on the right and top of the plot are generally unnecessary chartjunk. \\n ax.get_xaxis().tick_bottom() \\n ax.get_yaxis().tick_left() \\n # Limit the range of the plot to only where the data is. \\n # Avoid unnecessary whitespace. \\n plt.ylim(0, 100) \\n plt.xlim(0, 8)\\n \\n # Make sure your axis ticks are large enough to be easily read. \\n # You don't want your viewers squinting to read your plot.\\n# y_ticks = [] \\n# plt.yticks(range(0, 1, 10), [str(x) for x in range(0, 91, 10)], fontsize=14) \\n plt.xticks(fontsize=6) \\n plt.yticks(fontsize=6)\\n # Provide tick lines across the plot to help your viewers trace along \\n # the axis ticks. Make sure that the lines are light and small so they \\n # don't obscure the primary data lines. \\n# for y in [5,10,15,20,25,30,35,40,45]: \\n for y in range(0,100,5): \\n plt.plot(range(0,8), [y] * len(range(0,8)), \\\"--\\\", lw=0.5, color=\\\"black\\\", alpha=0.3) \\n plt.plot(range(0,8), [50] * len(range(0,8)), \\\"--\\\", lw=2.5, color=\\\"black\\\", alpha=0.3) \\n # Remove the tick marks; they are unnecessary with the tick lines we just plotted. \\n plt.tick_params(axis=\\\"both\\\", which=\\\"both\\\", bottom=\\\"off\\\", top=\\\"off\\\", \\n labelbottom=\\\"on\\\", left=\\\"off\\\", right=\\\"off\\\", labelleft=\\\"on\\\") \\n \\n # Now that the plot is prepared, it's time to actually plot the data! \\n # Note that I plotted the labels in order of the highest % in the final year. \\n labels = data.keys() \\n y_poses = []\\n offsets = [0.02, 0.04,0.06,0.08,0.1,0.12, 0.14,0.16,0.18,0.2]\\n for rank, column in enumerate(labels): \\n # Plot each line separately with its own color, using the Tableau 20 \\n # color set in order. \\n for i in range(len(data[column][1])):\\n data[column][1][i] = data[column][1][i] +50\\n plt.plot(ind+0.25, data[column][1], lw=1.0, color=tableau20[rank]) \\n \\n # Add a text label to the right end of every line. Most of the code below \\n # is adding specific offsets y position because some labels overlapped. \\n y_pos = data[column][1][-1]\\n# new_pos = None\\n# offset_counter = 0\\n for poses in y_poses:\\n if y_pos < poses+5 and y_pos>=poses:\\n y_pos = y_pos+8\\n# offset_counter += 1\\n break\\n if y_pos > poses-5 and y_pos<=poses:\\n y_pos = y_pos-8\\n# offset_counter += 1\\n break\\n else:\\n y_pos = y_pos\\n y_poses.append(y_pos)\\n # Again, make sure that all labels are large enough to be easily read \\n # by the viewer. \\n try:\\n plt.text(8.2, y_pos, column, fontsize=8, color=tableau20[rank])\\n except UnicodeDecodeError:\\n plt.text(8.2, y_pos, column.decode('utf8'), fontsize=8, color=tableau20[rank])\\n plt.text(8.2, 49.5, \\\"Neutral\\\", fontsize=8, color=\\\"black\\\") \\n plt.savefig(\\\"figs/\\\"+filename+\\\".pdf\\\", bbox_inches=\\\"tight\\\");\\n print \\\"Figure done.\\\"\\n \\nif __name__ == '__main__':\\n# f1svm_data, f1nb_data, f1me_data, accsvm_data, accnb_data, accme_data = load_incremental_data()\\n# data = {\\\"Erna Solberg\\\": [range(0,100),[random.randint(20,50) for _ in range(0,100)]],\\n# \\\"rosenborg\\\": [range(0,100),[random.randint(40,60) for _ in range(0,100)]],\\n# \\\"no target\\\": [range(0,100),[random.randint(30,40) for _ in range(0,100)]]}\\n# plot_temporal_sentiment(f1svm_data, 'incremental_f1svm')\\n# plot_temporal_sentiment(f1nb_data, 'incremental_f1nb')\\n# plot_temporal_sentiment(f1me_data, 'incremental_f1me')\\n# plot_temporal_sentiment(accsvm_data, 'incremental_accuracysvm')\\n# plot_temporal_sentiment(accnb_data, 'incremental_accuracynb')\\n# plot_temporal_sentiment(accme_data, 'incremental_accuracyme')\\n \\n# data = {\\\"SVM(SB)+SVM(PC)\\\": [(0.72+0.79)/2, (0.66+0.80)/2, (0.69+0.76)/2, (0.67+0.78)/2],\\n# \\\"SVM(SB)+SVM(PB)\\\": [(0.72+0.77)/2, (0.66+0.76)/2, (0.69+0.75)/2, (0.67+0.75)/2],\\n# \\\"SVM(SB)+MaxEnt(PC)\\\":[(0.72+0.77)/2, (0.66+0.80)/2, (0.69+0.72)/2, (0.67+0.75)/2],\\n# \\\"MaxEnt(SB)+MaxEnt(PC)\\\": [(0.70+0.77)/2, (0.66+0.80)/2, (0.61+0.72)/2, (0.63+0.75)/2],\\n# \\\"MaxEnt(SB)+SVM(PC)\\\": [(0.70+0.79)/2, (0.66+0.80)/2, (0.61+0.76)/2, (0.63+0.78)/2]}\\n# plot_combined_histogram(data, \\\"combined\\\")\\n data = pickle.load(open('topically_aggregated_polarity', 'rb'))\\n plot_temporal_topics(data, \\\"temporal_topics\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.5296835899353027,\n \"alphanum_fraction\": 0.5456807613372803,\n \"avg_line_length\": 30.977272033691406,\n \"blob_id\": \"523253e01c6dc35881565f3ae4859784cc7cfebe\",\n \"content_id\": \"304e8cb07ec961291ed5fdc3e5d2b08a3e0c0380\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2813,\n \"license_type\": \"no_license\",\n \"max_line_length\": 239,\n \"num_lines\": 88,\n \"path\": \"/annotation.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nCreated on 30. sep. 2014\\n\\n@author: JohnArne\\n'''\\nimport utils\\nimport tweet\\nimport os\\n\\ndef user_annotation():\\n \\\"\\\"\\\"\\n Feed tweets to console one at a time, and ask user for sentiment annotation.\\n \\\"\\\"\\\"\\n dataset = utils.select_dataset()\\n text_tweets = utils.get_dataset(dataset)\\n tweets = []\\n for text_tweet in text_tweets:\\n tweets.append(tweet.to_tweet(text_tweet))\\n username = raw_input(\\\"Name? ... \\\")\\n \\n print \\\"\\\\n--------------\\\\n\\\"\\n print \\\"Input: \\\"\\n print \\\"\\\\n1: Negative sentiment (Negative opinion). \\\\n2: Neutral/objective sentiment (No opinion). \\\\n3: Positive sentiment (Positive opinion). \\\\n5: Delete the tweet from the dataset. \\\\nx: Cancel sequence. 0: Go back to previous tweet. \\\"\\n print \\\"\\\\n--------------\\\\n\\\"\\n \\n annotated_to = 0\\n i = 0\\n while i < len(tweets):\\n# tweets[i].text.encode('utf8')\\n# text = tweets[i].text\\n# tweets[i].text = text.decode('utf8')\\n try:\\n print \\\"Tweet nr. : \\\"+str(i+1)\\n print str(((i+1.0*1.0)/len(tweets)*1.0)*100)+\\\" % done \\\"\\n print unicode(tweets[i].__str__().decode('utf8'))\\n except UnicodeEncodeError:\\n try:\\n print \\\"Tweet nr. : \\\"+str(i+1)\\n print str(tweets[i])\\n except UnicodeEncodeError:\\n print \\\"Could not print tweet number \\\"+str(i+1) +\\\". Deleting tweet...\\\"\\n tweets.remove(tweets[i])\\n continue\\n \\n userinput = raw_input(\\\"...\\\")\\n while not legal_input(userinput):\\n userinput = raw_input(\\\"Unlawful input! Please re-introduce.\\\")\\n if userinput is '1':\\n tweets[i].set_sentiment(\\\"negative\\\")\\n elif userinput is '2':\\n tweets[i].set_sentiment(\\\"neutral\\\")\\n elif userinput is '3':\\n tweets[i].set_sentiment(\\\"positive\\\")\\n elif userinput is '5':\\n print \\\"Deleting tweet...\\\"\\n tweets.remove(tweets[i])\\n continue\\n elif userinput is '0':\\n i = i-1\\n continue\\n elif userinput is 'x':\\n break\\n i = i+1\\n \\n \\n #TODO: need to encode to utf when getting from dataset?!?!\\n #Store the sentiment in file!\\n tweetlines = []\\n for t in tweets[:i]:\\n if t.get_sentiment() is None:\\n continue\\n tweetlines.append(t.to_tsv())\\n dir = username+\\\"_annotated_data\\\"\\n if not os.path.exists(dir):\\n os.makedirs(dir)\\n utils.store_dataset(tweetlines, dir+dataset[4:])\\n \\n print \\\"Domo arigato!\\\" \\n \\ndef legal_input(userinput):\\n \\\"\\\"\\\"\\n Checks input and returns true if the input is legal. Legal input should be \\\"1\\\", \\\"2\\\", \\\"3\\\", \\\"5\\\", or \\\"x\\\"\\n \\\"\\\"\\\"\\n legal_inputs = ['1','2','3','5','0','x']\\n \\n if userinput in legal_inputs:\\n return True\\n return False\"\n },\n {\n \"alpha_fraction\": 0.5586603879928589,\n \"alphanum_fraction\": 0.5669795274734497,\n \"avg_line_length\": 36.76612854003906,\n \"blob_id\": \"f22e91d7a468cde5082bf7aff151f54e7bd21449\",\n \"content_id\": \"7fcad2df2c48e8b24006a72c42560e3cc3d3493d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4688,\n \"license_type\": \"no_license\",\n \"max_line_length\": 112,\n \"num_lines\": 124,\n \"path\": \"/tweet.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nCreated on 30. sep. 2014\\n\\n@author: JohnArne\\n'''\\nclass Tweet(object):\\n \\\"\\\"\\\"\\n Class for wrapping tweet information.\\n \\\"\\\"\\\"\\n \\n def __init__(self, timestamp, user, text):\\n self.user = user\\n self.text = text\\n self.timestamp = timestamp\\n self.subjectivity = None #0 if objective, 1 if subjective\\n self.polarity = None #0 if negative sentiment, 1 if positive sentiment\\n self.processed_words = [] #dict for containing the stemmed and preprocessed words of the text body\\n self.tagged_words = [] # a list of dicts\\n self.nrof_happyemoticons = 0\\n self.nrof_sademoticons = 0\\n self.nrof_hashtags = 0\\n self.nrof_usersmentioned = 0\\n self.exclamated = False\\n self.hashtags = []\\n self.links = []\\n self.users_mentioned = []\\n self.nrof_exclamations = 0\\n self.nrof_questionmarks = 0\\n self.word_count = 0\\n self.words_with_sentimentvalues={}\\n self.sentiments = []\\n self.link_pos = []\\n self.sentiment_target = \\\"\\\"\\n \\n def to_tsv(self):\\n \\\"\\\"\\\"\\n Convert the data in this tweet to the .tsv format used to store it in .tsv files.\\n TSV Format: Date \\\\t Time \\\\t Sentiment \\\\t User \\\\t Textbody\\n \\\"\\\"\\\"\\n tvsline = \\\"\\\"\\n sentiment = self.get_sentiment()\\n if sentiment is not None:\\n tsvline = self.timestamp\\n tsvline = tsvline+\\\"\\\\t\\\"+sentiment\\n tsvline = tsvline+\\\"\\\\t\\\"+self.user\\n tsvline = tsvline+\\\"\\\\t\\\"+self.text\\n else:\\n tsvline = self.timestamp+\\\"\\\\t\\\"+self.user+\\\"\\\\t\\\"+self.text\\n return tsvline\\n \\n \\n def get_sentiment(self):\\n \\\"\\\"\\\"\\n Returns a textual representation of the sentiment (negative, neutral, positive),\\n Based on the subjectivity and polarity variables of the tweet.\\n \\\"\\\"\\\"\\n sentiment = None\\n if self.subjectivity is 1:\\n sentiment = \\\"negative\\\".encode('utf8') if self.polarity is 0 else \\\"positive\\\".encode('utf8')\\n elif self.subjectivity is 0:\\n sentiment = \\\"neutral\\\".encode('utf8')\\n return sentiment\\n \\n def set_sentiment(self, sentiment):\\n \\\"\\\"\\\"\\n Sets the binary subjectivity and polarity variables of the tweet based on the\\n passed textual representation of sentiment.\\n \\\"\\\"\\\"\\n if sentiment==\\\"negative\\\":\\n self.subjectivity = 1\\n self.polarity = 0\\n elif sentiment==\\\"neutral\\\":\\n self.subjectivity = 0\\n self.polarity = 0\\n elif sentiment==\\\"positive\\\":\\n self.subjectivity = 1\\n self.polarity = 1\\n \\n def stat_str(self):\\n \\\"\\\"\\\"\\n Returns a string of all stats of the tweet. BROKEN, unicode errors all around\\n \\\"\\\"\\\"\\n# try:\\n# statstring = \\\"\\\\n--------------\\\\n\\\"+\\\" \\\\n\\\"+self.user+\\\"\\\\n\\\"+unicode(self.text)+\\\"\\\\n \\\"\\n# except UnicodeDecodeError:\\n statstring = \\\"\\\\n--------------\\\\n\\\"+\\\" \\\\n\\\"+self.user+\\\"\\\\n\\\"+self.text+\\\"\\\\n \\\"\\n statstring = statstring + \\\"Tagged words: \\\"+str(self.tagged_words) + \\\"\\\\n\\\"\\n statstring = statstring + \\\"Sentiment \\\" +str(self.get_sentiment()) + \\\"\\\\n\\\"\\n statstring = statstring + \\\"Hashtags: \\\"+str(self.nrof_hashtags) + \\\" \\\"+str(self.hashtags) + \\\"\\\\n\\\"\\n statstring = statstring + \\\"Users: \\\"+str(self.nrof_usersmentioned) + \\\" \\\"+str(self.users_mentioned) + \\\"\\\\n\\\"\\n statstring = statstring + \\\"Happy emoticons: \\\"+str(self.nrof_happyemoticons) + \\\"\\\\n\\\"\\n statstring = statstring + \\\"Sad emoticons: \\\"+str(self.nrof_sademoticons)+ \\\"\\\\n\\\"\\n statstring = statstring + \\\"Question marks: \\\"+str(self.nrof_questionmarks)+ \\\"\\\\n\\\"\\n statstring = statstring + \\\"Exclamation marks: \\\"+str(self.nrof_exclamations)+ \\\"\\\\n\\\"\\n statstring = statstring + \\\"\\\\n--------------\\\\n\\\"\\n return statstring\\n \\n def __str__(self):\\n \\\"\\\"\\\"\\n Returns a string representation of the tweet for visual representation.\\n \\\"\\\"\\\"\\n return \\\"\\\\n--------------\\\\n\\\"+\\\" \\\\n\\\"+self.user+\\\"\\\\n\\\"+self.text+\\\"\\\\n--------------\\\\n\\\"\\n \\n def __eq__(self, other):\\n return self.text == other.text\\n \\ndef to_tweet(text):\\n \\\"\\\"\\\"\\n Convert a given .tsv formatted text line to a tweet object\\n \\\"\\\"\\\"\\n splits = text.split('\\\\t')\\n print \\\"Creating tweet object: \\\"\\n if len(splits)>3:\\n print \\\"Splitted into more than 3...\\\"\\n for split in splits:\\n print split\\n tweet = Tweet(splits[0], splits[2], splits[3])\\n tweet.set_sentiment(splits[1])\\n else:\\n print \\\"Splitted into less than 3...\\\"\\n for split in splits:\\n print split\\n tweet = Tweet(splits[0], splits[1], splits[2])\\n return tweet\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.5520133972167969,\n \"alphanum_fraction\": 0.5822147727012634,\n \"avg_line_length\": 26.952381134033203,\n \"blob_id\": \"502f11442a19c42bd965ab57c482c52e16a37246\",\n \"content_id\": \"d13b70f662befcb367ff23593c8d46d17fe9918a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 596,\n \"license_type\": \"no_license\",\n \"max_line_length\": 104,\n \"num_lines\": 21,\n \"path\": \"/models/nb.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nCreated on 19. mars 2014\\n\\n@author: JohnArne\\n'''\\nfrom model import Model\\nfrom sklearn.naive_bayes import MultinomialNB \\n\\n\\nclass NB(Model):\\n \\\"\\\"\\\"\\n Class implementing the Multinomial Naive Bayes learning method.\\n \\\"\\\"\\\"\\n \\n def __init__(self, train_tweets, train_targets, vect_options, tfidf_options):\\n self.classifier = MultinomialNB()\\n \\n extra_params ={ \\n 'clf__alpha': (0.1, 0.3, 0.5, 0.7, 0.8, 1.0)\\n }\\n super(NB, self).__init__(train_tweets, train_targets, vect_options, tfidf_options, extra_params)\\n\\n \"\n },\n {\n \"alpha_fraction\": 0.6105527877807617,\n \"alphanum_fraction\": 0.6170854568481445,\n \"avg_line_length\": 31.639345169067383,\n \"blob_id\": \"62123936db66fcecf44724466176a7c066206e9c\",\n \"content_id\": \"42440e1a88f3576abd14f3fa7ce5c960426a20c8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2003,\n \"license_type\": \"no_license\",\n \"max_line_length\": 157,\n \"num_lines\": 61,\n \"path\": \"/tagger.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n'''\\nCreated on 30. sep. 2014\\n\\n@author: JohnArne\\n'''\\n\\n\\n\\nimport requests\\n\\nclass Tagger():\\n \\\"\\\"\\\"\\n Interfaces the POS tagger for classification.\\n \\\"\\\"\\\"\\n \\n def __init__(self):\\n \\n #Request init connect to smarttagger\\n self.url = \\\"http://smarttagger.herokuapp.com/tag\\\"\\n \\n \\n def tag_text(self, text):\\n \\\"\\\"\\\"\\n Tags a text sequence using the current tagger.\\n \\\"\\\"\\\"\\n \\n# print \\\"Tagging: \\\"+unicode(text.decode('utf8'))\\n par = {\\\"text\\\": text, \\\"raw\\\": \\\"raw\\\", \\\"format\\\": \\\"json\\\"}\\n r = requests.post(self.url, data=par)\\n tagged_words = {}\\n try:\\n results = r.json()[\\\"phrases\\\"]\\n tagged_words = results\\n except ValueError as e:\\n print \\\"Unable to get JSON: \\\" +str(e)\\n print r.reason\\n print r.status_code\\n if len(tagged_words)<1:\\n return None\\n else:\\n return tagged_words[0]\\n \\n\\n\\nif __name__==\\\"__main__\\\":\\n tagger = Tagger()\\n \\n texts = []\\n texts.append(u\\\"Viss Russland kritikken erna solberg framførte i FN var skjult, korleis i hulaste har den då hamna på framsida av VG i dag?\\\")\\n texts.append(u\\\"borgebrende Hoyre erna solberg Hvem skal gjøre møkkajobbene da?\\\")\\n texts.append(u\\\"erna solberg Siv Jensen FrP jensstoltenberg jonasgahrstore Skremmende at regjeringen vil selge aksjer i statlige selskap til utlandet.\\\")\\n texts.append(u\\\"CSpange Aftenposten erna solberg Sannsynlige grunner ingen, heller ikke de 120, venter resultater. Og Kina og USA uteblir. Neste gang...\\\")\\n texts.append(u\\\"ElinJoval konservativ erna solberg det Elin sa! Jeg vil ha mer tid til å være sammen med elevene.\\\")\\n texts.append(u\\\"Skulle ønske konservativ og erna solberg hjalp til å styrke lærernes status. Vi er gode! Vi trenger tid til å gjøre jobben vår bedre!\\\")\\n \\n for text in texts:\\n print unicode(text)\\n \\n for text in texts:\\n print tagger.tag_text(text)\"\n },\n {\n \"alpha_fraction\": 0.6507903933525085,\n \"alphanum_fraction\": 0.6570028066635132,\n \"avg_line_length\": 37.20052719116211,\n \"blob_id\": \"26054ff21e0e3738fed7bba6acf6800e41c70023\",\n \"content_id\": \"332fe385b0b46ee3cc9b16c74970cd59e0fd0529\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 14487,\n \"license_type\": \"no_license\",\n \"max_line_length\": 260,\n \"num_lines\": 379,\n \"path\": \"/entity_extraction.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nCreated on 4. jan. 2015\\n\\n@author: JohnArne\\n\\n-Ta en gruppe tweets.\\n-Prov a finne entitetene i hver enkelt tweet.\\n- kjor clustering pa tweets, eller pa bare entitetsnavn... grupper entitetene etter clusters og nominer den mest frekvente entiteten som overordnet navn\\n'''\\nimport lexicon.pos_mappings\\nfrom lexicon import pos_mappings\\nfrom tweet import Tweet\\nimport utils\\nfrom models.features import get_sentiment_values\\nimport classifier\\nimport pickle\\nfrom sklearn import metrics\\nimport plotting\\nfrom sklearn.feature_extraction.text import CountVectorizer\\nimport math\\n\\n\\ndef perform_entity_extraction(tweets, sentimentvalues, breakword_min_freq=0.2, breakword_range=2, use_sentiment_values=False, use_pmi=False, vocabulary=None, cluster=False, use_minibatch=False, use_idf=False, use_hasher=False):\\n \\\"\\\"\\\"\\n Takes in a list of correctly predicted subjective tweets and sentimentvalues, in addition to several optional parameters, and attempts entity extraction on all the tweeets.\\n \\\"\\\"\\\"\\n print len(tweets)\\n sub_clf = classifier.get_optimal_subjectivity_classifier()\\n #Get all the correctly classified subjective tweets\\n if use_pmi and vocabulary==None: vocabulary = create_vocabulary(tweets) \\n if use_sentiment_values:\\n entities = find_entities(sub_clf, tweets, breakword_min_freq, breakword_range, use_pmi, vocabulary=vocabulary, sentimentvalues=sentimentvalues)\\n else:\\n entities = find_entities(sub_clf, tweets, breakword_min_freq, breakword_range, use_pmi, vocabulary=vocabulary)\\n \\n if cluster:\\n #use clustering to group together tweets, \\n #then choose the entities with the greatest freqiencies within each cluster as the sentiment target for all in the cluster\\n #but not if the target is already none...\\n cluster_tweets(tweets, use_minibatch, use_idf, use_hasher)\\n \\n \\n for i in xrange(len(entities)):\\n entities[i] = entities[i][0] if len(entities[i])>0 else None\\n print entities\\n return entities\\n\\ndef find_entities(sub_clf, tweets, min_freq, breakword_range, use_pmi=False, vocabulary=None, sentimentvalues=None):\\n \\\"\\\"\\\"\\n Takes in a subjectivity classifier and the tweets, and attempts to find the target of the classified sentiment.\\n Return a textual description of the entity.\\n \\\"\\\"\\\"\\n if sentimentvalues!=None:\\n entities = [find_entity(sub_clf, t, min_freq, breakword_range, use_pmi, vocabulary=vocabulary, sentimentvalues=s) for t,s in zip(tweets,sentimentvalues)]\\n else:\\n entities = [find_entity(sub_clf, t, min_freq, breakword_range, use_pmi, vocabulary=vocabulary) for t in tweets]\\n return entities\\n \\n \\ndef find_entity(sub_clf, t, min_freq, breakword_range, use_pmi=False, vocabulary=None, sentimentvalues=None):\\n \\\"\\\"\\\"\\n Attempts at identifying the entity of a single tweet, utilizing sentiment values if not none.\\n \\\"\\\"\\\"\\n #get a list of possibilities for this tweet\\n possibilities = get_possible_entities(t)\\n if len(possibilities)<1:\\n for hashtag in t.hashtags:\\n if len(hashtag)>1:\\n return [hashtag]\\n return []\\n if len(possibilities)==1:\\n return possibilities\\n \\n #Get breakwords from breakdown classification\\n breakwords = breakdown_classify(sub_clf, t)\\n \\n breakwords = cutoff_breakwords(breakwords, min_freq)\\n #get sentimental words if given values\\n sentimentwords = []\\n if sentimentvalues!=None:\\n sentimentwords = get_sentimentwords(sentimentvalues)\\n \\n #Perform an intersection of the breakwords and sentimental words\\n# print \\\"Text: \\\", t.text\\n# print \\\"Possible entities: \\\",possibilities\\n# print \\\"Hashtags: \\\",t.hashtags\\n# print \\\"Shifting words: \\\",breakwords\\n# print \\\"Sentimental words: \\\",sentimentwords\\n sentiment_points = [val for val in sentimentwords if val in breakwords]\\n# print \\\"Intersection: \\\", sentiment_points\\n if len(sentiment_points)<1: sentiment_points = list(set(breakwords + sentimentwords))\\n# print \\\"New intersection: \\\", sentiment_points\\n \\n possibilities = cutoff_possibilities(t.text.lower(), possibilities, sentiment_points, breakword_range)\\n# print \\\"Possibilities after cutoff: \\\", possibilities\\n# raw_input(\\\"Continue?\\\")\\n\\n if use_pmi:\\n #Use PMI to disambiguate between possibilities\\n pmi = []\\n for p in possibilities:\\n if p is None: continue\\n for s in sentiment_points:\\n if s is None: continue\\n pmi.append([calculate_pmi(p,s,vocabulary), p])\\n if len(pmi)>0: \\n possibilities = [max(pmi)[1]]\\n \\n if len(possibilities)>0:\\n return possibilities \\n else:\\n if len(t.hashtags)>0:\\n for hashtag in t.hashtags:\\n if len(hashtag)>1:\\n return [hashtag]\\n return []\\n \\n# if len(possibilities)>0:\\n# return t.hashtags[0] if len(t.hashtags)>0 else possibilities\\n# else:\\n# return t.hashtags\\n# \\n #decide entity from possibilities based on the sentiment points\\n #calculate the \\\"center\\\" of the sentiment points\\n #choose the entity which is closest to the center of the sentiment points\\n #or choose the entity closest to the first sentiment points...\\n \\n \\ndef calculate_pmi(entity, sentiword, vocabulary):\\n \\\"\\\"\\\"\\n Calculates the pointwise mutual information between two given words.\\n \\\"\\\"\\\"\\n unigrams_freq = float(sum(vocabulary[0].values()))\\n prob_entity = vocabulary[0][entity] / unigrams_freq\\n prob_sentiword = vocabulary[0][sentiword] / unigrams_freq\\n try:\\n prob_both = vocabulary[1][\\\" \\\".join([unicode(entity),unicode(sentiword)])] / float(sum(vocabulary[1].values()))\\n except KeyError:\\n return 0.0\\n except UnicodeDecodeError:\\n print \\\"UnicodeError\\\"\\n return 0.0\\n return math.log(prob_both/float(prob_entity*prob_sentiword),2)\\n \\ndef create_vocabulary(tweets):\\n \\\"\\\"\\\"\\n Creates a bigram + unigram vocabulary of the given tweet texts.\\n \\\"\\\"\\\"\\n print \\\"Creating vocabulary...\\\"\\n vocabulary = []\\n unigrams_freq = {}\\n bigrams_freq = {}\\n texts= [t.text.lower() for t in tweets]\\n for text in texts:\\n for phrase in text.split('.'):\\n phrase = phrase.replace(',',' ')\\n unigrams = phrase.split(\\\" \\\")\\n unigrams = [u for u in unigrams if len(u)>1]\\n extended_bigrams = [x+\\\" \\\"+y for x,y in zip(unigrams[0::2],unigrams[1::2])] + [x+\\\" \\\"+y for x,y in zip(unigrams[1::2],unigrams[2::2])] + [x+\\\" \\\"+y for x,y in zip(unigrams[0::2],unigrams[2::2])] + [x+\\\" \\\"+y for x,y in zip(unigrams[1::2],unigrams[3::2])]\\n for unigram in unigrams:\\n unigrams_freq[unigram] = unigrams_freq.get(unigram, 0) + 1\\n for bigram in extended_bigrams:\\n bigrams_freq[bigram] = bigrams_freq.get(bigram, 0) + 1\\n vocabulary = [unigrams_freq, bigrams_freq]\\n return vocabulary\\n\\ndef is_entity(clf, t, entity, sentimentvalues=None):\\n \\\"\\\"\\\"\\n Takes in a classifier, a tweet, and an entity, returns a binary value corresponding to whether each entity is the sentiment entity of each tweet.\\n \\\"\\\"\\\"\\n \\n return False\\n \\ndef get_possible_entities(t):\\n \\\"\\\"\\\"\\n Takes in a tweet, and returns a list of the possible entities for that tweet.\\n \\\"\\\"\\\"\\n possible_entities = []\\n for phrase in t.tagged_words:\\n for word in phrase:\\n try:\\n entity = word['word']\\n if word['pos'] ==\\\"Np\\\":\\n if len(entity)>1:\\n possible_entities.append(entity.lower())\\n except KeyError:\\n continue\\n return possible_entities\\n \\ndef get_sentimentwords(sentimentvalues):\\n \\\"\\\"\\\"\\n returns the words that contain sentimental value.\\n \\\"\\\"\\\"\\n sentimentwords = []\\n for word in sentimentvalues.keys():\\n if sentimentvalues[word][0]>0 or sentimentvalues[word][1]>0:\\n sentimentwords.append(word)\\n return sentimentwords\\n\\n\\ndef breakdown_classify(clf, t):\\n \\\"\\\"\\\"\\n Classify substring permutations of the tweet in order to find a shifting point in the subjectivity classification\\n \\\"\\\"\\\"\\n orig_class = clf.classify([t])\\n breakwords = subclassify(t.text.lower(), clf, orig_class)\\n# breakwords = []\\n# print \\\"Breakdown classification\\\"\\n# for substring_paths in substrings:\\n# for substring_and_rmword in substring_paths:\\n# print substring_and_rmword['substring'], \\\" rm:\\\",substring_and_rmword['removed_word']\\n# #Classify each substring, append causal word when sentiment changes from original\\n# new_class = clf.classify_text([\\\" \\\".join(substring_and_rmword['substring'])])\\n# print \\\"New class: \\\",new_class\\n# if new_class!=orig_class: \\n# breakwords.append(substring_and_rmword['removed_word'])\\n# break\\n# print \\\"Original prediction: \\\",orig_class\\n return breakwords\\n \\n\\n\\ndef subclassify(t, clf, orig):\\n \\\"\\\"\\\"\\n Takes in a text, a classifier, and an original class. Returns all the break words where the class shifts. FIXXX!\\n \\\"\\\"\\\"\\n phrases = t.split(\\\",\\\")\\n words = []\\n for phrase in phrases:\\n words = words + phrase.split(\\\" \\\")\\n length = len(words)\\n breakwords = []\\n for i in xrange(length):\\n breakwords.append(clf_sub(words[:i]+words[i+1:], words[i], clf, orig)) \\n\\n return breakwords\\n \\ndef clf_sub(words, removed_word, clf, orig):\\n \\\"\\\"\\\"\\n FIIIIX!!! Return on classification shift!!! yesaaaaa!\\n \\\"\\\"\\\"\\n if len(words)==1: return removed_word if clf.classify_text([\\\" \\\".join(words)])!=orig else None\\n breakword = None\\n# print \\\" \\\".join(words), \\\"Removed word: \\\", removed_word\\n for i in xrange(len(words)-1):\\n if clf.classify_text([\\\" \\\".join(words)])!=orig:\\n# print \\\"Swithed class!\\\"\\n return removed_word\\n return clf_sub(words[:i]+words[i+1:], words[i], clf, orig)\\n return breakword\\n \\ndef cutoff_breakwords(breakwords, min_freq):\\n \\\"\\\"\\\"\\n Cuts of breakwords below the given frequency. Returns a list of uniques, where the belowfreqs have been removed\\n \\\"\\\"\\\"\\n breakword_freq = int(round(len(breakwords)*min_freq))\\n# print \\\"Breakwords before cutoff \\\",breakwords, min_freq\\n frequencies = {}\\n #remove breakwords with a lower frequency\\n for word in breakwords:\\n frequencies[word] = frequencies.get(word,0)+1\\n uniquelist = list(set(breakwords))\\n for key in frequencies:\\n if frequencies[key]0 else None for t in tweets]\\n \\ndef reduce_entities(entities):\\n \\\"\\\"\\\"\\n Reduce entities to binary so as to test with actual targets.\\n \\\"\\\"\\\"\\n reduced = []\\n for entity in entities:\\n reduced.append(1 if entity in rosenborg_model else 0)\\n return reduced\\n\\ndef get_scores(targets, predictions):\\n accuracy = metrics.accuracy_score(targets, predictions)\\n precision = metrics.precision_score(targets, predictions)\\n recall = metrics.recall_score(targets, predictions)\\n f1_score = metrics.f1_score(targets, predictions)\\n return accuracy, precision, recall, f1_score\\n\\ndef create_model(text):\\n model = [text]\\n f = open(text+\\\"_model\\\", \\\"wb\\\")\\n pickle.dump(model,f)\\n f.close()\\n \\ndef append_to_model(name, text):\\n model = pickle.load(name+\\\"_model\\\")\\n model.append(text)\\n model = list(set(model))\\n f = open(name+\\\"_model\\\", \\\"wb\\\")\\n pickle.dump(model, f)\\n f.close()\\n \\ndef cluster_tweets(tweets, max_features, use_minibatch, use_idf, use_hasher):\\n \\\"\\\"\\\"\\n Performs k-means clustering on tweets.\\n \\\"\\\"\\\"\\n \\n \\n return None\\n\\n\\n\\ndef perform_and_test_extraction():\\n datasetnr = 1\\n tweets = utils.get_pickles(datasetnr)\\n vocabulary = create_vocabulary(utils.get_all_pickles())\\n \\n sentimentvalues = get_sentiment_values(datasetnr)\\n tweets, targets = utils.get_entity_test_and_targets()\\n entities = perform_entity_extraction(tweets, sentimentvalues, breakword_range=3)\\n hashtag_entities = get_hashtag_entities(tweets)\\n \\n pmi_entities = perform_entity_extraction(tweets, sentimentvalues, breakword_range=8, use_pmi=True, vocabulary=vocabulary)\\n #TESTIFY!\\n reduced_entities = reduce_entities(entities)\\n reduced_hashtags = reduce_entities(hashtag_entities)\\n reduced_pmis = reduce_entities(pmi_entities)\\n \\n data = {}\\n accuracy, precision, recall, f1_score = get_scores(targets, reduced_entities)\\n print \\\"Entity Scores: \\\", accuracy, precision, recall, f1_score\\n data[\\\"Custom\\\"] = [accuracy, precision, recall, f1_score] \\n accuracy, precision, recall, f1_score = get_scores(targets, reduced_hashtags)\\n data[\\\"Hashtags\\\"] = [accuracy, precision, recall, f1_score]\\n print \\\"Hashtag Scores: \\\", accuracy, precision, recall, f1_score\\n accuracy, precision, recall, f1_score = get_scores(targets, reduced_pmis)\\n print \\\"PMI Scores: \\\", accuracy, precision, recall, f1_score\\n data[\\\"Custom+PMI\\\"] = [accuracy, precision, recall, f1_score]\\n #send to plotting\\n plotting.plot_entity_histogram(data, \\\"entity_extraction\\\")\\n \\n \\nrosenborg_model = [\\\"rosenborg\\\",\\\"rosenborgs\\\",\\\"rosenborgms\\\", \\\"rbk\\\",\\\"rbks\\\",\\\"rosenborg2\\\" ]\\n \\nif __name__ == '__main__':\\n #test substringify\\n# substrings = subc(string)\\n# print substrings\\n# print len(substrings)\\n #test breakdown clf\\n# breakdown_classify(\\\"adwd\\\", Tweet(\\\"12313\\\", \\\"johnaren\\\", \\\"jeg liker at\\\"))\\n perform_and_test_extraction()\\n \\n \"\n },\n {\n \"alpha_fraction\": 0.5698412656784058,\n \"alphanum_fraction\": 0.5984126925468445,\n \"avg_line_length\": 29.649999618530273,\n \"blob_id\": \"3d0de48a877dac685544d2d1f4c03f06196825bd\",\n \"content_id\": \"7341c010223f1b5f2d098fe041786b1224d104ac\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 630,\n \"license_type\": \"no_license\",\n \"max_line_length\": 105,\n \"num_lines\": 20,\n \"path\": \"/models/svm.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nCreated on 19. mars 2014\\n\\n@author: JohnArne\\n'''\\nfrom model import Model\\nfrom sklearn.linear_model import SGDClassifier\\nfrom sklearn.svm import LinearSVC\\n\\nclass SVM(Model):\\n \\\"\\\"\\\"\\n Class implementing the Support Vector Machines classsification model.\\n \\\"\\\"\\\"\\n \\n def __init__(self, train_tweets, train_targets, vect_options, tfidf_options):\\n self.classifier = LinearSVC()\\n extra_params = {\\n 'clf__C': (0.1, 0.3, 0.5, 0.7, 0.8, 1.0)\\n }\\n super(SVM, self).__init__(train_tweets, train_targets, vect_options, tfidf_options, extra_params)\\n \\n \"\n },\n {\n \"alpha_fraction\": 0.5687612295150757,\n \"alphanum_fraction\": 0.5821185111999512,\n \"avg_line_length\": 36.84239196777344,\n \"blob_id\": \"88fd87586f1c74510f4110cabdd8cc027758b9a0\",\n \"content_id\": \"1961cb5ce98a7350685a9cd75a8a0f69aa4b9bd2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 13925,\n \"license_type\": \"no_license\",\n \"max_line_length\": 134,\n \"num_lines\": 368,\n \"path\": \"/models/features.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nCreated on 27. nov. 2014\\n\\n@author: JohnArne\\n'''\\nfrom sklearn.feature_extraction.text import CountVectorizer\\nimport numpy as np\\nimport pickle\\n\\ndef get_feature_set(tweet,featureset,sentimentvalues):\\n if(featureset=='SA'):\\n return get_feature_set_SA(tweet)\\n elif(featureset=='SB'): \\n return get_feature_set_SB(tweet)\\n elif(featureset=='SC'): \\n return get_feature_set_SC(tweet,sentimentvalues)\\n elif(featureset=='SC2'): \\n return get_feature_set_SC2(tweet,sentimentvalues)\\n elif(featureset=='PA'): \\n return get_feature_set_PA(tweet)\\n elif(featureset=='PB'): \\n return get_feature_set_PB(tweet)\\n elif(featureset=='PC'): \\n return get_feature_set_PC(tweet,sentimentvalues)\\n elif(featureset=='PC2'): \\n return get_feature_set_PC2(tweet,sentimentvalues)\\n \\n\\ndef get_feature_set_SA(tweet):\\n \\\"\\\"\\\"\\n Retrieves a list of tweets objects and returns feature set SA, which is only text frequencies...\\n \\\"\\\"\\\"\\n features= {}\\n return features\\n\\ndef get_feature_set_SB(tweet):\\n \\\"\\\"\\\"\\n Creates a dict with grammatical features to be included in classification. Returns it to the classification model.\\n Features to be included: pos-tags, \\n \\\"\\\"\\\"\\n #pos-tag frequencies\\n# print \\\"Tagged words in tweet: \\\", tweet.tagged_words\\n pos_tag_freq = {}\\n additional_freq = {}\\n for phrase in tweet.tagged_words:\\n for word in phrase:\\n try:\\n tag = word['pos']\\n pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# if tag=='PRtinf':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='ADJS':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='ADJ':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='NP':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='DET':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='P':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n if tag in ADJECTIVES:\\n additional_freq['adjectives'] = additional_freq.get(tag, 0) + 1\\n elif tag in ADVERBS: \\n additional_freq['adverbs'] = additional_freq.get(tag, 0) + 1\\n elif tag in PRONOUNS:\\n additional_freq['pronoun'] = 1\\n except KeyError:\\n continue\\n# print \\\"Tag frequencies: \\\", pos_tag_freq\\n for key in pos_tag_freq.keys():\\n pos_tag_freq[key] = pos_tag_freq[key]*1.0\\n #number of adjectives in sentence, number of adverbs in sentence(except ikke), pronoun in sentence(binary) \\n #Number of exclamation marks, number of emoticons,\\n emoticons = tweet.nrof_happyemoticons+tweet.nrof_sademoticons\\n if emoticons>0:\\n additional_freq['emoticons'] = emoticons*1.0\\n if tweet.nrof_exclamations>0:\\n additional_freq['exclamations'] = tweet.nrof_exclamations*1.0\\n \\n# print \\\"Additional frequencies: \\\", additional_freq\\n# raw_input(\\\"Continue?\\\")\\n \\n #Concatenate the dicts\\n features= dict(pos_tag_freq.items() + additional_freq.items())\\n# print \\\"All features: \\\", features\\n# raw_input(\\\"Continue?\\\")\\n return features\\n\\n\\ndef get_feature_set_SC(tweet, sentimentvalues):\\n \\\"\\\"\\\"\\n Retrieves a list of tweets objects and returns feature set SC.\\n \\\"\\\"\\\"\\n pos_tag_freq = {}\\n additional_freq = {}\\n for phrase in tweet.tagged_words:\\n for word in phrase:\\n try:\\n tag = word['pos']\\n pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# if tag=='PRtinf':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='ADJS':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='ADJ':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='NP':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='DET':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='P':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n if tag in ADJECTIVES:\\n additional_freq['adjectives'] = additional_freq.get(tag, 0) + 1\\n elif tag in ADVERBS: \\n additional_freq['adverbs'] = additional_freq.get(tag, 0) + 1\\n elif tag in PRONOUNS:\\n additional_freq['pronoun'] = 1\\n except KeyError:\\n continue\\n for key in pos_tag_freq.keys():\\n pos_tag_freq[key] = pos_tag_freq[key]*1.0\\n #number of adjectives in sentence, number of adverbs in sentence(except ikke), pronoun in sentence(binary) \\n #Number of exclamation marks, number of emoticons,\\n emoticons = tweet.nrof_happyemoticons+tweet.nrof_sademoticons\\n if emoticons>0:\\n additional_freq['emoticons'] = emoticons*1.0\\n if tweet.nrof_exclamations>0:\\n additional_freq['exclamations'] = tweet.nrof_exclamations*1.0\\n \\n #Add lexicon values\\n #total subjectivity score from word polarities, total objectivity score, number of subjective words, number of objective words, e\\n sub_score = 0.0\\n obj_score = 0.0\\n nrof_subwords = 0\\n nrof_objwords = 0\\n for word in sentimentvalues.keys():\\n if sentimentvalues[word][0]>0:\\n sub_score = sub_score + sentimentvalues[word][0]\\n nrof_subwords = nrof_subwords + 1\\n if sentimentvalues[word][1]>0:\\n sub_score = sub_score + sentimentvalues[word][1]\\n nrof_subwords = nrof_subwords + 1\\n if sentimentvalues[word][2]>0:\\n obj_score = obj_score + sentimentvalues[word][2]\\n nrof_objwords = nrof_objwords + 1\\n if sub_score>0:\\n additional_freq[\\\"sub_score\\\"] = sub_score+1.0\\n if obj_score>0:\\n additional_freq[\\\"obj_score\\\"] = obj_score+1.0\\n if nrof_subwords>0:\\n additional_freq[\\\"subjective_words\\\"] = nrof_subwords*1.0\\n if nrof_objwords>0:\\n additional_freq[\\\"objective_words\\\"] = nrof_objwords*1.0\\n \\n #Concatenate the dicts\\n features= dict(pos_tag_freq.items() + additional_freq.items())\\n \\n return features\\n\\ndef get_feature_set_SC2(tweet, sentimentvalues):\\n \\\"\\\"\\\"\\n Retrieves a list of tweets objects and returns feature set SC.\\n \\\"\\\"\\\"\\n pos_tag_freq = {}\\n additional_freq = {}\\n for phrase in tweet.tagged_words:\\n for word in phrase:\\n try:\\n tag = word['pos']\\n pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n if tag in ADJECTIVES:\\n additional_freq['adjectives'] = additional_freq.get(tag, 0) + 1\\n elif tag in ADVERBS: \\n additional_freq['adverbs'] = additional_freq.get(tag, 0) + 1\\n elif tag in PRONOUNS:\\n additional_freq['pronoun'] = 1\\n except KeyError:\\n continue\\n for key in pos_tag_freq.keys():\\n pos_tag_freq[key] = pos_tag_freq[key]*1.0\\n #number of adjectives in sentence, number of adverbs in sentence(except ikke), pronoun in sentence(binary) \\n #Number of exclamation marks, number of emoticons,\\n emoticons = tweet.nrof_happyemoticons+tweet.nrof_sademoticons\\n if emoticons>0:\\n additional_freq['emoticons'] = emoticons*1.0\\n if tweet.nrof_exclamations>0:\\n additional_freq['exclamations'] = tweet.nrof_exclamations*1.0\\n \\n #Add lexicon values\\n #total subjectivity score from word polarities, total objectivity score, number of subjective words, number of objective words, e\\n sub_score = sentimentvalues[0]+sentimentvalues[1]\\n obj_score = sentimentvalues[2]\\n if sub_score>0:\\n additional_freq[\\\"sub_score\\\"] = sub_score+1.0\\n if obj_score>0:\\n additional_freq[\\\"obj_score\\\"] = obj_score+1.0\\n \\n #Concatenate the dicts\\n features= dict(pos_tag_freq.items() + additional_freq.items())\\n \\n return features\\n\\n\\ndef get_feature_set_PA(tweet):\\n \\\"\\\"\\\"\\n Retrieves a list of tweets objects and returns feature set PA, which is noone... Only word tokens.\\n \\\"\\\"\\\"\\n features= {}\\n return features\\n\\ndef get_feature_set_PB(tweet):\\n \\\"\\\"\\\"\\n Retrieves a list of tweets objects and returns feature set PB.\\n \\\"\\\"\\\"\\n features= {\\n 'text_length': np.log(len(tweet.text))\\n } #ADD ADDITIONAL FEATURES\\n if tweet.nrof_sademoticons>0:\\n features['sademoticons'] = tweet.nrof_sademoticons\\n if tweet.nrof_happyemoticons>0:\\n features['happyemoticons'] = tweet.nrof_happyemoticons\\n \\n return features\\n\\ndef get_feature_set_PC(tweet, sentimentvalues):\\n \\\"\\\"\\\"\\n Retrieves a list of tweets objects and returns feature set PC.\\n \\\"\\\"\\\"\\n features= {\\n 'text_length': np.log(len(tweet.text))\\n } #ADD ADDITIONAL FEATURES\\n if tweet.nrof_sademoticons>0:\\n features['sademoticons'] = tweet.nrof_sademoticons\\n if tweet.nrof_happyemoticons>0:\\n features['happyemoticons'] = tweet.nrof_happyemoticons\\n \\n for phrase in tweet.tagged_words:\\n for word in phrase:\\n try:\\n tag = word['pos']\\n features[tag] = features.get(tag, 0) + 1\\n if tag in ADJECTIVES:\\n features['adjectives'] = features.get(tag, 0) + 1\\n elif tag in ADVERBS: \\n features['adverbs'] = features.get(tag, 0) + 1\\n elif tag in PRONOUNS:\\n features['pronoun'] = 1\\n except KeyError:\\n continue\\n for key in features.keys():\\n features[key] = features[key]*1.0\\n \\n #Add lexical features\\n # total polarity score, number of positive words, number of negative words\\n pos_score = 0\\n neg_score = 0\\n nrof_pos_words = 0\\n nrof_neg_words = 0\\n for word in sentimentvalues.keys():\\n if sentimentvalues[word][0]>0:\\n nrof_pos_words = nrof_pos_words + 1\\n pos_score = pos_score + sentimentvalues[word][0]\\n if sentimentvalues[word][1]>0:\\n nrof_neg_words = nrof_neg_words + 1\\n neg_score = neg_score + sentimentvalues[word][1]\\n\\n if neg_score>0:\\n features['neg_score'] = neg_score+1.0\\n if pos_score>0:\\n features['pos_score'] = pos_score+1.0\\n if nrof_pos_words>0:\\n features['positive_words'] = nrof_pos_words*1.0\\n if nrof_neg_words>0:\\n features['negative_words'] = nrof_neg_words*1.0\\n \\n return features\\n\\ndef get_feature_set_PC2(tweet, sentimentvalues):\\n \\\"\\\"\\\"\\n Retrieves a list of tweets objects and returns feature set PC.\\n \\\"\\\"\\\"\\n features= {\\n 'text_length': np.log(len(tweet.text))\\n } #ADD ADDITIONAL FEATURES\\n if tweet.nrof_sademoticons>0:\\n features['sademoticons'] = tweet.nrof_sademoticons\\n if tweet.nrof_happyemoticons>0:\\n features['happyemoticons'] = tweet.nrof_happyemoticons\\n \\n for phrase in tweet.tagged_words:\\n for word in phrase:\\n try:\\n tag = word['pos']\\n features[tag] = features.get(tag, 0) + 1\\n if tag in ADJECTIVES:\\n features['adjectives'] = features.get(tag, 0) + 1\\n elif tag in ADVERBS: \\n features['adverbs'] = features.get(tag, 0) + 1\\n elif tag in PRONOUNS:\\n features['pronoun'] = 1\\n except KeyError:\\n continue\\n for key in features.keys():\\n features[key] = features[key]*1.0\\n \\n #Add lexical features\\n # total polarity score, number of positive words, number of negative words\\n pos_score = sentimentvalues[0]\\n neg_score = sentimentvalues[1]\\n\\n if pos_score>0:\\n features['pos_score'] = pos_score+1.0\\n if neg_score>0:\\n features['neg_score'] = neg_score+1.0\\n \\n return features\\n\\ndef get_sentiment_values(setnr):\\n \\\"\\\"\\\"\\n Gets the pickles of sentiment values\\n \\\"\\\"\\\"\\n if setnr==None:\\n setnr = int(raw_input(\\\"Get which pickle set? 0: RandomSet 1: RoseborgSet 2: ErnaSet 3: All three ...\\\"))\\n \\n if setnr is 3:\\n #fetch all sets and append them together\\n tweets = []\\n for pickleset in sentiment_pickles:\\n tweets = tweets + pickle.load(open(pickleset, 'rb'))\\n return tweets\\n else:\\n tweets = pickle.load(open(sentiment_pickles[setnr], 'rb'))\\n return tweets\\n \\n return tweets\\n\\ndef get_google_sentiment_values(setnr):\\n \\\"\\\"\\\"\\n Gets the pickles of sentiment values\\n \\\"\\\"\\\"\\n if setnr==None:\\n setnr = int(raw_input(\\\"Get which pickle set? 0: RandomSet 1: RoseborgSet 2: ErnaSet 3: All three ...\\\"))\\n \\n if setnr is 3:\\n #fetch all sets and append them together\\n tweets = []\\n for pickleset in google_sentiment_pickles:\\n tweets = tweets + pickle.load(open(pickleset, 'rb'))\\n return tweets\\n else:\\n tweets = pickle.load(open(google_sentiment_pickles[setnr], 'rb'))\\n return tweets\\n \\n return tweets\\n\\nsentiment_pickles = ['models/sentimentvalues_random_dataset',\\n 'models/sentimentvalues_rosenborg_dataset',\\n 'models/sentimentvalues_erna_dataset']\\ngoogle_sentiment_pickles = ['models/google_sentimentvalues_random_dataset',\\n 'models/google_sentimentvalues_rosenborg_dataset',\\n 'models/google_sentimentvalues_erna_dataset']\\n\\nADJECTIVES = ['ADJ','ADJC','ADJS']\\nADVERBS = ['ADV','ADVm','ADVneg','ADVplc','ADVtemp']\\nPRONOUNS = ['PN','PNabs','PNana','PNdem','PNposs','PNrefl','PNrel']\\nNOUNS = ['CN','N','Nbare','Ncomm','NDV','NFEM','NMASC','NNEUT','NNO','Np','Nrel','Nspat']\"\n },\n {\n \"alpha_fraction\": 0.688524603843689,\n \"alphanum_fraction\": 0.688524603843689,\n \"avg_line_length\": 21.18181800842285,\n \"blob_id\": \"630d2a21d03fda68594b42eea7a2258fb712853c\",\n \"content_id\": \"eb45d3f1c19fd2e2e270f0dd06640d6a3c1dac61\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 244,\n \"license_type\": \"no_license\",\n \"max_line_length\": 107,\n \"num_lines\": 11,\n \"path\": \"/README.md\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Twitter-sentiment\\n=================\\n\\n**An attempt at Twitter temporal sentiment analysis.**\\n\\nAuthor: John Arne Øye\\n\\nIntroduction\\n------------\\n\\nThis system is written as part of a master thesis at NTNU (Norwegian University of Science and Technology).\\n\"\n },\n {\n \"alpha_fraction\": 0.5715023279190063,\n \"alphanum_fraction\": 0.5751161575317383,\n \"avg_line_length\": 29.746030807495117,\n \"blob_id\": \"b4a6e496d32b4a420b9471439292c3d8f043c1fc\",\n \"content_id\": \"7497df1091a8d1c60e63dc5b147f40f23a3fb6a7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1937,\n \"license_type\": \"no_license\",\n \"max_line_length\": 105,\n \"num_lines\": 63,\n \"path\": \"/retriever_tweepy.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nimport tweepy\\nimport utils\\n\\nOAUTH_API_KEY = \\\"JvgeRvICbMtWYcmhTug3w\\\"\\nOAUTH_API_SECRET = \\\"CzIwJm5yUi6hTHeLjrYMHZIMoszkNCD1MqgHFfO5qI\\\"\\n\\nACCESS_TOKEN = \\\"462254796-mLqIDTfa1e0ODYfksV1CiEunCIT5MuJ3avvp2kt9\\\"\\nACCESS_SECRET = \\\"EsRjaoF8ZAkQSNEk8s72Kf3aEStFV3k4epBLMsefDZtKd\\\"\\n\\nclass TweetRetriever(object):\\n \\\"\\\"\\\"\\n Handler for retrieving tweets using the twitter API through Tweepy.\\n \\\"\\\"\\\"\\n \\n query = \\\"\\\"\\n def __init__(self, query):\\n auth = tweepy.OAuthHandler(OAUTH_API_KEY, OAUTH_API_SECRET)\\n auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)\\n self.api = tweepy.API(auth)\\n print \\\"Connection to Twitter API is up.\\\"\\n \\n arguments = query.split(' ')\\n if len(arguments)>2:\\n self.since=arguments[len(arguments)-2]\\n self.until=arguments[len(arguments)-1]\\n self.query = \\\" \\\".join(arguments[:len(arguments)-2])\\n else:\\n self.since=None\\n self.until=None\\n self.query = query\\n\\n def retrieve_for_dataset(self):\\n \\\"\\\"\\\"\\n Return a sample of tweets and add to current dataset text file\\n \\\"\\\"\\\"\\n if self.since == None and self.until==None:\\n c = tweepy.Cursor(self.api.search, q=self.query, lang=\\\"no\\\")\\n else:\\n c = tweepy.Cursor(self.api.search, q=self.query, since=self.since,until=self.until,lang=\\\"no\\\")\\n results = []\\n print self.query\\n print self.since\\n print self.until\\n for tweet in c.items(500):\\n results.append(tweet)\\n results_list = utils.get_resultsets_text(results)\\n dataset = utils.select_complete_dataset()\\n utils.append_to_dataset(results_list, dataset)\\n print \\\"Fetched \\\"+str(len(results_list)) +\\\" tweets\\\"\\n \\n def retrieve_as_tweets(self):\\n \\\"\\\"\\\"\\n Fetch a sample of tweets and return them as tweets objects\\n \\\"\\\"\\\"\\n tweets = []\\n return tweets\\n \\n def retrieve_stream(self):\\n \\\"\\\"\\\"\\n Fetch tweets from the twitter stream.\\n \\\"\\\"\\\"\\n tweets =[]\\n return tweets\"\n },\n {\n \"alpha_fraction\": 0.4615384638309479,\n \"alphanum_fraction\": 0.5128205418586731,\n \"avg_line_length\": 10.55555534362793,\n \"blob_id\": \"baec6f9d4330090dff0d27ee4d2311934e4b725f\",\n \"content_id\": \"64dec11894cc644a4e723a6cddf64c4241c2f569\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 117,\n \"license_type\": \"no_license\",\n \"max_line_length\": 24,\n \"num_lines\": 9,\n \"path\": \"/kmeans.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nCreated on 10. jan. 2015\\n\\n@author: JohnArne\\n'''\\n\\nclass Kmeans(object):\\n \\n def __init__(self):\\n \\n \"\n },\n {\n \"alpha_fraction\": 0.5413320064544678,\n \"alphanum_fraction\": 0.5523491501808167,\n \"avg_line_length\": 46.486488342285156,\n \"blob_id\": \"f98005b01c6811418472ffd856d5d64bc8110949\",\n \"content_id\": \"e91b562645c6b61244398498aa4a1e454cc24502\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 14069,\n \"license_type\": \"no_license\",\n \"max_line_length\": 139,\n \"num_lines\": 296,\n \"path\": \"/analyzer.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nCreated on 7. nov. 2014\\n\\n@author: JohnArne\\n'''\\nfrom lexicon import pos_mappings\\nfrom operator import itemgetter\\nclass Analyzer:\\n \\n def __init__(self, dataset, tweets):\\n self.dataset = dataset\\n self.tweets = tweets\\n \\n def analyze(self):\\n \\\"\\\"\\\"\\n Performs an analysis of the given dataset.\\n \\\"\\\"\\\"\\n print \\\"Analyzing... \\\"\\n stats = Stats(self.dataset)\\n \\n stats.nrof_tweets = len(self.tweets)\\n users = []\\n pos_freqs = {}\\n #'ADJ','ADJC','ADJS','ADV', 'PNrefl',\\n # 'PN','NFEM','NMASC','DET','CONJS','N','P','INTRJC','V','Np','PRtinf','CONJ','NNEUT'\\n for tweet in self.tweets:\\n stats.nrof_words = stats.nrof_words + tweet.word_count\\n users.append(tweet.user)\\n \\n if tweet.get_sentiment()==\\\"negative\\\":\\n stats.nrof_negativetweets = stats.nrof_negativetweets + 1\\n for phrase in tweet.tagged_words:\\n for word in phrase:\\n if \\\"pos\\\" not in word.keys(): continue\\n pos_freqs[word[\\\"pos\\\"]] = pos_freqs.get(word[\\\"pos\\\"],0) +1\\n if word[\\\"pos\\\"] in pos_mappings.ADJECTIVES:\\n stats.nrof_adjectives = stats.nrof_adjectives + 1\\n stats.nrof_adjectives_in_negative = stats.nrof_adjectives_in_negative + 1 \\n if word[\\\"pos\\\"] in pos_mappings.NOUNS:\\n stats.nrof_nouns =stats.nrof_nouns +1 \\n stats.nrof_nouns_in_negative = stats.nrof_nouns_in_negative+1\\n if word[\\\"pos\\\"] in pos_mappings.ADVERBS:\\n stats.nrof_adverbs = stats.nrof_adverbs+1\\n stats.nrof_adverbs_in_negative = stats.nrof_adverbs_in_negative+1\\n if word[\\\"pos\\\"] in pos_mappings.VERBS:\\n stats.nrof_verbs = stats.nrof_verbs+1\\n elif tweet.get_sentiment()==\\\"neutral\\\":\\n stats.nrof_neutraltweets = stats.nrof_neutraltweets + 1\\n for phrase in tweet.tagged_words:\\n for word in phrase:\\n if \\\"pos\\\" not in word.keys(): continue\\n pos_freqs[word[\\\"pos\\\"]] = pos_freqs.get(word[\\\"pos\\\"],0) +1\\n if word[\\\"pos\\\"] in pos_mappings.ADJECTIVES:\\n stats.nrof_adjectives = stats.nrof_adjectives + 1\\n stats.nrof_adjectives_in_neutral = stats.nrof_adjectives_in_neutral + 1 \\n if word[\\\"pos\\\"] in pos_mappings.NOUNS:\\n stats.nrof_nouns =stats.nrof_nouns +1\\n stats.nrof_nouns_in_neutral = stats.nrof_nouns_in_neutral+1 \\n if word[\\\"pos\\\"] in pos_mappings.ADVERBS:\\n stats.nrof_adverbs = stats.nrof_adverbs+1\\n stats.nrof_adverbs_in_neutral = stats.nrof_adverbs_in_neutral+1\\n if word[\\\"pos\\\"] in pos_mappings.VERBS:\\n stats.nrof_verbs = stats.nrof_verbs+1\\n elif tweet.get_sentiment()==\\\"positive\\\":\\n stats.nrof_positivetweets = stats.nrof_positivetweets + 1\\n for phrase in tweet.tagged_words:\\n for word in phrase:\\n if \\\"pos\\\" not in word.keys(): continue\\n pos_freqs[word[\\\"pos\\\"]] = pos_freqs.get(word[\\\"pos\\\"],0) +1\\n if word[\\\"pos\\\"] in pos_mappings.ADJECTIVES:\\n stats.nrof_adjectives = stats.nrof_adjectives + 1\\n stats.nrof_adjectives_in_postive = stats.nrof_adjectives_in_postive + 1\\n if word[\\\"pos\\\"] in pos_mappings.NOUNS:\\n stats.nrof_nouns =stats.nrof_nouns +1\\n stats.nrof_nouns_in_postive = stats.nrof_nouns_in_postive+1\\n if word[\\\"pos\\\"] in pos_mappings.ADVERBS:\\n stats.nrof_adverbs =stats.nrof_adverbs +1\\n stats.nrof_adverbs_in_postive = stats.nrof_adverbs_in_postive+1\\n if word[\\\"pos\\\"] in pos_mappings.VERBS:\\n stats.nrof_verbs = stats.nrof_verbs+1\\n stats.nrof_links = stats.nrof_links + len(tweet.links)\\n stats.nrof_users_mentioned = stats.nrof_users_mentioned + len(tweet.users_mentioned)\\n stats.nrof_emoticons = stats.nrof_emoticons + tweet.nrof_happyemoticons + tweet.nrof_sademoticons\\n \\n avg_list = []\\n pos_list = []\\n if 'PNposs' in pos_freqs.keys(): pos_freqs.pop('PNposs')\\n if 'Ncomm' in pos_freqs.keys(): pos_freqs.pop('Ncomm')\\n print \\\"POStag averages \\\"\\n for key in pos_freqs.keys():\\n print key, \\\" \\\", pos_freqs[key]\\n pos_list.append(key)\\n avg_list.append(pos_freqs[key]*1.0/stats.nrof_tweets)\\n \\n sortedlists = [list(x) for x in zip(*sorted(zip(pos_list,avg_list), key=itemgetter(0)))]\\n avg_list = sortedlists[1]\\n pos_list = sortedlists[0]\\n for p,a in zip(pos_list, avg_list):\\n print p, \\\" \\\",a\\n stats.nrof_users = len(set(users))\\n stats.compute()\\n stats.store_tex()\\n #Return list to go to plottings\\n return avg_list, [stats.avg_adjectives, stats.avg_adverbs, stats.avg_nouns, stats.avg_verbs]\\n \\n \\n \\ndef pos_tag_analyze(tweets, postfix=\\\"\\\"):\\n \\\"\\\"\\\"\\n Perform a comparison of POS tags between different sentiment classes in the dataset.\\n \\\"\\\"\\\"\\n data = {} #dict to contain all the pos tags and their given values\\n #instantiate dict\\n for t in tweets:\\n for phrase in t.tagged_words:\\n for word in phrase:\\n try:\\n tag = word['pos']\\n if tag==\\\"PNrefl\\\": tag = \\\"PN\\\"\\n if tag==\\\"PNposs\\\": tag = \\\"PN\\\"\\n if tag==\\\"Ncomm\\\": tag = \\\"N\\\"\\n data[tag] = [0 for _ in xrange(4)]\\n except KeyError:\\n continue\\n #Count the pos tag frequencies for the different tweet classes\\n #A dict of lists, containing frequencies for [subjective,objective,positive,negative]\\n for t in tweets:\\n for phrase in t.tagged_words:\\n for word in phrase:\\n try:\\n tag = word['pos']\\n if tag==\\\"PNrefl\\\": tag = \\\"PN\\\"\\n if tag==\\\"PNposs\\\": tag = \\\"PN\\\"\\n if tag==\\\"Ncomm\\\": tag = \\\"N\\\"\\n if t.subjectivity==1:\\n #subjective\\n data[tag][0] = data[tag][0] + 1\\n if t.polarity==1:\\n #positive\\n data[tag][2] = data[tag][2] + 1\\n else:\\n #negative\\n data[tag][3] = data[tag][3] + 1\\n else:\\n #objective\\n data[tag][1] = data[tag][1] + 1\\n except KeyError:\\n continue\\n #Calculate\\n subjectivity_data ={}\\n polarity_data = {}\\n for key in data.keys():\\n print key, \\\" \\\",data[key]\\n subjectivity_data[key] = (data[key][0] - data[key][1]*1.0) / (data[key][0] + data[key][1]*1.0)\\n polarity_data[key] = (data[key][3] - data[key][2]*1.0) / (data[key][3] + data[key][2]*1.0)\\n if key==\\\"ADJC\\\" and polarity_data[key]>0.6: polarity_data[key]=polarity_data[key]-0.3\\n\\n for key in data.keys():\\n print key, \\\" \\\",subjectivity_data[key]\\n print key, \\\" \\\",polarity_data[key]\\n return subjectivity_data, polarity_data\\n\\n def sentiment_class_analysis(self, dataset2, tweets2, dataset3, tweets3):\\n \\\"\\\"\\\"\\n Compare all three datasets with each other, with respect to their sentiment annotations.\\n \\\"\\\"\\\"\\n \\n \\n \\nclass Stats:\\n \\\"\\\"\\\"\\n Contains and formats the statistics behind a dataset analysis.\\n \\\"\\\"\\\"\\n \\n def __init__(self, dataset):\\n self.dataset = dataset\\n self.nrof_tweets = 0\\n self.nrof_words = 0\\n self.nrof_users = 0\\n self.nrof_adjectives = 0\\n self.nrof_nouns = 0\\n self.nrof_verbs = 0\\n self.nrof_adverbs = 0\\n self.nrof_links = 0\\n self.nrof_users_mentioned = 0\\n self.nrof_emoticons = 0\\n \\n \\n self.nrof_negativetweets = 0\\n self.nrof_neutraltweets = 0\\n self.nrof_positivetweets = 0\\n \\n self.nrof_adjectives_in_negative = 0\\n self.nrof_adjectives_in_neutral = 0\\n self.nrof_adjectives_in_postive = 0\\n\\n self.nrof_nouns_in_negative = 0\\n self.nrof_nouns_in_neutral = 0\\n self.nrof_nouns_in_postive = 0\\n \\n self.nrof_adverbs_in_negative = 0\\n self.nrof_adverbs_in_neutral = 0\\n self.nrof_adverbs_in_postive = 0\\n \\n #computational variables\\n \\n self.avg_words = 0\\n self.avg_adjectives = 0\\n self.avg_nouns = 0\\n self.avg_verbs = 0\\n self.avg_adverbs = 0\\n self.tweetsperuser = 0\\n \\n self.prc_negativetweets = 0.0\\n self.prc_neutraltweets = 0.0\\n self.prc_positivetweets = 0.0\\n \\n #Stores the average number of adjectives in different classes of tweets.\\n self.avg_adjectives_in_negative = 0.0\\n self.avg_adjectives_in_neutral = 0.0\\n self.avg_adjectives_in_positive = 0.0\\n \\n self.avg_nouns_in_negative = 0.0\\n self.avg_nouns_in_neutral = 0.0 #For POS tag analysis\\n self.avg_nouns_in_positive = 0.0\\n \\n self.avg_adverbs_in_negative = 0.0\\n self.avg_adverbs_in_neutral = 0.0 \\n self.avg_adverbs_in_positive = 0.0\\n \\n def compute(self):\\n \\\"\\\"\\\"\\n Prompts the computation of statistics not explicitly given.\\n \\\"\\\"\\\"\\n \\n self.avg_words = self.division_else_zero(self.nrof_words, self.nrof_tweets)\\n self.avg_adjectives = self.division_else_zero(self.nrof_adjectives, self.nrof_tweets)\\n self.avg_nouns = self.division_else_zero(self.nrof_nouns, self.nrof_tweets)\\n self.avg_verbs = self.division_else_zero(self.nrof_verbs, self.nrof_tweets)\\n self.avg_adverbs = self.division_else_zero(self.nrof_adverbs, self.nrof_tweets)\\n self.tweetsperuser = self.division_else_zero(self.nrof_tweets, self.nrof_users)\\n \\n self.prc_negativetweets = self.division_else_zero(self.nrof_negativetweets, self.nrof_tweets) * 100\\n self.prc_neutraltweets = self.division_else_zero(self.nrof_neutraltweets, self.nrof_tweets) * 100\\n self.prc_positivetweets = self.division_else_zero(self.nrof_positivetweets, self.nrof_tweets) * 100\\n \\n self.avg_adjectives_in_negative = self.division_else_zero(self.nrof_adjectives_in_negative, self.nrof_negativetweets)\\n self.avg_adjectives_in_neutral = self.division_else_zero(self.nrof_adjectives_in_neutral, self.nrof_neutraltweets)\\n self.avg_adjectives_in_positive = self.division_else_zero(self.nrof_adjectives_in_postive, self.nrof_positivetweets)\\n \\n self.avg_nouns_in_negative = self.division_else_zero(self.nrof_nouns_in_negative, self.nrof_negativetweets)\\n self.avg_nouns_in_neutral = self.division_else_zero(self.nrof_nouns_in_neutral, self.nrof_neutraltweets)\\n self.avg_nouns_in_positive = self.division_else_zero(self.nrof_nouns_in_postive, self.nrof_positivetweets)\\n \\n self.avg_adverbs_in_negative = self.division_else_zero(self.nrof_adverbs_in_negative, self.nrof_negativetweets)\\n self.avg_adverbs_in_neutral = self.division_else_zero(self.nrof_adverbs_in_neutral, self.nrof_neutraltweets)\\n self.avg_adverbs_in_positive = self.division_else_zero(self.nrof_adverbs_in_postive, self.nrof_positivetweets)\\n\\n \\n \\n def store_tex(self):\\n \\\"\\\"\\\"\\n Stores the statistics of the given dataset as a .tex friendly text file.\\n \\\"\\\"\\\"\\n file = open(\\\"stats_tex/\\\"+str(self.dataset), \\\"w\\\")\\n printstring = \\\"\\\\\\\\begin{table} \\\\n \\\\\\\\begin{center} \\\\n \\\\\\\\caption{Table of statistics for \\\"+self.dataset+\\\"}\\\"\\n printstring = printstring + \\\"\\\\n \\\\\\\\begin{tabular}{|l|r|}\\\"\\n printstring = printstring+ \\\"\\\\n Number of tweets & \\\"+str(self.nrof_tweets) + \\\"\\\\\\\\\\\\\\\\\\\"\\n printstring = printstring+ \\\"\\\\n Words & \\\"+str(self.nrof_words) + \\\"\\\\\\\\\\\\\\\\\\\"\\n printstring = printstring+ \\\"\\\\n Users & \\\"+str(self.nrof_users) + \\\"\\\\\\\\\\\\\\\\\\\"\\n printstring = printstring+ \\\"\\\\n \\\\\\\\hline\\\"\\n printstring = printstring+ \\\"\\\\n Users mentioned & \\\"+str(self.nrof_users_mentioned) + \\\"\\\\\\\\\\\\\\\\\\\"\\n printstring = printstring+ \\\"\\\\n Links & \\\"+str(self.nrof_users_mentioned) + \\\"\\\\\\\\\\\\\\\\\\\"\\n printstring = printstring+ \\\"\\\\n Emoticons & = \\\"+str(self.nrof_emoticons) + \\\"\\\\\\\\\\\\\\\\\\\"\\n \\n printstring = printstring+ \\\"\\\\n \\\\\\\\hline\\\"\\n printstring = printstring+ \\\"\\\\n Tweets per user & \\\"+str(self.tweetsperuser) + \\\"\\\\\\\\\\\\\\\\\\\"\\n printstring = printstring+ \\\"\\\\n Words per tweet & \\\"+str(self.avg_words) + \\\"\\\\\\\\\\\\\\\\\\\"\\n \\n printstring = printstring+ \\\"\\\\n \\\\\\\\hline\\\"\\n printstring = printstring+ \\\"\\\\n Negative tweets & \\\"+str(self.nrof_negativetweets)+\\\"(\\\"+str(self.prc_negativetweets)+\\\"\\\\\\\\%)\\\" + \\\"\\\\\\\\\\\\\\\\\\\"\\n printstring = printstring+ \\\"\\\\n Neutral tweets & \\\"+str(self.nrof_neutraltweets)+ \\\"(\\\"+str(self.prc_neutraltweets)+\\\"\\\\\\\\%)\\\" + \\\"\\\\\\\\\\\\\\\\\\\"\\n printstring = printstring+ \\\"\\\\n Positive tweets & \\\"+str(self.nrof_positivetweets)+ \\\"(\\\" +str(self.prc_positivetweets)+\\\"\\\\\\\\%)\\\" + \\\"\\\\\\\\\\\\\\\\\\\"\\n printstring = printstring+ \\\"\\\\n \\\\\\\\end{tabular} \\\\n \\\\\\\\end{center} \\\\n \\\\\\\\end{table} \\\\n\\\"\\n file.write(printstring)\\n file.close()\\n \\n \\n def division_else_zero(self, variable1, variable2):\\n \\\"\\\"\\\"\\n Devides the first variable with the second variable, if the second is not 0, else returns 0.\\n \\\"\\\"\\\"\\n if variable2!=0:\\n return (variable1*1.0 / variable2*1.0)\\n else:\\n return 0.0\\n \\n \"\n },\n {\n \"alpha_fraction\": 0.6199748516082764,\n \"alphanum_fraction\": 0.6262562870979309,\n \"avg_line_length\": 29.922330856323242,\n \"blob_id\": \"1dcf59b81bb72790bb46edd6a5b6603fab94ffd6\",\n \"content_id\": \"d04a147b8b9308a8905cc0be93ae76cb94bf5f35\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3186,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 103,\n \"path\": \"/twitter/retriever.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"WINDOWS-1252\",\n \"text\": \"'''\\nCreated on 12. feb. 2014\\n\\n@author: JohnArne\\n'''\\n\\nfrom __future__ import unicode_literals\\nimport requests\\nimport json\\nfrom datetime import date, timedelta\\nfrom urlparse import parse_qs\\n#from requests_oauthlib import OAuth\\n\\nREQUEST_TOKEN_URL = \\\"https://api.twitter.com/oauth/request_token\\\"\\nAUTHORIZE_URL = \\\"https://api.twitter.com/oauth/authorize?oauth_token=\\\"\\nACCESS_TOKEN_URL = \\\"https://api.twitter.com/oauth/access_token\\\"\\n\\nCONSUMER_KEY = \\\"bERRpxqRNywXn2goGyDLA\\\"\\nCONSUMER_SECRET = \\\"EesTZzoqKNXerlntfkmXNqnW5BKBvRjJIeoBtqOe2c\\\"\\n\\nOAUTH_TOKEN = \\\"14317755-wlQ7wAY2S5oGnHpVnpTuPEjhbZ73OBPUrDWCWyiC5\\\"\\nOAUTH_TOKEN_SECRET = \\\"2mVNpK0PC45sKOK290oDBlYaDtzBMkeZR2qhnOGynQ\\\"\\n\\ndef setup_oauth(config):\\n \\\"\\\"\\\"Authorize your app via identifier.\\\"\\\"\\\"\\n # Request token\\n oauth = OAuth1(config['CONSUMER_KEY'], client_secret=config['CONSUMER_SECRET'])\\n r = requests.post(url=REQUEST_TOKEN_URL, auth=oauth)\\n credentials = parse_qs(r.content)\\n\\n resource_owner_key = credentials.get('oauth_token')[0]\\n resource_owner_secret = credentials.get('oauth_token_secret')[0]\\n\\n # Authorize\\n authorize_url = AUTHORIZE_URL + resource_owner_key\\n print 'Please go here and authorize: ' + authorize_url\\n\\n verifier = raw_input('Please input the verifier: ')\\n oauth = OAuth1(config['CONSUMER_KEY'],\\n client_secret=config['CONSUMER_SECRET'],\\n resource_owner_key=resource_owner_key,\\n resource_owner_secret=resource_owner_secret,\\n verifier=verifier)\\n\\n # Finally, Obtain the Access Token\\n r = requests.post(url=ACCESS_TOKEN_URL, auth=oauth)\\n credentials = parse_qs(r.content)\\n token = credentials.get('oauth_token')[0]\\n secret = credentials.get('oauth_token_secret')[0]\\n\\n return token, secret\\n\\n\\ndef get_oauth(config):\\n oauth = OAuth1(config['CONSUMER_KEY'],\\n client_secret=config['CONSUMER_SECRET'],\\n resource_owner_key=config['OAUTH_TOKEN'],\\n resource_owner_secret=config['OAUTH_TOKEN_SECRET'])\\n return oauth\\n\\nclass Tweet:\\n FIELDS = ('id', 'text', 'lang')\\n\\n def __init__(self, data):\\n for field in self.FIELDS:\\n setattr(self, field, data[field])\\n self.user = data['user']['screen_name']\\n self.data = data\\n self.sentiment = None\\n self.filtered_text = None\\n\\n def __unicode__(self):\\n s = u\\\"\\\"\\n if self.sentiment:\\n s = (u\\\"<%s> \\\" % self.sentiment).ljust(11)\\n return s + u\\\"@%s: «%s»\\\" % (self.user, self.text)\\n\\nclass Twitter:\\n RESOURCE_URL_TEMPLATE = \\\"https://api.twitter.com/1.1/%s.json\\\"\\n\\n def __init__(self, config):\\n self.oauth = get_oauth(config)\\n \\n def api_resource(self, resource):\\n return Twitter.RESOURCE_URL_TEMPLATE % resource\\n\\n def api_request(self, resource, payload):\\n url = self.api_resource(resource)\\n r = requests.get(url=url, auth=self.oauth, params=payload)\\n return r.json()\\n\\n def search(self, term, result_type='popular', count=10):\\n payload = {\\n 'q': term,\\n 'result_type': result_type,\\n 'count': count,\\n 'lang': 'en',\\n }\\n data = self.api_request(\\\"search/tweets\\\", payload)\\n return data[\\\"statuses\\\"]\\n\\nclass NotEnoughTweetsError(ValueError):\\n pass\"\n },\n {\n \"alpha_fraction\": 0.48388251662254333,\n \"alphanum_fraction\": 0.501074492931366,\n \"avg_line_length\": 40.6119384765625,\n \"blob_id\": \"d6e40d30e9f40583359cf77caa686252d2025878\",\n \"content_id\": \"d79138a520833e960cf282d1b16a57577323f25f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2792,\n \"license_type\": \"no_license\",\n \"max_line_length\": 286,\n \"num_lines\": 67,\n \"path\": \"/twitter/retrieve_curl.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nCreated on 10. mars 2014\\n\\n@author: JohnArne\\n'''\\n#Retrieves tweets from website using curl calls, and stores it locally.\\n\\nimport urllib\\nimport urllib2\\nimport json\\n#import pycurl\\nimport requests\\n\\n\\n#def retrieve_tweets_curl():\\n# url_string = 'http://vm-6123.idi.ntnu.no:9200/_all/_search?pretty'\\n# query_string = '{\\\"from\\\":0, \\\"size\\\":100, \\\"query\\\": {\\\"match_all\\\": {}}, \\\"filter\\\": {\\\"bool\\\": {\\\"must\\\": [ {\\\"match_all\\\": {}}, {\\\"terms\\\": {\\\"_type\\\": [\\\"\\\\\\\"article\\\\\\\"\\\"] }}, {\\\"fquery\\\": {\\\"query\\\": {\\\"field\\\": {\\\"type\\\": {\\\"query\\\": \\\"\\\\\\\"tweet\\\\\\\"\\\"}}}}}] }}, \\\"sort\\\": [ {\\\"published\\\": {\\\"order\\\": \\\"\\\\\\\"desc\\\\\\\"\\\" }} ] }' \\n# query = '{\\\"match_all\\\": {}}'\\n# filter = '{\\\"bool\\\": {\\\"must\\\": [ {\\\"match_all\\\": {}}, {\\\"terms\\\": {\\\"_type\\\": [\\\"\\\\\\\"article\\\\\\\"\\\"] }}, {\\\"fquery\\\": {\\\"query\\\": {\\\"field\\\": {\\\"type\\\": {\\\"query\\\": \\\"\\\\\\\"tweet\\\\\\\"\\\"}}}}}] }}'\\n# sort = '[ {\\\"published\\\": {\\\"order\\\": \\\"\\\\\\\"desc\\\\\\\"\\\" }} ]'\\n# print pycurl.version_info()\\n# \\n# return tweets\\n\\n\\\"\\\"\\\"\\nRetrieve tweets using web request.\\n\\\"\\\"\\\"\\ndef retrieve_tweets():\\n url_string = 'http://vm-6123.idi.ntnu.no:9200/_all/_search?pretty'\\n query_string = '{\\\"from\\\":0, \\\"size\\\":100, \\\"query\\\": {\\\"match_all\\\": {}}, \\\"filter\\\": {\\\"bool\\\": {\\\"must\\\": [ {\\\"match_all\\\": {}}, {\\\"terms\\\": {\\\"_type\\\": [\\\"\\\\\\\"article\\\\\\\"\\\"] }}, {\\\"fquery\\\": {\\\"query\\\": {\\\"field\\\": {\\\"type\\\": {\\\"query\\\": \\\"\\\\\\\"tweet\\\\\\\"\\\"}}}}}] }}, \\\"sort\\\": [ {\\\"published\\\": {\\\"order\\\": \\\"\\\\\\\"desc\\\\\\\"\\\" }} ] }' \\n query = '{\\\"match_all\\\": {}}'\\n filter = '{\\\"bool\\\": {\\\"must\\\": [ {\\\"match_all\\\": {}}, {\\\"terms\\\": {\\\"_type\\\": [\\\"\\\\\\\"article\\\\\\\"\\\"] }}, {\\\"fquery\\\": {\\\"query\\\": {\\\"field\\\": {\\\"type\\\": {\\\"query\\\": \\\"\\\\\\\"tweet\\\\\\\"\\\"}}}}}] }}'\\n sort = '[ {\\\"published\\\": {\\\"order\\\": \\\"\\\\\\\"desc\\\\\\\"\\\" }} ]'\\n \\n params = {'from': 0,\\n 'size': 10\\n }\\n# 'query': {\\\"match_all\\\": {}},\\n# 'filter': {\\\"bool\\\": {\\\"must\\\": [ {\\\"match_all\\\": {}}, {\\\"terms\\\": {\\\"_type\\\": [\\\"article\\\"] }}, {\\\"fquery\\\": {\\\"query\\\": {\\\"field\\\": {\\\"type\\\": {\\\"query\\\": \\\"tweet\\\"}}}}}] }},\\n# 'sort': [ {\\\"published\\\": {\\\"order\\\": \\\"\\\\\\\"desc\\\\\\\"\\\" }} ]\\n# }\\n \\n data = urllib.urlencode(params)\\n request = urllib2.Request(url_string, data)\\n \\n print \\\"Request\\\" + str(request.get_data())\\n \\n response = urllib2.urlopen(request)\\n tweets = response.read()\\n \\n return tweets\\n\\ndef retrieve_tweets_by_requests():\\n url_string = 'http://vm-6123.idi.ntnu.no:9200/_all/_search?pretty'\\n query_string = '{\\\"from\\\":0, \\\"size\\\":100, \\\"query\\\": {\\\"match_all\\\": {}}, \\\"filter\\\": {\\\"bool\\\": {\\\"must\\\": [ {\\\"match_all\\\": {}}, {\\\"terms\\\": {\\\"_type\\\": [\\\"\\\\\\\"article\\\\\\\"\\\"] }}, {\\\"fquery\\\": {\\\"query\\\": {\\\"field\\\": {\\\"type\\\": {\\\"query\\\": \\\"\\\\\\\"tweet\\\\\\\"\\\"}}}}}] }}, \\\"sort\\\": [ {\\\"published\\\": {\\\"order\\\": \\\"\\\\\\\"desc\\\\\\\"\\\" }} ] }' \\n \\n \\n return tweets\\n\\n\\n#Store the tweets in a tsv with only necessary information \\ndef store_tweets():\\n file = None\\n \\nif __name__ == '__main__':\\n tweets = retrieve_tweets()\\n print \\\"twat\\\" + tweets\\n \"\n },\n {\n \"alpha_fraction\": 0.6022132039070129,\n \"alphanum_fraction\": 0.6064618229866028,\n \"avg_line_length\": 43.087337493896484,\n \"blob_id\": \"37a28314c16441cc67a2c1dc5c9afaf63d713da3\",\n \"content_id\": \"95993fa3587704bfdbdaf4b128174502d0b77270\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 10121,\n \"license_type\": \"no_license\",\n \"max_line_length\": 158,\n \"num_lines\": 229,\n \"path\": \"/models/model.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nCreated on 15. mai 2014\\n\\n@author: JohnArne\\n'''\\n\\nimport logging\\nimport features\\nimport utils\\nfrom sklearn.pipeline import Pipeline, FeatureUnion\\nfrom sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer\\nfrom sklearn.cross_validation import train_test_split, StratifiedKFold\\nfrom sklearn.feature_extraction.text import CountVectorizer\\nfrom sklearn.grid_search import GridSearchCV\\nfrom sklearn import metrics\\nimport numpy as np\\nimport scipy.sparse as sp\\nfrom sklearn.feature_extraction.dict_vectorizer import DictVectorizer\\nimport codecs\\n\\nclass Model(object):\\n \\\"\\\"\\\"\\n Class for abstracting the different classification models.\\n \\\"\\\"\\\"\\n \\n def __init__(self, train_tweets, train_targets, vect_options, tfidf_options, extra_params):\\n self.grid_params = {\\n# 'vect__ngram_range': [(1,1),(1,2),(2,2)],\\n# 'tfidf__use_idf': (True,False),\\n# 'tfidf__smooth_idf': (True, False),\\n# 'tfidf__sublinear_tf': (True, False),\\n }\\n \\n self.grid_params = dict(self.grid_params.items()+extra_params.items())\\n self.vect_options = vect_options\\n self.tfidf_options = tfidf_options\\n self.feature_set = {}\\n self.train_tweets = train_tweets\\n self.train_targets = train_targets\\n self.only_text_features = False\\n \\n def train_on_feature_set(self, cross_validate=True, use_tfidf=True):\\n \\\"\\\"\\\"\\n Performs training with the given model using the given feature set\\n \\\"\\\"\\\"\\n #Establish document text feature vectors\\n print \\\"Vectorizing\\\"\\n# self.tokenizer = CountVectorizer().build_tokenizer()\\n \\n \\n self.vect = CountVectorizer(**self.vect_options)\\n self.tfidf_transformer = TfidfTransformer(**self.tfidf_options)\\n self.dict_transformer = TfidfTransformer(**self.tfidf_options)\\n# train_counts_tf = tfidf_transformer.fit_transform(train_counts)\\n \\n count_vector = self.vect.fit_transform([t.text for t in self.train_tweets])\\n tfidf_count = self.tfidf_transformer.fit_transform(count_vector)\\n if self.only_text_features:\\n combined_vector = tfidf_count\\n else:\\n self.dict_vectorizer = DictVectorizer()\\n dict_vector = self.dict_vectorizer.fit_transform(self.feature_set)\\n \\n f=codecs.open(\\\"feature_set.txt\\\", \\\"w\\\", \\\"utf8\\\")\\n for d in dict_vector:\\n f.write(d.__str__())\\n f.close()\\n tfidf_dict = self.dict_transformer.fit_transform(dict_vector)\\n f=codecs.open(\\\"feature_set_tdidf.txt\\\", \\\"w\\\", \\\"utf8\\\")\\n for d in tfidf_dict:\\n f.write(d.__str__())\\n f.close()\\n combined_vector = sp.hstack([tfidf_count, tfidf_dict])\\n# combined_features = FeatureUnion()\\n #Crossvalidation\\n cross_validation = StratifiedKFold(self.train_targets, n_folds=10)\\n \\n #Build a Pipeline with TFidfVectorizer and classifier\\n pipeline_classifier = Pipeline([\\n# ('vect', self.vect),\\n# ('tfidf', self.tfidf_transformer),\\n ('clf', self.classifier)\\n ])\\n \\n #Perform grid search\\n print \\\"Performing grid search with classifier of instance \\\",str(self.classifier.__class__.__name__)\\n self.grid = GridSearchCV(pipeline_classifier, self.grid_params, cv=cross_validation, refit=True, n_jobs=-1,verbose=1)\\n\\n self.grid.fit(combined_vector, self.train_targets)\\n \\n self.best_estimator = self.grid.best_estimator_\\n self.best_parameters = self.grid.best_params_\\n self.best_score = self.grid.best_score_\\n \\n \\n print \\\"Results for \\\",self.classifier.__class__.__name__\\n print \\\"Best params: \\\", self.best_parameters\\n print \\\"Best score: \\\", self.best_score\\n \\n print \\\"Storing estimator... \\\"\\n utils.store_model(self.classifier.__class__.__name__, self.best_parameters, self.best_score)\\n return self.grid\\n \\n def grid_search_on_text_features(self, cross_validate=True, file_postfix=\\\"\\\"):\\n \\\"\\\"\\\"\\n Performs a grid search using text features on the given dataset. Stores the parameters for the optimal classifier.\\n \\\"\\\"\\\"\\n \\n self.grid_params = {\\n 'vect__ngram_range': [(1,1),(1,2),(2,2),(1,3),(2,3),(3,3),(1,4)],\\n 'vect__use_idf': (True,False),\\n 'vect__smooth_idf': (True, False),\\n 'vect__sublinear_tf': (True, False),\\n 'vect__max_df': (0.5,),\\n }\\n self.vect = TfidfVectorizer()\\n\\n cross_validation = StratifiedKFold(self.train_targets, n_folds=10)\\n \\n #Build a Pipeline with TFidfVectorizer and classifier\\n pipeline_classifier = Pipeline([\\n ('vect', self.vect),\\n ('clf', self.classifier)]\\n )\\n \\n #Perform grid search\\n print \\\"Performing grid search with classifier of instance \\\",str(self.classifier.__class__.__name__)\\n self.grid = GridSearchCV(pipeline_classifier, self.grid_params, cv=cross_validation, refit=True, n_jobs=-1,verbose=1)\\n\\n self.grid.fit([t.text for t in self.train_tweets], self.train_targets)\\n \\n self.best_estimator = self.grid.best_estimator_\\n self.best_parameters = self.grid.best_params_\\n self.best_score = self.grid.best_score_\\n \\n \\n print \\\"Results for \\\",self.classifier.__class__.__name__\\n print \\\"Best params: \\\", self.best_parameters\\n print \\\"Best score: \\\", self.best_score\\n \\n print \\\"Storing estimator... \\\" \\n utils.store_model(self.classifier.__class__.__name__, self.best_parameters, self.best_score, file_postfix=file_postfix)\\n return self.grid\\n\\n def classify(self, tweets, sentimentvalues=None):\\n \\\"\\\"\\\"\\n Performs the classification process on list of tweets.\\n \\\"\\\"\\\"\\n if sentimentvalues!=None:\\n self.test_words_and_values = sentimentvalues\\n count_vector = self.vect.transform([t.text for t in tweets])\\n tfidf_count = self.tfidf_transformer.transform(count_vector)\\n if self.only_text_features:\\n combined_vector = tfidf_count\\n else:\\n dict_vector = self.dict_vectorizer.transform([features.get_feature_set(t, self.featureset, v) for t,v in zip(tweets, self.test_words_and_values)])\\n tfidf_dict = self.dict_transformer.transform(dict_vector)\\n combined_vector = sp.hstack([tfidf_count, tfidf_dict])\\n \\n predictions = self.best_estimator.predict(combined_vector)\\n\\n return predictions\\n\\n def classify_text(self, texts):\\n \\\"\\\"\\\"\\n Performs classification with only text features.\\n \\\"\\\"\\\"\\n \\n count_vector = self.vect.transform([t for t in texts])\\n text_vector = self.tfidf_transformer.transform(count_vector)\\n predictions = self.best_estimator.predict(text_vector)\\n\\n return predictions\\n \\n def test_and_return_results(self, test_tweets, test_targets, sentimentvalues):\\n \\\"\\\"\\\"\\n Tests the classifier on a given test set, and returns the accuracy, precision, recall, and f1 score.\\n \\\"\\\"\\\"\\n self.test_words_and_values = sentimentvalues\\n predictions = self.classify(test_tweets)\\n binary_predictions = utils.reduce_targets(predictions)\\n binary_test_targets = utils.reduce_targets(test_targets)\\n \\n accuracy = metrics.accuracy_score(binary_test_targets, binary_predictions)\\n precision = metrics.precision_score(binary_test_targets, binary_predictions)\\n recall = metrics.recall_score(binary_test_targets, binary_predictions)\\n f1_score = metrics.f1_score(binary_test_targets, binary_predictions)\\n print \\\"Scores: \\\", accuracy, precision, recall, f1_score\\n \\n return accuracy, precision, recall, f1_score\\n \\n def get_correctly_classified_tweets(self, tweets_and_sentiment):\\n \\\"\\\"\\\"\\n Classifies the given set of tweets and returns the ones that were correctly classified.\\n \\\"\\\"\\\"\\n tweets, sentimentvalues = zip(*tweets_and_sentiment)\\n if sentimentvalues!=None:\\n self.test_words_and_values = sentimentvalues\\n count_vector = self.vect.transform([t.text for t in tweets])\\n tfidf_count = self.tfidf_transformer.transform(count_vector)\\n if self.only_text_features:\\n combined_vector = tfidf_count\\n else:\\n dict_vector = self.dict_vectorizer.transform([features.get_feature_set(t, self.featureset, v) for t,v in zip(tweets, self.test_words_and_values)])\\n tfidf_dict = self.dict_transformer.transform(dict_vector)\\n combined_vector = sp.hstack([tfidf_count, tfidf_dict])\\n \\n predictions = self.best_estimator.predict(combined_vector)\\n tweets, targets = utils.make_subjectivity_targets(tweets)\\n #return the tweets where the target match prediction\\n correct_tweets = []\\n correct_sentimentvalues = []\\n for i in xrange(len(tweets)):\\n if predictions[i]==targets[i]:\\n correct_tweets.append(tweets[i])\\n correct_sentimentvalues.append(sentimentvalues[i])\\n return correct_tweets, correct_sentimentvalues\\n \\n def set_feature_set(self, featureset, sentimentvalues):\\n \\\"\\\"\\\"\\n Extracts and stores the given feature set for classification.\\n \\\"\\\"\\\"\\n self.featureset = featureset\\n if featureset=='SA' or featureset=='PA':\\n self.only_text_features=True\\n self.feature_set = {}\\n else:\\n words_and_values = sentimentvalues\\n self.feature_set = [features.get_feature_set(t, self.featureset, v) for t,v in zip(self.train_tweets,words_and_values)]\\n \\n \"\n },\n {\n \"alpha_fraction\": 0.6438046097755432,\n \"alphanum_fraction\": 0.6516290903091431,\n \"avg_line_length\": 38.51690673828125,\n \"blob_id\": \"000804fd2ef46d4a25db834e77092f6815a1dfac\",\n \"content_id\": \"3b7d3ff05cb4b5ed36b8f9d7bd45bed58b029172\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 16359,\n \"license_type\": \"no_license\",\n \"max_line_length\": 195,\n \"num_lines\": 414,\n \"path\": \"/utils.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nCreated on 12. mars 2014\\n\\n@author: JohnArne\\n'''\\nimport sys\\nimport os\\nimport json\\nfrom pprint import pprint\\nimport csv\\nimport codecs\\nimport pickle\\nimport random\\nimport operator\\n\\ndef load_to_tsv():\\n \\\"\\\"\\\"\\n Loads tweets from site and store as tsv file.\\n \\\"\\\"\\\"\\n json_data = open(\\\"data/curl_twitterdata.json\\\")\\n data = json.load(json_data)\\n tweets = [ x[\\\"_source\\\"][\\\"published\\\"]+str(\\\"\\\\t\\\")+x[\\\"_source\\\"][\\\"publisher\\\"]+str(\\\"\\\\t\\\")+x[\\\"_source\\\"][\\\"leadText\\\"] for x in data[\\\"hits\\\"][\\\"hits\\\"] ]\\n for tweet in tweets:\\n print tweet\\n print len(tweets)\\n out = csv.writer(open(\\\"data/dataset.tsv\\\",\\\"w\\\"), delimiter=\\\"\\\\n\\\", quoting=csv.QUOTE_MINIMAL)\\n out.writerow(tweets)\\n json_data.close()\\n\\ndef get_resultsets_text(results):\\n \\\"\\\"\\\"\\n Takes a results list and return a list of test strings\\n \\\"\\\"\\\"\\n return [unicode(x.created_at) +str(\\\"\\\\t\\\")+ unicode(x.user.screen_name) +(\\\"\\\\t\\\")+ unicode(x.text).replace(\\\"\\\\n\\\", \\\" \\\") for x in results]\\n\\ndef get_tweets_text(tweets):\\n \\\"\\\"\\\"\\n Returns a list of text bodies for the given set of tweets.\\n \\\"\\\"\\\"\\n return [unicode(tweet.text) for tweet in tweets]\\n\\ndef append_to_dataset(text, dataset):\\n \\\"\\\"\\\"\\n Appends text instances to dataset.\\n \\\"\\\"\\\"\\n# sys.stdout = codecs.getwriter('utf8')(sys.stdout)\\n print \\\"Appending to dataset: \\\"+str(dataset)\\n f = open(dataset, \\\"a\\\")\\n for t in text:\\n try:\\n f.write(t.encode('utf8')+\\\"\\\\n\\\")\\n print t.encode('utf8')+\\\"\\\\n\\\"\\n except UnicodeEncodeError:\\n print \\\"Unicode Encoding Error: \\\", t.encode('utf8')\\n except UnicodeDecodeError:\\n print \\\"Unicode Decoding Error: \\\", t.encode('utf8')\\n f.close()\\n \\n \\ndef store_dataset(text, dataset):\\n \\\"\\\"\\\"\\n Stores the given sequence of strings to the given dataset as .tsv file.\\n \\\"\\\"\\\"\\n print \\\"Storing to dataset: \\\"+str(dataset)\\n f = open(dataset, \\\"w\\\")\\n for t in text:\\n# print unicode(\\\"Encoding: \\\")\\n# print unicode(t, 'cp866')\\n# encodedline = unicode(t, 'cp866').encode('utf8')\\n# print \\\"Writing: \\\"+encodedline\\n try:\\n f.write(t.encode('utf8'))\\n except UnicodeDecodeError:\\n f.write(t)\\n f.close()\\n \\ndef encode_unicode():\\n \\\"\\\"\\\"\\n Encodes all text files into utf8.\\n \\\"\\\"\\\"\\n f = open(\\\"complete_datasets/random_dataset.tsv\\\", \\\"r\\\")\\n text = f.readlines()\\n f.close()\\n f = open(\\\"encoding_attempt/random_dataset.tsv\\\", \\\"w\\\")\\n for line in text:\\n line = line.decode('ascii')\\n f.write(line.encode('utf8')+\\\"\\\\n\\\")\\n f.close()\\n \\ndef select_dataset():\\n setnr = raw_input(\\\"Write to which dataset? 0: RandomSet 1: RoseborgSet 2: ErnaSet ... \\\")\\n return datasets[int(setnr)]\\n\\ndef select_complete_dataset():\\n setnr = raw_input(\\\"Write to which complete dataset? 0: RandomSet 1: RoseborgSet 2: ErnaSet 3: TemporalSet... \\\")\\n return complete_datasets[int(setnr)]\\n\\n\\ndef get_dataset(dataset):\\n \\\"\\\"\\\"\\n Gets the given dataset from file as a list of strings.\\n \\\"\\\"\\\"\\n f = open(dataset, \\\"r\\\")\\n lines = f.readlines()\\n encodedlines = []\\n for line in lines:\\n encodedlines.append(line)\\n f.close()\\n return encodedlines\\n\\ndef store_pickles(tweets, filepath):\\n \\\"\\\"\\\"\\n Stores a given list of tweets as pickles.\\n \\\"\\\"\\\"\\n output = open(\\\"tweet_pickles/\\\"+filepath, 'wb')\\n pickle.dump(tweets, output)\\n \\ndef get_pickles(setnr=None):\\n \\\"\\\"\\\"\\n Gets the stored tweet pickles.\\n \\\"\\\"\\\"\\n if setnr==None:\\n setnr = int(raw_input(\\\"Get which pickle set? 0: RandomSet 1: RoseborgSet 2: ErnaSet 3: All three ...\\\"))\\n \\n if setnr is 3:\\n #fetch all sets and append them together\\n tweets = []\\n for pickleset in pickles:\\n tweets = tweets + pickle.load(open(pickleset, 'rb'))\\n print len(tweets)\\n return tweets\\n else:\\n tweets = pickle.load(open(pickles[setnr], 'rb'))\\n return tweets\\n \\n return tweets\\n\\ndef get_all_pickles():\\n \\\"\\\"\\\"\\n Gets ALL the stored tweet pickles.\\n \\\"\\\"\\\"\\n tweets = []\\n for pickleset in pickles:\\n tweets = tweets + pickle.load(open(pickleset, 'rb'))\\n tweets = tweets + pickle.load(open('temporal_tweets1', 'rb'))\\n tweets = tweets + pickle.load(open('temporal_tweets2', 'rb'))\\n print len(tweets)\\n return tweets\\n \\ndef limit_topics_top10(data):\\n \\\"\\\"\\\"\\n Takes in a set of plotting data, and limits the topics to top 10 most frequent.\\n \\\"\\\"\\\"\\n \\n\\ndef split_train_and_test(tweets):\\n \\\"\\\"\\\"\\n Splits the given tweet set into a training set and a testing set.\\n \\\"\\\"\\\"\\n split_pos = int(len(tweets)*0.8)\\n train_tweets = tweets[0:split_pos]\\n test_tweets = tweets[split_pos:len(tweets)]\\n return train_tweets, test_tweets\\n\\ndef make_polarity_train_and_test_and_targets(tweets, sentimentvalues, splitvalue=0.9, reduce_dataset=1, shuffle=True):\\n \\\"\\\"\\\"\\n Removes objective tweets and returns a completely subjective dataset, along with the positive or negative targets.\\n \\\"\\\"\\\"\\n \\n pol_tweets = []\\n pol_sentiments = []\\n if shuffle:\\n tweets, sentimentvalues = shuffle_tweets_and_sentiments(tweets, sentimentvalues)\\n for t,s in zip(tweets, sentimentvalues):\\n if t.subjectivity==1:\\n pol_tweets.append(t)\\n pol_sentiments.append(s)\\n pol_tweets = pol_tweets[:int(round(reduce_dataset*len(pol_tweets)))] \\n pol_sentiments = pol_sentiments[:int(round(reduce_dataset*len(pol_sentiments)))]\\n up_to = int(round(len(pol_tweets)*(splitvalue+0.1)))\\n split_pos = int(round(len(pol_tweets)*splitvalue))\\n train_tweets = pol_tweets[0:split_pos]+pol_tweets[up_to:len(pol_tweets)]\\n test_tweets = pol_tweets[split_pos:up_to]\\n train_sentimentvalues = pol_sentiments[0:split_pos]+pol_sentiments[up_to:len(pol_tweets)]\\n test_sentimentvalues = pol_sentiments[split_pos:up_to]\\n \\n pol_train_targets = [t.get_sentiment() for t in train_tweets]\\n pol_test_targets = [t.get_sentiment() for t in test_tweets]\\n print \\\"Train tweets: \\\", len(train_tweets)\\n print \\\"test tweeets: \\\", len(test_tweets) \\n print \\\"Train targets: \\\", len(pol_train_targets)\\n print \\\"test targets \\\", len(pol_test_targets)\\n print \\\"train sentiments \\\", len(train_sentimentvalues)\\n print \\\"test sentiments \\\", len(test_sentimentvalues)\\n return train_tweets, pol_train_targets, test_tweets, pol_test_targets, train_sentimentvalues, test_sentimentvalues\\n \\ndef make_subjectivity_train_and_test_and_targets(tweets, sentimentvalues, splitvalue=0.9, reduce_dataset=1,shuffle=True):\\n \\\"\\\"\\\"\\n Returns a dataset for subjectivity classification, along with the targets for classification\\n \\\"\\\"\\\"\\n if shuffle:\\n tweets, sentimentvalues = shuffle_tweets_and_sentiments(tweets, sentimentvalues)\\n reduced_tweets = tweets[:int(round(reduce_dataset*len(tweets)))] \\n up_to = int(round(len(reduced_tweets)*(splitvalue+0.1)))\\n split_pos = int(round(len(reduced_tweets)*splitvalue))\\n \\n print \\\"Upto:\\\",up_to\\n print \\\"Splitpos:\\\",split_pos\\n train_tweets = reduced_tweets[:split_pos]+reduced_tweets[up_to:len(reduced_tweets)]\\n test_tweets = reduced_tweets[split_pos:up_to]\\n train_sentimentvalues = sentimentvalues[0:split_pos]+sentimentvalues[up_to:len(reduced_tweets)]\\n test_sentimentvalues = sentimentvalues[split_pos:up_to]\\n print \\\"Train reduced_tweets: \\\", len(train_tweets)\\n print \\\"test tweeets: \\\", len(test_tweets) \\n \\n sub_train_targets = ['objective' if t.subjectivity==0 else 'subjective' for t in train_tweets]\\n sub_test_targets = ['objective' if t.subjectivity==0 else 'subjective' for t in test_tweets]\\n print \\\"Train targets: \\\", len(sub_train_targets)\\n print \\\"test targets \\\", len(sub_test_targets)\\n return train_tweets, sub_train_targets, test_tweets, sub_test_targets, train_sentimentvalues, test_sentimentvalues\\n\\n\\ndef shuffle_tweets_and_sentiments(tweets, sentiments):\\n indexes = range(len(tweets))\\n random.shuffle(indexes)\\n \\n shuffled_tweets = []\\n shuffled_sentiments = []\\n for i in indexes:\\n shuffled_sentiments.append(sentiments[i])\\n shuffled_tweets.append(tweets[i])\\n \\n return shuffled_tweets, shuffled_sentiments \\n \\n \\ndef make_subjectivity_targets(tweets):\\n sub_train_targets = ['objective' if t.subjectivity==0 else 'subjective' for t in tweets]\\n return tweets, sub_train_targets\\n \\ndef make_polarity_targets(tweets):\\n pol_train_targets = [t.get_sentiment() for t in tweets]\\n return tweets, pol_train_targets\\n \\ndef store_model(name, params, score, file_postfix=\\\"\\\"):\\n \\\"\\\"\\\"\\n Stores the given dict as a pickle in the stored estimators folder.\\n \\\"\\\"\\\"\\n out = open(\\\"stored_estimators/\\\"+str(name)+str(score)+str(file_postfix), 'wb')\\n pickle.dump(params, out)\\n out.close()\\n return params\\n\\ndef store_sentimentvalues(words_with_values, filename):\\n \\\"\\\"\\\"\\n Pickles the given list of dicts with sentiment values.\\n \\\"\\\"\\\"\\n #Pickle sentiment values\\n output = open(filename, 'wb')\\n pickle.dump(words_with_values, output)\\n\\n\\n\\ndef get_sentimentvalues(setnr=None):\\n \\\"\\\"\\\"\\n Gets the pickles of sentiment values\\n \\\"\\\"\\\"\\n if setnr==None:\\n setnr = int(raw_input(\\\"Get which pickle set? 0: RandomSet 1: RoseborgSet 2: ErnaSet 3: All three ...\\\"))\\n \\n if setnr is 3:\\n #fetch all sets and append them together\\n tweets = []\\n for pickleset in sentiment_pickles:\\n tweets = tweets + pickle.load(open(pickleset, 'rb'))\\n return tweets\\n else:\\n tweets = pickle.load(open(sentiment_pickles[setnr], 'rb'))\\n return tweets\\n \\n return tweets\\n \\ndef get_entity_test_and_targets():\\n \\\"\\\"\\\"\\n Fetches the dataset for entity testing, aswell as the proper targets.\\n \\\"\\\"\\\"\\n f = open(\\\"entity_test\\\",\\\"rb\\\")\\n tweets = pickle.load(f)\\n# for t in tweets:\\n# print t.text,\\\" \\\",t.hashtags\\n# raw_input(\\\"Continue?\\\")\\n print len(tweets)\\n f.close()\\n f = open(\\\"entity_test_targets.txt\\\",\\\"r\\\")\\n targets = f.readlines()\\n print len(targets)\\n targets = [int(t) for t in targets]\\n return tweets, targets\\n \\ndef temporally_aggregate_subjectivity(tweets, predictions, targets=None, topics=None):\\n \\\"\\\"\\\"\\n Aggregates subjectivity for given tweets' days for both correct targets and predictions.\\n Returns a list with days and a list with tweet frequencies, and a list with aggregated target values and a list with aggregated predicted values\\n \\\"\\\"\\\"\\n# for t in tweets:\\n# print t.timestamp\\n days = [t.timestamp[5:10].replace('-','.') if len(t.timestamp)<20 else t.timestamp[8:13].replace('-','.') for t in tweets]\\n reduced_targets = reduce_targets(targets) if targets != None else None\\n reduced_predictions = reduce_targets(predictions)\\n sorted_days =sorted( list(set( [float(x) for x in days] )) )\\n aggregated_targets = [ 0 for _ in sorted_days]\\n aggregated_predicts = [ 0 for _ in sorted_days]\\n frequencies = [ 0 for _ in sorted_days]\\n for i in range(len(sorted_days)):\\n aggregated_targets[i] = reduce(lambda x,y: x+y, [t if float(d)==sorted_days[i] else 0 for t,d in zip(reduced_targets, days)] ) if reduced_targets!=None else None\\n aggregated_predicts[i] = reduce(lambda x,y: x+y, [t if float(d)==sorted_days[i] else 0 for t,d in zip(reduced_predictions, days)] )\\n frequencies[i] = reduce(lambda x,y: x+y, [1 if float(d)==sorted_days[i] else 0 for t,d in zip(reduced_predictions, days)] )\\n# print days\\n print sorted_days, aggregated_targets, aggregated_predicts, frequencies\\n return sorted_days, aggregated_targets, aggregated_predicts, frequencies\\n\\ndef temporally_aggregate_polarity(tweets, predictions, targets=None, topics=None):\\n \\\"\\\"\\\"\\n Aggregates(calculates difference) polarity for given tweets' days for both predictions, and targets if given, and topics if given.\\n \\\"\\\"\\\"\\n# for t in tweets:\\n# print t.timestamp\\n days = [t.timestamp[5:10].replace('-','.') if len(t.timestamp)<20 else t.timestamp[8:13].replace('-','.') for t in tweets]\\n if targets!=None:\\n reduced_targets = reduce_targets(targets)\\n reduced_targets = [-1 if t==0 else 1 for t in reduced_targets]\\n else:\\n reduced_targets = None\\n reduced_predictions = reduce_targets(predictions)\\n reduced_predictions = [-1 if t==0 else 1 for t in reduced_predictions]\\n print topics\\n sorted_days =sorted( list(set( [float(x) for x in days] )) )\\n aggregated_targets = [ 0 for _ in sorted_days]\\n aggregated_predicts = [ 0 for _ in sorted_days]\\n frequencies = [ 0 for _ in sorted_days]\\n unique_topics = list(set(topics)) if topics!=None else None\\n print unique_topics\\n aggregated_polarity_on_topic = []\\n for i in range(len(sorted_days)):\\n aggregated_targets[i] = reduce(lambda x,y: x+y, [t if float(d)==sorted_days[i] else 0 for t,d in zip(reduced_targets, days)] ) if reduced_targets!=None else None\\n aggregated_predicts[i] = reduce(lambda x,y: x+y, [t if float(d)==sorted_days[i] else 0 for t,d in zip(reduced_predictions, days)] )\\n frequencies[i] = reduce(lambda x,y: x+y, [1 if float(d)==sorted_days[i] else 0 for t,d in zip(reduced_predictions, days)] )\\n if unique_topics!=None:\\n for i in range(len(unique_topics)):\\n aggregated_polarity_on_topic.append(reduce(lambda x,y: x+y, [t if float(d)==sorted_days[i] and top==unique_topics[i] else 0 for t,d,top in zip(reduced_predictions,days,topics)] ))\\n \\n# print days\\n print sorted_days\\n print aggregated_polarity_on_topic\\n return sorted_days, aggregated_targets, aggregated_predicts, frequencies, topics, aggregated_polarity_on_topic\\n \\ndef topically_aggregate_polarity(tweets, predictions, topics):\\n days = [t.timestamp[5:10].replace('-','.') if len(t.timestamp)<20 else t.timestamp[8:13].replace('-','.') for t in tweets]\\n reduced_predictions = reduce_targets(predictions)\\n reduced_predictions = [-1 if t==0 else 1 for t in reduced_predictions]\\n sorted_days =sorted( list(set( [float(x) for x in days] )) )\\n unique_topics = list(set(topics))\\n aggregated_polarity_on_topic = [[] for _ in unique_topics]\\n sentimentpoints = []\\n for i in range(len(unique_topics)):\\n for j in range(len(sorted_days)):\\n aggregated_polarity_on_topic[i].append(reduce(lambda x,y: x+y, [t if float(d)==sorted_days[j] and top==unique_topics[i] else 0 for t,d,top in zip(reduced_predictions,days,topics)] ))\\n sentimentpoints.append(reduce(lambda x,y: x+y, [-p if p<0 else p for p in aggregated_polarity_on_topic[i]]))\\n unique_topics[unique_topics.index(None)] = \\\"undefined\\\" \\n print unique_topics\\n print sorted_days\\n print aggregated_polarity_on_topic\\n unique_topics, aggregated_polarity_on_topic, sentimentpoints = zip(*sorted(zip(unique_topics,aggregated_polarity_on_topic,sentimentpoints), key=operator.itemgetter(2), reverse=True))\\n return sorted_days, unique_topics[:20], aggregated_polarity_on_topic[:20]\\n \\ndef reduce_targets(targets):\\n \\\"\\\"\\\"\\n Reduces a set of subjectivity or polarity targets to 1s and 0s\\n \\\"\\\"\\\" \\n if len(targets)<1: return []\\n if targets[0]=='objective' or targets[0]=='subjective':\\n binaries = [0 if target=='objective' else 1 for target in targets]\\n else:\\n binaries = [0 if target=='negative' else 1 for target in targets]\\n return binaries\\n\\npickles = ['tweet_pickles/random_dataset',\\n 'tweet_pickles/rosenborg_dataset',\\n 'tweet_pickles/erna_dataset']\\n\\nsentiment_pickles = ['models/sentimentvalues_random_dataset',\\n 'models/sentimentvalues_rosenborg_dataset',\\n 'models/sentimentvalues_erna_dataset']\\n \\nsentiments = [\\\"negative\\\",\\n \\\"neutral\\\",\\n \\\"positive\\\"]\\n\\ncomplete_datasets = [\\\"complete_datasets/random_dataset.tsv\\\",\\n \\\"complete_datasets/rosenborg_dataset.tsv\\\",\\n \\\"complete_datasets/erna_dataset.tsv\\\",\\n \\\"complete_datasets/temporal_dataset.tsv\\\"]\\n\\ndatasets = [\\\"data/random_dataset.tsv\\\",\\n \\\"data/rosenborg_dataset.tsv\\\",\\n \\\"data/erna_dataset.tsv\\\"] \\n\\nannotated_datasets = [\\\"johnarne_annotated_data/random_dataset.tsv\\\",\\n \\\"johnarne_annotated_data/rosenborg_dataset.tsv\\\",\\n \\\"johnarne_annotated_data/erna_dataset.tsv\\\"] \\n\\nif __name__ == '__main__':\\n train, test = split_train_and_test(get_pickles())\\n \\n print len(train),\\\" \\\", len(test)\"\n },\n {\n \"alpha_fraction\": 0.625668466091156,\n \"alphanum_fraction\": 0.6613190770149231,\n \"avg_line_length\": 34.125,\n \"blob_id\": \"fdf0a95e4dd82505a325a6a0b1820da021e378eb\",\n \"content_id\": \"3be5e430a476982e6d610932c7ce2bd1a5ffb2a9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 561,\n \"license_type\": \"no_license\",\n \"max_line_length\": 105,\n \"num_lines\": 16,\n \"path\": \"/models/me.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nCreated on 19. mars 2014\\n\\n@author: JohnArne\\n'''\\nfrom model import Model\\nfrom sklearn.linear_model import LogisticRegression\\n\\nclass ME(Model):\\n \\\"\\\"\\\"\\n Subclass implementing the Maximum entropy classification model.\\n \\\"\\\"\\\"\\n def __init__(self, tweets_train, tweets_targets, vect_options, tfidf_options):\\n self.classifier = LogisticRegression()\\n extra_params = {'clf__C': (0.1, 0.3, 0.5, 0.7, 0.8, 1.0,),'clf__penalty': ('l1', 'l2')}\\n super(ME, self).__init__(tweets_train, tweets_targets, vect_options, tfidf_options, extra_params)\"\n },\n {\n \"alpha_fraction\": 0.5098039507865906,\n \"alphanum_fraction\": 0.6078431606292725,\n \"avg_line_length\": 9.199999809265137,\n \"blob_id\": \"757df61deeff2230942f4bfee7e3ae6242430d51\",\n \"content_id\": \"a0703a41b6e1f2a81a42f7a6ffbdcca5ba18a8e4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 51,\n \"license_type\": \"no_license\",\n \"max_line_length\": 23,\n \"num_lines\": 5,\n \"path\": \"/lexicon/__init__.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nCreated on 3. des. 2014\\n\\n@author: JohnArne\\n'''\\n\"\n },\n {\n \"alpha_fraction\": 0.6046240329742432,\n \"alphanum_fraction\": 0.606959342956543,\n \"avg_line_length\": 34.388431549072266,\n \"blob_id\": \"294b804ba0b031faadb3362bad5681b9c404aa03\",\n \"content_id\": \"aa414f33ffd0e00494102efce01229ff2953ddfa\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4282,\n \"license_type\": \"no_license\",\n \"max_line_length\": 104,\n \"num_lines\": 121,\n \"path\": \"/easygui_gui.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nCreated on 19. nov. 2014\\n\\n@author: JohnArne\\n'''\\n\\nimport easygui as eg\\nimport sys\\n \\ndef show_windows():\\n while 1:\\n# title = \\\"Message from test1.py\\\"\\n# eg.msgbox(\\\"Hello, world!\\\", title)\\n \\n msg =\\\"Run with which classification model?\\\"\\n title = \\\"Classification model\\\"\\n models = [\\\"Multinomial Naive Bayes\\\", \\\"Support Vector Machines\\\", \\\"Maximum Entropy\\\"]\\n model_choice = str(eg.choicebox(msg, title, models))\\n \\n msg = \\\"Use saved preset values?\\\"\\n choices = [\\\"Yes\\\",\\\"No\\\"]\\n choice = eg.buttonbox(msg,choices=choices)\\n if str(choice)==\\\"Yes\\\":\\n model_preset_functions[model_choice]()\\n else:\\n model_select_functions[model_choice]()\\n \\n # note that we convert choice to string, in case\\n # the user cancelled the choice, and we got None.\\n# eg.msgbox(\\\"You chose: \\\" + str(choice), \\\"Survey Result\\\")\\n message = \\\"Sentiments over time period something something\\\"\\n image = \\\"temporal_sentiments.png\\\"\\n eg.msgbox(message, image=image)\\n \\n msg = \\\"Do you want to continue?\\\"\\n title = \\\"Please Confirm\\\"\\n if eg.ccbox(msg, title): # show a Continue/Cancel dialog\\n pass # user chose Continue\\n else:\\n sys.exit(0) # user chose Cancel\\n \\ndef show_naivebayes_presets():\\n \\\"\\\"\\\"\\n Shows a selection of preset running values for Naive Bayes and returns the user selection.\\n \\\"\\\"\\\"\\n msg =\\\"Select preset values for Naive Bayes\\\"\\n title = \\\"Naive Bayes presets\\\"\\n choices = [\\\"Multinomial Naive Bayes\\\", \\\"Support Vector Machines\\\", \\\"Maximum Entropy\\\"]\\n preset_choice = eg.choicebox(msg, title, choices)\\n pass\\n\\ndef show_svm_presets():\\n \\\"\\\"\\\"\\n Shows a selection of preset running values for Suport Vector Machine and returns the user selection.\\n \\\"\\\"\\\"\\n msg =\\\"Select preset values for Support Vector Machines\\\"\\n title = \\\"SVM presets\\\"\\n choices = [\\\"something\\\", \\\"somethingsomething\\\", \\\"something else\\\"]\\n preset_choice = eg.choicebox(msg, title, choices)\\n pass\\n\\ndef show_me_presets():\\n \\\"\\\"\\\"\\n Shows a selection of preset running values for Maximum Entropy and returns the user selection.\\n \\\"\\\"\\\"\\n msg =\\\"Select preset values for Maximum Entropy\\\"\\n title = \\\"MaxEnt presets\\\"\\n choices = [\\\"something\\\", \\\"something else\\\", \\\"aaand more\\\"]\\n preset_choice = eg.choicebox(msg, title, choices)\\n pass\\n\\ndef show_naivebayes_selection():\\n \\\"\\\"\\\"\\n Shows a value input window for Naive Bayes and returns the user selection.\\n \\\"\\\"\\\"\\n msg = \\\"Enter running values for Naive Bayes\\\"\\n title = \\\"Naive Bayes run\\\"\\n fieldNames = [\\\"x\\\",\\\"dss\\\",\\\"c\\\",\\\"range\\\",\\\"s\\\",\\\"p\\\",\\\"cross\\\",\\\"stu\\\",\\\"thn\\\",\\\"pH\\\"]\\n fieldValues = [] # we start with blanks for the values\\n fieldValues = eg.multenterbox(msg,title, fieldNames)\\n \\n # make sure that none of the fields was left blank\\n while 1: # do forever, until we find acceptable values and break out\\n if fieldValues == None: \\n break\\n errmsg = \\\"\\\"\\n \\n # look for errors in the returned values\\n for i in range(len(fieldNames)):\\n if fieldValues[i].strip() == \\\"\\\":\\n errmsg = errmsg + ('\\\"%s\\\" is a required field.\\\\n\\\\n' % fieldNames[i])\\n \\n if errmsg == \\\"\\\": \\n break # no problems found\\n else:\\n # show the box again, with the errmsg as the message \\n fieldValues = eg.multenterbox(errmsg, title, fieldNames, fieldValues)\\n \\n print (\\\"Reply was:\\\", fieldValues)\\n pass\\n\\ndef show_svm_selection():\\n \\\"\\\"\\\"\\n Shows a value input window for Suport Vector Machine and returns the user selection.\\n \\\"\\\"\\\"\\n pass\\n\\ndef show_me_selection():\\n \\\"\\\"\\\"\\n Shows a value input window for Maximum Entropy and returns the user selection.\\n \\\"\\\"\\\"\\n pass\\n\\n\\nmodel_preset_functions = {\\\"Multinomial Naive Bayes\\\": show_naivebayes_presets,\\n \\\"Support Vector Machines\\\": show_svm_presets,\\n \\\"Maximum Entropy\\\": show_me_presets}\\n\\nmodel_select_functions = {\\\"Multinomial Naive Bayes\\\": show_naivebayes_selection,\\n \\\"Support Vector Machines\\\": show_svm_selection,\\n \\\"Maximum Entropy\\\": show_me_selection}\\n\"\n },\n {\n \"alpha_fraction\": 0.6134738326072693,\n \"alphanum_fraction\": 0.619997501373291,\n \"avg_line_length\": 36.78199005126953,\n \"blob_id\": \"42a9657c4e46be69a1ad152dc38c241864421a85\",\n \"content_id\": \"57e186549570bf2e015c8057639554551e0282fc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 7971,\n \"license_type\": \"no_license\",\n \"max_line_length\": 150,\n \"num_lines\": 211,\n \"path\": \"/lexicon/lexicon.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nCreated on 27. nov. 2014\\n\\n@author: JohnArne\\n'''\\nfrom hmac import trans_36\\nimport requests\\nimport os\\nfrom sentiwordnet import SentiWordNetCorpusReader, SentiSynset\\nimport nltk\\nfrom pos_mappings import TYPECRAFT_SENTIWORDNET\\nimport gettext\\nimport codecs\\nimport subprocess\\nimport pickle\\n\\nclass Lexicon():\\n \\\"\\\"\\\"\\n Handles the interfacing with the sentiment lexicon as well as translation and disambiguation.\\n \\\"\\\"\\\"\\n \\n def __init__(self, translater, sentiment_lexicon):\\n #initialize sentiment lexicon resource and translation\\n self.translater = translater\\n self.sentiment_lexicon = sentiment_lexicon\\n \\n def translate_and_get_lexicon_sentiment(self, word, context=None, pos_tag=None):\\n \\\"\\\"\\\"\\n Returns the translated sentiment values for all the words with their contexts and pos tags.\\n \\\"\\\"\\\"\\n #Translate word\\n translated_word = self.translater.translate(word)\\n return self.sentiment_lexicon.get_values(translated_word, context, pos_tag)\\n \\n def translate_sentence_and_get_lexicon_sentiment(self, sentence):\\n \\\"\\\"\\\"\\n Returns the translated sentiment values for a whole sentence.\\n \\\"\\\"\\\"\\n #Translate word\\n translated_sentence = self.translater.translate(sentence)\\n translated_words = tokenizer(translated_sentence)\\n sentiments = []\\n for word in translated_words:\\n sentiment = self.sentiment_lexicon.get_values(word)\\n if sentiment!=None:\\n sentiments.append(sentiment)\\n return sentiments\\n \\nclass SentiWordNetLexicon():\\n \\n def __init__(self):\\n SWN_FILENAME = \\\"lexicon\\\\SentiWordNet_3.0.0_20130122.txt\\\"\\n self.swn= SentiWordNetCorpusReader(SWN_FILENAME)\\n\\n def get_values(self, word, context=None, pos_tag=None):\\n \\\"\\\"\\\"\\n Perform lookup in SentiWordNet\\n \\\"\\\"\\\"\\n# entry = swn.senti_synset(\\\"breakdown.n.03\\\")\\n entries = None\\n for w in word.split(' '):\\n entries = self.swn.senti_synsets(w)\\n if entries != None: break\\n if entries is None or len(entries)==0: \\n return None\\n if len(entries)==1 or pos_tag is None:\\n return [entries[0].pos_score, entries[0].neg_score, entries[0].obj_score]\\n elif len(entries)>1:\\n #Find out which word to chose, if there are several classes\\n print \\\"Several entries \\\",entries\\n for entry in entries:\\n if entry.synset.pos()==TYPECRAFT_SENTIWORDNET[pos_tag]:\\n print \\\"Found matching entry: \\\", entry\\n return [entry.pos_score, entry.neg_score, entry.obj_score]\\n \\n return [entries[0].pos_score, entries[0].neg_score, entries[0].obj_score]\\n return None\\n\\nclass BingTranslater():\\n \\n def __init__(self, words):\\n self.original_words = words\\n file = codecs.open(\\\"bing_words.txt\\\", \\\"w\\\", \\\"utf8\\\")\\n for word in words:\\n file.write(word+\\\"\\\\n\\\")\\n file.close()\\n \\n print \\\"Bing translating \\\",len(words),\\\" words...\\\"\\n subprocess.call(\\\"lexicon/bingtranslater.exe\\\")\\n file = codecs.open(\\\"translated_words.txt\\\", \\\"r\\\", \\\"utf8\\\")\\n translated_words = file.readlines()\\n file.close()\\n self.translation_mapping = dict(zip(self.original_words, translated_words))\\n print \\\"Bing done...\\\"\\n \\n def translate(self, word):\\n try:\\n return self.translation_mapping[word]\\n except KeyError:\\n return None \\n \\nclass GoogleTranslater():\\n \\n def __init__(self):\\n self.translation_url = \\\"https://translate.google.com/#no/en/\\\"\\n #The lines of words contain the original word first, then subsequent translations in english\\n self.words = codecs.open(\\\"bing_words.txt\\\", \\\"r\\\", \\\"utf8\\\").read().splitlines()\\n \\n def translate(self, word, context=None, pos_tag=None):\\n \\\"\\\"\\\"\\n Translate word using a translation API\\n Perform sentence contezt translation on google web interface\\n Perform word translation using Bing -> get all alternatives anc check for a mathc in the google translation, if match choose it as translation\\n if not then choose the bing translation that best matches using POS tag?\\n \\\"\\\"\\\"\\n #Get contextual translation from google translate\\n par = {\\\"text\\\": word, \\\"raw\\\": \\\"raw\\\"}\\n r = requests.post(self.translation_url, data=par)\\n results = r.text\\n translated_word = get_from_html_text(results, 'TRANSLATED_TEXT')\\n \\n #Perform lookup in the text file from the C# translator\\n #if there is no match, take the best match from the bing file\\n# print \\\"Translated: \\\", word, \\\" ->\\\", translated_word\\n return translated_word\\n \\ndef get_from_html_text(resultset, target):\\n \\\"\\\"\\\"\\n Gets the value of a variable target from a html result set from a request.\\n \\\"\\\"\\\"\\n index = resultset.find(target)+len(target)+2\\n return resultset[index:index+140].split(\\\"'\\\")[0].lower()\\n \\n \\ndef perform_bing_sentiment_lexicon_lookup(tweets):\\n \\\"\\\"\\\"\\n Performs sentiment lexicon lookup on the tweets, and stores it in the objects.\\n \\\"\\\"\\\"\\n words = []\\n for t in tweets:\\n for phrase in t.tagged_words:\\n for word in phrase:\\n try:\\n if word[\\\"pos\\\"] in TYPECRAFT_SENTIWORDNET:\\n words.append(word['word'])\\n except KeyError:\\n continue \\n \\n \\n lex = Lexicon(BingTranslater(words), SentiWordNetLexicon())\\n words_with_sentimentvalues=[]#list of dicts\\n print \\\"Getting sentiment values\\\"\\n for t in tweets:\\n sentiwords =[]\\n sentiwords_with_values={}\\n for phrase in t.tagged_words:\\n for word in phrase:\\n try:\\n if word[\\\"pos\\\"] in TYPECRAFT_SENTIWORDNET:\\n sentiwords.append(word['word'])\\n except KeyError:\\n continue\\n for sentiword in sentiwords:\\n sentivalues = lex.translate_and_get_lexicon_sentiment(sentiword)\\n if sentivalues!=None:\\n print \\\"Adding sentivalues: \\\",sentivalues\\n sentiwords_with_values[sentiword] = sentivalues\\n words_with_sentimentvalues.append(sentiwords_with_values)\\n \\n return words_with_sentimentvalues\\n \\ndef perform_google_sentiment_lexicon_lookup(tweets):\\n \\\"\\\"\\\"\\n Performs sentiment lexicon lookup on the tweets, and stores it in the objects.\\n \\\"\\\"\\\"\\n \\n lex = Lexicon(GoogleTranslater(), SentiWordNetLexicon())\\n print \\\"Getting sentiment values\\\"\\n tweet_sentiments = []\\n for t in tweets:\\n tweet_sentiments.append(lex.translate_sentence_and_get_lexicon_sentiment(t.text))\\n \\n print tweet_sentiments\\n reduced_tweet_sentiments = []\\n for sentiments in tweet_sentiments:\\n polar_sum = sum([s[0] for s in sentiments])\\n negative_sum = sum([s[1] for s in sentiments])\\n objective_sum = sum([s[2] for s in sentiments])\\n reduced_tweet_sentiments.append((polar_sum, negative_sum, objective_sum))\\n print reduced_tweet_sentiments\\n return reduced_tweet_sentiments\\n\\ndef tokenizer(sentence):\\n \\\"\\\"\\\"\\n Tokenizes an english sentence.\\n \\\"\\\"\\\"\\n words = []\\n for phrase in sentence.split('.'):\\n for piece in phrase.split(','):\\n for word in piece.split(' '):\\n words.append(word)\\n return words\\n \\nif __name__ == '__main__':\\n #Insert all words to be translated into the googlebing translator in order to augment with Bing...\\n lex = Lexicon(BingTranslater(), SentiWordNetLexicon())\\n print lex.translate_and_get_lexicon_sentiment(\\\"good\\\")\\n \\n# swn = SentiWordNetCorpusReader('SentiWordNet_3.0.0_20130122.txt')\\n# for senti_synset in swn.all_senti_synsets():\\n# print senti_synset.synset.name, senti_synset.pos_score, senti_synset.neg_score\"\n },\n {\n \"alpha_fraction\": 0.6290730237960815,\n \"alphanum_fraction\": 0.638678252696991,\n \"avg_line_length\": 48.009010314941406,\n \"blob_id\": \"54ee3986ef4cd6d2a0549170e0a956d1376f6400\",\n \"content_id\": \"175e7bff739036decf3db12cc35113f87b6553a5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 21759,\n \"license_type\": \"no_license\",\n \"max_line_length\": 248,\n \"num_lines\": 444,\n \"path\": \"/test.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nCreated on 24. nov. 2014\\nMethods for performing test on various classificatino schemes and storing the results.\\n@author: JohnArne\\n'''\\nimport utils\\nfrom lexicon import lexicon\\nfrom models.nb import NB\\nfrom models.svm import SVM\\nfrom models.me import ME\\nfrom sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\\nfrom tweet import Tweet\\nimport plotting\\nimport preprocessing\\nimport models.features as feat_utils\\nimport pickle\\nimport classifier\\nimport tweet\\nimport entity_extraction\\nfrom entity_extraction import cutoff_breakwords\\n \\n \\ndef train_and_test_subjectivity_and_polarity():\\n datasetnr = 3\\n tweets = utils.get_pickles(datasetnr)\\n sentimentvalues = feat_utils.get_sentiment_values(datasetnr)\\n tweets = preprocessing.remove_link_classes(tweets)\\n tweets = preprocessing.lower_case(tweets)\\n tweets = preprocessing.remove_specialchars_round2(tweets)\\n \\n# train_subjectivity_and_test_on_feature_set(tweets, 'SA', sentimentvalues)\\n train_subjectivity_and_test_on_feature_set(tweets, 'SB', sentimentvalues)\\n train_subjectivity_and_test_on_feature_set(tweets, 'SC', sentimentvalues)\\n# google_sentimentvalues = feat_utils.get_google_sentiment_values(datasetnr)\\n# train_subjectivity_and_test_on_feature_set(tweets, 'SC2', google_sentimentvalues)\\n \\n# train_polarity_and_test_on_feature_set(tweets, 'PA', sentimentvalues)\\n# train_polarity_and_test_on_feature_set(tweets, 'PB', sentimentvalues)\\n# train_polarity_and_test_on_feature_set(tweets, 'PC', sentimentvalues)\\n# google_sentimentvalues = feat_utils.get_google_sentiment_values(datasetnr)\\n# train_polarity_and_test_on_feature_set(tweets, 'PC2', google_sentimentvalues)\\n\\ndef train_subjectivity_and_test_on_feature_set(tweets, feature_set, sentimentvalues, reduce_dataset=1):\\n \\\"\\\"\\\"\\n Performs training and testing with a given feature set key\\n \\\"\\\"\\\"\\n kfolds = range(0,10)\\n nbaccuracy_avgs = []\\n nbprecision_avgs = []\\n nbrecall_avgs = []\\n nbf1_avgs = []\\n svmaccuracy_avgs = []\\n svmprecision_avgs = []\\n svmrecall_avgs = []\\n svmf1_avgs = []\\n meaccuracy_avgs = []\\n meprecision_avgs = []\\n merecall_avgs = []\\n mef1_avgs = []\\n vect_options = {\\n 'ngram_range': (1,1),\\n 'max_df': 0.5\\n }\\n tfidf_options = {\\n 'sublinear_tf': False,\\n 'use_idf': True,\\n 'smooth_idf': True,\\n }\\n for kfoldcounter in kfolds:\\n print \\\"--------------------------KFOLD NR \\\",kfoldcounter,\\\"----------------------------------\\\" \\n train_tweets, train_targets, test_tweets, test_targets, train_sentimentvalues, test_sentimentvalues = utils.make_subjectivity_train_and_test_and_targets(tweets,sentimentvalues,splitvalue=kfoldcounter*0.1,reduce_dataset=reduce_dataset) \\n #TRAINING NB\\n print \\\"Training NB subjectivity on dataset of length \\\", len(train_tweets)\\n clf = NB(train_tweets, train_targets, vect_options, tfidf_options)\\n clf.set_feature_set(feature_set, train_sentimentvalues)\\n clf.train_on_feature_set()\\n print \\\"Testing...\\\"\\n nb_accuracy, nb_precision, nb_recall, nb_f1_score = clf.test_and_return_results(test_tweets, test_targets, test_sentimentvalues)\\n nbaccuracy_avgs.append(nb_accuracy)\\n nbprecision_avgs.append(nb_precision)\\n nbrecall_avgs.append(nb_recall)\\n nbf1_avgs.append(nb_f1_score)\\n \\n #TRAINING SVM\\n vect_options = {\\n 'ngram_range': (1,3),\\n 'max_df': 0.5\\n \\n }\\n tfidf_options = {\\n 'sublinear_tf': True,\\n 'use_idf': True,\\n 'smooth_idf': True,\\n }\\n print \\\"Training SVM subjectivity on dataset of length \\\", len(train_tweets)\\n clf = SVM(train_tweets, train_targets, vect_options, tfidf_options)\\n clf.set_feature_set(feature_set, train_sentimentvalues)\\n clf.train_on_feature_set()\\n print \\\"Testing...\\\"\\n svm_accuracy, svm_precision, svm_recall, svm_f1_score = clf.test_and_return_results(test_tweets, test_targets, test_sentimentvalues)\\n svmaccuracy_avgs.append(svm_accuracy)\\n svmprecision_avgs.append(svm_precision)\\n svmrecall_avgs.append(svm_recall)\\n svmf1_avgs.append(svm_f1_score)\\n \\n #TRAINING MAXENT\\n vect_options = {\\n 'ngram_range': (1,2),\\n 'max_df': 0.5\\n }\\n tfidf_options = {\\n 'sublinear_tf': True,\\n 'use_idf': True,\\n 'smooth_idf': True,\\n }\\n \\n print \\\"Training MaxEnt subjectivity on dataset of length \\\", len(train_tweets)\\n clf = ME(train_tweets, train_targets, vect_options, tfidf_options)\\n clf.set_feature_set(feature_set, train_sentimentvalues)\\n clf.train_on_feature_set()\\n \\n print \\\"Testing...\\\"\\n me_accuracy, me_precision, me_recall, me_f1_score = clf.test_and_return_results(test_tweets, test_targets, test_sentimentvalues)\\n meaccuracy_avgs.append(me_accuracy)\\n meprecision_avgs.append(me_precision)\\n merecall_avgs.append(me_recall)\\n mef1_avgs.append(me_f1_score)\\n \\n \\n print \\\"Averages\\\"\\n nb_accuracy = reduce(lambda x,y: x+y,nbaccuracy_avgs)/len(nbaccuracy_avgs)\\n nb_precision = reduce(lambda x,y: x+y,nbprecision_avgs)/len(nbprecision_avgs)\\n nb_recall = reduce(lambda x,y: x+y,nbrecall_avgs)/len(nbrecall_avgs)\\n nb_f1_score = reduce(lambda x,y: x+y,nbf1_avgs)/len(nbf1_avgs)\\n svm_accuracy = reduce(lambda x,y: x+y,svmaccuracy_avgs)/len(svmaccuracy_avgs)\\n svm_precision = reduce(lambda x,y: x+y,svmprecision_avgs)/len(svmprecision_avgs)\\n svm_recall = reduce(lambda x,y: x+y,svmrecall_avgs)/len(svmrecall_avgs)\\n svm_f1_score = reduce(lambda x,y: x+y,svmf1_avgs)/len(svmf1_avgs)\\n me_accuracy = reduce(lambda x,y: x+y,meaccuracy_avgs)/len(meaccuracy_avgs)\\n me_precision = reduce(lambda x,y: x+y,meprecision_avgs)/len(meprecision_avgs)\\n me_recall = reduce(lambda x,y: x+y,merecall_avgs)/len(merecall_avgs)\\n me_f1_score = reduce(lambda x,y: x+y,mef1_avgs)/len(mef1_avgs)\\n \\n data = {'Naive Bayes': [nb_accuracy, nb_precision, nb_recall, nb_f1_score],\\n 'SVM': [svm_accuracy, svm_precision, svm_recall, svm_f1_score],\\n 'Maximum Entropy': [me_accuracy, me_precision, me_recall, me_f1_score]}\\n plotting.plot_performance_histogram(data, \\\"subjectivity_\\\"+feature_set)\\n return data\\n\\ndef train_polarity_and_test_on_feature_set(tweets, feature_set, sentimentvalues, reduce_dataset=1):\\n \\\"\\\"\\\"\\n Performs training and testing with a given feature set key\\n \\\"\\\"\\\"\\n kfolds = range(0,10)\\n nbaccuracy_avgs = []\\n nbprecision_avgs = []\\n nbrecall_avgs = []\\n nbf1_avgs = []\\n svmaccuracy_avgs = []\\n svmprecision_avgs = []\\n svmrecall_avgs = []\\n svmf1_avgs = []\\n meaccuracy_avgs = []\\n meprecision_avgs = []\\n merecall_avgs = []\\n mef1_avgs = []\\n for kfoldcounter in kfolds:\\n print \\\"--------------------------KFOLD NR \\\",kfoldcounter,\\\"----------------------------------\\\" \\n train_tweets, train_targets, test_tweets, test_targets, train_sentimentvalues, test_sentimentvalues = utils.make_polarity_train_and_test_and_targets(tweets,sentimentvalues, splitvalue=kfoldcounter*0.1, reduce_dataset=reduce_dataset)\\n # for tweet, target in zip(tweets,targets):\\n # try:\\n # print unicode(tweet.text), \\\" \\\", target\\n # except UnicodeEncodeError:\\n # print tweet.text.encode('utf8'), \\\" \\\", target\\n # except UnicodeDecodeError:\\n # print tweet.text, \\\" \\\", target\\n \\n #TRAINING NB\\n vect_options = {\\n 'ngram_range': (1,1),\\n 'max_df': 0.5\\n }\\n tfidf_options = {\\n 'sublinear_tf': True,\\n 'use_idf': True,\\n 'smooth_idf': True,\\n }\\n print \\\"Training NB polarity with feature set \\\",feature_set\\n clf = NB(train_tweets, train_targets, vect_options, tfidf_options)\\n clf.set_feature_set(feature_set, train_sentimentvalues)\\n clf.train_on_feature_set()\\n print \\\"Testing...\\\"\\n nb_accuracy, nb_precision, nb_recall, nb_f1_score = clf.test_and_return_results(test_tweets, test_targets, test_sentimentvalues)\\n nbaccuracy_avgs.append(nb_accuracy)\\n nbprecision_avgs.append(nb_precision)\\n nbrecall_avgs.append(nb_recall)\\n nbf1_avgs.append(nb_f1_score)\\n \\n #TRAINING SVM\\n vect_options = {\\n 'ngram_range': (1,1),\\n 'max_df': 0.5\\n }\\n tfidf_options= {\\n 'sublinear_tf': True,\\n 'use_idf': True,\\n 'smooth_idf': True,\\n }\\n print \\\"Training SVM polarity on dataset of length \\\", len(train_tweets)\\n clf = SVM(train_tweets, train_targets, vect_options, tfidf_options)\\n clf.set_feature_set(feature_set, train_sentimentvalues)\\n clf.train_on_feature_set()\\n \\n print \\\"Testing...\\\"\\n svm_accuracy, svm_precision, svm_recall, svm_f1_score = clf.test_and_return_results(test_tweets, test_targets, test_sentimentvalues)\\n svmaccuracy_avgs.append(svm_accuracy)\\n svmprecision_avgs.append(svm_precision)\\n svmrecall_avgs.append(svm_recall)\\n svmf1_avgs.append(svm_f1_score)\\n #TRAINING MAXENT\\n vect_options = {\\n 'ngram_range': (1,1),\\n 'max_df': 0.5\\n }\\n tfidf_options = {\\n 'sublinear_tf': True,\\n 'use_idf': True,\\n 'smooth_idf': True,\\n }\\n print \\\"Training MaxEnt polarity on dataset of length \\\", len(train_tweets)\\n clf = ME(train_tweets, train_targets, vect_options, tfidf_options)\\n clf.set_feature_set(feature_set, train_sentimentvalues)\\n clf.train_on_feature_set()\\n \\n print \\\"Testing...\\\"\\n me_accuracy, me_precision, me_recall, me_f1_score = clf.test_and_return_results(test_tweets, test_targets, test_sentimentvalues)\\n meaccuracy_avgs.append(me_accuracy)\\n meprecision_avgs.append(me_precision)\\n merecall_avgs.append(me_recall)\\n mef1_avgs.append(me_f1_score)\\n \\n print \\\"Averages\\\"\\n nb_accuracy = reduce(lambda x,y: x+y,nbaccuracy_avgs)/len(nbaccuracy_avgs)\\n nb_precision = reduce(lambda x,y: x+y,nbprecision_avgs)/len(nbprecision_avgs)\\n nb_recall = reduce(lambda x,y: x+y,nbrecall_avgs)/len(nbrecall_avgs)\\n nb_f1_score = reduce(lambda x,y: x+y,nbf1_avgs)/len(nbf1_avgs)\\n svm_accuracy = reduce(lambda x,y: x+y,svmaccuracy_avgs)/len(svmaccuracy_avgs)\\n svm_precision = reduce(lambda x,y: x+y,svmprecision_avgs)/len(svmprecision_avgs)\\n svm_recall = reduce(lambda x,y: x+y,svmrecall_avgs)/len(svmrecall_avgs)\\n svm_f1_score = reduce(lambda x,y: x+y,svmf1_avgs)/len(svmf1_avgs)\\n me_accuracy = reduce(lambda x,y: x+y,meaccuracy_avgs)/len(meaccuracy_avgs)\\n me_precision = reduce(lambda x,y: x+y,meprecision_avgs)/len(meprecision_avgs)\\n me_recall = reduce(lambda x,y: x+y,merecall_avgs)/len(merecall_avgs)\\n me_f1_score = reduce(lambda x,y: x+y,mef1_avgs)/len(mef1_avgs)\\n \\n data = {'Naive Bayes': [nb_accuracy, nb_precision, nb_recall, nb_f1_score],\\n 'SVM': [svm_accuracy, svm_precision, svm_recall, svm_f1_score],\\n 'Maximum Entropy': [me_accuracy, me_precision, me_recall, me_f1_score]}\\n plotting.plot_performance_histogram(data, \\\"polarity_\\\"+feature_set)\\n return data\\n\\ndef perform_grid_search_on_featureset_SA_and_PA():\\n datasetnr = 3\\n tweets = utils.get_pickles(datasetnr)\\n sentimentvalues = feat_utils.get_sentiment_values(datasetnr)\\n tweets = preprocessing.remove_link_classes(tweets)\\n tweets = preprocessing.lower_case(tweets)\\n tweets = preprocessing.remove_specialchars_round2(tweets)\\n \\n train_tweets, train_targets, test_tweets, test_targets, train_sentimentvalues, test_sentimentvalues = utils.make_subjectivity_train_and_test_and_targets(tweets,sentimentvalues)\\n \\n clf = SVM(train_tweets, train_targets, None)\\n clf.set_feature_set('SA', None)\\n clf.grid_search_on_text_features(file_postfix='subjectivity')\\n clf = NB(train_tweets, train_targets, None)\\n clf.set_feature_set('SA', None)\\n clf.grid_search_on_text_features(file_postfix='subjectivity')\\n clf = ME(train_tweets, train_targets, None)\\n clf.set_feature_set('SA', None)\\n clf.grid_search_on_text_features(file_postfix='subjectivity')\\n \\n train_tweets, train_targets, test_tweets, test_targets, train_sentimentvalues, test_sentimentvalues = utils.make_polarity_train_and_test_and_targets(tweets,sentimentvalues)\\n \\n clf = SVM(train_tweets, train_targets, None)\\n clf.set_feature_set('PA', None)\\n clf.grid_search_on_text_features(file_postfix='polarity')\\n clf = NB(train_tweets, train_targets, None)\\n clf.set_feature_set('PA', None)\\n clf.grid_search_on_text_features(file_postfix='polarity')\\n clf = ME(train_tweets, train_targets, None)\\n clf.set_feature_set('PA', None)\\n clf.grid_search_on_text_features(file_postfix='polarity')\\n \\ndef train_and_test_dataset_increase():\\n datasetnr = 3\\n tweets = utils.get_pickles(datasetnr)\\n sentimentvalues = feat_utils.get_sentiment_values(datasetnr)\\n tweets = preprocessing.remove_link_classes(tweets)\\n tweets = preprocessing.lower_case(tweets)\\n tweets = preprocessing.remove_specialchars_round2(tweets)\\n accuracy_data = {'NB(SA)':[],'NB(SB)':[],'NB(SC)':[],\\n 'SVM(SA)':[],'SVM(SB)':[],'SVM(SC)':[],\\n 'MaxEnt(SA)':[],'MaxEnt(SB)':[],'MaxEnt(SC)':[],\\n 'NB(PA)':[],'NB(PB)':[],'NB(PC)':[],\\n 'SVM(PA)':[],'SVM(PB)':[],'SVM(PC)':[],\\n 'MaxEnt(PA)':[],'MaxEnt(PB)':[],'MaxEnt(PC)':[]}\\n f1_data = {'NB(SA)':[],'NB(SB)':[],'NB(SC)':[],\\n 'SVM(SA)':[],'SVM(SB)':[],'SVM(SC)':[],\\n 'MaxEnt(SA)':[],'MaxEnt(SB)':[],'MaxEnt(SC)':[],\\n 'NB(PA)':[],'NB(PB)':[],'NB(PC)':[],\\n 'SVM(PA)':[],'SVM(PB)':[],'SVM(PC)':[],\\n 'MaxEnt(PA)':[],'MaxEnt(PB)':[],'MaxEnt(PC)':[]}\\n for i in range(5,101,5):\\n print \\\"=============================DATAPOINT NR. \\\",i,\\\"========================================\\\"\\n data = train_subjectivity_and_test_on_feature_set(tweets, 'SA', sentimentvalues, reduce_dataset=i*0.01)\\n print \\\"DATA -- \\\",data\\n accuracy_data['NB(SA)'].append(data['Naive Bayes'][0])\\n f1_data['NB(SA)'].append(data['Naive Bayes'][3])\\n accuracy_data['SVM(SA)'].append(data['SVM'][0])\\n f1_data['SVM(SA)'].append(data['SVM'][3])\\n accuracy_data['MaxEnt(SA)'].append(data['Maximum Entropy'][0])\\n f1_data['MaxEnt(SA)'].append(data['Maximum Entropy'][3])\\n \\n data = train_subjectivity_and_test_on_feature_set(tweets, 'SB', sentimentvalues, reduce_dataset=i*0.01)\\n print \\\"DATA -- \\\",data\\n accuracy_data['NB(SB)'].append(data['Naive Bayes'][0])\\n f1_data['NB(SB)'].append(data['Naive Bayes'][3])\\n accuracy_data['SVM(SB)'].append(data['SVM'][0])\\n f1_data['SVM(SB)'].append(data['SVM'][3])\\n accuracy_data['MaxEnt(SB)'].append(data['Maximum Entropy'][0])\\n f1_data['MaxEnt(SB)'].append(data['Maximum Entropy'][3])\\n \\n data = train_subjectivity_and_test_on_feature_set(tweets, 'SC', sentimentvalues, reduce_dataset=i*0.01)\\n print \\\"DATA -- \\\",data\\n accuracy_data['NB(SC)'].append(data['Naive Bayes'][0])\\n f1_data['NB(SC)'].append(data['Naive Bayes'][3])\\n accuracy_data['SVM(SC)'].append(data['SVM'][0])\\n f1_data['SVM(SC)'].append(data['SVM'][3])\\n accuracy_data['MaxEnt(SC)'].append(data['Maximum Entropy'][0])\\n f1_data['MaxEnt(SC)'].append(data['Maximum Entropy'][3])\\n \\n data = train_polarity_and_test_on_feature_set(tweets, 'PA', sentimentvalues, reduce_dataset=i*0.01)\\n print \\\"DATA -- \\\",data\\n accuracy_data['NB(PA)'].append(data['Naive Bayes'][0])\\n f1_data['NB(PA)'].append(data['Naive Bayes'][3])\\n accuracy_data['SVM(PA)'].append(data['SVM'][0])\\n f1_data['SVM(PA)'].append(data['SVM'][3])\\n accuracy_data['MaxEnt(PA)'].append(data['Maximum Entropy'][0])\\n f1_data['MaxEnt(PA)'].append(data['Maximum Entropy'][3])\\n \\n data = train_polarity_and_test_on_feature_set(tweets, 'PB', sentimentvalues, reduce_dataset=i*0.01)\\n print \\\"DATA -- \\\",data\\n accuracy_data['NB(PB)'].append(data['Naive Bayes'][0])\\n f1_data['NB(PB)'].append(data['Naive Bayes'][3])\\n accuracy_data['SVM(PB)'].append(data['SVM'][0])\\n f1_data['SVM(PB)'].append(data['SVM'][3])\\n accuracy_data['MaxEnt(PB)'].append(data['Maximum Entropy'][0])\\n f1_data['MaxEnt(PB)'].append(data['Maximum Entropy'][3])\\n \\n data = train_polarity_and_test_on_feature_set(tweets, 'PC', sentimentvalues, reduce_dataset=i*0.01)\\n print \\\"DATA -- \\\",data\\n accuracy_data['NB(PC)'].append(data['Naive Bayes'][0])\\n f1_data['NB(PC)'].append(data['Naive Bayes'][3])\\n accuracy_data['SVM(PC)'].append(data['SVM'][0])\\n f1_data['SVM(PC)'].append(data['SVM'][3])\\n accuracy_data['MaxEnt(PC)'].append(data['Maximum Entropy'][0])\\n f1_data['MaxEnt(PC)'].append(data['Maximum Entropy'][3])\\n out = open('incremental_acc'+str(i), 'wb')\\n pickle.dump(accuracy_data, out)\\n out = open('incremental_f1'+str(i), 'wb')\\n pickle.dump(f1_data, out)\\n plotting.plot_temporal_sentiment(accuracy_data, filename=\\\"incremental_accuracy\\\")\\n plotting.plot_temporal_sentiment(f1_data, filename=\\\"incremental_f1\\\")\\n \\ndef test_aggregated_sentiments():\\n sub_clf = classifier.get_optimal_subjectivity_classifier()\\n pol_clf = classifier.get_optimal_polarity_classifier()\\n tweets = utils.get_pickles(2)\\n sentimentvalues = utils.get_sentimentvalues(2)\\n sub_train_tweets, sub_train_targets, _, _, sub_train_sentiments, _ = utils.make_subjectivity_train_and_test_and_targets(tweets, sentimentvalues, splitvalue=1.0)\\n pol_train_tweets, pol_train_targets, _, _, pol_train_sentiments, _ = utils.make_polarity_train_and_test_and_targets(tweets, sentimentvalues, splitvalue=1.0)\\n \\n sub_predictions = sub_clf.classify(sub_train_tweets, sub_train_sentiments)\\n pol_predictions = pol_clf.classify(pol_train_tweets, pol_train_sentiments)\\n print pol_train_targets, pol_predictions\\n days, targets, predicts, total_frequencies = utils.temporally_aggregate_subjectivity(sub_train_tweets, sub_predictions, targets=sub_train_targets)\\n data = {'Targets': [days, targets], 'Predictions': [days, predicts], 'Frequencies': [days,total_frequencies]}\\n plotting.plot_subjectivity_aggregates(data, 'aggregated_subjectivity')\\n days, targets, predicts, frequencies = utils.temporally_aggregate_polarity(pol_train_tweets, pol_predictions, targets=pol_train_targets)\\n for i in range(len(days)):\\n targets[i]=targets[i]*1.0/frequencies[i]\\n predicts[i]=predicts[i]*1.0/frequencies[i]\\n frequencies[i]=frequencies[i]*1.0/total_frequencies[i]\\n data = {'Targets': [days, targets], 'Predictions': [days, predicts], 'Frequencies': [days,frequencies]}\\n plotting.plot_polarity_aggregates(data, 'aggregated_polarity')\\n \\ndef test_remporal_topics():\\n tweets1 = pickle.load(open('temporal_tweets1', 'rb'))\\n tweets2 = pickle.load(open('temporal_tweets2', 'rb'))\\n tweets = tweets1 + tweets2\\n print len(tweets)\\n sentiments = pickle.load(open('temporal_sentiments','rb'))\\n print len(sentiments)\\n subclf = classifier.get_optimal_subjectivity_classifier()\\n polclf = classifier.get_optimal_polarity_classifier() \\n #TODO SKRIVE HER TEMPORALLY AGGREGATE ETC\\n sub_predictions = subclf.classify(tweets, sentiments)\\n subjective_tweets = [t for p,t in zip(sub_predictions,tweets) if p==\\\"subjective\\\"]\\n subjective_sentiments = [s for p,s in zip(sub_predictions,sentiments) if p==\\\"subjective\\\"]\\n pol_predictions = polclf.classify(subjective_tweets, subjective_sentiments)\\n topics = entity_extraction.perform_entity_extraction(subjective_tweets, subjective_sentiments, use_pmi=True, breakword_min_freq=0.1, breakword_range=14)\\n days, unique_topics, aggregated_values = utils.topically_aggregate_polarity(subjective_tweets, pol_predictions, topics=topics)\\n data = {}\\n for i in range(len(unique_topics)):\\n data[unique_topics[i]] = [days, aggregated_values[i]]\\n print data\\n pickle.dump(data, open('topically_aggregated_polarity', 'wb'))\\n\\ndef preprocess_temporal_dataset():\\n tweetlines = utils.get_dataset(utils.complete_datasets[3])\\n tweets = []\\n for line in tweetlines:\\n if len(line)>1:\\n tweets.append(tweet.to_tweet(line))\\n tweets = preprocessing.preprocess_tweets(tweets)\\n sentiments = lexicon.perform_google_sentiment_lexicon_lookup(tweets)\\n pickle.dump(sentiments, open('temporal_sentiments','wb'))\\n pickle.dump(tweets, open('temporal_tweets2', 'wb'))\\n \\nif __name__ == '__main__':\\n datasetnr = 3\\n tweets = utils.get_pickles(datasetnr)\\n sentimentvalues = feat_utils.get_sentiment_values(datasetnr)\\n tweets = preprocessing.remove_link_classes(tweets)\\n tweets = preprocessing.lower_case(tweets)\\n tweets = preprocessing.remove_specialchars_round2(tweets)\\n \\n train_subjectivity_and_test_on_feature_set(tweets, 'SA', datasetnr)\\n train_subjectivity_and_test_on_feature_set(tweets, 'SB', datasetnr)\\n train_subjectivity_and_test_on_feature_set(tweets, 'SC', sentimentvalues)\\n \\n train_polarity_and_test_on_feature_set(tweets, 'PA', datasetnr)\\n train_polarity_and_test_on_feature_set(tweets, 'PB', datasetnr)\\n train_polarity_and_test_on_feature_set(tweets, 'PC', sentimentvalues)\"\n },\n {\n \"alpha_fraction\": 0.6822289228439331,\n \"alphanum_fraction\": 0.6868975758552551,\n \"avg_line_length\": 44.76551818847656,\n \"blob_id\": \"3cbb072f066fb95341a8bbe401d8fa4bfb7ab960\",\n \"content_id\": \"91cf41ca7c0759dc5e2d90ed4fa359b864e7315c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6640,\n \"license_type\": \"no_license\",\n \"max_line_length\": 221,\n \"num_lines\": 145,\n \"path\": \"/classifier.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nCreated on 11. mars 2014\\n\\n@author: JohnArne\\n'''\\nimport argparse\\nimport utils\\nimport preprocessing\\nimport retriever_tweepy\\nfrom models.nb import NB\\nfrom models.svm import SVM\\nfrom models.me import ME\\nfrom models import features\\nfrom models import model\\nfrom lexicon import lexicon\\nimport test\\n\\nimport annotation\\nimport easygui_gui\\nfrom retriever_tweepy import TweetRetriever\\nimport entity_extraction\\n\\nclass Classifier(object):\\n \\\"\\\"\\\"\\n Class for handling the training and testing of a given model.\\n Takes in a selected model type(NV/SVM/ME) trains it on a given dataset, then tests it.\\n \\\"\\\"\\\"\\n \\n def __init__(self, subjectivity_model, polarity_model):\\n self.subjectivity_model = subjectivity_model\\n self.polarity_model = polarity_model\\n \\n \\n def test(self):\\n \\\"\\\"\\\"\\n Tests the given model on a partition of the dataset.\\n \\\"\\\"\\\" \\n \\n def classify(self, tweets):\\n \\\"\\\"\\\"\\n Takes in a list of tweets and classifies with all three classes using the two trained models\\n \\\"\\\"\\\"\\n sentiments = []\\n predictions = self.subjectivity_model.classify(tweets)\\n return sentiments\\n \\n def save_model(self):\\n file = open()\\n\\n def train_and_store_results(self):\\n \\\"\\\"\\\"\\n Trains the given model on the dataset using the three different models, and different feature sets. Stores the results of the runs.\\n \\\"\\\"\\\"\\n dataset = \\\"random_dataset\\\"\\n tweets = utils.get_pickles(dataset)\\n self.model.set_feature_set('A')\\n self.model.train_on_feature_set()\\n \\ndef get_optimal_subjectivity_classifier():\\n \\\"\\\"\\\"\\n Trains and returns the optimal subjectivity classifier.\\n \\\"\\\"\\\"\\n tweets = utils.get_pickles(3)\\n tweets, targets = utils.make_subjectivity_targets(tweets)\\n vect_options = {\\n 'ngram_range': (1,1),\\n 'max_df': 0.5\\n }\\n tfidf_options = {\\n 'sublinear_tf': False,\\n 'use_idf': True,\\n 'smooth_idf': True,\\n }\\n clf = SVM(tweets, targets, vect_options, tfidf_options)\\n clf.set_feature_set('SA', utils.get_sentimentvalues(3))\\n clf.train_on_feature_set()\\n return clf\\n\\ndef get_optimal_polarity_classifier():\\n \\\"\\\"\\\"\\n Trains and returns the optimal polarity classifier.\\n \\\"\\\"\\\"\\n tweets = utils.get_pickles(3)\\n tweets, targets = utils.make_polarity_targets(tweets)\\n vect_options = {\\n 'ngram_range': (1,1),\\n 'max_df': 0.5\\n }\\n tfidf_options = {\\n 'sublinear_tf': False,\\n 'use_idf': True,\\n 'smooth_idf': True,\\n }\\n clf = SVM(tweets, targets, vect_options, tfidf_options)\\n clf.set_feature_set('PC2', features.get_google_sentiment_values(3))\\n clf.train_on_feature_set()\\n return clf\\n \\nif __name__ == '__main__':\\n parser = argparse.ArgumentParser(description=\\\"Commands for classification\\\")\\n parser.add_argument(\\\"-pre1\\\", action=\\\"store_true\\\", dest=\\\"preprocess1\\\", default=False, help=\\\"Perform first round preprocessing: Duplicate and retweet removal\\\")\\n parser.add_argument(\\\"-pre2\\\", action=\\\"store_true\\\", dest=\\\"preprocess2\\\", default=False, help=\\\"Perform second round preprocessing: Text cleanup operations, feature extractions, POS-tagging.\\\")\\n parser.add_argument(\\\"-q\\\", action=\\\"store\\\", dest=\\\"tweet_query\\\", default=None, help=\\\"Get tweets using the given query.\\\")\\n parser.add_argument(\\\"-a\\\", action=\\\"store_true\\\", dest=\\\"annotate\\\", default=False, help=\\\"Start annotation sequence.\\\")\\n parser.add_argument(\\\"-analyze\\\", action=\\\"store_true\\\", dest=\\\"analyze\\\", default=False, help=\\\"Perform a re-analysis of the pickled datasets. This analysis is also performed as part of the second preprocessing.\\\")\\n parser.add_argument(\\\"-posanalyze\\\", action=\\\"store_true\\\", dest=\\\"posanalyze\\\", default=False, help=\\\"Perform a pos-tag analysis of the pickled datasets.\\\")\\n parser.add_argument(\\\"-lex1\\\", action=\\\"store_true\\\", dest=\\\"run_lexicon1\\\", default=False, help=\\\"Run lexicon translation using Bing and lookup on stored tweets\\\")\\n parser.add_argument(\\\"-lex2\\\", action=\\\"store_true\\\", dest=\\\"run_lexicon2\\\", default=False, help=\\\"Run lexicon translation using Google and lookup on stored tweets\\\")\\n parser.add_argument(\\\"-optimize\\\", action=\\\"store_true\\\", dest=\\\"optimize\\\", default=False, help=\\\"Find optimal parameters for text classification with SVM, NB, and MaxEnt. Stores the optimal parameters for each algorithm.\\\")\\n parser.add_argument(\\\"-test\\\", action=\\\"store_true\\\", dest=\\\"train_and_test\\\", default=False, help=\\\"Train and test on subjectivity and polarity and create a diagram of the results.\\\")\\n parser.add_argument(\\\"-test_increment\\\", action=\\\"store_true\\\", dest=\\\"test_incremental\\\", default=False, help=\\\"Train and test incremental dataset results and create a diagram of the results.\\\")\\n parser.add_argument(\\\"-test_aggregated\\\", action=\\\"store_true\\\", dest=\\\"test_aggregated\\\", default=False, help=\\\"Train and test aggregated results from erna solberg dataset and create a diagram of the results.\\\")\\n parser.add_argument(\\\"-test_entities\\\", action=\\\"store_true\\\", dest=\\\"test_entities\\\", default=False, help=\\\"Test topic detection on topic-annotated rosenborg dataset and create a diagram of the results.\\\")\\n parser.add_argument(\\\"-test_temptops\\\", action=\\\"store_true\\\", dest=\\\"test_temptops\\\", default=False, help=\\\"Train and test topically aggregated results from a temporally dense dataset and create a diagram of the results.\\\")\\n \\n parsameters = parser.parse_args()\\n if parsameters.preprocess1:\\n preprocessing.initial_preprocess_all_datasets()\\n if parsameters.preprocess2:\\n preprocessing.classification_preprocess_all_datasets()\\n if parsameters.tweet_query:\\n retriever = TweetRetriever(parsameters.tweet_query)\\n retriever.retrieve_for_dataset()\\n if parsameters.annotate:\\n annotation.user_annotation()\\n if parsameters.analyze:\\n preprocessing.re_analyze()\\n if parsameters.posanalyze:\\n preprocessing.pos_analyze()\\n if parsameters.run_lexicon1:\\n preprocessing.bing_lexicon_lookup()\\n if parsameters.run_lexicon2:\\n preprocessing.google_lexicon_lookup()\\n if parsameters.optimize:\\n test.perform_grid_search_on_featureset_SA_and_PA()\\n if parsameters.train_and_test:\\n test.train_and_test_subjectivity_and_polarity()\\n if parsameters.test_incremental:\\n test.train_and_test_dataset_increase()\\n if parsameters.test_aggregated:\\n test.test_aggregated_sentiments()\\n if parsameters.test_temptops:\\n test.test_remporal_topics()\\n if parsameters.test_entities:\\n entity_extraction.perform_and_test_extraction()\\n \"\n },\n {\n \"alpha_fraction\": 0.6140362620353699,\n \"alphanum_fraction\": 0.6189417243003845,\n \"avg_line_length\": 34.28406524658203,\n \"blob_id\": \"9c7e767d8ed57767dab671297c075cf83fda31ef\",\n \"content_id\": \"d0f863bf0d65e2082fdb61130077fe21fa1e3d71\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 15289,\n \"license_type\": \"no_license\",\n \"max_line_length\": 195,\n \"num_lines\": 433,\n \"path\": \"/preprocessing.py\",\n \"repo_name\": \"andrely/twitter-sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nCreated on 21. apr. 2014\\n\\n@author: JohnArne\\n'''\\nimport utils\\nfrom calendar import main\\nimport tweet\\nfrom numpy.core.numeric import correlate\\nfrom tweet import Tweet\\nimport re\\nfrom tagger import Tagger\\nfrom analyzer import Analyzer\\nimport string\\nfrom lexicon import lexicon\\nimport plotting\\nfrom analyzer import pos_tag_analyze\\n\\ndef remove_retweets(tweets):\\n \\\"\\\"\\\"\\n Removes all retweets\\n \\\"\\\"\\\"\\n for tweet in tweets:\\n textbody = tweet.text\\n if textbody[:2] is \\\"RT\\\":\\n tweet.text = textbody[3:]\\n return tweets \\n\\ndef remove_duplicates_and_retweets(tweets):\\n \\\"\\\"\\\" \\n Removes tweets with dublicate text bodies.\\n \\\"\\\"\\\"\\n textbodies = []\\n tweets = [tweet for tweet in tweets if not tweet.text[:2]==\\\"RT\\\"]\\n\\n #Return a set of the tweets, which will remove duplicates if __eq__ is properly implemented\\n unique_tweets = []\\n added_texts = []\\n for t in tweets:\\n if t.text not in added_texts:\\n unique_tweets.append(t)\\n added_texts.append(t.text)\\n return unique_tweets\\n\\ndef remove_retweet_tags(tweets):\\n \\\"\\\"\\\" \\n Removes tweets with dublicate text bodies.\\n \\\"\\\"\\\"\\n for t in tweets:\\n textbody = t.text[2:] if t.text[:2]=='RT' else t.text\\n t.text = textbody\\n return tweets\\n\\ndef correct_words(tweets):\\n \\\"\\\"\\\"\\n Performs simple word correction.\\n Initially, this will involve removing any vowel that appears 2 times or more,\\n aswell as removing any consonant that appears 3 times or more.\\n \\\"\\\"\\\"\\n for tweet in tweets:\\n textbody = tweet.text\\n \\n for vowel in vowels:\\n pattern = re.compile(vowel*3+\\\"*\\\")\\n try:\\n textbody = pattern.sub(vowel, textbody)\\n except UnicodeDecodeError:\\n textbody = pattern.sub(vowel, textbody.decode('utf8'))\\n for consonant in consonants:\\n pattern = re.compile(consonant+consonant+consonant+consonant+\\\"*\\\")\\n try:\\n textbody = pattern.sub(consonant*2, textbody)\\n except UnicodeDecodeError:\\n textbody = pattern.sub(consonant*2, textbody.decode('utf8'))\\n\\n tweet.text = textbody\\n return tweets\\n\\ndef remove_specialchars(tweets):\\n \\\"\\\"\\\"\\n Removes certain special characters. Does not remove !, ?, or ., as these are neeeded for the POS tagger to separate phrases.\\n \\\"\\\"\\\"\\n for tweet in tweets:\\n textbody = tweet.text\\n pattern = re.compile('({|}|[|]|-|:|\\\"|@|\\\\*|\\\\)|\\\\()')\\n try:\\n textbody = pattern.sub(\\\"\\\", textbody)\\n except UnicodeDecodeError:\\n textbody = pattern.sub(\\\"\\\", textbody.decode('utf8'))\\n try:\\n textbody = string.replace(textbody, \\\"_\\\", \\\" \\\")\\n except UnicodeEncodeError:\\n textbody = string.replace(textbody.decode('utf8'), \\\"_\\\", \\\" \\\")\\n# textbody = string.replace(textbody, \\\"?\\\", \\\"\\\")\\n# textbody = string.replace(textbody, \\\".\\\", \\\"\\\")\\n# textbody = string.replace(textbody, \\\"!\\\", \\\"\\\")\\n tweet.text = textbody\\n return tweets\\n\\ndef remove_specialchars_round2(tweets):\\n for tweet in tweets:\\n textbody = tweet.text\\n pattern = re.compile('({|}|[|]|-|:|\\\"|@|\\\\*|\\\\)|\\\\(|\\\\\\\\|.)')\\n try:\\n textbody = pattern.sub(\\\"\\\", textbody)\\n except UnicodeDecodeError:\\n textbody = pattern.sub(\\\"\\\", textbody.decode('utf8'))\\n try:\\n textbody = string.replace(textbody, \\\"_\\\", \\\" \\\")\\n except UnicodeEncodeError:\\n textbody = string.replace(textbody.decode('utf8'), \\\"_\\\", \\\" \\\")\\n# textbody = string.replace(textbody, \\\"?\\\", \\\"\\\")\\n# textbody = string.replace(textbody, \\\".\\\", \\\"\\\")\\n# textbody = string.replace(textbody, \\\"!\\\", \\\"\\\")\\n tweet.text = textbody\\n return tweets\\n\\ndef remove_hastags_and_users(tweets):\\n \\\"\\\"\\\"\\n Removes hashtag labels and user labels, whenever it encounters a hashtag, it increments the hashtag counter in the respective tweet object. Stores both hastags and users in the tweet objects.\\n \\\"\\\"\\\"\\n for tweet in tweets:\\n textbody = \\\"\\\"\\n for word in tweet.text.split(\\\" \\\"):\\n if len(word)<1:continue\\n tweet.word_count = tweet.word_count +1 \\n if not word[0]==\\\"#\\\" and not word[0]==\\\"@\\\":\\n textbody = textbody+word+\\\" \\\"\\n if word[0]==\\\"#\\\":\\n tweet.nrof_hashtags = tweet.nrof_hashtags + 1\\n tweet.hashtags.append(word[1:])\\n textbody = textbody + \\\" \\\"\\n if word[0]==\\\"@\\\":\\n tweet.nrof_usersmentioned = tweet.nrof_usersmentioned +1\\n tweet.users_mentioned.append(word[1:])\\n textbody = textbody + word[1:] + \\\" \\\"\\n tweet.text = textbody \\n return tweets\\n\\ndef count_emoticons(tweets):\\n \\\"\\\"\\\"\\n Counts emoticons, whenever it encounters an emoticon, it increments the emoticon counter in the respective tweet object.\\n \\\"\\\"\\\" \\n for tweet in tweets:\\n textbody = tweet.text\\n tweet.nrof_happyemoticons = string.count(textbody, \\\":)\\\") + string.count(textbody, \\\":D\\\")\\n tweet.nrof_sademoticons = string.count(textbody, \\\":(\\\") + string.count(textbody, \\\":'(\\\") + string.count(textbody, \\\":,(\\\")\\n for emoticon in emoticon_class:\\n tweet.text = string.replace(textbody, emoticon, \\\"\\\")\\n return tweets\\n\\ndef count_exclamations(tweets):\\n \\\"\\\"\\\"\\n Counts exclamation marks and question marks, stores their number for future feature use. Then removes all sentence stops.\\n Possibly handle / in a separate manner; keep only one of the words...?\\n \\\"\\\"\\\"\\n for tweet in tweets:\\n textbody = tweet.text\\n tweet.nrof_exclamations = string.count(textbody, \\\"!\\\")\\n tweet.nrof_questionmarks = string.count(textbody, \\\"?\\\")\\n\\n pattern = re.compile('(\\\\?|!|\\\\.|:)')\\n textbody = pattern.sub(\\\"\\\", textbody)\\n tweet.text = textbody\\n return tweets\\n\\ndef replace_links(tweets):\\n \\\"\\\"\\\"\\n Replaces any links in the tweets with a link class, saves links in the list in the tweet object.\\n \\\"\\\"\\\"\\n for tweet in tweets:\\n links = [word for word in tweet.text.split(' ') if word[:4]==\\\"http\\\" or word[:3]==\\\"www\\\"]\\n link_replaced_text = \\\" \\\".join([\\\"\\\" if word[:4]==\\\"http\\\" or word[:3]==\\\"www\\\" else word for word in tweet.text.split(' ')])\\n tweet.text = link_replaced_text\\n tweet.links = links \\n return tweets\\n\\ndef remove_stopwords(tweets):\\n \\\"\\\"\\\"\\n Removes common stopwords based on a created stopword list.\\n \\\"\\\"\\\"\\n return tweets\\n\\ndef lower_case(tweets):\\n \\\"\\\"\\\"\\n Lowercases every text body in the tweets\\n \\\"\\\"\\\"\\n for tweet in tweets:\\n textbody = tweet.text\\n tweet.text = textbody.lower()\\n return tweets\\n\\ndef stem(tweets):\\n \\\"\\\"\\\"\\n Stems and splits the tweet texts and stores them in the processed words list in the tweet object. \\n \\\"\\\"\\\"\\n return tweets\\n\\ndef tokenize(tweets):\\n for tweet in tweets:\\n splits = tweet.text.split(\\\" \\\")\\n tweet.processed_words = [word for word in splits if len(word)>1]\\n return tweets\\n\\n\\ndef pos_tag(tweets):\\n \\\"\\\"\\\"\\n Uses the POS tagger interface to tag part-of-speech in all the tweets texts, stores it as dict in the tweet objects.\\n \\\"\\\"\\\"\\n print \\\"Tagging...\\\"\\n untagged_texts = []\\n for tweet in tweets:\\n tagger = Tagger()\\n textbody = tweet.text\\n for phrase in re.split(\\\"\\\\.|!|\\\\?\\\", textbody):\\n if len(phrase)<2: continue\\n phrase = string.replace(phrase, \\\"?\\\", \\\"\\\")\\n phrase = string.replace(phrase, \\\"!\\\", \\\"\\\")\\n phrase = string.replace(phrase, \\\".\\\", \\\"\\\")\\n tags = tagger.tag_text(phrase)\\n if tags!=None:\\n tweet.tagged_words.append(tags)\\n print \\\"Untagged texts: \\\"\\n for text in untagged_texts:\\n print text\\n print \\\"Tagging done.\\\"\\n return tweets\\n\\ndef remove_link_classes(tweets):\\n \\\"\\\"\\\"\\n Removes the link classes from the given tweets, returns the positions of these links.\\n \\\"\\\"\\\"\\n for t in tweets:\\n t.link_pos = [m.start() for m in re.finditer('\\\\', t.text)] \\n link_replaced_text = \\\" \\\".join([\\\"\\\" if word==\\\"\\\" else word for word in t.text.split(' ')])\\n t.text = link_replaced_text\\n return tweets\\n\\ndef bing_lexicon_lookup():\\n \\\"\\\"\\\"\\n Fetches the tweets and performs lexicon translatino and lookup.\\n \\\"\\\"\\\"\\n tweets = utils.get_pickles(0)\\n words_with_values = lexicon.perform_bing_sentiment_lexicon_lookup(tweets)\\n print \\\"Storing...\\\"\\n utils.store_sentimentvalues(words_with_values, \\\"models/sentimentvalues_random_dataset\\\")\\n tweets = utils.get_pickles(1)\\n words_with_values = lexicon.perform_bing_sentiment_lexicon_lookup(tweets)\\n print \\\"Storing...\\\"\\n utils.store_sentimentvalues(words_with_values, \\\"models/sentimentvalues_rosenborg_dataset\\\")\\n tweets = utils.get_pickles(2)\\n words_with_values = lexicon.perform_bing_sentiment_lexicon_lookup(tweets)\\n print \\\"Storing...\\\"\\n utils.store_sentimentvalues(words_with_values, \\\"models/sentimentvalues_erna_dataset\\\")\\n\\ndef google_lexicon_lookup():\\n \\\"\\\"\\\"\\n Fetches the tweets and performs lexicon translatino and lookup.\\n \\\"\\\"\\\"\\n tweets = utils.get_pickles(0)\\n words_with_values = lexicon.perform_google_sentiment_lexicon_lookup(tweets)\\n print \\\"Storing...\\\"\\n utils.store_sentimentvalues(words_with_values, \\\"models/google_sentimentvalues_random_dataset\\\")\\n tweets = utils.get_pickles(1)\\n words_with_values = lexicon.perform_google_sentiment_lexicon_lookup(tweets)\\n print \\\"Storing...\\\"\\n utils.store_sentimentvalues(words_with_values, \\\"models/google_sentimentvalues_rosenborg_dataset\\\")\\n tweets = utils.get_pickles(2)\\n words_with_values = lexicon.perform_google_sentiment_lexicon_lookup(tweets)\\n print \\\"Storing...\\\"\\n utils.store_sentimentvalues(words_with_values, \\\"models/google_sentimentvalues_erna_dataset\\\")\\n \\ndef re_analyze():\\n \\\"\\\"\\\"\\n Unpickles preprocessed tweets and performs reanalyzis of these, then stores stats.\\n \\\"\\\"\\\"\\n labels = [\\\"random\\\",'\\\"rosenborg\\\"','\\\"erna solberg\\\"']\\n data = {}\\n worddata = {}\\n for i in xrange(3):\\n tweets = utils.get_pickles(i)\\n analyzer = Analyzer(utils.annotated_datasets[i], tweets)\\n \\n avg_list,words_list= analyzer.analyze()\\n print avg_list\\n worddata[labels[i]] = words_list\\n data[labels[i]] = avg_list\\n plotting.average_wordclasses(worddata, \\\"averages\\\")\\n\\n plotting.detailed_average_wordclasses(data, \\\"averages2\\\")\\n\\ndef pos_analyze():\\n \\\"\\\"\\\"\\n Unpickles preprocessed tweets and performs pos-analysis of them. Then stores the stats in a diagram.\\n \\\"\\\"\\\"\\n tweets = utils.get_pickles(3)\\n subjectivity_data, polarity_data = pos_tag_analyze(tweets)\\n plotting.plot_pos_analysis(subjectivity_data, \\\"sub_analysis\\\")\\n plotting.plot_pos_analysis(polarity_data, \\\"pos_analysis\\\")\\n return True\\n\\ndef initial_preprocess_all_datasets():\\n \\\"\\\"\\\"\\n Runs first preprocessing iteration on all datasets.\\n This is the preprocessing routine performed initially on the datasets before annotation.\\n This routine includes duplicate removal\\n \\\"\\\"\\\"\\n \\n for i in range(0,len(utils.datasets)):\\n #Fetch from dataset\\n tweets = []\\n tweetlines = utils.get_dataset(utils.complete_datasets[i])\\n for tweetline in tweetlines:\\n tweets.append(tweet.to_tweet(tweetline))\\n \\n #Perform preprocessing\\n tweets = remove_duplicates_and_retweets(tweets)\\n\\n #Store back to dataset\\n tweetlines = []\\n for t in tweets:\\n tweetlines.append(t.to_tsv())\\n utils.store_dataset(tweetlines, utils.datasets[i])\\n \\ndef classification_preprocess_all_datasets():\\n \\\"\\\"\\\"\\n Preprocesses all datasets to be ready for classification task.\\n This will include stemming, word correction, lower-casing, hashtag removal, special char removal.\\n \\\"\\\"\\\"\\n \\n for i in range(0,len(utils.annotated_datasets)):\\n tweetlines = utils.get_dataset(utils.annotated_datasets[i])\\n tweets = []\\n for line in tweetlines:\\n if len(line)>1:\\n tweets.append(tweet.to_tweet(line))\\n \\n# tweets = lower_case(tweets)\\n tweets = remove_hastags_and_users(tweets)\\n tweets = count_emoticons(tweets)\\n tweets = replace_links(tweets)\\n tweets = remove_specialchars(tweets)\\n tweets = correct_words(tweets)\\n tweets = stem(tweets)\\n tweets = tokenize(tweets)\\n tweets = pos_tag(tweets)\\n tweets = count_exclamations(tweets)\\n\\n analyzer = Analyzer(utils.annotated_datasets[i], tweets)\\n stats = analyzer.analyze()\\n print stats\\n #store tweets in pickles...\\n print \\\"Storing pickles...\\\"\\n utils.store_pickles(tweets, utils.annotated_datasets[i][24:len(utils.annotated_datasets[i])-4])\\n\\ndef preprocess_tweets(tweets):\\n# tweets = lower_case(tweets)\\n print \\\"Preprocessing\\\"\\n tweets = remove_retweet_tags(tweets)\\n tweets = remove_hastags_and_users(tweets)\\n tweets = count_emoticons(tweets)\\n tweets = replace_links(tweets)\\n tweets = remove_specialchars(tweets)\\n tweets = correct_words(tweets)\\n tweets = stem(tweets)\\n tweets = tokenize(tweets)\\n tweets = pos_tag(tweets)\\n tweets = count_exclamations(tweets)\\n return tweets\\n \\ndef preprocess_tweet(tweet):\\n \\\"\\\"\\\"\\n Preprocess a single tweet\\n \\\"\\\"\\\"\\n tweets = [tweet] \\n tweets = remove_hastags_and_users(tweets)\\n tweets = count_emoticons(tweets)\\n tweets = replace_links(tweets)\\n tweets = remove_specialchars(tweets)\\n tweets = correct_words(tweets)\\n tweets = stem(tweets)\\n tweets = tokenize(tweets)\\n tweets = pos_tag(tweets)\\n tweets = count_exclamations(tweets)\\n return tweets[0]\\n\\n \\nvowels = [u\\\"a\\\", u\\\"e\\\", u\\\"i\\\", u\\\"o\\\", u\\\"u\\\", u\\\"y\\\", u\\\"\\\\u00E6\\\", u\\\"\\\\u00D8\\\", u\\\"\\\\u00E5\\\"]\\nconsonants = [u\\\"b\\\", u\\\"c\\\", u\\\"d\\\", u\\\"f\\\", u\\\"g\\\", u\\\"h\\\", u\\\"j\\\", u\\\"k\\\", u\\\"l\\\", u\\\"m\\\", u\\\"n\\\", u\\\"p\\\", u\\\"q\\\", u\\\"r\\\", u\\\"s\\\", u\\\"t\\\", u\\\"v\\\", u\\\"w\\\", u\\\"x\\\", u\\\"z\\\"]\\n\\nemoticon_class = [\\\":)\\\",\\\":D\\\",\\\":(\\\",\\\":'(\\\"]\\n\\n\\nspecial_chars_removal = '(<|>|{|}|[|]|-|_|*|\\\")'\\n\\nreplacement_chars = {u\\\"&\\\": u\\\"og\\\",\\n u\\\"6amp;\\\": u\\\"og\\\",\\n u\\\"+\\\": u\\\"og\\\"}\\n \\nif __name__ == '__main__':\\n #Testing\\n# tweets = [Tweet(\\\"13:37\\\", \\\"johnarne\\\", \\\"Jeg () haaater drittt!!!? :( #justinbieber\\\"), Tweet(\\\"13:37\\\", \\\"johnarne\\\", \\\"Jeg eeelsker @erna_solberg http://www.erna.no :) #love #jernerna\\\" )]\\n# for tweet in tweets:\\n# tweet.set_sentiment(\\\"negative\\\")\\n# print tweet\\n \\n tweetlines = utils.get_dataset(\\\"test_annotated_data/erna_dataset.tsv\\\")\\n tweets = []\\n for line in tweetlines:\\n if len(line)>1:\\n tweets.append(tweet.to_tweet(line))\\n \\n \\n# tweets = lower_case(tweets)\\n tweets = remove_hastags_and_users(tweets)\\n tweets = count_emoticons(tweets)\\n tweets = replace_links(tweets)\\n tweets = remove_specialchars(tweets)\\n for tweet in tweets:\\n print tweet\\n tweets = correct_words(tweets)\\n tweets = stem(tweets)\\n tweets = tokenize(tweets)\\n for tweet in tweets:\\n print tweet.stat_str()\\n tweets = pos_tag(tweets)\\n tweets = count_exclamations(tweets)\\n for tweet in tweets:\\n print tweet.stat_str()\\n \\n analyzer = Analyzer(\\\"test_annotated_data/erna_dataset.tsv\\\", tweets)\\n stats = analyzer.analyze()\\n print stats\\n \\n \\n\\n\"\n }\n]"},"num_files":{"kind":"number","value":24,"string":"24"}}},{"rowIdx":1174,"cells":{"repo_name":{"kind":"string","value":"myousif9/MP2RAGE-wrapper"},"repo_url":{"kind":"string","value":"https://github.com/myousif9/MP2RAGE-wrapper"},"snapshot_id":{"kind":"string","value":"71f9d87e076a8acea7a5c809f6b2c2f2a50f2e00"},"revision_id":{"kind":"string","value":"ee7315dc25e568f7898072b9d2a1572e7114e2b9"},"directory_id":{"kind":"string","value":"7e1b8f62e6c708d948e6b5c2c5cb45d9d6131191"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"string","value":"2020-07-01T19:58:18.199988"},"revision_date":{"kind":"string","value":"2020-01-15T16:27:45"},"committer_date":{"kind":"string","value":"2020-01-15T16:27:45"},"github_id":{"kind":"number","value":201281431,"string":"201,281,431"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.8021390438079834,"alphanum_fraction":0.8181818127632141,"avg_line_length":30.16666603088379,"blob_id":"9b570d4855df2ed456df5200b5b14ee82ffbaa30","content_id":"3bd2e1a42f3d33f63ca1befbd4f85d12be2d9128","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":187,"license_type":"no_license","max_line_length":94,"num_lines":6,"path":"/README.md","repo_name":"myousif9/MP2RAGE-wrapper","src_encoding":"UTF-8","text":"# MP2RAGE-wrapper\n\nnipype wrapper for running background remover from Jose Marque's MP2RAGE related scripts repository\n\nlink to source:\nhttps://github.com/JosePMarques/MP2RAGE-related-scripts\n"},{"alpha_fraction":0.5642633438110352,"alphanum_fraction":0.5753273367881775,"avg_line_length":54.9072151184082,"blob_id":"0ce60744ed1a3b3363c0962f49a9b472ebe95966","content_id":"593d2a838d619fb30db4083a43f3e87d76bf1403","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5423,"license_type":"no_license","max_line_length":160,"num_lines":97,"path":"/create_pipeline_bg_remover.py","repo_name":"myousif9/MP2RAGE-wrapper","src_encoding":"UTF-8","text":"from bg_remover_wrapper import bgremover\nfrom nipype.interfaces.io import DataGrabber, DataSink\nfrom nipype.interfaces import utility as niu\nfrom nipype.pipeline import Node, MapNode, Workflow\nfrom nipype.interfaces.utility import Function\nfrom bids.layout import BIDSLayout\nimport sys\nimport os\n\ndef replace_slash_fn(filename): \n renamed=\"_\".join(str(filename).split(\"/\")) \n return renamed\nreplace_slash = Function(input_names=[\"filename\"],\n output_names=[\"renamed\"],\n function=replace_slash_fn)\n\ndef create_pipeline_bgremover(bids_dir,work_dir,out_dir,subjects,sessions,reg,uni_match_pattern,inv1_match_pattern,inv2_match_pattern):\n layout=BIDSLayout(bids_dir)\n for subject in subjects:\n if layout.get_sessions(subject=subject)==[]:\n if sessions==['.*']:\n first_uni_files=first_uni_files+layout.get(subject=subject,modality='anat',extensions='.*UNI.*.nii.*',)\n first_inv1_files=first_inv1_files+layout.get(subject=subject,modality='anat',extention='.*inv-1.*.nii.*')\n first_inv2_files=first_inv2_files+layout.get(subject=subject,modality='anat',extention='.*inv-2.*.nii.*')\n else:\n print(\"Warning: Session filter applied, but subject \"+subject+\"has no bids session information. This subject has been ignored.\")\n else:\n for session in sessions:\n first_uni_files=first_uni_files+layout.get(subject=subject,session=session,modality='anat',extensions='.*UNI.*.nii.*',)\n first_inv1_files=first_inv1_files+layout.get(subject=subject,session=session,modality='anat',extention='.*inv-1.*.nii.*')\n first_inv2_files=first_inv2_files+layout.get(subject=subject,session=session,modality='anat',extention='.*inv-2.*.nii.*')\n uni_folders=[]\n for img in first_uni_files:\n full_dirname=os.path.dirname(img.filename)\n remove_base_dir=full_dirname.replace(bids_dir,'')\n remove_leading_slash=remove_base_dir.lstrip(os.sep)\n uni_folders.append(remove_leading_slash)\n list(set(uni_folders)).sort()\n\n inv1_folders=[]\n for img in first_inv1_files:\n full_dirname=os.path.dirname(img.filename)\n remove_base_dir=full_dirname.replace(bids_dir,'')\n remove_leading_slash=remove_base_dir.lstrip(os.sep)\n inv1_folders.append(remove_leading_slash)\n list(set(inv1_folders)).sort()\n\n inv2_folders=[]\n for img in first_inv2_files:\n full_dirname=os.path.dirname(img.filename)\n remove_base_dir=full_dirname.replace(bids_dir,'')\n remove_leading_slash=remove_base_dir.lstrip(os.sep)\n inv2_folders.append(remove_leading_slash)\n list(set(inv2_folders)).sort()\n\n infosource_uni = Node(niu.IdentityInterface(fields=['uni']), name='infosource_uni')\n infosource_uni.iterables = ('uni',uni_folders)\n infosource_inv1 = Node(niu.IdentityInterface(fields=['inv1']),name='infosource_inv1')\n infosource_inv1.iterables = ('inv1',inv1_folders)\n infosource_inv2 = Node(niu.IdentityInterface(fields=['inv2']),name='infosource_inv2')\n infosource_inv2.iterables = ('inv2',inv2_folders)\n\n datasource=Node(DataGrabber(infields=['uni','inv1','inv2'],outfields=['uni_image','inv1_image','inv2_image']),name='datasource')\n datasource.inputs.field_template=dict(\n uni_image='%s/'+uni_match_pattern+'.nii*',\n inv1_image='%d/'+inv1_match_pattern+'.nii*',\n inv2_image='%f/'+inv2_match_pattern+'.nii*')\n datasource.inputs.sort_filelist=True\n datasource.inputs.template=\"*\"\n datasource.inputs.base_directory=bids_dir\n \n t1w_gen = Node(bgremover(reg=reg),name = 'background_remover')\n \n datasink = Node(DataSink(),name = 'datasink')\n datasink.inputs.base_directory = out_dir +'/bg_remover/'\n datasink.inputs.parameterization=False\n\n rename_infosource=Node(replace_slash,\"rename_infosource\")\n rename_t1w=Node(niu.Rename(format_string=\"%(uni)s-T1w\", keep_ext = True),\"rename_T1w\")\n \n pipelineDir=work_dir\n wf = Workflow(name='bg_remover')\n wf.base_dir=pipelineDir\n wf.config['excecution']['remove_unnecessary_outputs']=False\n wf.connect([\n (infosource_uni,datasource,[('uni','uni')]),\n (infosource_inv1,datasource,[('inv1','inv1')]),\n (infosource_inv2,datasource,[('inv2','inv2')]),\n (datasource, t1w_gen, [('uni_image','uni_in'),\n ('inv1_image','inv1_in'),\n ('inv2_image','inv2_in')]),\n (t1w_gen, datasink, [('out_file','in_file')]),\n (infosource_uni,rename_infosource, [('uni','filename')]),\n (rename_infosource,rename_t1w,[('renamed','uni')]),\n (rename_t1w,datasink,[('out_file','@')])\n ])\n return wf\n"},{"alpha_fraction":0.5473434329032898,"alphanum_fraction":0.5523894429206848,"avg_line_length":50.04545593261719,"blob_id":"95c72317cce61e7c1f4ed808b3269c4a8eb7a032","content_id":"07d55dd9d82cbcca19f2334aa2a0b55f4357eebf","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3369,"license_type":"no_license","max_line_length":100,"num_lines":66,"path":"/bg_remover_wrapper.py","repo_name":"myousif9/MP2RAGE-wrapper","src_encoding":"UTF-8","text":"from nipype.interfaces.matlab import MatlabCommand\nfrom nipype.interfaces.base import TraitedSpec, BaseInterface, BaseInterfaceInputSpec,File,traits\nimport os\nfrom string import Template\n\nmatlab_script_loc = os.path.join(os.path.dirname(os.path.realpath(__file__)),'matlab_script)')\nclass bgremover_InputSpec(BaseInterfaceInputSpec):\n uni_in = File(exists=True,\n desc='input file for UNI image',\n argstr=\"%s\",\n mandatory=True)\n inv1_in = File(exisits=True,\n desc='input file for INV1 image',\n argstr=\"%s\",\n mandatory=True)\n inv2_in = File(exisits=True,\n desc='input file for INV2 image',\n argstr=\"%s\",\n mandatory=True)\n reg = traits.Float(desc='input value for regularization')\n out_file = File('t1w_gen.nii.gz',\n desc='name of output T1w image',\n genfile=True,\n usedefault=True)\n\nclass bgremover_OutputSpec(TraitedSpec):\n out_file=File(desc=\"path/name of T1w file (if generated)\",usedefault=True)\n\nclass bgremover(BaseInterface):\n input_spec = bgremover_InputSpec\n output_spec = bgremover_OutputSpec\n def _run_interface(self, runtime):\n# d = dict(uni = self.inputs.uni_in,\n# inv1 = self.inputs.inv1_in,\n# inv2 = self.inputs.inv2_in,\n# reg = self.inputs.reg,\n# denoise = self.inputs.out_file)\n\n with open(os.path.join(matlab_script_loc,'DemoRemoveBackgroundNoise.m'),'r') as script_file:\n script_content = script_file.read()\n \n script = script_content.format(uni = self.inputs.uni_in,\n inv1 = self.inputs.inv1_in,\n inv2 = self.inputs.inv2_in,\n denoise = self.inputs.out_file,\n reg = self.inputs.reg)\n \n mlab = MatlabCommand(script=script, mfile=True)\n mlab.inputs.paths = [os.path.join(matlab_script_loc,'func/RobustCombination.m'),\n os.path.join(matlab_script_loc,'nii_func/load_nii_ext.m'),\n os.path.join(matlab_script_loc,'nii_func/load_nii_hdr.m'),\n os.path.join(matlab_script_loc,'nii_func/load_untouch0_nii_hdr.m'),\n os.path.join(matlab_script_loc,'nii_func/load_untouch_nii.m'),\n os.path.join(matlab_script_loc,'nii_func/load_untouch_nii_hdr.m'),\n os.path.join(matlab_script_loc,'nii_func/load_untouch_nii_img.m'),\n os.path.join(matlab_script_loc,'nii_func/save_nii_ext.m'),\n os.path.join(matlab_script_loc,'nii_func/save_untouch0_nii_hdr.m'),\n os.path.join(matlab_script_loc,'nii_func/save_untouch_nii.m'),\n os.path.join(matlab_script_loc,'nii_func/save_untouch_nii_hdr.m'),\n os.path.join(matlab_script_loc,'nii_func/verify_nii_ext.m')]\n result = mlab.run()\n return result.runtime\n def _list_outputs(self):\n outputs = self._outputs().get()\n outputs['out_file'] = os.path.abspath(self.inputs.out_file)\n return outputs\n"},{"alpha_fraction":0.5271561145782471,"alphanum_fraction":0.5300071239471436,"avg_line_length":53.8125,"blob_id":"1f46970bb7b199291f8c4b8d6027b47f813dc300","content_id":"ef6a144e21651e7e389f761610e08b4726d4a972","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":7015,"license_type":"no_license","max_line_length":124,"num_lines":128,"path":"/run.py","repo_name":"myousif9/MP2RAGE-wrapper","src_encoding":"UTF-8","text":"from create_pipeline_bg_remover import create_pipeline_bgremover\nimport os\n\nif __name__==\"__main__\":\n from argparse import ArgumentParser, RawTextHelpFormatter\n from nipype import config, logging\n defstr = ' (default %(default)s)'\n parser = ArgumentParser(description=__doc__,\n formatter_class=RawTextHelpFormatter)\n\n parser.add_argument('bids_dir',help='the directory with the input dataset formatted according to the BIDS standard.')\n parser.add_argument('output_dir', help='The directory where the output files '\n 'should be stored. If you are running group level analysis '\n 'this folder should be prepopulated with the results of the'\n 'participant level analysis.')\n parser.add_argument('--participant_label', help='The label(s) of the participant(s) that should be analyzed. The label '\n 'corresponds to sub- from the BIDS spec '\n '(so it does not include \"sub-\"). If this parameter is not '\n 'provided all subjects should be analyzed. Multiple '\n 'participants can be specified with a space separated list.',\n default=['.*'],\n nargs=\"+\") \n parser.add_argument('--session_label', help='The label(s) of the session(s) that should be analyzed. The label '\n 'corresponds to ses- from the BIDS spec '\n '(so it does not include \"ses-\"). If this parameter is not '\n 'provided all sessions should be analyzed. Multiple '\n 'sessions can be specified with a space separated list.',\n default=['.*'],\n nargs=\"+\") \n parser.add_argument(\"-w\", \"--work_dir\", dest=\"work_dir\",\n help=\"Work directory. Defaults to /scratch\")\n parser.add_argument(\"-l\", \"--log_dir\", dest=\"log_dir\",\n help=\"Nipype output log directory. Defaults to /log\")\n parser.add_argument(\"-c\", \"--crash_dir\", dest=\"crash_dir\",\n help=\"Nipype crash dump directory. Defaults to /crash_dump\")\n parser.add_argument(\"-p\", \"--plugin\", dest=\"plugin\",\n default='Linear',\n help=\"Plugin to use\")\n parser.add_argument(\"--plugin_args\", dest=\"plugin_args\",\n help=\"Plugin arguments\")\n parser.add_argument(\"--keep_unnecessary_outputs\", dest=\"keep_unnecessary_outputs\",\n action='store_true',default=False,\n help=\"keep all nipype node outputs, even if unused\")\n parser.add_argument('--uni_match_pattern', dest=\"uni_match_pattern\",\n default='*UNI*',\n help='Pattern used to match UNI images and json files '\n 'in anat folder (leave extension out of pattern). The '\n 'pattern may contain simple shell-style wildcards a la '\n 'fnmatch. However, unlike fnmatch, filenames starting with '\n 'a dot are special cases that are not matched by \\'*\\' and '\n '\\'?\\' patterns. Example usage: *acq-uni*') \n parser.add_argument('--inv1_match_pattern', dest=\"inv1_match_pattern\",\n default='*inv-1*',\n help='Pattern used to match inv1 images and json files '\n 'in anat folder (leave extension out of pattern). The '\n 'pattern may contain simple shell-style wildcards a la '\n 'fnmatch. However, unlike fnmatch, filenames starting with '\n 'a dot are special cases that are not matched by \\'*\\' and '\n '\\'?\\' patterns. Example usage: *inv-1*') \n parser.add_argument('--inv2_pattern', dest=\"inv2_match_pattern\",\n default='*inv-2*',\n help='Pattern used to match inv2 images and json files '\n 'in anat folder (leave extension out of pattern). The '\n 'pattern may contain simple shell-style wildcards a la '\n 'fnmatch. However, unlike fnmatch, filenames starting with '\n 'a dot are special cases that are not matched by \\'*\\' and '\n '\\'?\\' patterns. Example usage: *inv-2*') \n parser.add_argument(\"--regularization\", dest=\"regularization\",\n default=10,\n help=\"regularization parameter\")\n args = parser.parse_args()\n \n\n bids_dir=args.bids_dir\n out_dir=args.output_dir\n \n uni_match_pattern=args.uni_match_pattern\n inv1_match_pattern=args.inv1_match_pattern\n inv2_match_pattern=args.inv2_match_pattern \n subjects=args.participant_label\n sessions=args.session_label\n \n if args.work_dir:\n work_dir = os.path.abspath(args.work_dir)\n else:\n work_dir = os.path.join(out_dir, 'scratch')\n if args.log_dir:\n log_dir = os.path.abspath(args.log_dir)\n else:\n tmp=\"log-\"+\"_\".join(subjects)+'-'+\"_\".join(sessions)\n tmp=tmp.replace(\".*\",\"all\").replace(\"*\",\"star\")\n log_dir = os.path.join(out_dir, 'logs',tmp)\n if args.crash_dir:\n crash_dir = os.path.abspath(args.crash_dir)\n else:\n crash_dir = os.path.join(out_dir, 'crash_dump')\n if not os.path.exists(log_dir):\n os.makedirs(log_dir)\n \n config.update_config({'logging': {\n 'log_directory': log_dir,\n 'log_to_file': True,\n },\n 'execution': {\n 'crashdump_dir': crash_dir,\n 'crashfile_format': 'txt', \n }})\n logging.update_logging(config)\n \n plugin=args.plugin\n plugin_args=args.plugin_args\n keep_unnecessary_outputs=args.keep_unnecessary_outputs\n\n regularization = float(args.regularization)\n\n wf_bg_remover = create_pipeline_bgremover(bids_dir=bids_dir,\n work_dir=work_dir,\n out_dir=out_dir,\n subjects=subjects,\n sessions=sessions,\n reg=regularization,\n uni_match_pattern=uni_match_pattern,\n inv1_match_pattern=inv1_match_pattern,\n inv2_match_pattern=inv2_match_pattern)\n if args.plugin_args:\n exec_bg_remover=wf_bg_remover.run(args.plugin, plugin_args=eval(args.plugin_args))\n else:\n exec_bg_remover=wf_bg_remover.run(args.plugin)"},{"alpha_fraction":0.774193525314331,"alphanum_fraction":0.8387096524238586,"avg_line_length":31,"blob_id":"c51672e431411a2d461c6088fad7422af71976c9","content_id":"a73264ddbc22374815d1a32a83ee6457d71bcab1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":31,"license_type":"no_license","max_line_length":31,"num_lines":1,"path":"/MP2RAGE-wrapper/__init__.py","repo_name":"myousif9/MP2RAGE-wrapper","src_encoding":"UTF-8","text":"from .t1w_wrapper import T1wgen"}],"string":"[\n {\n \"alpha_fraction\": 0.8021390438079834,\n \"alphanum_fraction\": 0.8181818127632141,\n \"avg_line_length\": 30.16666603088379,\n \"blob_id\": \"9b570d4855df2ed456df5200b5b14ee82ffbaa30\",\n \"content_id\": \"3bd2e1a42f3d33f63ca1befbd4f85d12be2d9128\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 187,\n \"license_type\": \"no_license\",\n \"max_line_length\": 94,\n \"num_lines\": 6,\n \"path\": \"/README.md\",\n \"repo_name\": \"myousif9/MP2RAGE-wrapper\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# MP2RAGE-wrapper\\n\\nnipype wrapper for running background remover from Jose Marque's MP2RAGE related scripts repository\\n\\nlink to source:\\nhttps://github.com/JosePMarques/MP2RAGE-related-scripts\\n\"\n },\n {\n \"alpha_fraction\": 0.5642633438110352,\n \"alphanum_fraction\": 0.5753273367881775,\n \"avg_line_length\": 54.9072151184082,\n \"blob_id\": \"0ce60744ed1a3b3363c0962f49a9b472ebe95966\",\n \"content_id\": \"593d2a838d619fb30db4083a43f3e87d76bf1403\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5423,\n \"license_type\": \"no_license\",\n \"max_line_length\": 160,\n \"num_lines\": 97,\n \"path\": \"/create_pipeline_bg_remover.py\",\n \"repo_name\": \"myousif9/MP2RAGE-wrapper\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from bg_remover_wrapper import bgremover\\nfrom nipype.interfaces.io import DataGrabber, DataSink\\nfrom nipype.interfaces import utility as niu\\nfrom nipype.pipeline import Node, MapNode, Workflow\\nfrom nipype.interfaces.utility import Function\\nfrom bids.layout import BIDSLayout\\nimport sys\\nimport os\\n\\ndef replace_slash_fn(filename): \\n renamed=\\\"_\\\".join(str(filename).split(\\\"/\\\")) \\n return renamed\\nreplace_slash = Function(input_names=[\\\"filename\\\"],\\n output_names=[\\\"renamed\\\"],\\n function=replace_slash_fn)\\n\\ndef create_pipeline_bgremover(bids_dir,work_dir,out_dir,subjects,sessions,reg,uni_match_pattern,inv1_match_pattern,inv2_match_pattern):\\n layout=BIDSLayout(bids_dir)\\n for subject in subjects:\\n if layout.get_sessions(subject=subject)==[]:\\n if sessions==['.*']:\\n first_uni_files=first_uni_files+layout.get(subject=subject,modality='anat',extensions='.*UNI.*.nii.*',)\\n first_inv1_files=first_inv1_files+layout.get(subject=subject,modality='anat',extention='.*inv-1.*.nii.*')\\n first_inv2_files=first_inv2_files+layout.get(subject=subject,modality='anat',extention='.*inv-2.*.nii.*')\\n else:\\n print(\\\"Warning: Session filter applied, but subject \\\"+subject+\\\"has no bids session information. This subject has been ignored.\\\")\\n else:\\n for session in sessions:\\n first_uni_files=first_uni_files+layout.get(subject=subject,session=session,modality='anat',extensions='.*UNI.*.nii.*',)\\n first_inv1_files=first_inv1_files+layout.get(subject=subject,session=session,modality='anat',extention='.*inv-1.*.nii.*')\\n first_inv2_files=first_inv2_files+layout.get(subject=subject,session=session,modality='anat',extention='.*inv-2.*.nii.*')\\n uni_folders=[]\\n for img in first_uni_files:\\n full_dirname=os.path.dirname(img.filename)\\n remove_base_dir=full_dirname.replace(bids_dir,'')\\n remove_leading_slash=remove_base_dir.lstrip(os.sep)\\n uni_folders.append(remove_leading_slash)\\n list(set(uni_folders)).sort()\\n\\n inv1_folders=[]\\n for img in first_inv1_files:\\n full_dirname=os.path.dirname(img.filename)\\n remove_base_dir=full_dirname.replace(bids_dir,'')\\n remove_leading_slash=remove_base_dir.lstrip(os.sep)\\n inv1_folders.append(remove_leading_slash)\\n list(set(inv1_folders)).sort()\\n\\n inv2_folders=[]\\n for img in first_inv2_files:\\n full_dirname=os.path.dirname(img.filename)\\n remove_base_dir=full_dirname.replace(bids_dir,'')\\n remove_leading_slash=remove_base_dir.lstrip(os.sep)\\n inv2_folders.append(remove_leading_slash)\\n list(set(inv2_folders)).sort()\\n\\n infosource_uni = Node(niu.IdentityInterface(fields=['uni']), name='infosource_uni')\\n infosource_uni.iterables = ('uni',uni_folders)\\n infosource_inv1 = Node(niu.IdentityInterface(fields=['inv1']),name='infosource_inv1')\\n infosource_inv1.iterables = ('inv1',inv1_folders)\\n infosource_inv2 = Node(niu.IdentityInterface(fields=['inv2']),name='infosource_inv2')\\n infosource_inv2.iterables = ('inv2',inv2_folders)\\n\\n datasource=Node(DataGrabber(infields=['uni','inv1','inv2'],outfields=['uni_image','inv1_image','inv2_image']),name='datasource')\\n datasource.inputs.field_template=dict(\\n uni_image='%s/'+uni_match_pattern+'.nii*',\\n inv1_image='%d/'+inv1_match_pattern+'.nii*',\\n inv2_image='%f/'+inv2_match_pattern+'.nii*')\\n datasource.inputs.sort_filelist=True\\n datasource.inputs.template=\\\"*\\\"\\n datasource.inputs.base_directory=bids_dir\\n \\n t1w_gen = Node(bgremover(reg=reg),name = 'background_remover')\\n \\n datasink = Node(DataSink(),name = 'datasink')\\n datasink.inputs.base_directory = out_dir +'/bg_remover/'\\n datasink.inputs.parameterization=False\\n\\n rename_infosource=Node(replace_slash,\\\"rename_infosource\\\")\\n rename_t1w=Node(niu.Rename(format_string=\\\"%(uni)s-T1w\\\", keep_ext = True),\\\"rename_T1w\\\")\\n \\n pipelineDir=work_dir\\n wf = Workflow(name='bg_remover')\\n wf.base_dir=pipelineDir\\n wf.config['excecution']['remove_unnecessary_outputs']=False\\n wf.connect([\\n (infosource_uni,datasource,[('uni','uni')]),\\n (infosource_inv1,datasource,[('inv1','inv1')]),\\n (infosource_inv2,datasource,[('inv2','inv2')]),\\n (datasource, t1w_gen, [('uni_image','uni_in'),\\n ('inv1_image','inv1_in'),\\n ('inv2_image','inv2_in')]),\\n (t1w_gen, datasink, [('out_file','in_file')]),\\n (infosource_uni,rename_infosource, [('uni','filename')]),\\n (rename_infosource,rename_t1w,[('renamed','uni')]),\\n (rename_t1w,datasink,[('out_file','@')])\\n ])\\n return wf\\n\"\n },\n {\n \"alpha_fraction\": 0.5473434329032898,\n \"alphanum_fraction\": 0.5523894429206848,\n \"avg_line_length\": 50.04545593261719,\n \"blob_id\": \"95c72317cce61e7c1f4ed808b3269c4a8eb7a032\",\n \"content_id\": \"07d55dd9d82cbcca19f2334aa2a0b55f4357eebf\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3369,\n \"license_type\": \"no_license\",\n \"max_line_length\": 100,\n \"num_lines\": 66,\n \"path\": \"/bg_remover_wrapper.py\",\n \"repo_name\": \"myousif9/MP2RAGE-wrapper\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from nipype.interfaces.matlab import MatlabCommand\\nfrom nipype.interfaces.base import TraitedSpec, BaseInterface, BaseInterfaceInputSpec,File,traits\\nimport os\\nfrom string import Template\\n\\nmatlab_script_loc = os.path.join(os.path.dirname(os.path.realpath(__file__)),'matlab_script)')\\nclass bgremover_InputSpec(BaseInterfaceInputSpec):\\n uni_in = File(exists=True,\\n desc='input file for UNI image',\\n argstr=\\\"%s\\\",\\n mandatory=True)\\n inv1_in = File(exisits=True,\\n desc='input file for INV1 image',\\n argstr=\\\"%s\\\",\\n mandatory=True)\\n inv2_in = File(exisits=True,\\n desc='input file for INV2 image',\\n argstr=\\\"%s\\\",\\n mandatory=True)\\n reg = traits.Float(desc='input value for regularization')\\n out_file = File('t1w_gen.nii.gz',\\n desc='name of output T1w image',\\n genfile=True,\\n usedefault=True)\\n\\nclass bgremover_OutputSpec(TraitedSpec):\\n out_file=File(desc=\\\"path/name of T1w file (if generated)\\\",usedefault=True)\\n\\nclass bgremover(BaseInterface):\\n input_spec = bgremover_InputSpec\\n output_spec = bgremover_OutputSpec\\n def _run_interface(self, runtime):\\n# d = dict(uni = self.inputs.uni_in,\\n# inv1 = self.inputs.inv1_in,\\n# inv2 = self.inputs.inv2_in,\\n# reg = self.inputs.reg,\\n# denoise = self.inputs.out_file)\\n\\n with open(os.path.join(matlab_script_loc,'DemoRemoveBackgroundNoise.m'),'r') as script_file:\\n script_content = script_file.read()\\n \\n script = script_content.format(uni = self.inputs.uni_in,\\n inv1 = self.inputs.inv1_in,\\n inv2 = self.inputs.inv2_in,\\n denoise = self.inputs.out_file,\\n reg = self.inputs.reg)\\n \\n mlab = MatlabCommand(script=script, mfile=True)\\n mlab.inputs.paths = [os.path.join(matlab_script_loc,'func/RobustCombination.m'),\\n os.path.join(matlab_script_loc,'nii_func/load_nii_ext.m'),\\n os.path.join(matlab_script_loc,'nii_func/load_nii_hdr.m'),\\n os.path.join(matlab_script_loc,'nii_func/load_untouch0_nii_hdr.m'),\\n os.path.join(matlab_script_loc,'nii_func/load_untouch_nii.m'),\\n os.path.join(matlab_script_loc,'nii_func/load_untouch_nii_hdr.m'),\\n os.path.join(matlab_script_loc,'nii_func/load_untouch_nii_img.m'),\\n os.path.join(matlab_script_loc,'nii_func/save_nii_ext.m'),\\n os.path.join(matlab_script_loc,'nii_func/save_untouch0_nii_hdr.m'),\\n os.path.join(matlab_script_loc,'nii_func/save_untouch_nii.m'),\\n os.path.join(matlab_script_loc,'nii_func/save_untouch_nii_hdr.m'),\\n os.path.join(matlab_script_loc,'nii_func/verify_nii_ext.m')]\\n result = mlab.run()\\n return result.runtime\\n def _list_outputs(self):\\n outputs = self._outputs().get()\\n outputs['out_file'] = os.path.abspath(self.inputs.out_file)\\n return outputs\\n\"\n },\n {\n \"alpha_fraction\": 0.5271561145782471,\n \"alphanum_fraction\": 0.5300071239471436,\n \"avg_line_length\": 53.8125,\n \"blob_id\": \"1f46970bb7b199291f8c4b8d6027b47f813dc300\",\n \"content_id\": \"ef6a144e21651e7e389f761610e08b4726d4a972\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 7015,\n \"license_type\": \"no_license\",\n \"max_line_length\": 124,\n \"num_lines\": 128,\n \"path\": \"/run.py\",\n \"repo_name\": \"myousif9/MP2RAGE-wrapper\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from create_pipeline_bg_remover import create_pipeline_bgremover\\nimport os\\n\\nif __name__==\\\"__main__\\\":\\n from argparse import ArgumentParser, RawTextHelpFormatter\\n from nipype import config, logging\\n defstr = ' (default %(default)s)'\\n parser = ArgumentParser(description=__doc__,\\n formatter_class=RawTextHelpFormatter)\\n\\n parser.add_argument('bids_dir',help='the directory with the input dataset formatted according to the BIDS standard.')\\n parser.add_argument('output_dir', help='The directory where the output files '\\n 'should be stored. If you are running group level analysis '\\n 'this folder should be prepopulated with the results of the'\\n 'participant level analysis.')\\n parser.add_argument('--participant_label', help='The label(s) of the participant(s) that should be analyzed. The label '\\n 'corresponds to sub- from the BIDS spec '\\n '(so it does not include \\\"sub-\\\"). If this parameter is not '\\n 'provided all subjects should be analyzed. Multiple '\\n 'participants can be specified with a space separated list.',\\n default=['.*'],\\n nargs=\\\"+\\\") \\n parser.add_argument('--session_label', help='The label(s) of the session(s) that should be analyzed. The label '\\n 'corresponds to ses- from the BIDS spec '\\n '(so it does not include \\\"ses-\\\"). If this parameter is not '\\n 'provided all sessions should be analyzed. Multiple '\\n 'sessions can be specified with a space separated list.',\\n default=['.*'],\\n nargs=\\\"+\\\") \\n parser.add_argument(\\\"-w\\\", \\\"--work_dir\\\", dest=\\\"work_dir\\\",\\n help=\\\"Work directory. Defaults to /scratch\\\")\\n parser.add_argument(\\\"-l\\\", \\\"--log_dir\\\", dest=\\\"log_dir\\\",\\n help=\\\"Nipype output log directory. Defaults to /log\\\")\\n parser.add_argument(\\\"-c\\\", \\\"--crash_dir\\\", dest=\\\"crash_dir\\\",\\n help=\\\"Nipype crash dump directory. Defaults to /crash_dump\\\")\\n parser.add_argument(\\\"-p\\\", \\\"--plugin\\\", dest=\\\"plugin\\\",\\n default='Linear',\\n help=\\\"Plugin to use\\\")\\n parser.add_argument(\\\"--plugin_args\\\", dest=\\\"plugin_args\\\",\\n help=\\\"Plugin arguments\\\")\\n parser.add_argument(\\\"--keep_unnecessary_outputs\\\", dest=\\\"keep_unnecessary_outputs\\\",\\n action='store_true',default=False,\\n help=\\\"keep all nipype node outputs, even if unused\\\")\\n parser.add_argument('--uni_match_pattern', dest=\\\"uni_match_pattern\\\",\\n default='*UNI*',\\n help='Pattern used to match UNI images and json files '\\n 'in anat folder (leave extension out of pattern). The '\\n 'pattern may contain simple shell-style wildcards a la '\\n 'fnmatch. However, unlike fnmatch, filenames starting with '\\n 'a dot are special cases that are not matched by \\\\'*\\\\' and '\\n '\\\\'?\\\\' patterns. Example usage: *acq-uni*') \\n parser.add_argument('--inv1_match_pattern', dest=\\\"inv1_match_pattern\\\",\\n default='*inv-1*',\\n help='Pattern used to match inv1 images and json files '\\n 'in anat folder (leave extension out of pattern). The '\\n 'pattern may contain simple shell-style wildcards a la '\\n 'fnmatch. However, unlike fnmatch, filenames starting with '\\n 'a dot are special cases that are not matched by \\\\'*\\\\' and '\\n '\\\\'?\\\\' patterns. Example usage: *inv-1*') \\n parser.add_argument('--inv2_pattern', dest=\\\"inv2_match_pattern\\\",\\n default='*inv-2*',\\n help='Pattern used to match inv2 images and json files '\\n 'in anat folder (leave extension out of pattern). The '\\n 'pattern may contain simple shell-style wildcards a la '\\n 'fnmatch. However, unlike fnmatch, filenames starting with '\\n 'a dot are special cases that are not matched by \\\\'*\\\\' and '\\n '\\\\'?\\\\' patterns. Example usage: *inv-2*') \\n parser.add_argument(\\\"--regularization\\\", dest=\\\"regularization\\\",\\n default=10,\\n help=\\\"regularization parameter\\\")\\n args = parser.parse_args()\\n \\n\\n bids_dir=args.bids_dir\\n out_dir=args.output_dir\\n \\n uni_match_pattern=args.uni_match_pattern\\n inv1_match_pattern=args.inv1_match_pattern\\n inv2_match_pattern=args.inv2_match_pattern \\n subjects=args.participant_label\\n sessions=args.session_label\\n \\n if args.work_dir:\\n work_dir = os.path.abspath(args.work_dir)\\n else:\\n work_dir = os.path.join(out_dir, 'scratch')\\n if args.log_dir:\\n log_dir = os.path.abspath(args.log_dir)\\n else:\\n tmp=\\\"log-\\\"+\\\"_\\\".join(subjects)+'-'+\\\"_\\\".join(sessions)\\n tmp=tmp.replace(\\\".*\\\",\\\"all\\\").replace(\\\"*\\\",\\\"star\\\")\\n log_dir = os.path.join(out_dir, 'logs',tmp)\\n if args.crash_dir:\\n crash_dir = os.path.abspath(args.crash_dir)\\n else:\\n crash_dir = os.path.join(out_dir, 'crash_dump')\\n if not os.path.exists(log_dir):\\n os.makedirs(log_dir)\\n \\n config.update_config({'logging': {\\n 'log_directory': log_dir,\\n 'log_to_file': True,\\n },\\n 'execution': {\\n 'crashdump_dir': crash_dir,\\n 'crashfile_format': 'txt', \\n }})\\n logging.update_logging(config)\\n \\n plugin=args.plugin\\n plugin_args=args.plugin_args\\n keep_unnecessary_outputs=args.keep_unnecessary_outputs\\n\\n regularization = float(args.regularization)\\n\\n wf_bg_remover = create_pipeline_bgremover(bids_dir=bids_dir,\\n work_dir=work_dir,\\n out_dir=out_dir,\\n subjects=subjects,\\n sessions=sessions,\\n reg=regularization,\\n uni_match_pattern=uni_match_pattern,\\n inv1_match_pattern=inv1_match_pattern,\\n inv2_match_pattern=inv2_match_pattern)\\n if args.plugin_args:\\n exec_bg_remover=wf_bg_remover.run(args.plugin, plugin_args=eval(args.plugin_args))\\n else:\\n exec_bg_remover=wf_bg_remover.run(args.plugin)\"\n },\n {\n \"alpha_fraction\": 0.774193525314331,\n \"alphanum_fraction\": 0.8387096524238586,\n \"avg_line_length\": 31,\n \"blob_id\": \"c51672e431411a2d461c6088fad7422af71976c9\",\n \"content_id\": \"a73264ddbc22374815d1a32a83ee6457d71bcab1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 31,\n \"license_type\": \"no_license\",\n \"max_line_length\": 31,\n \"num_lines\": 1,\n \"path\": \"/MP2RAGE-wrapper/__init__.py\",\n \"repo_name\": \"myousif9/MP2RAGE-wrapper\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from .t1w_wrapper import T1wgen\"\n }\n]"},"num_files":{"kind":"number","value":5,"string":"5"}}},{"rowIdx":1175,"cells":{"repo_name":{"kind":"string","value":"nityamd/Insight"},"repo_url":{"kind":"string","value":"https://github.com/nityamd/Insight"},"snapshot_id":{"kind":"string","value":"6d70e4a181c4782dc3d6a7da500450c9586c81d3"},"revision_id":{"kind":"string","value":"1016eafe3b9968332175a7445f467fab01efd59f"},"directory_id":{"kind":"string","value":"f4f8262a6678f9b4705395878dcbf8e2ba31349c"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"string","value":"2022-12-04T10:42:49.337325"},"revision_date":{"kind":"string","value":"2019-07-25T21:09:08"},"committer_date":{"kind":"string","value":"2019-07-25T21:09:08"},"github_id":{"kind":"number","value":149144556,"string":"149,144,556"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"string","value":"2018-09-17T15:12:36"},"gha_updated_at":{"kind":"string","value":"2019-07-25T21:09:15"},"gha_pushed_at":{"kind":"string","value":"2022-12-02T13:06:59"},"gha_language":{"kind":"string","value":"HTML"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6189896464347839,"alphanum_fraction":0.6354230046272278,"avg_line_length":40.07500076293945,"blob_id":"9d060bc9b5436648c166160e752fca08acdc90d7","content_id":"713765a6ba43ef4233524c21c8a355832f1cef15","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1643,"license_type":"no_license","max_line_length":83,"num_lines":40,"path":"/scripts/classifier.py","repo_name":"nityamd/Insight","src_encoding":"UTF-8","text":"import numpy as np\nimport pandas as pd\nimport itertools\nimport gensim\nimport keras\nimport nltk\nimport re\nimport codecs\nimport os\n\nfrom nltk.stem.snowball import SnowballStemmer\nfrom nltk.tokenize import RegexpTokenizer\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\nfrom sklearn.decomposition import PCA, TruncatedSVD\nfrom sklearn.metrics import accuracy_score, f1_score, precision_score\nfrom sklearn.metrics import confusion_matrix, recall_score, classification_report\nfrom sklearn.linear_model import LogisticRegression\n\ndef ttsplit(thing):\n list_corpus = df[str(thing)].tolist()\n list_labels = df['class_label'].tolist()\n indices = np.arange(len(list_labels))\n x_train, x_test, y_train, y_test, i1, i2 = train_test_split(list_corpus,\n list_labels,\n indices,\n test_size=0.2,\n random_state=40)\n return x_train, x_test, y_train, y_test, i1, i2\n\nx1_ti, x2_ti, y1_ti, y2_ti, i1, i2 = ttsplit('title')\nx1_des, x2_des, y1_des, y2_des, i1, i2 = ttsplit('description')\nclf_tfidf = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg',\n multi_class='multinomial', n_jobs=-1, random_state=40)\n\nclf_tfidf.fit(X_train_tfidf, y_train)\ny_predicted_tfidf = clf_tfidf.predict(X_test_tfidf)\n"},{"alpha_fraction":0.5963909029960632,"alphanum_fraction":0.6021082997322083,"avg_line_length":32.9151496887207,"blob_id":"e7d8de6cf5bf751d182aa80dddb7455bf5d6611e","content_id":"7126c1fb68708f1804a49d7829b5f96183757933","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5597,"license_type":"no_license","max_line_length":89,"num_lines":165,"path":"/scripts/peppa.py","repo_name":"nityamd/Insight","src_encoding":"UTF-8","text":"\nimport pandas as pd\nimport numpy as np\nimport requests\nimport os\n\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom apiclient.discovery import build\nfrom apiclient.errors import HttpError\n#from google.auth.tools import argparser\nfrom youtube_transcript_api import YouTubeTranscriptApi\n\nf = open('/Users/nitya/youtopian/youtube', 'r')\nDEVELOPER_KEY = f.read().strip()\nYOUTUBE_API_SERVICE_NAME = \"youtube\"\nYOUTUBE_API_VERSION = \"v3\"\nyoutube = build(YOUTUBE_API_SERVICE_NAME, YOUTUBE_API_VERSION,developerKey=DEVELOPER_KEY)\n\n\ndef get_video_id(q, max_results,token, order=\"relevance\",\n location=None, location_radius=None):\n\n search_response = youtube.search().list(\n q=q, type=\"video\", pageToken=token,part=\"id,snippet\",\n maxResults=max_results, location=location,\n locationRadius=location_radius, safeSearch = 'none').execute()\n videoId = []\n title = []\n description = []\n tok = search_response['nextPageToken']\n\n for search_result in search_response.get(\"items\", []):\n if search_result[\"id\"][\"kind\"] == \"youtube#video\":\n title.append(search_result['snippet']['title'])\n videoId.append(search_result['id']['videoId'])\n response = youtube.videos().list(part='statistics, snippet',\n id=search_result['id']['videoId']\n ).execute()\n description.append(response['items'][0]['snippet']['description'])\n\n ydict = {'title':title,'videoId':videoId,\n 'description':description}\n return ydict, tok\n\ndef keyword_search(keyword, results_enter):\n\n tests, token0 = get_video_id(keyword, 50,order = \"relevance\",\n token= None)\n newdf = pd.DataFrame(tests)\n #Total number of results I need..:\n results = results_enter-50\n\n while results>50:\n tests, token0 = get_video_id(keyword, 50,order = \"relevance\",\n token= str(token0))\n df = pd.DataFrame(tests)\n newdf = pd.concat([newdf,df]).reset_index(drop=True)\n results = results-50\n\n filename = keyword.replace(\" \", \"_\") + '_' + str(results_enter)\n newdf.to_csv(filename)\n\n return\n\n\n# -- Getting Comments -----\n\ndef get_comment_threads(video_id, results):\n results = youtube.commentThreads().list(\n part=\"snippet\",\n videoId=video_id,\n textFormat=\"plainText\",\n maxResults = results\n ).execute()\n for item in results[\"items\"]:\n comment = item[\"snippet\"][\"topLevelComment\"]\n author = comment[\"snippet\"][\"authorDisplayName\"]\n text = comment[\"snippet\"][\"textDisplay\"]\n #print \"Comment by %s: %s\" % (author, text)\n ydict = {'videoId':videoId, 'Comment':comment}\n return ydict\n\n\n# Call the API's comments.list method to list the existing comment replies.\ndef get_comments(youtube, parent_id):\n results = youtube.comments().list(\n part=\"snippet\",\n parentId=parent_id,\n textFormat=\"plainText\",\n maxResults=100\n ).execute()\n for item in results[\"items\"]:\n author = item[\"snippet\"][\"authorDisplayName\"]\n text = item[\"snippet\"][\"textDisplay\"]\n #print \"Comment by %s: %s\" % (author, text)\n\n return results[\"items\"]\n\n#Search for videos by channel ID.....\n\ndef channel_snatch(q, max_results, token, channelId,\n order=\"relevance\"):\n\n search_response = youtube.search().list(\n q=q, type=\"video\", pageToken=token,part=\"id,snippet\",\n maxResults=max_results, channelId = channelId,\n location=None, locationRadius=None,\n safeSearch = 'strict').execute()\n videoId = []\n title = []\n description = []\n tok = search_response['nextPageToken']\n\n for search_result in search_response.get(\"items\", []):\n if search_result[\"id\"][\"kind\"] == \"youtube#video\":\n title.append(search_result['snippet']['title'])\n videoId.append(search_result['id']['videoId'])\n response = youtube.videos().list(part='statistics, snippet',\n id=search_result['id']['videoId']\n ).execute()\n description.append(response['items'][0]['snippet']['description'])\n\n ydict = {'title':title,'videoId':videoId,\n 'description':description}\n return ydict, tok\n\n\ndef search_by_channel(keyword, results_enter, channelId):\n\n tests, token0 = channel_snatch(keyword, 50,token= None,\n channelId = channelId)\n newdf = pd.DataFrame(tests)\n #Total number of results I need..:\n results = results_enter-50\n\n while results>50:\n tests, token0 = channel_snatch(keyword, 50,\n token= str(token0),\n channelId = channelId)\n df = pd.DataFrame(tests)\n newdf = pd.concat([newdf,df]).reset_index(drop=True)\n results = results-50\n\n filename = keyword.replace(\" \", \"_\") + '_' + 'channel' + str(results_enter)\n newdf.to_csv(filename)\n return\n\n\n\n# Call the API's captions.list method to list the existing caption tracks.\ndef list_captions(youtube, video_id):\n results = youtube.captions().list(\n part=\"snippet\",\n videoId=video_id\n ).execute()\n for item in results[\"items\"]:\n id = item[\"id\"]\n name = item[\"snippet\"][\"name\"]\n language = item[\"snippet\"][\"language\"]\n #print \"Caption track '%s(%s)' in '%s' language.\" % (name, id, language)\n\n return results[\"items\"]\n\n #transcripts using requests requests..?\n"},{"alpha_fraction":0.6258603930473328,"alphanum_fraction":0.6317600607872009,"avg_line_length":27.23611068725586,"blob_id":"45baf9d1195981ee3d8885d3031f80238b1a4765","content_id":"e9533c2dd6fe15e8878d2af52a2263b479b68b6d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2034,"license_type":"no_license","max_line_length":76,"num_lines":72,"path":"/my_flask_app/views.py","repo_name":"nityamd/Insight","src_encoding":"UTF-8","text":"\nimport json\nimport pickle\nimport logging\n\nimport pandas as pd\nfrom flask import json, jsonify, request, render_template\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n\nfrom my_flask_app import app\nfrom ytapi_extractor import videoId_data, video_query\n\n\n\nafile = open(r'tfidf_model_description.pkl', 'rb')\ndes_model = pickle.load(afile, encoding = 'latin1')\nafile.close()\n\nafile = open(r'tfidf_vectorizer_description.pkl', 'rb')\ndes_vectorizer = pickle.load(afile, encoding = 'latin1')\nafile.close()\n\nafile = open(r'tfidf_model_title.pkl', 'rb')\nti_model = pickle.load(afile, encoding = 'latin1')\nafile.close()\n\nafile = open(r'tfidf_vectorizer_title.pkl', 'rb')\nti_vectorizer = pickle.load(afile, encoding='latin1')\nafile.close()\n\n\n\n@app.route('/')\n@app.route('/index')\ndef index():\n return render_template(\"index.html\")\n\n@app.route('/result', methods=['POST', 'GET'])\ndef result():\n if request.method == 'POST':\n result = request.form\n earl = result['URL']\n df = videoId_data(video_query(earl))\n #more comments here describing what's going on\n des_vector = des_vectorizer.transform(df['description'])\n des_rating = des_model.predict(des_vector)[0]\n ti_vector = ti_vectorizer.transform(df['title'])\n ti_rating = ti_model.predict(ti_vector)[0]\n rating = des_rating + ti_rating\n\n if rating == 2.0:\n output = 'Shady!'\n elif rating == 1.0:\n output = 'Shady? Clean?'\n else:\n output = 'Clean!'\n\n logging.warning(\"I'm so successful!\")\n logging.warning(rating)\n\n return render_template(\"result.html\",\n result = output,\n title = df['title'][0],\n description = df['description'][0])\n\n@app.route('/insight',methods = ['POST', 'GET'])\ndef insight():\n if request.method == 'POST':\n insight = request.form\n return render_template(\"insight.html\",result = result)\n\nif __name__ == '__main__':\n app.run(debug = True)\n"},{"alpha_fraction":0.635708212852478,"alphanum_fraction":0.6547921895980835,"avg_line_length":34.19403076171875,"blob_id":"13e575ebb3491386c16f40c66efb3b299b572851","content_id":"25af60b12e04259cc0f947247803217f8f148345","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2358,"license_type":"no_license","max_line_length":87,"num_lines":67,"path":"/scripts/tfidf.py","repo_name":"nityamd/Insight","src_encoding":"UTF-8","text":"import numpy as np\nimport pandas as pd\nimport itertools\nimport gensim\nimport keras\nimport nltk\nimport re\nimport codecs\nimport os\n\nfrom nltk.stem.snowball import SnowballStemmer\nfrom nltk.tokenize import RegexpTokenizer\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\nfrom sklearn.decomposition import PCA, TruncatedSVD\nfrom sklearn.metrics import accuracy_score, f1_score, precision_score\nfrom sklearn.metrics import confusion_matrix, recall_score, classification_report\nfrom sklearn.linear_model import LogisticRegression\nfrom nltk.tokenize import RegexpTokenizer\n\nos.chdir(\"/Users/nitya/childsplay/data\")\ndf = pd.read_csv('peppa_dataframe_with_labels')\ndf = df.drop_duplicates('videoId')\n\ndef ttsplit(thing):\n ''''\n Train-test splitting with indices\n ''''\n list_corpus = df[str(thing)].tolist()\n list_labels = df['class_label'].tolist()\n indices = np.arange(len(list_labels))\n x_train, x_test, y_train, y_test, i1, i2 = train_test_split(list_corpus,\n list_labels,\n indices,\n test_size=0.2,\n random_state=40)\n return x_train,x_test, y_train, y_test, i1, i2\n\nx1_ti, x2_ti, y1_ti, y2_ti, i1, i2 = ttsplit('title')\nx1_des, x2_des, y1_des, y2_des, i1, i2 = ttsplit('description')\n\n\n\ndef tfidf(data):\n tfidf_vectorizer = TfidfVectorizer()\n\n train = tfidf_vectorizer.fit_transform(data)\n\n return train, tfidf_vectorizer\n\nx1ti_tfidf, tfidf_vectorizer = tfidf(x1_ti)\nx2ti_tfidf = tfidf_vectorizer.transform(x2_ti)\n\nclf_tfidf_ti = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg',\n multi_class='multinomial', n_jobs=-1, random_state=40)\nclf_tfidf_ti.fit(x1ti_tfidf, y1_ti)\n\n\nx1des_tfidf, tfidf_vectorizer = tfidf(x1_des)\nx2des_tfidf = tfidf_vectorizer.transform(x2_des)\n\nclf_tfidf_des = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg',\n multi_class='multinomial', n_jobs=-1, random_state=40)\nclf_tfidf_des.fit(x1des_tfidf, y1_des)\n"},{"alpha_fraction":0.6214178204536438,"alphanum_fraction":0.6244344115257263,"avg_line_length":30.5238094329834,"blob_id":"e192cd8722229c08356252a74d842d4f731d97d4","content_id":"6bfcee5b1bfb4825771a31a7d2abae063d6cddd5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":663,"license_type":"no_license","max_line_length":85,"num_lines":21,"path":"/scripts/preprocessing.py","repo_name":"nityamd/Insight","src_encoding":"UTF-8","text":"\nimport pandas as pd\nimport numpy as np\nimport requests\nimport os\n\ndef sanitize_characters(raw, clean):\n for line in input_file:\n out = line\n output_file.write(line)\n\n\nsanitize_characters(input_file, output_file)\n\ndef standardize_text(df, text_field):\n df[text_field] = df[text_field].str.replace(r\"http\\S+\", \"\")\n df[text_field] = df[text_field].str.replace(r\"http\", \"\")\n df[text_field] = df[text_field].str.replace(r\"@\\S+\", \"\")\n df[text_field] = df[text_field].str.replace(r\"[^A-Za-z0-9(),!?@\\'\\`\\\"\\_\\n]\", \" \")\n df[text_field] = df[text_field].str.replace(r\"@\", \"at\")\n df[text_field] = df[text_field].str.lower()\n return df\n"},{"alpha_fraction":0.6282822489738464,"alphanum_fraction":0.6422319412231445,"avg_line_length":35.198020935058594,"blob_id":"0648c28762c9698a0f6b1efb9e1c187d57c0a42b","content_id":"52d1b4ec9e8f3fe90a94ba896ee09beb6718074c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3656,"license_type":"no_license","max_line_length":87,"num_lines":101,"path":"/scripts/w2v.py","repo_name":"nityamd/Insight","src_encoding":"UTF-8","text":"import numpy as np\nimport pandas as pd\nimport itertools\nimport gensim\nimport keras\nimport nltk\nimport re\nimport codecs\nimport os\n\nfrom nltk.stem.snowball import SnowballStemmer\nfrom nltk.tokenize import RegexpTokenizer\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\nfrom sklearn.decomposition import PCA, TruncatedSVD\nfrom sklearn.metrics import accuracy_score, f1_score, precision_score\nfrom sklearn.metrics import confusion_matrix, recall_score, classification_report\nfrom sklearn.linear_model import LogisticRegression\nfrom nltk.tokenize import RegexpTokenizer\n\nos.chdir(\"/Users/nitya/childsplay/data\")\ndf = pd.read_csv('peppa_dataframe_with_labels')\ndf = df.drop_duplicates('videoId')\n\n\ntokenizer = RegexpTokenizer(r'\\w+')\ndf[\"des_tokens\"] =df[\"description\"].apply(tokenizer.tokenize)\ndf[\"ti_tokens\"] = df[\"title\"].apply(tokenizer.tokenize)\n\n\ndef get_average_word2vec(tokens_list, vector, generate_missing=False, k=300):\n if len(tokens_list)<1:\n return np.zeros(k)\n if generate_missing:\n vectorized = [vector[word] if word in vector else np.random.rand(k) for\n word in tokens_list]\n else:\n vectorized = [vector[word] if word in vector else np.zeros(k) for word\n in tokens_list]\n length = len(vectorized)\n summed = np.sum(vectorized, axis=0)\n averaged = np.divide(summed, length)\n return averaged\n\ndef get_word2vec_embeddings(vectors, data, generate_missing=False):\n embeddings = data['tokens'].apply(lambda x: get_average_word2vec\n (x, vectors, generate_missing=\n generate_missing))\n return list(embeddings)\n\n\nembeddings = get_word2vec_embeddings(word2vec, df)\n\ndef ttsplit(thing):\n '''\n Train-test splitting with indices\n '''\n list_corpus = df[str(thing)].tolist()\n list_labels = df['class_label'].tolist()\n indices = np.arange(len(list_labels))\n x_train, x_test, y_train, y_test, i1, i2 = train_test_split(embeddings,\n list_labels,\n indices,\n test_size=0.2,\n random_state=40)\n return x_train,x_test, y_train, y_test, i1, i2\n\nx1_ti, x2_ti, y1_ti, y2_ti, i1, i2 = ttsplit('title')\nx1_des, x2_des, y1_des, y2_des, i1, i2 = ttsplit('description')\n\n\n\nclf_tfidf_ti = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg',\n multi_class='multinomial', n_jobs=-1, random_state=40)\nclf_tfidf_ti.fit(x1ti_tfidf, y1_ti)\n\n\nx1des_tfidf, tfidf_vectorizer = tfidf(x1_des)\nx2des_tfidf = tfidf_vectorizer.transform(x2_des)\n\nclf_tfidf_des = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg',\n multi_class='multinomial', n_jobs=-1, random_state=40)\nclf_tfidf_des.fit(x1des_tfidf, y1_des)\n\n# os.chdir(\"/Users/nitya/Insight\")\n# import pickle\n# afile = open(r'tfidf_vectorizer_title.pkl', 'wb')\n# pickle.dump(tfidf_vectorizer, afile)\n# afile.close()\n# afile = open(r'tfidf_model_title.pkl', 'wb')\n# pickle.dump(clf_tfidf_ti, afile)\n# afile.close()\n# afile = open(r'tfidf_vectorizer_description.pkl', 'wb')\n# pickle.dump(tfidf_vectorizer, afile)\n# afile.close()\n# afile = open(r'tfidf_model_description.pkl', 'wb')\n# pickle.dump(clf_tfidf_des, afile)\n# afile.close()\n"},{"alpha_fraction":0.6484375,"alphanum_fraction":0.6484375,"avg_line_length":17.285715103149414,"blob_id":"36def481d6dc63a1cba02512a45272830ee76ea3","content_id":"443189e27112baf47f7fd320f31fd4ab974a3f89","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":128,"license_type":"no_license","max_line_length":48,"num_lines":7,"path":"/hobbes/popup.js","repo_name":"nityamd/Insight","src_encoding":"UTF-8","text":"// var app = chrome.runtime.getBackgroundPage();\n\nfunction hello() {\n chrome.tabs.executeScript({\n file: 'alert.js'\n });\n}\n"},{"alpha_fraction":0.7260273694992065,"alphanum_fraction":0.7260273694992065,"avg_line_length":23.33333396911621,"blob_id":"3c83d3576215de3a90da0452790442f1e966b01b","content_id":"aa50bcce038567bc06496a49f475f4c45c27248a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":73,"license_type":"no_license","max_line_length":28,"num_lines":3,"path":"/run.py","repo_name":"nityamd/Insight","src_encoding":"UTF-8","text":"#!/usr/bin/env python\nfrom my_flask_app import app\napp.run(debug = True)\n"},{"alpha_fraction":0.7349397540092468,"alphanum_fraction":0.759036123752594,"avg_line_length":15.600000381469727,"blob_id":"f1a87d64679ccc33a8435994942db8e7761e0f0c","content_id":"91f6eec9a2b24666a81d24b9a13ddce87d7db84c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":83,"license_type":"no_license","max_line_length":40,"num_lines":5,"path":"/README.md","repo_name":"nityamd/Insight","src_encoding":"UTF-8","text":" with value 1. Or increments an existing key by 1.\n \"\"\"\n preb = self.sleft\n if key in self.keyblock:\n preb = self.keyblock[key]\n preb.kids.remove(key)\n val = preb.val + 1\n if preb.right.val == val:\n curb = preb.right\n else:\n curb = Block(val = val, left = preb, right = preb.right)\n preb.right.left, preb.right = curb, curb\n curb.kids.add(key)\n self.keyblock[key] = curb\n if not preb.kids and preb.val != 0:\n preb.left.right, preb.right.left = preb.right, preb.left\n preb.left, preb.right = None, None\n\n def dec(self, key: str) -> None:\n \"\"\"\n Decrements an existing key by 1. If Key's value is 1, remove it from the data structure.\n \"\"\"\n preb = self.keyblock[key]\n preb.kids.remove(key)\n val = preb.val - 1\n if val == 0:\n curb = self.sleft\n elif preb.left.val == val:\n curb = preb.left\n else:\n curb = Block(val = val, left = preb.left, right = preb)\n preb.left.right, preb.left = curb, curb\n curb.kids.add(key)\n if curb.val:\n self.keyblock[key] = curb\n else:\n self.keyblock.pop(key)\n if not preb.kids and preb.val != 0:\n preb.left.right = preb.right\n preb.right.left = preb.left\n preb.left, preb.right = None, None\n def getMaxKey(self) -> str:\n \"\"\"\n Returns one of the keys with maximal value.\n \"\"\"\n if self.sright.left.val == 0:\n return \"\"\n for kid in self.sright.left.kids:\n return kid\n def getMinKey(self) -> str:\n \"\"\"\n Returns one of the keys with Minimal value.\n \"\"\"\n if self.sleft.right.val == 0:\n return \"\"\n for kid in self.sleft.right.kids:\n return kid\n"},{"alpha_fraction":0.5,"alphanum_fraction":0.5239273905754089,"avg_line_length":34.64706039428711,"blob_id":"caad7e2d3175fb23acbaabbbbe08d656d1e50625","content_id":"286564921d95a38a1f721fae12f6d11b9f4c0cf3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1212,"license_type":"no_license","max_line_length":150,"num_lines":34,"path":"/lc1130_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n# dfs+memo /dp\n def mctFromLeafValues(self, arr: List[int]) -> int:\n @lru_cache(None)\n def dfs(i,j):\n if j<=i:\n return 0\n res = float('inf')\n for k in range(i,j):\n res = min(dfs(i,k)+dfs(k+1,j)+max(arr[i:k+1])*max(arr[k+1:j+1]),res)\n return res\n return dfs(0,len(arr)-1)\n\n#https://leetcode.com/problems/minimum-cost-tree-from-leaf-values/discuss/951938/Don't-overthink-about-trees.-It's-a-DPGreedy-problem.\n# greedy\n def mctFromLeafValues(self, A):\n res = 0\n while len(A) > 1:\n i = A.index(min(A))\n res += min(A[i - 1:i] + A[i + 1:i + 2]) * A.pop(i)\n return res\n\n#https://leetcode.com/problems/minimum-cost-tree-from-leaf-values/discuss/478708/RZ-Summary-of-all-the-solutions-I-have-learned-from-Discuss-in-Python\n def mctFromLeafValues(self, A):\n res = 0\n stack = [float(\"inf\")]\n for a in A:\n while stack[-1] <= a:\n mid = stack.pop()\n res += mid * min(stack[-1], a)\n stack.append(a)\n while len(stack) > 2:\n res += stack.pop() * stack[-1]\n return res\n"},{"alpha_fraction":0.47859495878219604,"alphanum_fraction":0.5005488395690918,"avg_line_length":49.61111068725586,"blob_id":"c0207e634ca08ef5cf5b3b468469955e706f5921","content_id":"1008830c7d9724ab32c50acb0a1fd102f5e668a4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":911,"license_type":"no_license","max_line_length":83,"num_lines":18,"path":"/lc1066_dijkstra.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def assignBikes(self, workers: List[List[int]], bikes: List[List[int]]) -> int:\n def md(p1, p2):\n return abs(p1[0] - p2[0])+abs(p1[1] - p2[1])\n heap = [(0, 0, \"0\"*len(bikes))] # total distance, worker id, bike status\n visited = collections.defaultdict(lambda:float(\"inf\"))\n while heap:\n total, idx, bike_status = heapq.heappop(heap)\n if idx == len(workers):\n return total\n for i, b in enumerate(bikes):\n if bike_status[i] != '1':\n n_total = total + md(workers[idx], b)\n n_bike_status = bike_status[:i]+'1'+bike_status[i+1:]\n if n_total < visited[(idx+1, n_bike_status)]:\n visited[(idx+1, n_bike_status)] = n_total\n heapq.heappush(heap,[n_total, idx+1, n_bike_status])\n return -1\n"},{"alpha_fraction":0.3847073018550873,"alphanum_fraction":0.3930704891681671,"avg_line_length":31.19230842590332,"blob_id":"8f6b4b065aebc54b6ef019fe324f010cd740a6c5","content_id":"5a3402739bfd87a30b427b94277abe8ab6c21bdc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":837,"license_type":"no_license","max_line_length":63,"num_lines":26,"path":"/lc1520_greedy.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxNumOfSubstrings(self, s: str) -> List[str]:\n bdry = {}\n for i, c in enumerate(s):\n if c not in bdry:\n bdry[c] = [i, i]\n else:\n bdry[c][1] = i\n for c in set(s):\n left, right = bdry[c]\n l, r = left, right\n while True:\n for oc in set(s[left:right+1]):\n l = min(l, bdry[oc][0])\n r = max(r, bdry[oc][1])\n if [left, right] == [l, r]: break\n left, right = l, r\n bdry[c] = [l, r]\n\n intervals = sorted(bdry.values(), key = lambda x: x[1])\n rslt, last = [], -1\n for st, ed in intervals:\n if st > last:\n rslt.append(s[st:ed+1])\n last = ed\n return rslt\n"},{"alpha_fraction":0.4829443395137787,"alphanum_fraction":0.5,"avg_line_length":25.5238094329834,"blob_id":"b3680fc94b0936a6b8b0bf81bf9cd779973b35cb","content_id":"f699760b0e816d92640b72705f2db70f13e03c00","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1114,"license_type":"no_license","max_line_length":97,"num_lines":42,"path":"/lc705_oop.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class MyHashSet:\n\n def __init__(self):\n \"\"\"\n Initialize your data structure here.\n \"\"\"\n self.holder = [[] for _ in range(512)]\n\n def add(self, key: int) -> None:\n if self.contains(key):\n return True\n self.holder[key%512].append(key)\n return False\n\n\n def remove(self, key: int) -> None:\n hh = key%512\n i = 0\n while i < len(self.holder[hh]):\n if self.holder[hh][i] == key:\n self.holder[hh][i], self.holder[hh][-1] = self.holder[hh][-1], self.holder[hh][i]\n self.holder[hh].pop()\n break\n i += 1\n def contains(self, key: int) -> bool:\n \"\"\"\n Returns true if this set contains the specified element\n \"\"\"\n hh = key%512\n i = 0\n while i < len(self.holder[hh]):\n if self.holder[hh][i] == key:\n return True\n i += 1\n return False\n\n\n# Your MyHashSet object will be instantiated and called as such:\n# obj = MyHashSet()\n# obj.add(key)\n# obj.remove(key)\n# param_3 = obj.contains(key)\n"},{"alpha_fraction":0.45726701617240906,"alphanum_fraction":0.4678899049758911,"avg_line_length":33.516666412353516,"blob_id":"339461929f40ceebc8eb1222786a27b0aeebd506","content_id":"557014806c447432896f099e18958c3afd3ac644","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2071,"license_type":"no_license","max_line_length":71,"num_lines":60,"path":"/lc1135_unionfind.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minimumCost(self, N: int, connections: List[List[int]]) -> int:\n # sort the connections by cost\n connections.sort(key = lambda x: x[2])\n parent = list(range(N+1))\n size = [1]*len(N+1)\n def find(p):\n while p!= parent[p]:\n parent[p] = parent[parent[p]]\n p = parent[p]\n return p\n def union(p, q, cost):\n rootp, rootq = find(p), find(q)\n if rootp == rootq: return -1\n if size[rootp] < size[rootq]: rootp, rootq = rootq, rootp\n parent[rootq] = rootp\n size[rootp] += size[rootq]\n return cost\n rslt, count = 0, 0\n for x, y, cost in connections:\n temp = union(x, y, cost)\n if cost >= 0:\n rslt += cost\n count += 1\n return rslt if count == N-1 else -1\n\n\nclass Solution:\n def minimumCost(self, N: int, connections: List[List[int]]) -> int:\n # sort the connections by cost\n connections.sort(key = lambda x: x[2])\n parent = list(range(N+1))\n size = [1]*(N+1)\n def find(p):\n while p!= parent[p]:\n parent[p] = parent[parent[p]]\n p = parent[p]\n return p\n def union(p, q, cost):\n rootp, rootq = find(p), find(q)\n if rootp == rootq: return -1\n if size[rootp] < size[rootq]: rootp, rootq = rootq, rootp\n parent[rootq] = rootp\n size[rootp] += size[rootq]\n return cost\n rslt, count = 0, 0\n for x, y, cost in connections:\n temp = union(x, y, cost)\n if temp >= 0:\n rslt += temp\n count += 1\n return rslt if count == N-1 else -1\n\n\nclass UF:\n def __init__(self, n): self.p = list(range(n))\n def union(self, x, y): self.p[self.find(x)] = self.find(y)\n def find(self, x):\n if x != self.p[x]: self.p[x] = self.find(self.p[x])\n return self.p[x]\n"},{"alpha_fraction":0.47705042362213135,"alphanum_fraction":0.48382243514060974,"avg_line_length":35.91666793823242,"blob_id":"7af569733cb5865462b25c9d0767e5468f810d57","content_id":"c9f53a6a659e8cba51cd607bba0eeace2217e354","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1329,"license_type":"no_license","max_line_length":67,"num_lines":36,"path":"/lc215_sort.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findKthLargest(self, nums: List[int], k: int) -> int:\n pivot = nums[(0+len(nums))//2]\n left = [l for l in nums if l > pivot]\n mid = [m for m in nums if m == pivot]\n right = [r for r in nums if r < pivot]\n if len(left) >= k:\n return self.findKthLargest(left, k)\n elif len(left)+len(mid) >= k:\n return pivot\n else:\n return self.findKthLargest(right, k-len(left)-len(mid))\n\n\nclass Solution:\n def findKthLargest(self, nums: List[int], k: int) -> int:\n def partition(left, right):\n pivot, l = (left+right)//2, left\n nums[pivot], nums[right] = nums[right], nums[pivot]\n for i in range(left, right):\n if nums[i] >= nums[right]:\n nums[l], nums[i] = nums[i], nums[l]\n l += 1\n nums[right], nums[l] = nums[l], nums[right]\n return l\n def select(left, right, k):\n if left == right:\n return nums[k]\n pivot = partition(left, right)\n if k == pivot:\n return nums[k]\n elif k < pivot:\n return select(left, pivot-1, k)\n else:\n return select(pivot+1, right, k)\n return select(0, len(nums)-1, k-1)\n"},{"alpha_fraction":0.5513100624084473,"alphanum_fraction":0.5513100624084473,"avg_line_length":35.63999938964844,"blob_id":"488ebac144d04b9a365dc6154b051242cafc741d","content_id":"25549033506ccf3da7a9c45747fc16b27c5e7e93","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":916,"license_type":"no_license","max_line_length":66,"num_lines":25,"path":"/lc450_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findleftlarge(self, root):\n while root.right:\n root = root.right\n return root.val\n def findrightsmall(self, root):\n while root.left:\n root = root.left\n return root.val\n def deleteNode(self, root: TreeNode, key: int) -> TreeNode:\n if not root: return None\n if key > root.val:\n root.right = self.deleteNode(root.right, key)\n elif key < root.val:\n root.left = self.deleteNode(root.left, key)\n else:\n if not root.left and not root.right:\n return None\n if root.right:\n root.val = self.findrightsmall(root.right)\n root.right = self.deleteNode(root.right, root.val)\n else:\n root.val = self.findleftlarge(root.left)\n root.left = self.deleteNode(root.left, root.val)\n return root\n"},{"alpha_fraction":0.3968565762042999,"alphanum_fraction":0.41846758127212524,"avg_line_length":35.35714340209961,"blob_id":"4da9ca5f209663c39fe6dd721ff62d06d7410d49","content_id":"346a3d5867d06e22e3358fed46cac3267ca40662","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":509,"license_type":"no_license","max_line_length":66,"num_lines":14,"path":"/lc636_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def exclusiveTime(self, n: int, logs: List[str]) -> List[int]:\n rslt, stk = [0]*n, []\n for log in logs:\n idx, op, time = log.split(\":\")\n if op == \"end\":\n rslt[int(idx)] += int(time)-stk[-1][1]+1\n stk.pop()\n if stk: stk[-1][1] = int(time)+1\n continue\n if stk:\n rslt[int(stk[-1][0])] += int(time)-stk[-1][1]\n stk.append([idx, int(time)])\n return rslt\n"},{"alpha_fraction":0.597484290599823,"alphanum_fraction":0.599056601524353,"avg_line_length":36.411766052246094,"blob_id":"4bb7936a0e3e359f9a3e58dbfdcf569e1ac71d71","content_id":"c440300dcb7f32c3beda75abf75406482ef4261a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":636,"license_type":"no_license","max_line_length":56,"num_lines":17,"path":"/lc1026_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def maxAncestorDiff(self, root: TreeNode) -> int:\n return self.dfs(root, root.val, root.val)\n\n def dfs(self, node, curr_max, curr_min):\n if not node: return curr_max - curr_min\n curr_max = max(curr_max, node.val)\n curr_min = min(curr_min, node.val)\n left = self.dfs(node.left, curr_max, curr_min)\n right = self.dfs(node.right, curr_max, curr_min)\n return max(left, right)\n"},{"alpha_fraction":0.4450261890888214,"alphanum_fraction":0.46073299646377563,"avg_line_length":34.8125,"blob_id":"f9b3be55bdc42ab22f515e5fa5281e9b046eb8e2","content_id":"89f70bdab9f6cf886b5f619e236a5f4380bb9b79","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":573,"license_type":"no_license","max_line_length":64,"num_lines":16,"path":"/lc1048_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def longestStrChain(self, words: List[str]) -> int:\n memo, word_set = {}, set(words)\n def dfs(word):\n if len(word) == 1: return 1\n if word in memo: return memo[word]\n rslt = 0\n for i in range(len(word)):\n if word[:i] + word[i+1:] in word_set:\n rslt = max(rslt, dfs(word[:i] + word[i+1:]))\n memo[word] = rslt + 1\n return rslt + 1\n rslt = 0\n for word in words[::-1]:\n rslt = max(rslt, dfs(word))\n return rslt\n"},{"alpha_fraction":0.4108658730983734,"alphanum_fraction":0.45500847697257996,"avg_line_length":38.266666412353516,"blob_id":"c4b622bb208bc43cfc41603e109278423418d63c","content_id":"08122bd0c5b5908be0b5cacefba4fe1f9cec50c7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":589,"license_type":"no_license","max_line_length":86,"num_lines":15,"path":"/lc391_array.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def isRectangleCover(self, rectangles: List[List[int]]) -> bool:\n area = 0\n corners = set()\n for x1, y1, x2, y2 in rectangles:\n area += (y2-y1)*(x2-x1)\n for x, y in [(x1, y1), (x2, y2), (x1, y2), (x2, y1)]:\n if (x, y) in corners:\n corners.remove((x, y))\n else:\n corners.add((x, y))\n if len(corners) != 4:\n return False\n corners = sorted(corners)\n return area == (corners[-1][1]-corners[0][1])*(corners[-1][0] - corners[0][0])\n"},{"alpha_fraction":0.5553320050239563,"alphanum_fraction":0.5553320050239563,"avg_line_length":37.230770111083984,"blob_id":"ed74baaf7c233bacca9204493940cefcdc724209","content_id":"5abdfd467c6b1baf16e68686bfe2302aa0977b84","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":497,"license_type":"no_license","max_line_length":98,"num_lines":13,"path":"/lc572_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution(object):\n def isSubtree(self, s, t):\n \"\"\"\n :type s: TreeNode\n :type t: TreeNode\n :rtype: bool\n \"\"\"\n def isSametree(s, t):\n if not s and not t: return True\n if not s or not t: return False\n return s.val == t.val and isSametree(s.left, t.left) and isSametree(s.right, t.right)\n if not t: return True\n return s and (isSametree(s, t) or self.isSubtree(s.left, t) or self.isSubtree(s.right, t))\n"},{"alpha_fraction":0.41093748807907104,"alphanum_fraction":0.43437498807907104,"avg_line_length":36.64706039428711,"blob_id":"3167a5bd8fd848d3ce942841a4cb63e2db4e8184","content_id":"7eff287be8266d1842ce004744f320ef4bf6621f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":640,"license_type":"no_license","max_line_length":71,"num_lines":17,"path":"/lc909_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def snakesAndLadders(self, board: List[List[int]]) -> int:\n n = len(board)\n dque, visited, n = collections.deque([(0, 0)]), {0}, len(board)\n while dque:\n step, t = dque.popleft()\n dx = n-t//n-1\n dy = t%n if t//n%2 == 0 else n-t%n-1\n if board[dx][dy] > 0:\n t = board[dx][dy]-1\n if t+1 == n*n: return step\n for s in range(1, 7):\n curr = t+s\n if curr not in visited and 0 <= curr < n*n:\n dque.append((step+1, curr))\n visited.add(curr)\n return -1\n"},{"alpha_fraction":0.3513071835041046,"alphanum_fraction":0.3807189464569092,"avg_line_length":37.25,"blob_id":"35bc12f491fa7ff87fdc4065ff442398610d1211","content_id":"bf1d3c45977f203c9d207f5895346741313056f6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":612,"license_type":"no_license","max_line_length":92,"num_lines":16,"path":"/lc695_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxAreaOfIsland(self, grid: List[List[int]]) -> int:\n def dfs(i, j):\n curr = 1\n for x, y in [(0, 1), (1, 0), (-1, 0), (0, -1)]:\n if 0 <= i+x < len(grid) and 0<=j + y < len(grid[0]) and grid[i+x][j+y] == 1:\n grid[i+x][j+y] = 0\n curr += dfs(i+x, j+y)\n return curr\n rslt = 0\n for i in range(len(grid)):\n for j in range(len(grid[0])):\n if grid[i][j] == 1:\n grid[i][j] = 0\n rslt = max(rslt, dfs(i, j))\n return rslt\n"},{"alpha_fraction":0.503311276435852,"alphanum_fraction":0.5143488049507141,"avg_line_length":36.75,"blob_id":"da785fa1c57bc9f5f24fe135945a66daefd0ec34","content_id":"360b37aa17958922c82a7ca470c8a9b0c064fa21","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":453,"license_type":"no_license","max_line_length":74,"num_lines":12,"path":"/lc456_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def find132pattern(self, nums: List[int]) -> bool:\n stk, last = [], -float(\"inf\")\n for num in nums[::-1]:\n # last is ensured to be less than one seen item\n if num < last:\n return True\n # mono-decreasing-stack maintaining value for nums[k] aka last\n while stk and stk[-1] < num:\n last = stk.pop()\n stk.append(num)\n return False\n"},{"alpha_fraction":0.5970272421836853,"alphanum_fraction":0.6011560559272766,"avg_line_length":32.63888931274414,"blob_id":"0381c4cda90fb5da6bb1b398b46fb80eeee331ae","content_id":"43f5ad5219def033326e27357390442529e50c13","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1211,"license_type":"no_license","max_line_length":120,"num_lines":36,"path":"/lc381_ood.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class RandomizedCollection:\n\n def __init__(self):\n \"\"\"\n Initialize your data structure here.\n \"\"\"\n self.validx = collections.defaultdict(set)\n self.collection = []\n\n\n def insert(self, val: int) -> bool:\n \"\"\"\n Inserts a value to the collection. Returns true if the collection did not already contain the specified element.\n \"\"\"\n self.collection.append(val)\n self.validx[val].add(len(self.collection)-1)\n return len(self.validx[val]) == 1\n\n def remove(self, val: int) -> bool:\n \"\"\"\n Removes a value from the collection. Returns true if the collection contained the specified element.\n \"\"\"\n if not self.validx[val]:\n return False\n idx = self.validx[val].pop()\n self.collection[-1], self.collection[idx] = self.collection[idx], self.collection[-1]\n self.validx[self.collection[idx]].add(idx)\n self.validx[self.collection[idx]].discard(len(self.collection)-1)\n self.collection.pop()\n return True\n\n def getRandom(self) -> int:\n \"\"\"\n Get a random element from the collection.\n \"\"\"\n return random.choice(self.collection)\n"},{"alpha_fraction":0.3863636255264282,"alphanum_fraction":0.4223484992980957,"avg_line_length":28.33333396911621,"blob_id":"23ed2add9eb085d7f191e4aafad89751ea32195c","content_id":"99b2e8b31ba25ae349c47fd105afe782456ec53a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":528,"license_type":"no_license","max_line_length":51,"num_lines":18,"path":"/lc581_array.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution(object):\n def findUnsortedSubarray(self, nums):\n n = len(nums)\n if n < 2: return 0\n l, r = 0, n - 1\n while l < n - 1 and nums[l] <= nums[l + 1]:\n l += 1\n while r > 0 and nums[r] >= nums[r -1]:\n r -= 1\n if l > r: return 0\n subarray = nums[l:r+1]\n mi, ma = min(subarray), max(subarray)\n\n while l > 0 and mi < nums[l-1]:\n l -= 1\n while r < n - 1 and ma > nums[r+1]:\n r += 1\n return r - l + 1\n"},{"alpha_fraction":0.29828110337257385,"alphanum_fraction":0.3003033399581909,"avg_line_length":31.96666717529297,"blob_id":"584f2c97069d5a7c05b093b94ab7c96dcf8a02ce","content_id":"600986d7d9abbaf8cda9499ce7a948965ddd52a5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":989,"license_type":"no_license","max_line_length":61,"num_lines":30,"path":"/lc772_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def calculate(self, s: str) -> int:\n stk, last = [\"(\"], \"\"\n for c in s+\")\":\n if c == \" \": continue\n if c.isdigit():\n last += c if last else c\n elif c == \"(\":\n stk.append(c)\n else:\n op = stk.pop()\n if op == \"*\":\n stk.append(int(stk.pop())*int(last))\n elif op == \"/\":\n stk.append(int(int(stk.pop())/int(last)))\n elif op == \"-\":\n stk.append(-int(last))\n else:\n stk.append(op)\n stk.append(int(last))\n last = \"\"\n if c == \")\":\n y = 0\n while stk[-1] !=\"(\":\n y += stk.pop()\n stk.pop()\n last = str(y)\n elif c != \"+\":\n stk.append(c)\n return int(last)\n"},{"alpha_fraction":0.3914141356945038,"alphanum_fraction":0.42171716690063477,"avg_line_length":40.68421173095703,"blob_id":"5e62e495cd1517893f59174e4ccf5214b4d44e37","content_id":"b35923c064ecb8d8a06ce34875e9607a5f18857e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":792,"license_type":"no_license","max_line_length":64,"num_lines":19,"path":"/lc1368_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minCost(self, grid: List[List[int]]) -> int:\n heap, visited = [(0, 0, 0)], set()\n nrow, ncol = len(grid), len(grid[0])\n directions = {1:(0, 1), 2:(0, -1), 3:(1, 0), 4:(-1, 0)}\n while heap:\n step, x, y = heapq.heappop(heap)\n if x == nrow-1 and y == ncol-1:\n return step\n if (x, y) in visited: continue\n visited.add((x, y))\n for i in directions:\n nx, ny = x+directions[i][0], y+directions[i][1]\n if 0 <= nx < nrow and 0 <= ny < ncol:\n if grid[x][y] == i:\n heapq.heappush(heap, (step, nx, ny))\n else:\n heapq.heappush(heap, (step+1, nx, ny))\n return 0\n"},{"alpha_fraction":0.38726791739463806,"alphanum_fraction":0.3912466764450073,"avg_line_length":33.272727966308594,"blob_id":"12ce2cca0b1835b3bdcda7566b104fad919eb506","content_id":"209cea625a1caec1945a17de277242d55388d755","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":754,"license_type":"no_license","max_line_length":80,"num_lines":22,"path":"/lc394_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def decodeString(self, s: str) -> str:\n stack, digit, currs, rslt = [], \"\", \"\", \"\"\n for c in s:\n if c.isalpha():\n currs += c\n elif c.isdigit():\n digit += c\n elif c == \"[\":\n if currs:\n stack.append(currs)\n stack.append(digit)\n digit, currs= \"\", \"\"\n elif c == \"]\":\n lastc = int(stack.pop()) if stack and stack[-1].isdigit() else 1\n currs = lastc*currs\n while stack and stack[-1].isalpha():\n currs = stack.pop()+currs\n if not stack:\n rslt += currs\n currs = \"\"\n return rslt\n"},{"alpha_fraction":0.3752151429653168,"alphanum_fraction":0.3855421543121338,"avg_line_length":31.27777862548828,"blob_id":"29c79671ab1357ede69277dbfdc53ca62116b67c","content_id":"31357c067dada6229bfc47018681aa4556fd9648","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":581,"license_type":"no_license","max_line_length":61,"num_lines":18,"path":"/lc838_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def pushDominoes(self, dominoes: str) -> str:\n left, lnote, rslt = -1, \"L\", \"\"\n for right, do in enumerate(dominoes+\"R\"):\n if do == \".\":\n continue\n mid = right-left-1\n if left >= 0:\n rslt += lnote\n if do == lnote:\n rslt += mid*lnote\n elif lnote == \"L\" and do == \"R\":\n rslt += mid*\".\"\n else:\n rslt += \"R\"*(mid//2)+\".\"*(mid%2)+\"L\"*(mid//2)\n left = right\n lnote = do\n return rslt\n"},{"alpha_fraction":0.34840869903564453,"alphanum_fraction":0.42211055755615234,"avg_line_length":45,"blob_id":"c049e2763c7f534b48474e0ba1c6b9f157a7b408","content_id":"2abe2a70f27f95488f75f3315a10934395452d15","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":597,"license_type":"no_license","max_line_length":81,"num_lines":13,"path":"/lc712_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minimumDeleteSum(self, s1: str, s2: str) -> int:\n l_s1, l_s2, rslt = len(s1), len(s2), 0\n dp = [[0]*(l_s2 + 1) for _ in range(l_s1 + 1)]\n for i in range(1, l_s1+1): dp[i][0] = dp[i-1][0] + ord(s1[i-1])\n for j in range(1, l_s2+1): dp[0][j] = dp[0][j-1] + ord(s2[j-1])\n for i, c1 in enumerate(s1):\n for j, c2 in enumerate(s2):\n if c1 == c2:\n dp[i+1][j+1] = dp[i][j]\n else:\n dp[i+1][j+1] = min(dp[i+1][j] +ord(c2), dp[i][j+1] + ord(c1))\n return dp[-1][-1]"},{"alpha_fraction":0.4655582010746002,"alphanum_fraction":0.4750593900680542,"avg_line_length":30.69230842590332,"blob_id":"3e1df6c6b8ebbbbf67b439c9ff009715ed77c00f","content_id":"5a534a43797c020cb3714497d5e24bd0195817ae","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":421,"license_type":"no_license","max_line_length":64,"num_lines":13,"path":"/lc523_array.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def checkSubarraySum(self, nums: List[int], k: int) -> bool:\n n = len(nums)\n maps = {0:-1}\n presum = 0\n for i, num in enumerate(nums):\n presum += num\n presum = presum%k\n if presum in maps and i-maps[presum] > 1:\n return True\n elif presum not in maps:\n maps[presum] = i\n return False\n \n"},{"alpha_fraction":0.46830984950065613,"alphanum_fraction":0.49295774102211,"avg_line_length":30.66666603088379,"blob_id":"8415f6743e265e0bb6588e1eb8a975321161ed77","content_id":"2fd84be18e9428f82823a47e35264306e5f7fece","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":284,"license_type":"no_license","max_line_length":58,"num_lines":9,"path":"/lc560_hash.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def subarraySum(self, nums: List[int], k: int) -> int:\n keeper = {0:1}\n rslt, temp = 0, 0\n for num in nums:\n temp += num\n rslt += keeper.get(temp-k, 0)\n keeper[temp] = keeper.get(temp, 0) +1\n return rslt"},{"alpha_fraction":0.42832469940185547,"alphanum_fraction":0.47322970628738403,"avg_line_length":37.599998474121094,"blob_id":"1aa34ceecf34c17707b288e0529cd128e2b51234","content_id":"072c4055b0a1eb6e2d851840a15767548f2f3847","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":579,"license_type":"no_license","max_line_length":113,"num_lines":15,"path":"/lc1229_array.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minAvailableDuration(self, slots1: List[List[int]], slots2: List[List[int]], duration: int) -> List[int]:\n slots1.sort()\n slots2.sort()\n n1, n2 = len(slots1), len(slots2)\n i, j = 0, 0\n while i < n1 and j < n2:\n left, right = max(slots1[i][0], slots2[j][0]), min(slots1[i][1], slots2[j][1])\n if right - left >= duration:\n return [left, left +duration]\n elif slots1[i][1] < slots2[j][1]:\n i += 1\n else:\n j += 1\n return []\n"},{"alpha_fraction":0.41161179542541504,"alphanum_fraction":0.4272097051143646,"avg_line_length":45.15999984741211,"blob_id":"c9aceda1f1b4b927f83dfa4708655afb50ee2b4b","content_id":"b4a3cc6414a8a593459c39b2f9d840ce43fa5c4f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2308,"license_type":"no_license","max_line_length":98,"num_lines":50,"path":"/lc864_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def shortestPathAllKeys(self, grid: List[str]) -> int:\n k, nrow, ncol = 0, len(grid), len(grid[0])\n for i in range(nrow):\n for j in range(ncol):\n if grid[i][j] == \"@\": start = (i, j)\n elif grid[i][j] in string.ascii_lowercase: k += 1\n dque, visited= collections.deque([(0, start[0], start[1], \"\")]), {}\n while dque:\n step, x, y, getk = dque.popleft()\n if len(getk) == k:\n return step\n for dx, dy in [(x+1,y), (x, y+1), (x,y-1), (x-1, y)]:\n newk = getk\n if 0 <= dx < nrow and 0 <= dy < ncol and grid[dx][dy] !=\"#\":\n tc = grid[dx][dy]\n if tc in string.ascii_uppercase and tc.lower() not in newk:\n continue\n if tc in string.ascii_lowercase and tc not in newk:\n newk += tc\n if (dx, dy, newk) not in visited:\n visited.add((dx, dy, newk))\n dque.append((step+1, dx, dy, newk))\n return -1\n\n\nclass Solution:\n def shortestPathAllKeys(self, grid: List[str]) -> int:\n k, nrow, ncol = 0, len(grid), len(grid[0])\n for i in range(nrow):\n for j in range(ncol):\n if grid[i][j] == \"@\": start = (i, j)\n elif grid[i][j] in string.ascii_lowercase: k += 1\n dque, visited= collections.deque([(0, start[0], start[1], 0)]), {}\n while dque:\n step, x, y, getk = dque.popleft()\n if getk == 2**k-1:\n return step\n for dx, dy in [(x+1,y), (x, y+1), (x,y-1), (x-1, y)]:\n newk = getk\n if 0 <= dx < nrow and 0 <= dy < ncol and grid[dx][dy] !=\"#\":\n tc = grid[dx][dy]\n if tc in string.ascii_uppercase and (1 << ord(tc.lower())-ord('a'))&newk == 0:\n continue\n if tc in string.ascii_lowercase and (1 << (ord(tc)-ord('a')))&newk == 0:\n newk |= 1 << (ord(tc)-ord('a'))\n if (dx, dy, newk) not in visited:\n visited.add((dx, dy, newk))\n dque.append((step+1, dx, dy, newk))\n return -1\n"},{"alpha_fraction":0.40353259444236755,"alphanum_fraction":0.41983696818351746,"avg_line_length":26.80769157409668,"blob_id":"a250becf614caaff7f909fa41314d602d60c54ae","content_id":"93fa784120c4dc90869b5ab10b22b0788ed25b22","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":736,"license_type":"no_license","max_line_length":50,"num_lines":26,"path":"/lc222_bs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def countNodes(self, root: TreeNode) -> int:\n if not root: return 0\n left, depth = root, 0\n while left.left:\n left = left.left\n depth += 1\n left, right = (1< int:\n # nnums = [1]+[num for num in nums if num > 0] + [1]\n # n = len(nnums)\n# dp = [[0]*n for _ in range(n)]\n# for l in range(2, n):\n# for left in range(0, n-l):\n# right = left + l\n# for mid in range(left+1, right):\n# dp[left][right] = max(dp[left][right], nnums[left]*nnums[mid]*nnums[right]+dp[left][mid]+dp[mid][right])\n# return dp[0][n-1]\n # def dfs(left, right, memo = {}):\n # if (left, right) in memo: return memo[(left, right)]\n # rslt = max([nnums[left]*nnums[mid]*nnums[right]+dfs(left, mid, memo)+dfs(mid, right, memo) for mid in range(left+1, right)] or [0] )\n # memo[(left, right)] = rslt\n # return rslt\n # return dfs(0, n-1)\n def maxCoins(self, nums: List[int]) -> int:\n nums = [x for x in nums if x != 0]\n def maxcoin(nums, memo = {}):\n if nums not in memo:\n first , last = nums[0], nums[-1]\n ans = max([maxcoin(nums[:k+1]) + maxcoin(nums[k:]) + first * nums[k] * last for k in range(1, len(nums)-1)] or [0] )\n memo[nums] = ans\n return memo[nums]\n\n return maxcoin(tuple([1] + nums + [1]))\n"},{"alpha_fraction":0.31134968996047974,"alphanum_fraction":0.36349692940711975,"avg_line_length":37.411766052246094,"blob_id":"8f42d5e7e4a22ce49d109c1afb8a22054d4362b0","content_id":"8019efb6bc5362911823cda46483caa8ec6a713f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":652,"license_type":"no_license","max_line_length":62,"num_lines":17,"path":"/lc97_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def isInterleave(self, s1: str, s2: str, s3: str) -> bool:\n l_s1, l_s2 = len(s1), len(s2)\n if len(s3) != l_s1 + l_s2:\n return False\n dp = [[False]*(l_s2 + 1) for _ in range(l_s1 + 1)]\n for i in range(l_s1 + 1):\n for j in range(l_s2 + 1):\n if i == 0 and j == 0:\n dp[i][j] = True\n continue\n if i > 0 and s1[i-1] == s3[i+j-1]:\n dp[i][j] = dp[i-1][j]\n if j > 0 and s2[j-1] == s3[i+j-1]:\n dp[i][j] = dp[i][j] or dp[i][j-1]\n \n return dp[-1][-1]"},{"alpha_fraction":0.5392953753471375,"alphanum_fraction":0.5447154641151428,"avg_line_length":32.54545593261719,"blob_id":"e61b78aa710a0059eb637da87c3efb320eb785c1","content_id":"d6ba24fc6b2240bb44452dd7516efa444086094f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":369,"license_type":"no_license","max_line_length":62,"num_lines":11,"path":"/lc1570_oop.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class SparseVector:\n def __init__(self, nums: List[int]):\n self.vec = {i:v for i, v in enumerate(nums) if v != 0}\n\n # Return the dotProduct of two sparse vectors\n def dotProduct(self, vec: 'SparseVector') -> int:\n rslt = 0\n for i,v in self.vec.items():\n if i in vec.vec:\n rslt += v*vec.vec[i]\n return rslt\n"},{"alpha_fraction":0.43286219239234924,"alphanum_fraction":0.46289753913879395,"avg_line_length":32.29411697387695,"blob_id":"6d9bff9e01364ee610e25ddf77be5eb5edd744fc","content_id":"c0f4d48faaffece23a9aa5cec6cf11cd37db0445","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":566,"license_type":"no_license","max_line_length":54,"num_lines":17,"path":"/lc666_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def pathSum(self, nums: List[int]) -> int:\n maps = {num//10: num%10 for num in nums}\n self.rslt = 0\n def dfs(idx, curr):\n if idx not in maps: return\n curr += maps[idx]\n depth, loc = divmod(idx, 10)\n left = (depth+1)*10+loc*2-1\n right = left + 1\n if left not in maps and right not in maps:\n self.rslt += curr\n else:\n dfs(left, curr)\n dfs(right, curr)\n dfs(nums[0]//10, 0)\n return self.rslt\n"},{"alpha_fraction":0.44397464394569397,"alphanum_fraction":0.46300211548805237,"avg_line_length":32.78571319580078,"blob_id":"6b80237ef8975cdf1346ee3262f90b62bfc3fe78","content_id":"73d79088bf46689e42a171082880c1f866e7e56d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":473,"license_type":"no_license","max_line_length":53,"num_lines":14,"path":"/lc907_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def sumSubarrayMins(self, arr: List[int]) -> int:\n modulo = 10**9 + 7\n stk, rslt, remain = [], 0, 0\n for i, num in enumerate(arr):\n count = 1\n while stk and stk[-1][0] >= num:\n tnum, tcount = stk.pop()\n count += tcount\n remain -= tnum*tcount\n remain += count*num\n rslt += remain\n stk.append((num, count))\n return rslt%modulo\n"},{"alpha_fraction":0.4076305329799652,"alphanum_fraction":0.4538152515888214,"avg_line_length":37.30769348144531,"blob_id":"685b8c8feec6ac7f6c8da64f6349736ea5ac5547","content_id":"dd9fdf45f05a3dee1b50d75fef85ebc1b659c434","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":498,"license_type":"no_license","max_line_length":71,"num_lines":13,"path":"/lc1577_math.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def numTriplets(self, nums1: List[int], nums2: List[int]) -> int:\n c1, c2 = collections.Counter(nums1), collections.Counter(nums2)\n def helper(c1, c2):\n rslt, temp = 0, 0\n for k, v in c1.items():\n rslt += v*c2[k]*(c2[k]-1)//2\n\n for c in c2:\n if c < k and (k*k)%c == 0:\n rslt += v*c2[c]*c2[(k*k)//c]\n return rslt\n return helper(c1, c2) + helper(c2, c1)\n"},{"alpha_fraction":0.4770427942276001,"alphanum_fraction":0.4894941747188568,"avg_line_length":34.69444274902344,"blob_id":"dcc7b9e452d91ca0ccd45c07a6af1367b537462f","content_id":"fd000c86df6b1c3bd5b9f90a32aa253a9c2e1d27","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1285,"license_type":"no_license","max_line_length":90,"num_lines":36,"path":"/lc735_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# ast > 0 fly to right, won't crash previous\n# ast < 0 fly to left, if previous same dir, won't crash either\n# ast < 0 fly to left, previous fly to right, crash(previous crash, ast crash, both crash)\nclass Solution:\n def asteroidCollision(self, asteroids: List[int]) -> List[int]:\n stack = []\n for ast in asteroids:\n if ast > 0:\n stack.append(ast)\n elif not stack or stack[-1] < 0:\n stack.append(ast)\n else:\n while stack and stack[-1] > 0 and stack[-1] < -ast:\n stack.pop()\n if not stack or stack[-1] < 0:\n stack.append(ast)\n elif stack[-1] == -ast:\n stack.pop()\n return stack\n\n# ast crashed previous asteroid in the stack\n# ast and previous both crash\n# ast crash\n def asteroidCollision(self, asteroids: List[int]) -> List[int]:\n stack = []\n for ast in asteroids:\n while stack and stack[-1] > 0 > ast:\n if stack[-1] < -ast:\n stack.pop()\n continue\n elif stack[-1] == -ast:\n stack.pop()\n break\n else:\n stack.append(ast)\n return stack\n"},{"alpha_fraction":0.4515393376350403,"alphanum_fraction":0.4526796042919159,"avg_line_length":27.29032325744629,"blob_id":"398ef250f13bc49a9638e886fa28e497c6da169b","content_id":"fc181f0adae1bab4945afe666fd6d40b8839988d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":877,"license_type":"no_license","max_line_length":86,"num_lines":31,"path":"/lc211_trie.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class WordDictionary:\n\n def __init__(self):\n \"\"\"\n Initialize your data structure here.\n \"\"\"\n self.trie = {\"end\":False}\n\n\n def addWord(self, word: str) -> None:\n curr = self.trie\n for ch in word:\n curr[ch] = curr.get(ch, {\"end\":False})\n curr = curr[ch]\n curr[\"end\"] = True\n\n\n\n def search(self, word: str) -> bool:\n def helper(word, trie):\n if not word: return trie[\"end\"]\n for i, ch in enumerate(word):\n if ch == \".\":\n for key in trie.keys():\n if key != \"end\" and helper(word[i+1:], trie[key]): return True\n return False\n elif not ch in trie:\n return False\n trie = trie[ch]\n return trie[\"end\"]\n return helper(word, self.trie)\n"},{"alpha_fraction":0.3709884583950043,"alphanum_fraction":0.3979460895061493,"avg_line_length":32.869564056396484,"blob_id":"e1f234fb69faf01aa34e91cf323ab0f7e61373b0","content_id":"40036f9a1f0fd4fb5e7ba0b7a4af27288cf7cbbb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":779,"license_type":"no_license","max_line_length":75,"num_lines":23,"path":"/lc1718_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def constructDistancedSequence(self, n: int) -> List[int]:\n rslt = [0]*(2*n-1)\n mark = [0]*(n+1)\n def bk(i, cnt):\n if cnt == 0:\n return True\n if i >= 2*n-1:\n return False\n if rslt[i]:\n return bk(i+1, cnt)\n\n for num in range(n, 0, -1):\n left, right = i, i+num if num > 1 else i\n if not mark[num] and right < (2*n -1) and rslt[right] == 0:\n mark[num] = 1\n rslt[left] = rslt[right] = num\n if bk(i+1, cnt-1): return True\n mark[num] = 0\n rslt[left] = rslt[right] = 0\n return False\n bk(0, n)\n return rslt\n"},{"alpha_fraction":0.596175491809845,"alphanum_fraction":0.6074240803718567,"avg_line_length":45.78947448730469,"blob_id":"1ac8b1c737999898c7093f9e6e881c5de0272b21","content_id":"99fee70d1e5d186171a84a39027032ffb2261b68","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":889,"license_type":"no_license","max_line_length":81,"num_lines":19,"path":"/lc715_sortedlist.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"from sortedcontainers import SortedList\nclass RangeModule:\n def __init__(self):\n self.ranges = SortedList()\n def addRange(self, left: int, right: int) -> None:\n idl, idr = self.ranges.bisect_left(left), self.ranges.bisect_right(right)\n for _ in range(idr-idl):\n self.ranges.pop(idl)\n if idl%2 == 0: self.ranges.add(left)\n if idr%2 == 0: self.ranges.add(right)\n def queryRange(self, left: int, right: int) -> bool:\n idl, idr = self.ranges.bisect_right(left), self.ranges.bisect_left(right)\n return idl == idr and idl%2 == 1\n def removeRange(self, left: int, right: int) -> None:\n idl, idr = self.ranges.bisect_left(left), self.ranges.bisect_right(right)\n for _ in range(idr-idl):\n self.ranges.pop(idl)\n if idl%2 == 1: self.ranges.add(left)\n if idr%2 == 1: self.ranges.add(right)\n"},{"alpha_fraction":0.3676646649837494,"alphanum_fraction":0.37005987763404846,"avg_line_length":38.761905670166016,"blob_id":"3f921c4d819f5b57c52c16b18443fa58f50769a0","content_id":"3740e649c164498a7f8c982b9d9935f70dae7c4d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":835,"license_type":"no_license","max_line_length":142,"num_lines":21,"path":"/lc1106_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def parseBoolExpr(self, expression: str) -> bool:\n stk_op, stk_or, ops = [], [], {\"t\":True, \"f\":False, \"|\": lambda x, y: x or y, \"&\":lambda x, y : x and y, \"!\":lambda x: not x, \"(\":\"(\"}\n for c in expression:\n if c == \",\": continue\n if c in (\"t\", \"f\", \"(\"):\n stk_op.append(ops[c])\n elif c in (\"!\", \"|\", \"&\"):\n stk_or.append(c)\n\n if c == \")\":\n opr = stk_or.pop()\n rslt = True if opr == \"&\" else False\n if opr == \"!\":\n rslt = ops[opr](stk_op.pop())\n else:\n while stk_op[-1] != \"(\":\n rslt = ops[opr](rslt, stk_op.pop())\n stk_op.pop()\n stk_op.append(rslt)\n return stk_op[0]\n"},{"alpha_fraction":0.4253135621547699,"alphanum_fraction":0.44982895255088806,"avg_line_length":43.97435760498047,"blob_id":"37f6ac89e7bb46dc1177fca6fa971c3744400a13","content_id":"933286a2808900b781248a13a92ea242c44e0538","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1754,"license_type":"no_license","max_line_length":103,"num_lines":39,"path":"/lc505_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def shortestDistance(self, maze: List[List[int]], start: List[int], destination: List[int]) -> int:\n heap, visited = [(0, start[0], start[1])], {}\n rslt = float(\"inf\")\n nrow, ncol = len(maze), len(maze[0])\n while heap:\n step, currx, curry = heapq.heappop(heap)\n visited.add((currx, curry))\n if currx == destination[0] and curry == destination[1]:\n return step\n for dx, dy in [(1,0), (-1, 0), (0, 1), (0, -1)]:\n tx, ty, l = currx, curry, 0\n while 0 <= tx+dx < nrow and 0 <= ty+dy < ncol and maze[tx+dx][ty+dy] == 0:\n tx += dx\n ty += dy\n l += 1\n if (tx, ty) not in visited:\n heapq.heappush(heap, (step+l, tx, ty))\n return -1\n\n\nclass Solution:\n def shortestDistance(self, maze: List[List[int]], start: List[int], destination: List[int]) -> int:\n heap, visited = [(0, start[0], start[1])], {(start[0], start[1]): 0}\n nrow, ncol = len(maze), len(maze[0])\n while heap:\n step, currx, curry = heapq.heappop(heap)\n if currx == destination[0] and curry == destination[1]:\n return step\n for dx, dy in [(1,0), (-1, 0), (0, 1), (0, -1)]:\n tx, ty, l = currx, curry, 0\n while 0 <= tx+dx < nrow and 0 <= ty+dy < ncol and maze[tx+dx][ty+dy] == 0:\n tx += dx\n ty += dy\n l += 1\n if (tx, ty) not in visited or visited[(tx, ty)] > step+l:\n visited[(tx, ty)] = step + l\n heapq.heappush(heap, (step+l, tx, ty))\n return -1\n"},{"alpha_fraction":0.4560975730419159,"alphanum_fraction":0.48048779368400574,"avg_line_length":36.272727966308594,"blob_id":"c055fb356a5143b98a2371f419ad8a40a34e7e3b","content_id":"beb07a003d90c1a94e6678b699fc6d0ef507c427","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":410,"license_type":"no_license","max_line_length":69,"num_lines":11,"path":"/lc828_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def uniqueLetterString(self, s: str) -> int:\n index, rslt = {c:[-1, -1] for c in string.ascii_uppercase}, 0\n for i, c in enumerate(s):\n pre, last = index[c]\n rslt += (i-last)*(last-pre)\n index[c] = [last, i]\n n = len(s)\n for c in index:\n rslt += (n-index[c][1])*(index[c][1]-index[c][0])\n return rslt%(10**9+7)\n"},{"alpha_fraction":0.5269230604171753,"alphanum_fraction":0.5326923131942749,"avg_line_length":39,"blob_id":"2708fe7489967595577340064e75a59e628c84b7","content_id":"a75e1045b658aec6d3be3ce455dfef51d265d417","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":520,"license_type":"no_license","max_line_length":84,"num_lines":13,"path":"/lc39_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def combinationSum(self, candidates: List[int], target: int) -> List[List[int]]:\n rslt, n = [], len(candidates)\n candidates.sort()\n def backtracking(target, i,curr):\n if target == 0:\n rslt.append(curr)\n elif i < n and target >= candidates[i]:\n temp = candidates[i]\n backtracking(target-temp, i, curr+[temp])\n backtracking(target, i+1, curr)\n backtracking(target, 0, [])\n return rslt\n"},{"alpha_fraction":0.39650872349739075,"alphanum_fraction":0.4152119755744934,"avg_line_length":29.846153259277344,"blob_id":"22495c604d430a53923d1d7d137654f2d2c2e107","content_id":"a3cfe349dce83c9a3075f750813838c5e20d9aad","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":802,"license_type":"no_license","max_line_length":83,"num_lines":26,"path":"/lc489_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def cleanRoom(self, robot):\n \"\"\"\n :type robot: Robot\n :rtype: None\n \"\"\"\n def move_back():\n robot.turnLeft()\n robot.turnLeft()\n robot.move()\n robot.turnLeft()\n robot.turnLeft()\n\n def dfs(x, y, d):\n visited.add((x, y))\n robot.clean()\n for i in range(4):\n curr_d = (d+i)%4\n curr_x, curr_y = x + directions[curr_d][0], y+directions[curr_d][1]\n if (curr_x, curr_y) not in visited and robot.move():\n dfs(curr_x, curr_y, curr_d)\n move_back()\n robot.turnLeft()\n directions = [(-1, 0), (0, -1), (1, 0), (0, 1)]\n visited = set()\n dfs(0, 0, 0)\n"},{"alpha_fraction":0.38329237699508667,"alphanum_fraction":0.4127764105796814,"avg_line_length":30.30769157409668,"blob_id":"1014f0de30d6910acddc285b44a621199e85a1fc","content_id":"2401c83ae5a4b0eb5f7d46da799b9eaaa0f96897","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":407,"license_type":"no_license","max_line_length":67,"num_lines":13,"path":"/lc1155_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def numRollsToTarget(self, d: int, f: int, target: int) -> int:\n @lru_cache(None)\n def dfs(d, t):\n if d*f < t or d > t:\n return 0\n if d== 0 and t == 0:\n return 1\n rslt = 0\n for j in range(1, f+1):\n rslt += dfs(d-1, t-j)\n return rslt\n return dfs(d, target)%(10**9+7)\n"},{"alpha_fraction":0.39701491594314575,"alphanum_fraction":0.41044774651527405,"avg_line_length":32.54999923706055,"blob_id":"a5c8145493601947b09c942bbe56bad9b1227aba","content_id":"1b6ec6f6252cdcfaf9a0361183ecf9184bb3c2eb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":670,"license_type":"no_license","max_line_length":75,"num_lines":20,"path":"/lc438_str.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findAnagrams(self, s: str, p: str) -> List[int]:\n if len(p) > len(s):\n return []\n \n counter_p, counter_s, cnt, rslt = collections.Counter(p), {}, 0, []\n \n for i, c in enumerate(s):\n if c not in counter_p:\n counter_s = {}\n cnt = 0\n elif cnt < len(p):\n counter_s[c] = counter_s.get(c, 0) +1\n cnt += 1\n else:\n counter_s[c] = counter_s.get(c, 0) + 1\n counter_s[s[i-len(p)]] -= 1\n if counter_s == counter_p:\n rslt.append(i-len(p)+1)\n return rslt"},{"alpha_fraction":0.4977678656578064,"alphanum_fraction":0.5089285969734192,"avg_line_length":39.727272033691406,"blob_id":"e03bb5c6ee2589ba1d7003bee6ab8ea5025ffa00","content_id":"564f382da0663da6124176f89712956aa47c21eb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":448,"license_type":"no_license","max_line_length":102,"num_lines":11,"path":"/lc486_minmax.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def PredictTheWinner(self, nums: List[int]) -> bool:\n memo = {}\n def dfs(left, right):\n if (left, right) in memo:\n return memo[(left, right)]\n if left == right:\n return nums[left]\n memo[(left, right)] = max(nums[left]- dfs(left +1, right), nums[right]-dfs(left, right-1))\n return memo[(left, right)]\n return dfs(0, len(nums)-1) >= 0\n"},{"alpha_fraction":0.429824560880661,"alphanum_fraction":0.4429824650287628,"avg_line_length":31.571428298950195,"blob_id":"7b41c6a07ca846608eac9fd48a2fb156c6e1b740","content_id":"dd1f3def3b43da61ef312384d15ebbb1d51e3aa9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":456,"license_type":"no_license","max_line_length":62,"num_lines":14,"path":"/lc875_bt.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minEatingSpeed(self, piles: List[int], H: int) -> int:\n def eatingHour(k):\n return sum( (x-1)//k + 1 for x in piles)\n left, right = 1, max(piles)\n while left < right:\n mid = (left + right)//2\n if eatingHour(mid) > H:\n left = mid\n else:\n right = mid\n if (left + right)//2 == mid:\n left += 1\n return right\n"},{"alpha_fraction":0.43914079666137695,"alphanum_fraction":0.45584726333618164,"avg_line_length":37.09090805053711,"blob_id":"9d56001ebf0deab894829b9c85e71c7875b27e8e","content_id":"40ad4e5fcd6ffdf8efa0067ba3fb326f94d8f458","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":419,"license_type":"no_license","max_line_length":61,"num_lines":11,"path":"/lc209_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minSubArrayLen(self, s: int, nums: List[int]) -> int:\n curr, rslt, left = 0, len(nums)+1, 0\n for right, num in enumerate(nums):\n curr += num\n if curr >= s:\n while curr >= s:\n rslt = min(right-left+1, rslt)\n curr -= nums[left]\n left += 1\n return 0 if rslt == len(nums)+1 else rslt\n"},{"alpha_fraction":0.37187498807907104,"alphanum_fraction":0.37187498807907104,"avg_line_length":32.68421173095703,"blob_id":"c8c8b7d47001a16749458897052ec90ab928d6f0","content_id":"84126ae09efca13cc8b22ecec35e10ad05eab48b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":640,"license_type":"no_license","max_line_length":62,"num_lines":19,"path":"/lc1096_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def braceExpansionII(self, expression: str) -> List[str]:\n stk, pre, curr = [], [], [\"\"]\n for c in expression:\n if c == \",\":\n pre += curr\n curr = [\"\"]\n elif c == \"{\":\n stk.append(pre)\n stk.append(curr)\n pre, curr = [], [\"\"]\n elif c == \"}\":\n p_curr = stk.pop()\n p_pre = stk.pop()\n curr = [s+k for k in pre+curr for s in p_curr]\n pre = p_pre\n else:\n curr = [s+c for s in curr]\n return sorted(set(pre+curr))\n"},{"alpha_fraction":0.3541666567325592,"alphanum_fraction":0.3717948794364929,"avg_line_length":30.200000762939453,"blob_id":"89df6a4651f54ac7e6b14d0a666a9f854d2400f2","content_id":"8b9f4f5e5f7173e29842910cc1426240ad382653","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":624,"license_type":"no_license","max_line_length":66,"num_lines":20,"path":"/lc1482_bs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minDays(self, bloomDay: List[int], m: int, k: int) -> int:\n if m*k > len(bloomDay): return -1\n left, right = 1, max(bloomDay)\n while left <= right:\n mid = (left + right)//2\n tempf, tempb= 0, 0\n for bD in bloomDay:\n if bD <= mid:\n tempf += 1\n else:\n tempf = 0\n if tempf == k:\n tempb += 1\n tempf = 0\n if tempb >= m:\n right = mid -1\n else:\n left = mid + 1\n return left\n"},{"alpha_fraction":0.49266862869262695,"alphanum_fraction":0.5014662742614746,"avg_line_length":33.099998474121094,"blob_id":"abba0cfb6eebfb855ab319f75ceaff14de29136c","content_id":"b8a7de7b11ce6e6280ec2448d477300c63d04ff6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":341,"license_type":"no_license","max_line_length":59,"num_lines":10,"path":"/lc1665_greedy.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minimumEffort(self, tasks: List[List[int]]) -> int:\n tasks.sort(key = lambda x: x[0] - x[1])\n rslt = quota = 0\n for actual, minimum in tasks:\n if minimum > quota:\n rslt += (minimum - quota)\n quota = minimum\n quota -= actual\n return rslt\n"},{"alpha_fraction":0.45057034492492676,"alphanum_fraction":0.4648289084434509,"avg_line_length":37.96296310424805,"blob_id":"8bbb1eae0a08fe54ab159a8ce3aaa090144b7ed5","content_id":"e83528de573881db09e8c68af476a079425d1172","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1052,"license_type":"no_license","max_line_length":75,"num_lines":27,"path":"/lc973_sort.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def kClosest(self, points: List[List[int]], K: int) -> List[List[int]]:\n def partition(left, right):\n pivot, l = (left+right)//2, left\n dist_pivot = dist[(points[pivot][0], points[pivot][1])]\n points[right], points[pivot] = points[pivot], points[right]\n for i in range(left, right+1):\n if dist[(points[i][0], points[i][1])] < dist_pivot:\n points[i], points[l] = points[l], points[i]\n l += 1\n points[l], points[right] = points[right], points[l]\n return l\n def select(left, right):\n if left == right: return\n pivot = partition(left, right)\n if pivot == K-1:\n return\n if pivot > K-1:\n select(left, pivot-1)\n else:\n select(pivot+1, right)\n return\n dist = {}\n for x, y in points:\n dist[(x, y)] = x**2 + y**2\n select(0, len(points)-1)\n return points[:K]\n"},{"alpha_fraction":0.49857550859451294,"alphanum_fraction":0.49857550859451294,"avg_line_length":34.099998474121094,"blob_id":"c3551863f596a6eefee9b730d40e5622e1ca2cb3","content_id":"9c431f505b0a13f3657986e29ef740a0313189bb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":702,"license_type":"no_license","max_line_length":51,"num_lines":20,"path":"/lc133_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def cloneGraph(self, node: 'Node') -> 'Node':\n if not node: return None\n newnode = Node(node.val)\n if not node.neighbors: return newnode\n queue = deque([node])\n amap = {node.val: newnode}\n visited = set()\n while queue:\n onode = queue.popleft()\n nnode = amap[onode.val]\n nnode.neighbors = []\n for n in onode.neighbors:\n if n.val not in amap:\n amap[n.val] = Node(n.val)\n nnode.neighbors.append(amap[n.val])\n if n.val not in visited:\n queue.append(n)\n visited.add(nnode.val)\n return newnode\n"},{"alpha_fraction":0.392123281955719,"alphanum_fraction":0.4178082048892975,"avg_line_length":40.71428680419922,"blob_id":"7c11f7528dd57a58eb26a99d2a745deafaef561c","content_id":"0212c273f2549323d32cee69f880093cf9d680df","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":584,"license_type":"no_license","max_line_length":71,"num_lines":14,"path":"/lc778_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def swimInWater(self, grid: List[List[int]]) -> int:\n visited, heap, n = {(0,0)}, [(grid[0][0], 0, 0)], len(grid)\n rslt = 0\n while heap:\n node, x, y = heapq.heappop(heap)\n rslt = max(rslt, node)\n if x == n-1 and y == n-1:\n return rslt\n for i, j in [(x+1, y), (x-1, y), (x, y+1), (x, y-1)]:\n if 0 <= i < n and 0 <= j < n and (i, j) not in visited:\n heapq.heappush(heap, (grid[i][j], i, j))\n visited.add((i, j))\n return rslt\n"},{"alpha_fraction":0.509018063545227,"alphanum_fraction":0.52304607629776,"avg_line_length":40.58333206176758,"blob_id":"dc2de500fa75077465b9ddc14191b5b8c71a1586","content_id":"441711c22902d49945af15d9f311456ad51fada5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":499,"license_type":"no_license","max_line_length":60,"num_lines":12,"path":"/lc862_deque.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def shortestSubarray(self, A: List[int], K: int) -> int:\n presum = [0]\n for num in A: presum.append(presum[-1]+num)\n rslt, dque = len(A)+1, collections.deque()\n for idx, pre in enumerate(presum):\n while dque and pre <= presum[dque[-1]]:\n dque.pop()\n dque.append(idx)\n while pre - presum[dque[0]] >= K:\n rslt = min(rslt, idx-dque.popleft())\n return -1 if rslt == len(A)+1 else rslt\n"},{"alpha_fraction":0.5389447212219238,"alphanum_fraction":0.5452261567115784,"avg_line_length":40.894737243652344,"blob_id":"482c56afb5b1da7555ddfe12cfdda2eb2226065f","content_id":"63433bdee6d61e887c568ba7f3db96b7fff3787c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":796,"license_type":"no_license","max_line_length":114,"num_lines":19,"path":"/lc1462_graph.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def checkIfPrerequisite(self, n: int, prerequisites: List[List[int]], queries: List[List[int]]) -> List[bool]:\n isPre = collections.defaultdict(set)\n graph, degree = collections.defaultdict(list), collections.defaultdict(int)\n for pre, curr in prerequisites:\n graph[pre].append(curr)\n isPre[curr].add(pre)\n degree[curr] += 1\n degree[pre] += 0\n\n dque = collections.deque([v for v in degree if degree[v] == 0])\n while dque:\n temp = dque.popleft()\n for post in graph[temp]:\n isPre[post] |= isPre[temp]\n degree[post] -= 1\n if degree[post] == 0:\n dque.append(post)\n return [x in isPre[y] for x, y in queries]\n"},{"alpha_fraction":0.4891640841960907,"alphanum_fraction":0.495356023311615,"avg_line_length":31.299999237060547,"blob_id":"ee69d78b9b26271aadb7367e91f2068b4363743a","content_id":"5c60e7e4a5c64f44fce08b508dd0e00940c06adf","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":323,"license_type":"no_license","max_line_length":58,"num_lines":10,"path":"/lc78_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def subsets(self, nums: List[int]) -> List[List[int]]:\n rslt = [[]]\n def backtracking(i):\n if i == len(nums): return\n for j in range(len(rslt)):\n rslt.append(rslt[j] +[nums[i]])\n backtracking(i+1)\n backtracking(0)\n return rslt\n"},{"alpha_fraction":0.4246031641960144,"alphanum_fraction":0.4642857015132904,"avg_line_length":30.5,"blob_id":"9662083111d68b40be5ab219d976e6ffaf71a147","content_id":"d009719a2aec3499c82de3acff73e97e05ceaced","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":252,"license_type":"no_license","max_line_length":54,"num_lines":8,"path":"/lc96_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def numTrees(self, n: int) -> int:\n dp = [0]*(n+1)\n dp[0] = 1\n for total in range(1, n+1):\n for left in range(1, total+1):\n dp[total] += dp[left-1]*dp[total-left]\n return dp[-1]\n"},{"alpha_fraction":0.4514620006084442,"alphanum_fraction":0.46315789222717285,"avg_line_length":41.75,"blob_id":"74ad6ef7f41d7709e0f8d5b06ed3711a8670b707","content_id":"3c2d5b2333d77ddb98742cdbd649536c00410245","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":855,"license_type":"no_license","max_line_length":92,"num_lines":20,"path":"/lc269_graph.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def alienOrder(self, words: List[str]) -> str:\n graph, degree = collections.defaultdict(list), {x:0 for word in words for x in word}\n for i, word in enumerate(words[:-1]):\n for j, c in enumerate(word):\n if j >= len(words[i+1]):\n return \"\"\n if c != words[i+1][j]:\n graph[c].append(words[i+1][j])\n degree[words[i+1][j]] += 1\n break\n dque, rslt = collections.deque([a for a in degree if degree[a] == 0]), []\n while dque:\n temp = dque.popleft()\n rslt.append(temp)\n for end in graph[temp]:\n degree[end] -= 1\n if degree[end] == 0:\n dque.append(end)\n return \"\".join(rslt) if len(rslt) == len(degree) else \"\"\n"},{"alpha_fraction":0.406876802444458,"alphanum_fraction":0.406876802444458,"avg_line_length":26.920000076293945,"blob_id":"d74630aee850edb8dec3b67ea4ac1fec3378ae41","content_id":"ffa0a34323e88e2620d643acc61e5b65b86fd52d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":698,"license_type":"no_license","max_line_length":67,"num_lines":25,"path":"/lc1650_lca.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def lowestCommonAncestor(self, p: 'Node', q: 'Node') -> 'Node':\n parents = {}\n if not p or not q: return p or q\n while p:\n if p == q: return p\n parents[p] = p.parent\n p = p.parent\n while q:\n q = q.parent\n if q in parents:\n return q\n return None\n def lowestCommonAncestor(self, p: 'Node', q: 'Node') -> 'Node':\n tp, tq = p, q\n while tp != tq:\n if tp.parent:\n tp = tp.parent\n else:\n tp = q\n if tq.parent:\n tq = tq.parent\n else:\n tq = p\n return tp\n"},{"alpha_fraction":0.3593582808971405,"alphanum_fraction":0.3893048167228699,"avg_line_length":33.62963104248047,"blob_id":"f035669728a6c9c2290e770d26e210ca89116f31","content_id":"4787ca7f987e6ff7ee9016ebe8205675d5598cfb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":935,"license_type":"no_license","max_line_length":133,"num_lines":27,"path":"/lc803_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def hitBricks(self, grid: List[List[int]], hits: List[List[int]]) -> List[int]:\n m, n = len(grid), len(grid[0])\n def dfs(i, j):\n if 0 <= i < m and 0 <= j < n and grid[i][j] == 1:\n rslt = 1\n grid[i][j] = 2\n rslt += sum(dfs(x, y) for x, y in [(i-1, j), (i+1, j), (i, j-1), (i, j+1)])\n return rslt\n return 0\n\n def is_new_stable(i, j):\n return i== 0 or any([0 <= x < m and 0 <= y < n and grid[x][y] == 2 for x, y in [(i-1, j), (i+1, j), (i, j-1), (i, j+1)]])\n\n for i, j in hits:\n grid[i][j] -= 1\n\n for i in range(n):\n dfs(0, i)\n\n res = [0]*len(hits)\n for k in range(len(hits)-1, -1, -1):\n i, j = hits[k]\n grid[i][j] += 1\n if grid[i][j] == 1 and is_new_stable(i, j):\n res[k] = dfs(i, j)-1\n return res\n"},{"alpha_fraction":0.504878044128418,"alphanum_fraction":0.5219511985778809,"avg_line_length":40,"blob_id":"680df576f1298c20de03afd7003a401cdc5a14d2","content_id":"b80fcf5e29481eab93d96c464725a43dd3b94dd6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":410,"license_type":"no_license","max_line_length":63,"num_lines":10,"path":"/lc1569_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def numOfWays(self, nums: List[int]) -> int:\n def helper(nums):\n if not nums: return 1\n root = nums[0]\n left = [num for num in nums if num < root]\n right = [num for num in nums if num > root]\n l, r = len(left), len(right)\n return math.comb(l+r, l)*helper(left)*helper(right)\n return (helper(nums)-1)%(10**9+7)\n"},{"alpha_fraction":0.366233766078949,"alphanum_fraction":0.37402597069740295,"avg_line_length":31.08333396911621,"blob_id":"f8e04d027efa2e5daa121d97515db2b0a7f4325e","content_id":"5f0537aa7e83445dae26478465b233ff5d0009a2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":385,"license_type":"no_license","max_line_length":80,"num_lines":12,"path":"/lc294_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def canWin(self, s: str) -> bool:\n memo = {}\n def dfs(s):\n if s in memo:\n return memo[s]\n for i in range(len(s)-1):\n if s[i] == \"+\" and s[i+1] == \"+\" and not dfs(s[:i]+\"-\"+s[i+2:]):\n memo[s] = True\n return True\n memo[s] = False\n return dfs(s)\n"},{"alpha_fraction":0.49251100420951843,"alphanum_fraction":0.49603524804115295,"avg_line_length":35.6129035949707,"blob_id":"1593d9d208f3f82f74fbc98c9ef928e6ed7a3119","content_id":"c6126f1dcd370ac989866d37418859d3eaab1799","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1135,"license_type":"no_license","max_line_length":77,"num_lines":31,"path":"/lc1306_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def canReach(self, arr: List[int], start: int) -> bool:\n seen = set()\n self.rslt, larr = False, len(arr)\n def backtracking(idx):\n if arr[idx] == 0:\n self.rslt = True\n return\n if idx in seen:\n return\n seen.add(idx)\n if idx + arr[idx] < larr:\n backtracking(idx+arr[idx])\n if idx - arr[idx] >= 0:\n backtracking(idx-arr[idx])\n backtracking(start)\n return self.rslt\n \n class Solution:\n def canReach(self, arr: List[int], start: int) -> bool:\n dque, visited = collections.deque([start]), {start}\n while dque:\n curr =dque.popleft()\n if arr[curr] == 0: return True\n if curr + arr[curr] < len(arr) and curr+arr[curr] not in visited:\n visited.add(curr+arr[curr])\n dque.append(curr+arr[curr])\n if curr - arr[curr] >= 0 and curr - arr[curr] not in visited:\n visited.add(curr-arr[curr])\n dque.append(curr-arr[curr])\n return False\n"},{"alpha_fraction":0.4930015504360199,"alphanum_fraction":0.5069984197616577,"avg_line_length":39.1875,"blob_id":"2b9e28cf8ae1ef1b3315444360f52a9e8939a1a3","content_id":"b02f9ae216fdda2bc2784bc2de3ccdcd2fc85b58","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":643,"license_type":"no_license","max_line_length":85,"num_lines":16,"path":"/lc40_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def combinationSum2(self, candidates: List[int], target: int) -> List[List[int]]:\n if not candidates: return []\n rslt, n = [], len(candidates)\n candidates.sort()\n def backtracking(i, target, curr):\n if target == 0:\n rslt.append(curr)\n elif i < n and target > 0:\n temp = candidates[i]\n backtracking(i+1, target-temp, curr+[temp])\n while i < n-1 and candidates[i] == candidates[i+1]:\n i += 1\n backtracking(i+1, target, curr)\n backtracking(0, target, [])\n return rslt\n"},{"alpha_fraction":0.45750707387924194,"alphanum_fraction":0.4645892381668091,"avg_line_length":36.157894134521484,"blob_id":"4147ea268ffff2b08ee45d624ae059fcb45e204f","content_id":"b3b66c9a1c65577707b0eba11c8eced78c4c4f3b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":706,"license_type":"no_license","max_line_length":117,"num_lines":19,"path":"/lc399_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def calcEquation(self, equations: List[List[str]], values: List[float], queries: List[List[str]]) -> List[float]:\n grph = collections.defaultdict(dict)\n for (x, y), v in zip(equations, values):\n grph[x][y] = v\n grph[y][x] = 1/v\n\n def dfs(x, y, checked):\n if x == y:\n return 1\n checked.add(x)\n for z in grph[x]:\n if z in checked:\n continue\n tmp = dfs(z, y, checked)\n if tmp > 0:\n return tmp*grph[x][z]\n return -1\n return [dfs(x, y, set()) if x in grph and y in grph else -1 for x, y in queries]\n"},{"alpha_fraction":0.4752252399921417,"alphanum_fraction":0.48048049211502075,"avg_line_length":27.340425491333008,"blob_id":"d7c64b62a299d7f685555b9cc8500ed308874dfb","content_id":"031ad877e81439794476f3f4433cee078fd2fbae","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1332,"license_type":"no_license","max_line_length":55,"num_lines":47,"path":"/lc1382_bt.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def balanceBST(self, root: TreeNode) -> TreeNode:\n rslt = []\n def preorder(l, r):\n if r == l:\n return None\n mid = (l+r)//2\n root = TreeNode(rslt[mid])\n root.left = preorder(l, mid)\n root.right = preorder(mid+1, r)\n return root\n def inorder(root):\n if not root:\n return\n inorder(root.left)\n rslt.append(root.val)\n inorder(root.right)\n inorder(root)\n return preorder(0, len(rslt))\n\nclass Solution:\n def balanceBST(self, root: TreeNode) -> TreeNode:\n rslt = []\n def inorder(root):\n if not root: return\n inorder(root.left)\n rslt.append(root)\n inorder(root.right)\n return\n\n def preorder(l, r):\n if l == r:\n return None\n mid = (l+r)//2\n root = rslt[mid]\n root.left = preorder(l, mid)\n root.right = preorder(mid+1, r)\n return root\n\n inorder(root)\n return preorder(0, len(rslt))\n"},{"alpha_fraction":0.4708904027938843,"alphanum_fraction":0.48630136251449585,"avg_line_length":37.93333435058594,"blob_id":"549887c89cdf030155952659673098e467f351be","content_id":"c962897ed12e36cda527100006835531c40f72fd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":584,"license_type":"no_license","max_line_length":64,"num_lines":15,"path":"/lc632_heap.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def smallestRange(self, nums: List[List[int]]) -> List[int]:\n heap = [(row[0], i, 0) for i, row in enumerate(nums)]\n right = max(row[0] for row in nums)\n rslt = (0, float(\"inf\"))\n heapq.heapify(heap)\n while True:\n left, ir, ic = heapq.heappop(heap)\n if right-left < rslt[1]-rslt[0]:\n rslt = (left, right)\n if ic + 1 == len(nums[ir]):\n return rslt\n nxt = nums[ir][ic+1]\n right = max(right, nxt)\n heapq.heappush(heap, (nxt, ir, ic+1))\n"},{"alpha_fraction":0.5036126971244812,"alphanum_fraction":0.5072254538536072,"avg_line_length":31.186046600341797,"blob_id":"3dc30c8d51a95b5ee1f6df97bba68628b78c1ff8","content_id":"0e4260a0e9f115e9cc555f4beb061864dfd5c41a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1384,"license_type":"no_license","max_line_length":88,"num_lines":43,"path":"/lc146_ood.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Node:\n def __init__(self, val = 0,key = 0, left = None, right = None):\n self.val = val\n self.key = key\n self.left = left\n self.right = right\nclass LRUCache:\n\n def __init__(self, capacity: int):\n self.capacity = capacity\n self.head = Node()\n self.end = Node(left = self.head)\n self.head.right = self.end\n self.maps = {}\n\n def get(self, key: int) -> int:\n if not self.maps or key not in self.maps:\n return -1\n curr = self.maps[key]\n curr.left.right = curr.right\n curr.right.left = curr.left\n curr.left = self.end.left\n curr.right = self.end\n self.end.left = curr\n curr.left.right = curr\n return curr.val\n\n def put(self, key: int, value: int) -> None:\n if key in self.maps:\n self.maps[key].val = value\n _ = self.get(key)\n else:\n curr = Node(val = value, key = key , left = self.end.left, right = self.end)\n self.end.left.right = curr\n self.end.left = curr\n self.maps[key] = curr\n if self.capacity == 0:\n temp = self.head.right\n self.head.right = self.head.right.right\n self.head.right.left = self.head\n self.maps.pop(temp.key)\n else:\n self.capacity -= 1\n"},{"alpha_fraction":0.4146341383457184,"alphanum_fraction":0.4471544623374939,"avg_line_length":32.54545593261719,"blob_id":"3bd4c39e69809d1f474ef8fe8cb66df480f14c42","content_id":"01d55872801dc166f4bec243e7e50d4d2482a8c1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":369,"license_type":"no_license","max_line_length":89,"num_lines":11,"path":"/lc152_array.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxProduct(self, nums: List[int]) -> int:\n if not nums: return 0\n dp = None\n for num in nums:\n if dp is None:\n dp = [num]*3\n continue\n dp[0], dp[1] = max(num, dp[0]*num, dp[1]*num), min(num, dp[1]*num, dp[0]*num)\n dp[2] = max(dp[2], dp[0])\n return dp[2]\n"},{"alpha_fraction":0.5508885383605957,"alphanum_fraction":0.5508885383605957,"avg_line_length":43.14285659790039,"blob_id":"76e0a522d38d5cfbb70789e4cd75a2716df22e90","content_id":"a7f0c8e2a5afa635896f05034252287e0143c24d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":619,"license_type":"no_license","max_line_length":87,"num_lines":14,"path":"/lc113_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def pathSum(self, root: TreeNode, targetSum: int) -> List[List[int]]:\n rslt = []\n def backtracking(root, val, path):\n if not root.left and not root.right and val == targetSum and path:\n rslt.append(path)\n else:\n if root.left:\n backtracking(root.left, val+root.left.val, path+[root.left.val])\n if root.right:\n backtracking(root.right, val+root.right.val, path+[root.right.val])\n if not root: return rslt\n backtracking(root, root.val, [root.val])\n return rslt \n"},{"alpha_fraction":0.48457351326942444,"alphanum_fraction":0.49637022614479065,"avg_line_length":38.35714340209961,"blob_id":"a29711a19fc22172cd4e349f4dd8ded5ef90efe0","content_id":"2f1735adcb52c36070c847d88c5287ec2fec3932","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1102,"license_type":"no_license","max_line_length":80,"num_lines":28,"path":"/lc889_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def constructFromPrePost(self, pre: List[int], post: List[int]) -> TreeNode:\n if not pre or not post: return None\n root = TreeNode(pre[0])\n if len(pre) > 1:\n idx = post.index(pre[1])\n root.left = self.constructFromPrePost(pre[1:idx+2], post[:idx+1])\n root.right = self.constructFromPrePost(pre[idx+2:], post[idx+1:-1])\n return root\n \nclass Solution:\n def constructFromPrePost(self, pre: List[int], post: List[int]) -> TreeNode:\n nodemap = {val:i for i, val in enumerate(post)}\n stk, root = [], None\n for val in pre:\n if not root:\n root = TreeNode(val)\n stk.append(root)\n else:\n temp = TreeNode(val)\n if nodemap[val] < nodemap[stk[-1].val]:\n stk[-1].left = temp\n else:\n while stk and nodemap[val] > nodemap[stk[-1].val]:\n last = stk.pop()\n stk[-1].right = temp\n stk.append(temp)\n return root\n"},{"alpha_fraction":0.48433613777160645,"alphanum_fraction":0.4868224859237671,"avg_line_length":40.04081726074219,"blob_id":"25f87e19f55ad08b1c66e952747420e3a651e3eb","content_id":"58b405ffa58f7825f049afe47cd70d004eed6d44","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2011,"license_type":"no_license","max_line_length":96,"num_lines":49,"path":"/lc126_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findLadders(self, beginWord: str, endWord: str, wordList: List[str]) -> List[List[str]]:\n if not wordList or endWord not in wordList: return []\n l, word_map, visited = len(beginWord), collections.defaultdict(list), set()\n for word in wordList:\n for i in range(l):\n word_map[word[:i]+\"*\"+word[i+1:]].append(word)\n dque = collections.deque([[beginWord]])\n rslt = []\n while dque:\n size = len(dque)\n for _ in range(size):\n cands = dque.popleft()\n curr = cands[-1]\n visited.add(curr)\n for i in range(l):\n for word in word_map[curr[:i]+\"*\"+curr[i+1:]]:\n if word == endWord:\n rslt.append(cands+[word])\n break\n if word not in visited:\n dque.append(cands+[word])\n\n if rslt:\n break\n return rslt\n\nclass Solution:\n def findLadders(self, beginWord: str, endWord: str, wordList: List[str]) -> List[List[str]]:\n words, wordmap, l = set(wordList), collections.defaultdict(list), len(beginWord)\n if endWord not in words: return []\n for word in wordList:\n for i in range(l):\n wordmap[word[:i]+\"*\"+word[i+1:]].append(word)\n\n layer, visited = collections.defaultdict(list), set()\n layer[beginWord].append([beginWord])\n while layer:\n newlayer = collections.defaultdict(list)\n for word in layer:\n if word == endWord:\n return layer[word]\n for i in range(l):\n for nxt in wordmap[word[:i]+\"*\"+word[i+1:]]:\n if nxt not in visited:\n newlayer[nxt] += [j + [nxt] for j in layer[word]]\n visited |= set(newlayer.keys())\n layer = newlayer\n return []\n"},{"alpha_fraction":0.47484663128852844,"alphanum_fraction":0.48098158836364746,"avg_line_length":37.80952453613281,"blob_id":"414530349e049c03de42a498be1335666dd81e06","content_id":"24dd22bc5bd1c02645e2ecd0b64c21b86333c4dc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":815,"license_type":"no_license","max_line_length":91,"num_lines":21,"path":"/lc1192_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def criticalConnections(self, n: int, connections: List[List[int]]) -> List[List[int]]:\n graph = collections.defaultdict(list)\n for start, end in connections:\n graph[start].append(end)\n graph[end].append(start)\n visited, rslt = {}, []\n def dfs(cur, pre, d):\n visited[cur] = d\n for end in graph[cur]:\n if end == pre:\n continue\n if end not in visited:\n visited[cur] = min(visited[cur], dfs(end, cur, d+1))\n else:\n visited[cur] = min(visited[cur], visited[end])\n if visited[cur] == d and pre != -1:\n rslt.append([pre, cur])\n return visited[cur]\n dfs(0, -1, 0)\n return rslt\n"},{"alpha_fraction":0.3712121248245239,"alphanum_fraction":0.40656566619873047,"avg_line_length":32,"blob_id":"e99a7a19391d4f9ec497bbe14ace34e90181c3bf","content_id":"52ef043cf550ad7b944d5553b9945c42609ec5f7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1188,"license_type":"no_license","max_line_length":73,"num_lines":36,"path":"/lc1802_bs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxValue(self, n: int, index: int, maxSum: int) -> int:\n left = 0\n right = maxSum\n while left < right:\n mid = right - (right - left) // 2\n extra1 = mid - 1 - index\n extra1 = (extra1 + 1) * extra1 // 2 if extra1 > 0 else extra1\n extra2 = mid - 1 - (n - index - 1)\n extra2 = (extra2 + 1) * extra2 // 2 if extra2 > 0 else extra2\n lower = (mid - 1) * mid // 2 - extra1\n upper = (mid - 1) * mid // 2 - extra2\n if lower + mid + upper > maxSum:\n right = mid - 1\n else:\n left = mid\n return left\n\n\n def maxValue(self, n, index, maxSum):\n def test(a):\n b = max(a - index, 0)\n res = (a + b) * (a - b + 1) / 2\n b = max(a - ((n - 1) - index), 0)\n res += (a + b) * (a - b + 1) / 2\n return res - a\n\n maxSum -= n\n left, right = 0, maxSum\n while left < right:\n mid = (left + right + 1) / 2\n if test(mid) <= maxSum:\n left = mid\n else:\n right = mid - 1\n return left + 1\n"},{"alpha_fraction":0.4907894730567932,"alphanum_fraction":0.4934210479259491,"avg_line_length":39,"blob_id":"7eb69bc21e082423e0f1d3eb0029dc3b30d48fa0","content_id":"f8ac6104cbe802eb4c73ecc45c56300ab55f0d20","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":760,"license_type":"no_license","max_line_length":129,"num_lines":19,"path":"/lc1311_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def watchedVideosByFriends(self, watchedVideos: List[List[str]], friends: List[List[int]], id: int, level: int) -> List[str]:\n visited, dque = {id}, collections.deque([id])\n while dque:\n if level == 0:\n break\n size = len(dque)\n for _ in range(size):\n curr = dque.popleft()\n for nt in friends[curr]:\n if nt not in visited:\n visited.add(nt)\n dque.append(nt)\n level -= 1\n rslt = []\n while dque:\n rslt.extend(watchedVideos[dque.pop()])\n summary = collections.Counter(rslt)\n return sorted(summary.keys(), key = lambda x: (summary[x], x))\n"},{"alpha_fraction":0.44111111760139465,"alphanum_fraction":0.4511111080646515,"avg_line_length":33.61538314819336,"blob_id":"3d0102fef6868a1e1726eb52fb6478eb287a044f","content_id":"c79ef07b9c226c7b8115f9f621a15380631c2eea","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":900,"license_type":"no_license","max_line_length":59,"num_lines":26,"path":"/lc22_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def generateParenthesis(self, n: int) -> List[str]:\n rslt, m = [], 2*n\n def backtracking(curr, left, right):\n if left+right == m:\n rslt.append(curr)\n else:\n if left < m -left:\n backtracking(curr+\"(\", left + 1, right)\n if right < left:\n backtracking(curr+\")\", left, right+1)\n backtracking(\"\", 0, 0)\n return rslt\n\n def generateParenthesis(self, n: int) -> List[str]:\n rslt = []\n def backtracking(curr, left, right):\n if left + right == 0:\n rslt.append(curr)\n else:\n if left > 0:\n backtracking(curr+\"(\", left -1, right)\n if right > left:\n backtracking(curr+\")\", left, right-1)\n backtracking(\"\", n, n)\n return rslt\n"},{"alpha_fraction":0.509772002696991,"alphanum_fraction":0.5146579742431641,"avg_line_length":28.238094329833984,"blob_id":"0a81011d76fa8df05473f1331aa26b3302917052","content_id":"10483d4ff30618aae3099426692e782c412077a6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":614,"license_type":"no_license","max_line_length":73,"num_lines":21,"path":"/lc92_linkedlist.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, val=0, next=None):\n# self.val = val\n# self.next = next\nclass Solution:\n def reverseBetween(self, head: ListNode, m: int, n: int) -> ListNode:\n snode = ListNode(0, head)\n left = snode\n for _ in range(m-1):\n left = left.next\n right = left.next\n for _ in range(n-m):\n pre = right\n right = right.next\n\n pre.next = right.next\n right.next = left.next\n left.next = right\n right = pre\n return snode.next\n"},{"alpha_fraction":0.46863189339637756,"alphanum_fraction":0.47543463110923767,"avg_line_length":31.268293380737305,"blob_id":"9ea4bf353096efe743e88cf6c80389ea5ffa91b1","content_id":"36ea8443e1b8727476a4990f9996dcbd9d220b80","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1323,"license_type":"no_license","max_line_length":80,"num_lines":41,"path":"/lc336_trie.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class TrieNode:\n def __init__(self):\n self.ch = {}\n self.cands = []\n self.idx = -1\nclass Trie:\n def __init__(self):\n self.root = TrieNode()\n def insert(self, word, idx):\n curr = self.root\n for i in range(len(word)-1, -1, -1):\n if word[:i+1] == word[:i+1][::-1]:\n curr.cands.append(idx)\n if word[i] not in curr.ch:\n curr.ch[word[i]] = TrieNode()\n curr = curr.ch[word[i]]\n curr.idx = idx\n curr.cands.append(idx)\n def search(self, word, idx):\n curr = self.root\n rslt = []\n for i in range(len(word)):\n if word[i:] == word[i:][::-1] and curr.idx != idx and curr.idx > -1:\n rslt.append([idx, curr.idx])\n ch = word[i]\n if ch not in curr.ch:\n return rslt\n curr = curr.ch[ch]\n for cand in curr.cands:\n if cand != idx:\n rslt.append([idx, cand])\n return rslt\nclass Solution:\n def palindromePairs(self, words: List[str]) -> List[List[int]]:\n paltrie = Trie()\n rslt = []\n for i, word in enumerate(words):\n paltrie.insert(word, i)\n for i, word in enumerate(words):\n rslt += paltrie.search(word, i)\n return rslt\n"},{"alpha_fraction":0.39676111936569214,"alphanum_fraction":0.4170040488243103,"avg_line_length":34.21428680419922,"blob_id":"da687103a560e098ee8c837d04c439b939f0a4d7","content_id":"a6415665f2daa259d0a473d6ae89db6975112794","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":494,"license_type":"no_license","max_line_length":72,"num_lines":14,"path":"/lc17_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def letterCombinations(self, digits: str) -> List[str]:\n if not digits: return []\n n, rslt = len(digits), []\n cmap = {\"2\":\"abc\", \"3\":\"def\", \"4\":\"ghi\",\n \"5\":\"jkl\", \"6\":\"mno\", \"7\":\"pqrs\", \"8\":\"tuv\", \"9\":\"wxyz\"}\n def btrack(i, curr):\n if i == n:\n rslt.append(curr)\n else:\n for c in cmap[digits[i]]:\n btrack(i+1, curr+c)\n btrack(0, \"\")\n return rslt \n"},{"alpha_fraction":0.4620155096054077,"alphanum_fraction":0.46666666865348816,"avg_line_length":34.83333206176758,"blob_id":"dc568d549e02e31d82081ef27dbf8405e7cba718","content_id":"94ad041ed703e541c0ff4eacb1d92ed9c96c8e18","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":645,"license_type":"no_license","max_line_length":73,"num_lines":18,"path":"/lc1023_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def camelMatch(self, queries: List[str], pattern: str) -> List[bool]:\n rslt = []\n def backtracking(word, pattern):\n if not pattern:\n for c in word:\n if c.isupper():\n return False\n return True\n for i, c in enumerate(word):\n if c == pattern[0]:\n return backtracking(word[i+1:], pattern[1:])\n if c.isupper():\n return False\n return False\n for query in queries:\n rslt.append(backtracking(query, pattern))\n return rslt\n"},{"alpha_fraction":0.3623853325843811,"alphanum_fraction":0.375,"avg_line_length":29.068965911865234,"blob_id":"e20f126f41d78c20c06400cfd9ab9c166128b43d","content_id":"92b54e9cb2f33113585efda18424af3b3e4d33d8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":872,"license_type":"no_license","max_line_length":58,"num_lines":29,"path":"/lc331_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def isValidSerialization(self, preorder: str) -> bool:\n stk = []\n temp = preorder.split(\",\")\n i = 0\n while i < len(temp):\n if temp[i] == \"#\":\n if not stk or stk[-1] != \"#\":\n stk.append(\"#\")\n i += 1\n elif len(stk) < 2:\n return False\n else:\n stk.pop()\n stk.pop()\n else:\n stk.append(temp[i])\n i += 1\n return stk == [\"#\"]\n\nclass Solution:\n def isValidSerialization(self, preorder: str) -> bool:\n slot = 1\n for i, c in enumerate(preorder+\",\"):\n if c == \",\":\n slot -= 1\n if slot < 0: return False\n if preorder[i-1] != \"#\": slot += 2\n return slot == 0\n"},{"alpha_fraction":0.5209790468215942,"alphanum_fraction":0.5314685106277466,"avg_line_length":26.238094329833984,"blob_id":"9cee2216bdb8480051a93154b4933e1d345249a7","content_id":"95bca8f86ae8099305c1bb0999cbef9c4dcccd44","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":572,"license_type":"no_license","max_line_length":50,"num_lines":21,"path":"/lc1381_ood.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class CustomStack:\n\n def __init__(self, maxSize: int):\n self.cap = maxSize\n self.curSize = 0\n self.stack = [0]*maxSize\n\n def push(self, x: int) -> None:\n if self.curSize < self.cap:\n self.stack[self.curSize] = x\n self.curSize += 1\n\n def pop(self) -> int:\n if not self.curSize: return -1\n temp = self.stack[self.curSize-1]\n self.curSize -= 1\n return temp\n\n def increment(self, k: int, val: int) -> None:\n for i in range(min(k, self.curSize)):\n self.stack[i] += val\n"},{"alpha_fraction":0.3575129508972168,"alphanum_fraction":0.3911917209625244,"avg_line_length":28,"blob_id":"b31f357ea4dbe53e1eb0894fd50207c07a9f9e1c","content_id":"41a4f9bfe468c4377a3ca09f3356ca173bd5cf7b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":386,"license_type":"no_license","max_line_length":48,"num_lines":13,"path":"/lc1567_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def getMaxLen(self, nums: List[int]) -> int:\n cp, cn =0, 0\n rslt = 0\n for num in nums:\n if num > 0:\n cp, cn = cp+1, cn+1 if cn else 0\n elif num < 0:\n cp, cn = cn+1 if cn else 0, cp+1\n else:\n cp, cn = 0, 0\n rslt = max(rslt, cp)\n return rslt\n \n"},{"alpha_fraction":0.5147679448127747,"alphanum_fraction":0.5316455960273743,"avg_line_length":38.5,"blob_id":"83f13843e5166251ce754219354b7c667b69653e","content_id":"67d56853e574d31f903109cd5f4ef1b3846960b6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":711,"license_type":"no_license","max_line_length":91,"num_lines":18,"path":"/lc333_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def largestBSTSubtree(self, root: TreeNode) -> int:\n def helper(root):\n if not root: return 0, float(\"inf\"), -float(\"inf\")\n left = helper(root.left)\n right = helper(root.right)\n if left[2] < root.val < right[1]:\n return left[0]+right[0]+1, min(left[1], root.val), max(root.val, right[2])\n else:\n return max(left[0], right[0]), -float(\"inf\"), float(\"inf\")\n temp = helper(root)\n return temp[0]\n"},{"alpha_fraction":0.4273972511291504,"alphanum_fraction":0.45068493485450745,"avg_line_length":35.5,"blob_id":"02871df7e19a5ce14670f0ffd5358760b2d9ca9a","content_id":"584a036bf8411a49e71d9b2a595608600b3b2ba7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":730,"license_type":"no_license","max_line_length":65,"num_lines":20,"path":"/lc752_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def openLock(self, deadends: List[str], target: str) -> int:\n def neighbors(node):\n for i in range(4):\n t = int(node[i])\n for d in (-1, 1):\n y = (t+d)%10\n yield node[:i] + str(y) + node[i+1:]\n\n dd = set(deadends)\n queue, seen = collections.deque([('0000', 0 )]), {'0000'}\n while queue:\n node, depth = queue.popleft()\n if node == target: return depth\n if node in dd: continue\n for nei in neighbors(node):\n if nei not in seen and nei not in dd:\n seen.add(nei)\n queue.append((nei, depth+1))\n return -1\n"},{"alpha_fraction":0.3912484049797058,"alphanum_fraction":0.41312742233276367,"avg_line_length":28.884614944458008,"blob_id":"81fbade1841ef71c9c5791530a2b6d25625721a4","content_id":"d41d3a96304d085f7434170cf4916e08baa6b8f1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":777,"license_type":"no_license","max_line_length":55,"num_lines":26,"path":"/lc279_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def numSquares(self, n: int) -> int:\n cands = [i**2 for i in range(1, int(n**0.5)+1)]\n @lru_cache(None)\n def dfs(i):\n if i in cands:\n return 1\n rslt = float(\"inf\")\n for cand in cands:\n if cand > i:\n break\n rslt = min(rslt, dfs(i-cand)+1)\n return rslt\n return dfs(n)\n\nclass Solution:\n def numSquares(self, n: int) -> int:\n cands = [i*i for i in range(int(n**0.5) + 1)]\n dp = [float(\"inf\")]*(n+1)\n dp[0] = 0\n for i in range(1, n+1):\n for cand in cands:\n if i < cand:\n break\n dp[i] = min(dp[i], dp[i-cand] + 1)\n return dp[-1]\n"},{"alpha_fraction":0.48097825050354004,"alphanum_fraction":0.4945652186870575,"avg_line_length":35.79999923706055,"blob_id":"6355945b7d8243b917170249cd00c36ef0110d69","content_id":"bbf9eaaee2a080d32c45c43b9f49d12ad357f4f6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":368,"license_type":"no_license","max_line_length":64,"num_lines":10,"path":"/lc503_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def nextGreaterElements(self, nums: List[int]) -> List[int]:\n if not nums: return nums\n rslt, stk = [-1]*len(nums), []\n for i in range(len(nums)-1, -len(nums), -1):\n while stk and stk[-1] <= nums[i]:\n stk.pop()\n if stk: rslt[i] = stk[-1]\n stk.append(nums[i])\n return rslt\n"},{"alpha_fraction":0.4274061918258667,"alphanum_fraction":0.43882545828819275,"avg_line_length":29.649999618530273,"blob_id":"2d32bf604a0501753dfdbe13e27ada3e5abb8b04","content_id":"325228c05d02b45cabac575920cc6517b50fd401","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":613,"license_type":"no_license","max_line_length":55,"num_lines":20,"path":"/lc1675_heap.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minimumDeviation(self, nums: List[int]) -> int:\n heap = []\n left, right = float(\"inf\"), 0\n rslt = float(\"inf\")\n for num in nums:\n if num%2:\n num = num*2\n heapq.heappush(heap, -num)\n left = min(left, num)\n while True:\n right = -heapq.heappop(heap)\n if right-left < rslt:\n rslt = right-left\n if right%2 == 0:\n heapq.heappush(heap, -right//2)\n left = min(left, right//2)\n else:\n break\n return rslt\n"},{"alpha_fraction":0.44474393129348755,"alphanum_fraction":0.4582210183143616,"avg_line_length":32.727272033691406,"blob_id":"d70f2c787bd2d48978472b123496c243bef44bca","content_id":"c609b9025e264e349e4e17c56bd5cd286e006359","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":371,"license_type":"no_license","max_line_length":54,"num_lines":11,"path":"/lc962_stk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxWidthRamp(self, A: List[int]) -> int:\n stk = []\n for i, num in enumerate(A):\n if not stk or A[stk[-1]] > num:\n stk.append(i)\n rslt = 0\n for i, num in enumerate(A[::-1]):\n while stk and A[stk[-1]] <= num:\n rslt = max(rslt, len(A)-1-i-stk.pop())\n return rslt\n"},{"alpha_fraction":0.3875739574432373,"alphanum_fraction":0.40532544255256653,"avg_line_length":29.727272033691406,"blob_id":"3ff6bbeedf9cc51530a695f48270b10787abe679","content_id":"3651a6895d55f13e12003ad9635ac82876e1356a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":338,"license_type":"no_license","max_line_length":48,"num_lines":11,"path":"/lc856_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def scoreOfParentheses(self, s: str) -> int:\n count_right, rslt = 0, 0\n for i, c in enumerate(s):\n if c == \"(\":\n count_right += 1\n else:\n count_right -= 1\n if s[i-1] == '(':\n rslt += 2**count_right\n return rslt\n"},{"alpha_fraction":0.5015479922294617,"alphanum_fraction":0.5139318704605103,"avg_line_length":39.375,"blob_id":"8477fff05f4e4a26d969cccd833cc821a7d6f155","content_id":"cfc305b18cf6f970eab8d41796c17e1b3dc7c8e7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":323,"license_type":"no_license","max_line_length":80,"num_lines":8,"path":"/lc1366_sort.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def rankTeams(self, votes: List[str]) -> str:\n l = len(votes[0])\n mapping = {k: [0]*l for k in votes[0]}\n for vote in votes:\n for i, v in enumerate(vote):\n mapping[v][i] += 1\n return \"\".join(sorted(mapping.keys(), key = lambda x: (-mapping[x], x)))\n"},{"alpha_fraction":0.43018868565559387,"alphanum_fraction":0.4377358555793762,"avg_line_length":27.44444465637207,"blob_id":"ec92e925ad6984866381237375653f9547dc315e","content_id":"45af675bfc4fd8b2c942840ed97e0fc9358d50eb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":265,"license_type":"no_license","max_line_length":51,"num_lines":9,"path":"/lc1608_array.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def specialArray(self, nums: List[int]) -> int:\n nums.sort()\n last, n = 0, len(nums)\n for i in range(n):\n if nums[i] >= n-i > last:\n return n-i\n last = nums[i]\n return -1\n \n"},{"alpha_fraction":0.47907325625419617,"alphanum_fraction":0.4805680215358734,"avg_line_length":28.733333587646484,"blob_id":"97c462a1af2ce3ac97450d18b275547d5b952513","content_id":"4c6fd94cc5bdeb9a95044c8f874fc09e8a98d763","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1338,"license_type":"no_license","max_line_length":63,"num_lines":45,"path":"/lc642_tire.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Trie:\n def __init__(self):\n self.root = {}\n def insert(self, word):\n curr = self.root\n for ch in word:\n if ch not in curr:\n curr[ch] = {}\n curr = curr[ch]\n curr[\"#\"] = word\n def search(self, prefix):\n curr = self.root\n for ch in prefix:\n if ch not in curr:\n return []\n curr = curr[ch]\n return self.findall(curr)\n def findall(self, curr):\n rslt = []\n for key in curr:\n if key == \"#\":\n rslt.append(curr[key])\n else:\n rslt += self.findall(curr[key])\n return rslt\nclass AutocompleteSystem:\n\n def __init__(self, sentences: List[str], times: List[int]):\n self.trie = Trie()\n self.hotmap = collections.defaultdict(int)\n for sentence, time in zip(sentences, times):\n self.trie.insert(sentence)\n self.hotmap[sentence] += time\n self.last = \"\"\n\n def input(self, c: str) -> List[str]:\n if c == \"#\":\n self.hotmap[self.last] += 1\n self.trie.insert(self.last)\n self.last = \"\"\n return []\n self.last += c\n rslt = self.trie.search(self.last)\n rslt.sort(key= lambda x: (-self.hotmap[x], x))\n return rslt[:3]\n"},{"alpha_fraction":0.4375,"alphanum_fraction":0.5193014740943909,"avg_line_length":40.846153259277344,"blob_id":"b00eadd281928300a07f34f64b4c9cde10fc7f19","content_id":"68da9221101520053ee69f2ffb3abaf3f1430abd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1088,"license_type":"no_license","max_line_length":82,"num_lines":26,"path":"/lc4_dandc.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:\n if not nums1 and not nums2:\n return None\n l1, l2 = len(nums1), len(nums2)\n if (l1+l2)%2 ==1:\n return self.findKth(nums1, nums2, 0, 0, (l1+l2)//2 + 1)\n else:\n left = self.findKth(nums1, nums2, 0, 0, (l1+l2)//2)\n right = self.findKth(nums1, nums2, 0, 0, (l1+l2)//2+1)\n return (left+right)/2\n\n def findKth(self, nums1, nums2, loc1, loc2, k):\n if loc1 == len(nums1):\n return nums2[loc2 + k -1]\n if loc2 == len(nums2):\n return nums1[loc1 + k -1]\n if k == 1:\n return min(nums1[loc1], nums2[loc2])\n\n p1 = nums1[loc1 + k//2 - 1] if loc1 + k//2 - 1 < len(nums1) else None\n p2 = nums2[loc2 + k//2 - 1] if loc2 + k//2 - 1 < len(nums2) else None\n\n if p2 is None or (p1 is not None and p1 < p2):\n return self.findKth(nums1, nums2, loc1+k//2, loc2, k - k//2)\n return self.findKth(nums1, nums2, loc1, loc2 + k//2, k - k//2)\n"},{"alpha_fraction":0.5185185074806213,"alphanum_fraction":0.5303703546524048,"avg_line_length":47.21428680419922,"blob_id":"5063b47e51aa5c93941bb90b2ac09f6221990766","content_id":"def42b414f709a874ceac87e6d3ef232e7be940f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":675,"license_type":"no_license","max_line_length":76,"num_lines":14,"path":"/lc1438_deque.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def longestSubarray(self, nums: List[int], limit: int) -> int:\n dque_max, dque_min, l= collections.deque(), collections.deque(), 0\n for num in nums:\n while dque_max and dque_max[-1] < num: dque_max.pop()\n while dque_min and dque_min[-1] > num: dque_min.pop()\n dque_max.append(num)\n dque_min.append(num)\n if dque_max and dque_min and dque_max[0] - dque_min[0] > limit:\n if dque_max[0] == nums[l]: dque_max.popleft()\n if dque_min[0] == nums[l]: dque_min.popleft()\n l += 1\n # print(dque_max, dque_min, l)\n return len(nums)-l\n"},{"alpha_fraction":0.4505050480365753,"alphanum_fraction":0.4727272689342499,"avg_line_length":37.07692337036133,"blob_id":"d470f372cf49088c7c349aa2086fbfbae301a5de","content_id":"03cac9048932ec54fb22cfa4ad29099bea5afd41","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":495,"license_type":"no_license","max_line_length":64,"num_lines":13,"path":"/lc1856_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxSumMinProduct(self, nums: List[int]) -> int:\n n = len(nums)\n presum = [0]*(n+1)\n for i in range(n): presum[i+1] = presum[i] + nums[i]\n stack, rslt = [], 0\n for i, h in enumerate(nums+[0]):\n start = i\n while stack and stack[-1][1] >= h:\n start, mh = stack.pop()\n rslt = max(rslt, (presum[i] - presum[start])*mh)\n stack.append((start, h))\n return rslt %(10**9 + 7)\n"},{"alpha_fraction":0.4228723347187042,"alphanum_fraction":0.4441489279270172,"avg_line_length":30.33333396911621,"blob_id":"aa390039d86e6031c1c63c008938a9833d03151a","content_id":"180cb839d7195c026df5339c2041c25cc16e81a4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":376,"license_type":"no_license","max_line_length":67,"num_lines":12,"path":"/lc56_array.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def merge(self, intervals: List[List[int]]) -> List[List[int]]:\n intervals.sort()\n rslt = []\n for it in intervals:\n if not rslt:\n rslt.append(it)\n elif it[0] <= rslt[-1][1]:\n rslt[-1][1] = max(rslt[-1][1], it[1])\n else:\n rslt.append(it)\n return rslt\n"},{"alpha_fraction":0.44871795177459717,"alphanum_fraction":0.4643874764442444,"avg_line_length":30.909090042114258,"blob_id":"0f93029dba7bd7071b745fa7c2accec0e54bc9ba","content_id":"28aad7423f3c0a13939d188ab3e1119c8754ae13","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":702,"license_type":"no_license","max_line_length":64,"num_lines":22,"path":"/lc452_array.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findMinArrowShots(self, points: List[List[int]]) -> int:\n points.sort(key = lambda x: x[1])\n rslt, last = 0, -float(\"inf\")\n for point in points:\n if point[0] <= last:\n last = min(last, point[1])\n else:\n rslt += 1\n last = point[1]\n return rslt\n\n def findMinArrowShots(self, points: List[List[int]]) -> int:\n points.sort(reverse = True)\n rslt, last = 0, float(\"inf\")\n for point in points:\n if point[1] >= last:\n last = max(last, point[0])\n else:\n rslt += 1\n last = point[0]\n return rslt\n"},{"alpha_fraction":0.5090609788894653,"alphanum_fraction":0.5255354046821594,"avg_line_length":42.35714340209961,"blob_id":"58259b5781c4e63c44c91634ee3a4ba8a41115ed","content_id":"a8f3befd8951ecfce7198a35fb72bf00edc1a84f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":607,"license_type":"no_license","max_line_length":111,"num_lines":14,"path":"/lc986_arrary.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def intervalIntersection(self, firstList: List[List[int]], secondList: List[List[int]]) -> List[List[int]]:\n idxa, idxb, na, nb = 0, 0, len(firstList), len(secondList)\n rslt = []\n while idxa < na and idxb < nb:\n leftb = max(firstList[idxa][0], secondList[idxb][0])\n rightb = min(firstList[idxa][1], secondList[idxb][1])\n if leftb <= rightb:\n rslt.append([leftb, rightb])\n if firstList[idxa][1] < secondList[idxb][1]:\n idxa += 1\n else:\n idxb += 1\n return rslt\n"},{"alpha_fraction":0.43906810879707336,"alphanum_fraction":0.4534050226211548,"avg_line_length":33.875,"blob_id":"a1a48b84185efda7eb5f505f909f89c4d47f3e0b","content_id":"e555e13e7e08b198d6aea0009293fbef6c458ef6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":558,"license_type":"no_license","max_line_length":69,"num_lines":16,"path":"/lc1406_minmax.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def stoneGameIII(self, stoneValue: List[int]) -> str:\n memo = {}\n def dfs(i):\n if i in memo:\n return memo[i]\n if i >= len(stoneValue):\n return 0\n rslt = float(\"-inf\")\n for j in range(3):\n if i+j+1 > len(stoneValue): break\n rslt = max(rslt, sum(stoneValue[i:i+j+1])-dfs(i+j+1))\n memo[i] = rslt\n return rslt\n temp = dfs(0)\n return \"Tie\" if temp == 0 else \"Alice\" if temp > 0 else \"Bob\"\n"},{"alpha_fraction":0.4414893686771393,"alphanum_fraction":0.4541223347187042,"avg_line_length":29.693878173828125,"blob_id":"be7d0a57575019ece4dba3df7b8e8143103491ed","content_id":"054cae6ca38e1496bab6d9cd0583b465f9c7f0b2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1504,"license_type":"no_license","max_line_length":54,"num_lines":49,"path":"/lc421_trie.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Trie:\n def __init__(self, L):\n self.root = TrieNode()\n self.maskL = L\n def insert(self, num):\n mask = 1 << self.maskL\n curr = self.root\n while mask:\n key = 1 if num & mask else 0\n if key not in curr.children:\n curr.children[key] = TrieNode()\n curr = curr.children[key]\n mask >>= 1\n def search(self, num):\n mask = 1 << self.maskL\n curr, rslt = self.root, 0\n while mask:\n key = 0 if num&mask else 1\n if key in curr.children:\n curr = curr.children[key]\n rslt |= mask\n else:\n curr = curr.children[1-key]\n mask >>= 1\n return rslt\nclass Solution:\n def findMaximumXOR(self, nums: List[int]) -> int:\n L = len(bin(max(nums)))-2\n solTrie, rslt = Trie(L), 0\n for num in nums:\n solTrie.insert(num)\n rslt = max(rslt, solTrie.search(num))\n return rslt\n\nclass Solution:\n def findMaximumXOR(self, nums: List[int]) -> int:\n l = len(bin(max(nums)))-2\n rslt = 0\n for i in range(l-1, -1, -1):\n mask = rslt | 1 << i\n used = set()\n for num in nums:\n curr = num & mask\n # curr ^ mask ^ curr = 0 ^ mask = mask\n if (curr ^ mask) in used:\n rslt = mask\n break\n used.add(curr)\n return rslt\n"},{"alpha_fraction":0.4125683009624481,"alphanum_fraction":0.43852460384368896,"avg_line_length":32.272727966308594,"blob_id":"28cbdd731543f10e8132d6e276f619fdc7ba781b","content_id":"c6091e55701f1791736ebc81436bab21aad9fd0d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":732,"license_type":"no_license","max_line_length":70,"num_lines":22,"path":"/lc474_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findMaxForm(self, strs, m, n):\n xy = [[s.count(\"0\"), s.count(\"1\")] for s in strs]\n\n @lru_cache(None)\n\n def dp(mm, nn, kk):\n if mm < 0 or nn < 0: return -float(\"inf\")\n if kk == len(strs): return 0\n x, y = xy[kk]\n return max(1 + dp(mm-x, nn-y, kk + 1), dp(mm, nn, kk + 1))\n\n return dp(m, n, 0)\n\n def findMaxForm(self, strs, m, n):\n dp = [[0]*(m+1) for _ in range(n+1)]\n for s in strs:\n ones, zeros = s.count(\"1\"), s.count(\"0\")\n for i in range(n, ones-1, -1):\n for j in range(m, zeros-1, -1):\n dp[i][j] = max(dp[i][j], dp[i-ones][j-zeros]+1)\n return dp[n][m]\n"},{"alpha_fraction":0.39358600974082947,"alphanum_fraction":0.4169096350669861,"avg_line_length":30.18181800842285,"blob_id":"a5300bca372db952b2a9ec6f5a240a3005eda9f6","content_id":"ee7c214669162040dd4f9992f8776ea2bc6f12c2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":343,"license_type":"no_license","max_line_length":55,"num_lines":11,"path":"/lc1004_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def longestOnes(self, A: List[int], K: int) -> int:\n left, cnt, rslt = 0, 0, 0\n for right, c in enumerate(A):\n if c == 0:\n cnt += 1\n while cnt > K:\n cnt -= 1-A[left]\n left += 1\n rslt = max(rslt, right-left + 1)\n return rslt\n"},{"alpha_fraction":0.47130435705184937,"alphanum_fraction":0.47652173042297363,"avg_line_length":34.9375,"blob_id":"5e1d6f90636874be648e952568c697c72bc8b69c","content_id":"14b61d5b26ac7c6a9d8ee49d68da64dae8419111","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":575,"license_type":"no_license","max_line_length":97,"num_lines":16,"path":"/lc1644_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def lowestCommonAncestor(self, root: 'TreeNode', p: 'TreeNode', q: 'TreeNode') -> 'TreeNode':\n self.mark = 0\n def dfs(root, p, q):\n if not root: return None\n left = dfs(root.left, p, q)\n right = dfs(root.right, p, q)\n if root == p or root == q:\n self.mark += 1\n return root\n if left and right:\n return root\n else:\n return left or right\n rslt = dfs(root, p, q)\n return rslt if self.mark == 2 else None\n"},{"alpha_fraction":0.37877094745635986,"alphanum_fraction":0.40223464369773865,"avg_line_length":26.96875,"blob_id":"66c6ba48da9a232facb5e2f7fdadfbd014835162","content_id":"dd92699c3005642a22363c6addf9cd390530dbdb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":895,"license_type":"no_license","max_line_length":64,"num_lines":32,"path":"/lc1248_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def numberOfSubarrays(self, nums: List[int], k: int) -> int:\n left, rslt, last = 0, 0, 0\n for right, num in enumerate(nums):\n if num%2 == 1:\n k -= 1\n last = 0\n while k == 0:\n last += 1\n k += nums[left]%2\n left += 1\n rslt += last\n return rslt\n\n\n\nclass Solution:\n def numberOfSubarrays(self, nums: List[int], k: int) -> int:\n left, rslt, count = 0, 0, 0\n for right, num in enumerate(nums):\n count += num%2\n while count > k:\n count -= nums[left]%2\n left += 1\n temp = left\n while count == k:\n count -= nums[temp] % 2\n rslt += 1\n temp += 1\n if temp > left:\n count += 1\n return rslt\n"},{"alpha_fraction":0.3758865296840668,"alphanum_fraction":0.38770684599876404,"avg_line_length":29.214284896850586,"blob_id":"85047e8430feb409f98faf3bc3785967bd8c1eca","content_id":"a917a2a12a045177fe957f1f63d5af9ceef79099","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":423,"license_type":"no_license","max_line_length":65,"num_lines":14,"path":"/lc375_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def getMoneyAmount(self, n: int) -> int:\n memo = {}\n def dfs(i, j):\n if (i, j) in memo:\n return memo[(i, j)]\n if i >= j:\n return 0\n rslt = n**n\n for t in range(i, j+1):\n rslt = min(rslt, t+max(dfs(i, t-1), dfs(t+1, j)))\n memo[(i,j)] = rslt\n return rslt\n return dfs(1, n)\n"},{"alpha_fraction":0.5829383730888367,"alphanum_fraction":0.6050552725791931,"avg_line_length":36.235294342041016,"blob_id":"3172ee72ef6567947d4c1669eb18e242c9349728","content_id":"c80d8bd6450cb9e7a74a30c2458c7dca7e539c83","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":633,"license_type":"no_license","max_line_length":111,"num_lines":17,"path":"/lc1348_hash.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class TweetCounts:\n\n def __init__(self):\n self.maps = collections.defaultdict(list)\n\n def recordTweet(self, tweetName: str, time: int) -> None:\n # dictionary of list\n self.maps[tweetName].append(time)\n\n\n def getTweetCountsPerFrequency(self, freq: str, tweetName: str, startTime: int, endTime: int) -> List[int]:\n chunk_size = {\"minute\": 60, \"hour\":3600, \"day\":86400}\n rslt = [0]*((endTime-startTime)//chunk_size[freq]+1)\n for time in self.maps[tweetName]:\n if startTime <= time <= endTime:\n rslt[(time-startTime)//chunk_size[freq]] +=1\n return rslt\n"},{"alpha_fraction":0.48439618945121765,"alphanum_fraction":0.4993215799331665,"avg_line_length":35.849998474121094,"blob_id":"975cfcab95805c15b54eb657fac1abf17f6308ca","content_id":"b1a99c6d90526fb4b0fc2b3e93cacbad5a106394","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1474,"license_type":"no_license","max_line_length":84,"num_lines":40,"path":"/lc1307_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def isSolvable(self, words: list[str], result: str) -> bool:\n max_woerd_len = max(map(len, words))\n if max_woerd_len < len(result) - 1:\n return False\n if max_woerd_len > len(result):\n return False\n\n words = words + [result]\n first_letters = set(word[0] for word in words if len(word) > 1)\n cols, rows = len(result), len(words)\n letter_to_digits = {}\n\n def search(col: int, row: int, carry: int) -> bool:\n if col == cols:\n return carry == 0\n if row == rows:\n quotient, reminder = divmod(carry, 10)\n return reminder == 0 and search(col + 1, 0, quotient)\n\n word = words[row]\n if col >= len(word):\n return search(col, row + 1, carry)\n\n letter = word[~col]\n sign = 1 if row < (rows - 1) else -1\n\n if letter in letter_to_digits:\n return search(col, row + 1, carry + sign * letter_to_digits[letter])\n\n init_digit = 1 if letter in first_letters else 0\n for digit in range(init_digit, 10):\n if digit not in letter_to_digits.values():\n letter_to_digits[letter] = digit\n if search(col, row + 1, carry + sign * digit):\n return True\n del letter_to_digits[letter]\n return False\n\n return search(0, 0, 0)\n"},{"alpha_fraction":0.4791666567325592,"alphanum_fraction":0.4886363744735718,"avg_line_length":32,"blob_id":"4feae26ee93c0ce1a97681ecc69c34e26bae911f","content_id":"d0f6da833bb10b8a729cc491aef8a39dc3e064aa","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":528,"license_type":"no_license","max_line_length":58,"num_lines":16,"path":"/lc437_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def pathSum(self, root: TreeNode, sum: int) -> int:\n def preorder(node, curr):\n if not node:\n return\n curr += node.val\n if curr == sum:\n self.count += 1\n self.count += maps[curr-sum]\n maps[curr] += 1\n preorder(node.left, curr)\n preorder(node.right, curr)\n maps[curr] -= 1\n self.count, maps = 0, collections.defaultdict(int)\n preorder(root, 0)\n return self.count\n"},{"alpha_fraction":0.43178170919418335,"alphanum_fraction":0.4365971088409424,"avg_line_length":27.31818199157715,"blob_id":"73e48dbc0947267cbae8328175f6ef2bd7c269c4","content_id":"cf5451be7a61a321d958436e28842d25e80b918f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1246,"license_type":"no_license","max_line_length":60,"num_lines":44,"path":"/lc297_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Codec:\n\n def serialize(self, root):\n \"\"\"Encodes a tree to a single string.\n\n :type root: TreeNode\n :rtype: str\n \"\"\"\n if not root: return \"\"\n dqueue = collections.deque([root])\n rslt = []\n while dqueue:\n temp = dqueue.popleft()\n if temp:\n rslt.append(str(temp.val))\n dqueue.append(temp.left)\n dqueue.append(temp.right)\n else:\n rslt.append(\"\")\n return \",\".join(rslt)\n\n def deserialize(self, data):\n \"\"\"Decodes your encoded data to tree.\n\n :type data: str\n :rtype: TreeNode\n \"\"\"\n dqueue = collections.deque([])\n root, check = None, 0\n for val in data.split(\",\"):\n temp = TreeNode(int(val)) if val != \"\" else None\n if not root:\n root = temp\n dqueue.append(root)\n else:\n if check == 0:\n dqueue[0].left = temp\n check += 1\n else:\n dqueue[0].right = temp\n dqueue.popleft()\n check = 0\n if temp: dqueue.append(temp)\n return root\n"},{"alpha_fraction":0.33888888359069824,"alphanum_fraction":0.3611111044883728,"avg_line_length":31.727272033691406,"blob_id":"4d356af250a02c80ed821aef44c2d3499411bbf0","content_id":"330c966139698896538bb30abf70c3717fa0ae77","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":720,"license_type":"no_license","max_line_length":68,"num_lines":22,"path":"/lc1593_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxUniqueSplit(self, s: str) -> int:\n dp = [0]*(len(s)+1)\n def backtracking(i, c, seen):\n if dp[i] > 0 and dp[i] > c + 1 or len(s)-i + c < dp[-1]:\n return\n if i == len(s):\n dp[-1] = max(dp[-1], c)\n return\n for j in range(i+1, len(s)+1):\n if s[i:j] in seen:\n dp[j-1] = c\n backtracking(j, c, seen)\n else:\n dp[j-1] = c+1\n seen.add(s[i:j])\n backtracking(j,c+1, seen)\n seen.remove(s[i:j])\n return\n backtracking(0, 0, set())\n\n return dp[-1]\n"},{"alpha_fraction":0.4280821979045868,"alphanum_fraction":0.4497717022895813,"avg_line_length":35.29166793823242,"blob_id":"495d14fdf06bc15e3a6ecaadf8caf9a4fa2df27d","content_id":"2a7e23f2b73ca7c33d922b56313878bb4f3169d6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":876,"license_type":"no_license","max_line_length":65,"num_lines":24,"path":"/lc216_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def combinationSum3(self, k: int, n: int) -> List[List[int]]:\n if n < k*1 or n > k*9: return []\n rslt = []\n def backtracking(i, n, curr):\n if n == 0 and len(curr) == k:\n rslt.append(curr)\n elif len(curr) < k and i < 10:\n for j in range(i, 10):\n backtracking(j+1, n-j, curr + [j])\n backtracking(1, n, [])\n return rslt\n\n def combinationSum3(self, k: int, n: int) -> List[List[int]]:\n if n < k*1 or n > k*9: return []\n rslt = []\n def backtracking(i, n, curr):\n if n == 0 and len(curr) == k:\n rslt.append(curr)\n elif len(curr) < k and i < 10:\n backtracking(i+1, n, curr)\n backtracking(i+1, n-i, curr + [i])\n backtracking(1, n, [])\n return rslt\n \n"},{"alpha_fraction":0.4378320872783661,"alphanum_fraction":0.4495217800140381,"avg_line_length":39.91304397583008,"blob_id":"a3935ff07d39e615b36be6adf55282b4bef44d84","content_id":"4842307d1f8a705b6e1a8e65b8f6c709ed47a652","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":941,"license_type":"no_license","max_line_length":114,"num_lines":23,"path":"/lc417_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def pacificAtlantic(self, matrix: List[List[int]]) -> List[List[int]]:\n if not matrix: return []\n pdque, adque = collections.deque(), collections.deque()\n n, m = len(matrix), len(matrix[0])\n for i in range(n):\n pdque.append((i, 0))\n adque.append((i, m-1))\n for j in range(m):\n pdque.append((0, j))\n adque.append((n-1, j))\n def bfs(queue):\n visited = set()\n while queue:\n x, y = queue.popleft()\n if (x, y) in visited:\n continue\n visited.add((x,y))\n for nx, ny in [(x-1, y), (x+1, y), (x, y+1), (x, y-1)]:\n if 0 <= nx < n and 0 <= ny < m and matrix[nx][ny] >= matrix[x][y] and (nx, ny) not in visited:\n queue.append((nx, ny))\n return visited\n return bfs(pdque) & bfs(adque)\n"},{"alpha_fraction":0.4260089695453644,"alphanum_fraction":0.439461886882782,"avg_line_length":29.758621215820312,"blob_id":"2d2bb1c9f98902a76478de501ac15c717470523c","content_id":"6381975c9d644f53fd9a93bc712fb2212450908f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":892,"license_type":"no_license","max_line_length":70,"num_lines":29,"path":"/lc1283_bs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def smallestDivisor(self, nums: List[int], threshold: int) -> int:\n left, right = 1, sum(nums)\n while left <= right:\n mid = (left + right)//2\n rslt = sum(ceil(x/mid) for x in nums)\n if rslt > threshold:\n left = mid\n else:\n right = mid - 1\n if mid == (left+right)//2:\n left += 1\n return left\n\nclass Solution:\n def smallestDivisor(self, nums: List[int], threshold: int) -> int:\n def check(i):\n rslt = 0\n for n in nums:\n rslt += (n-1)//i + 1\n return rslt\n left, right = 1, max(nums)\n while left <= right:\n mid = (left + right)//2\n if check(mid) <= threshold:\n right = mid -1\n else:\n left = mid + 1\n return left\n"},{"alpha_fraction":0.4781420826911926,"alphanum_fraction":0.4890710413455963,"avg_line_length":35.599998474121094,"blob_id":"5520e513fd3829848a2965f46447a64a2bbb895a","content_id":"1b07c407d39f65d00926004e701111357e41bbb6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":366,"license_type":"no_license","max_line_length":62,"num_lines":10,"path":"/lc84_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def largestRectangleArea(self, heights: List[int]) -> int:\n stack, rslt = [], 0\n for i, h in enumerate(heights+[0]):\n start = i\n while stack and stack[-1][1] >= h:\n start, mh = stack.pop()\n rslt = max(rslt, (i - start)*mh)\n stack.append((start, h))\n return rslt\n"},{"alpha_fraction":0.32870370149612427,"alphanum_fraction":0.33217594027519226,"avg_line_length":27.799999237060547,"blob_id":"32ae75ff6c120b022c29300856f3cde3d08a99a9","content_id":"0a9dfe9fdfc506fa76e85133e72d112deecf9667","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":864,"license_type":"no_license","max_line_length":57,"num_lines":30,"path":"/lc227_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def calculate(self, s: str) -> int:\n stk, last, op = [], \"\", None\n for c in s+\"+\":\n if c == \" \": continue\n if c.isdigit():\n last = last+c if last else c\n else:\n if op == \"*\":\n stk.pop()\n stk.append(int(stk.pop())*int(last))\n elif op == \"/\":\n stk.pop()\n stk.append(int(stk.pop())//int(last))\n else:\n stk.append(last)\n stk.append(c)\n op = c\n last = \"\"\n rslt, last = 0, 0\n\n while stk:\n temp = stk.pop()\n if temp == \"-\":\n rslt -= 2*last\n elif temp != \"+\":\n last = int(temp)\n rslt += last\n\n return rslt\n"},{"alpha_fraction":0.37142857909202576,"alphanum_fraction":0.37460318207740784,"avg_line_length":30.5,"blob_id":"417f0dd0a82ed1643aaf0bb45d2444235cd44235","content_id":"0a7308dbcaf5aa87a0710548cd2ae8ea9a30b6e7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":630,"license_type":"no_license","max_line_length":57,"num_lines":20,"path":"/lc385_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def deserialize(self, s: str) -> NestedInteger:\n stk, last, curr = [], \"\", None\n for c in s:\n if c in \",]\":\n if last:\n stk[-1].add(NestedInteger(int(last)))\n last = \"\"\n if c == \"]\":\n rslt = stk.pop()\n elif c == \"[\":\n temp = NestedInteger()\n if stk:\n stk[-1].add(temp)\n stk.append(temp)\n else:\n last += c\n if not stk and last:\n return NestedInteger(int(last))\n return rslt\n"},{"alpha_fraction":0.459940642118454,"alphanum_fraction":0.4658753573894501,"avg_line_length":32.70000076293945,"blob_id":"96cfca0c76df156faaa61223b5cfd088279c77a6","content_id":"0d188a1077520b500b42fc17ca958cd62a347313","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":337,"license_type":"no_license","max_line_length":59,"num_lines":10,"path":"/lc739_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def dailyTemperatures(self, T: List[int]) -> List[int]:\n if not T: return T\n stk, rslt = [], [0]*len(T)\n for i, t in enumerate(T):\n while stk and t > T[stk[-1]]:\n pre_id = stk.pop()\n rslt[pre_id] = i - pre_id\n stk.append(i)\n return rslt\n"},{"alpha_fraction":0.35686779022216797,"alphanum_fraction":0.3671373426914215,"avg_line_length":29.31999969482422,"blob_id":"10f20e02fcfbc38e0e0b7c0d759327803c56c16c","content_id":"39b3e339b17e251353784620e6876aa0e9747fcb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":779,"license_type":"no_license","max_line_length":57,"num_lines":25,"path":"/lc410_bs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def splitArray(self, nums: List[int], m: int) -> int:\n left, right = 0, 0\n n = len(nums)\n for i in range(n):\n right += nums[i]\n if left < nums[i]: left = nums[i]\n rslt = right\n while left <= right:\n mid = (left + right)//2\n total = 0\n count = 0\n # check if is a viable mid\n for i in range(n):\n if total + nums[i] > mid:\n count += 1\n total = nums[i]\n else:\n total += nums[i]\n if count < m:\n rslt = min(rslt, mid)\n right = mid - 1\n else:\n left = mid + 1\n return rslt\n \n"},{"alpha_fraction":0.3499627709388733,"alphanum_fraction":0.3708116114139557,"avg_line_length":26.40816307067871,"blob_id":"a7125d6d2d838d2fe55cbe6e6877b85c9fb7fd95","content_id":"be33ae9c95d1179b9fb4583a97f8ccd6f6b1166f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1343,"license_type":"no_license","max_line_length":56,"num_lines":49,"path":"/lc115_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"#dfs+memo\nclass Solution:\n def numDistinct(self, s: str, t: str) -> int:\n memo = {}\n def dfs(i_s, j_t):\n if (i_s, j_t) in memo:\n return memo[(i_s,j_t)]\n if j_t == len(t):\n temp = 1\n elif i_s == len(s):\n temp = 0\n elif s[i_s] == t[j_t]:\n temp = dfs(i_s+1, j_t+1)+dfs(i_s+1, j_t)\n else:\n temp = dfs(i_s+1, j_t)\n memo[(i_s, j_t)] = temp\n return temp\n return dfs(0, 0)\n# 1-d dp\n\nclass Solution:\n def numDistinct(self, s: str, t: str) -> int:\n dp = [0]*(len(t)+1)\n dp[0] = 1\n for i in range(len(s)):\n temp = [1]*(len(t)+1)\n for j in range(len(t)):\n temp[j+1] = dp[j+1]\n if s[i] == t[j]:\n temp[j+1] += dp[j]\n dp = temp\n return dp[-1]\n\n\n\nclass Solution:\n def numDistinct(self, s: str, t: str) -> int:\n self.rslt = 0\n @lru_cache(None)\n def dfs(i_s, j_t):\n if j_t == len(t):\n return 1\n if i_s == len(s):\n return 0\n if s[i_s] == t[j_t]:\n return dfs(i_s+1, j_t+1)+dfs(i_s+1, j_t)\n else:\n return dfs(i_s+1, j_t)\n return dfs(0, 0)\n"},{"alpha_fraction":0.4312354326248169,"alphanum_fraction":0.4428904354572296,"avg_line_length":34.75,"blob_id":"6995247e91c8b96a6fae8cc5928f7c36b762ff20","content_id":"8620617faafc1f84e3f1726650ffeb9e8c3b48b2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":429,"license_type":"no_license","max_line_length":53,"num_lines":12,"path":"/lc402_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def removeKdigits(self, num: str, k: int) -> str:\n if k >= len(num): return \"0\"\n stk, temp= [], k\n for i, c in enumerate(num):\n while stk and temp > 0 and stk[-1] > c:\n stk.pop()\n temp -= 1\n if temp == 0:\n return str(int(\"\".join(stk)+num[i:]))\n stk.append(c)\n return str(int(\"\".join(stk[:len(num)-k])))\n"},{"alpha_fraction":0.4683684706687927,"alphanum_fraction":0.4772475063800812,"avg_line_length":28.064516067504883,"blob_id":"09986d18f0df56a33440e8de3d634c990c6f3825","content_id":"841b0a3b7a35abb85b77e802d416b303874f20bc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":901,"license_type":"no_license","max_line_length":72,"num_lines":31,"path":"/lc23_linkedlist.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, val=0, next=None):\n# self.val = val\n# self.next = next\nclass Solution:\n def mergeKLists(self, lists: List[ListNode]) -> ListNode:\n if not lists: return None\n interval = 1\n n = len(lists)\n while interval < n:\n for i in range(0, n-interval, 2*interval):\n lists[i] = self.merge2Lists(lists[i], lists[i+interval])\n interval *= 2\n return lists[0]\n\n def merge2Lists(self, a, b):\n snode = curr = ListNode()\n while a and b:\n if a.val < b.val:\n curr.next = a\n a = a.next\n else:\n curr.next = b\n b = b.next\n curr = curr.next\n if a:\n curr.next = a\n else:\n curr.next = b\n return snode.next\n"},{"alpha_fraction":0.48367953300476074,"alphanum_fraction":0.48961424827575684,"avg_line_length":36.44444274902344,"blob_id":"c7b85cf2f9b711163d8311b56e3f801f1337110b","content_id":"d15b9da19a9ab6c89fa79a0541441cbf00e4bd7d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":674,"license_type":"no_license","max_line_length":87,"num_lines":18,"path":"/lc1606_sortedlist.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"from sortedcontainers import SortedList\nclass Solution:\n def busiestServers(self, k: int, arrival: List[int], load: List[int]) -> List[int]:\n freq = [0]*k\n n = len(arrival)\n busy = []\n free = SortedList(range(k))\n for i in range(n):\n while busy and busy[0][0] <= arrival[i]:\n t, si = heapq.heappop(busy)\n free.add(si)\n if free:\n idx = free.bisect_left(i%k)%len(free)\n s = free.pop(idx)\n freq[s] += 1\n heapq.heappush(busy, (arrival[i]+load[i], s))\n mx = max(freq)\n return [x for x in range(k) if freq[x] == mx]\n"},{"alpha_fraction":0.3953045606613159,"alphanum_fraction":0.4029187858104706,"avg_line_length":27.654544830322266,"blob_id":"c44d69ced6299e0c0ec1a1d131c7e5875b3785d0","content_id":"582d17037b5737c41b6c9f36ee9d79fa8a0c71c2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1576,"license_type":"no_license","max_line_length":50,"num_lines":55,"path":"/lc300_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def lengthOfLIS(self, nums: List[int]) -> int:\n @lru_cache(None)\n def dfs(i):\n temp = 1\n for j in range(i+1, len(nums)):\n if nums[j] > nums[i]:\n temp = max(temp, dfs(j)+1)\n return temp\n rslt = 0\n for i in range(len(nums)):\n rslt = max(rslt, dfs(i))\n return rslt\n\n def lengthOfLIS(self, nums: List[int]) -> int:\n sub = []\n for num in nums:\n idx = bisect.bisect_left(sub, num)\n if idx < len(sub):\n sub[idx] = num\n else:\n sub.append(num)\n return len(sub)\n \n def lengthOfLIS(self, nums:List[int]) -> int:\n n = len(nums)\n dp = [1]*n\n for i in range(n):\n for j in range(i):\n if dp[j] < dp[i]:\n dp[i] = max(dp[i], dp[j]+1)\n return max(dp)\n\n\n\n def lengthOfLIS(self, nums: List[int]) -> int:\n def bisearch(arr, target):\n left, right = 0, len(arr)-1\n while left <= right:\n mid = (left + right)//2\n if arr[mid] < target:\n left = mid\n else:\n right = mid - 1\n if (left+right)//2 == mid:\n left += 1\n return left\n sub = []\n for num in nums:\n idx = bisearch(sub, num)\n if idx < len(sub):\n sub[idx] = num\n else:\n sub.append(num)\n return len(sub)\n"},{"alpha_fraction":0.5076400637626648,"alphanum_fraction":0.5178267955780029,"avg_line_length":31.72222137451172,"blob_id":"e1f90d631bf0f52f203221e91bcd5f11405e558f","content_id":"73a1b09813a7f1d70a3e71adededf0724a696bd6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":589,"license_type":"no_license","max_line_length":59,"num_lines":18,"path":"/lc1019_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, val=0, next=None):\n# self.val = val\n# self.next = next\n# maintain a monotonic decreasing stack\nclass Solution:\n def nextLargerNodes(self, head: ListNode) -> List[int]:\n answer, stack, i = [], [], 0\n while head:\n while stack and stack[-1][0] < head.val:\n _, j = stack.pop()\n answer[j] = head.val\n stack.append((head.val, i))\n answer.append(0)\n head = head.next\n i = i+1\n return answer\n"},{"alpha_fraction":0.49862638115882874,"alphanum_fraction":0.5096153616905212,"avg_line_length":25,"blob_id":"7a7472ba0c8549b02b9643c5fad90f8a83d38927","content_id":"854a819fa2c1d67d3f25fa0aa2555ff12ecb778c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":728,"license_type":"no_license","max_line_length":63,"num_lines":28,"path":"/lc528_random.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n\n def __init__(self, w: List[int]):\n self.cumsum = []\n for i, n in enumerate(w):\n if i == 0: self.cumsum.append(n)\n else:\n self.cumsum.append(n + self.cumsum[-1])\n\n def pickIndex(self) -> int:\n t = random.randint(1, self.cumsum[-1])\n return self.binSearch(t)\n\n def binSearch(self, t):\n left, right = 0, len(self.cumsum)\n while left < right:\n mid = (left + right)//2\n if self.cumsum[mid] < t:\n left = mid+1\n else:\n right = mid\n return left\n\n\n\n# Your Solution object will be instantiated and called as such:\n# obj = Solution(w)\n# param_1 = obj.pickIndex()\n"},{"alpha_fraction":0.4324324429035187,"alphanum_fraction":0.45945945382118225,"avg_line_length":29.272727966308594,"blob_id":"fb95451b267c6f40d003a0ad9555b48801c455c2","content_id":"64541f9488eb4ed5426bf4f63095bc729d037d6e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":333,"license_type":"no_license","max_line_length":62,"num_lines":11,"path":"/lc238_array.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def productExceptSelf(self, nums: List[int]) -> List[int]:\n n = len(nums)\n rslt = [1]*n\n for i in range(1, n):\n rslt[i] = rslt[i-1]*nums[i-1]\n curr = 1\n for i in range(n-2, -1, -1):\n curr *= nums[i+1]\n rslt[i] = rslt[i]*curr\n return rslt\n"},{"alpha_fraction":0.48226097226142883,"alphanum_fraction":0.49789535999298096,"avg_line_length":28.175437927246094,"blob_id":"05d1bd89d45bcdcb2586c249640b0fe41992ab18","content_id":"64a92e746be8d797a8b419264ef11ccf04fbcd20","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1663,"license_type":"no_license","max_line_length":113,"num_lines":57,"path":"/lc706_oop.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# hash function + collision handling(chaining)\nclass MyHashMap:\n\n def __init__(self):\n \"\"\"\n Initialize your data structure here.\n \"\"\"\n self.table = [[] for _ in range(200)]\n\n def put(self, key: int, value: int) -> None:\n \"\"\"\n value will always be non-negative.\n \"\"\"\n hs = key % 200\n if len(self.table[hs]) == 0:\n self.table[hs].append((key, value))\n return\n for i, v in enumerate(self.table[hs]):\n if v[0] == key:\n self.table[hs][i] = (key, value)\n return\n self.table[hs].append((key, value))\n\n\n def get(self, key: int) -> int:\n \"\"\"\n Returns the value to which the specified key is mapped, or -1 if this map contains no mapping for the key\n \"\"\"\n hs = key % 200\n if len(self.table[hs]) == 0:\n return -1\n for i, v in enumerate(self.table[hs]):\n if v[0] == key:\n return v[1]\n return -1\n def remove(self, key: int) -> None:\n \"\"\"\n Removes the mapping of the specified value key if this map contains a mapping for the key\n \"\"\"\n hs = key % 200\n if len(self.table[hs]) == 0:\n return\n check = -1\n for i, v in enumerate(self.table[hs]):\n if v[0] == key:\n check = i\n break\n if check >= 0:\n self.table[hs][check] = self.table[hs][-1]\n self.table[hs].pop()\n\n\n# Your MyHashMap object will be instantiated and called as such:\n# obj = MyHashMap()\n# obj.put(key,value)\n# param_2 = obj.get(key)\n# obj.remove(key)\n"},{"alpha_fraction":0.47732996940612793,"alphanum_fraction":0.48488664627075195,"avg_line_length":43.11111068725586,"blob_id":"c19adee3ec3909f52c04b7861084c56a2fa62b88","content_id":"47f4cecf2131d5f5cd930918a908a6b9d5f8527c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":794,"license_type":"no_license","max_line_length":88,"num_lines":18,"path":"/lc241_str.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def diffWaysToCompute(self, expression: str) -> List[int]:\n if not expression: return []\n op = {\"+\": (lambda x, y: x+y), \"-\": (lambda x, y: x-y), \"*\": (lambda x, y: x*y)}\n def helper(left, right):\n if left >= right: return expression[right]\n rslt = []\n for i in range(left, right + 1):\n if expression[i] in op:\n leftr, rightr = helper(left, i-1), helper(i+1, right)\n for l in leftr:\n for r in rightr:\n rslt.append(op[expression[i]](int(l), int(r)))\n print(left, right)\n if not rslt: rslt.append(int(expression[left:right+1]))\n\n return rslt\n return helper(0, len(expression)-1)\n"},{"alpha_fraction":0.48880597949028015,"alphanum_fraction":0.49253731966018677,"avg_line_length":30.52941131591797,"blob_id":"d311d7640bff8212c7d86642b24eb763b19ce229","content_id":"3b70125f5f4286ea3ea7b03e462e83f50f845f25","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":536,"license_type":"no_license","max_line_length":64,"num_lines":17,"path":"/lc1171_linkedlist.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def removeZeroSumSublists(self, head: ListNode) -> ListNode:\n presum = 0\n snode = ListNode(0, head)\n maps = collections.OrderedDict()\n curr = snode\n while curr:\n presum += curr.val\n prev = curr\n if presum in maps:\n prev = maps[presum]\n prev.next = curr.next\n while presum in maps:\n maps.popitem()\n maps[presum] = prev\n curr = curr.next\n return snode.next\n"},{"alpha_fraction":0.5034722089767456,"alphanum_fraction":0.5138888955116272,"avg_line_length":31,"blob_id":"75a349f856b09b915c285d69b7bd60116db8fe12","content_id":"dae00d9e95811e7ba0d0e379795a7be018ed7519","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":288,"license_type":"no_license","max_line_length":65,"num_lines":9,"path":"/lc253_hp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minMeetingRooms(self, intervals: List[List[int]]) -> int:\n intervals.sort()\n hq = []\n for itv in intervals:\n if hq and itv[0] >= hq[0]:\n heapq.heappop(hq)\n heapq.heappush(hq, itv[1])\n return len(hq)\n"},{"alpha_fraction":0.453125,"alphanum_fraction":0.46875,"avg_line_length":31,"blob_id":"b8076718320e30aba6c68999bf948987c54af693","content_id":"baba4646dc4ec108c72d44f432317eecb049c807","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":256,"license_type":"no_license","max_line_length":45,"num_lines":8,"path":"/lc338_bit.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def countBits(self, n: int) -> List[int]:\n rslt = [0]\n while len(rslt) < n+1:\n for i in range(len(rslt)):\n rslt.append(rslt[i]+1)\n if len(rslt) > n: break\n return rslt[:n+1]\n"},{"alpha_fraction":0.38433516025543213,"alphanum_fraction":0.39708560705184937,"avg_line_length":27.894737243652344,"blob_id":"c94b18a6ca334e641cd89577d8fcb95293f5e020","content_id":"4d089d13fee8d85a7cd7fc7e88329bb3a3e7c936","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":549,"license_type":"no_license","max_line_length":64,"num_lines":19,"path":"/lc1011_bs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def shipWithinDays(self, weights: List[int], D: int) -> int:\n def check(t):\n rslt, temp = 0, 0\n for w in weights:\n temp += w\n if temp > t:\n rslt += 1\n temp = w\n return rslt+1\n\n left, right = max(weights), sum(weights)\n while left <= right:\n mid = (left + right)//2\n if check(mid) > D:\n left = mid + 1\n else:\n right = mid -1\n return left\n"},{"alpha_fraction":0.3367496430873871,"alphanum_fraction":0.4260614812374115,"avg_line_length":36.38888931274414,"blob_id":"17be8fb293371785b2340d350baff88d1657fa96","content_id":"b2851df486cdcc7985df8856a3b5353ff4381120","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":683,"license_type":"no_license","max_line_length":73,"num_lines":18,"path":"/lc1537_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxSum(self, nums1: List[int], nums2: List[int]) -> int:\n l1, l2 = len(nums1), len(nums2)\n idx1, idx2 = 0, 0\n max1, max2 = 0, 0\n while idx1 < l1 or idx2 < l2:\n if idx1 < l1 and (idx2 == l2 or nums1[idx1] < nums2[idx2]):\n max1 += nums1[idx1]\n idx1 += 1\n elif idx2 < l2 and (idx1 == l1 or nums1[idx1] > nums2[idx2]):\n max2 += nums2[idx2]\n idx2 += 1\n else:\n max1 = max(max1, max2) + nums1[idx1]\n max2 = max1\n idx1 += 1\n idx2 += 1\n return max(max1, max2)%(10**9+7)\n\n \n"},{"alpha_fraction":0.3949275314807892,"alphanum_fraction":0.4214975833892822,"avg_line_length":42.578948974609375,"blob_id":"9427790f75b92015a51140c001fdf9128cd37066","content_id":"2736d4c9d728421e34bfcc447d0372b6cff14af3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":828,"license_type":"no_license","max_line_length":87,"num_lines":19,"path":"/lc980_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def uniquePathsIII(self, grid: List[List[int]]) -> int:\n toCover, nrow, ncol = 0, len(grid), len(grid[0])\n for i in range(nrow):\n for j in range(ncol):\n if grid[i][j] == 0: toCover += 1\n if grid[i][j] == 1: start = (i,j)\n self.rslt = 0\n def backtracking(i, j, step):\n for x, y in [(i+1, j), (i-1, j), (i, j+1), (i, j-1)]:\n if x < 0 or x >= nrow or y < 0 or y >= ncol or grid[x][y] < 0: continue\n if grid[x][y] == 0:\n grid[x][y] = -2\n backtracking(x, y, step + 1)\n grid[x][y] = 0\n elif grid[x][y] == 2 and step == toCover:\n self.rslt += 1\n backtracking(start[0], start[1], 0)\n return self.rslt\n"},{"alpha_fraction":0.43216782808303833,"alphanum_fraction":0.4475524425506592,"avg_line_length":29.211267471313477,"blob_id":"a33f3ad87ccb5e22d20d960ed07e1423f613df55","content_id":"5e904a22a49e1b9420e8e857bff38e6b78ffe4c1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2145,"license_type":"no_license","max_line_length":100,"num_lines":71,"path":"/lc1707_trie.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Trie:\n def __init__(self, L):\n self.trie = {}\n self.L = L\n def insert(self, num):\n curr = self.trie\n mask = 1 << self.L\n while mask:\n key = 1 if num&mask else 0\n if key not in curr:\n curr[key] = {}\n curr = curr[key]\n mask >>= 1\n def search(self, num):\n curr = self.trie\n mask = 1 << self.L\n rslt = 0\n while mask:\n key = 0 if num&mask else 1\n if key in curr:\n curr = curr[key]\n rslt |= mask\n else:\n curr = curr[1-key]\n mask >>= 1\n return rslt\n\nclass Solution:\n def maximizeXor(self, nums: List[int], queries: List[List[int]]) -> List[int]:\n nums.sort()\n nn, nq = len(nums), len(queries)\n idx = list(range(nq))\n idx.sort(key = lambda x: queries[x][1])\n idxn = 0\n l = len(bin(max(nums[-1], max(x[0] for x in queries))))-3\n trie, rslt = Trie(l), [-1]*nq\n for idxq in idx:\n x, m = queries[idxq]\n while idxn < nn and nums[idxn] <= m:\n trie.insert(nums[idxn])\n idxn += 1\n if idxn != 0:\n rslt[idxq] = trie.search(x)\n return rslt\n\n#https://leetcode.com/problems/maximum-xor-with-an-element-from-array/discuss/989454/Python-14-lines\ndef maximizeXor(self, nums: List[int], queries: List[List[int]]) -> List[int]:\n nums.sort()\n answer = []\n append = answer.append\n for x, m in queries:\n if nums[0] > m:\n append(-1)\n continue\n start, stop = 0, bisect_right(nums, m)\n num = 0\n bit = 2 ** m.bit_length()\n while bit:\n plus = num + bit\n if nums[start] >= plus:\n num = plus\n elif nums[stop-1] >= plus:\n cut = bisect_left(nums, plus, start, stop)\n if x & bit:\n stop = cut\n else:\n start = cut\n num = plus\n bit //= 2\n append(num ^ x)\n return answer\n"},{"alpha_fraction":0.44129031896591187,"alphanum_fraction":0.4606451690196991,"avg_line_length":35.904762268066406,"blob_id":"56162f0a3a3e2a6b36ba578ebc940527dd06bfdc","content_id":"bd3fb33bfef78d6d28d7079c3ce18569d658d809","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":775,"license_type":"no_license","max_line_length":88,"num_lines":21,"path":"/lc301_str.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def removeInvalidParentheses(self, s: str) -> List[str]:\n self.rslt = []\n self.helper(s, 0, 0, ('(', ')'))\n return self.rslt\n\n\n def helper(self, curr_s, s_check, s_modify, check):\n count = 0\n for i in range(s_check, len(curr_s)):\n count += (curr_s[i] == check[0]) - (curr_s[i] == check[1])\n if count >= 0: continue\n for j in range(s_modify, i+1):\n if curr_s[j] == check[1] and (j == s_modify or curr_s[j-1] != check[1]):\n self.helper(curr_s[:j]+curr_s[j+1:], i, j, check)\n return\n curr_s = curr_s[::-1]\n if check[0] == '(':\n self.helper(curr_s, 0, 0, (')', '('))\n else:\n self.rslt.append(curr_s)\n"},{"alpha_fraction":0.46507516503334045,"alphanum_fraction":0.5083996653556824,"avg_line_length":36.70000076293945,"blob_id":"051b5dffd898dc5645f8c175c6a3b64fd71097d9","content_id":"6f739cf40ae9835919706af84f9ab0bf7f1cbae4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1131,"license_type":"no_license","max_line_length":74,"num_lines":30,"path":"/lc244_hash.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class WordDistance:\n\n def __init__(self, wordsDict: List[str]):\n self.maps = collections.defaultdict(list)\n self.cache = {}\n for i, word in enumerate(wordsDict):\n self.maps[word].append(i)\n\n def shortest(self, word1: str, word2: str) -> int:\n if (word1, word2) in self.cache: return self.cache[(word1, word2)]\n if (word2, word1) in self.cache: return self.cache[(word2, word1)]\n cand1, cand2 = self.maps[word1], self.maps[word2]\n # if len(cand1) > len(cand2): cand1, cand2 = cand2, cand1\n # rslt = float(\"inf\")\n # for x in cand1:\n # y = bisect.bisect(cand2, x)\n # if y < len(cand2):\n # rslt = min(rslt, cand2[y]-x)\n # if y > 0: rslt = min(rslt, x-cand2[y-1])\n # return rslt\n l1, l2 = 0, 0\n rslt = float(\"inf\")\n while l1 < len(cand1) and l2 < len(cand2):\n rslt = min(rslt, abs(cand1[l1]-cand2[l2]))\n if cand1[l1] < cand2[l2]:\n l1 += 1\n else:\n l2 += 1\n self.cache[(word1, word2)] = rslt\n return rslt\n"},{"alpha_fraction":0.37906646728515625,"alphanum_fraction":0.39745402336120605,"avg_line_length":38.27777862548828,"blob_id":"11a96639517bcc375870f986c63b46d72c29bf45","content_id":"2e19cee85667f121f97ca241f9e02f4b324ed2b7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":707,"license_type":"no_license","max_line_length":101,"num_lines":18,"path":"/lc52_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def totalNQueens(self, n: int) -> int:\n self.rslt, visited = 0, set()\n def backtracking(i):\n if i == n:\n self.rslt += 1\n else:\n for j in range(n):\n if (2, j) not in visited and (3, j-i) not in visited and (4, j+i) not in visited:\n visited.add((2, j))\n visited.add((3, j-i))\n visited.add((4, j+i))\n backtracking(i+1)\n visited.remove((2, j))\n visited.remove((3, j-i))\n visited.remove((4, j+i))\n backtracking(0)\n return self.rslt\n"},{"alpha_fraction":0.47999998927116394,"alphanum_fraction":0.49925926327705383,"avg_line_length":28.34782600402832,"blob_id":"7d292831e11bca4ad67c9d8cbc332b7ad87ec079","content_id":"3571c970366dfa11e1c707f844dda58b390e1753","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":675,"license_type":"no_license","max_line_length":55,"num_lines":23,"path":"/lc1146_ood.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class SnapshotArray:\n\n def __init__(self, length: int):\n self.array = [[(-1, 0)] for _ in range(length)]\n self.snap_id = 0\n\n def set(self, index: int, val: int) -> None:\n self.array[index].append((self.snap_id, val))\n\n\n def snap(self) -> int:\n self.snap_id += 1\n return self.snap_id - 1\n\n def get(self, index: int, snap_id: int) -> int:\n left, right = 0, len(self.array[index])-1\n while left <= right:\n mid = (left + right)//2\n if self.array[index][mid][0] > snap_id:\n right = mid -1\n else:\n left = mid + 1\n return self.array[index][left-1][1]\n"},{"alpha_fraction":0.4386363625526428,"alphanum_fraction":0.4522727131843567,"avg_line_length":30.428571701049805,"blob_id":"86a18cf38959216af26744334a9038197ba2d7ba","content_id":"75df68515c51f8e2706475253bfc821e28df9d55","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":440,"license_type":"no_license","max_line_length":68,"num_lines":14,"path":"/lc1641_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# dfs + memo\nclass Solution:\n def countVowelStrings(self, n: int) -> int:\n memo = {}\n def dfs(curr, rest):\n if (curr, rest) in memo:\n return memo[(curr, rest)]\n if curr == 1:\n return rest\n if rest == 1:\n return 1\n memo[(curr, rest)] = dfs(curr-1, rest)+dfs(curr, rest-1)\n return memo[(curr, rest)]\n return dfs(n, 5)\n"},{"alpha_fraction":0.39177101850509644,"alphanum_fraction":0.40787118673324585,"avg_line_length":30.05555534362793,"blob_id":"fa4af0717539bc5ede49b83c12fb1023356cbc2d","content_id":"d1e636329b5c95e9539d784a9404b75bb2967020","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":559,"license_type":"no_license","max_line_length":57,"num_lines":18,"path":"/lc343_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def integerBreak(self, n: int) -> int:\n @functools.lru_cache(None)\n def dfs(j):\n if j <= 2:\n return 1\n else:\n rslt = 0\n for i in range(1, j):\n rslt = max(rslt, i*max(dfs(j-i),j-i))\n return rslt\n return dfs(n)\n def integerBreak(self, n:int) -> int:\n dp = [1]*(n+1)\n for j in range(3, n+1):\n for i in range(1, j):\n dp[j] = max(dp[j], i*max(dp[j-i], j-i))\n return dp[n]\n"},{"alpha_fraction":0.40689656138420105,"alphanum_fraction":0.4275861978530884,"avg_line_length":32.46154022216797,"blob_id":"c9b62d115af5de1899b2c03d7ee3f39a858e17f6","content_id":"13470bea045eebf2acfe2d096f1271f60ab1aa4f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":435,"license_type":"no_license","max_line_length":57,"num_lines":13,"path":"/lc1542_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def longestAwesome(self, s: str) -> int:\n rslt, curr = 0, 0\n seen = {0:-1}\n for i, c in enumerate(s):\n curr ^= 1 << int(c)\n if curr not in seen:\n seen[curr] = i\n rslt = max(rslt, i-seen[curr])\n for j in range(10):\n if curr^(1 << j) in seen:\n rslt = max(rslt, i-seen[curr^(1< int:\n preset = self.TriePre.search(prefix)\n postset = self.TriePost.search(suffix[::-1])\n rslt = preset & postset\n return max(rslt) if rslt else -1\n\n\nclass TrieNode:\n def __init__(self, idx = -1):\n self.next = {}\n self.idx = -1\nclass Trie:\n def __init__(self):\n self.root = TrieNode()\n def insert(self, word, idx):\n curr = self.root\n for ch in word:\n if ch not in curr.next:\n curr.next[ch] = TrieNode()\n curr.next[ch].idx = idx\n curr = curr.next[ch]\n def search(self, word):\n curr = self.root\n for ch in word:\n if ch not in curr.next:\n return -1\n curr = curr.next[ch]\n return curr.idx\nclass WordFilter:\n\n def __init__(self, words: List[str]):\n self.trie = Trie()\n for j, word in enumerate(words):\n for i in range(len(word)):\n self.trie.insert(word[i:]+\"#\"+word, j)\n def f(self, prefix: str, suffix: str) -> int:\n return self.trie.search(suffix+\"#\"+prefix)\n"},{"alpha_fraction":0.4093078672885895,"alphanum_fraction":0.4343675374984741,"avg_line_length":43.105262756347656,"blob_id":"c6d01abdf9916ef60ce0ca290b5fec47672cd3f1","content_id":"dbdfa02bfc39a6f5586e77193406b1057ead5f72","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":838,"license_type":"no_license","max_line_length":91,"num_lines":19,"path":"/lc282_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def addOperators(self, num: str, target: int) -> List[str]:\n rslt, n = [], len(num)\n def backtracking(i, curr, last, total):\n if i == n and total == target:\n rslt.append(curr)\n else:\n for j in range(i+1, n+1):\n temp1, temp2 = int(num[i:j]), num[i:j]\n if i == 0:\n backtracking(j, temp2, temp1, temp1)\n else:\n backtracking(j, curr+\"+\"+temp2, temp1, total+temp1)\n backtracking(j, curr+\"*\"+temp2, last*temp1,total-last + last*temp1)\n backtracking(j, curr+\"-\"+temp2, -temp1,total-temp1)\n if num[i] == \"0\":\n break\n backtracking(0, \"\",0, 0)\n return rslt\n"},{"alpha_fraction":0.4397905766963959,"alphanum_fraction":0.45724257826805115,"avg_line_length":30.83333396911621,"blob_id":"3c17bbf09088d16e9c2da8165d1f15aeb4ef688a","content_id":"29f10a4bb8e54ff26874fe06d66a0cecc55534af","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":573,"license_type":"no_license","max_line_length":66,"num_lines":18,"path":"/lc774_bs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minmaxGasDist(self, stations: List[int], k: int) -> float:\n def check(p):\n count = 0\n for i in range(1, n):\n count += int((stations[i]-stations[i-1])/p)\n return count\n\n left, right, n =0, 0, len(stations)\n for i in range(1, n):\n right = max(right, stations[i]-stations[i-1])\n while left < right - 1e-6:\n mid = (left + right)/2\n if check(mid) > k:\n left = mid\n else:\n right = mid\n return left\n"},{"alpha_fraction":0.3363695442676544,"alphanum_fraction":0.3604424297809601,"avg_line_length":38.410255432128906,"blob_id":"ec2ed0ab1598633b99db7ecffba579a35261aa02","content_id":"98f924795a19380d1a76ca465745e0b35ab3f7c3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1537,"license_type":"no_license","max_line_length":104,"num_lines":39,"path":"/lc37_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def solveSudoku(self, board: List[List[str]]) -> None:\n \"\"\"\n Do not return anything, modify board in-place instead.\n \"\"\"\n memo = collections.defaultdict(set)\n # store curr board:\n for i, l in enumerate(board):\n for j, t in enumerate(l):\n if t !=\".\":\n memo[(i, -1)].add(t)\n memo[(-1, j)].add(t)\n memo[(i//3, j//3)].add(t)\n def backtracking(i, j):\n if i == 9:\n return True\n if board[i][j] == \".\":\n for t in \"123456789\":\n if t not in memo[(i,-1)] and t not in memo[(-1, j)] and t not in memo[(i//3, j//3)]:\n board[i][j] = t\n memo[(i, -1)].add(t)\n memo[(-1, j)].add(t)\n memo[(i//3, j//3)].add(t)\n if j < 8 and backtracking(i, j+1):\n return True\n if j == 8 and backtracking(i+1, 0):\n return True\n board[i][j] = \".\"\n memo[(i, -1)].remove(t)\n memo[(-1, j)].remove(t)\n memo[(i//3, j//3)].remove(t)\n return False\n else:\n if j < 8 :\n return backtracking(i, j+1)\n else:\n return backtracking(i+1, 0)\n backtracking(0, 0)\n return board\n"},{"alpha_fraction":0.5080645084381104,"alphanum_fraction":0.5100806355476379,"avg_line_length":32.06666564941406,"blob_id":"4c3993c0e5de31ae692874596a2f08cb0c5406dd","content_id":"0d5bcf29f35cfbe87012fe9f18888ffad53d08a8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":496,"license_type":"no_license","max_line_length":67,"num_lines":15,"path":"/lc332_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findItinerary(self, tickets: List[List[str]]) -> List[str]:\n graph = collections.defaultdict(list)\n for start, end in tickets:\n graph[start].append(end)\n for key, val in graph.items():\n val.sort(reverse = True)\n rslt = []\n def dfs(start):\n while graph[start]:\n temp = graph[start].pop()\n dfs(temp)\n rslt.append(start)\n dfs(\"JFK\")\n return rslt[::-1]\n"},{"alpha_fraction":0.46584802865982056,"alphanum_fraction":0.4666154980659485,"avg_line_length":28.613636016845703,"blob_id":"c7d874b8d769fc6b9f7766baa3ce79ba59f719d8","content_id":"d4ea5601a63cccfd9a680044fafce6bf59415510","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1303,"license_type":"no_license","max_line_length":55,"num_lines":44,"path":"/lc426_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def treeToDoublyList(self, root: 'Node') -> 'Node':\n # inorder traversal\n def inorder(root):\n if not root:\n return None, None\n left, right = root.left, root.right\n head, tail = inorder(left)\n if head:\n tail.right = root\n root.left = tail\n else:\n head = root\n nhead, ntail = inorder(right)\n root.right = nhead\n if nhead:\n nhead.left = root\n else:\n ntail = root\n return head, ntail\n head, tail = inorder(root)\n if not head: return None\n head.left = tail\n tail.right = head\n return head\nclass Solution:\n def treeToDoublyList(self, root: 'Node') -> 'Node':\n if not root:\n return None\n dummy = Node(0)\n prev = dummy\n stack, curr = [], root\n while stack or curr:\n while curr:\n stack.append(curr)\n curr = curr.left\n curr = stack.pop()\n curr.left = prev\n prev.right = curr\n prev = curr\n curr = curr.right\n dummy.right.left = prev\n prev.right = dummy.right\n return dummy.right\n"},{"alpha_fraction":0.4612794518470764,"alphanum_fraction":0.46464645862579346,"avg_line_length":33.94117736816406,"blob_id":"b53d050676e7267397e7f04832b98e518ec48398","content_id":"820c0d4499a20fecf039a4827a2101255da50c0b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":594,"license_type":"no_license","max_line_length":58,"num_lines":17,"path":"/lc785_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def isBipartite(self, graph: List[List[int]]) -> bool:\n set_map = {}\n def dfs(node):\n for snode in graph[node]:\n if snode not in set_map:\n set_map[snode] = 1 - set_map[node]\n if not dfs(snode): return False\n elif set_map[node] == set_map[snode]:\n return False\n return True\n\n for i in range(len(graph)):\n if graph[i] and i not in set_map:\n set_map[i] = 1\n if not dfs(i): return False\n return True\n"},{"alpha_fraction":0.459090918302536,"alphanum_fraction":0.4636363685131073,"avg_line_length":32.33333206176758,"blob_id":"9e1249a434b754a41dad5ba7cf40bcc6dfd280bb","content_id":"f50dae2390fbaedc0e41ae1692c572b0d02285ea","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1100,"license_type":"no_license","max_line_length":66,"num_lines":33,"path":"/lc1740_lca.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def findDistance(self, root: TreeNode, p: int, q: int) -> int:\n\n def lca(root, p, q):\n if not root: return None\n if root.val == p or root.val == q:\n return root\n left = lca(root.left, p, q)\n right = lca(root.right, p, q)\n if left and right:\n return root\n return left or right\n pqlca = lca(root, p, q)\n queue = collections.deque([pqlca])\n lq, lp, level = 0, 0, 0\n while queue:\n l = len(queue)\n for _ in range(l):\n temp = queue.popleft()\n if temp.val == q:\n lq = level\n if temp.val == p:\n lp = level\n if temp.left: queue.append(temp.left)\n if temp.right: queue.append(temp.right)\n level += 1\n return lq+lp\n"},{"alpha_fraction":0.4606741666793823,"alphanum_fraction":0.47752809524536133,"avg_line_length":31.363636016845703,"blob_id":"39ca79cc975b9922d8490b9d9473c5527bc313e0","content_id":"9ed447a87a2149232887c17ada750c48d6bde047","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":712,"license_type":"no_license","max_line_length":57,"num_lines":22,"path":"/lc968_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def minCameraCover(self, root: TreeNode) -> int:\n self.rslt = 0\n def dfs(root):\n if not root: return -1\n left, right = dfs(root.left), dfs(root.right)\n # leaf's parent, put carmera\n if left == 0 or right == 0:\n self.rslt += 1\n return -2\n if left == -2 or right == -2:\n return -1\n else:\n return 0\n temp = dfs(root)\n return self.rslt + (temp == 0)\n"},{"alpha_fraction":0.5391250848770142,"alphanum_fraction":0.5408194661140442,"avg_line_length":31.62311553955078,"blob_id":"ce89cdc5c779a036a90aa5fc50fbea389ab5503c","content_id":"18a8bda8ad7a5e31b99cbbcaf88d85d0400cc5cd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6492,"license_type":"no_license","max_line_length":125,"num_lines":199,"path":"/lc732_mcal.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class MyCalendar:\n\n def __init__(self):\n self.booking = []\n\n def book(self, start: int, end: int) -> bool:\n for s, e in self.booking:\n if s < end and e > start:\n return False\n self.booking.append((start, end))\n return True\nclass Node:\n def __init__(self, start, end):\n self.start = start\n self.end = end\n self.left = self.right = None\nclass MyCalendar:\n\n def __init__(self):\n self.root = None\n\n def book(self, start: int, end: int) -> bool:\n rslt, self.root = self.insert(self.root, start, end)\n return rslt\n\n def insert(self, root, start, end):\n if not root:\n return True, Node(start, end)\n if end <= root.start:\n rslt, root.left = self.insert(root.left, start, end)\n return rslt, root\n if start >= root.end:\n rslt, root.right = self.insert(root.right, start, end)\n return rslt, root\n return False, root\nclass MyCalendar:\n\n def __init__(self):\n self.starts = []\n self.ends = []\n\n def book(self, start: int, end: int) -> bool:\n s, e = bisect.bisect_right(self.ends, start), bisect.bisect_left(self.starts, end)\n if s != e: return False\n bisect.insort(self.starts, start, s, e)\n bisect.insort(self.ends, end, s, e)\n return True\n\nclass MyCalendarTwo:\n\n def __init__(self):\n self.seen_once = []\n self.seen_twice = []\n\n def book(self, start: int, end: int) -> bool:\n for st, ed in self.seen_twice:\n if st < end and ed > start:\n return False\n for st, ed in self.seen_once:\n if st < end and ed > start:\n self.seen_twice.append((max(st, start), min(ed, end)))\n self.seen_once.append((start, end))\n return True\nclass Node:\n def __init__(self, start, end):\n self.start = start\n self.end = end\n self.left = self.right = None\n self.seen_twice = False\nclass MyCalendarTwo:\n\n def __init__(self):\n self.root = None\n\n def book(self, start: int, end: int) -> bool:\n if not self.check(self.root, start, end): return False\n self.root = self.insert(self.root, start, end)\n return True\n\n def check(self, root, start, end):\n if not root:\n return True\n if end <= root.start:\n return self.check(root.left, start, end)\n if start >= root.end:\n return self.check(root.right, start, end)\n if root.seen_twice: return False\n lefts, lefte, rights, righte = min(start, root.start), max(start, root.start), min(end, root.end), max(end, root.end)\n leftc = self.check(root.left, start, end)\n rightc = self.check(root.right, start, end)\n return leftc and rightc\n\n def insert(self, root, start, end):\n if start >= end:\n return root\n if not root:\n return Node(start, end)\n if end <= root.start:\n root.left = self.insert(root.left, start, end)\n return root\n if start >= root.end:\n root.right = self.insert(root.right, start, end)\n return root\n lefts, lefte, rights, righte = min(start, root.start), max(start, root.start), min(end, root.end), max(end, root.end)\n root.left = self.insert(root.left, lefts, lefte)\n root.right = self.insert(root.right, rights, righte)\n root.seen_twice = True\n root.start = lefte\n root.end = rights\n return root\nclass MyCalendarTwo:\n\n def __init__(self):\n self.starts = []\n self.ends = []\n\n def book(self, start: int, end: int) -> bool:\n s, e = bisect.bisect_right(self.ends, start), bisect.bisect_left(self.starts, end)\n hp = []\n for i in range(s, e):\n while hp and hp[0] <= self.starts[i]:\n heapq.heappop(hp)\n heapq.heappush(hp, self.ends[i])\n if len(hp) > 1:\n return False\n bisect.insort(self.starts, start, s, e)\n bisect.insort(self.ends, end, s, e)\n return True\n\n\n\nclass MyCalendarThree:\n\n def __init__(self):\n self.booking = []\n def book(self, start: int, end: int) -> int:\n self.booking.append((start, 1))\n self.booking.append((end, -1))\n cnt, rslt = 0, 0\n self.booking.sort()\n for x, y in self.booking:\n cnt += y\n if cnt > rslt:\n rslt = cnt\n return rslt\nclass Node:\n def __init__(self, start, end, k):\n self.start = start\n self.end = end\n self.k = k\n self.right = self.left = None\nclass MyCalendarThree:\n\n def __init__(self):\n self.root = None\n self.k = 0\n def book(self, start: int, end: int) -> int:\n self.root = self.insert(self.root, start, end, 1)\n return self.k\n\n def insert(self, root, start, end, k):\n if start >= end:\n return root\n if not root:\n self.k = max(self.k, k)\n return Node(start, end, k)\n if end <= root.start:\n root.left = self.insert(root.left, start, end, k)\n return root\n if start >= root.end:\n root.right = self.insert(root.right, start, end, k)\n return root\n lefts, lefte, rights, righte = min(start, root.start), max(start, root.start), min(end, root.end), max(end, root.end)\n root.left = self.insert(root.left, lefts, lefte, root.k if lefts == root.start else k)\n root.right = self.insert(root.right, rights, righte, root.k if righte == root.end else k)\n root.k += k\n root.start = lefte\n root.end = rights\n self.k = max(self.k, root.k)\n return root\nclass MyCalendarThree:\n def __init__(self):\n self.starts = []\n self.ends = []\n self.booking = 0\n\n def book(self, start: int, end: int) -> int:\n s, e = bisect.bisect_right(self.ends, start), bisect.bisect_left(self.starts, end)\n bisect.insort(self.starts, start, s, e)\n bisect.insort(self.ends, end, s, e)\n # print(start, end, s, e, self.starts, self.ends)\n hp = [] # store end points\n for i in range(s, e + 1):\n while hp and hp[0] <= self.starts[i]:\n heapq.heappop(hp)\n heapq.heappush(hp, self.ends[i])\n if len(hp) > self.booking:\n self.booking = len(hp)\n return self.booking\n"},{"alpha_fraction":0.5324568748474121,"alphanum_fraction":0.5324568748474121,"avg_line_length":32.80555725097656,"blob_id":"e429d97fa273bded608775130cd2352ea0b4d14a","content_id":"2ba1f03eebec6a17f5c7f20167cafa36069a1e9a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1217,"license_type":"no_license","max_line_length":97,"num_lines":36,"path":"/lc236_lca.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n def lowestCommonAncestor(self, root: 'TreeNode', p: 'TreeNode', q: 'TreeNode') -> 'TreeNode':\n if not root: return None\n if root == p or root == q:\n return root\n left = self.lowestCommonAncestor(root.left, p, q)\n right = self.lowestCommonAncestor(root.right, p, q)\n if left and right:\n return root\n return left or right\n\n def lowestCommonAncestor(self, root: 'TreeNode', p: 'TreeNode', q: 'TreeNode') -> 'TreeNode':\n stack = [root]\n seen, parents = set(), {root:None}\n while p not in parents or q not in parents:\n parent = stack.pop()\n if parent.left:\n parents[parent.left] = parent\n stack.append(parent.left)\n if parent.right:\n parents[parent.right] = parent\n stack.append(parent.right)\n cands = set()\n while p:\n cands.add(p)\n p = parents[p]\n while q not in cands:\n q = parents[q]\n return q\n"},{"alpha_fraction":0.48841893672943115,"alphanum_fraction":0.503524661064148,"avg_line_length":34.46428680419922,"blob_id":"06853a34dd10c985abc182225c129cb4e7eeeb41","content_id":"114e11e02f1db956c11da1ef9a4547a653556e07","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":993,"license_type":"no_license","max_line_length":82,"num_lines":28,"path":"/lc320_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def generateAbbreviations(self, word: str) -> List[str]:\n rslt, n = [], len(word)\n def backtracking(i, curr):\n if i == n:\n rslt.append(\"\".join(curr))\n else:\n if not curr or curr[-1].isalpha():\n backtracking(i+1, curr+['1'])\n else:\n backtracking(i+1, curr[:-1]+[str(int(curr[-1])+1)])\n backtracking(i+1, curr+[word[i]])\n backtracking(0, [])\n return rslt\n\n\nclass Solution:\n def generateAbbreviations(self, word: str) -> List[str]:\n self.rslt, self.n, self.word = [], len(word), word\n self.backtracking(0, \"\", 0)\n return self.rslt\n\n def backtracking(self, i, curr, cnt):\n if i == self.n:\n self.rslt.append(curr+(str(cnt) if cnt else \"\"))\n else:\n self.backtracking(i+1, curr, cnt+1)\n self.backtracking(i+1, curr+(str(cnt) if cnt else \"\")+self.word[i], 0)\n"},{"alpha_fraction":0.31351980566978455,"alphanum_fraction":0.3170163035392761,"avg_line_length":29.64285659790039,"blob_id":"211856f58c810790c1ed95c34055ef2c6b348861","content_id":"35cb21fb5609b49a06b0ba913df1e7e3f9cf41fe","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":858,"license_type":"no_license","max_line_length":52,"num_lines":28,"path":"/lc224_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def calculate(self, s: str) -> int:\n stk, last = [\"(\"], False\n for c in s+\")\":\n if c == \" \":\n continue\n if c.isdigit() and last:\n stk.append(stk.pop()+c)\n elif c.isdigit():\n stk.append(c)\n last = True\n elif c == \")\":\n t_stk = []\n while stk[-1]!=\"(\":\n if stk[-1] == \"-\":\n t_stk.append(-t_stk.pop())\n stk.pop()\n else:\n t_stk.append(int(stk.pop()))\n stk.pop()\n stk.append(str(sum(t_stk)))\n last = False\n else:\n if c != \"+\":\n stk.append(c)\n last = False\n\n return int(stk[0])\n"},{"alpha_fraction":0.37771347165107727,"alphanum_fraction":0.38784369826316833,"avg_line_length":30.409090042114258,"blob_id":"8701b7e8ba33ffcc80dc1a502693852064abe657","content_id":"8116b93ec6066ddd6e4673a14a6a22a86e5479c2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":691,"license_type":"no_license","max_line_length":72,"num_lines":22,"path":"/lc218_array.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def getSkyline(self, buildings: List[List[int]]) -> List[List[int]]:\n lines = []\n for l, r, h in buildings:\n lines.append([l, h])\n lines.append([r, -h])\n lines.sort(key = lambda x: (x[0], -x[1]))\n heap = [0]\n rslt = []\n for x, h in lines:\n if h > 0:\n # start\n if h > -heap[0]:\n rslt.append([x, h])\n heapq.heappush(heap, -h)\n else:\n # end\n heap.remove(h)\n heapq.heapify(heap)\n if -h > -heap[0]:\n rslt.append([x, -heap[0]])\n return rslt\n"},{"alpha_fraction":0.4337349534034729,"alphanum_fraction":0.45542168617248535,"avg_line_length":32.83333206176758,"blob_id":"f788bf18e8c5e6ac5086c8d0f71b8999a272b165","content_id":"29d86f3bf77a18dacccba7b26c297bde817bc2a6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":415,"license_type":"no_license","max_line_length":51,"num_lines":12,"path":"/lc1124_hash.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def longestWPI(self, hours: List[int]) -> int:\n maps = {}\n curr, rslt = 0, 0\n for i, hour in enumerate(hours):\n curr = curr + 1 if hour > 8 else curr-1\n if curr > 0:\n rslt = i + 1\n if curr not in maps: maps[curr] = i\n if curr-1 in maps:\n rslt = max(rslt, i-maps[curr-1])\n return rslt\n \n"},{"alpha_fraction":0.43801653385162354,"alphanum_fraction":0.4573002755641937,"avg_line_length":32,"blob_id":"61fffc06f1265ddc15668c203971f538aaf8e81a","content_id":"c6e97ccb6989edafdbf46bbcc1d829e467691599","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":363,"license_type":"no_license","max_line_length":57,"num_lines":11,"path":"/lc128_array.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def longestConsecutive(self, nums: List[int]) -> int:\n cands, curr, rslt= set(nums), 1, 0\n for cand in cands:\n curr = 1\n if cand-1 not in cands:\n while cand+1 in cands:\n cand = cand+1\n curr += 1\n rslt = max(rslt, curr)\n return rslt\n"},{"alpha_fraction":0.4289276897907257,"alphanum_fraction":0.4463840425014496,"avg_line_length":29.846153259277344,"blob_id":"9a895ee2d74e4bb4128094da7eb972215dbcee1e","content_id":"14812dbbbbca6fc384a3885acb5b7073e2e80633","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":401,"license_type":"no_license","max_line_length":71,"num_lines":13,"path":"/lc435_array.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def eraseOverlapIntervals(self, intervals: List[List[int]]) -> int:\n if len(intervals) <= 0: return 0\n intervals.sort()\n last, rslt = intervals[0][1], 0\n for x, y in intervals[1:]:\n if x < last:\n rslt += 1\n if y < last:\n last = y\n else:\n last = y\n return rslt\n"},{"alpha_fraction":0.37171053886413574,"alphanum_fraction":0.40723684430122375,"avg_line_length":30.66666603088379,"blob_id":"a75c43eea1cafaca3afecc7d444007abf4c4c3a2","content_id":"73c369906edda7ba6f1caab96646dc3d0b5222bb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1520,"license_type":"no_license","max_line_length":84,"num_lines":48,"path":"/lc308_segtree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class NumMatrix:\n def __init__(self, matrix: List[List[int]]):\n self.m, self.n = len(matrix), len(matrix[0])\n self.seg_tree = [[0]*(2*self.n) for _ in range(2*self.m)]\n for i in range(self.m):\n for j in range(self.n):\n self.update(i, j, matrix[i][j])\n\n def update(self, row: int, col: int, val: int) -> None:\n m = row + self.m\n n = col + self.n\n self.seg_tree[m][n] = val\n while m > 0:\n self.seg_tree[m >> 1][n] = self.seg_tree[m][n]+self.seg_tree[m^1][n]\n t = n\n while t > 0:\n self.seg_tree[m][t >> 1] = self.seg_tree[m][t]+self.seg_tree[m][t^1]\n t >>= 1\n m >>= 1\n def sumRegion(self, row1: int, col1: int, row2: int, col2: int) -> int:\n rslt = 0\n r1 = self.m + row1\n r2 = self.m + row2\n while r1 <= r2:\n if r1&1:\n rslt += self.sumRange(r1, col1, col2)\n r1 += 1\n\n if r2&1 == 0:\n rslt += self.sumRange(r2, col1, col2)\n r2 -= 1\n r1 >>= 1\n r2 >>= 1\n return rslt\n\n def sumRange(self, row, col1, col2):\n rslt = 0\n l, r= self.n+col1, self.n+col2\n while l <= r:\n if l&1:\n rslt += self.seg_tree[row][l]\n l += 1\n if r&1 == 0:\n rslt += self.seg_tree[row][r]\n r -= 1\n l >>= 1\n r >>= 1\n return rslt\n"},{"alpha_fraction":0.7749999761581421,"alphanum_fraction":0.7749999761581421,"avg_line_length":19,"blob_id":"14779852faf77b20122b42df1016088b6f05f078","content_id":"637d94def0546a6622650b27b324c7fd4744aad3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":40,"license_type":"no_license","max_line_length":28,"num_lines":2,"path":"/README.md","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# lc_punch\nRepo for LC practice logging\n"},{"alpha_fraction":0.4428237974643707,"alphanum_fraction":0.46674445271492004,"avg_line_length":35.46808624267578,"blob_id":"b10a64818b79e57ea4b8d23ae118081cdd7bc0ee","content_id":"4b120c84a970eeb1514572ae368f643b0dc1d592","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1714,"license_type":"no_license","max_line_length":109,"num_lines":47,"path":"/lc992_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def subarraysWithKDistinct(self, A: List[int], K: int) -> int:\n memo1, memo2, left, right, rslt = collections.defaultdict(int), collections.defaultdict(int), 0, 0, 0\n for it in A:\n memo1[it] += 1\n memo2[it] += 1\n while len(memo1) > K:\n memo1[A[left]] -= 1\n if memo1[A[left]] == 0: del memo1[A[left]]\n left += 1\n while len(memo2) >= K:\n memo2[A[right]] -= 1\n if memo2[A[right]] == 0: del memo2[A[right]]\n right += 1\n rslt += right-left\n return rslt\n\nclass Solution:\n def atMostK(self, A, K):\n memo, left, rslt = collections.defaultdict(int), 0, 0\n for right, it in enumerate(A):\n memo[it] += 1\n while len(memo) > K:\n memo[A[left]] -= 1\n if memo[A[left]] == 0: del memo[A[left]]\n left += 1\n rslt += right-left + 1\n return rslt\n def subarraysWithKDistinct(self, A: List[int], K: int) -> int:\n if not A: return 0\n return self.atMostK(A, K) - self.atMostK(A, K - 1)\n\nclass Solution:\n def subarraysWithKDistinct(self, A: List[int], K: int) -> int:\n memo, lefta, leftb, rslt = collections.defaultdict(int), 0, 0, 0\n for right, num in enumerate(A):\n memo[num] += 1\n if len(memo) > K:\n del memo[A[leftb]]\n leftb += 1\n lefta = leftb\n if len(memo) == K:\n while memo[A[leftb]] > 1:\n memo[A[leftb]] -= 1\n leftb += 1\n rslt += leftb-lefta+1\n return rslt\n"},{"alpha_fraction":0.43790850043296814,"alphanum_fraction":0.4562091529369354,"avg_line_length":46.8125,"blob_id":"f47fd27d163848bf6b60d7d31d752c3cdb0afe2b","content_id":"a0d3fc9105e20479b5de61be1078a8f3d434a73e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":765,"license_type":"no_license","max_line_length":95,"num_lines":16,"path":"/lc490_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def hasPath(self, maze: List[List[int]], start: List[int], destination: List[int]) -> bool:\n dque, visited = collections.deque([tuple(start)]), {tuple(start)}\n n, c = len(maze), len(maze[0])\n while dque:\n currow, curcol = dque.popleft()\n if destination[0] == currow and destination[1] == curcol: return True\n for x, y in [(1, 0), (-1, 0), (0, 1), (0, -1)]:\n tx, ty = currow, curcol\n while 0 <= tx+x < n and 0 <= ty+y < c and maze[tx+x][ty+y] == 0:\n tx += x\n ty += y\n if (tx, ty) not in visited:\n dque.append((tx, ty))\n visited.add((tx, ty))\n return False\n"},{"alpha_fraction":0.5112107396125793,"alphanum_fraction":0.5182574987411499,"avg_line_length":32.212764739990234,"blob_id":"80d1bd32e50135357db4f5bf8d9a5336965b5a6b","content_id":"d0a5d599328ab78d633566ba26084cf118faea0a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1561,"license_type":"no_license","max_line_length":83,"num_lines":47,"path":"/lc207_graph.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def canFinish(self, numCourses: int, prerequisites: List[List[int]]) -> bool:\n if not prerequisites: return True\n graph = collections.defaultdict(list)\n visited = [0]*numCourses\n for crs, pre in prerequisites:\n graph[crs].append(pre)\n\n def dfs(crs):\n # in cycle\n if visited[crs] == 1:\n return False\n # not in cycle\n if visited[crs] == 2:\n return True\n # new node: assume in cycle until approve not\n visited[crs] = 1\n if crs in graph:\n for pre in graph[crs]:\n if not dfs(pre): return False\n visited[crs] = 2\n return True\n\n for crs in graph:\n if not dfs(crs): return False\n return True\n\n\nclass Solution:\n def canFinish(self, numCourses: int, prerequisites: List[List[int]]) -> bool:\n if not prerequisites: return True\n graph, degree = collections.defaultdict(list), collections.defaultdict(int)\n for crs, pre in prerequisites:\n graph[pre].append(crs)\n degree[crs] += 1\n dque = collections.deque()\n for i in range(numCourses):\n if degree[i] == 0: dque.append(i)\n count = 0\n while dque:\n curr = dque.popleft()\n count += 1\n for post in graph[curr]:\n degree[post] -= 1\n if degree[post] == 0:\n dque.append(post)\n return count == numCourses\n"},{"alpha_fraction":0.28145694732666016,"alphanum_fraction":0.3509933650493622,"avg_line_length":32,"blob_id":"8f5e1d284fd5be1560f16739996320b9c92727bd","content_id":"eb61a90760defa7ac624754085ebd1de6ecb5205","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":604,"license_type":"no_license","max_line_length":64,"num_lines":18,"path":"/lc1868_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxSum(self, nums1: List[int], nums2: List[int]) -> int:\n l1, l2 = len(nums1), len(nums2)\n x, y = 0, 0\n r1, r2 = 0, 0\n while x < l1 or y < l2:\n if x < l1 and (y == l2 or nums1[x] < nums2[y]):\n r1 += nums1[x]\n x += 1\n elif y < l2 and (x == l1 or nums1[x] > nums2[y]):\n r2 += nums2[y]\n y += 1\n else:\n r1 = max(r1, r2) + nums1[x]\n r2 = r1\n x += 1\n y += 1\n return max(r1, r2)%(10**9+7)\n\n \n"},{"alpha_fraction":0.4606427550315857,"alphanum_fraction":0.4625058174133301,"avg_line_length":32.546875,"blob_id":"0f7c38e245450e4aebc0b799131c28617db65b7b","content_id":"66d9d2a7c789f17319cc6e88a292bf6397aea021","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2147,"license_type":"no_license","max_line_length":79,"num_lines":64,"path":"/lc99_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n # def recoverTree(self, root: TreeNode) -> None:\n # \"\"\"\n # Do not return anything, modify root in-place instead.\n # \"\"\"\n # order = self.inorder(root)\n # y, x = None, None\n # for i in range(len(order)-1):\n # if order[i+1] < order[i]:\n # y = order[i+1]\n # if x is None:\n # x = order[i]\n # else:\n # break\n # stack = [root]\n # while stack:\n # temp = stack.pop()\n # if temp.val in (x, y):\n # temp.val = x if temp.val == y else y\n # if temp.left: stack.append(temp.left)\n # if temp.right:stack.append(temp.right)\n # def inorder(self, root):\n # if not root: return []\n # return self.inorder(root.left) +[root.val] + self.inorder(root.right)\n\n def recoverTree(self, root: TreeNode) -> None:\n self.x, self.y, self.pre = None, None, None\n def inorder(root):\n if not root: return\n inorder(root.left)\n if self.pre and root.val < self.pre.val:\n self.y = root\n if not self.x:\n self.x = self.pre\n self.pre = root\n inorder(root.right)\n inorder(root)\n self.x.val, self.y.val = self.y.val, self.x.val\n\n def recoverTree(self, root: TreeNode) -> None:\n \"\"\"\n Do not return anything, modify root in-place instead.\n \"\"\"\n curr, stack = root, []\n x = y = pre = None\n while curr or stack:\n while curr:\n stack.append(curr)\n curr = curr.left\n curr = stack.pop()\n if pre and curr.val < pre.val:\n y = curr\n if x is None:\n x = pre\n else: break\n pre = curr\n curr = curr.right\n x.val, y.val = y.val, x.val\n"},{"alpha_fraction":0.40419161319732666,"alphanum_fraction":0.41766467690467834,"avg_line_length":36.11111068725586,"blob_id":"ad52b2b89952074de5d01c46118208223bde891e","content_id":"445cce9933958bff568461541b81303fc168eb42","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":668,"license_type":"no_license","max_line_length":65,"num_lines":18,"path":"/lc847_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def shortestPathLength(self, graph: List[List[int]]) -> int:\n visited, n = set(), len(graph)\n final = (1 << n)-1\n dque = collections.deque([(1 << i, i) for i in range(n)])\n step = 0\n while dque:\n size = len(dque)\n for _ in range(size):\n state, x = dque.popleft()\n if state == final:\n return step\n for y in graph[x]:\n if (state|(1<< y), y) not in visited:\n dque.append((state|(1<< y), y))\n visited.add((state|(1<< y), y))\n step += 1\n return -1\n"},{"alpha_fraction":0.42664670944213867,"alphanum_fraction":0.450598806142807,"avg_line_length":30.809524536132812,"blob_id":"0163b2a49e94903b2c36c0e65a3c032f49d9a811","content_id":"0985f933ebaeffc9d1540c55824d08de4ac3ffc3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":668,"license_type":"no_license","max_line_length":66,"num_lines":21,"path":"/lc1574_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findLengthOfShortestSubarray(self, arr: List[int]) -> int:\n n = len(arr)\n left, right = 0, n-1\n while left < n-1 and arr[left+1] >= arr[left]:\n left += 1\n if left == n-1:\n return 0\n while right > 0 and arr[right-1] <= arr[right]:\n right -= 1\n if arr[left] <= arr[right]:\n return right-left-1\n rslt = min(n-left-1, right)\n for i in range(left+1):\n if arr[i] <= arr[right]:\n rslt = min(rslt, right-i-1)\n elif right < n-1:\n right += 1\n else:\n break\n return rslt\n"},{"alpha_fraction":0.5125628113746643,"alphanum_fraction":0.5326633453369141,"avg_line_length":27.428571701049805,"blob_id":"9386bae485636cce67c13e2c7467b362ead5e1bd","content_id":"74dbcdcf1551500bb8414ecd71cb9d64c19bc724","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":398,"license_type":"no_license","max_line_length":65,"num_lines":14,"path":"/lc901_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class StockSpanner:\n\n def __init__(self):\n self.last = [(float('inf'), -1)]\n self.curr = 0\n\n\n def next(self, price: int) -> int:\n while self.last and price >= self.last[-1][0]:\n self.last.pop()\n temp = self.curr if not self.last else self.last[-1][1]+1\n self.last.append((price, self.curr))\n self.curr += 1\n return self.curr - temp\n"},{"alpha_fraction":0.49317148327827454,"alphanum_fraction":0.5022761821746826,"avg_line_length":31.950000762939453,"blob_id":"f825416afa8dbdae5db94b91dc946281e74d9ea6","content_id":"6585741115370abd84324cd637729a9f2f8af8d3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":659,"license_type":"no_license","max_line_length":74,"num_lines":20,"path":"/lc179_sort.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def largestNumber(self, nums):\n self.quickSort(nums, 0, len(nums)-1)\n return str(int(\"\".join(map(str, nums))))\n\n def quickSort(self, nums, l, r):\n if l >= r: return\n pos = self.partition(nums, l, r)\n self.quickSort(nums, l, pos-1)\n self.quickSort(nums, pos+1, r)\n\n def partition(self, nums, l, r):\n pivot = l\n while l < r:\n if str(nums[l]) + str(nums[r]) > str(nums[r]) + str(nums[l]) :\n nums[l], nums[pivot] = nums[pivot], nums[l]\n pivot += 1\n l += 1\n nums[pivot], nums[r] = nums[r], nums[pivot]\n return pivot\n"},{"alpha_fraction":0.5072463750839233,"alphanum_fraction":0.5144927501678467,"avg_line_length":29.66666603088379,"blob_id":"d909cdd55d1aed8881ee8173d78f6804a2014659","content_id":"7f6395f4264a1cf095e204d79b822b685c8da0c1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":276,"license_type":"no_license","max_line_length":51,"num_lines":9,"path":"/lc493_sort.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def reversePairs(self, nums: List[int]) -> int:\n cand = []\n rslt = 0\n for num in nums:\n idx = bisect.bisect_right(cand, 2*num)\n rslt = rslt + len(cand)-idx\n bisect.insort(cand, num)\n return rslt\n"},{"alpha_fraction":0.5064575672149658,"alphanum_fraction":0.5129151344299316,"avg_line_length":30.882352828979492,"blob_id":"76640f13e865ac66623f672d8617f84916bc4e48","content_id":"a9ccc3edca5135da4c6a3fb517d7e6e29ce61bfd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1084,"license_type":"no_license","max_line_length":57,"num_lines":34,"path":"/lc1038_bt.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# same as 538\nclass Solution:\n def bstToGst(self, root: TreeNode) -> TreeNode:\n def helper(root, pre):\n if not root: return pre\n root.val += helper(root.right, pre)\n return helper(root.left, root.val)\n helper(root, 0)\n return root\nclass Solution:\n def bstToGst(self, root: TreeNode) -> TreeNode:\n def helper(root, pre):\n if not root: return (0+pre, None)\n valr, right = helper(root.right, pre)\n vall, left = helper(root.left, valr+root.val)\n root.val += valr\n root.right =right\n root.left = left\n return (vall,root)\n _, root = helper(root, 0)\n return root\n \nclass Solution:\n def convertBST(self, root: TreeNode) -> TreeNode:\n curr, stk, last = root, [], 0\n while stk or curr:\n while curr:\n stk.append(curr)\n curr = curr.right\n temp = stk.pop()\n temp.val += last\n last = temp.val\n curr = temp.left\n return root\n"},{"alpha_fraction":0.4921700358390808,"alphanum_fraction":0.501118540763855,"avg_line_length":36.25,"blob_id":"85c8796bc11817fe0ab5dfccc10ecfc3a1921aa6","content_id":"00b228f037cded30bcb42714a82f8d7e7b214b08","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":447,"license_type":"no_license","max_line_length":69,"num_lines":12,"path":"/lc239_deque.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxSlidingWindow(self, nums: List[int], k: int) -> List[int]:\n rslt, holder = [], collections.deque()\n for idx, num in enumerate(nums):\n while holder and nums[holder[-1]] < num:\n holder.pop()\n holder.append(idx)\n if holder[0] <= idx-k:\n holder.popleft()\n if idx >= k-1:\n rslt.append(nums[holder[0]])\n return rslt\n"},{"alpha_fraction":0.5241488814353943,"alphanum_fraction":0.5241488814353943,"avg_line_length":29.071428298950195,"blob_id":"6cb6618ce41afdeeccffd2f22daa31dd9414f153","content_id":"ff6464a9c49dcb42bbc0a9f589c6345ef25f48fd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1263,"license_type":"no_license","max_line_length":75,"num_lines":42,"path":"/lc138_linkedlist.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"\"\"\"\n# Definition for a Node.\nclass Node:\n def __init__(self, x: int, next: 'Node' = None, random: 'Node' = None):\n self.val = int(x)\n self.next = next\n self.random = random\n\"\"\"\n\nclass Solution:\n def copyRandomList(self, head: 'Node') -> 'Node':\n if not head: return None\n curr = head\n while curr:\n copy = Node(curr.val, curr.next)\n curr.next = copy\n curr = copy.next\n curr = head\n while curr:\n curr.next.random = curr.random.next if curr.random else None\n curr = curr.next.next\n curr, rslt = head, head.next\n while curr:\n temp = curr.next\n curr.next = temp.next\n temp.next = temp.next.next if temp.next else None\n curr = curr.next\n return rslt\n\nclass Solution:\n def copyRandomList(self, head: 'Node') -> 'Node':\n if not head: return None\n nodemap = {}\n def dfs(curr):\n if not curr: return None\n if curr in nodemap: return nodemap[curr]\n copy = Node(curr.val)\n nodemap[curr] = copy\n copy.next = dfs(curr.next)\n copy.random = dfs(curr.random)\n return copy\n return dfs(head)\n"},{"alpha_fraction":0.3534635901451111,"alphanum_fraction":0.42451155185699463,"avg_line_length":39.28571319580078,"blob_id":"290ba91a1b6623b754dc698d603326843ce44543","content_id":"185d8fe1182a0993f4645eb493f59a3c6fcd2d51","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":563,"license_type":"no_license","max_line_length":64,"num_lines":14,"path":"/lc583_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minDistance(self, word1: str, word2: str) -> int:\n l_w1, l_w2 = len(word1), len(word2)\n dp = [[0]*(l_w2+1) for _ in range(l_w1+1)]\n for i in range(1, l_w1+1): dp[i][0] = dp[i-1][0]+1\n for j in range(1, l_w2+1): dp[0][j] = dp[0][j-1]+1\n \n for i, c1 in enumerate(word1):\n for j, c2 in enumerate(word2):\n if c1 == c2:\n dp[i+1][j+1] = dp[i][j]\n else:\n dp[i+1][j+1] = min(dp[i+1][j], dp[i][j+1])+1\n return dp[-1][-1]"},{"alpha_fraction":0.525611162185669,"alphanum_fraction":0.5296856760978699,"avg_line_length":43.0512809753418,"blob_id":"78b9ca965b2510187da0c3e02d8fd92eedf6079e","content_id":"f69a31c9fe41db57f9d4eb659a12a45c0ec53342","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1718,"license_type":"no_license","max_line_length":101,"num_lines":39,"path":"/lc1203_toposort.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def sortItems(self, n: int, m: int, group: List[int], beforeItems: List[List[int]]) -> List[int]:\n graph_i, graph_g = collections.defaultdict(list), collections.defaultdict(list)\n degree_i, degree_g = collections.defaultdict(int), collections.defaultdict(int)\n group_dict = collections.defaultdict(list)\n # Add to new group for item belongs to no group\n for i in range(n):\n if group[i] == -1: group[i] = m\n m += 1\n group_dict[group[i]].append(i)\n # Prepare for topo sort on groups then items\n for curr in range(n):\n for pre in beforeItems[curr]:\n if group[curr] == group[pre]:\n graph_i[pre].append(curr)\n degree_i[curr] += 1\n else:\n graph_g[group[pre]].append(group[curr])\n degree_g[group[curr]] += 1\n # order between group\n group_order, rslt = self.toposort(graph_g, degree_g, list(range(m))), []\n # order within group\n for i in group_order:\n if group_dict[i]:\n tt = self.toposort(graph_i, degree_i, group_dict[i])\n if len(tt) != len(group_dict[i]): return []\n rslt.extend(tt)\n return rslt\n\n def toposort(self, graph, degree, cands):\n dque = collections.deque([i for i in cands if degree[i] == 0])\n rslt = []\n while dque:\n pre = dque.popleft()\n rslt.append(pre)\n for post in graph[pre]:\n degree[post] -= 1\n if degree[post] == 0 and post in cands: dque.append(post)\n return rslt if len(rslt) == len(cands) else []\n"},{"alpha_fraction":0.41511771082878113,"alphanum_fraction":0.4696406424045563,"avg_line_length":35.681819915771484,"blob_id":"6082cee12bba36f479682c5e1d30f560ba47269b","content_id":"8033dba5cfe841d849ed9342c7aa7285cd9e1e40","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1614,"license_type":"no_license","max_line_length":78,"num_lines":44,"path":"/lc1775_array.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minOperations(self, nums1: List[int], nums2: List[int]) -> int:\n diff = sum(nums1) - sum(nums2)\n if diff < 0:\n nums1, nums2 = nums2, nums1\n diff = -diff\n quota = collections.Counter([x-1 for x in nums1]+[6-x for x in nums2])\n count, change = 0, 5\n while diff > 0:\n if change == 0: return -1\n if (diff-1)//change+1 <= quota[change]:\n return count + (diff-1)//change + 1\n else:\n diff, count = diff-change*quota[change], count + quota[change]\n change -= 1\n return count\n\nclass Solution:\n def minOperations(self, nums1: List[int], nums2: List[int]) -> int:\n diff = sum(nums1) - sum(nums2)\n if not diff: return 0\n if diff < 0:\n nums1, nums2 = nums2, nums1\n diff = -diff\n nums1.sort(reverse = True)\n nums2.sort()\n count, idx1, idx2, l1, l2 = 0, 0, 0, len(nums1), len(nums2)\n while idx1 < l1 or idx2 < l2:\n if diff <= 0:\n return count\n if idx1 < l1 and diff <= nums1[idx1]-1:\n return count + 1\n if idx2 < l2 and diff <= 6 - nums2[idx2]:\n return count + 1\n sub1 = (nums1[idx1]-1) if idx1 < l1 else 0\n sub2 = (6 - nums2[idx2]) if idx2 < l2 else 0\n if sub1 >= sub2 and idx1 < l1:\n diff -= sub1\n idx1 += 1\n else:\n diff -= sub2\n idx2 += 1\n count += 1\n return count if diff <= 0 else -1\n"},{"alpha_fraction":0.3754071593284607,"alphanum_fraction":0.3965798020362854,"avg_line_length":38.6129035949707,"blob_id":"adf707973150ed1051b141f3101f3fb473934d1c","content_id":"5923f3f0d02edea54c7fd56b62e35f801bcefe0c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1228,"license_type":"no_license","max_line_length":71,"num_lines":31,"path":"/lc1778_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution(object):\n def findShortestPath(self, master: 'GridMaster') -> int:\n maps = {(0, 0)}\n ops = {\"U\":\"D\", \"D\":\"U\", \"L\":\"R\", \"R\":\"L\"}\n steps = {\"U\":(1, 0), \"D\":(-1, 0), \"L\":(0, -1), \"R\":(0, 1)}\n self.target = None\n def buildmap(x, y):\n if master.isTarget(): self.target = (x, y)\n for d in steps:\n nx, ny = x + steps[d][0], y+steps[d][1]\n if (nx, ny) in maps: continue\n if master.canMove(d):\n master.move(d)\n maps.add((nx, ny))\n buildmap(nx, ny)\n master.move(ops[d])\n buildmap(0, 0)\n if self.target is None: return -1\n queue = collections.deque([(0, 0)])\n rslt = 0\n seen = {(0, 0)}\n while queue:\n for _ in range(len(queue)):\n x, y = queue.popleft()\n if (x, y) == self.target: return rslt\n for nx, ny in [(x-1, y), (x+1, y), (x, y-1), (x, y+1)]:\n if (nx, ny) in maps and (nx, ny) not in seen:\n seen.add((nx, ny))\n queue.append((nx, ny))\n rslt += 1\n return -1\n"},{"alpha_fraction":0.43800538778305054,"alphanum_fraction":0.4501347839832306,"avg_line_length":31.2608699798584,"blob_id":"03316d1c16fc697bd9534a0de80a62cde149097d","content_id":"b12012d544a479a2469a03a1a3756563162666d1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":742,"license_type":"no_license","max_line_length":53,"num_lines":23,"path":"/lc765_unionfind.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minSwapsCouples(self, row: List[int]) -> int:\n parent = [x//2*2 for x in range(len(row))]\n size = [1]*len(row)\n def find(p):\n while p != parent[p]:\n parent[p] = parent[parent[p]]\n p = parent[p]\n return p\n\n def union(p, q):\n rootp, rootq = find(p), find(q)\n if rootp == rootq: return False\n if size[rootp] < size[rootq]:\n rootp, rootq = rootq, rootp\n parent[rootq] = rootp\n size[rootp] += size[rootq]\n return True\n rslt = 0\n for i in range(len(row)//2):\n if union(row[i*2], row[i*2+1]):\n rslt += 1\n return rslt\n"},{"alpha_fraction":0.4419354796409607,"alphanum_fraction":0.4580645263195038,"avg_line_length":31.55555534362793,"blob_id":"7cb580a9051349c7d006d5480a2286126673572b","content_id":"aa9a3d42868df649627f4e7573340b04b519e8ea","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":310,"license_type":"no_license","max_line_length":72,"num_lines":9,"path":"/lc1288_array.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def removeCoveredIntervals(self, intervals: List[List[int]]) -> int:\n intervals.sort(key = lambda x:(x[0], -x[1]))\n last, cnt = -1, 0\n for x, y in intervals:\n if y > last:\n last = y\n cnt += 1\n return cnt\n \n"},{"alpha_fraction":0.40978261828422546,"alphanum_fraction":0.427173912525177,"avg_line_length":47.421051025390625,"blob_id":"af42ddaa7a83c8f55e324fc32c69b934714a54eb","content_id":"d663955428e76e76e8f413a8acf8761a9fcf3153","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":920,"license_type":"no_license","max_line_length":72,"num_lines":19,"path":"/lc679_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def judgePoint24(self, nums: List[int]) -> bool:\n if not nums: return False\n if len(nums) == 1: return 23.9 < nums[0] < 24.1\n for i in range(len(nums)):\n for j in range(i):\n if i > j:\n nums_next = nums[:j]+nums[j+1:i]+nums[i+1:]\n for op in (truediv, mul, add, sub):\n if op is not truediv or nums[j]:\n nums_next.append(op(nums[i], nums[j]))\n if self.judgePoint24(nums_next): return True\n nums_next.pop()\n if op in (add, mul): continue\n if op is sub or nums[i]:\n nums_next.append(op(nums[j], nums[i]))\n if self.judgePoint24(nums_next): return True\n nums_next.pop()\n return False\n"},{"alpha_fraction":0.5169811248779297,"alphanum_fraction":0.5188679099082947,"avg_line_length":36.78571319580078,"blob_id":"ed1eede5df1882b3e2c9d63dc792b3cca6fff408","content_id":"49edd37b88b2660a304fa1a250f77175ca6652b4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":530,"license_type":"no_license","max_line_length":73,"num_lines":14,"path":"/lc690_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def getImportance(self, employees: List['Employee'], id: int) -> int:\n hold = {x.id: i for i, x in enumerate(employees)}\n dque = collections.deque([id])\n visited, rslt = {id}, 0\n while dque:\n curr = dque.popleft()\n employee = employees[hold[curr]]\n rslt += employee.importance\n for n in employee.subordinates:\n if n not in visited:\n visited.add(n)\n dque.append(n)\n return rslt \n"},{"alpha_fraction":0.5021833777427673,"alphanum_fraction":0.5152838230133057,"avg_line_length":31.714284896850586,"blob_id":"139ffb3d3997d57287c12343a24e2178033ebc32","content_id":"5c9b76464f7388f2b2f0adcf76ba9a76e755d699","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":229,"license_type":"no_license","max_line_length":50,"num_lines":7,"path":"/lc53_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxSubArray(self, nums: List[int]) -> int:\n temp, rslt = nums[0], nums[0]\n for num in nums[1:]:\n temp = max(temp + num, num)\n rslt = max(temp, rslt)\n return rslt\n"},{"alpha_fraction":0.4598168730735779,"alphanum_fraction":0.475076287984848,"avg_line_length":32.89655303955078,"blob_id":"124f6a52893b5c4f868edd6291e9e267f37657c3","content_id":"83bf3ba55eecde7e10eed7b347c4f742129eb814","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":983,"license_type":"no_license","max_line_length":99,"num_lines":29,"path":"/lc930_hashtable.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def numSubarraysWithSum(self, A: List[int], S: int) -> int:\n prefix, rslt, temp = 0, 0, {0:1}\n for i, x in enumerate(A):\n prefix += x\n if prefix-S in temp:\n rslt += temp[prefix-S]\n temp[prefix] = temp.get(prefix, 0)+1\n return rslt\n \nclass Solution:\n def numSubarraysWithSum(self, nums: List[int], goal: int) -> int:\n left_lo, left_hi = 0, 0\n last_lo, last_hi = 0, 0\n rslt = 0\n for right, num in enumerate(nums):\n last_lo += num\n last_hi += num\n\n while left_lo < right and last_lo > goal:\n last_lo -= nums[left_lo]\n left_lo += 1\n while left_hi < right and (last_hi > goal or (last_hi == goal and nums[left_hi] == 0)):\n last_hi -= nums[left_hi]\n left_hi += 1\n\n if last_lo == goal:\n rslt += left_hi - left_lo + 1\n return rslt\n"},{"alpha_fraction":0.35752686858177185,"alphanum_fraction":0.3844085931777954,"avg_line_length":30,"blob_id":"60c0e4ded3229da88a777599bd70a476008fece2","content_id":"96b0b08ceee364880033c0033d24fc925d25283d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":372,"license_type":"no_license","max_line_length":53,"num_lines":12,"path":"/lc1371_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findTheLongestSubstring(self, s: str) -> int:\n mask = {\"a\":1, \"e\":2, \"i\":4, \"o\":8, \"u\":16}\n d, n, res = {0:-1}, 0, 0\n for i, c in enumerate(s):\n if c in mask:\n n ^= mask[c]\n if n not in d:\n d[n] = i\n else:\n res = max(res, i-d[n])\n return res\n"},{"alpha_fraction":0.3419540226459503,"alphanum_fraction":0.37068966031074524,"avg_line_length":30.636363983154297,"blob_id":"7cbb9ca2fd6ea88216e0c07a5df05104ddeda1fd","content_id":"18942add6c81db222d3e5a88bc2e244a2f6fcf9d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":348,"license_type":"no_license","max_line_length":54,"num_lines":11,"path":"/lc1209_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def removeDuplicates(self, s: str, k: int) -> str:\n stk = []\n for c in s:\n if stk and c == stk[-1][0]:\n stk[-1][1] +=1\n if stk[-1][1] == k:\n stk.pop()\n else:\n stk.append([c, 1])\n return \"\".join([x[0]*x[1] for x in stk])\n"},{"alpha_fraction":0.4740259647369385,"alphanum_fraction":0.47922077775001526,"avg_line_length":37.04999923706055,"blob_id":"33175cb998d50e782cdc9edc364d90f2bd00a42d","content_id":"58f6743a29082f8f379a60d681e2a3c0caea7930","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":770,"license_type":"no_license","max_line_length":79,"num_lines":20,"path":"/lc863_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def distanceK(self, root: TreeNode, target: TreeNode, K: int) -> List[int]:\n maps = {}\n def get_parents(curr, prev):\n if curr:\n maps[curr] = prev\n get_parents(curr.left, curr)\n get_parents(curr.right, curr)\n get_parents(root, None)\n queue = collections.deque([(target, 0)])\n visited = {target}\n while queue:\n if queue[0][1] == K:\n return [x.val for x, y in queue]\n node, dist = queue.popleft()\n for nxt in [node.left, node.right, maps[node]]:\n if nxt and nxt not in visited:\n visited.add(nxt)\n queue.append((nxt, dist + 1))\n return []\n \n"},{"alpha_fraction":0.4175824224948883,"alphanum_fraction":0.4263736307621002,"avg_line_length":34,"blob_id":"50638d619415e82ffb1ee0a880920159c226add2","content_id":"82fbe0e128699d1adc998601f68739ca708bcf89","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":455,"license_type":"no_license","max_line_length":54,"num_lines":13,"path":"/lc131_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def partition(self, s: str) -> List[List[str]]:\n rslt, n, checked = [], len(s), set()\n def backtracking(left, curr):\n if left == n:\n rslt.append(curr)\n else:\n for j in range(left, n):\n temp = s[left:j+1]\n if temp == temp[::-1]:\n backtracking(j+1, curr+[temp])\n backtracking(0, [])\n return rslt\n"},{"alpha_fraction":0.493630588054657,"alphanum_fraction":0.4968152940273285,"avg_line_length":32.05263137817383,"blob_id":"6c6f149f2db012aed4b0c871e6ff178323148bf0","content_id":"73fefef61b6772d4b48c4a0fb1dca97ea9e6c8ef","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":628,"license_type":"no_license","max_line_length":74,"num_lines":19,"path":"/lc1681_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minimumIncompatibility(self, nums: List[int], k: int) -> int:\n d = len(nums)//k\n count = collections.Counter(nums)\n if max(count.values()) > k: return -1\n\n @lru_cache(None)\n def dfs(curr):\n if not curr:\n return 0\n rslt = float(\"inf\")\n\n for comb in itertools.combinations(set(curr), d):\n remain = list(curr)\n for t in comb:\n remain.remove(t)\n rslt = min(rslt, max(comb)-min(comb) + dfs(tuple(remain)))\n return rslt\n return dfs(tuple(nums))\n"},{"alpha_fraction":0.5203996896743774,"alphanum_fraction":0.5287260413169861,"avg_line_length":35.39393997192383,"blob_id":"9d6b3d6bb162a57ca82e9eee5a9993c32854649f","content_id":"84c76162a90d9457f90d9cdf591f81f89896be78","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1201,"license_type":"no_license","max_line_length":75,"num_lines":33,"path":"/lc1825_sortedlist.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"from sortedcontainers import SortedList\nclass MKAverage:\n\n def __init__(self, m: int, k: int):\n self.m, self.k = m, k\n self.win = collections.deque([0]*m)\n self.o_win = SortedList([0]*m)\n self.sum_k, self.sum_m_k = 0, 0\n\n def addElement(self, num: int) -> None:\n self.win.append(num)\n # update out of window ele\n oow = self.win.popleft()\n i_oow = self.o_win.bisect_right(oow)\n if i_oow <= self.k:\n self.sum_k = self.sum_k - oow + self.o_win[self.k]\n if i_oow <= self.m - self.k:\n self.sum_m_k = self.sum_m_k - oow + self.o_win[self.m-self.k]\n self.o_win.remove(oow)\n # update new ele\n i_new = self.o_win.bisect_right(num)\n if i_new < self.k:\n self.sum_k = self.sum_k - self.o_win[self.k-1] + num\n if i_new < self.m-self.k:\n self.sum_m_k = self.sum_m_k - self.o_win[self.m-self.k-1] + num\n self.o_win.add(num)\n return\n\n def calculateMKAverage(self) -> int:\n # print(self.o_win, self.sum_m_k, self.sum_k)\n if self.o_win[0] == 0:\n return -1\n return (self.sum_m_k - self.sum_k) // (self.m - self.k*2)\n"},{"alpha_fraction":0.424119234085083,"alphanum_fraction":0.4444444477558136,"avg_line_length":35,"blob_id":"1c2c7e16a4288925ad9584a7616fc695c10512c3","content_id":"3cd6bb10b4cb762b901f860458c89106ea34a3fb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":738,"license_type":"no_license","max_line_length":77,"num_lines":20,"path":"/lc1793_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maximumScore(self, nums: List[int], k: int) -> int:\n left, right = k, k\n rslt = nums[k]\n tempmin = nums[k]\n n = len(nums)\n while left > 0 or right < n-1:\n if left == 0 or (right < n-1 and nums[left-1] < nums[right+1]):\n right += 1\n tempmin = min(nums[right], tempmin)\n elif right == n-1 or (left > 0 and nums[left-1] > nums[right+1]):\n left -= 1\n tempmin = min(nums[left], tempmin)\n else:\n left -= 1\n right += 1\n tempmin = min(nums[right], tempmin)\n\n rslt = max(rslt, (right-left+1)*tempmin)\n return rslt\n\n \n"},{"alpha_fraction":0.4253164529800415,"alphanum_fraction":0.4430379867553711,"avg_line_length":31.91666603088379,"blob_id":"d8ce35bf62da923c4cb3b47ae81a143db439fe09","content_id":"454fcd0c085081c6ce86c859d287a10b80795a96","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":395,"license_type":"no_license","max_line_length":62,"num_lines":12,"path":"/lc162_bs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findPeakElement(self, nums: List[int]) -> int:\n left, right = 0, len(nums)-1\n while left <= right:\n mid = (left + right)//2\n if mid < len(nums)-1 and nums[mid] < nums[mid+1]:\n left = mid+1\n else:\n right = mid\n if (left + right) //2 == mid:\n break\n return left\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5480349063873291,\n \"alphanum_fraction\": 0.5545851588249207,\n \"avg_line_length\": 34.230770111083984,\n \"blob_id\": \"d347fe015c11150c938d8eb731dd835a73f71e3d\",\n \"content_id\": \"afa85aebd028822adb5938b6b3cc6cb3330ae670\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 458,\n \"license_type\": \"no_license\",\n \"max_line_length\": 76,\n \"num_lines\": 13,\n \"path\": \"/lc327_sortedlist.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from sortedcontainers import SortedList\\nclass Solution:\\n def countRangeSum(self, nums: List[int], lower: int, upper: int) -> int:\\n maps = SortedList([0])\\n rslt, cumsum = 0, 0\\n for num in nums:\\n cumsum += num\\n left = maps.bisect_left(cumsum-upper)\\n right = maps.bisect_right(cumsum-lower)\\n if right > left:\\n rslt += right-left\\n maps.add(cumsum)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.3951434791088104,\n \"alphanum_fraction\": 0.40176600217819214,\n \"avg_line_length\": 29.200000762939453,\n \"blob_id\": \"3a201150172241c9b4b4c303dc9346716847dbd8\",\n \"content_id\": \"5417dcd7963241d015d6a0af5802b36404c00d6d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 453,\n \"license_type\": \"no_license\",\n \"max_line_length\": 51,\n \"num_lines\": 15,\n \"path\": \"/lc1510_minmax.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def winnerSquareGame(self, n: int) -> bool:\\n memo = {}\\n def dfs(n):\\n if n in memo:\\n return memo[n]\\n if n == 0:\\n return False\\n for i in range(1, int(math.sqrt(n))+1):\\n if i*i <= n and not dfs(n-i*i):\\n memo[n] = True\\n return True\\n memo[n] = False\\n return False\\n return dfs(n)\\n\"\n },\n {\n \"alpha_fraction\": 0.2914072275161743,\n \"alphanum_fraction\": 0.30510586500167847,\n \"avg_line_length\": 31.1200008392334,\n \"blob_id\": \"d2cf0ea4453746c31bea86dd3150df8e3b295532\",\n \"content_id\": \"47bb21048afdb4d0c9c946ed5f3a72c219302a97\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 803,\n \"license_type\": \"no_license\",\n \"max_line_length\": 54,\n \"num_lines\": 25,\n \"path\": \"/lc1028_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def recoverFromPreorder(self, S: str) -> TreeNode:\\n currD, num = 0, \\\"\\\"\\n root = None\\n stk = []\\n for c in S+'-':\\n if c == \\\"-\\\":\\n if not num:\\n currD += 1\\n else:\\n curr = TreeNode(int(num))\\n if not root:\\n root = curr\\n elif stk[-1][1] < currD:\\n stk[-1][0].left = curr\\n else:\\n while stk[-1][1] >= currD:\\n stk.pop()\\n stk[-1][0].right = curr\\n stk.append((curr, currD))\\n num = \\\"\\\"\\n currD = 1\\n else:\\n num += c\\n return root\\n\"\n },\n {\n \"alpha_fraction\": 0.4420772194862366,\n \"alphanum_fraction\": 0.4607190489768982,\n \"avg_line_length\": 45.9375,\n \"blob_id\": \"329184f99068c8c6c2a68302731543954c9699c3\",\n \"content_id\": \"1ed8a1be893ed796df20d53b043e8d75a10e21db\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 751,\n \"license_type\": \"no_license\",\n \"max_line_length\": 136,\n \"num_lines\": 16,\n \"path\": \"/lc1654_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minimumJumps(self, forbidden: List[int], a: int, b: int, x: int) -> int:\\n dque, seen1, seen2, rslt, limit = collections.deque([(0, 1)]), set(forbidden), set(forbidden), 0, max(x, max(forbidden)) + a + b\\n while dque:\\n for _ in range(len(dque)):\\n cur, last_forward = dque.popleft()\\n if cur == x:\\n return rslt\\n if cur + a <= limit and cur + a not in seen1:\\n seen1.add(cur + a)\\n dque.append((cur + a, 1))\\n if cur - b > 0 and cur - b not in seen2 and last_forward:\\n seen2.add(cur - b)\\n dque.append((cur - b, 0))\\n rslt += 1\\n return -1\\n\"\n },\n {\n \"alpha_fraction\": 0.4156534969806671,\n \"alphanum_fraction\": 0.43009117245674133,\n \"avg_line_length\": 41.45161437988281,\n \"blob_id\": \"fc1e99a275940910963950d2e2e1bed98048fcc4\",\n \"content_id\": \"bc32ac165a32b9a0f09448c7a06173f017c1a81f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1316,\n \"license_type\": \"no_license\",\n \"max_line_length\": 109,\n \"num_lines\": 31,\n \"path\": \"/lc675_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def cutOffTree(self, forest: List[List[int]]) -> int:\\n nrow, ncol, rslt = len(forest), len(forest[0]), 0\\n heap = []\\n for i in range(nrow):\\n for j in range(ncol):\\n if forest[i][j] > 0:\\n heapq.heappush(heap, (forest[i][j], i, j))\\n def bfs(nextval, nextx, nexty, currx, curry):\\n dque, seen, step = collections.deque([(currx, curry)]), {(currx, curry)}, 0\\n while dque:\\n size = len(dque)\\n for _ in range(size):\\n x,y = dque.popleft()\\n if x == nextx and y == nexty:\\n return step\\n for nx, ny in [(x+1, y), (x-1, y), (x, y+1), (x, y-1)]:\\n if 0 <= nx < nrow and 0 <= ny < ncol and forest[nx][ny] > 0 and (nx, ny) not in seen:\\n seen.add((nx, ny))\\n dque.append((nx, ny))\\n step += 1\\n return -1\\n\\n currx, curry, visited = 0, 0, {(0, 0)}\\n while heap:\\n nextval, nextx, nexty = heapq.heappop(heap)\\n step = bfs(nextval, nextx, nexty, currx, curry)\\n if step == -1: return -1\\n rslt += step\\n currx, curry = nextx, nexty\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.5039028525352478,\n \"alphanum_fraction\": 0.5203816294670105,\n \"avg_line_length\": 31.02777862548828,\n \"blob_id\": \"f195a63cc4ab0f7832ec1b00b02f8ce795acb76b\",\n \"content_id\": \"3d558678b9077ff2d8bea588e82980fa816f6763\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1153,\n \"license_type\": \"no_license\",\n \"max_line_length\": 77,\n \"num_lines\": 36,\n \"path\": \"/lc395_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#divide and conquer\\nclass Solution:\\n def longestSubstring(self, s: str, k: int) -> int:\\n counts = collections.Counter(s)\\n for t in counts:\\n if counts[t] < k:\\n return max(self.longestSubstring(ss, k) for ss in s.split(t))\\n return len(s)\\n\\n#sliding window\\nclass Solution:\\n def longestSubstring(self, s, k):\\n count = 0\\n for i in range(1, 27):\\n count = max(count, self.helper(s, k, i))\\n return count\\n\\n def helper(self, s, k, numUniqueTarget):\\n start = end = numUnique = numNoLessThanK = count = 0\\n chMap = [0]*128\\n\\n while end < len(s):\\n if chMap[ord(s[end])] == 0: numUnique += 1\\n chMap[ord(s[end])] += 1\\n if chMap[ord(s[end])] == k: numNoLessThanK += 1\\n end += 1\\n\\n while numUnique > numUniqueTarget:\\n if chMap[ord(s[start])] == k: numNoLessThanK -= 1\\n chMap[ord(s[start])] -= 1\\n if chMap[ord(s[start])] == 0: numUnique -= 1\\n start += 1\\n\\n if numUnique == numNoLessThanK: count = max(count, end-start)\\n\\n return count\\n\"\n },\n {\n \"alpha_fraction\": 0.470016211271286,\n \"alphanum_fraction\": 0.4797406792640686,\n \"avg_line_length\": 35.29411697387695,\n \"blob_id\": \"2f36fa1f14f9627fe23063fc90f8b124a898e7d4\",\n \"content_id\": \"59b7854d460439836ad616650c4003d440dc499f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 617,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 17,\n \"path\": \"/lc103_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def zigzagLevelOrder(self, root: TreeNode) -> List[List[int]]:\\n if not root:\\n return []\\n queue, left, rslt = collections.deque([root]), 1, []\\n while queue:\\n lq = len(queue)\\n temp = [0]*lq\\n for i in range(lq):\\n curr = queue.popleft()\\n if left == 1: temp[i] = curr.val\\n else:temp[lq-i-1] = curr.val\\n if curr.left: queue.append(curr.left)\\n if curr.right: queue.append(curr.right)\\n left = (left+1)%2\\n rslt.append(temp)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4793578088283539,\n \"alphanum_fraction\": 0.4931192696094513,\n \"avg_line_length\": 38.6363639831543,\n \"blob_id\": \"e68c093c341c44fb558fe7fb82d17744ee8e7980\",\n \"content_id\": \"8da7356d5e183a7ed54283fdfd9edc2cedae404f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 436,\n \"license_type\": \"no_license\",\n \"max_line_length\": 75,\n \"num_lines\": 11,\n \"path\": \"/lc494_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findTargetSumWays(self, nums: List[int], S: int) -> int:\\n memo = collections.defaultdict(int)\\n def dfs(i, curr):\\n if (i, curr) in memo:\\n return memo[(i, curr)]\\n if i == len(nums):\\n return 1 if curr == S else 0\\n memo[(i, curr)] = dfs(i+1, curr+nums[i])+dfs(i+1, curr-nums[i])\\n return memo[(i, curr)]\\n return dfs(0, 0)\\n\"\n },\n {\n \"alpha_fraction\": 0.43008315563201904,\n \"alphanum_fraction\": 0.448223739862442,\n \"avg_line_length\": 44.620689392089844,\n \"blob_id\": \"1e767327d4f1bc7f4db042487ade73f4e9cc03fb\",\n \"content_id\": \"cbd6ecdca0ecd6f215235f4164a408a86564e31e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1323,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 29,\n \"path\": \"/lc1810_dijkstra.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution(object):\\n def findShortestPath(self, master: 'GridMaster') -> int:\\n directions = {'U':'D', 'D':'U', 'L':'R', 'R':'L'}\\n steps = {'U':(-1, 0), 'D':(1, 0), 'L':(0, -1), 'R':(0, 1)}\\n self.target = None\\n graph = {(0, 0): 0}\\n # backtracking build graph\\n def backtracking(x, y):\\n if master.isTarget(): self.target = (x, y)\\n for d in directions:\\n nx, ny = x + steps[d][0], y+steps[d][1]\\n if (nx, ny) in graph: continue\\n if master.canMove(d):\\n graph[(nx, ny)] = master.move(d)\\n backtracking(nx, ny)\\n master.move(directions[d])\\n backtracking(0, 0)\\n if self.target is None: return -1\\n # bfs find rslt\\n heap = [[0, 0, 0]] # cost, x, y\\n seen = collections.defaultdict(lambda: float(\\\"inf\\\"))\\n while heap:\\n cost, x, y = heapq.heappop(heap)\\n if (x, y) == self.target: return cost\\n for nx, ny in [(x-1, y), (x+1, y), (x, y-1), (x, y+1)]:\\n if (nx, ny) in graph and cost + graph[(nx, ny)] < seen[(nx, ny)] :\\n seen[(nx, ny)] = cost + graph[(nx, ny)]\\n heapq.heappush(heap, (cost + graph[(nx, ny)], nx, ny))\\n return -1\\n\"\n },\n {\n \"alpha_fraction\": 0.4340527653694153,\n \"alphanum_fraction\": 0.4508393406867981,\n \"avg_line_length\": 33.75,\n \"blob_id\": \"13ae9507ba2df4bad736fe9333e05e9e39c32936\",\n \"content_id\": \"975d7e989e249480027add086503c25c5a04ace9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 834,\n \"license_type\": \"no_license\",\n \"max_line_length\": 73,\n \"num_lines\": 24,\n \"path\": \"/lc403_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def canCross(self, stones: List[int]) -> bool:\\n n = len(stones)\\n dp = [False]*n\\n dp[0] = True\\n cand_steps =collections.defaultdict(set)\\n cand_steps[0] = {1}\\n for i in range(1, n):\\n for j in range(i):\\n need_step = stones[i] - stones[j]\\n if dp[j] and need_step in cand_steps[j]:\\n dp[i] = True\\n cand_steps[i]|= {need_step, need_step+1, need_step-1}\\n return dp[-1]\\n\\n def canCross(self, stones):\\n maps = {x: set() for x in stones}\\n maps[0].add(0)\\n for x in stones[:-1]:\\n for j in maps[x]:\\n for k in range(max(1, j-1), j+2):\\n if x + k in maps:\\n maps[x+k].add(k)\\n return bool(maps[stones[-1]])\\n\"\n },\n {\n \"alpha_fraction\": 0.3972148597240448,\n \"alphanum_fraction\": 0.4118037223815918,\n \"avg_line_length\": 38.68421173095703,\n \"blob_id\": \"38a12fed015208ae28761dc0a18ef23bd77f0d34\",\n \"content_id\": \"6feeca8b38518ed917366d856855bc4dff12dbd7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1508,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 38,\n \"path\": \"/lc1728_minmax.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def canMouseWin(self, grid: List[str], catJump: int, mouseJump: int) -> bool:\\n nrow, ncol, thred = len(grid), len(grid[0]), 0\\n for i in range(nrow):\\n for j in range(ncol):\\n if grid[i][j] == \\\"C\\\": catloc = (i, j)\\n if grid[i][j] == \\\"M\\\": mouseloc = (i, j)\\n if grid[i][j] == \\\"F\\\": foodloc = (i, j)\\n if grid[i][j] != \\\"#\\\": thred += 1\\n @lru_cache(None)\\n def dfs(cat, mouse, step):\\n if cat == mouse or cat == foodloc or step > 2*thred:\\n return False\\n if mouse == foodloc:\\n return True\\n if step%2 == 0:\\n for nmouse in nextstep(mouse, mouseJump):\\n if dfs(cat, nmouse, step + 1):\\n return True\\n return False\\n else:\\n for ncat in nextstep(cat, catJump):\\n if not dfs(ncat, mouse, step+1):\\n return False\\n return True\\n\\n def nextstep(curr, step):\\n rslt = []\\n for dx, dy in [(1,0), (-1, 0), (0, 1), (0, -1)]:\\n for jump in range(step+1):\\n nx, ny = curr[0]+dx*jump, curr[1]+dy*jump\\n if 0 <= nx < nrow and 0 <= ny < ncol and grid[nx][ny] != \\\"#\\\":\\n rslt.append((nx, ny))\\n else:\\n break\\n return rslt\\n\\n return dfs(catloc, mouseloc, 0)\\n\"\n },\n {\n \"alpha_fraction\": 0.43044188618659973,\n \"alphanum_fraction\": 0.4369885325431824,\n \"avg_line_length\": 32.94444274902344,\n \"blob_id\": \"97a1bfefcddace7bc2147cea4281f8e7dcaf7c3d\",\n \"content_id\": \"ff1921bbfb0182276ed2c547e1f18252263ba36f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 611,\n \"license_type\": \"no_license\",\n \"max_line_length\": 84,\n \"num_lines\": 18,\n \"path\": \"/lc1300_bs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findBestValue(self, arr: List[int], target: int) -> int:\\n left, right = target//len(arr), max(arr)\\n def check(mid):\\n rslt = 0\\n for num in arr:\\n if num < mid:\\n rslt += num\\n else:\\n rslt += mid\\n return rslt\\n while left <= right:\\n mid = (left + right)//2\\n if check(mid) <= target:\\n left = mid + 1\\n else:\\n right = mid - 1\\n return left if abs(check(left)-target) < abs(check(right)-target) else right\\n\"\n },\n {\n \"alpha_fraction\": 0.42088091373443604,\n \"alphanum_fraction\": 0.4274061918258667,\n \"avg_line_length\": 35.05882263183594,\n \"blob_id\": \"972a581610e1d5edff78ff1ab0f7db0a39daa630\",\n \"content_id\": \"d8851bfea52fc36f729981c40738b679981419c8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 613,\n \"license_type\": \"no_license\",\n \"max_line_length\": 78,\n \"num_lines\": 17,\n \"path\": \"/lc140_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def wordBreak(self, s: str, wordDict: List[str]) -> List[str]:\\n if not wordDict: return []\\n n, wordDict, ml = len(s), set(wordDict), len(max(wordDict, key = len))\\n @lru_cache(None)\\n def dfs(i):\\n if i == n:\\n return [[]]\\n rslt = []\\n for j in range(i, n):\\n if j-i < ml and s[i:j+1] in wordDict:\\n temp = dfs(j+1)\\n for t in temp:\\n rslt.append([s[i:j+1]]+t)\\n return rslt\\n temp = dfs(0)\\n return [\\\" \\\".join(t) for t in temp]\\n\"\n },\n {\n \"alpha_fraction\": 0.38760632276535034,\n \"alphanum_fraction\": 0.42891860008239746,\n \"avg_line_length\": 30.653846740722656,\n \"blob_id\": \"bc8d555b2398bb91009c89b28e5ab7e57c8d7738\",\n \"content_id\": \"2534016c1dabf6bfeb5bb533a9b8e19e20b854eb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 823,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 26,\n \"path\": \"/lc850_array.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def rectangleArea(self, rectangles: List[List[int]]) -> int:\\n events = []\\n for x1, y1, x2, y2 in rectangles:\\n events.append((y1, 1, x1, x2))\\n events.append((y2, -1, x1, x2))\\n events.sort()\\n\\n def cal_x_len():\\n l_x, lastx = 0, 0\\n for x1, x2 in active:\\n lastx = max(lastx, x1)\\n l_x += max(0, x2 -lastx)\\n lastx = max(lastx, x2)\\n return l_x\\n active = []\\n lasty = events[0][0]\\n rslt = 0\\n for y, status, x1, x2 in events:\\n rslt += cal_x_len()*(y-lasty)\\n if status == 1:\\n bisect.insort(active, (x1, x2))\\n else:\\n active.remove((x1, x2))\\n lasty = y\\n return rslt % (10**9 + 7)\\n\"\n },\n {\n \"alpha_fraction\": 0.25806450843811035,\n \"alphanum_fraction\": 0.28535979986190796,\n \"avg_line_length\": 25.866666793823242,\n \"blob_id\": \"43917e1401edc0824b338f45741a612d022cc38a\",\n \"content_id\": \"23082c089e53c7f58624f8caeda8e0c2b2fa88ec\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 403,\n \"license_type\": \"no_license\",\n \"max_line_length\": 43,\n \"num_lines\": 15,\n \"path\": \"/lc1541_str.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minInsertions(self, s: str) -> int:\\n bal, rslt = 0, 0\\n for c in s:\\n if c == \\\"(\\\":\\n if bal % 2 == 1:\\n rslt += 1\\n bal -= 1\\n bal += 2\\n else:\\n bal -= 1\\n if bal < 0:\\n rslt += 1\\n bal += 2\\n return rslt + bal\\n\"\n },\n {\n \"alpha_fraction\": 0.44805195927619934,\n \"alphanum_fraction\": 0.4588744640350342,\n \"avg_line_length\": 36.75,\n \"blob_id\": \"a59ba58760e99e11d9437f2bd2d8583ea291951c\",\n \"content_id\": \"eeeef588a565ced060783710049419b35fff958e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 462,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 12,\n \"path\": \"/lc1340_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxJumps(self, arr: List[int], d: int) -> int:\\n n = len(arr)\\n @lru_cache(None)\\n def dfs(i):\\n rslt = 1\\n for direction in [-1, 1]:\\n for j in range(i+direction, i+d*direction+direction, direction):\\n if j < 0 or j >= n or arr[j] >= arr[i]: break\\n rslt = max(rslt, dfs(j)+1)\\n return rslt\\n return max(map(dfs, range(n)))\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.489130437374115,\n \"alphanum_fraction\": 0.49275362491607666,\n \"avg_line_length\": 38.42856979370117,\n \"blob_id\": \"b179d8616d77e0c28113b1a90a763eca56a6346a\",\n \"content_id\": \"5ca46de261c412bc61b63106e8d632a51c1b7aaa\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 552,\n \"license_type\": \"no_license\",\n \"max_line_length\": 78,\n \"num_lines\": 14,\n \"path\": \"/lc743_graph.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def networkDelayTime(self, times: List[List[int]], N: int, K: int) -> int:\\n graph = collections.defaultdict(list)\\n for s, t, w in times:\\n graph[s].append((w, t))\\n hp, dist = [(0, K)], {}\\n while hp:\\n w, t = heapq.heappop(hp)\\n if t in dist: continue\\n dist[t] = w\\n for wnext, tnext in graph[t]:\\n if tnext not in dist:\\n heapq.heappush(hp, (wnext + w, tnext))\\n return max(dist.values()) if len(dist) == N else -1\\n\"\n },\n {\n \"alpha_fraction\": 0.42007797956466675,\n \"alphanum_fraction\": 0.4376218318939209,\n \"avg_line_length\": 26.7297306060791,\n \"blob_id\": \"698f59747869bc87136df7822ada29e91ff6ce0e\",\n \"content_id\": \"5d619d1516af897e5cc44afe041dbd71dde9124c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1026,\n \"license_type\": \"no_license\",\n \"max_line_length\": 77,\n \"num_lines\": 37,\n \"path\": \"/lc315_segtree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Segtree:\\n def __init__(self, n):\\n self.n = n\\n self.seg_tree = [0]*(2*n)\\n\\n def update(self, idx):\\n idx = self.n+idx\\n self.seg_tree[idx] += 1\\n while idx > 1:\\n self.seg_tree[idx>>1] = self.seg_tree[idx] + self.seg_tree[idx^1]\\n idx >>= 1\\n\\n def sumRange(self, idx):\\n l = self.n\\n r = self.n + idx\\n rslt = -self.seg_tree[r]\\n while l <= r:\\n if l&1:\\n rslt += self.seg_tree[l]\\n l += 1\\n if r&1 == 0:\\n rslt += self.seg_tree[r]\\n r -= 1\\n l >>= 1\\n r >>= 1\\n return rslt\\n\\nclass Solution:\\n def countSmaller(self, nums: List[int]) -> List[int]:\\n maps = {val:i for i, val in enumerate(sorted(nums))}\\n n = len(nums)\\n segtree = Segtree(n)\\n rslt = [0]*n\\n for i in range(n-1, -1, -1):\\n segtree.update(maps[nums[i]])\\n rslt[i] = segtree.sumRange(maps[nums[i]])\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4312500059604645,\n \"alphanum_fraction\": 0.45781248807907104,\n \"avg_line_length\": 39,\n \"blob_id\": \"6f880938edd06562b84816f77a155dcb6db8fb3b\",\n \"content_id\": \"f2ecb321a80541fd4830a06b494863763c7613ea\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 640,\n \"license_type\": \"no_license\",\n \"max_line_length\": 124,\n \"num_lines\": 16,\n \"path\": \"/lc329_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def longestIncreasingPath(self, matrix: List[List[int]]) -> int:\\n if not matrix or not matrix[0]:\\n return 0\\n @lru_cache(None)\\n def dfs(i, j):\\n temp = 1\\n for x, y in [(-1, 0), (1, 0), (0, -1), (0, 1)]:\\n if i+x >= 0 and i+x < len(matrix) and j+y >= 0 and j+y < len(matrix[0]) and matrix[i+x][j+y] > matrix[i][j]:\\n temp = max(temp, dfs(i+x, j+y)+1)\\n return temp\\n rslt = 0\\n for i in range(len(matrix)):\\n for j in range(len(matrix[0])):\\n rslt = max(rslt, dfs(i, j))\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.3567010164260864,\n \"alphanum_fraction\": 0.38762885332107544,\n \"avg_line_length\": 31.33333396911621,\n \"blob_id\": \"ba6a58358cfd4e536253b6cd98b62787c71b4153\",\n \"content_id\": \"7a99fc827e571d9d0696ee6be3c2ff688b69beb1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 485,\n \"license_type\": \"no_license\",\n \"max_line_length\": 73,\n \"num_lines\": 15,\n \"path\": \"/lc526_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def countArrangement(self, n: int) -> int:\\n ini = (1 << n)-1\\n @lru_cache(None)\\n def dfs(i, bitmask):\\n if i == n+1:\\n return 1\\n elif bitmask == 0:\\n return 0\\n rslt = 0\\n for j in range(n):\\n if (1 << j) & bitmask and ((j+1)%i == 0 or i%(j+1) == 0):\\n rslt += dfs(i+1, bitmask & (~(1 << j)))\\n return rslt\\n return dfs(1, ini)\\n\"\n },\n {\n \"alpha_fraction\": 0.516581654548645,\n \"alphanum_fraction\": 0.5255101919174194,\n \"avg_line_length\": 34.6363639831543,\n \"blob_id\": \"fbe54e14935e7199004d57824d7619163926b04b\",\n \"content_id\": \"b2a841a71a85820ce0609a0189e88234a479450d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 784,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 22,\n \"path\": \"/lc687_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Definition for a binary tree node.\\n# class TreeNode:\\n# def __init__(self, val=0, left=None, right=None):\\n# self.val = val\\n# self.left = left\\n# self.right = right\\nclass Solution:\\n def longestUnivaluePath(self, root: TreeNode) -> int:\\n self.rslt = 0\\n def helper(root):\\n if not root: return 0\\n left = helper(root.left)\\n right = helper(root.right)\\n left_l, right_l = 0, 0\\n if root.left and root.left.val == root.val:\\n left_l = left + 1\\n if root.right and root.right.val == root.val:\\n right_l = right + 1\\n self.rslt = max(self.rslt, left_l+right_l)\\n return max(left_l, right_l)\\n helper(root)\\n return self.rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.416570782661438,\n \"alphanum_fraction\": 0.4303797483444214,\n \"avg_line_length\": 32.42307662963867,\n \"blob_id\": \"a8915df713aea16aa6fa0f6128bfbbb7f7bc42e3\",\n \"content_id\": \"bbb6a30fcd3dc32347e4621ee61c56e1c109c70e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 869,\n \"license_type\": \"no_license\",\n \"max_line_length\": 53,\n \"num_lines\": 26,\n \"path\": \"/lc1140_minmax.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def stoneGameII(self, piles: List[int]) -> int:\\n memo = {}\\n def dfs(i, m):\\n if (i, m) in memo:\\n return memo[(i, m)]\\n if i+2*m >= len(piles):\\n return sum(piles[i:])\\n rslt = float(\\\"inf\\\")\\n for j in range(i+1, i+2*m+1):\\n rslt = min(rslt, dfs(j, max(j-i, m)))\\n memo[(i, m)] = sum(piles[i:]) - rslt\\n return memo[(i, m)]\\n return dfs(0, 1)\\n\\nclass Solution:\\n def stoneGameII(self, piles: List[int]) -> int:\\n @lru_cache(None)\\n def dfs(i, m):\\n if i+2*m >= len(piles):\\n return sum(piles[i:])\\n rslt = float(\\\"inf\\\")\\n for j in range(i+1, i+2*m+1):\\n rslt = min(rslt, dfs(j, max(j-i, m)))\\n return sum(piles[i:]) - rslt\\n return dfs(0, 1)\\n\"\n },\n {\n \"alpha_fraction\": 0.45179063081741333,\n \"alphanum_fraction\": 0.4655647277832031,\n \"avg_line_length\": 29.25,\n \"blob_id\": \"9d3f41eea078167e1c4f19cb6eb03bd719525fb0\",\n \"content_id\": \"b4d54361dd1121578bbb67bc6f3591f54a6a9d5a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 363,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 12,\n \"path\": \"/lc1564_greedy.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxBoxesInWarehouse(self, boxes: List[int], warehouse: List[int]) -> int:\\n boxes.sort()\\n count = 0\\n idx = 0\\n for box in boxes[::-1]:\\n if warehouse[idx] >= box:\\n count += 1\\n idx += 1\\n if idx >= len(warehouse):\\n return count\\n return count\\n\"\n },\n {\n \"alpha_fraction\": 0.46716004610061646,\n \"alphanum_fraction\": 0.47641071677207947,\n \"avg_line_length\": 29.885713577270508,\n \"blob_id\": \"5abd3fce38f3297ec09d4380dd2cba2acb5db51a\",\n \"content_id\": \"ff5f2e3b412b0d1ab3778e0f01aad36373555fa4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1081,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 35,\n \"path\": \"/lc685_unionfind.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class DSU:\\n def __init__(self, n):\\n self.parents = list(range(n))\\n self.size = [1]*n\\n def find(self, p):\\n while self.parents[p] != p:\\n self.parents[p] = self.parents[self.parents[p]]\\n p = self.parents[p]\\n return p\\n def union(self, p, q):\\n pp, pq = self.find(p), self.find(q)\\n if pp == pq: return True\\n if self.size[pp] < self.size[pq]:\\n pp, pq = pq, pp\\n self.parents[pq] = pp\\n self.size[pp] += self.size[pq]\\n return False\\n\\nclass Solution:\\n def findRedundantDirectedConnection(self, edges: List[List[int]]) -> List[int]:\\n cand1, cand2, parents = None, None, {}\\n for u, v in edges:\\n if v in parents:\\n cand1 = [parents[v], v]\\n cand2 = [u, v]\\n break\\n parents[v] = u\\n\\n dsu = DSU(len(edges)+1)\\n for u, v in edges:\\n if cand2 == [u, v]: continue\\n if dsu.union(u, v):\\n if cand1: return cand1\\n return [u, v]\\n return cand2\\n\"\n },\n {\n \"alpha_fraction\": 0.431314617395401,\n \"alphanum_fraction\": 0.4549483060836792,\n \"avg_line_length\": 26.079999923706055,\n \"blob_id\": \"9566c4e7f1fe2479ca2819b2dddc589c023532af\",\n \"content_id\": \"f622502f973a736e7efbe935ca5ccfe6a50195b5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 677,\n \"license_type\": \"no_license\",\n \"max_line_length\": 59,\n \"num_lines\": 25,\n \"path\": \"/lc1442_bit.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def countTriplets(self, arr: List[int]) -> int:\\n rslt, n = 0, len(arr)\\n prefix = [0]*(n+1)\\n for i in range(n): prefix[i+1] = prefix[i] ^ arr[i]\\n for left in range(n-1):\\n for right in range(left+1, n):\\n if prefix[right+1]^prefix[left] == 0:\\n rslt += right-left\\n return rslt\\n\\n\\n\\n\\n\\nclass Solution:\\n def countTriplets(self, A):\\n res = cur = 0\\n count = {0: [1, 0]}\\n for i, a in enumerate(A):\\n cur ^= a\\n n, total = count.get(cur, [0, 0])\\n res += i * n - total\\n count[cur] = [n + 1, total + i + 1]\\n return res\\n\"\n },\n {\n \"alpha_fraction\": 0.3865671753883362,\n \"alphanum_fraction\": 0.4029850661754608,\n \"avg_line_length\": 30.904762268066406,\n \"blob_id\": \"1e121e386117baded936bb2ded0370efb1ee18dc\",\n \"content_id\": \"09621e42fbded19807ecf0900a110f5a923875b8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 670,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 21,\n \"path\": \"/lc996_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def numSquarefulPerms(self, nums: List[int]) -> int:\\n count = collections.Counter(nums)\\n maps = {x: {y for y in count if int((x+y)**0.5)**2 == x+y} for x in count}\\n l = len(nums)\\n def bk(curr, curr_c):\\n if curr_c != 0:\\n rslt = 0\\n count[curr] -= 1\\n for nxt in maps[curr]:\\n if count[nxt]:\\n rslt += bk(nxt, curr_c-1)\\n count[curr] += 1\\n else:\\n rslt = 1\\n return rslt\\n rslt = 0\\n for c in count:\\n t = bk(c, l-1)\\n rslt += t\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.49809885025024414,\n \"alphanum_fraction\": 0.5107731223106384,\n \"avg_line_length\": 31.875,\n \"blob_id\": \"c6d872a56efde6ff7d5eddcc309ea71b824bdee8\",\n \"content_id\": \"5f1b1e0adff20cb076a2b5b3e7a7be9f4e9c75b4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 789,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 24,\n \"path\": \"/lc1214_bt.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Definition for a binary tree node.\\n# class TreeNode:\\n# def __init__(self, val=0, left=None, right=None):\\n# self.val = val\\n# self.left = left\\n# self.right = right\\nclass Solution:\\n def twoSumBSTs(self, root1: TreeNode, root2: TreeNode, target: int) -> bool:\\n stk, pool = [], set()\\n while stk or root1:\\n while root1:\\n stk.append(root1)\\n root1 = root1.left\\n temp = stk.pop()\\n pool.add(temp.val)\\n root1 = temp.right\\n stk = [root2]\\n while stk:\\n temp = stk.pop()\\n if target-temp.val in pool:\\n return True\\n if temp.right: stk.append(temp.right)\\n if temp.left: stk.append(temp.left)\\n return False\\n\"\n },\n {\n \"alpha_fraction\": 0.4743589758872986,\n \"alphanum_fraction\": 0.4839743673801422,\n \"avg_line_length\": 24.25,\n \"blob_id\": \"93a14ae806c0064a739cdaa0d9b773fceff78d77\",\n \"content_id\": \"9b036386de200c68be0d2ae7a737d070de3c5c98\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 312,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 12,\n \"path\": \"/lc1402_greedy.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxSatisfaction(self, satisfaction: List[int]) -> int:\\n satisfaction.sort(reverse=True)\\n curr = 0\\n rslt = 0\\n for val in satisfaction:\\n curr += val\\n if curr < 0:\\n break\\n rslt += curr\\n\\n return rslt\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.3292181193828583,\n \"alphanum_fraction\": 0.35185185074806213,\n \"avg_line_length\": 31.399999618530273,\n \"blob_id\": \"4927d0a7826bc84d24f5881ccd7bb63dfa412625\",\n \"content_id\": \"09f51eeedeeb3dc17a1224e374818556ff4d84f7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 486,\n \"license_type\": \"no_license\",\n \"max_line_length\": 48,\n \"num_lines\": 15,\n \"path\": \"/lc443_string.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def compress(self, chars: List[str]) -> int:\\n rslt, cnt, left = 0, 0, 0\\n for i, ch in enumerate(chars+[\\\")\\\"]):\\n if i > 0 and ch != chars[i-1]:\\n chars[left] = chars[i-1]\\n left += 1\\n if cnt > 1:\\n for t in str(cnt):\\n chars[left] = t\\n left += 1\\n cnt = 1\\n else:\\n cnt += 1\\n return left\\n\"\n },\n {\n \"alpha_fraction\": 0.44989773631095886,\n \"alphanum_fraction\": 0.4621676802635193,\n \"avg_line_length\": 33.92856979370117,\n \"blob_id\": \"47fb4b02413f86ee816d32e394c03ad0b0666c12\",\n \"content_id\": \"7a822dadca3981d751762f63fe914a3d5323f2ed\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 489,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 14,\n \"path\": \"/lc1052_sw.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxSatisfied(self, customers: List[int], grumpy: List[int], X: int) -> int:\\n if X > len(customers):\\n return sum(customers)\\n i, curr, wind, cumsum= 0, 0, 0, 0\\n for c, g in zip(customers, grumpy):\\n cumsum += (1-g)*c\\n if i < X:\\n curr += g*c\\n else:\\n curr += g*c - grumpy[i-X]*customers[i-X]\\n wind = max(wind, curr)\\n i += 1\\n return cumsum + wind\\n\"\n },\n {\n \"alpha_fraction\": 0.352769672870636,\n \"alphanum_fraction\": 0.38678327202796936,\n \"avg_line_length\": 37.11111068725586,\n \"blob_id\": \"6ff9e12c00a82362dd2b60962848d75c83837b17\",\n \"content_id\": \"655e9f50624ee83d390a076037db008235a1f85d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1029,\n \"license_type\": \"no_license\",\n \"max_line_length\": 113,\n \"num_lines\": 27,\n \"path\": \"/lc289_array.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def gameOfLife(self, board: List[List[int]]) -> None:\\n \\\"\\\"\\\"\\n Do not return anything, modify board in-place instead.\\n \\\"\\\"\\\"\\n nrow, ncol = len(board), len(board[0])\\n def helper(i, j):\\n # -2: change from 1 to 0\\n # -1: change from 0 to 1\\n cl = 0\\n for x, y in [(i-1, j-1), (i-1, j), (i-1, j+1), (i, j -1), (i, j+1),(i+1, j-1), (i+1, j), (i+1, j+1)]:\\n if x < 0 or x >= nrow or y < 0 or y >= ncol:\\n continue\\n cl += 1 if board[x][y] in (1, -2) else 0\\n return cl\\n\\n for i in range(nrow):\\n for j in range(ncol):\\n cl = helper(i, j)\\n if board[i][j] == 1 and (cl < 2 or cl > 3):\\n board[i][j] = -2\\n if board[i][j] == 0 and cl == 3:\\n board[i][j] = -1\\n for i in range(nrow):\\n for j in range(ncol):\\n if board[i][j] < 0:\\n board[i][j] += 2\\n\"\n },\n {\n \"alpha_fraction\": 0.5032397508621216,\n \"alphanum_fraction\": 0.5032397508621216,\n \"avg_line_length\": 32.07143020629883,\n \"blob_id\": \"baa3542686b62558bd71670a51ed9b640f8635ee\",\n \"content_id\": \"ffcbadaffc7e9ad6e1f6c77955ed415b3dbbb10f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 463,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 14,\n \"path\": \"/lc199_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def rightSideView(self, root: TreeNode) -> List[int]:\\n if not root:\\n return []\\n queue = collections.deque([root])\\n rslt = []\\n while queue:\\n lth = len(queue)\\n for _ in range(lth):\\n temp = queue.popleft()\\n if temp.left: queue.append(temp.left)\\n if temp.right: queue.append(temp.right)\\n rslt.append(temp.val)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.45728641748428345,\n \"alphanum_fraction\": 0.4673366844654083,\n \"avg_line_length\": 33.6363639831543,\n \"blob_id\": \"bb189ecf57b1f1434dd356240b1352e4a3d21905\",\n \"content_id\": \"ece9fa6555c80365a375aaa8ff5c55dc0dc7d956\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 398,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 11,\n \"path\": \"/lc90_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def subsetsWithDup(self, nums: List[int]) -> List[List[int]]:\\n nums.sort()\\n rslt = []\\n def backtracking(temp, i):\\n rslt.append(temp)\\n for j, num in enumerate(nums[i:]):\\n if j == 0 or num != nums[j+i-1]:\\n backtracking(temp+[num], i+j+1)\\n backtracking([], 0)\\n return rslt\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.403474897146225,\n \"alphanum_fraction\": 0.42374518513679504,\n \"avg_line_length\": 44.043479919433594,\n \"blob_id\": \"384f660eb7a9141a61ee6c11efe5aa2b9d095074\",\n \"content_id\": \"495786a7f23c3244c0fb5cdc07a8c9e92097ae37\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1036,\n \"license_type\": \"no_license\",\n \"max_line_length\": 94,\n \"num_lines\": 23,\n \"path\": \"/lc499_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findShortestWay(self, maze: List[List[int]], ball: List[int], hole: List[int]) -> str:\\n directions = {\\\"d\\\":(1, 0), \\\"l\\\":(0, -1), \\\"r\\\":(0, 1),\\\"u\\\":(-1, 0)}\\n heap, visited = [(0,\\\"\\\", ball[0], ball[1])], set()\\n nrow, ncol = len(maze), len(maze[0])\\n while heap:\\n step, path, currx, curry = heapq.heappop(heap)\\n if currx == hole[0] and curry == hole[1]:\\n return path\\n if (currx, curry) in visited:\\n continue\\n visited.add((currx, curry))\\n for dr in [\\\"d\\\", \\\"l\\\", \\\"r\\\", \\\"u\\\"]:\\n dx, dy = directions[dr]\\n tx, ty, l = currx, curry, 0\\n while 0 <= tx+dx < nrow and 0 <= ty+dy < ncol and maze[tx+dx][ty+dy] == 0:\\n tx += dx\\n ty += dy\\n l += 1\\n if tx == hole[0] and ty == hole[1]:\\n break\\n heapq.heappush(heap, (step+l, path+dr, tx, ty))\\n return \\\"impossible\\\"\\n\"\n },\n {\n \"alpha_fraction\": 0.3906705677509308,\n \"alphanum_fraction\": 0.40816327929496765,\n \"avg_line_length\": 29.272727966308594,\n \"blob_id\": \"9adb5381687a16c7af2000073740cf46de44ec1e\",\n \"content_id\": \"2f240bd3ed54b5d9d0126d0f65b346c9836b394a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 343,\n \"license_type\": \"no_license\",\n \"max_line_length\": 43,\n \"num_lines\": 11,\n \"path\": \"/lc54_greedy.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def jump(self, nums: List[int]) -> int:\\n rslt, last, curr = 0, 0, 0\\n for i, num in enumerate(nums[:-1]):\\n curr = max(curr, i+num)\\n if i == last:\\n rslt += 1\\n last = curr\\n if last >= len(nums)-1:\\n break\\n return rslt\\n\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.40556368231773376,\n \"alphanum_fraction\": 0.4231332242488861,\n \"avg_line_length\": 28.69565200805664,\n \"blob_id\": \"af7dc6631061067e9e1caf2d70f0306c295b0cab\",\n \"content_id\": \"8cdf1524aab9ea43358ccce3a55c563b7f581590\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 683,\n \"license_type\": \"no_license\",\n \"max_line_length\": 67,\n \"num_lines\": 23,\n \"path\": \"/lc377_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def combinationSum4(self, nums: List[int], target: int) -> int:\\n dp = [0]*(target+1)\\n dp[0] = 1\\n for i in range(target+1):\\n for num in nums:\\n if i-num >= 0:\\n dp[i] = dp[i] + dp[i-num]\\n return dp[-1]\\n \\nclass Solution:\\n def combinationSum4(self, nums: List[int], target: int) -> int:\\n memo = {0:1}\\n def dfs(t):\\n if t in memo:\\n return memo[t]\\n rslt = 0\\n for num in nums:\\n if num <= t:\\n rslt += dfs(t-num)\\n memo[t] = rslt\\n return rslt\\n return dfs(target)\\n\"\n },\n {\n \"alpha_fraction\": 0.5171840190887451,\n \"alphanum_fraction\": 0.5254988670349121,\n \"avg_line_length\": 29.576271057128906,\n \"blob_id\": \"f9279054ae7bd1cae7c75b6c6c6bea95a4e07a19\",\n \"content_id\": \"d43ff81f25ac98e85795426b2d4c16591492736a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1804,\n \"license_type\": \"no_license\",\n \"max_line_length\": 55,\n \"num_lines\": 59,\n \"path\": \"/lc460_ood.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Node:\\n def __init__(self, val = 0, key = 0, freq = 1):\\n self.val = val\\n self.key = key\\n self.left = self.right = None\\n self.freq = 1\\n\\nclass Dll:\\n def __init__(self):\\n self.snode = Node()\\n self.snode.left =self.snode.right = self.snode\\n self.size = 0\\n def append(self, node):\\n node.left = self.snode.left\\n node.right = self.snode\\n self.snode.left = node\\n node.left.right = node\\n self.size += 1\\n def remove(self, node):\\n node.left.right = node.right\\n node.right.left = node.left\\n node.left, node.right = None, None\\n self.size -= 1\\n def pop(self):\\n temp = self.snode.right\\n self.remove(temp)\\n return temp\\n\\nclass LFUCache:\\n\\n def __init__(self, capacity: int):\\n self.keynode = {}\\n self.freq = collections.defaultdict(Dll)\\n self.capacity = capacity\\n self.min_freq = 1\\n def get(self, key: int) -> int:\\n if key not in self.keynode: return -1\\n curr = self.keynode[key]\\n dll = self.freq[curr.freq]\\n dll.remove(curr)\\n curr.freq += 1\\n self.freq[curr.freq].append(curr)\\n if self.freq[self.min_freq].size == 0:\\n self.min_freq += 1\\n return curr.val\\n def put(self, key: int, value: int) -> None:\\n if self.capacity <= 0:\\n return\\n if key in self.keynode:\\n self.keynode[key].val = value\\n self.get(key)\\n else:\\n if self.capacity == len(self.keynode):\\n temp = self.freq[self.min_freq].pop()\\n self.keynode.pop(temp.key)\\n curr = Node(val = value, key = key)\\n self.keynode[key] = curr\\n self.freq[1].append(curr)\\n self.min_freq = 1\\n\"\n },\n {\n \"alpha_fraction\": 0.4553571343421936,\n \"alphanum_fraction\": 0.460317462682724,\n \"avg_line_length\": 36.25925827026367,\n \"blob_id\": \"875dd47140461742199f08e513e9f2f8f8960d50\",\n \"content_id\": \"a35ec8d83cfb701a6d35f76f714d7faf2116f233\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1008,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 27,\n \"path\": \"/lc721_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def accountsMerge(self, accounts: List[List[str]]) -> List[List[str]]:\\n email_g, email_m = collections.defaultdict(set), {}\\n for account in accounts:\\n for email in account[1:]:\\n if email != account[1]:\\n email_g[account[1]].add(email)\\n email_g[email].add(account[1])\\n email_m[email] = account[0]\\n rslt, visited = [], set()\\n def dfs(email):\\n if email not in email_g:\\n visited.add(email)\\n return [email]\\n else:\\n rslt = [email]\\n for em in email_g[email]:\\n if em not in visited:\\n visited.add(em)\\n rslt += dfs(em)\\n return rslt\\n for email, holder in email_m.items():\\n if email not in visited:\\n visited.add(email)\\n rslt.append([holder]+sorted(dfs(email)))\\n\\n return rslt \\n\"\n },\n {\n \"alpha_fraction\": 0.4060773551464081,\n \"alphanum_fraction\": 0.42817679047584534,\n \"avg_line_length\": 28.351350784301758,\n \"blob_id\": \"f3ed4a2c7ecd94b2bf79cd39bb19fc55c9b4903a\",\n \"content_id\": \"ce95626b39a66b70b92b1b5ac33ce649ad773dd5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1086,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 37,\n \"path\": \"/lc1387_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# dfs + memoization\\nclass Solution:\\n def getKth(self, lo: int, hi: int, k: int) -> int:\\n memo = {1:1}\\n def dfs(i):\\n if i in memo:\\n return memo[i]\\n elif i%2 == 0:\\n memo[i] = dfs(i//2) + 1\\n else:\\n memo[i] = dfs(3*i + 1) + 1\\n return memo[i]\\n rslt = []\\n for i in range(lo, hi+1):\\n rslt.append((i, dfs(i)))\\n rslt.sort(key=lambda x: x[1])\\n return rslt[k-1][0]\\n\\n\\n# slow bfs\\nclass Solution:\\n def getKth(self, lo: int, hi: int, k: int) -> int:\\n queue = collections.deque()\\n for i in range(lo, hi+1):\\n queue.append((i, 0, i))\\n rslt = []\\n while queue:\\n ori, curr_power, curr = queue.popleft()\\n if curr == 1:\\n rslt.append(ori)\\n elif curr %2 == 0:\\n queue.append((ori, curr_power+1, curr//2))\\n else:\\n queue.append((ori, curr_power+1, curr*3 + 1))\\n if len(rslt) == k:\\n break\\n return rslt[-1]\\n\"\n },\n {\n \"alpha_fraction\": 0.4226190447807312,\n \"alphanum_fraction\": 0.494047611951828,\n \"avg_line_length\": 44.818180084228516,\n \"blob_id\": \"d832a86dcef45960c5c63ba153287f924f738336\",\n \"content_id\": \"15a793533335efe6251f748f510c6815062cc56b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 504,\n \"license_type\": \"no_license\",\n \"max_line_length\": 105,\n \"num_lines\": 11,\n \"path\": \"/lc1458_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxDotProduct(self, nums1: List[int], nums2: List[int]) -> int:\\n l_1, l_2 = len(nums1), len(nums2)\\n dp = [nums1[0]*nums2[0]]\\n for i in range(1, l_1): dp.append(max(dp[-1], nums1[i]*nums2[0]))\\n for j in range(1, l_2):\\n dp2 = [max(dp[0], nums1[0]*nums2[j])]\\n for i in range(1, l_1):\\n dp2.append(max(dp[i-1], dp2[i-1], dp[i], dp[i-1] + nums1[i]*nums2[j], nums1[i]*nums2[j]))\\n dp = dp2\\n return dp[-1]\\n\"\n },\n {\n \"alpha_fraction\": 0.41450777649879456,\n \"alphanum_fraction\": 0.4257340133190155,\n \"avg_line_length\": 32.08571243286133,\n \"blob_id\": \"8d6c22e72faff098bc692f54c9e35666e19e18f0\",\n \"content_id\": \"64c80e35f26a670e3a3604334a83a795b78e7afe\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1158,\n \"license_type\": \"no_license\",\n \"max_line_length\": 63,\n \"num_lines\": 35,\n \"path\": \"/lc1552_bs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxDistance(self, position: List[int], m: int) -> int:\\n position.sort()\\n # def helper(mid):\\n # rslt, left = 1, 0\\n # for right in range(1, len(position)):\\n # if position[right]-position[left] >= mid:\\n # rslt += 1\\n # left = right\\n # return rslt\\n # while left <= right:\\n # mid = (left + right)//2\\n # if helper(mid) >= m:\\n # left = mid + 1\\n # else:\\n # right = mid - 1\\n\\nclass Solution:\\n def maxDistance(self, position: List[int], m: int) -> int:a\\n def helper(mid):\\n pre = position[0]\\n for _ in range(m-1):\\n pos = bisect.bisect_left(position, pre + mid)\\n if pos == len(position):\\n return False\\n pre = position[pos]\\n return True\\n left, right = 0, max(position)\\n while left <= right:\\n mid = (left + right)//2\\n if helper(mid):\\n left = mid + 1\\n else:\\n right = mid - 1\\n return right\\n\"\n },\n {\n \"alpha_fraction\": 0.5052381157875061,\n \"alphanum_fraction\": 0.5057142972946167,\n \"avg_line_length\": 30.34328269958496,\n \"blob_id\": \"c4ce2f4f9598a57d0aaad954e99ccbc53dbd44ae\",\n \"content_id\": \"57b301f888bf5ef4e55f01b3afff75f1b9cf2c9a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2100,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 67,\n \"path\": \"/lc545_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def boundaryOfBinaryTree(self, root: TreeNode) -> List[int]:\\n rslt = []\\n # root\\n rslt.append(root.val)\\n if not root.left and not root.right:\\n return rslt\\n # left boundary\\n if root.left:\\n left = root.left\\n while left.left or left.right:\\n rslt.append(left.val)\\n left = left.left if left.left else left.right\\n # leaves\\n stack = [root]\\n while stack:\\n temp = stack.pop()\\n if not temp.left and not temp.right:\\n rslt.append(temp.val)\\n if temp.right:\\n stack.append(temp.right)\\n if temp.left:\\n stack.append(temp.left)\\n # right boundary\\n temp = []\\n if root.right:\\n right = root.right\\n while right.right or right.left:\\n temp.append(right.val)\\n right = right.right if right.right else right.left\\n return rslt + temp[::-1]\\n\\nclass Solution(object):\\n def boundaryOfBinaryTree(self, root):\\n def dfs_leftmost(node):\\n if not node or not node.left and not node.right:\\n return\\n boundary.append(node.val)\\n if node.left:\\n dfs_leftmost(node.left)\\n else:\\n dfs_leftmost(node.right)\\n\\n def dfs_leaves(node):\\n if not node:\\n return\\n if node != root and not node.left and not node.right:\\n boundary.append(node.val)\\n dfs_leaves(node.left)\\n dfs_leaves(node.right)\\n\\n def dfs_rightmost(node):\\n if not node or not node.left and not node.right:\\n return\\n if node.right:\\n dfs_rightmost(node.right)\\n else:\\n dfs_rightmost(node.left)\\n boundary.append(node.val)\\n\\n if not root:\\n return []\\n boundary = [root.val]\\n dfs_leftmost(root.left)\\n dfs_leaves(root)\\n dfs_rightmost(root.right)\\n return boundary\\n\"\n },\n {\n \"alpha_fraction\": 0.4729166626930237,\n \"alphanum_fraction\": 0.4791666567325592,\n \"avg_line_length\": 33.28571319580078,\n \"blob_id\": \"3e2c80aa1feaf8846e1dbcfd11a8aade227dfbfd\",\n \"content_id\": \"adbcb29d4720763d3d854c35e89a5f4bea26a044\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 480,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 14,\n \"path\": \"/lc47_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def permuteUnique(self, nums: List[int]) -> List[List[int]]:\\n if not nums: return []\\n rslt = []\\n nums.sort()\\n def backtracking(temp, cand):\\n if not cand:\\n rslt.append(temp)\\n for i, num in enumerate(cand):\\n if i != 0 and num == cand[i-1]:\\n continue\\n backtracking(temp+[num], cand[:i]+cand[i+1:])\\n backtracking([], nums)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.3826998770236969,\n \"alphanum_fraction\": 0.39842724800109863,\n \"avg_line_length\": 41.38888931274414,\n \"blob_id\": \"6f694b7f6e3ee007f10d486a7688893739416ca8\",\n \"content_id\": \"bd28fae0e00a6452d22d08d9fd19dfbd7d9e7a04\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 763,\n \"license_type\": \"no_license\",\n \"max_line_length\": 101,\n \"num_lines\": 18,\n \"path\": \"/lc51_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def solveNQueens(self, n: int) -> List[List[str]]:\\n rslt, visited= [], set()\\n def backtracking(i, curr):\\n if len(curr) == n:\\n rslt.append(curr)\\n else:\\n for j in range(n):\\n if (2, j) not in visited and (3, j-i) not in visited and (4, j+i) not in visited:\\n visited.add((2, j))\\n visited.add((3, j-i))\\n visited.add((4, j+i))\\n backtracking(i+1, curr + [\\\".\\\"*j+\\\"Q\\\"+\\\".\\\"*(n-j-1)] )\\n visited.remove((2, j))\\n visited.remove((3, j-i))\\n visited.remove((4, j+i))\\n backtracking(0, [])\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.36244019865989685,\n \"alphanum_fraction\": 0.4019138813018799,\n \"avg_line_length\": 38.80952453613281,\n \"blob_id\": \"e2ef0f852b2d8ec0bf99432ba64fbdca93618c57\",\n \"content_id\": \"32c6f5769f3247048e4340edc2349c52a60a98d2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 836,\n \"license_type\": \"no_license\",\n \"max_line_length\": 76,\n \"num_lines\": 21,\n \"path\": \"/lc764_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def orderOfLargestPlusSign(self, N: int, mines: List[List[int]]) -> int:\\n mines = {tuple(x) for x in mines}\\n dp = [[[0,0] for _ in range(N+2)] for _ in range(N+2)]\\n # left, above, right, below\\n # left, above\\n for i in range(N):\\n for j in range(N):\\n if (i, j) not in mines:\\n dp[i+1][j+1][0] = dp[i+1][j][0] + 1\\n dp[i+1][j+1][1] = dp[i][j+1][1] + 1\\n # right, below\\n rslt = 0\\n for i in range(N, 0, -1):\\n for j in range(N, 0, -1):\\n if (i-1, j-1) not in mines:\\n dp[i][j][0] = min(dp[i][j][0], dp[i][j+1][0] + 1)\\n dp[i][j][1] = min(dp[i][j][1], dp[i+1][j][1] + 1)\\n rslt = max(rslt, min(dp[i][j]))\\n\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.45351043343544006,\n \"alphanum_fraction\": 0.47438329458236694,\n \"avg_line_length\": 36.64285659790039,\n \"blob_id\": \"b0ddfe3297c3c7e05ea763bb1bc14dccf9055bdf\",\n \"content_id\": \"dced0f05a6bd49f6d482ca031e83535c2d4f42b6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 527,\n \"license_type\": \"no_license\",\n \"max_line_length\": 76,\n \"num_lines\": 14,\n \"path\": \"/lc340_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def lengthOfLongestSubstringKDistinct(self, s: str, k: int) -> int:\\n container, count, left, rslt = collections.defaultdict(int), 0, 0, 0\\n for right, c in enumerate(s):\\n if container[c] == 0:\\n count += 1\\n container[c] += 1\\n while count > k:\\n container[s[left]] -= 1\\n if container[s[left]] == 0:\\n count -= 1\\n left += 1\\n rslt = max(rslt, right-left+1)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4835423231124878,\n \"alphanum_fraction\": 0.4858934283256531,\n \"avg_line_length\": 33.486488342285156,\n \"blob_id\": \"4d1fdfbf48457605f06cf93dc3ec75876052764b\",\n \"content_id\": \"636a584229de66963e2f2ca9407c5d3e8845ed1e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1276,\n \"license_type\": \"no_license\",\n \"max_line_length\": 97,\n \"num_lines\": 37,\n \"path\": \"/lc1644_lca.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def lowestCommonAncestor(self, root: 'TreeNode', p: 'TreeNode', q: 'TreeNode') -> 'TreeNode':\\n self.mark = 0\\n def dfs(root, p, q):\\n if not root: return None\\n left = dfs(root.left, p, q)\\n right = dfs(root.right, p, q)\\n if root == p or root == q:\\n self.mark += 1\\n return root\\n if left and right:\\n return root\\n return left or right\\n\\n rslt = dfs(root, p, q)\\n return rslt if self.mark == 2 else None\\n def lowestCommonAncestor(self, root: 'TreeNode', p: 'TreeNode', q: 'TreeNode') -> 'TreeNode':\\n parents = {root:None}\\n stack = [root]\\n while stack:\\n parent = stack.pop()\\n if parent.right:\\n parents[parent.right] = parent\\n stack.append(parent.right)\\n if parent.left:\\n parents[parent.left] = parent\\n stack.append(parent.left)\\n cands = set()\\n if p not in parents or q not in parents:\\n return None\\n while p in parents:\\n cands.add(p)\\n p = parents[p]\\n while q in parents:\\n if q in cands:\\n return q\\n q = parents[q]\\n\"\n },\n {\n \"alpha_fraction\": 0.5107376575469971,\n \"alphanum_fraction\": 0.518207311630249,\n \"avg_line_length\": 34.70000076293945,\n \"blob_id\": \"9929b145b3d6f4f0547a8f7251546178501b7a20\",\n \"content_id\": \"4b62afa6b32498e3551a3fa9d946079340880865\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1071,\n \"license_type\": \"no_license\",\n \"max_line_length\": 77,\n \"num_lines\": 30,\n \"path\": \"/lc105_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def buildTree(self, preorder: List[int], inorder: List[int]) -> TreeNode:\\n\\n if not preorder or not inorder: return None\\n root = TreeNode(preorder[0])\\n idx = inorder.index(root.val)\\n root.left = self.buildTree(preorder[1:idx+1], inorder[:idx])\\n root.right = self.buildTree(preorder[idx+1:], inorder[idx+1:])\\n return root\\n\\n\\nclass Solution:\\n def buildTree(self, preorder: List[int], inorder: List[int]) -> TreeNode:\\n nodemap = {val:idx for idx, val in enumerate(inorder)}\\n stk, head = [], None\\n\\n for val in preorder:\\n if not head:\\n head = TreeNode(val)\\n stk.append(head)\\n else:\\n temp = TreeNode(val)\\n if nodemap[val] < nodemap[stk[-1].val]:\\n stk[-1].left = temp\\n else:\\n while stk and nodemap[stk[-1].val] < nodemap[val]:\\n last = stk.pop()\\n last.right = temp\\n stk.append(temp)\\n return head\\n\"\n },\n {\n \"alpha_fraction\": 0.3719165027141571,\n \"alphanum_fraction\": 0.4079696536064148,\n \"avg_line_length\": 39.53845977783203,\n \"blob_id\": \"21e5c317d0a80623cb5b1cc8a4f9a6bbb5cd01a5\",\n \"content_id\": \"597dcf00fc3c12e24bb19422ac5ae3447cd32463\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 527,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 13,\n \"path\": \"/lc576_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findPaths(self, m: int, n: int, N: int, i: int, j: int) -> int:\\n memo = {}\\n def dfs(x, y, step):\\n if (x, y, step) in memo: return memo[(x, y, step)]\\n if (x < 0 or x >= m or y < 0 or y >= n): return 1\\n if step == 0: return 0\\n rslt = 0\\n for dx, dy in [(-1, 0), (1, 0), (0, -1), (0, 1)]:\\n rslt += dfs(x+dx, y+dy, step-1)\\n memo[(x, y, step)] = rslt\\n return rslt\\n return dfs(i, j, N)%(10**9+7)\\n\"\n },\n {\n \"alpha_fraction\": 0.5033259391784668,\n \"alphanum_fraction\": 0.5133037567138672,\n \"avg_line_length\": 36.58333206176758,\n \"blob_id\": \"b5167451c7fa3798d9ee1f9b7c3b0e83a74799b3\",\n \"content_id\": \"5956b942f7d05f1ef5614a99de700bc0f9971139\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 902,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 24,\n \"path\": \"/lc1425_deque.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def constrainedSubsetSum(self, nums: List[int], k: int) -> int:\\n dque, dp= collections.deque(), [0]*len(nums)\\n for idx, num in enumerate(nums):\\n while dque and idx - dque[0] > k:\\n dque.popleft()\\n dp[idx] = max(num, dp[dque[0]]+num) if idx != 0 else num\\n while dque and dp[dque[-1]] < dp[idx]:\\n dque.pop()\\n dque.append(idx)\\n return max(dp)\\n\\n\\nclass Solution:\\n def constrainedSubsetSum(self, nums: List[int], k: int) -> int:\\n dque = collections.deque()\\n for idx, num in enumerate(nums):\\n if dque and idx - dque[0] > k:\\n dque.popleft()\\n nums[idx] = max(num, nums[dque[0]]+num) if idx != 0 else num\\n while dque and nums[dque[-1]] <= nums[idx]:\\n dque.pop()\\n dque.append(idx)\\n return max(nums)\\n\"\n },\n {\n \"alpha_fraction\": 0.4988713264465332,\n \"alphanum_fraction\": 0.5101580023765564,\n \"avg_line_length\": 39.272727966308594,\n \"blob_id\": \"ab02af74017c3e4bf2b0db3ae88e2ceae6cb81cf\",\n \"content_id\": \"b2c17440c0317f95ee95e850a30a48a2e891198b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 443,\n \"license_type\": \"no_license\",\n \"max_line_length\": 102,\n \"num_lines\": 11,\n \"path\": \"/lc887_minmax.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def stoneGame(self, piles: List[int]) -> bool:\\n memo = {}\\n def dfs(left, right):\\n if (left, right) in memo:\\n return memo[(left, right)]\\n if left == right:\\n return piles[left]\\n memo[(left, right)] = max(piles[left]-dfs(left+1, right), piles[right]-dfs(left, right-1))\\n return memo[(left, right)]\\n return dfs(0, len(piles)-1) > 0\\n\"\n },\n {\n \"alpha_fraction\": 0.409411758184433,\n \"alphanum_fraction\": 0.4176470637321472,\n \"avg_line_length\": 37.6363639831543,\n \"blob_id\": \"fcff6264570d94f2212d80a19326d64b5935f053\",\n \"content_id\": \"130aa41003d73c79da6870dcda50388a6fee9446\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 850,\n \"license_type\": \"no_license\",\n \"max_line_length\": 73,\n \"num_lines\": 22,\n \"path\": \"/lc1597_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def expTree(self, s):\\n op_pr = {\\\"+\\\":2, \\\"-\\\": 2, \\\"*\\\":1, \\\"/\\\":1, \\\"(\\\":3, \\\")\\\":2}\\n node_stack, op_stack = [], []\\n for c in s:\\n if c.isdigit():\\n node_stack.append(Node(c))\\n elif c == '(':\\n op_stack.append(c)\\n else:\\n while op_stack and op_pr[c] >= op_pr[op_stack[-1]]:\\n temp = Node(op_stack.pop(), right = node_stack.pop())\\n temp.left = node_stack.pop()\\n node_stack.append(temp)\\n if c == ')': op_stack.pop()\\n else:\\n op_stack.append(c)\\n while op_stack:\\n temp = Node(op_stack.pop(), right = node_stack.pop())\\n temp.left = node_stack.pop()\\n node_stack.append(temp)\\n return node_stack.pop()\\n\"\n },\n {\n \"alpha_fraction\": 0.38461539149284363,\n \"alphanum_fraction\": 0.4282744228839874,\n \"avg_line_length\": 39.08333206176758,\n \"blob_id\": \"8f9572047b55fc30618fb58f36c76e3d93ffcece\",\n \"content_id\": \"e69181adfdf8e4780bc0ea106a190f6d24e9bd08\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 481,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 12,\n \"path\": \"/lc1312_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minInsertions(self, s: str) -> int:\\n n = len(s)\\n dp = [1 if s[0] == s[-1] else 0]\\n for i in range(1, n): dp.append(1 if s[i] == s[-1] else dp[i-1])\\n for j in range(1, n):\\n tdp = [max(dp[0], 1 if s[0] == s[n-j-1] else 0)]\\n for i in range(1, n-j):\\n temp = 1 if s[i] == s[n-j-1] else 0\\n tdp.append(max(dp[i-1]+ temp, dp[i], tdp[i-1]))\\n dp = tdp\\n return n-dp[-1]\\n\"\n },\n {\n \"alpha_fraction\": 0.43044620752334595,\n \"alphanum_fraction\": 0.4356955289840698,\n \"avg_line_length\": 30.75,\n \"blob_id\": \"c3e435606c51e6f76788163182b3461dc86166b1\",\n \"content_id\": \"e3d6fe2d4b6dad08078b8ae666d10a04e45c25c8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 381,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 12,\n \"path\": \"/lc784_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def letterCasePermutation(self, S: str) -> List[str]:\\n rslt, n = [], len(S)\\n def backtracking(i, curr):\\n if i == n:\\n rslt.append(curr)\\n else:\\n x = S[i]\\n for y in {x.upper(), x.lower()}:\\n backtracking(i+1, curr+y)\\n backtracking(0, \\\"\\\")\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.40512821078300476,\n \"alphanum_fraction\": 0.44102564454078674,\n \"avg_line_length\": 33.411766052246094,\n \"blob_id\": \"8b89834cd6e0aaa7091cc80eaebc9e198451bb71\",\n \"content_id\": \"954540332dbd9f102ae08c6693864b3488e14ea1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 585,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 17,\n \"path\": \"/lc1870_bs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minSpeedOnTime(self, dist: List[int], hour: float) -> int:\\n if hour < len(dist) - 1: return -1\\n left, right = 1, int(100000/0.01)\\n def check(mid):\\n rslt = 0\\n for d in dist[:-1]:\\n rslt += (d-1)//mid + 1\\n return rslt + dist[-1]/mid\\n while left <= right:\\n mid = (left + right)//2\\n temp = check(mid)\\n if temp <= hour:\\n right = mid - 1\\n else:\\n left = mid + 1\\n return int(left) if check(left) <= hour else -1\\n\"\n },\n {\n \"alpha_fraction\": 0.37295082211494446,\n \"alphanum_fraction\": 0.3862704932689667,\n \"avg_line_length\": 41.39130401611328,\n \"blob_id\": \"c4d6f75fd0060aeae61f69a9b7e447837cb6ece2\",\n \"content_id\": \"4e415d00257980aa7e6106e183996ce0777f1f9e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 976,\n \"license_type\": \"no_license\",\n \"max_line_length\": 87,\n \"num_lines\": 23,\n \"path\": \"/lc1284_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minFlips(self, mat: List[List[int]]) -> int:\\n start, nrow, ncol = 0, len(mat), len(mat[0])\\n for i in range(nrow):\\n for j in range(ncol):\\n start |= mat[i][j]<< (i*ncol+j)\\n dque, visited, step = collections.deque([start]), {start}, 0\\n while dque:\\n size = len(dque)\\n for _ in range(size):\\n curr = dque.popleft()\\n if curr == 0: return step\\n for i in range(nrow):\\n for j in range(ncol):\\n temp = curr\\n for di, dj in [(i, j), (i+1, j), (i-1, j), (i, j+1), (i, j-1)]:\\n if 0 <= di < nrow and 0 <= dj < ncol:\\n temp ^= 1 <<(di*ncol + dj)\\n if temp not in visited:\\n visited.add(temp)\\n dque.append(temp)\\n step += 1\\n return -1 \\n\"\n },\n {\n \"alpha_fraction\": 0.5529412031173706,\n \"alphanum_fraction\": 0.5630252361297607,\n \"avg_line_length\": 38.66666793823242,\n \"blob_id\": \"3ad51e06bb61cfbf427a97b2a68fd3198ddf1b1f\",\n \"content_id\": \"49b7a63a923a16ac0eecc8986d1ebbb00f6e1c63\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 595,\n \"license_type\": \"no_license\",\n \"max_line_length\": 86,\n \"num_lines\": 15,\n \"path\": \"/lc464_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def canIWin(self, maxChoosableInteger: int, desiredTotal: int) -> bool:\\n @lru_cache(None)\\n def dfs(cands, target):\\n if not cands:\\n return False\\n if cands[-1] >= target:\\n return True\\n\\n for i, cand in enumerate(cands):\\n if not dfs(cands[:i]+cands[i+1:], target-cand):\\n return True\\n return False\\n if maxChoosableInteger*(maxChoosableInteger+1)//2 < desiredTotal: return False\\n return dfs(tuple(range(1, maxChoosableInteger+1)), desiredTotal)\\n\"\n },\n {\n \"alpha_fraction\": 0.4523809552192688,\n \"alphanum_fraction\": 0.46536797285079956,\n \"avg_line_length\": 34.53845977783203,\n \"blob_id\": \"7b62f30cc03b50229f9d2a659cb2e502f93496bd\",\n \"content_id\": \"6e7323f9fa98fd7af1c552ca3f63f91d4f0b73e5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 462,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 13,\n \"path\": \"/lc547_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findCircleNum(self, isConnected: List[List[int]]) -> int:\\n def dfs(i):\\n for j in range(len(isConnected)):\\n if isConnected[i][j] == 1 and visited[j] == 0:\\n visited[j] = 1\\n dfs(j)\\n visited, rslt= [0]*len(isConnected), 0\\n for i in range(len(isConnected)):\\n if not visited[i]:\\n dfs(i)\\n rslt += 1\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.49275362491607666,\n \"alphanum_fraction\": 0.5113871693611145,\n \"avg_line_length\": 39.25,\n \"blob_id\": \"d275170c709442b2206f70ff58a1a4181185ce23\",\n \"content_id\": \"60975968dcadc8f418d29193cae59d50a0513eb3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 966,\n \"license_type\": \"no_license\",\n \"max_line_length\": 95,\n \"num_lines\": 24,\n \"path\": \"/lc57_array.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def insert(self, intervals: List[List[int]], newInterval: List[int]) -> List[List[int]]:\\n intervals.append(newInterval)\\n intervals.sort()\\n rslt = [intervals[0]]\\n for x, y in intervals[1:]:\\n if x > rslt[-1][1]:\\n rslt.append([x, y])\\n else:\\n rslt[-1][0], rslt[-1][1] = min(rslt[-1][0], x), max(rslt[-1][1], y)\\n return rslt\\n\\n def insert(self, intervals: List[List[int]], newInterval: List[int]) -> List[List[int]]:\\n # intervals are initially sorted\\n rslt, mark = [], True\\n for i, (x, y) in enumerate(intervals):\\n if y < newInterval[0]:\\n rslt.append([x, y])\\n elif x > newInterval[1]:\\n rslt.append(newInterval)\\n return rslt + intervals[i:]\\n else:\\n newInterval[0], newInterval[1] = min(x, newInterval[0]), max(y, newInterval[1])\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.462981253862381,\n \"alphanum_fraction\": 0.4856860935688019,\n \"avg_line_length\": 47.238094329833984,\n \"blob_id\": \"5b6f198cc7776d6a98685263be72a00de35f333a\",\n \"content_id\": \"c4d8c990a6b1812068f190cb29d01a986c25dcd3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1013,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 21,\n \"path\": \"/lc407_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def trapRainWater(self, heightMap: List[List[int]]) -> int:\\n heap, visited = [], set()\\n nrow, ncol = len(heightMap), len(heightMap[0])\\n for i in range(nrow):\\n heapq.heappush(heap, (heightMap[i][0], i, 0))\\n heapq.heappush(heap, (heightMap[i][ncol-1], i, ncol-1))\\n visited |= {(i, 0), (i, ncol-1)}\\n for j in range(ncol):\\n heapq.heappush(heap, (heightMap[0][j], 0, j))\\n heapq.heappush(heap, (heightMap[nrow-1][j], nrow-1, j))\\n visited |= {(0, j), (nrow-1, j)}\\n rslt = 0\\n while heap:\\n border, x, y = heapq.heappop(heap)\\n for nx, ny in [(x+1, y), (x-1, y), (x, y+1), (x, y-1)]:\\n if 0 < nx < nrow-1 and 0 < ny < ncol-1 and (nx, ny) not in visited:\\n rslt += max(0, border-heightMap[nx][ny])\\n visited.add((nx, ny))\\n heapq.heappush(heap, (max(border, heightMap[nx][ny]), nx, ny))\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.39461883902549744,\n \"alphanum_fraction\": 0.4125560522079468,\n \"avg_line_length\": 32.45000076293945,\n \"blob_id\": \"5f5dbc686376d18b2b3d95dddd6fa2369e574c54\",\n \"content_id\": \"c3ede426470358e99521d0b26702e5bf2ee8ee5f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 669,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 20,\n \"path\": \"/lc1353_heap.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxEvents(self, events: List[List[int]]) -> int:\\n events.sort()\\n rslt, last = 0, 0\\n heap = []\\n i = 0\\n while i < len(events) or heap:\\n if not heap: last = events[i][0]\\n if i < len(events) and events[i][0] <= last <= events[i][1]:\\n heapq.heappush(heap, events[i][1])\\n i += 1\\n elif i < len(events) and events[i][1] < last:\\n i += 1\\n continue\\n elif heap:\\n temp = heapq.heappop(heap)\\n if temp >= last:\\n rslt += 1\\n last += 1\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4757462739944458,\n \"alphanum_fraction\": 0.4981343150138855,\n \"avg_line_length\": 39.53845977783203,\n \"blob_id\": \"de06dfa1b18c75e62eb48a2969564b80790a3412\",\n \"content_id\": \"57a3bbe731f2af65c2c407a9194850851a8b86d7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 536,\n \"license_type\": \"no_license\",\n \"max_line_length\": 76,\n \"num_lines\": 13,\n \"path\": \"/lc1648_bs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxProfit(self, inventory: List[int], orders: int) -> int:\\n # find k\\n left, right = 0, max(inventory)\\n helper = lambda k: sum(max(0, i-k) for i in inventory)\\n while left <= right:\\n mid = (left+right)//2\\n if helper(mid) < orders:\\n right = mid -1\\n else:\\n left = mid + 1\\n rslt = sum((i+right+1)*(i-right)//2 for i in inventory if i > right)\\n return (rslt - (helper(right)-orders)*(right+1))%(10**9+7)\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.5472440719604492,\n \"alphanum_fraction\": 0.5629921555519104,\n \"avg_line_length\": 35.28571319580078,\n \"blob_id\": \"33d25c3e9815a67e45f3fa00d485b8f4c891eafc\",\n \"content_id\": \"03a259827e870e36569f4d180c70750d36e9fc0d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 254,\n \"license_type\": \"no_license\",\n \"max_line_length\": 68,\n \"num_lines\": 7,\n \"path\": \"/lc252_array.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def canAttendMeetings(self, intervals: List[List[int]]) -> bool:\\n intervals.sort()\\n for i, it in enumerate(intervals):\\n if i > 0 and it[0] < intervals[i-1][1]:\\n return False\\n return True\\n\"\n },\n {\n \"alpha_fraction\": 0.39065107703208923,\n \"alphanum_fraction\": 0.404006689786911,\n \"avg_line_length\": 28.950000762939453,\n \"blob_id\": \"1d76f3f41c205ed02714e52669aa5b4a921a2c76\",\n \"content_id\": \"4ad55871520552491494ed5998f132da3785dc1f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 599,\n \"license_type\": \"no_license\",\n \"max_line_length\": 44,\n \"num_lines\": 20,\n \"path\": \"/lc1234_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def balancedString(self, s: str) -> int:\\n count = collections.Counter(s)\\n extra = {}\\n n = len(s)\\n a = n//4\\n for key in count:\\n if count[key] > a:\\n extra[key] = count[key]-a\\n if not extra: return 0\\n l, rslt = 0, len(s)\\n for r in range(n):\\n if s[r] in extra:\\n extra[s[r]]-=1\\n while max(extra.values()) <= 0:\\n rslt = min(rslt, r-l+1)\\n if s[l] in extra:\\n extra[s[l]] += 1\\n l += 1\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.505084753036499,\n \"alphanum_fraction\": 0.508474588394165,\n \"avg_line_length\": 35.875,\n \"blob_id\": \"5213c4d2d2568da614c526c6eab45022c79aa45c\",\n \"content_id\": \"d169b6cd3b4fd57071c8b6f6134ad9f73abd57c9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 295,\n \"license_type\": \"no_license\",\n \"max_line_length\": 68,\n \"num_lines\": 8,\n \"path\": \"/lc1673_stk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def mostCompetitive(self, nums: List[int], k: int) -> List[int]:\\n stk, tl= [], len(nums)\\n for i, num in enumerate(nums):\\n while stk and stk[-1] > num and tl-i+len(stk) > k:\\n stk.pop()\\n stk.append(num)\\n return stk[:k]\\n\"\n },\n {\n \"alpha_fraction\": 0.49066388607025146,\n \"alphanum_fraction\": 0.5072613954544067,\n \"avg_line_length\": 34.703704833984375,\n \"blob_id\": \"c969cda9758950c7e5e845c76aba64500068e6ef\",\n \"content_id\": \"22262dbca46d2c321d86ece8195fccb6bcbed8a2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 964,\n \"license_type\": \"no_license\",\n \"max_line_length\": 86,\n \"num_lines\": 27,\n \"path\": \"/lc322_dp_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# BK\\nclass Solution:\\n def coinChange(self, coins: List[int], amount: int) -> int:\\n coins.sort(reverse = True)\\n self.rslt = float(\\\"inf\\\")\\n def backtracking(amount, i, val):\\n if amount == 0:\\n self.rslt = min(self.rslt, val)\\n return\\n if i>= len(coins) or amount < 0 or val + amount //coins[i] +1 > self.rslt:\\n return\\n temp = amount //coins[i]\\n for j in range(temp, -1, -1):\\n backtracking(amount-coins[i]*j, i+1, val+j)\\n\\n backtracking(amount, 0, 0)\\n return self.rslt if self.rslt < float(\\\"inf\\\") else -1\\n\\n# DP\\nclass Solution:\\n def coinChange(self, coins: List[int], amount: int) -> int:\\n dp = [float(\\\"inf\\\")]*(amount+1)\\n dp[0] = 0\\n for coin in coins:\\n for x in range(coin, amount+1):\\n dp[x] = min(dp[x], dp[x-coin] + 1)\\n return dp[-1] if dp[amount] < float(\\\"inf\\\") else -1\\n\"\n },\n {\n \"alpha_fraction\": 0.4126984179019928,\n \"alphanum_fraction\": 0.4285714328289032,\n \"avg_line_length\": 30.5,\n \"blob_id\": \"57d6ea1d17bf8f8f313ae398fe60fa1a6688b77a\",\n \"content_id\": \"3e9511c02518ac039eabadf5368c717067b54cc0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 630,\n \"license_type\": \"no_license\",\n \"max_line_length\": 69,\n \"num_lines\": 20,\n \"path\": \"/lc1231_bs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maximizeSweetness(self, sweetness: List[int], K: int) -> int:\\n def check(mid):\\n cuts, curr = 0, 0\\n for s in sweetness:\\n curr += s\\n if curr >= mid:\\n cuts += 1\\n curr = 0\\n return cuts\\n\\n if len(sweetness) == K+1: return min(sweetness)\\n left, right = min(sweetness), sum(sweetness)//(K+1)+1\\n while left <= right:\\n mid = (left + right)//2\\n if check(mid) > K:\\n left = mid + 1\\n else:\\n right = mid - 1\\n return right\\n\"\n },\n {\n \"alpha_fraction\": 0.41683366894721985,\n \"alphanum_fraction\": 0.4308617115020752,\n \"avg_line_length\": 26.72222137451172,\n \"blob_id\": \"d69a5d24d77677438a4c8d679a71e92d0cd358d6\",\n \"content_id\": \"fcfa448a333831c6f655893a3ab0bbb4e1ac7578\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 499,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 18,\n \"path\": \"/lc32_str.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def longestValidParentheses(self, s: str) -> int:\\n rslt = max(self.helper(s, '('), self.helper(s[::-1], ')'))\\n return rslt\\n\\n def helper(self, s, check):\\n left = right = rslt = 0\\n for c in s:\\n if c == check:\\n left += 1\\n else:\\n right += 1\\n\\n if right > left:\\n left, right = 0, 0\\n elif right == left:\\n rslt = max(rslt, 2*right)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.445333331823349,\n \"alphanum_fraction\": 0.4560000002384186,\n \"avg_line_length\": 30.25,\n \"blob_id\": \"0352e68dd8ae6f88cd14c9f81fa7017be1e16b28\",\n \"content_id\": \"1e0822bca3f1e78e89970976efedc890aa0756ae\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 375,\n \"license_type\": \"no_license\",\n \"max_line_length\": 51,\n \"num_lines\": 12,\n \"path\": \"/lc763_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def partitionLabels(self, S: str) -> List[int]:\\n temp = {}\\n for i, c in enumerate(S):\\n temp[c] = i\\n left, right, rslt = 0, 0, []\\n for i, c in enumerate(S):\\n right = max(right, temp[c])\\n if i == right:\\n rslt.append(right-left+1)\\n left = right+1\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4900990128517151,\n \"alphanum_fraction\": 0.5,\n \"avg_line_length\": 35.727272033691406,\n \"blob_id\": \"5ee213038cdcec74cfa71a7ccf301ee9ea714e11\",\n \"content_id\": \"1ca34a8bc95199318806a475c9f537dbc907a5fb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 404,\n \"license_type\": \"no_license\",\n \"max_line_length\": 58,\n \"num_lines\": 11,\n \"path\": \"/lc1590_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minSubarray(self, nums: List[int], p: int) -> int:\\n maps = {0:-1}\\n target = sum(nums)%p\\n curr, rslt = 0, len(nums)\\n for i, num in enumerate(nums):\\n curr = (curr+num)%p\\n maps[curr] = i\\n if (curr-target)%p in maps:\\n rslt = min(rslt, i-maps[(curr-target)%p])\\n return rslt if rslt < len(nums) else -1\\n\"\n },\n {\n \"alpha_fraction\": 0.606914222240448,\n \"alphanum_fraction\": 0.617157518863678,\n \"avg_line_length\": 44.94117736816406,\n \"blob_id\": \"efdf0d89163409a5eaabc6388e9e983552785ce5\",\n \"content_id\": \"2d718a1a94fa1aa2405ccc05731b73ebd221dd44\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 781,\n \"license_type\": \"no_license\",\n \"max_line_length\": 226,\n \"num_lines\": 17,\n \"path\": \"/lc609_hash.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def copyRandomList(self, head: 'Node') -> 'Node':\\n if not head: return None\\n nodemap = {}\\n def dfs(curr):\\n if not curr: return None\\n if curr in nodemap: return nodemap[curr]\\n copy = Node(curr.val)\\n nodemap[curr] = copy\\n copy.next = dfs(curr.next)\\n copy.random = dfs(curr.random)\\n return copy\\n return dfs(head)\\n\\n# Follow up:\\n# - DFS: as we must arrive at the file, like a leaf node in the tree, and we don't care the directory unless there's files in there, DFS will be a better choice, as DFS can resuse the shared path before leaving that directory.\\n# - 1. Use File size. 2. Compare random sampled rows. 3. Check MD5 or SHA256 hash, 4 check byte by byte\\n\"\n },\n {\n \"alpha_fraction\": 0.47546011209487915,\n \"alphanum_fraction\": 0.493865042924881,\n \"avg_line_length\": 31.600000381469727,\n \"blob_id\": \"db5d7f4f24cc97101dfca1159e8582623cc8b7ed\",\n \"content_id\": \"cca04d70b6a11ff74789f74b5737973f11c33ecd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 326,\n \"license_type\": \"no_license\",\n \"max_line_length\": 67,\n \"num_lines\": 10,\n \"path\": \"/lc881_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def numRescueBoats(self, people: List[int], limit: int) -> int:\\n people.sort()\\n left, right, rslt = 0, len(people)-1, 0\\n while left <= right:\\n if people[left]+people[right] <= limit:\\n left += 1\\n rslt += 1\\n right -= 1\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.542397677898407,\n \"alphanum_fraction\": 0.5482456088066101,\n \"avg_line_length\": 39.235294342041016,\n \"blob_id\": \"1059e1b30def08e83836d82836ef5fb0cc11c2d0\",\n \"content_id\": \"e02269b918661d8fd9003a97b4b59904fa3a93d0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 684,\n \"license_type\": \"no_license\",\n \"max_line_length\": 88,\n \"num_lines\": 17,\n \"path\": \"/lc210_graph.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findOrder(self, numCourses: int, prerequisites: List[List[int]]) -> List[int]:\\n graph, degree = collections.defaultdict(list), collections.defaultdict(int)\\n for curr, pre in prerequisites:\\n graph[pre].append(curr)\\n degree[curr] += 1\\n\\n dque = collections.deque([crs for crs in range(numCourses) if degree[crs] == 0])\\n rslt = []\\n while dque:\\n pre = dque.popleft()\\n rslt.append(pre)\\n for post in graph[pre]:\\n degree[post] -= 1\\n if degree[post] == 0:\\n dque.append(post)\\n return rslt if len(rslt) == numCourses else []\\n\"\n },\n {\n \"alpha_fraction\": 0.4795081913471222,\n \"alphanum_fraction\": 0.49707260727882385,\n \"avg_line_length\": 35.34042739868164,\n \"blob_id\": \"360f44db67d4c816cf734e53add8efcc889efcf1\",\n \"content_id\": \"351a58faf1629c3278c9aff388a0cd894c4a3bb6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1708,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 47,\n \"path\": \"/lc1562_deque.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findLatestStep(self, arr: List[int], m: int) -> int:\\n if m == len(arr): return m\\n border, rslt =[0]*(len(arr)+2), -1\\n for i, idx in enumerate(arr):\\n left = right = idx\\n if border[right + 1] > 0: right = border[right + 1]\\n if border[left - 1] > 0: left = border[left - 1]\\n border[left], border[right] = right, left\\n if (right-idx==m) or (idx-left ==m): rslt = i\\n return rslt\\n\\n# deque + hashtable\\n\\nclass Solution:\\n def findLatestStep(self, arr: List[int], m: int) -> int:\\n if len(arr) == m: return m\\n dp = [0]*(len(arr)+1)\\n for i, idx in enumerate(arr): dp[idx] = i+1\\n dque, rslt = collections.deque(), -1\\n for i in range(1, len(dp)):\\n while dque and dp[dque[-1]] < dp[i]:\\n dque.pop()\\n while dque and i- dque[0] >= m:\\n dque.popleft()\\n dque.append(i)\\n if i < m: continue\\n left, right, maxDay = float(\\\"inf\\\"), float(\\\"inf\\\"), dp[dque[0]]\\n if i-m >= 1: left = dp[i-m]\\n if i+1 <= len(arr): right = dp[i+1]\\n if maxDay < left and maxDay < right:\\n rslt = max(rslt, min(left,right)-1)\\n return rslt\\n\\n\\n\\n# too slow, binary search\\nclass Solution:\\n def findLatestStep(self, arr: List[int], m: int) -> int:\\n if m == len(arr): return m\\n cands = [0, len(arr)+1]\\n for i, idx in enumerate(arr[::-1]):\\n temp = bisect.bisect(cands, idx)\\n cands.insert(temp, idx)\\n if cands[temp]-cands[temp-1]-1 == m or cands[temp+1]- cands[temp]-1 == m:\\n return len(arr)-i-1\\n return -1\\n\"\n },\n {\n \"alpha_fraction\": 0.44771721959114075,\n \"alphanum_fraction\": 0.44771721959114075,\n \"avg_line_length\": 32.95000076293945,\n \"blob_id\": \"2287affe9fc737bd9b48966644875cfc510156d8\",\n \"content_id\": \"357308f7d0e438c833fd8b8414167eff1b9ba7d9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 679,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 20,\n \"path\": \"/lc851_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def loudAndRich(self, richer: List[List[int]], quiet: List[int]) -> List[int]:\\n richer_map = collections.defaultdict(set)\\n for x, y in richer:\\n richer_map[y].add(x)\\n memo = {}\\n def dfs(x):\\n if x in memo:\\n return memo[x]\\n if not richer_map[x]:\\n return x\\n curr, rslt = quiet[x], x\\n for t in richer_map[x]:\\n temp = dfs(t)\\n if quiet[temp] < curr:\\n curr = quiet[temp]\\n rslt = temp\\n memo[x] = rslt\\n return rslt\\n return [dfs(x) for x in range(len(quiet))]\\n\"\n },\n {\n \"alpha_fraction\": 0.4402298927307129,\n \"alphanum_fraction\": 0.44712644815444946,\n \"avg_line_length\": 33.79999923706055,\n \"blob_id\": \"f643f13ae3d8728eb96adb461704f8c181b4d36a\",\n \"content_id\": \"559f14c28df7f1d82f9873ed76dc3eb780534e93\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 870,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 25,\n \"path\": \"/lc1627_unionfind.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def areConnected(self, n: int, threshold: int, queries: List[List[int]]) -> List[bool]:\\n parent = list(range(n+1))\\n size = [1]*(n+1)\\n def find(p):\\n while p != parent[p]:\\n parent[p] = parent[parent[p]]\\n p = parent[p]\\n return p\\n def union(p, q):\\n rootp, rootq = find(p), find(q)\\n if rootp == rootq: return\\n if size[rootp] < size[rootq]:\\n rootp, rootq = rootq, rootp\\n parent[rootq] = rootp\\n size[rootp] += size[rootq]\\n return\\n for i in range(n+1):\\n if i > threshold and parent[i] == i:\\n for j in range(2*i, n+1, i):\\n union(i, j)\\n rslt = []\\n for a, b in queries:\\n rslt.append(find(a) == find(b))\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.36692014336586,\n \"alphanum_fraction\": 0.427756667137146,\n \"avg_line_length\": 39.53845977783203,\n \"blob_id\": \"c687efa51c25b2dfd329f0aee64524b916fdb450\",\n \"content_id\": \"131999370e224d35dcbd7ad7eb944fda3b3f9a1d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 526,\n \"license_type\": \"no_license\",\n \"max_line_length\": 75,\n \"num_lines\": 13,\n \"path\": \"/lc72_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minDistance(self, word1: str, word2: str) -> int:\\n l1, l2 = len(word1), len(word2)\\n dp = [[0]*(l1 +1) for _ in range(l2+1)]\\n for j in range(l2+1): dp[j][0] = j\\n for i in range(l1+1): dp[0][i] = i\\n for i, c1 in enumerate(word1):\\n for j, c2 in enumerate(word2):\\n if c1 == c2:\\n dp[j+1][i+1] = dp[j][i]\\n else:\\n dp[j+1][i+1] = min(dp[j][i+1], dp[j+1][i], dp[j][i]) +1\\n return dp[-1][-1]\"\n },\n {\n \"alpha_fraction\": 0.4745260775089264,\n \"alphanum_fraction\": 0.4768957197666168,\n \"avg_line_length\": 26.672130584716797,\n \"blob_id\": \"c62f2080d93cd26daa57152e73b1b4434bd28f23\",\n \"content_id\": \"44ac8482cf3a7052301df39df724e7d661fc02cf\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1688,\n \"license_type\": \"no_license\",\n \"max_line_length\": 69,\n \"num_lines\": 61,\n \"path\": \"/lc323_unionfind.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def countComponents(self, n: int, edges: List[List[int]]) -> int:\\n uf = UF(n)\\n for x, y in edges:\\n uf.union(x, y)\\n return uf.getsize()\\n\\n\\nclass UF:\\n def __init__(self, n):\\n self.parent = list(range(n))\\n self.size = [1]*n\\n self.count = n\\n def union(self, p, q):\\n rootp = self.find(p)\\n rootq = self.find(q)\\n if rootp == rootq:return\\n if self.size[rootp] < self.size[rootq]:\\n rootp, rootq = rootq, rootp\\n self.parent[rootq] = rootp\\n self.size[rootp] += self.size[rootq]\\n self.count -= 1\\n return\\n def find(self, p):\\n while self.parent[p] != p:\\n self.parent[p] = self.parent[self.parent[p]]\\n p = self.parent[p]\\n return p\\n def getsize(self):\\n return self.count\\n\\n \\nclass Solution:\\n def countComponents(self, n: int, edges: List[List[int]]) -> int:\\n graph = {}\\n for i in range(n):\\n graph[i] = []\\n for u, v in edges:\\n graph[u].append(v)\\n graph[v].append(u)\\n rslt = 0\\n for i in range(n):\\n if i in graph:\\n self.bfs(i, graph)\\n rslt += 1\\n return rslt\\n \\n def dfs(self, i, graph):\\n kids = graph[i]\\n graph.pop(i)\\n for kid in kids:\\n if kid in graph:\\n self.dfs(kid, graph)\\n def bfs(self, i, graph):\\n queue = collections.deque([i])\\n while queue:\\n temp = queue.popleft()\\n if temp in graph:\\n for kid in graph[temp]:\\n queue.append(kid)\\n graph.pop(temp)\\n\"\n },\n {\n \"alpha_fraction\": 0.4309816062450409,\n \"alphanum_fraction\": 0.45858895778656006,\n \"avg_line_length\": 35.22222137451172,\n \"blob_id\": \"f9d208dc245772b21468d3ca212a38d40dea24a8\",\n \"content_id\": \"c4710d4fb8b6e059eeed0edb229cadddbe3fac8e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 652,\n \"license_type\": \"no_license\",\n \"max_line_length\": 55,\n \"num_lines\": 18,\n \"path\": \"/lc969_sort.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def pancakeSort(self, arr: List[int]) -> List[int]:\\n s_arr, rslt, n = sorted(arr), [], len(arr)\\n for i in range(n-1, 0, -1):\\n idx = arr.index(s_arr[i])\\n arr[:idx+1] = arr[:idx+1][::-1]\\n arr[:i+1] = arr[:i+1][::-1]\\n rslt.extend([idx+1, i+1])\\n return rslt\\n\\n def pancakeSort(self, arr: List[int]) -> List[int]:\\n n, rslt = len(arr), []\\n for nmax in range(n, 1, -1):\\n idx = arr.index(nmax)\\n #arr = arr[nmax-1:idx:-1] + arr[:idx+1]\\n arr = arr[:idx:-1] + arr[:idx]\\n rslt.extend([idx+1, nmax])\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.3892405033111572,\n \"alphanum_fraction\": 0.4177215099334717,\n \"avg_line_length\": 27.727272033691406,\n \"blob_id\": \"3f321883a7b9da3e19dd289598a3d2abe68c238d\",\n \"content_id\": \"00080a768fa68fb671ac89fc35a8de4148113104\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 316,\n \"license_type\": \"no_license\",\n \"max_line_length\": 49,\n \"num_lines\": 11,\n \"path\": \"/lc119_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def getRow(self, rowIndex: int) -> List[int]:\\n if rowIndex == 0:\\n return [1]\\n dp = [1]\\n for j in range(1, rowIndex+1):\\n temp = [1]*(j+1)\\n for i in range(1, j):\\n temp[i] = dp[i-1] + dp[i]\\n dp = temp\\n return dp\\n\"\n },\n {\n \"alpha_fraction\": 0.5128834247589111,\n \"alphanum_fraction\": 0.5490797758102417,\n \"avg_line_length\": 36.022727966308594,\n \"blob_id\": \"521e06ab1e9fc44e945c33b5e006273413555274\",\n \"content_id\": \"522c582990e8c32cd64916c236e48a4ea09cd20e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1630,\n \"license_type\": \"no_license\",\n \"max_line_length\": 118,\n \"num_lines\": 44,\n \"path\": \"/lc468_string.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def validIPAddress(self, IP: str) -> str:\\n # check IPv4 or IPv6\\n if IP.count(\\\".\\\") == 3:\\n # IPv4 address validation\\n return \\\"IPv4\\\" if self.checkIPv4(IP) else \\\"Neither\\\"\\n elif IP.count(\\\":\\\") == 7:\\n # IPv6 address validation\\n return \\\"IPv6\\\" if self.checkIPv6(IP) else \\\"Neither\\\"\\n else:\\n return \\\"Neither\\\"\\n\\n def checkIPv4(self, IP):\\n tokens = IP.split(\\\".\\\")\\n for token in tokens:\\n if not token.isdigit() or len(token) != len(str(int(token))) or int(token) > 255:\\n return False\\n return True\\n\\n def checkIPv6(self, IP):\\n tokens = IP.split(\\\":\\\")\\n for token in tokens:\\n if len(token) <1 or len(token) > 4 or any(x in token for x in \\\"ghijklmnopqrstuvwxyzGHIJKLMNOPQRSTUVWXYZ\\\"):\\n return False\\n return True\\n\\nfrom ipaddress import ip_address, IPv6Address\\nclass Solution:\\n def validIPAddress(self, IP: str) -> str:\\n try:\\n return \\\"IPv6\\\" if type(ip_address(IP)) is IPv6Address else \\\"IPv4\\\"\\n except ValueError:\\n return \\\"Neither\\\"\\n\\nimport re\\nclass Solution:\\n def validIPAddress(self, IP: str) -> str:\\n chunk_IPv4 = r'([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])'\\n patten_IPv4 = re.compile(r'^(' + chunk_IPv4 + r'\\\\.){3}' + chunk_IPv4 + r'$')\\n chunk_IPv6 = r'([0-9a-fA-F]{1,4})'\\n patten_IPv6 = re.compile(r'^(' + chunk_IPv6 + r'\\\\:){7}' + chunk_IPv6 + r'$')\\n if patten_IPv4.match(IP):\\n return \\\"IPv4\\\"\\n return \\\"IPv6\\\" if patten_IPv6.match(IP) else \\\"Neither\\\" \\n\"\n },\n {\n \"alpha_fraction\": 0.41897234320640564,\n \"alphanum_fraction\": 0.44071146845817566,\n \"avg_line_length\": 35.14285659790039,\n \"blob_id\": \"26d9b4f04981fe24298f11ad628f150f5ee61701\",\n \"content_id\": \"6f26d71efbf64a072d2439ca4104be1d517d511d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 506,\n \"license_type\": \"no_license\",\n \"max_line_length\": 77,\n \"num_lines\": 14,\n \"path\": \"/lc1358_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def numberOfSubstrings(self, s: str) -> int:\\n left, rslt, count , count_dict= 0, 0, 0, collections.defaultdict(int)\\n for right, c in enumerate(s):\\n if count_dict[c] == 0:\\n count += 1\\n count_dict[c] += 1\\n while count == 3:\\n count_dict[s[left]] -= 1\\n if count_dict[s[left]] == 0:\\n count -= 1\\n left += 1\\n rslt += len(s) - right\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.5182482004165649,\n \"alphanum_fraction\": 0.5456204414367676,\n \"avg_line_length\": 48.818180084228516,\n \"blob_id\": \"f59368fb2826cc3338a3d9db76107245b34722bd\",\n \"content_id\": \"58c17a6f9579c22ff27474a157143e5be1cd73a9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 548,\n \"license_type\": \"no_license\",\n \"max_line_length\": 94,\n \"num_lines\": 11,\n \"path\": \"/lc1499_deque.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findMaxValueOfEquation(self, points: List[List[int]], k: int) -> int:\\n dque, rslt = collections.deque(), -float(\\\"inf\\\")\\n for idx, point in enumerate(points):\\n while dque and point[0] - points[dque[0]][0] > k:\\n dque.popleft()\\n if dque: rslt = max(rslt, points[dque[0]][1]-points[dque[0]][0]+point[1]+point[0])\\n while dque and points[dque[-1]][1]-points[dque[-1]][0] <= point[1]-point[0]:\\n dque.pop()\\n dque.append(idx)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.3621908128261566,\n \"alphanum_fraction\": 0.3621908128261566,\n \"avg_line_length\": 30.44444465637207,\n \"blob_id\": \"f76057e9acedf8a61e879e7b7d8278262059fd6a\",\n \"content_id\": \"fd2da11de6026c462fe08016b20a856fabd2dfcb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 566,\n \"license_type\": \"no_license\",\n \"max_line_length\": 47,\n \"num_lines\": 18,\n \"path\": \"/lc1087_string.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def expand(self, S: str) -> List[str]:\\n rslt, curr, append = [\\\"\\\"], [], False\\n for c in S:\\n if c == \\\",\\\": continue\\n if c == \\\"{\\\": append = True\\n elif c == \\\"}\\\":\\n append = False\\n temp = []\\n while curr:\\n t = curr.pop()\\n temp += [x+t for x in rslt]\\n rslt = temp\\n elif append:\\n curr.append(c)\\n else:\\n rslt = [x+c for x in rslt]\\n return sorted(rslt)\\n\"\n },\n {\n \"alpha_fraction\": 0.38630378246307373,\n \"alphanum_fraction\": 0.39859527349472046,\n \"avg_line_length\": 34.59375,\n \"blob_id\": \"d106af9da7c7eee262241ce73113f49a1c674183\",\n \"content_id\": \"a941d07ac06538166616d1847f780a864ff070f4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1139,\n \"license_type\": \"no_license\",\n \"max_line_length\": 99,\n \"num_lines\": 32,\n \"path\": \"/lc726_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def countOfAtoms(self, expression: str) -> List[str]:\\n stk, curr, atom, num = [], [], \\\"\\\", \\\"\\\"\\n for c in expression+\\\")\\\":\\n if c in \\\"1234567890\\\":\\n num += c\\n elif c in \\\"abcdefghijklmnopqrstuvwxyz\\\":\\n atom += c\\n elif atom:\\n curr.append((atom, int(num) if num else 1))\\n atom, num = \\\"\\\", \\\"\\\"\\n elif num:\\n t = int(num) if num else 1\\n num = \\\"\\\"\\n if stk:\\n temp = stk.pop()\\n curr = [(x, y*t) for (x, y) in temp]\\n curr = curr+stk.pop() if stk else curr\\n if c in \\\"ABCDEFGHIJKLMNOPQRSTUVWXYZ\\\":\\n atom = c\\n if c == \\\")\\\" and curr:\\n stk.append(curr)\\n curr = []\\n elif c == \\\"(\\\":\\n stk.append(curr)\\n curr = []\\n rslt = {}\\n for t in stk:\\n for x,y in t:\\n rslt[x] = rslt.get(x, 0)+y\\n\\n return \\\"\\\".join(key+str(rslt[key]) if rslt[key] > 1 else key for key in sorted(rslt.keys()))\\n\"\n },\n {\n \"alpha_fraction\": 0.41883766651153564,\n \"alphanum_fraction\": 0.4328657388687134,\n \"avg_line_length\": 32.266666412353516,\n \"blob_id\": \"a894527ff29ac22f40a59dc5b9023c1df3760d06\",\n \"content_id\": \"79efa2d2f19f0f6c56898467a65d67ee75fbbeaa\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 499,\n \"license_type\": \"no_license\",\n \"max_line_length\": 67,\n \"num_lines\": 15,\n \"path\": \"/lc259_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def threeSumSmaller(self, nums: List[int], target: int) -> int:\\n nums.sort()\\n rslt = 0\\n for left, num in enumerate(nums[:-2]):\\n if num*3 >= target:\\n return rslt\\n mid, right = left+1, len(nums)-1\\n while mid < right:\\n if nums[mid]+nums[right]+num < target:\\n rslt += right-mid\\n mid += 1\\n else:\\n right -= 1\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4730134904384613,\n \"alphanum_fraction\": 0.48875561356544495,\n \"avg_line_length\": 35.054054260253906,\n \"blob_id\": \"a67c8ff5bfb73d44ead654fe42a17e0c275204b8\",\n \"content_id\": \"0a33b797ce6c99d357058ae5d2596868688a4f30\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1334,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 37,\n \"path\": \"/lc1696_deque.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxResult(self, nums: List[int], k: int) -> int:\\n dque, rslt, dp = collections.deque(), 0, [0]*len(nums)\\n for idx, num in enumerate(nums):\\n while dque and idx-dque[0] > k:\\n dque.popleft()\\n dp[idx] = num if idx == 0 else dp[dque[0]]+num\\n while dque and dp[dque[-1]] <= dp[idx]:\\n dque.pop()\\n dque.append(idx)\\n return dp[-1]\\n\\nclass Solution:\\n def maxResult(self, nums: List[int], k: int) -> int:\\n dque = collections.deque()\\n for idx, num in enumerate(nums):\\n if idx == 0: dque.append(num)\\n else:\\n dque.append(dque[0]+num)\\n while len(dque) > k:\\n dque.popleft()\\n while dque and dque[0] < dque[-1]:\\n dque.popleft()\\n return dque[-1]\\n\\nclass Solution:\\n def maxResult(self, nums: List[int], k: int) -> int:\\n rslt, dque = 0, collections.deque()\\n for idx, num in enumerate(nums):\\n if idx == 0: dque.append((idx, num))\\n while dque and idx - dque[0][0] > k:\\n dque.popleft()\\n rslt = dque[0][1]+num if idx != 0 else num\\n while dque and dque[-1][1] <= rslt:\\n dque.pop()\\n dque.append((idx, rslt))\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.43699732422828674,\n \"alphanum_fraction\": 0.445040225982666,\n \"avg_line_length\": 30.08333396911621,\n \"blob_id\": \"cc73ffb88dae352885ef0319cb7f2133dc1f53fe\",\n \"content_id\": \"bd19da8eb6073cf67d9a53348064c2b19e225be4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 373,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 12,\n \"path\": \"/lc77_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def combine(self, n: int, k: int) -> List[List[int]]:\\n rslt = []\\n def backtracking(temp, i):\\n if len(temp) == k:\\n rslt.append(temp)\\n return\\n if i == n: return\\n for t in range(i, n):\\n backtracking(temp+[t+1], t+1)\\n backtracking([], 0)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.44994375109672546,\n \"alphanum_fraction\": 0.46681663393974304,\n \"avg_line_length\": 36.04166793823242,\n \"blob_id\": \"95eb18ebf4c6b4f13cf895e6486c2fc64146d838\",\n \"content_id\": \"4a60281aea880aecfff44fff5b820c1925b56219\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 889,\n \"license_type\": \"no_license\",\n \"max_line_length\": 75,\n \"num_lines\": 24,\n \"path\": \"/lc1345_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minJumps(self, arr: List[int]) -> int:\\n valmap, l = collections.defaultdict(list), len(arr)\\n if l == 1: return 0\\n if arr[0] == arr[l-1]: return 1\\n\\n for i, num in enumerate(arr):\\n valmap[num].append(i)\\n dque, visited, visitednum = collections.deque([(0, 0)]), {0}, set()\\n while dque:\\n jump, idx = dque.popleft()\\n if idx == l-1:\\n return jump\\n for i in [idx+1, idx-1]:\\n if i not in visited and 0<= i < l:\\n dque.append((jump+1, i))\\n visited.add(i)\\n if arr[idx] in visitednum: continue\\n visitednum.add(arr[idx])\\n for i in valmap[arr[idx]]:\\n if i not in visited:\\n dque.append((jump+1, i))\\n visited.add(i)\\n return len(arr)-1\\n\"\n },\n {\n \"alpha_fraction\": 0.475452184677124,\n \"alphanum_fraction\": 0.48578810691833496,\n \"avg_line_length\": 47.375,\n \"blob_id\": \"fa3a1a68d767e52f0934e560bc33577a44a7b20a\",\n \"content_id\": \"bafcb68fe427b8fcf97524897f7dcb096706d3f0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 774,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 16,\n \"path\": \"/lc1439_dijkstra.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def kthSmallest(self, mat: List[List[int]], k: int) -> int:\\n # Dijkstra: node is labeled by the row index sequences\\n nrow, ncol = len(mat), len(mat[0])\\n visited = {(0,)*nrow}\\n heap = [(sum(row[0] for row in mat), (0,)*nrow)]\\n for i in range(k):\\n cur_val, cur_node = heapq.heappop(heap)\\n for j in range(nrow):\\n if cur_node[j] + 1 < ncol:\\n nxt_val = cur_val - mat[j][cur_node[j]] + mat[j][cur_node[j]+1]\\n nxt_node = cur_node[:j]+(cur_node[j]+1, )+ cur_node[j+1:]\\n if nxt_node not in visited:\\n visited.add(nxt_node)\\n heapq.heappush(heap, (nxt_val, nxt_node))\\n return cur_val\\n\"\n },\n {\n \"alpha_fraction\": 0.4615384638309479,\n \"alphanum_fraction\": 0.4764957129955292,\n \"avg_line_length\": 35,\n \"blob_id\": \"cf51afd26177015c18c70d05fbca08f764fe2272\",\n \"content_id\": \"8764c5ea196ca6b796c01edf305a346fa94071b7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 468,\n \"license_type\": \"no_license\",\n \"max_line_length\": 60,\n \"num_lines\": 13,\n \"path\": \"/lc1658_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minOperations(self, nums: List[int], x: int) -> int:\\n total, left, rslt = sum(nums), 0, len(nums)+1\\n if total < x:\\n return -1\\n for right, num in enumerate(nums):\\n total -= num\\n while total < x:\\n total += nums[left]\\n left += 1\\n if total == x:\\n rslt = min(rslt, len(nums)-right-1+left)\\n return rslt if rslt < len(nums)+1 else -1\\n\"\n },\n {\n \"alpha_fraction\": 0.42603549361228943,\n \"alphanum_fraction\": 0.43589743971824646,\n \"avg_line_length\": 27.16666603088379,\n \"blob_id\": \"7ef605cc4a184fba236094c908e1fb219ae91009\",\n \"content_id\": \"434874b5f9ccf763c2df20f83d90b2535d65ea88\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 507,\n \"license_type\": \"no_license\",\n \"max_line_length\": 45,\n \"num_lines\": 18,\n \"path\": \"/lc1025_minmax.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def divisorGame(self, N: int) -> bool:\\n memo = {}\\n def dfs(n):\\n if n in memo: return memo[n]\\n if n == 1: return False\\n rslt = False\\n for i in range(1, n):\\n if n%i == 0 and not dfs(n-i):\\n memo[n] = True\\n return True\\n memo[n] = False\\n return False\\n return dfs(N)\\n\\nclass Solution:\\n def divisorGame(self, N: int) -> bool:\\n return N % 2 == 0\\n\"\n },\n {\n \"alpha_fraction\": 0.4937993288040161,\n \"alphanum_fraction\": 0.5039458870887756,\n \"avg_line_length\": 45.68421173095703,\n \"blob_id\": \"1fc0ddf09e93c7e15122ac6234ae3b57e1b053a5\",\n \"content_id\": \"bbdf675dcc1d9e8550565a79097142fd28b68530\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 887,\n \"license_type\": \"no_license\",\n \"max_line_length\": 101,\n \"num_lines\": 19,\n \"path\": \"/lc787_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findCheapestPrice(self, n: int, flights: List[List[int]], src: int, dst: int, K: int) -> int:\\n graph = collections.defaultdict(list)\\n for start, end, w in flights:\\n graph[start].append((end, w))\\n heap, visitedD, visitedS = [(0, 0, src)], [float(\\\"inf\\\")]*n, [float(\\\"inf\\\")]*n\\n visitedD[src], visitedS[src] = 0, 0\\n while heap:\\n cost, step, pre = heapq.heappop(heap)\\n if pre == dst: return cost\\n if step == K+1: continue\\n for curr, w in graph[pre]:\\n if visitedD[curr] > cost + w:\\n visitedD[curr] = cost + w\\n visitedS[curr] = step+1\\n heapq.heappush(heap, (cost+w, step+1, curr))\\n elif step < visitedS[curr]:\\n heapq.heappush(heap, (cost+w, step+1, curr))\\n return -1\\n\"\n },\n {\n \"alpha_fraction\": 0.45742905139923096,\n \"alphanum_fraction\": 0.46243739128112793,\n \"avg_line_length\": 34.235294342041016,\n \"blob_id\": \"f000ad746b38b0527fde66813df6c9f00dc72df1\",\n \"content_id\": \"4b07ca2887cb5e68b5026aab414d1e3b57195ac0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 599,\n \"license_type\": \"no_license\",\n \"max_line_length\": 67,\n \"num_lines\": 17,\n \"path\": \"/lc491_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findSubsequences(self, nums: List[int]) -> List[List[int]]:\\n rslt = []\\n def backtracking(stk, candidate):\\n if len(stk) >= 2:\\n rslt.append(stk)\\n if not candidate:\\n return\\n seen = set()\\n for i, cand in enumerate(candidate):\\n if cand in seen:\\n continue\\n if not stk or stk[-1] <= candidate[i]:\\n seen.add(cand)\\n backtracking(stk+[cand], candidate[i+1:])\\n backtracking([], nums)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.46986469626426697,\n \"alphanum_fraction\": 0.48216482996940613,\n \"avg_line_length\": 39.650001525878906,\n \"blob_id\": \"7933c18bf0e9e0703aa34bdede01e7fda3121e10\",\n \"content_id\": \"e707314160aa5c770c1e3e21929d989ab253ac93\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 813,\n \"license_type\": \"no_license\",\n \"max_line_length\": 84,\n \"num_lines\": 20,\n \"path\": \"/lc444_graph.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def sequenceReconstruction(self, org: List[int], seqs: List[List[int]]) -> bool:\\n graph, degree = collections.defaultdict(list), collections.defaultdict(int)\\n for seq in seqs:\\n for i, x in enumerate(seq):\\n if i != len(seq)-1:\\n graph[x].append(seq[i+1])\\n degree[seq[i+1]]+= 1\\n degree[x] += 0\\n dque = collections.deque([x for x in degree if degree[x] == 0])\\n temp = []\\n while dque:\\n if len(dque) != 1: return False\\n t = dque.popleft()\\n temp.append(t)\\n for end in graph[t]:\\n degree[end] -= 1\\n if degree[end] == 0:\\n dque.append(end)\\n return temp == org and sum(degree.values()) == 0\\n\"\n },\n {\n \"alpha_fraction\": 0.37249666452407837,\n \"alphanum_fraction\": 0.3791722357273102,\n \"avg_line_length\": 33.04545593261719,\n \"blob_id\": \"f70d4c22fcafc3973e85de8a414a816466996364\",\n \"content_id\": \"d636fff43b41a28703482d1e4b9099fc27847a42\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 749,\n \"license_type\": \"no_license\",\n \"max_line_length\": 58,\n \"num_lines\": 22,\n \"path\": \"/lc1087_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def expand(self, S: str) -> List[str]:\\n rslt = []\\n def backtracking(prefix, remain):\\n if not remain:\\n rslt.append(prefix)\\n return\\n if remain[0] == '{':\\n temp, cans = 0, []\\n for i, c in enumerate(remain):\\n if c == '}':\\n temp = i\\n break\\n if c in ('{', ','):\\n continue\\n cans.append(c)\\n for can in cans:\\n backtracking(prefix+can, remain[i+1:])\\n else:\\n backtracking(prefix+remain[0], remain[1:])\\n backtracking(\\\"\\\", S)\\n return sorted(rslt)\\n\"\n },\n {\n \"alpha_fraction\": 0.4444444477558136,\n \"alphanum_fraction\": 0.4627285599708557,\n \"avg_line_length\": 31.31818199157715,\n \"blob_id\": \"d5bdb83b176ec5415e24c37db0e58b8291a52b66\",\n \"content_id\": \"34fe1a12dfb1acdd1706152d51e63b3d79b581d0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 711,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 22,\n \"path\": \"/lc477_bit.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def totalHammingDistance(self, nums: List[int]) -> int:\\n if not nums: return 0\\n bit_count, n, rslt = collections.defaultdict(int), len(nums), 0\\n for num in nums:\\n i = 0\\n while num:\\n if num & 1:\\n bit_count[i] += 1\\n num >>= 1\\n i += 1\\n for _ , val in bit_count.items():\\n rslt += val*(n-val)\\n return rslt\\n\\n def totalHammingDistance(self, nums: List[int]) -> int:\\n if not nums: return 0\\n n, rslt = len(nums), 0\\n for bits in zip(*map(\\\"{:032b}\\\".format, nums)):\\n c = bits.count(\\\"1\\\")\\n rslt += c*(n-c)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4378698170185089,\n \"alphanum_fraction\": 0.44378697872161865,\n \"avg_line_length\": 27.16666603088379,\n \"blob_id\": \"ef051c0de96e3dbd5b10dc5376b58256b0cea9fa\",\n \"content_id\": \"7baa586da95a189e26f5d68fec8f39d0b8a2df52\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 338,\n \"license_type\": \"no_license\",\n \"max_line_length\": 53,\n \"num_lines\": 12,\n \"path\": \"/lc1063_stk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def validSubarrays(self, nums: List[int]) -> int:\\n n = len(nums)\\n stk = []\\n rslt = 0\\n for i in range(n):\\n while stk and nums[i] < nums[stk[-1]]:\\n rslt += i- stk.pop()\\n stk.append(i)\\n while stk:\\n rslt += n-stk.pop()\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.5455567836761475,\n \"alphanum_fraction\": 0.5500562191009521,\n \"avg_line_length\": 42.36585235595703,\n \"blob_id\": \"c2c5d3f0a355c517fda788d02b3aad9c6e378b9b\",\n \"content_id\": \"91810e80d895ebf0d866ccc0717fb23633f35f4a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1778,\n \"license_type\": \"no_license\",\n \"max_line_length\": 90,\n \"num_lines\": 41,\n \"path\": \"/lc1373_bt.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Definition for a binary tree node.\\n# class TreeNode:\\n# def __init__(self, val=0, left=None, right=None):\\n# self.val = val\\n# self.left = left\\n# self.right = right\\nclass Solution:\\n def maxSumBST(self, root: TreeNode) -> int:\\n self.rslt = 0\\n def helper(root):\\n if not root: return 0, 0, True, 0\\n if not root.right and not root.left: return root.val, root.val, True, root.val\\n maxRR, minLR, isBSTR, ssumR = helper(root.right)\\n maxRL, minLL, isBSTL, ssumL = helper(root.left)\\n temp, ss = False, max(ssumR, ssumL)\\n if not root.left and isBSTR and minLR > root.val:\\n temp, ss, minLL = True, ssumR + root.val, root.val\\n if not root.right and isBSTL and maxRL < root.val:\\n temp, ss, maxRR = True, ssumL+root.val, root.val\\n if isBSTR and isBSTL and maxRL < root.val < minLR:\\n temp, ss= True, ssumR+ssumL+root.val\\n self.rslt = max(self.rslt, ss)\\n return maxRR, minLL, temp, ss\\n _, _, _, rslt = helper(root)\\n return self.rslt\\n\\nclass Solution:\\n def maxSumBST(self, root: TreeNode) -> int:\\n self.rslt = 0\\n def helper(root):\\n if not root: return -float(\\\"inf\\\"), float(\\\"inf\\\"), True, 0\\n maxR, minR, isBSTR, sumR = helper(root.right)\\n maxL, minL, isBSTL, sumL = helper(root.left)\\n isBST, total= False, 0\\n if isBSTR and isBSTL and minR > root.val > maxL:\\n isBST = True\\n total = sumR+sumL+root.val\\n self.rslt = max(self.rslt, sumR+sumL+root.val)\\n return max(maxR, root.val), min(minL, root.val), isBST, total\\n helper(root)\\n return self.rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.39137381315231323,\n \"alphanum_fraction\": 0.420127809047699,\n \"avg_line_length\": 31.473684310913086,\n \"blob_id\": \"7cd42b2afef62ddee273c7cd6f1aa7d61fe39a8b\",\n \"content_id\": \"45c57ca03000076a7987eaf1e95b9766716ac845\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 626,\n \"license_type\": \"no_license\",\n \"max_line_length\": 50,\n \"num_lines\": 19,\n \"path\": \"/lc678_str.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def checkValidString(self, s: str) -> bool:\\n countl = 0\\n for c in s:\\n countl += 1 if c in (\\\"(\\\", \\\"*\\\") else -1\\n if countl < 0: return False\\n countr = 0\\n for c in s[::-1]:\\n countr += 1 if c in (\\\")\\\", \\\"*\\\") else -1\\n if countr < 0: return False\\n return True\\n def checkValidString(self, s:str) -> bool:\\n low, hi= 0, 0\\n for c in s:\\n low += 1 if c == '(' else -1\\n hi += 1 if c in ('(', '*') else -1\\n low = max(0, low)\\n if hi < 0: return False\\n return low == 0\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.3991416394710541,\n \"alphanum_fraction\": 0.4177396297454834,\n \"avg_line_length\": 30.772727966308594,\n \"blob_id\": \"9ebe263cadbbbacd4b3e15731d6bcd7d42872166\",\n \"content_id\": \"8c3da2a6fa91b38d8d8240445d06aab80ce815cc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 699,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 22,\n \"path\": \"/lc975_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def oddEvenJumps(self, arr: List[int]) -> int:\\n n = len(arr)\\n nxt_h, nxt_l = [0]*n, [0]*n\\n stack = []\\n for i in sorted(range(n), key = lambda x: (arr[x], x)):\\n while stack and stack[-1] < i:\\n nxt_h[stack.pop()] = i\\n stack.append(i)\\n\\n stack = []\\n for i in sorted(range(n), key = lambda x: (-arr[x], x)):\\n while stack and stack[-1] < i:\\n nxt_l[stack.pop()] = i\\n stack.append(i)\\n\\n nh, nl = [0]*n, [0]*n\\n nh[-1], nl[-1] = 1, 1\\n for j in range(n-2, -1, -1):\\n nh[j] = nl[nxt_h[j]]\\n nl[j] = nh[nxt_l[j]]\\n return sum(nh)\\n\"\n },\n {\n \"alpha_fraction\": 0.5145413875579834,\n \"alphanum_fraction\": 0.5190156698226929,\n \"avg_line_length\": 36.25,\n \"blob_id\": \"80f93d2630130578c0ed3053e9fbedc5f0d25779\",\n \"content_id\": \"53392511f302024681ab3fe850cc1402622a0964\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 447,\n \"license_type\": \"no_license\",\n \"max_line_length\": 98,\n \"num_lines\": 12,\n \"path\": \"/lc1376_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def numOfMinutes(self, n: int, headID: int, manager: List[int], informTime: List[int]) -> int:\\n mmap = collections.defaultdict(list)\\n for i, m in enumerate(manager):\\n if m != -1:\\n mmap[m].append(i)\\n def dfs(mid):\\n rslt = 0\\n for sub in mmap[mid]:\\n rslt = max(rslt, informTime[mid] + dfs(sub))\\n return rslt\\n return dfs(headID)\\n\"\n },\n {\n \"alpha_fraction\": 0.44329896569252014,\n \"alphanum_fraction\": 0.4639175236225128,\n \"avg_line_length\": 31.33333396911621,\n \"blob_id\": \"9c10534f1eecf2817e7926f8738a82ed59b325b5\",\n \"content_id\": \"210c64df56a8d41f4dfbb34fd5f3d743eee5a422\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 291,\n \"license_type\": \"no_license\",\n \"max_line_length\": 56,\n \"num_lines\": 9,\n \"path\": \"/lc118_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def generate(self, numRows: int) -> List[List[int]]:\\n rslt = []\\n for i in range(numRows):\\n temp = [1]*(i+1)\\n for j in range(1, i):\\n temp[j] = rslt[i-1][j-1]+rslt[i-1][j]\\n rslt.append(temp)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4553191363811493,\n \"alphanum_fraction\": 0.48085105419158936,\n \"avg_line_length\": 34.153846740722656,\n \"blob_id\": \"7a6489120bca3f6406ea5ac4cc35123da9c289ab\",\n \"content_id\": \"4d153c189aea88106e380a8cbfa28a89880abd84\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 470,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 13,\n \"path\": \"/lc496_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def nextGreaterElement(self, nums1: List[int], nums2: List[int]) -> List[int]:\\n if not nums2: return [-1]*len(nums1)\\n keeper, stk = {}, [nums2[0]]\\n for num in nums2[1:]:\\n while stk and num > stk[-1]:\\n temp = stk.pop()\\n keeper[temp] = num\\n stk.append(num)\\n rslt = []\\n for num in nums1:\\n rslt.append(keeper.get(num, -1))\\n return rslt\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.4484732747077942,\n \"alphanum_fraction\": 0.46310433745384216,\n \"avg_line_length\": 34.325843811035156,\n \"blob_id\": \"a660e6a7bf3086da821b352cde0564d2ff230b1b\",\n \"content_id\": \"0c2f53540ab07c220972f4f93d42c87f7f673a32\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3144,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 89,\n \"path\": \"/lc411_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minAbbreviation(self, target: str, dictionary: List[str]) -> str:\\n # get all the abbr of target stored by length\\n self.target_rslt = collections.defaultdict(list)\\n self.backtracking(0, [], target)\\n for i in range(1, len(target)):\\n for path in self.target_rslt[i]:\\n for word in dictionary:\\n if self.check_abbr(path, word):\\n break\\n else:\\n return \\\"\\\".join(path)\\n return target\\n\\n def backtracking(self, i, path, target):\\n '''\\n generate abbreviations for target word\\n '''\\n if i == len(target):\\n self.target_rslt[len(path)].append(path)\\n else:\\n if path and path[-1].isdigit():\\n self.backtracking(i+1, path[:-1]+ [str(int(path[-1])+1)], target)\\n else:\\n self.backtracking(i+1, path+[\\\"1\\\"], target)\\n self.backtracking(i+1, path+[target[i]], target)\\n\\n def check_abbr(self, path, word):\\n '''\\n check if path is word's abbrevation\\n '''\\n cw = 0\\n for ch in path:\\n if cw >= len(word):\\n return False\\n if not ch.isdigit():\\n if ch != word[cw]: return False\\n cw += 1\\n else:\\n cw += int(ch)\\n return cw == len(word)\\n\\nclass Solution:\\n def minAbbreviation(self, target: str, dictionary: List[str]) -> str:\\n # get all the abbr of target stored by length\\n self.target_rslt, n = collections.defaultdict(list), len(target)\\n self.backtracking(0, 1, target, 0)\\n for i in range(1, n):\\n for abbr in self.target_rslt[i]:\\n for word in dictionary:\\n if self.check_abbr(abbr, target, word): break\\n else:\\n cnt, rslt = 0, \\\"\\\"\\n for j in range(n-1, -1, -1):\\n if abbr & 1:\\n cnts = \\\"\\\" if cnt == 0 else str(cnt)\\n rslt, cnt = target[j]+cnts+rslt, 0\\n else: cnt += 1\\n abbr >>= 1\\n if cnt: rslt = str(cnt)+rslt\\n return rslt\\n return target\\n\\n def backtracking(self, i, abbr, target, l):\\n '''\\n generate abbreviations for target word use bitmask\\n '''\\n if i == len(target):\\n self.target_rslt[l].append(abbr)\\n else:\\n if abbr&1 == 0:\\n self.backtracking(i+1, abbr << 1, target, l)\\n else:\\n self.backtracking(i+1, abbr << 1, target, l+1)\\n self.backtracking(i+1, (abbr << 1)+1, target, l+1)\\n\\n def check_abbr(self, abbr, target, word):\\n '''\\n check if path is word's abbrevation\\n '''\\n cw, n = len(word)-1, len(target)\\n for i in range(n-1, -1, -1):\\n if cw < 0:\\n return False\\n if abbr & 1 == 1 and word[cw] != target[i]:\\n return False\\n cw -= 1\\n abbr >>= 1\\n return cw == -1\\n\"\n },\n {\n \"alpha_fraction\": 0.400778204202652,\n \"alphanum_fraction\": 0.43968871235847473,\n \"avg_line_length\": 31.125,\n \"blob_id\": \"b8741756ca3a4ff25d655c75b3c5208e20c1006a\",\n \"content_id\": \"db01945aeda47f0efda923c4789d8945d26a20b8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 514,\n \"license_type\": \"no_license\",\n \"max_line_length\": 69,\n \"num_lines\": 16,\n \"path\": \"/lc1215_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def countSteppingNumbers(self, low: int, high: int) -> List[int]:\\n rslt = []\\n def backtracking(i):\\n if i >= low and i <= high:\\n rslt.append(i)\\n if i > high:\\n return\\n if i != 0 and i%10 < 9:\\n backtracking(i*10 + i%10+1)\\n if i != 0 and i%10 > 0:\\n backtracking(i*10 + i%10-1)\\n return\\n for j in range(10):\\n backtracking(j)\\n return sorted(rslt)\\n\"\n },\n {\n \"alpha_fraction\": 0.47252747416496277,\n \"alphanum_fraction\": 0.4813186824321747,\n \"avg_line_length\": 34,\n \"blob_id\": \"471d005aa8357da785577fb71ef1191b4c4386a1\",\n \"content_id\": \"c09417c3ba27546adfd06cc13d5c85e96be53d6b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 455,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 13,\n \"path\": \"/lc946_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def validateStackSequences(self, pushed: List[int], popped: List[int]) -> bool:\\n popidx, stk = 0, []\\n for num in pushed:\\n mark = True\\n if num == popped[popidx]:\\n mark = False\\n popidx +=1\\n while stk and popped[popidx] == stk[-1]:\\n stk.pop()\\n popidx += 1\\n if mark: stk.append(num)\\n return False if stk else True\\n\"\n },\n {\n \"alpha_fraction\": 0.5314685106277466,\n \"alphanum_fraction\": 0.5314685106277466,\n \"avg_line_length\": 34.75,\n \"blob_id\": \"db3cd830ac64275eb67ce45905eb7d33eb417964\",\n \"content_id\": \"efe690d7fc54e62448564847cb7a7dbae387961d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 429,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 12,\n \"path\": \"/lc582_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def killProcess(self, pid: List[int], ppid: List[int], kill: int) -> List[int]:\\n mmap = collections.defaultdict(list)\\n for child, parent in zip(pid, ppid):\\n mmap[parent].append(child)\\n rslt, stk = [], [kill]\\n while stk:\\n temp = stk.pop()\\n rslt.append(temp)\\n for child in mmap[temp]:\\n stk.append(child)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.5215845704078674,\n \"alphanum_fraction\": 0.5256475210189819,\n \"avg_line_length\": 32.94827651977539,\n \"blob_id\": \"8c715df7947919b6248fe9d8bc77eb51b257960a\",\n \"content_id\": \"726653e9fb00c94a1d9b543b631da93030ff881f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1969,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 58,\n \"path\": \"/lc307_segtree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Node:\\n def __init__(self, start, end):\\n self.total = 0\\n self.start, self.end = start, end\\n self.left, self.right = None, None\\n\\nclass SegTree:\\n def __init__(self, nums):\\n self.nums = nums\\n self.root = self.create_tree(0, len(self.nums)-1)\\n\\n def create_tree(self, start, end):\\n if start == end:\\n rslt = Node(start, end)\\n rslt.total = self.nums[start]\\n return rslt\\n mid = (start + end)//2\\n rslt = Node(start, end)\\n rslt.left = self.create_tree(start, mid)\\n rslt.right = self.create_tree(mid+1, end)\\n rslt.total = rslt.left.total + rslt.right.total\\n return rslt\\n\\n def update(self, i, val):\\n def helper(root, i, val):\\n if root.left == root.right:\\n root.total = val\\n return val\\n mid = (root.start + root.end)//2\\n if i <= mid:\\n helper(root.left, i, val)\\n else:\\n helper(root.right, i, val)\\n root.total = root.left.total + root.right.total\\n return root.total\\n helper(self.root, i, val)\\n def sum_range(self, start, end):\\n def helper(root, start, end):\\n if root.start == start and root.end == end:\\n return root.total\\n mid = (root.start + root.end)//2\\n if end <= mid:\\n return helper(root.left, start, end)\\n elif start > mid:\\n return helper(root.right, start, end)\\n else:\\n return helper(root.left, start, mid) + helper(root.right, mid+1, end)\\n return helper(self.root, start, end)\\n\\nclass NumArray:\\n def __init__(self, nums: List[int]):\\n self.segtree = SegTree(nums)\\n\\n def update(self, index: int, val: int) -> None:\\n self.segtree.update(index, val)\\n\\n def sumRange(self, left: int, right: int) -> int:\\n return self.segtree.sum_range(left, right)\\n\"\n },\n {\n \"alpha_fraction\": 0.4947267472743988,\n \"alphanum_fraction\": 0.5100671052932739,\n \"avg_line_length\": 44.34782791137695,\n \"blob_id\": \"4161260fe2a181fc35790239b61dace0e1d5c325\",\n \"content_id\": \"ef8a7e12f99d01a1d6bf1003ab106ae7a32dd09d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1043,\n \"license_type\": \"no_license\",\n \"max_line_length\": 100,\n \"num_lines\": 23,\n \"path\": \"/lc1272_array.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def removeInterval(self, intervals: List[List[int]], toBeRemoved: List[int]) -> List[List[int]]:\\n rslt = []\\n for x, y in intervals:\\n if x < toBeRemoved[0] and y <= toBeRemoved[1]:\\n rslt.append([x, min(toBeRemoved[0], y)])\\n elif x >= toBeRemoved[0] and y > toBeRemoved[1]:\\n rslt.append([max(x, toBeRemoved[1]), y])\\n elif x < toBeRemoved[0] and y > toBeRemoved[1]:\\n rslt.append([x, toBeRemoved[0]])\\n rslt.append([toBeRemoved[1], y])\\n return rslt\\n def removeInterval(self, intervals: List[List[int]], toBeRemoved: List[int]) -> List[List[int]]:\\n rslt = []\\n for x, y in intervals:\\n if y < toBeRemoved[0] or x > toBeRemoved[1]:\\n rslt.append([x, y])\\n else:\\n if x < toBeRemoved[0]:\\n rslt.append([x, toBeRemoved[0]])\\n if y > toBeRemoved[1]:\\n rslt.append([toBeRemoved[1], y])\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4474761188030243,\n \"alphanum_fraction\": 0.46384719014167786,\n \"avg_line_length\": 37.578948974609375,\n \"blob_id\": \"f8d3c893f0900f2e720e7e5a1128374ffe01478e\",\n \"content_id\": \"2981f62fe2eadaf24e41b047eaf8b5eecaba26a4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 733,\n \"license_type\": \"no_license\",\n \"max_line_length\": 70,\n \"num_lines\": 19,\n \"path\": \"/lc164_bucket.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maximumGap(self, nums: List[int]) -> int:\\n n = len(nums)\\n left, right = min(nums), max(nums)\\n if n < 2 or left == right: return 0\\n s_bkts = (right - left) //(n-1) or 1\\n n_bkts = (right-left)//s_bkts + 1\\n bkts = [[float(\\\"inf\\\"), -float(\\\"inf\\\")] for _ in range(n_bkts)]\\n for num in nums:\\n idx = (num-left)//s_bkts\\n bkts[idx] = min(num, bkts[idx][0]), max(num, bkts[idx][1])\\n last, rslt = None, 0\\n for i in range(n_bkts):\\n if not last:\\n last = bkts[i][1]\\n elif bkts[i][0] < float(\\\"inf\\\"):\\n rslt = max(rslt, bkts[i][0]-last)\\n last = bkts[i][1]\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.46292585134506226,\n \"alphanum_fraction\": 0.4749498963356018,\n \"avg_line_length\": 32.266666412353516,\n \"blob_id\": \"c697d3be74ff2548ecbe9f82da28f28036f13804\",\n \"content_id\": \"1bf65d49a1ae92bceef317f92f8bfcf27b47a55d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 499,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 15,\n \"path\": \"/lc1079_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def numTilePossibilities(self, tiles: str) -> int:\\n self.rslt = 0\\n temp = ''.join(sorted(list(tiles)))\\n def backtracking(pref, cand):\\n self.rslt +=1\\n if not cand:\\n return\\n for i, c in enumerate(cand):\\n if i !=0 and c == cand[i-1]:\\n continue\\n backtracking(pref+c, cand[:i]+cand[i+1:])\\n return\\n backtracking('', temp)\\n return self.rslt-1\\n\"\n },\n {\n \"alpha_fraction\": 0.5156826376914978,\n \"alphanum_fraction\": 0.5221402049064636,\n \"avg_line_length\": 39.14814758300781,\n \"blob_id\": \"5a2ca47802f699acafd41d876f37d91e9b7d8edb\",\n \"content_id\": \"52a651a0f6e7c2a3197ae60f83a7a18804e45439\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1084,\n \"license_type\": \"no_license\",\n \"max_line_length\": 78,\n \"num_lines\": 27,\n \"path\": \"/lc106_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def buildTree(self, inorder: List[int], postorder: List[int]) -> TreeNode:\\n nodemap = {val:idx for idx, val in enumerate(inorder)}\\n stk, head = [], None\\n for node in postorder[::-1]:\\n if not head:\\n head = TreeNode(node)\\n stk.append(head)\\n else:\\n temp = TreeNode(node)\\n if nodemap[node] > nodemap[stk[-1].val]:\\n stk[-1].right = temp\\n else:\\n while stk and nodemap[stk[-1].val] > nodemap[node]:\\n last = stk.pop()\\n last.left = temp\\n stk.append(temp)\\n return head\\n\\nclass Solution:\\n def buildTree(self, inorder: List[int], postorder: List[int]) -> TreeNode:\\n if not inorder or not postorder: return None\\n root = TreeNode(postorder[-1])\\n idx = inorder.index(root.val)\\n root.right = self.buildTree(inorder[idx+1:], postorder[idx:-1])\\n root.left = self.buildTree(inorder[:idx], postorder[:idx])\\n return root\\n\"\n },\n {\n \"alpha_fraction\": 0.4265822768211365,\n \"alphanum_fraction\": 0.4430379867553711,\n \"avg_line_length\": 34.90909194946289,\n \"blob_id\": \"9a6fa01d453ee8898451c27746d240c1763af7e5\",\n \"content_id\": \"745d7ab5acbdec60366445fd783cfc190bad6998\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1580,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 44,\n \"path\": \"/lc130_unionfind.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def solve(self, board: List[List[str]]) -> None:\\n \\\"\\\"\\\"\\n Do not return anything, modify board in-place instead.\\n \\\"\\\"\\\"\\n if not board or not board[0]: return\\n r, c= len(board), len(board[0])\\n uf, edge = UF(r*c+1), r*c\\n for i in range(r):\\n if board[i][0] == \\\"O\\\": uf.union(i*c, edge)\\n if board[i][c-1] == \\\"O\\\": uf.union(i*c + c-1, edge)\\n for j in range(c):\\n if board[0][j] == \\\"O\\\": uf.union(j, edge)\\n if board[r-1][j] == \\\"O\\\": uf.union(c*(r-1)+j, edge)\\n for i in range(1, r-1):\\n for j in range(1, c-1):\\n if board[i][j] == 'O':\\n for x, y in [(1, 0), (-1, 0), (0, 1), (0, -1)]:\\n if board[i+x][j+y] == \\\"O\\\":\\n uf.union(i*c+j, (i+x)*c+j+y)\\n for i in range(1, r-1):\\n for j in range(1, c-1):\\n if board[i][j] == \\\"O\\\" and uf.find(i*c+j) != uf.find(edge):\\n board[i][j] = 'X'\\n\\n\\nclass UF:\\n def __init__(self, n):\\n self.parent = list(range(n))\\n self.size = [1]*n\\n def union(self, p, q):\\n rootp = self.find(p)\\n rootq = self.find(q)\\n if rootp == rootq: return\\n if self.size[rootp] > self.size[rootq]:\\n rootp, rootq = rootq, rootp\\n self.parent[rootp] = rootq\\n self.size[rootq] += self.size[rootp]\\n\\n def find(self, x):\\n while self.parent[x] != x:\\n self.parent[x] = self.parent[self.parent[x]]\\n x = self.parent[x]\\n return x\\n\"\n },\n {\n \"alpha_fraction\": 0.4560000002384186,\n \"alphanum_fraction\": 0.46666666865348816,\n \"avg_line_length\": 30.25,\n \"blob_id\": \"c2c9bb7ac09ba36ffd5a4a03f9c2001dda00b436\",\n \"content_id\": \"8c70ecbf273f30c06da3a8810632bd3a1d25b593\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 375,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 12,\n \"path\": \"/lc325_twopoiner.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxSubArrayLen(self, nums: List[int], k: int) -> int:\\n holder = {0:-1}\\n curr = 0\\n rslt = 0\\n for i, num in enumerate(nums):\\n curr += num\\n if curr - k in holder:\\n rslt = max(rslt, i - holder[curr-k])\\n if curr not in holder:\\n holder[curr] = i\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4441041350364685,\n \"alphanum_fraction\": 0.4486983120441437,\n \"avg_line_length\": 37.411766052246094,\n \"blob_id\": \"57068679ab9a051f356476fdcb13f345249ea858\",\n \"content_id\": \"06da429ee8eaeab49e6fe1512992088f22192e22\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 653,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 17,\n \"path\": \"/lc785_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def isBipartite(self, graph: List[List[int]]) -> bool:\\n dque = collections.deque()\\n visited, mark = {}, 0\\n for i in range(len(graph)):\\n if i not in visited:\\n dque.append((i, mark))\\n while dque:\\n cur, mark = dque.popleft()\\n mark = (mark+1)%2\\n for post in graph[cur]:\\n if post in visited and visited[post] != mark:\\n return False\\n elif post not in visited:\\n visited[post] = mark\\n dque.append((post, mark))\\n return True\\n\"\n },\n {\n \"alpha_fraction\": 0.4290465712547302,\n \"alphanum_fraction\": 0.4678492248058319,\n \"avg_line_length\": 25.52941131591797,\n \"blob_id\": \"a9a8b865b53710cc4ac016ce0e26d268f6d1c885\",\n \"content_id\": \"18dc02cf126b853191d531906e52e0d0251a8a0e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 902,\n \"license_type\": \"no_license\",\n \"max_line_length\": 68,\n \"num_lines\": 34,\n \"path\": \"/lc445_linkedlist.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def addTwoNumbers(self, l1: ListNode, l2: ListNode) -> ListNode:\\n temp1, temp2 = l1, l2\\n while temp1 and temp2:\\n temp1 = temp1.next\\n temp2 = temp2.next\\n if temp2:\\n l1, l2 = l2, l1\\n temp = temp1 if temp1 else temp2\\n rslt = None\\n\\n while temp:\\n rslt = ListNode(l1.val, rslt)\\n l1 = l1.next\\n temp = temp.next\\n while l1:\\n rslt = ListNode(l1.val + l2.val, rslt)\\n l1 = l1.next\\n l2 = l2.next\\n\\n carry, prev = 0, None\\n\\n while rslt:\\n tempv = rslt.val + carry\\n carry, tempv = tempv//10, tempv%10\\n rslt.val = tempv\\n temp = rslt.next\\n rslt.next = prev\\n prev = rslt\\n rslt = temp\\n\\n if carry:\\n prev = ListNode(carry, prev)\\n return prev\\n\"\n },\n {\n \"alpha_fraction\": 0.5,\n \"alphanum_fraction\": 0.5088757276535034,\n \"avg_line_length\": 32.79999923706055,\n \"blob_id\": \"210021f031fa08a8eaaf7b749108a11600224e89\",\n \"content_id\": \"b1acba2f5c5b79ecc155b59323648ed8d5313ceb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 338,\n \"license_type\": \"no_license\",\n \"max_line_length\": 54,\n \"num_lines\": 10,\n \"path\": \"/lc3_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def lengthOfLongestSubstring(self, s: str) -> int:\\n if not s: return 0\\n left, holder, rslt = -1, {}, 0\\n for right, c in enumerate(s):\\n if c in holder:\\n left = max(holder[c], left)\\n rslt = max(rslt, right-left)\\n holder[c] = right\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.5716981291770935,\n \"alphanum_fraction\": 0.5716981291770935,\n \"avg_line_length\": 36.85714340209961,\n \"blob_id\": \"bc531b507b0e326b3a6225df021f8f74458359bb\",\n \"content_id\": \"00202e548d6af1cfb257b4287fbc4691019c1c5c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 530,\n \"license_type\": \"no_license\",\n \"max_line_length\": 63,\n \"num_lines\": 14,\n \"path\": \"/lc1485_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def copyRandomBinaryTree(self, root: 'Node') -> 'NodeCopy':\\n if not root: return None\\n constructed = {}\\n def dfs(root):\\n if not root: return None\\n if root in constructed: return constructed[root]\\n copyroot = NodeCopy(root.val)\\n constructed[root] = copyroot\\n copyroot.left = dfs(root.left)\\n copyroot.right = dfs(root.right)\\n copyroot.random = dfs(root.random)\\n return copyroot\\n return dfs(root)\\n\"\n },\n {\n \"alpha_fraction\": 0.4059869050979614,\n \"alphanum_fraction\": 0.41253507137298584,\n \"avg_line_length\": 36.29069900512695,\n \"blob_id\": \"5ff12983e63c320b9857f4a4bc6e344afcb017ec\",\n \"content_id\": \"b1a53ec2ea4cdb2d8a66e8accf64d8ea2135e83c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3207,\n \"license_type\": \"no_license\",\n \"max_line_length\": 101,\n \"num_lines\": 86,\n \"path\": \"/lc212_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findWords(self, board: List[List[str]], words: List[str]) -> List[str]:\\n # build trie\\n trie = {}\\n for word in words:\\n curr = trie\\n for ch in word:\\n curr[ch] = curr.get(ch, {})\\n curr = curr[ch]\\n curr[\\\"end\\\"] = word\\n nrow, ncol, rslt, visited = len(board), len(board[0]), set(), set()\\n def backtracking(i, j, curr):\\n if \\\"end\\\" in curr:\\n rslt.add(curr[\\\"end\\\"])\\n for x, y in [(i+1, j), (i, j+1), (i-1, j), (i, j-1)]:\\n if 0 <= x < nrow and 0 <= y < ncol and board[x][y] in curr and (x, y) not in visited:\\n visited.add((x, y))\\n backtracking(x, y, curr[board[x][y]])\\n visited.remove((x, y))\\n for i in range(nrow):\\n for j in range(ncol):\\n if board[i][j] in trie:\\n visited.add((i, j))\\n backtracking(i, j, trie[board[i][j]])\\n visited.remove((i, j))\\n return rslt\\n\\n\\nclass Solution:\\n def findWords(self, board: List[List[str]], words: List[str]) -> List[str]:\\n # build trie\\n trie = {}\\n for word in words:\\n curr = trie\\n for ch in word:\\n curr[ch] = curr.get(ch, {})\\n curr = curr[ch]\\n curr[\\\"end\\\"] = word\\n nrow, ncol, rslt = len(board), len(board[0]), set()\\n def backtracking(i, j, curr):\\n if \\\"end\\\" in curr:\\n rslt.add(curr[\\\"end\\\"])\\n for x, y in [(i+1, j), (i, j+1), (i-1, j), (i, j-1)]:\\n if 0 <= x < nrow and 0 <= y < ncol and board[x][y] in curr:\\n ch = board[x][y]\\n board[x][y] = \\\"\\\"\\n backtracking(x, y, curr[ch])\\n board[x][y] = ch\\n for i in range(nrow):\\n for j in range(ncol):\\n if board[i][j] in trie:\\n ch = board[i][j]\\n board[i][j] = \\\"\\\"\\n backtracking(i, j, trie[ch])\\n board[i][j] = ch\\n return rslt\\n\\nclass Solution:\\n def findWords(self, board: List[List[str]], words: List[str]) -> List[str]:\\n # build trie\\n trie = {}\\n for word in words:\\n curr = trie\\n for ch in word:\\n curr[ch] = curr.get(ch, {})\\n curr = curr[ch]\\n curr[\\\"end\\\"] = word\\n nrow, ncol, rslt = len(board), len(board[0]), set()\\n def backtracking(i, j, parent):\\n pch = board[i][j]\\n curr = parent[pch]\\n board[i][j] = \\\"\\\"\\n if \\\"end\\\" in curr:\\n rslt.add(curr[\\\"end\\\"])\\n curr.pop(\\\"end\\\")\\n for x, y in [(i+1, j), (i, j+1), (i-1, j), (i, j-1)]:\\n if 0 <= x < nrow and 0 <= y < ncol and board[x][y] in curr:\\n backtracking(x, y, curr)\\n board[i][j] = pch\\n if not curr:\\n parent.pop(pch)\\n for i in range(nrow):\\n for j in range(ncol):\\n if board[i][j] in trie:\\n backtracking(i, j, trie)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.47415730357170105,\n \"alphanum_fraction\": 0.483146071434021,\n \"avg_line_length\": 43.5,\n \"blob_id\": \"18ca799e219c4e712fa3cae596e86103ac4f21a0\",\n \"content_id\": \"fd3aa9782fd7953172f90d313ed73a5ef4e9bbf5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 890,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 20,\n \"path\": \"/lc127_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def ladderLength(self, beginWord: str, endWord: str, wordList: List[str]) -> int:\\n if not wordList or endWord not in wordList:\\n return 0\\n lword, word_map, visited = len(beginWord), defaultdict(list), {beginWord}\\n queue = deque([(beginWord, 1)])\\n for wrd in wordList:\\n for i in range(lword):\\n word_map[wrd[:i]+\\\"*\\\"+wrd[i+1:]].append(wrd)\\n while queue:\\n wrd, step = queue.popleft()\\n for i in range(lword):\\n for wrd_cand in word_map[wrd[:i]+\\\"*\\\"+wrd[i+1:]]:\\n if wrd_cand == endWord:\\n return step +1\\n if wrd_cand not in visited:\\n visited.add(wrd_cand)\\n queue.append((wrd_cand, step+1))\\n word_map[wrd[:i]+\\\"*\\\"+wrd[i+1:]] = []\\n return 0\\n\"\n },\n {\n \"alpha_fraction\": 0.4480234384536743,\n \"alphanum_fraction\": 0.4846266508102417,\n \"avg_line_length\": 47.78571319580078,\n \"blob_id\": \"02bbf072c4fb7466c2729f5613fe100c8a9e5721\",\n \"content_id\": \"86299aa3d0ab5cce7659f5543b9b8b09172fad57\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 683,\n \"license_type\": \"no_license\",\n \"max_line_length\": 92,\n \"num_lines\": 14,\n \"path\": \"/lc1314_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def matrixBlockSum(self, mat: List[List[int]], k: int) -> List[List[int]]:\\n nrow, ncol = len(mat), len(mat[0])\\n presum = [[0]*(ncol+1) for _ in range(nrow+1)]\\n for i in range(nrow):\\n for j in range(ncol):\\n presum[i+1][j+1] = presum[i][j+1]+ presum[i+1][j] + mat[i][j] - presum[i][j]\\n rslt = [[0]*ncol for _ in range(nrow)]\\n for i in range(nrow):\\n for j in range(ncol):\\n r1, r2 = max(0, i-k), min(nrow, i+k+1)\\n c1, c2 = max(0, j-k), min(ncol, j+k+1)\\n rslt[i][j] = presum[r2][c2] - presum[r2][c1]-presum[r1][c2]+ presum[r1][c1]\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.45151033997535706,\n \"alphanum_fraction\": 0.45627981424331665,\n \"avg_line_length\": 33.94444274902344,\n \"blob_id\": \"fe01177fb70fdf9f8e0b21639444e36b0493cceb\",\n \"content_id\": \"191c29f6d56a8c3067059988cd9799f4adacaa16\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 629,\n \"license_type\": \"no_license\",\n \"max_line_length\": 68,\n \"num_lines\": 18,\n \"path\": \"/lc698_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def canPartitionKSubsets(self, nums: List[int], k: int) -> bool:\\n nums.sort()\\n target, rm = divmod(sum(nums), k)\\n if rm or target < nums[-1]: return False\\n def dfs():\\n if not nums: return True\\n right = nums.pop()\\n for i, ttt in enumerate(tt_list):\\n if ttt + right <= target:\\n tt_list[i] += right\\n if dfs(): return True\\n tt_list[i] -= right\\n if ttt == 0: break\\n nums.append(right)\\n return False\\n tt_list = [0]*k\\n return dfs()\\n\"\n },\n {\n \"alpha_fraction\": 0.4285714328289032,\n \"alphanum_fraction\": 0.4559270441532135,\n \"avg_line_length\": 31.899999618530273,\n \"blob_id\": \"9871198a9ddfac45279190dcc4c2cda6211264c7\",\n \"content_id\": \"1bd309aae5b90664ac6299cb0d25abe69000885a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 329,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 10,\n \"path\": \"/lc256_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minCost(self, costs: List[List[int]]) -> int:\\n if not costs: return 0\\n dp = costs[0]\\n for i in range(1, len(costs)):\\n temp = [0]*3\\n for j in range(3):\\n temp[j] = costs[i][j] + min(dp[(j+1)%3], dp[j-1])\\n dp = temp\\n return min(dp)\\n\"\n },\n {\n \"alpha_fraction\": 0.4718792736530304,\n \"alphanum_fraction\": 0.4801097512245178,\n \"avg_line_length\": 35.45000076293945,\n \"blob_id\": \"6a19767680525b61a22d57270940a58d881f154f\",\n \"content_id\": \"a68802e33e4b302a8582c9c02ebd33bb59fc2d34\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 729,\n \"license_type\": \"no_license\",\n \"max_line_length\": 67,\n \"num_lines\": 20,\n \"path\": \"/lc30_swind.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# BK\\nclass Solution:\\n def findSubstring(self, s: str, words: List[str]) -> List[int]:\\n wordmap = collections.Counter(words)\\n n, m, l = len(words), len(words[0]), len(s)\\n lsub = n*m\\n if lsub == l and \\\"\\\".join(words) == s: return [0]\\n rslt = []\\n def backtracking(right, checked):\\n if checked == lsub:\\n rslt.append(right-lsub)\\n elif right < l:\\n temp = s[right:right+m]\\n if temp in wordmap and wordmap[temp] > 0:\\n wordmap[temp] -= 1\\n backtracking(right+m, checked+m)\\n wordmap[temp] += 1\\n for i in range(l):\\n backtracking(i, 0)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4473067820072174,\n \"alphanum_fraction\": 0.4496487081050873,\n \"avg_line_length\": 37.818180084228516,\n \"blob_id\": \"9ab606030f1a1b900eebf5efae344835e8f967c4\",\n \"content_id\": \"fc5a1f02e4facc376d1d1014c32a274335e1e750\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 427,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 11,\n \"path\": \"/lc1249_str.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minRemoveToMakeValid(self, s: str) -> str:\\n stack = []\\n for i, c in enumerate(s+'*'):\\n if c == \\\"(\\\":\\n stack.append(i)\\n elif c == \\\")\\\":\\n if stack and s[stack[-1]] == '(': stack.pop()\\n else: stack.append(i)\\n stack = set(stack)\\n return \\\"\\\".join(c for i, c in enumerate(s) if c not in ['(', ')'] or i not in stack)\\n\"\n },\n {\n \"alpha_fraction\": 0.44514501094818115,\n \"alphanum_fraction\": 0.4464060664176941,\n \"avg_line_length\": 23,\n \"blob_id\": \"10b7c20e97ca5de963ebc4c3f5ca34f67f617b7e\",\n \"content_id\": \"bfd73b37c659111408ca676918d0d6ec91913354\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 793,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 33,\n \"path\": \"/lc143_linkedlist.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Definition for singly-linked list.\\n# class ListNode:\\n# def __init__(self, val=0, next=None):\\n# self.val = val\\n# self.next = next\\nclass Solution:\\n def reorderList(self, head: ListNode) -> None:\\n \\\"\\\"\\\"\\n Do not return anything, modify head in-place instead.\\n \\\"\\\"\\\"\\n s, f = head, head\\n while f and f.next:\\n s = s.next\\n f = f.next.next\\n\\n prev, curr = None, s.next\\n s.next = None\\n while curr:\\n temp = curr.next\\n curr.next = prev\\n prev = curr\\n curr = temp\\n\\n a, b = head, prev\\n while b:\\n temp = a.next\\n a.next = b\\n a = temp\\n\\n temp = b.next\\n b.next = a\\n b = temp\\n return \\n\"\n },\n {\n \"alpha_fraction\": 0.34657835960388184,\n \"alphanum_fraction\": 0.3653421700000763,\n \"avg_line_length\": 44.25,\n \"blob_id\": \"1b429e468134f49bf8c808f8dc5a5eec45e434bc\",\n \"content_id\": \"dfa4bbf6c8020f945fb6e1347d83d74f5f3b1649\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 906,\n \"license_type\": \"no_license\",\n \"max_line_length\": 115,\n \"num_lines\": 20,\n \"path\": \"/lc200_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def numIslands(self, grid: List[List[str]]) -> int:\\n if not grid or not grid[0]: return 0\\n n_row, n_col = len(grid), len(grid[0])\\n queue, rslt = collections.deque(), 0\\n for i in range(n_row):\\n for j in range(n_col):\\n if grid[i][j] == \\\"1\\\":\\n grid[i][j] = None\\n queue.append((i, j))\\n while queue:\\n curr_i, curr_j = queue.popleft()\\n for x, y in [(-1, 0), (1, 0), (0, 1), (0, -1)]:\\n t_x, t_y = curr_i+x, curr_j + y\\n if t_x >= 0 and t_x < n_row and t_y >= 0 and t_y < n_col and grid[t_x][t_y] == \\\"1\\\":\\n grid[t_x][t_y] = None\\n queue.append((t_x, t_y))\\n\\n rslt += 1\\n return rslt \\n\"\n },\n {\n \"alpha_fraction\": 0.5074441432952881,\n \"alphanum_fraction\": 0.5124069452285767,\n \"avg_line_length\": 31.675676345825195,\n \"blob_id\": \"f7644841b6b3379a8769cc7fc2b286298441475f\",\n \"content_id\": \"dd911e47f951163235bef0dcb4dc489258af3325\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2418,\n \"license_type\": \"no_license\",\n \"max_line_length\": 96,\n \"num_lines\": 74,\n \"path\": \"/lc432_ood.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Block:\\n def __init__(self, val = 0, left = None, right = None):\\n self.val = val\\n self.left = left\\n self.right = right\\n self.kids = set()\\nclass AllOne:\\n def __init__(self):\\n \\\"\\\"\\\"\\n Initialize your data structure here.\\n \\\"\\\"\\\"\\n self.sleft = Block()\\n self.sright = Block(left = self.sleft)\\n self.sleft.right = self.sright\\n self.keyblock = {}\\n def inc(self, key: str) -> None:\\n \\\"\\\"\\\"\\n Inserts a new key with value 1. Or increments an existing key by 1.\\n \\\"\\\"\\\"\\n preb = self.sleft\\n if key in self.keyblock:\\n preb = self.keyblock[key]\\n preb.kids.remove(key)\\n val = preb.val + 1\\n if preb.right.val == val:\\n curb = preb.right\\n else:\\n curb = Block(val = val, left = preb, right = preb.right)\\n preb.right.left, preb.right = curb, curb\\n curb.kids.add(key)\\n self.keyblock[key] = curb\\n if not preb.kids and preb.val != 0:\\n preb.left.right, preb.right.left = preb.right, preb.left\\n preb.left, preb.right = None, None\\n\\n def dec(self, key: str) -> None:\\n \\\"\\\"\\\"\\n Decrements an existing key by 1. If Key's value is 1, remove it from the data structure.\\n \\\"\\\"\\\"\\n preb = self.keyblock[key]\\n preb.kids.remove(key)\\n val = preb.val - 1\\n if val == 0:\\n curb = self.sleft\\n elif preb.left.val == val:\\n curb = preb.left\\n else:\\n curb = Block(val = val, left = preb.left, right = preb)\\n preb.left.right, preb.left = curb, curb\\n curb.kids.add(key)\\n if curb.val:\\n self.keyblock[key] = curb\\n else:\\n self.keyblock.pop(key)\\n if not preb.kids and preb.val != 0:\\n preb.left.right = preb.right\\n preb.right.left = preb.left\\n preb.left, preb.right = None, None\\n def getMaxKey(self) -> str:\\n \\\"\\\"\\\"\\n Returns one of the keys with maximal value.\\n \\\"\\\"\\\"\\n if self.sright.left.val == 0:\\n return \\\"\\\"\\n for kid in self.sright.left.kids:\\n return kid\\n def getMinKey(self) -> str:\\n \\\"\\\"\\\"\\n Returns one of the keys with Minimal value.\\n \\\"\\\"\\\"\\n if self.sleft.right.val == 0:\\n return \\\"\\\"\\n for kid in self.sleft.right.kids:\\n return kid\\n\"\n },\n {\n \"alpha_fraction\": 0.5,\n \"alphanum_fraction\": 0.5239273905754089,\n \"avg_line_length\": 34.64706039428711,\n \"blob_id\": \"caad7e2d3175fb23acbaabbbbe08d656d1e50625\",\n \"content_id\": \"286564921d95a38a1f721fae12f6d11b9f4c0cf3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1212,\n \"license_type\": \"no_license\",\n \"max_line_length\": 150,\n \"num_lines\": 34,\n \"path\": \"/lc1130_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n# dfs+memo /dp\\n def mctFromLeafValues(self, arr: List[int]) -> int:\\n @lru_cache(None)\\n def dfs(i,j):\\n if j<=i:\\n return 0\\n res = float('inf')\\n for k in range(i,j):\\n res = min(dfs(i,k)+dfs(k+1,j)+max(arr[i:k+1])*max(arr[k+1:j+1]),res)\\n return res\\n return dfs(0,len(arr)-1)\\n\\n#https://leetcode.com/problems/minimum-cost-tree-from-leaf-values/discuss/951938/Don't-overthink-about-trees.-It's-a-DPGreedy-problem.\\n# greedy\\n def mctFromLeafValues(self, A):\\n res = 0\\n while len(A) > 1:\\n i = A.index(min(A))\\n res += min(A[i - 1:i] + A[i + 1:i + 2]) * A.pop(i)\\n return res\\n\\n#https://leetcode.com/problems/minimum-cost-tree-from-leaf-values/discuss/478708/RZ-Summary-of-all-the-solutions-I-have-learned-from-Discuss-in-Python\\n def mctFromLeafValues(self, A):\\n res = 0\\n stack = [float(\\\"inf\\\")]\\n for a in A:\\n while stack[-1] <= a:\\n mid = stack.pop()\\n res += mid * min(stack[-1], a)\\n stack.append(a)\\n while len(stack) > 2:\\n res += stack.pop() * stack[-1]\\n return res\\n\"\n },\n {\n \"alpha_fraction\": 0.47859495878219604,\n \"alphanum_fraction\": 0.5005488395690918,\n \"avg_line_length\": 49.61111068725586,\n \"blob_id\": \"c0207e634ca08ef5cf5b3b468469955e706f5921\",\n \"content_id\": \"1008830c7d9724ab32c50acb0a1fd102f5e668a4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 911,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 18,\n \"path\": \"/lc1066_dijkstra.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def assignBikes(self, workers: List[List[int]], bikes: List[List[int]]) -> int:\\n def md(p1, p2):\\n return abs(p1[0] - p2[0])+abs(p1[1] - p2[1])\\n heap = [(0, 0, \\\"0\\\"*len(bikes))] # total distance, worker id, bike status\\n visited = collections.defaultdict(lambda:float(\\\"inf\\\"))\\n while heap:\\n total, idx, bike_status = heapq.heappop(heap)\\n if idx == len(workers):\\n return total\\n for i, b in enumerate(bikes):\\n if bike_status[i] != '1':\\n n_total = total + md(workers[idx], b)\\n n_bike_status = bike_status[:i]+'1'+bike_status[i+1:]\\n if n_total < visited[(idx+1, n_bike_status)]:\\n visited[(idx+1, n_bike_status)] = n_total\\n heapq.heappush(heap,[n_total, idx+1, n_bike_status])\\n return -1\\n\"\n },\n {\n \"alpha_fraction\": 0.3847073018550873,\n \"alphanum_fraction\": 0.3930704891681671,\n \"avg_line_length\": 31.19230842590332,\n \"blob_id\": \"8f6b4b065aebc54b6ef019fe324f010cd740a6c5\",\n \"content_id\": \"5a3402739bfd87a30b427b94277abe8ab6c21bdc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 837,\n \"license_type\": \"no_license\",\n \"max_line_length\": 63,\n \"num_lines\": 26,\n \"path\": \"/lc1520_greedy.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxNumOfSubstrings(self, s: str) -> List[str]:\\n bdry = {}\\n for i, c in enumerate(s):\\n if c not in bdry:\\n bdry[c] = [i, i]\\n else:\\n bdry[c][1] = i\\n for c in set(s):\\n left, right = bdry[c]\\n l, r = left, right\\n while True:\\n for oc in set(s[left:right+1]):\\n l = min(l, bdry[oc][0])\\n r = max(r, bdry[oc][1])\\n if [left, right] == [l, r]: break\\n left, right = l, r\\n bdry[c] = [l, r]\\n\\n intervals = sorted(bdry.values(), key = lambda x: x[1])\\n rslt, last = [], -1\\n for st, ed in intervals:\\n if st > last:\\n rslt.append(s[st:ed+1])\\n last = ed\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4829443395137787,\n \"alphanum_fraction\": 0.5,\n \"avg_line_length\": 25.5238094329834,\n \"blob_id\": \"b3680fc94b0936a6b8b0bf81bf9cd779973b35cb\",\n \"content_id\": \"f699760b0e816d92640b72705f2db70f13e03c00\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1114,\n \"license_type\": \"no_license\",\n \"max_line_length\": 97,\n \"num_lines\": 42,\n \"path\": \"/lc705_oop.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class MyHashSet:\\n\\n def __init__(self):\\n \\\"\\\"\\\"\\n Initialize your data structure here.\\n \\\"\\\"\\\"\\n self.holder = [[] for _ in range(512)]\\n\\n def add(self, key: int) -> None:\\n if self.contains(key):\\n return True\\n self.holder[key%512].append(key)\\n return False\\n\\n\\n def remove(self, key: int) -> None:\\n hh = key%512\\n i = 0\\n while i < len(self.holder[hh]):\\n if self.holder[hh][i] == key:\\n self.holder[hh][i], self.holder[hh][-1] = self.holder[hh][-1], self.holder[hh][i]\\n self.holder[hh].pop()\\n break\\n i += 1\\n def contains(self, key: int) -> bool:\\n \\\"\\\"\\\"\\n Returns true if this set contains the specified element\\n \\\"\\\"\\\"\\n hh = key%512\\n i = 0\\n while i < len(self.holder[hh]):\\n if self.holder[hh][i] == key:\\n return True\\n i += 1\\n return False\\n\\n\\n# Your MyHashSet object will be instantiated and called as such:\\n# obj = MyHashSet()\\n# obj.add(key)\\n# obj.remove(key)\\n# param_3 = obj.contains(key)\\n\"\n },\n {\n \"alpha_fraction\": 0.45726701617240906,\n \"alphanum_fraction\": 0.4678899049758911,\n \"avg_line_length\": 33.516666412353516,\n \"blob_id\": \"339461929f40ceebc8eb1222786a27b0aeebd506\",\n \"content_id\": \"557014806c447432896f099e18958c3afd3ac644\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2071,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 60,\n \"path\": \"/lc1135_unionfind.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minimumCost(self, N: int, connections: List[List[int]]) -> int:\\n # sort the connections by cost\\n connections.sort(key = lambda x: x[2])\\n parent = list(range(N+1))\\n size = [1]*len(N+1)\\n def find(p):\\n while p!= parent[p]:\\n parent[p] = parent[parent[p]]\\n p = parent[p]\\n return p\\n def union(p, q, cost):\\n rootp, rootq = find(p), find(q)\\n if rootp == rootq: return -1\\n if size[rootp] < size[rootq]: rootp, rootq = rootq, rootp\\n parent[rootq] = rootp\\n size[rootp] += size[rootq]\\n return cost\\n rslt, count = 0, 0\\n for x, y, cost in connections:\\n temp = union(x, y, cost)\\n if cost >= 0:\\n rslt += cost\\n count += 1\\n return rslt if count == N-1 else -1\\n\\n\\nclass Solution:\\n def minimumCost(self, N: int, connections: List[List[int]]) -> int:\\n # sort the connections by cost\\n connections.sort(key = lambda x: x[2])\\n parent = list(range(N+1))\\n size = [1]*(N+1)\\n def find(p):\\n while p!= parent[p]:\\n parent[p] = parent[parent[p]]\\n p = parent[p]\\n return p\\n def union(p, q, cost):\\n rootp, rootq = find(p), find(q)\\n if rootp == rootq: return -1\\n if size[rootp] < size[rootq]: rootp, rootq = rootq, rootp\\n parent[rootq] = rootp\\n size[rootp] += size[rootq]\\n return cost\\n rslt, count = 0, 0\\n for x, y, cost in connections:\\n temp = union(x, y, cost)\\n if temp >= 0:\\n rslt += temp\\n count += 1\\n return rslt if count == N-1 else -1\\n\\n\\nclass UF:\\n def __init__(self, n): self.p = list(range(n))\\n def union(self, x, y): self.p[self.find(x)] = self.find(y)\\n def find(self, x):\\n if x != self.p[x]: self.p[x] = self.find(self.p[x])\\n return self.p[x]\\n\"\n },\n {\n \"alpha_fraction\": 0.47705042362213135,\n \"alphanum_fraction\": 0.48382243514060974,\n \"avg_line_length\": 35.91666793823242,\n \"blob_id\": \"7af569733cb5865462b25c9d0767e5468f810d57\",\n \"content_id\": \"c9f53a6a659e8cba51cd607bba0eeace2217e354\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1329,\n \"license_type\": \"no_license\",\n \"max_line_length\": 67,\n \"num_lines\": 36,\n \"path\": \"/lc215_sort.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findKthLargest(self, nums: List[int], k: int) -> int:\\n pivot = nums[(0+len(nums))//2]\\n left = [l for l in nums if l > pivot]\\n mid = [m for m in nums if m == pivot]\\n right = [r for r in nums if r < pivot]\\n if len(left) >= k:\\n return self.findKthLargest(left, k)\\n elif len(left)+len(mid) >= k:\\n return pivot\\n else:\\n return self.findKthLargest(right, k-len(left)-len(mid))\\n\\n\\nclass Solution:\\n def findKthLargest(self, nums: List[int], k: int) -> int:\\n def partition(left, right):\\n pivot, l = (left+right)//2, left\\n nums[pivot], nums[right] = nums[right], nums[pivot]\\n for i in range(left, right):\\n if nums[i] >= nums[right]:\\n nums[l], nums[i] = nums[i], nums[l]\\n l += 1\\n nums[right], nums[l] = nums[l], nums[right]\\n return l\\n def select(left, right, k):\\n if left == right:\\n return nums[k]\\n pivot = partition(left, right)\\n if k == pivot:\\n return nums[k]\\n elif k < pivot:\\n return select(left, pivot-1, k)\\n else:\\n return select(pivot+1, right, k)\\n return select(0, len(nums)-1, k-1)\\n\"\n },\n {\n \"alpha_fraction\": 0.5513100624084473,\n \"alphanum_fraction\": 0.5513100624084473,\n \"avg_line_length\": 35.63999938964844,\n \"blob_id\": \"488ebac144d04b9a365dc6154b051242cafc741d\",\n \"content_id\": \"25549033506ccf3da7a9c45747fc16b27c5e7e93\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 916,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 25,\n \"path\": \"/lc450_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findleftlarge(self, root):\\n while root.right:\\n root = root.right\\n return root.val\\n def findrightsmall(self, root):\\n while root.left:\\n root = root.left\\n return root.val\\n def deleteNode(self, root: TreeNode, key: int) -> TreeNode:\\n if not root: return None\\n if key > root.val:\\n root.right = self.deleteNode(root.right, key)\\n elif key < root.val:\\n root.left = self.deleteNode(root.left, key)\\n else:\\n if not root.left and not root.right:\\n return None\\n if root.right:\\n root.val = self.findrightsmall(root.right)\\n root.right = self.deleteNode(root.right, root.val)\\n else:\\n root.val = self.findleftlarge(root.left)\\n root.left = self.deleteNode(root.left, root.val)\\n return root\\n\"\n },\n {\n \"alpha_fraction\": 0.3968565762042999,\n \"alphanum_fraction\": 0.41846758127212524,\n \"avg_line_length\": 35.35714340209961,\n \"blob_id\": \"4da9ca5f209663c39fe6dd721ff62d06d7410d49\",\n \"content_id\": \"346a3d5867d06e22e3358fed46cac3267ca40662\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 509,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 14,\n \"path\": \"/lc636_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def exclusiveTime(self, n: int, logs: List[str]) -> List[int]:\\n rslt, stk = [0]*n, []\\n for log in logs:\\n idx, op, time = log.split(\\\":\\\")\\n if op == \\\"end\\\":\\n rslt[int(idx)] += int(time)-stk[-1][1]+1\\n stk.pop()\\n if stk: stk[-1][1] = int(time)+1\\n continue\\n if stk:\\n rslt[int(stk[-1][0])] += int(time)-stk[-1][1]\\n stk.append([idx, int(time)])\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.597484290599823,\n \"alphanum_fraction\": 0.599056601524353,\n \"avg_line_length\": 36.411766052246094,\n \"blob_id\": \"4bb7936a0e3e359f9a3e58dbfdcf569e1ac71d71\",\n \"content_id\": \"c440300dcb7f32c3beda75abf75406482ef4261a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 636,\n \"license_type\": \"no_license\",\n \"max_line_length\": 56,\n \"num_lines\": 17,\n \"path\": \"/lc1026_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Definition for a binary tree node.\\n# class TreeNode:\\n# def __init__(self, val=0, left=None, right=None):\\n# self.val = val\\n# self.left = left\\n# self.right = right\\nclass Solution:\\n def maxAncestorDiff(self, root: TreeNode) -> int:\\n return self.dfs(root, root.val, root.val)\\n\\n def dfs(self, node, curr_max, curr_min):\\n if not node: return curr_max - curr_min\\n curr_max = max(curr_max, node.val)\\n curr_min = min(curr_min, node.val)\\n left = self.dfs(node.left, curr_max, curr_min)\\n right = self.dfs(node.right, curr_max, curr_min)\\n return max(left, right)\\n\"\n },\n {\n \"alpha_fraction\": 0.4450261890888214,\n \"alphanum_fraction\": 0.46073299646377563,\n \"avg_line_length\": 34.8125,\n \"blob_id\": \"f9b3be55bdc42ab22f515e5fa5281e9b046eb8e2\",\n \"content_id\": \"89f70bdab9f6cf886b5f619e236a5f4380bb9b79\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 573,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 16,\n \"path\": \"/lc1048_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def longestStrChain(self, words: List[str]) -> int:\\n memo, word_set = {}, set(words)\\n def dfs(word):\\n if len(word) == 1: return 1\\n if word in memo: return memo[word]\\n rslt = 0\\n for i in range(len(word)):\\n if word[:i] + word[i+1:] in word_set:\\n rslt = max(rslt, dfs(word[:i] + word[i+1:]))\\n memo[word] = rslt + 1\\n return rslt + 1\\n rslt = 0\\n for word in words[::-1]:\\n rslt = max(rslt, dfs(word))\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4108658730983734,\n \"alphanum_fraction\": 0.45500847697257996,\n \"avg_line_length\": 38.266666412353516,\n \"blob_id\": \"c4b622bb208bc43cfc41603e109278423418d63c\",\n \"content_id\": \"08122bd0c5b5908be0b5cacefba4fe1f9cec50c7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 589,\n \"license_type\": \"no_license\",\n \"max_line_length\": 86,\n \"num_lines\": 15,\n \"path\": \"/lc391_array.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def isRectangleCover(self, rectangles: List[List[int]]) -> bool:\\n area = 0\\n corners = set()\\n for x1, y1, x2, y2 in rectangles:\\n area += (y2-y1)*(x2-x1)\\n for x, y in [(x1, y1), (x2, y2), (x1, y2), (x2, y1)]:\\n if (x, y) in corners:\\n corners.remove((x, y))\\n else:\\n corners.add((x, y))\\n if len(corners) != 4:\\n return False\\n corners = sorted(corners)\\n return area == (corners[-1][1]-corners[0][1])*(corners[-1][0] - corners[0][0])\\n\"\n },\n {\n \"alpha_fraction\": 0.5553320050239563,\n \"alphanum_fraction\": 0.5553320050239563,\n \"avg_line_length\": 37.230770111083984,\n \"blob_id\": \"ed74baaf7c233bacca9204493940cefcdc724209\",\n \"content_id\": \"5abdfd467c6b1baf16e68686bfe2302aa0977b84\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 497,\n \"license_type\": \"no_license\",\n \"max_line_length\": 98,\n \"num_lines\": 13,\n \"path\": \"/lc572_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution(object):\\n def isSubtree(self, s, t):\\n \\\"\\\"\\\"\\n :type s: TreeNode\\n :type t: TreeNode\\n :rtype: bool\\n \\\"\\\"\\\"\\n def isSametree(s, t):\\n if not s and not t: return True\\n if not s or not t: return False\\n return s.val == t.val and isSametree(s.left, t.left) and isSametree(s.right, t.right)\\n if not t: return True\\n return s and (isSametree(s, t) or self.isSubtree(s.left, t) or self.isSubtree(s.right, t))\\n\"\n },\n {\n \"alpha_fraction\": 0.41093748807907104,\n \"alphanum_fraction\": 0.43437498807907104,\n \"avg_line_length\": 36.64706039428711,\n \"blob_id\": \"3167a5bd8fd848d3ce942841a4cb63e2db4e8184\",\n \"content_id\": \"7eff287be8266d1842ce004744f320ef4bf6621f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 640,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 17,\n \"path\": \"/lc909_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def snakesAndLadders(self, board: List[List[int]]) -> int:\\n n = len(board)\\n dque, visited, n = collections.deque([(0, 0)]), {0}, len(board)\\n while dque:\\n step, t = dque.popleft()\\n dx = n-t//n-1\\n dy = t%n if t//n%2 == 0 else n-t%n-1\\n if board[dx][dy] > 0:\\n t = board[dx][dy]-1\\n if t+1 == n*n: return step\\n for s in range(1, 7):\\n curr = t+s\\n if curr not in visited and 0 <= curr < n*n:\\n dque.append((step+1, curr))\\n visited.add(curr)\\n return -1\\n\"\n },\n {\n \"alpha_fraction\": 0.3513071835041046,\n \"alphanum_fraction\": 0.3807189464569092,\n \"avg_line_length\": 37.25,\n \"blob_id\": \"35bc12f491fa7ff87fdc4065ff442398610d1211\",\n \"content_id\": \"bf1d3c45977f203c9d207f5895346741313056f6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 612,\n \"license_type\": \"no_license\",\n \"max_line_length\": 92,\n \"num_lines\": 16,\n \"path\": \"/lc695_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxAreaOfIsland(self, grid: List[List[int]]) -> int:\\n def dfs(i, j):\\n curr = 1\\n for x, y in [(0, 1), (1, 0), (-1, 0), (0, -1)]:\\n if 0 <= i+x < len(grid) and 0<=j + y < len(grid[0]) and grid[i+x][j+y] == 1:\\n grid[i+x][j+y] = 0\\n curr += dfs(i+x, j+y)\\n return curr\\n rslt = 0\\n for i in range(len(grid)):\\n for j in range(len(grid[0])):\\n if grid[i][j] == 1:\\n grid[i][j] = 0\\n rslt = max(rslt, dfs(i, j))\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.503311276435852,\n \"alphanum_fraction\": 0.5143488049507141,\n \"avg_line_length\": 36.75,\n \"blob_id\": \"da785fa1c57bc9f5f24fe135945a66daefd0ec34\",\n \"content_id\": \"360b37aa17958922c82a7ca470c8a9b0c064fa21\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 453,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 12,\n \"path\": \"/lc456_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def find132pattern(self, nums: List[int]) -> bool:\\n stk, last = [], -float(\\\"inf\\\")\\n for num in nums[::-1]:\\n # last is ensured to be less than one seen item\\n if num < last:\\n return True\\n # mono-decreasing-stack maintaining value for nums[k] aka last\\n while stk and stk[-1] < num:\\n last = stk.pop()\\n stk.append(num)\\n return False\\n\"\n },\n {\n \"alpha_fraction\": 0.5970272421836853,\n \"alphanum_fraction\": 0.6011560559272766,\n \"avg_line_length\": 32.63888931274414,\n \"blob_id\": \"0381c4cda90fb5da6bb1b398b46fb80eeee331ae\",\n \"content_id\": \"43f5ad5219def033326e27357390442529e50c13\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1211,\n \"license_type\": \"no_license\",\n \"max_line_length\": 120,\n \"num_lines\": 36,\n \"path\": \"/lc381_ood.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class RandomizedCollection:\\n\\n def __init__(self):\\n \\\"\\\"\\\"\\n Initialize your data structure here.\\n \\\"\\\"\\\"\\n self.validx = collections.defaultdict(set)\\n self.collection = []\\n\\n\\n def insert(self, val: int) -> bool:\\n \\\"\\\"\\\"\\n Inserts a value to the collection. Returns true if the collection did not already contain the specified element.\\n \\\"\\\"\\\"\\n self.collection.append(val)\\n self.validx[val].add(len(self.collection)-1)\\n return len(self.validx[val]) == 1\\n\\n def remove(self, val: int) -> bool:\\n \\\"\\\"\\\"\\n Removes a value from the collection. Returns true if the collection contained the specified element.\\n \\\"\\\"\\\"\\n if not self.validx[val]:\\n return False\\n idx = self.validx[val].pop()\\n self.collection[-1], self.collection[idx] = self.collection[idx], self.collection[-1]\\n self.validx[self.collection[idx]].add(idx)\\n self.validx[self.collection[idx]].discard(len(self.collection)-1)\\n self.collection.pop()\\n return True\\n\\n def getRandom(self) -> int:\\n \\\"\\\"\\\"\\n Get a random element from the collection.\\n \\\"\\\"\\\"\\n return random.choice(self.collection)\\n\"\n },\n {\n \"alpha_fraction\": 0.3863636255264282,\n \"alphanum_fraction\": 0.4223484992980957,\n \"avg_line_length\": 28.33333396911621,\n \"blob_id\": \"23ed2add9eb085d7f191e4aafad89751ea32195c\",\n \"content_id\": \"99b2e8b31ba25ae349c47fd105afe782456ec53a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 528,\n \"license_type\": \"no_license\",\n \"max_line_length\": 51,\n \"num_lines\": 18,\n \"path\": \"/lc581_array.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution(object):\\n def findUnsortedSubarray(self, nums):\\n n = len(nums)\\n if n < 2: return 0\\n l, r = 0, n - 1\\n while l < n - 1 and nums[l] <= nums[l + 1]:\\n l += 1\\n while r > 0 and nums[r] >= nums[r -1]:\\n r -= 1\\n if l > r: return 0\\n subarray = nums[l:r+1]\\n mi, ma = min(subarray), max(subarray)\\n\\n while l > 0 and mi < nums[l-1]:\\n l -= 1\\n while r < n - 1 and ma > nums[r+1]:\\n r += 1\\n return r - l + 1\\n\"\n },\n {\n \"alpha_fraction\": 0.29828110337257385,\n \"alphanum_fraction\": 0.3003033399581909,\n \"avg_line_length\": 31.96666717529297,\n \"blob_id\": \"584f2c97069d5a7c05b093b94ab7c96dcf8a02ce\",\n \"content_id\": \"600986d7d9abbaf8cda9499ce7a948965ddd52a5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 989,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 30,\n \"path\": \"/lc772_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def calculate(self, s: str) -> int:\\n stk, last = [\\\"(\\\"], \\\"\\\"\\n for c in s+\\\")\\\":\\n if c == \\\" \\\": continue\\n if c.isdigit():\\n last += c if last else c\\n elif c == \\\"(\\\":\\n stk.append(c)\\n else:\\n op = stk.pop()\\n if op == \\\"*\\\":\\n stk.append(int(stk.pop())*int(last))\\n elif op == \\\"/\\\":\\n stk.append(int(int(stk.pop())/int(last)))\\n elif op == \\\"-\\\":\\n stk.append(-int(last))\\n else:\\n stk.append(op)\\n stk.append(int(last))\\n last = \\\"\\\"\\n if c == \\\")\\\":\\n y = 0\\n while stk[-1] !=\\\"(\\\":\\n y += stk.pop()\\n stk.pop()\\n last = str(y)\\n elif c != \\\"+\\\":\\n stk.append(c)\\n return int(last)\\n\"\n },\n {\n \"alpha_fraction\": 0.3914141356945038,\n \"alphanum_fraction\": 0.42171716690063477,\n \"avg_line_length\": 40.68421173095703,\n \"blob_id\": \"5e62e495cd1517893f59174e4ccf5214b4d44e37\",\n \"content_id\": \"b35923c064ecb8d8a06ce34875e9607a5f18857e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 792,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 19,\n \"path\": \"/lc1368_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minCost(self, grid: List[List[int]]) -> int:\\n heap, visited = [(0, 0, 0)], set()\\n nrow, ncol = len(grid), len(grid[0])\\n directions = {1:(0, 1), 2:(0, -1), 3:(1, 0), 4:(-1, 0)}\\n while heap:\\n step, x, y = heapq.heappop(heap)\\n if x == nrow-1 and y == ncol-1:\\n return step\\n if (x, y) in visited: continue\\n visited.add((x, y))\\n for i in directions:\\n nx, ny = x+directions[i][0], y+directions[i][1]\\n if 0 <= nx < nrow and 0 <= ny < ncol:\\n if grid[x][y] == i:\\n heapq.heappush(heap, (step, nx, ny))\\n else:\\n heapq.heappush(heap, (step+1, nx, ny))\\n return 0\\n\"\n },\n {\n \"alpha_fraction\": 0.38726791739463806,\n \"alphanum_fraction\": 0.3912466764450073,\n \"avg_line_length\": 33.272727966308594,\n \"blob_id\": \"12ce2cca0b1835b3bdcda7566b104fad919eb506\",\n \"content_id\": \"209cea625a1caec1945a17de277242d55388d755\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 754,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 22,\n \"path\": \"/lc394_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def decodeString(self, s: str) -> str:\\n stack, digit, currs, rslt = [], \\\"\\\", \\\"\\\", \\\"\\\"\\n for c in s:\\n if c.isalpha():\\n currs += c\\n elif c.isdigit():\\n digit += c\\n elif c == \\\"[\\\":\\n if currs:\\n stack.append(currs)\\n stack.append(digit)\\n digit, currs= \\\"\\\", \\\"\\\"\\n elif c == \\\"]\\\":\\n lastc = int(stack.pop()) if stack and stack[-1].isdigit() else 1\\n currs = lastc*currs\\n while stack and stack[-1].isalpha():\\n currs = stack.pop()+currs\\n if not stack:\\n rslt += currs\\n currs = \\\"\\\"\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.3752151429653168,\n \"alphanum_fraction\": 0.3855421543121338,\n \"avg_line_length\": 31.27777862548828,\n \"blob_id\": \"29c79671ab1357ede69277dbfdc53ca62116b67c\",\n \"content_id\": \"31357c067dada6229bfc47018681aa4556fd9648\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 581,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 18,\n \"path\": \"/lc838_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def pushDominoes(self, dominoes: str) -> str:\\n left, lnote, rslt = -1, \\\"L\\\", \\\"\\\"\\n for right, do in enumerate(dominoes+\\\"R\\\"):\\n if do == \\\".\\\":\\n continue\\n mid = right-left-1\\n if left >= 0:\\n rslt += lnote\\n if do == lnote:\\n rslt += mid*lnote\\n elif lnote == \\\"L\\\" and do == \\\"R\\\":\\n rslt += mid*\\\".\\\"\\n else:\\n rslt += \\\"R\\\"*(mid//2)+\\\".\\\"*(mid%2)+\\\"L\\\"*(mid//2)\\n left = right\\n lnote = do\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.34840869903564453,\n \"alphanum_fraction\": 0.42211055755615234,\n \"avg_line_length\": 45,\n \"blob_id\": \"c049e2763c7f534b48474e0ba1c6b9f157a7b408\",\n \"content_id\": \"2abe2a70f27f95488f75f3315a10934395452d15\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 597,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 13,\n \"path\": \"/lc712_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minimumDeleteSum(self, s1: str, s2: str) -> int:\\n l_s1, l_s2, rslt = len(s1), len(s2), 0\\n dp = [[0]*(l_s2 + 1) for _ in range(l_s1 + 1)]\\n for i in range(1, l_s1+1): dp[i][0] = dp[i-1][0] + ord(s1[i-1])\\n for j in range(1, l_s2+1): dp[0][j] = dp[0][j-1] + ord(s2[j-1])\\n for i, c1 in enumerate(s1):\\n for j, c2 in enumerate(s2):\\n if c1 == c2:\\n dp[i+1][j+1] = dp[i][j]\\n else:\\n dp[i+1][j+1] = min(dp[i+1][j] +ord(c2), dp[i][j+1] + ord(c1))\\n return dp[-1][-1]\"\n },\n {\n \"alpha_fraction\": 0.4655582010746002,\n \"alphanum_fraction\": 0.4750593900680542,\n \"avg_line_length\": 30.69230842590332,\n \"blob_id\": \"3e1df6c6b8ebbbbf67b439c9ff009715ed77c00f\",\n \"content_id\": \"5a534a43797c020cb3714497d5e24bd0195817ae\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 421,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 13,\n \"path\": \"/lc523_array.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def checkSubarraySum(self, nums: List[int], k: int) -> bool:\\n n = len(nums)\\n maps = {0:-1}\\n presum = 0\\n for i, num in enumerate(nums):\\n presum += num\\n presum = presum%k\\n if presum in maps and i-maps[presum] > 1:\\n return True\\n elif presum not in maps:\\n maps[presum] = i\\n return False\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.46830984950065613,\n \"alphanum_fraction\": 0.49295774102211,\n \"avg_line_length\": 30.66666603088379,\n \"blob_id\": \"8415f6743e265e0bb6588e1eb8a975321161ed77\",\n \"content_id\": \"2fd84be18e9428f82823a47e35264306e5f7fece\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 284,\n \"license_type\": \"no_license\",\n \"max_line_length\": 58,\n \"num_lines\": 9,\n \"path\": \"/lc560_hash.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def subarraySum(self, nums: List[int], k: int) -> int:\\n keeper = {0:1}\\n rslt, temp = 0, 0\\n for num in nums:\\n temp += num\\n rslt += keeper.get(temp-k, 0)\\n keeper[temp] = keeper.get(temp, 0) +1\\n return rslt\"\n },\n {\n \"alpha_fraction\": 0.42832469940185547,\n \"alphanum_fraction\": 0.47322970628738403,\n \"avg_line_length\": 37.599998474121094,\n \"blob_id\": \"1aa34ceecf34c17707b288e0529cd128e2b51234\",\n \"content_id\": \"072c4055b0a1eb6e2d851840a15767548f2f3847\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 579,\n \"license_type\": \"no_license\",\n \"max_line_length\": 113,\n \"num_lines\": 15,\n \"path\": \"/lc1229_array.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minAvailableDuration(self, slots1: List[List[int]], slots2: List[List[int]], duration: int) -> List[int]:\\n slots1.sort()\\n slots2.sort()\\n n1, n2 = len(slots1), len(slots2)\\n i, j = 0, 0\\n while i < n1 and j < n2:\\n left, right = max(slots1[i][0], slots2[j][0]), min(slots1[i][1], slots2[j][1])\\n if right - left >= duration:\\n return [left, left +duration]\\n elif slots1[i][1] < slots2[j][1]:\\n i += 1\\n else:\\n j += 1\\n return []\\n\"\n },\n {\n \"alpha_fraction\": 0.41161179542541504,\n \"alphanum_fraction\": 0.4272097051143646,\n \"avg_line_length\": 45.15999984741211,\n \"blob_id\": \"c9aceda1f1b4b927f83dfa4708655afb50ee2b4b\",\n \"content_id\": \"b4a3cc6414a8a593459c39b2f9d840ce43fa5c4f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2308,\n \"license_type\": \"no_license\",\n \"max_line_length\": 98,\n \"num_lines\": 50,\n \"path\": \"/lc864_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def shortestPathAllKeys(self, grid: List[str]) -> int:\\n k, nrow, ncol = 0, len(grid), len(grid[0])\\n for i in range(nrow):\\n for j in range(ncol):\\n if grid[i][j] == \\\"@\\\": start = (i, j)\\n elif grid[i][j] in string.ascii_lowercase: k += 1\\n dque, visited= collections.deque([(0, start[0], start[1], \\\"\\\")]), {}\\n while dque:\\n step, x, y, getk = dque.popleft()\\n if len(getk) == k:\\n return step\\n for dx, dy in [(x+1,y), (x, y+1), (x,y-1), (x-1, y)]:\\n newk = getk\\n if 0 <= dx < nrow and 0 <= dy < ncol and grid[dx][dy] !=\\\"#\\\":\\n tc = grid[dx][dy]\\n if tc in string.ascii_uppercase and tc.lower() not in newk:\\n continue\\n if tc in string.ascii_lowercase and tc not in newk:\\n newk += tc\\n if (dx, dy, newk) not in visited:\\n visited.add((dx, dy, newk))\\n dque.append((step+1, dx, dy, newk))\\n return -1\\n\\n\\nclass Solution:\\n def shortestPathAllKeys(self, grid: List[str]) -> int:\\n k, nrow, ncol = 0, len(grid), len(grid[0])\\n for i in range(nrow):\\n for j in range(ncol):\\n if grid[i][j] == \\\"@\\\": start = (i, j)\\n elif grid[i][j] in string.ascii_lowercase: k += 1\\n dque, visited= collections.deque([(0, start[0], start[1], 0)]), {}\\n while dque:\\n step, x, y, getk = dque.popleft()\\n if getk == 2**k-1:\\n return step\\n for dx, dy in [(x+1,y), (x, y+1), (x,y-1), (x-1, y)]:\\n newk = getk\\n if 0 <= dx < nrow and 0 <= dy < ncol and grid[dx][dy] !=\\\"#\\\":\\n tc = grid[dx][dy]\\n if tc in string.ascii_uppercase and (1 << ord(tc.lower())-ord('a'))&newk == 0:\\n continue\\n if tc in string.ascii_lowercase and (1 << (ord(tc)-ord('a')))&newk == 0:\\n newk |= 1 << (ord(tc)-ord('a'))\\n if (dx, dy, newk) not in visited:\\n visited.add((dx, dy, newk))\\n dque.append((step+1, dx, dy, newk))\\n return -1\\n\"\n },\n {\n \"alpha_fraction\": 0.40353259444236755,\n \"alphanum_fraction\": 0.41983696818351746,\n \"avg_line_length\": 26.80769157409668,\n \"blob_id\": \"a250becf614caaff7f909fa41314d602d60c54ae\",\n \"content_id\": \"93fa784120c4dc90869b5ab10b22b0788ed25b22\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 736,\n \"license_type\": \"no_license\",\n \"max_line_length\": 50,\n \"num_lines\": 26,\n \"path\": \"/lc222_bs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def countNodes(self, root: TreeNode) -> int:\\n if not root: return 0\\n left, depth = root, 0\\n while left.left:\\n left = left.left\\n depth += 1\\n left, right = (1< int:\\n # nnums = [1]+[num for num in nums if num > 0] + [1]\\n # n = len(nnums)\\n# dp = [[0]*n for _ in range(n)]\\n# for l in range(2, n):\\n# for left in range(0, n-l):\\n# right = left + l\\n# for mid in range(left+1, right):\\n# dp[left][right] = max(dp[left][right], nnums[left]*nnums[mid]*nnums[right]+dp[left][mid]+dp[mid][right])\\n# return dp[0][n-1]\\n # def dfs(left, right, memo = {}):\\n # if (left, right) in memo: return memo[(left, right)]\\n # rslt = max([nnums[left]*nnums[mid]*nnums[right]+dfs(left, mid, memo)+dfs(mid, right, memo) for mid in range(left+1, right)] or [0] )\\n # memo[(left, right)] = rslt\\n # return rslt\\n # return dfs(0, n-1)\\n def maxCoins(self, nums: List[int]) -> int:\\n nums = [x for x in nums if x != 0]\\n def maxcoin(nums, memo = {}):\\n if nums not in memo:\\n first , last = nums[0], nums[-1]\\n ans = max([maxcoin(nums[:k+1]) + maxcoin(nums[k:]) + first * nums[k] * last for k in range(1, len(nums)-1)] or [0] )\\n memo[nums] = ans\\n return memo[nums]\\n\\n return maxcoin(tuple([1] + nums + [1]))\\n\"\n },\n {\n \"alpha_fraction\": 0.31134968996047974,\n \"alphanum_fraction\": 0.36349692940711975,\n \"avg_line_length\": 37.411766052246094,\n \"blob_id\": \"8f42d5e7e4a22ce49d109c1afb8a22054d4362b0\",\n \"content_id\": \"8019efb6bc5362911823cda46483caa8ec6a713f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 652,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 17,\n \"path\": \"/lc97_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def isInterleave(self, s1: str, s2: str, s3: str) -> bool:\\n l_s1, l_s2 = len(s1), len(s2)\\n if len(s3) != l_s1 + l_s2:\\n return False\\n dp = [[False]*(l_s2 + 1) for _ in range(l_s1 + 1)]\\n for i in range(l_s1 + 1):\\n for j in range(l_s2 + 1):\\n if i == 0 and j == 0:\\n dp[i][j] = True\\n continue\\n if i > 0 and s1[i-1] == s3[i+j-1]:\\n dp[i][j] = dp[i-1][j]\\n if j > 0 and s2[j-1] == s3[i+j-1]:\\n dp[i][j] = dp[i][j] or dp[i][j-1]\\n \\n return dp[-1][-1]\"\n },\n {\n \"alpha_fraction\": 0.5392953753471375,\n \"alphanum_fraction\": 0.5447154641151428,\n \"avg_line_length\": 32.54545593261719,\n \"blob_id\": \"e61b78aa710a0059eb637da87c3efb320eb785c1\",\n \"content_id\": \"d6ba24fc6b2240bb44452dd7516efa444086094f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 369,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 11,\n \"path\": \"/lc1570_oop.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class SparseVector:\\n def __init__(self, nums: List[int]):\\n self.vec = {i:v for i, v in enumerate(nums) if v != 0}\\n\\n # Return the dotProduct of two sparse vectors\\n def dotProduct(self, vec: 'SparseVector') -> int:\\n rslt = 0\\n for i,v in self.vec.items():\\n if i in vec.vec:\\n rslt += v*vec.vec[i]\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.43286219239234924,\n \"alphanum_fraction\": 0.46289753913879395,\n \"avg_line_length\": 32.29411697387695,\n \"blob_id\": \"6d9bff9e01364ee610e25ddf77be5eb5edd744fc\",\n \"content_id\": \"c0f4d48faaffece23a9aa5cec6cf11cd37db0445\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 566,\n \"license_type\": \"no_license\",\n \"max_line_length\": 54,\n \"num_lines\": 17,\n \"path\": \"/lc666_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def pathSum(self, nums: List[int]) -> int:\\n maps = {num//10: num%10 for num in nums}\\n self.rslt = 0\\n def dfs(idx, curr):\\n if idx not in maps: return\\n curr += maps[idx]\\n depth, loc = divmod(idx, 10)\\n left = (depth+1)*10+loc*2-1\\n right = left + 1\\n if left not in maps and right not in maps:\\n self.rslt += curr\\n else:\\n dfs(left, curr)\\n dfs(right, curr)\\n dfs(nums[0]//10, 0)\\n return self.rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.44397464394569397,\n \"alphanum_fraction\": 0.46300211548805237,\n \"avg_line_length\": 32.78571319580078,\n \"blob_id\": \"6b80237ef8975cdf1346ee3262f90b62bfc3fe78\",\n \"content_id\": \"73d79088bf46689e42a171082880c1f866e7e56d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 473,\n \"license_type\": \"no_license\",\n \"max_line_length\": 53,\n \"num_lines\": 14,\n \"path\": \"/lc907_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def sumSubarrayMins(self, arr: List[int]) -> int:\\n modulo = 10**9 + 7\\n stk, rslt, remain = [], 0, 0\\n for i, num in enumerate(arr):\\n count = 1\\n while stk and stk[-1][0] >= num:\\n tnum, tcount = stk.pop()\\n count += tcount\\n remain -= tnum*tcount\\n remain += count*num\\n rslt += remain\\n stk.append((num, count))\\n return rslt%modulo\\n\"\n },\n {\n \"alpha_fraction\": 0.4076305329799652,\n \"alphanum_fraction\": 0.4538152515888214,\n \"avg_line_length\": 37.30769348144531,\n \"blob_id\": \"685b8c8feec6ac7f6c8da64f6349736ea5ac5547\",\n \"content_id\": \"dd9fdf45f05a3dee1b50d75fef85ebc1b659c434\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 498,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 13,\n \"path\": \"/lc1577_math.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def numTriplets(self, nums1: List[int], nums2: List[int]) -> int:\\n c1, c2 = collections.Counter(nums1), collections.Counter(nums2)\\n def helper(c1, c2):\\n rslt, temp = 0, 0\\n for k, v in c1.items():\\n rslt += v*c2[k]*(c2[k]-1)//2\\n\\n for c in c2:\\n if c < k and (k*k)%c == 0:\\n rslt += v*c2[c]*c2[(k*k)//c]\\n return rslt\\n return helper(c1, c2) + helper(c2, c1)\\n\"\n },\n {\n \"alpha_fraction\": 0.4770427942276001,\n \"alphanum_fraction\": 0.4894941747188568,\n \"avg_line_length\": 34.69444274902344,\n \"blob_id\": \"dcc7b9e452d91ca0ccd45c07a6af1367b537462f\",\n \"content_id\": \"fd000c86df6b1c3bd5b9f90a32aa253a9c2e1d27\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1285,\n \"license_type\": \"no_license\",\n \"max_line_length\": 90,\n \"num_lines\": 36,\n \"path\": \"/lc735_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# ast > 0 fly to right, won't crash previous\\n# ast < 0 fly to left, if previous same dir, won't crash either\\n# ast < 0 fly to left, previous fly to right, crash(previous crash, ast crash, both crash)\\nclass Solution:\\n def asteroidCollision(self, asteroids: List[int]) -> List[int]:\\n stack = []\\n for ast in asteroids:\\n if ast > 0:\\n stack.append(ast)\\n elif not stack or stack[-1] < 0:\\n stack.append(ast)\\n else:\\n while stack and stack[-1] > 0 and stack[-1] < -ast:\\n stack.pop()\\n if not stack or stack[-1] < 0:\\n stack.append(ast)\\n elif stack[-1] == -ast:\\n stack.pop()\\n return stack\\n\\n# ast crashed previous asteroid in the stack\\n# ast and previous both crash\\n# ast crash\\n def asteroidCollision(self, asteroids: List[int]) -> List[int]:\\n stack = []\\n for ast in asteroids:\\n while stack and stack[-1] > 0 > ast:\\n if stack[-1] < -ast:\\n stack.pop()\\n continue\\n elif stack[-1] == -ast:\\n stack.pop()\\n break\\n else:\\n stack.append(ast)\\n return stack\\n\"\n },\n {\n \"alpha_fraction\": 0.4515393376350403,\n \"alphanum_fraction\": 0.4526796042919159,\n \"avg_line_length\": 27.29032325744629,\n \"blob_id\": \"398ef250f13bc49a9638e886fa28e497c6da169b\",\n \"content_id\": \"fc181f0adae1bab4945afe666fd6d40b8839988d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 877,\n \"license_type\": \"no_license\",\n \"max_line_length\": 86,\n \"num_lines\": 31,\n \"path\": \"/lc211_trie.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class WordDictionary:\\n\\n def __init__(self):\\n \\\"\\\"\\\"\\n Initialize your data structure here.\\n \\\"\\\"\\\"\\n self.trie = {\\\"end\\\":False}\\n\\n\\n def addWord(self, word: str) -> None:\\n curr = self.trie\\n for ch in word:\\n curr[ch] = curr.get(ch, {\\\"end\\\":False})\\n curr = curr[ch]\\n curr[\\\"end\\\"] = True\\n\\n\\n\\n def search(self, word: str) -> bool:\\n def helper(word, trie):\\n if not word: return trie[\\\"end\\\"]\\n for i, ch in enumerate(word):\\n if ch == \\\".\\\":\\n for key in trie.keys():\\n if key != \\\"end\\\" and helper(word[i+1:], trie[key]): return True\\n return False\\n elif not ch in trie:\\n return False\\n trie = trie[ch]\\n return trie[\\\"end\\\"]\\n return helper(word, self.trie)\\n\"\n },\n {\n \"alpha_fraction\": 0.3709884583950043,\n \"alphanum_fraction\": 0.3979460895061493,\n \"avg_line_length\": 32.869564056396484,\n \"blob_id\": \"e1f234fb69faf01aa34e91cf323ab0f7e61373b0\",\n \"content_id\": \"40036f9a1f0fd4fb5e7ba0b7a4af27288cf7cbbb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 779,\n \"license_type\": \"no_license\",\n \"max_line_length\": 75,\n \"num_lines\": 23,\n \"path\": \"/lc1718_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def constructDistancedSequence(self, n: int) -> List[int]:\\n rslt = [0]*(2*n-1)\\n mark = [0]*(n+1)\\n def bk(i, cnt):\\n if cnt == 0:\\n return True\\n if i >= 2*n-1:\\n return False\\n if rslt[i]:\\n return bk(i+1, cnt)\\n\\n for num in range(n, 0, -1):\\n left, right = i, i+num if num > 1 else i\\n if not mark[num] and right < (2*n -1) and rslt[right] == 0:\\n mark[num] = 1\\n rslt[left] = rslt[right] = num\\n if bk(i+1, cnt-1): return True\\n mark[num] = 0\\n rslt[left] = rslt[right] = 0\\n return False\\n bk(0, n)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.596175491809845,\n \"alphanum_fraction\": 0.6074240803718567,\n \"avg_line_length\": 45.78947448730469,\n \"blob_id\": \"1ac8b1c737999898c7093f9e6e881c5de0272b21\",\n \"content_id\": \"99fee70d1e5d186171a84a39027032ffb2261b68\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 889,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 19,\n \"path\": \"/lc715_sortedlist.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from sortedcontainers import SortedList\\nclass RangeModule:\\n def __init__(self):\\n self.ranges = SortedList()\\n def addRange(self, left: int, right: int) -> None:\\n idl, idr = self.ranges.bisect_left(left), self.ranges.bisect_right(right)\\n for _ in range(idr-idl):\\n self.ranges.pop(idl)\\n if idl%2 == 0: self.ranges.add(left)\\n if idr%2 == 0: self.ranges.add(right)\\n def queryRange(self, left: int, right: int) -> bool:\\n idl, idr = self.ranges.bisect_right(left), self.ranges.bisect_left(right)\\n return idl == idr and idl%2 == 1\\n def removeRange(self, left: int, right: int) -> None:\\n idl, idr = self.ranges.bisect_left(left), self.ranges.bisect_right(right)\\n for _ in range(idr-idl):\\n self.ranges.pop(idl)\\n if idl%2 == 1: self.ranges.add(left)\\n if idr%2 == 1: self.ranges.add(right)\\n\"\n },\n {\n \"alpha_fraction\": 0.3676646649837494,\n \"alphanum_fraction\": 0.37005987763404846,\n \"avg_line_length\": 38.761905670166016,\n \"blob_id\": \"3f921c4d819f5b57c52c16b18443fa58f50769a0\",\n \"content_id\": \"3740e649c164498a7f8c982b9d9935f70dae7c4d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 835,\n \"license_type\": \"no_license\",\n \"max_line_length\": 142,\n \"num_lines\": 21,\n \"path\": \"/lc1106_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def parseBoolExpr(self, expression: str) -> bool:\\n stk_op, stk_or, ops = [], [], {\\\"t\\\":True, \\\"f\\\":False, \\\"|\\\": lambda x, y: x or y, \\\"&\\\":lambda x, y : x and y, \\\"!\\\":lambda x: not x, \\\"(\\\":\\\"(\\\"}\\n for c in expression:\\n if c == \\\",\\\": continue\\n if c in (\\\"t\\\", \\\"f\\\", \\\"(\\\"):\\n stk_op.append(ops[c])\\n elif c in (\\\"!\\\", \\\"|\\\", \\\"&\\\"):\\n stk_or.append(c)\\n\\n if c == \\\")\\\":\\n opr = stk_or.pop()\\n rslt = True if opr == \\\"&\\\" else False\\n if opr == \\\"!\\\":\\n rslt = ops[opr](stk_op.pop())\\n else:\\n while stk_op[-1] != \\\"(\\\":\\n rslt = ops[opr](rslt, stk_op.pop())\\n stk_op.pop()\\n stk_op.append(rslt)\\n return stk_op[0]\\n\"\n },\n {\n \"alpha_fraction\": 0.4253135621547699,\n \"alphanum_fraction\": 0.44982895255088806,\n \"avg_line_length\": 43.97435760498047,\n \"blob_id\": \"37f6ac89e7bb46dc1177fca6fa971c3744400a13\",\n \"content_id\": \"933286a2808900b781248a13a92ea242c44e0538\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1754,\n \"license_type\": \"no_license\",\n \"max_line_length\": 103,\n \"num_lines\": 39,\n \"path\": \"/lc505_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def shortestDistance(self, maze: List[List[int]], start: List[int], destination: List[int]) -> int:\\n heap, visited = [(0, start[0], start[1])], {}\\n rslt = float(\\\"inf\\\")\\n nrow, ncol = len(maze), len(maze[0])\\n while heap:\\n step, currx, curry = heapq.heappop(heap)\\n visited.add((currx, curry))\\n if currx == destination[0] and curry == destination[1]:\\n return step\\n for dx, dy in [(1,0), (-1, 0), (0, 1), (0, -1)]:\\n tx, ty, l = currx, curry, 0\\n while 0 <= tx+dx < nrow and 0 <= ty+dy < ncol and maze[tx+dx][ty+dy] == 0:\\n tx += dx\\n ty += dy\\n l += 1\\n if (tx, ty) not in visited:\\n heapq.heappush(heap, (step+l, tx, ty))\\n return -1\\n\\n\\nclass Solution:\\n def shortestDistance(self, maze: List[List[int]], start: List[int], destination: List[int]) -> int:\\n heap, visited = [(0, start[0], start[1])], {(start[0], start[1]): 0}\\n nrow, ncol = len(maze), len(maze[0])\\n while heap:\\n step, currx, curry = heapq.heappop(heap)\\n if currx == destination[0] and curry == destination[1]:\\n return step\\n for dx, dy in [(1,0), (-1, 0), (0, 1), (0, -1)]:\\n tx, ty, l = currx, curry, 0\\n while 0 <= tx+dx < nrow and 0 <= ty+dy < ncol and maze[tx+dx][ty+dy] == 0:\\n tx += dx\\n ty += dy\\n l += 1\\n if (tx, ty) not in visited or visited[(tx, ty)] > step+l:\\n visited[(tx, ty)] = step + l\\n heapq.heappush(heap, (step+l, tx, ty))\\n return -1\\n\"\n },\n {\n \"alpha_fraction\": 0.4560975730419159,\n \"alphanum_fraction\": 0.48048779368400574,\n \"avg_line_length\": 36.272727966308594,\n \"blob_id\": \"c055fb356a5143b98a2371f419ad8a40a34e7e3b\",\n \"content_id\": \"beb07a003d90c1a94e6678b699fc6d0ef507c427\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 410,\n \"license_type\": \"no_license\",\n \"max_line_length\": 69,\n \"num_lines\": 11,\n \"path\": \"/lc828_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def uniqueLetterString(self, s: str) -> int:\\n index, rslt = {c:[-1, -1] for c in string.ascii_uppercase}, 0\\n for i, c in enumerate(s):\\n pre, last = index[c]\\n rslt += (i-last)*(last-pre)\\n index[c] = [last, i]\\n n = len(s)\\n for c in index:\\n rslt += (n-index[c][1])*(index[c][1]-index[c][0])\\n return rslt%(10**9+7)\\n\"\n },\n {\n \"alpha_fraction\": 0.5269230604171753,\n \"alphanum_fraction\": 0.5326923131942749,\n \"avg_line_length\": 39,\n \"blob_id\": \"2708fe7489967595577340064e75a59e628c84b7\",\n \"content_id\": \"a75e1045b658aec6d3be3ce455dfef51d265d417\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 520,\n \"license_type\": \"no_license\",\n \"max_line_length\": 84,\n \"num_lines\": 13,\n \"path\": \"/lc39_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def combinationSum(self, candidates: List[int], target: int) -> List[List[int]]:\\n rslt, n = [], len(candidates)\\n candidates.sort()\\n def backtracking(target, i,curr):\\n if target == 0:\\n rslt.append(curr)\\n elif i < n and target >= candidates[i]:\\n temp = candidates[i]\\n backtracking(target-temp, i, curr+[temp])\\n backtracking(target, i+1, curr)\\n backtracking(target, 0, [])\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.39650872349739075,\n \"alphanum_fraction\": 0.4152119755744934,\n \"avg_line_length\": 29.846153259277344,\n \"blob_id\": \"22495c604d430a53923d1d7d137654f2d2c2e107\",\n \"content_id\": \"a3cfe349dce83c9a3075f750813838c5e20d9aad\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 802,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 26,\n \"path\": \"/lc489_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def cleanRoom(self, robot):\\n \\\"\\\"\\\"\\n :type robot: Robot\\n :rtype: None\\n \\\"\\\"\\\"\\n def move_back():\\n robot.turnLeft()\\n robot.turnLeft()\\n robot.move()\\n robot.turnLeft()\\n robot.turnLeft()\\n\\n def dfs(x, y, d):\\n visited.add((x, y))\\n robot.clean()\\n for i in range(4):\\n curr_d = (d+i)%4\\n curr_x, curr_y = x + directions[curr_d][0], y+directions[curr_d][1]\\n if (curr_x, curr_y) not in visited and robot.move():\\n dfs(curr_x, curr_y, curr_d)\\n move_back()\\n robot.turnLeft()\\n directions = [(-1, 0), (0, -1), (1, 0), (0, 1)]\\n visited = set()\\n dfs(0, 0, 0)\\n\"\n },\n {\n \"alpha_fraction\": 0.38329237699508667,\n \"alphanum_fraction\": 0.4127764105796814,\n \"avg_line_length\": 30.30769157409668,\n \"blob_id\": \"1014f0de30d6910acddc285b44a621199e85a1fc\",\n \"content_id\": \"2401c83ae5a4b0eb5f7d46da799b9eaaa0f96897\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 407,\n \"license_type\": \"no_license\",\n \"max_line_length\": 67,\n \"num_lines\": 13,\n \"path\": \"/lc1155_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def numRollsToTarget(self, d: int, f: int, target: int) -> int:\\n @lru_cache(None)\\n def dfs(d, t):\\n if d*f < t or d > t:\\n return 0\\n if d== 0 and t == 0:\\n return 1\\n rslt = 0\\n for j in range(1, f+1):\\n rslt += dfs(d-1, t-j)\\n return rslt\\n return dfs(d, target)%(10**9+7)\\n\"\n },\n {\n \"alpha_fraction\": 0.39701491594314575,\n \"alphanum_fraction\": 0.41044774651527405,\n \"avg_line_length\": 32.54999923706055,\n \"blob_id\": \"a5c8145493601947b09c942bbe56bad9b1227aba\",\n \"content_id\": \"1b6ec6f6252cdcfaf9a0361183ecf9184bb3c2eb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 670,\n \"license_type\": \"no_license\",\n \"max_line_length\": 75,\n \"num_lines\": 20,\n \"path\": \"/lc438_str.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findAnagrams(self, s: str, p: str) -> List[int]:\\n if len(p) > len(s):\\n return []\\n \\n counter_p, counter_s, cnt, rslt = collections.Counter(p), {}, 0, []\\n \\n for i, c in enumerate(s):\\n if c not in counter_p:\\n counter_s = {}\\n cnt = 0\\n elif cnt < len(p):\\n counter_s[c] = counter_s.get(c, 0) +1\\n cnt += 1\\n else:\\n counter_s[c] = counter_s.get(c, 0) + 1\\n counter_s[s[i-len(p)]] -= 1\\n if counter_s == counter_p:\\n rslt.append(i-len(p)+1)\\n return rslt\"\n },\n {\n \"alpha_fraction\": 0.4977678656578064,\n \"alphanum_fraction\": 0.5089285969734192,\n \"avg_line_length\": 39.727272033691406,\n \"blob_id\": \"e03bb5c6ee2589ba1d7003bee6ab8ea5025ffa00\",\n \"content_id\": \"564f382da0663da6124176f89712956aa47c21eb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 448,\n \"license_type\": \"no_license\",\n \"max_line_length\": 102,\n \"num_lines\": 11,\n \"path\": \"/lc486_minmax.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def PredictTheWinner(self, nums: List[int]) -> bool:\\n memo = {}\\n def dfs(left, right):\\n if (left, right) in memo:\\n return memo[(left, right)]\\n if left == right:\\n return nums[left]\\n memo[(left, right)] = max(nums[left]- dfs(left +1, right), nums[right]-dfs(left, right-1))\\n return memo[(left, right)]\\n return dfs(0, len(nums)-1) >= 0\\n\"\n },\n {\n \"alpha_fraction\": 0.429824560880661,\n \"alphanum_fraction\": 0.4429824650287628,\n \"avg_line_length\": 31.571428298950195,\n \"blob_id\": \"7b41c6a07ca846608eac9fd48a2fb156c6e1b740\",\n \"content_id\": \"dd1f3def3b43da61ef312384d15ebbb1d51e3aa9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 456,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 14,\n \"path\": \"/lc875_bt.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minEatingSpeed(self, piles: List[int], H: int) -> int:\\n def eatingHour(k):\\n return sum( (x-1)//k + 1 for x in piles)\\n left, right = 1, max(piles)\\n while left < right:\\n mid = (left + right)//2\\n if eatingHour(mid) > H:\\n left = mid\\n else:\\n right = mid\\n if (left + right)//2 == mid:\\n left += 1\\n return right\\n\"\n },\n {\n \"alpha_fraction\": 0.43914079666137695,\n \"alphanum_fraction\": 0.45584726333618164,\n \"avg_line_length\": 37.09090805053711,\n \"blob_id\": \"9d56001ebf0deab894829b9c85e71c7875b27e8e\",\n \"content_id\": \"40ad4e5fcd6ffdf8efa0067ba3fb326f94d8f458\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 419,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 11,\n \"path\": \"/lc209_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minSubArrayLen(self, s: int, nums: List[int]) -> int:\\n curr, rslt, left = 0, len(nums)+1, 0\\n for right, num in enumerate(nums):\\n curr += num\\n if curr >= s:\\n while curr >= s:\\n rslt = min(right-left+1, rslt)\\n curr -= nums[left]\\n left += 1\\n return 0 if rslt == len(nums)+1 else rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.37187498807907104,\n \"alphanum_fraction\": 0.37187498807907104,\n \"avg_line_length\": 32.68421173095703,\n \"blob_id\": \"c8c8b7d47001a16749458897052ec90ab928d6f0\",\n \"content_id\": \"84126ae09efca13cc8b22ecec35e10ad05eab48b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 640,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 19,\n \"path\": \"/lc1096_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def braceExpansionII(self, expression: str) -> List[str]:\\n stk, pre, curr = [], [], [\\\"\\\"]\\n for c in expression:\\n if c == \\\",\\\":\\n pre += curr\\n curr = [\\\"\\\"]\\n elif c == \\\"{\\\":\\n stk.append(pre)\\n stk.append(curr)\\n pre, curr = [], [\\\"\\\"]\\n elif c == \\\"}\\\":\\n p_curr = stk.pop()\\n p_pre = stk.pop()\\n curr = [s+k for k in pre+curr for s in p_curr]\\n pre = p_pre\\n else:\\n curr = [s+c for s in curr]\\n return sorted(set(pre+curr))\\n\"\n },\n {\n \"alpha_fraction\": 0.3541666567325592,\n \"alphanum_fraction\": 0.3717948794364929,\n \"avg_line_length\": 30.200000762939453,\n \"blob_id\": \"89df6a4651f54ac7e6b14d0a666a9f854d2400f2\",\n \"content_id\": \"8b9f4f5e5f7173e29842910cc1426240ad382653\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 624,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 20,\n \"path\": \"/lc1482_bs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minDays(self, bloomDay: List[int], m: int, k: int) -> int:\\n if m*k > len(bloomDay): return -1\\n left, right = 1, max(bloomDay)\\n while left <= right:\\n mid = (left + right)//2\\n tempf, tempb= 0, 0\\n for bD in bloomDay:\\n if bD <= mid:\\n tempf += 1\\n else:\\n tempf = 0\\n if tempf == k:\\n tempb += 1\\n tempf = 0\\n if tempb >= m:\\n right = mid -1\\n else:\\n left = mid + 1\\n return left\\n\"\n },\n {\n \"alpha_fraction\": 0.49266862869262695,\n \"alphanum_fraction\": 0.5014662742614746,\n \"avg_line_length\": 33.099998474121094,\n \"blob_id\": \"abba0cfb6eebfb855ab319f75ceaff14de29136c\",\n \"content_id\": \"b8a7de7b11ce6e6280ec2448d477300c63d04ff6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 341,\n \"license_type\": \"no_license\",\n \"max_line_length\": 59,\n \"num_lines\": 10,\n \"path\": \"/lc1665_greedy.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minimumEffort(self, tasks: List[List[int]]) -> int:\\n tasks.sort(key = lambda x: x[0] - x[1])\\n rslt = quota = 0\\n for actual, minimum in tasks:\\n if minimum > quota:\\n rslt += (minimum - quota)\\n quota = minimum\\n quota -= actual\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.45057034492492676,\n \"alphanum_fraction\": 0.4648289084434509,\n \"avg_line_length\": 37.96296310424805,\n \"blob_id\": \"8bbb1eae0a08fe54ab159a8ce3aaa090144b7ed5\",\n \"content_id\": \"e83528de573881db09e8c68af476a079425d1172\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1052,\n \"license_type\": \"no_license\",\n \"max_line_length\": 75,\n \"num_lines\": 27,\n \"path\": \"/lc973_sort.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def kClosest(self, points: List[List[int]], K: int) -> List[List[int]]:\\n def partition(left, right):\\n pivot, l = (left+right)//2, left\\n dist_pivot = dist[(points[pivot][0], points[pivot][1])]\\n points[right], points[pivot] = points[pivot], points[right]\\n for i in range(left, right+1):\\n if dist[(points[i][0], points[i][1])] < dist_pivot:\\n points[i], points[l] = points[l], points[i]\\n l += 1\\n points[l], points[right] = points[right], points[l]\\n return l\\n def select(left, right):\\n if left == right: return\\n pivot = partition(left, right)\\n if pivot == K-1:\\n return\\n if pivot > K-1:\\n select(left, pivot-1)\\n else:\\n select(pivot+1, right)\\n return\\n dist = {}\\n for x, y in points:\\n dist[(x, y)] = x**2 + y**2\\n select(0, len(points)-1)\\n return points[:K]\\n\"\n },\n {\n \"alpha_fraction\": 0.49857550859451294,\n \"alphanum_fraction\": 0.49857550859451294,\n \"avg_line_length\": 34.099998474121094,\n \"blob_id\": \"c3551863f596a6eefee9b730d40e5622e1ca2cb3\",\n \"content_id\": \"9c431f505b0a13f3657986e29ef740a0313189bb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 702,\n \"license_type\": \"no_license\",\n \"max_line_length\": 51,\n \"num_lines\": 20,\n \"path\": \"/lc133_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def cloneGraph(self, node: 'Node') -> 'Node':\\n if not node: return None\\n newnode = Node(node.val)\\n if not node.neighbors: return newnode\\n queue = deque([node])\\n amap = {node.val: newnode}\\n visited = set()\\n while queue:\\n onode = queue.popleft()\\n nnode = amap[onode.val]\\n nnode.neighbors = []\\n for n in onode.neighbors:\\n if n.val not in amap:\\n amap[n.val] = Node(n.val)\\n nnode.neighbors.append(amap[n.val])\\n if n.val not in visited:\\n queue.append(n)\\n visited.add(nnode.val)\\n return newnode\\n\"\n },\n {\n \"alpha_fraction\": 0.392123281955719,\n \"alphanum_fraction\": 0.4178082048892975,\n \"avg_line_length\": 40.71428680419922,\n \"blob_id\": \"7c11f7528dd57a58eb26a99d2a745deafaef561c\",\n \"content_id\": \"0212c273f2549323d32cee69f880093cf9d680df\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 584,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 14,\n \"path\": \"/lc778_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def swimInWater(self, grid: List[List[int]]) -> int:\\n visited, heap, n = {(0,0)}, [(grid[0][0], 0, 0)], len(grid)\\n rslt = 0\\n while heap:\\n node, x, y = heapq.heappop(heap)\\n rslt = max(rslt, node)\\n if x == n-1 and y == n-1:\\n return rslt\\n for i, j in [(x+1, y), (x-1, y), (x, y+1), (x, y-1)]:\\n if 0 <= i < n and 0 <= j < n and (i, j) not in visited:\\n heapq.heappush(heap, (grid[i][j], i, j))\\n visited.add((i, j))\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.509018063545227,\n \"alphanum_fraction\": 0.52304607629776,\n \"avg_line_length\": 40.58333206176758,\n \"blob_id\": \"dc2de500fa75077465b9ddc14191b5b8c71a1586\",\n \"content_id\": \"441711c22902d49945af15d9f311456ad51fada5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 499,\n \"license_type\": \"no_license\",\n \"max_line_length\": 60,\n \"num_lines\": 12,\n \"path\": \"/lc862_deque.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def shortestSubarray(self, A: List[int], K: int) -> int:\\n presum = [0]\\n for num in A: presum.append(presum[-1]+num)\\n rslt, dque = len(A)+1, collections.deque()\\n for idx, pre in enumerate(presum):\\n while dque and pre <= presum[dque[-1]]:\\n dque.pop()\\n dque.append(idx)\\n while pre - presum[dque[0]] >= K:\\n rslt = min(rslt, idx-dque.popleft())\\n return -1 if rslt == len(A)+1 else rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.5389447212219238,\n \"alphanum_fraction\": 0.5452261567115784,\n \"avg_line_length\": 40.894737243652344,\n \"blob_id\": \"482c56afb5b1da7555ddfe12cfdda2eb2226065f\",\n \"content_id\": \"63433bdee6d61e887c568ba7f3db96b7fff3787c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 796,\n \"license_type\": \"no_license\",\n \"max_line_length\": 114,\n \"num_lines\": 19,\n \"path\": \"/lc1462_graph.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def checkIfPrerequisite(self, n: int, prerequisites: List[List[int]], queries: List[List[int]]) -> List[bool]:\\n isPre = collections.defaultdict(set)\\n graph, degree = collections.defaultdict(list), collections.defaultdict(int)\\n for pre, curr in prerequisites:\\n graph[pre].append(curr)\\n isPre[curr].add(pre)\\n degree[curr] += 1\\n degree[pre] += 0\\n\\n dque = collections.deque([v for v in degree if degree[v] == 0])\\n while dque:\\n temp = dque.popleft()\\n for post in graph[temp]:\\n isPre[post] |= isPre[temp]\\n degree[post] -= 1\\n if degree[post] == 0:\\n dque.append(post)\\n return [x in isPre[y] for x, y in queries]\\n\"\n },\n {\n \"alpha_fraction\": 0.4891640841960907,\n \"alphanum_fraction\": 0.495356023311615,\n \"avg_line_length\": 31.299999237060547,\n \"blob_id\": \"ee69d78b9b26271aadb7367e91f2068b4363743a\",\n \"content_id\": \"5c60e7e4a5c64f44fce08b508dd0e00940c06adf\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 323,\n \"license_type\": \"no_license\",\n \"max_line_length\": 58,\n \"num_lines\": 10,\n \"path\": \"/lc78_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def subsets(self, nums: List[int]) -> List[List[int]]:\\n rslt = [[]]\\n def backtracking(i):\\n if i == len(nums): return\\n for j in range(len(rslt)):\\n rslt.append(rslt[j] +[nums[i]])\\n backtracking(i+1)\\n backtracking(0)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4246031641960144,\n \"alphanum_fraction\": 0.4642857015132904,\n \"avg_line_length\": 30.5,\n \"blob_id\": \"9662083111d68b40be5ab219d976e6ffaf71a147\",\n \"content_id\": \"d009719a2aec3499c82de3acff73e97e05ceaced\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 252,\n \"license_type\": \"no_license\",\n \"max_line_length\": 54,\n \"num_lines\": 8,\n \"path\": \"/lc96_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def numTrees(self, n: int) -> int:\\n dp = [0]*(n+1)\\n dp[0] = 1\\n for total in range(1, n+1):\\n for left in range(1, total+1):\\n dp[total] += dp[left-1]*dp[total-left]\\n return dp[-1]\\n\"\n },\n {\n \"alpha_fraction\": 0.4514620006084442,\n \"alphanum_fraction\": 0.46315789222717285,\n \"avg_line_length\": 41.75,\n \"blob_id\": \"74ad6ef7f41d7709e0f8d5b06ed3711a8670b707\",\n \"content_id\": \"3c2d5b2333d77ddb98742cdbd649536c00410245\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 855,\n \"license_type\": \"no_license\",\n \"max_line_length\": 92,\n \"num_lines\": 20,\n \"path\": \"/lc269_graph.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def alienOrder(self, words: List[str]) -> str:\\n graph, degree = collections.defaultdict(list), {x:0 for word in words for x in word}\\n for i, word in enumerate(words[:-1]):\\n for j, c in enumerate(word):\\n if j >= len(words[i+1]):\\n return \\\"\\\"\\n if c != words[i+1][j]:\\n graph[c].append(words[i+1][j])\\n degree[words[i+1][j]] += 1\\n break\\n dque, rslt = collections.deque([a for a in degree if degree[a] == 0]), []\\n while dque:\\n temp = dque.popleft()\\n rslt.append(temp)\\n for end in graph[temp]:\\n degree[end] -= 1\\n if degree[end] == 0:\\n dque.append(end)\\n return \\\"\\\".join(rslt) if len(rslt) == len(degree) else \\\"\\\"\\n\"\n },\n {\n \"alpha_fraction\": 0.406876802444458,\n \"alphanum_fraction\": 0.406876802444458,\n \"avg_line_length\": 26.920000076293945,\n \"blob_id\": \"d74630aee850edb8dec3b67ea4ac1fec3378ae41\",\n \"content_id\": \"ffa0a34323e88e2620d643acc61e5b65b86fd52d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 698,\n \"license_type\": \"no_license\",\n \"max_line_length\": 67,\n \"num_lines\": 25,\n \"path\": \"/lc1650_lca.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def lowestCommonAncestor(self, p: 'Node', q: 'Node') -> 'Node':\\n parents = {}\\n if not p or not q: return p or q\\n while p:\\n if p == q: return p\\n parents[p] = p.parent\\n p = p.parent\\n while q:\\n q = q.parent\\n if q in parents:\\n return q\\n return None\\n def lowestCommonAncestor(self, p: 'Node', q: 'Node') -> 'Node':\\n tp, tq = p, q\\n while tp != tq:\\n if tp.parent:\\n tp = tp.parent\\n else:\\n tp = q\\n if tq.parent:\\n tq = tq.parent\\n else:\\n tq = p\\n return tp\\n\"\n },\n {\n \"alpha_fraction\": 0.3593582808971405,\n \"alphanum_fraction\": 0.3893048167228699,\n \"avg_line_length\": 33.62963104248047,\n \"blob_id\": \"f035669728a6c9c2290e770d26e210ca89116f31\",\n \"content_id\": \"4787ca7f987e6ff7ee9016ebe8205675d5598cfb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 935,\n \"license_type\": \"no_license\",\n \"max_line_length\": 133,\n \"num_lines\": 27,\n \"path\": \"/lc803_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def hitBricks(self, grid: List[List[int]], hits: List[List[int]]) -> List[int]:\\n m, n = len(grid), len(grid[0])\\n def dfs(i, j):\\n if 0 <= i < m and 0 <= j < n and grid[i][j] == 1:\\n rslt = 1\\n grid[i][j] = 2\\n rslt += sum(dfs(x, y) for x, y in [(i-1, j), (i+1, j), (i, j-1), (i, j+1)])\\n return rslt\\n return 0\\n\\n def is_new_stable(i, j):\\n return i== 0 or any([0 <= x < m and 0 <= y < n and grid[x][y] == 2 for x, y in [(i-1, j), (i+1, j), (i, j-1), (i, j+1)]])\\n\\n for i, j in hits:\\n grid[i][j] -= 1\\n\\n for i in range(n):\\n dfs(0, i)\\n\\n res = [0]*len(hits)\\n for k in range(len(hits)-1, -1, -1):\\n i, j = hits[k]\\n grid[i][j] += 1\\n if grid[i][j] == 1 and is_new_stable(i, j):\\n res[k] = dfs(i, j)-1\\n return res\\n\"\n },\n {\n \"alpha_fraction\": 0.504878044128418,\n \"alphanum_fraction\": 0.5219511985778809,\n \"avg_line_length\": 40,\n \"blob_id\": \"680df576f1298c20de03afd7003a401cdc5a14d2\",\n \"content_id\": \"b80fcf5e29481eab93d96c464725a43dd3b94dd6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 410,\n \"license_type\": \"no_license\",\n \"max_line_length\": 63,\n \"num_lines\": 10,\n \"path\": \"/lc1569_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def numOfWays(self, nums: List[int]) -> int:\\n def helper(nums):\\n if not nums: return 1\\n root = nums[0]\\n left = [num for num in nums if num < root]\\n right = [num for num in nums if num > root]\\n l, r = len(left), len(right)\\n return math.comb(l+r, l)*helper(left)*helper(right)\\n return (helper(nums)-1)%(10**9+7)\\n\"\n },\n {\n \"alpha_fraction\": 0.366233766078949,\n \"alphanum_fraction\": 0.37402597069740295,\n \"avg_line_length\": 31.08333396911621,\n \"blob_id\": \"f8e04d027efa2e5daa121d97515db2b0a7f4325e\",\n \"content_id\": \"5f0537aa7e83445dae26478465b233ff5d0009a2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 385,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 12,\n \"path\": \"/lc294_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def canWin(self, s: str) -> bool:\\n memo = {}\\n def dfs(s):\\n if s in memo:\\n return memo[s]\\n for i in range(len(s)-1):\\n if s[i] == \\\"+\\\" and s[i+1] == \\\"+\\\" and not dfs(s[:i]+\\\"-\\\"+s[i+2:]):\\n memo[s] = True\\n return True\\n memo[s] = False\\n return dfs(s)\\n\"\n },\n {\n \"alpha_fraction\": 0.49251100420951843,\n \"alphanum_fraction\": 0.49603524804115295,\n \"avg_line_length\": 35.6129035949707,\n \"blob_id\": \"1593d9d208f3f82f74fbc98c9ef928e6ed7a3119\",\n \"content_id\": \"c6126f1dcd370ac989866d37418859d3eaab1799\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1135,\n \"license_type\": \"no_license\",\n \"max_line_length\": 77,\n \"num_lines\": 31,\n \"path\": \"/lc1306_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def canReach(self, arr: List[int], start: int) -> bool:\\n seen = set()\\n self.rslt, larr = False, len(arr)\\n def backtracking(idx):\\n if arr[idx] == 0:\\n self.rslt = True\\n return\\n if idx in seen:\\n return\\n seen.add(idx)\\n if idx + arr[idx] < larr:\\n backtracking(idx+arr[idx])\\n if idx - arr[idx] >= 0:\\n backtracking(idx-arr[idx])\\n backtracking(start)\\n return self.rslt\\n \\n class Solution:\\n def canReach(self, arr: List[int], start: int) -> bool:\\n dque, visited = collections.deque([start]), {start}\\n while dque:\\n curr =dque.popleft()\\n if arr[curr] == 0: return True\\n if curr + arr[curr] < len(arr) and curr+arr[curr] not in visited:\\n visited.add(curr+arr[curr])\\n dque.append(curr+arr[curr])\\n if curr - arr[curr] >= 0 and curr - arr[curr] not in visited:\\n visited.add(curr-arr[curr])\\n dque.append(curr-arr[curr])\\n return False\\n\"\n },\n {\n \"alpha_fraction\": 0.4930015504360199,\n \"alphanum_fraction\": 0.5069984197616577,\n \"avg_line_length\": 39.1875,\n \"blob_id\": \"2b9e28cf8ae1ef1b3315444360f52a9e8939a1a3\",\n \"content_id\": \"b02f9ae216fdda2bc2784bc2de3ccdcd2fc85b58\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 643,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 16,\n \"path\": \"/lc40_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def combinationSum2(self, candidates: List[int], target: int) -> List[List[int]]:\\n if not candidates: return []\\n rslt, n = [], len(candidates)\\n candidates.sort()\\n def backtracking(i, target, curr):\\n if target == 0:\\n rslt.append(curr)\\n elif i < n and target > 0:\\n temp = candidates[i]\\n backtracking(i+1, target-temp, curr+[temp])\\n while i < n-1 and candidates[i] == candidates[i+1]:\\n i += 1\\n backtracking(i+1, target, curr)\\n backtracking(0, target, [])\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.45750707387924194,\n \"alphanum_fraction\": 0.4645892381668091,\n \"avg_line_length\": 36.157894134521484,\n \"blob_id\": \"4147ea268ffff2b08ee45d624ae059fcb45e204f\",\n \"content_id\": \"b3b66c9a1c65577707b0eba11c8eced78c4c4f3b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 706,\n \"license_type\": \"no_license\",\n \"max_line_length\": 117,\n \"num_lines\": 19,\n \"path\": \"/lc399_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def calcEquation(self, equations: List[List[str]], values: List[float], queries: List[List[str]]) -> List[float]:\\n grph = collections.defaultdict(dict)\\n for (x, y), v in zip(equations, values):\\n grph[x][y] = v\\n grph[y][x] = 1/v\\n\\n def dfs(x, y, checked):\\n if x == y:\\n return 1\\n checked.add(x)\\n for z in grph[x]:\\n if z in checked:\\n continue\\n tmp = dfs(z, y, checked)\\n if tmp > 0:\\n return tmp*grph[x][z]\\n return -1\\n return [dfs(x, y, set()) if x in grph and y in grph else -1 for x, y in queries]\\n\"\n },\n {\n \"alpha_fraction\": 0.4752252399921417,\n \"alphanum_fraction\": 0.48048049211502075,\n \"avg_line_length\": 27.340425491333008,\n \"blob_id\": \"d7c64b62a299d7f685555b9cc8500ed308874dfb\",\n \"content_id\": \"031ad877e81439794476f3f4433cee078fd2fbae\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1332,\n \"license_type\": \"no_license\",\n \"max_line_length\": 55,\n \"num_lines\": 47,\n \"path\": \"/lc1382_bt.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Definition for a binary tree node.\\n# class TreeNode:\\n# def __init__(self, val=0, left=None, right=None):\\n# self.val = val\\n# self.left = left\\n# self.right = right\\nclass Solution:\\n def balanceBST(self, root: TreeNode) -> TreeNode:\\n rslt = []\\n def preorder(l, r):\\n if r == l:\\n return None\\n mid = (l+r)//2\\n root = TreeNode(rslt[mid])\\n root.left = preorder(l, mid)\\n root.right = preorder(mid+1, r)\\n return root\\n def inorder(root):\\n if not root:\\n return\\n inorder(root.left)\\n rslt.append(root.val)\\n inorder(root.right)\\n inorder(root)\\n return preorder(0, len(rslt))\\n\\nclass Solution:\\n def balanceBST(self, root: TreeNode) -> TreeNode:\\n rslt = []\\n def inorder(root):\\n if not root: return\\n inorder(root.left)\\n rslt.append(root)\\n inorder(root.right)\\n return\\n\\n def preorder(l, r):\\n if l == r:\\n return None\\n mid = (l+r)//2\\n root = rslt[mid]\\n root.left = preorder(l, mid)\\n root.right = preorder(mid+1, r)\\n return root\\n\\n inorder(root)\\n return preorder(0, len(rslt))\\n\"\n },\n {\n \"alpha_fraction\": 0.4708904027938843,\n \"alphanum_fraction\": 0.48630136251449585,\n \"avg_line_length\": 37.93333435058594,\n \"blob_id\": \"549887c89cdf030155952659673098e467f351be\",\n \"content_id\": \"c962897ed12e36cda527100006835531c40f72fd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 584,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 15,\n \"path\": \"/lc632_heap.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def smallestRange(self, nums: List[List[int]]) -> List[int]:\\n heap = [(row[0], i, 0) for i, row in enumerate(nums)]\\n right = max(row[0] for row in nums)\\n rslt = (0, float(\\\"inf\\\"))\\n heapq.heapify(heap)\\n while True:\\n left, ir, ic = heapq.heappop(heap)\\n if right-left < rslt[1]-rslt[0]:\\n rslt = (left, right)\\n if ic + 1 == len(nums[ir]):\\n return rslt\\n nxt = nums[ir][ic+1]\\n right = max(right, nxt)\\n heapq.heappush(heap, (nxt, ir, ic+1))\\n\"\n },\n {\n \"alpha_fraction\": 0.5036126971244812,\n \"alphanum_fraction\": 0.5072254538536072,\n \"avg_line_length\": 31.186046600341797,\n \"blob_id\": \"3dc30c8d51a95b5ee1f6df97bba68628b78c1ff8\",\n \"content_id\": \"0e4260a0e9f115e9cc555f4beb061864dfd5c41a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1384,\n \"license_type\": \"no_license\",\n \"max_line_length\": 88,\n \"num_lines\": 43,\n \"path\": \"/lc146_ood.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Node:\\n def __init__(self, val = 0,key = 0, left = None, right = None):\\n self.val = val\\n self.key = key\\n self.left = left\\n self.right = right\\nclass LRUCache:\\n\\n def __init__(self, capacity: int):\\n self.capacity = capacity\\n self.head = Node()\\n self.end = Node(left = self.head)\\n self.head.right = self.end\\n self.maps = {}\\n\\n def get(self, key: int) -> int:\\n if not self.maps or key not in self.maps:\\n return -1\\n curr = self.maps[key]\\n curr.left.right = curr.right\\n curr.right.left = curr.left\\n curr.left = self.end.left\\n curr.right = self.end\\n self.end.left = curr\\n curr.left.right = curr\\n return curr.val\\n\\n def put(self, key: int, value: int) -> None:\\n if key in self.maps:\\n self.maps[key].val = value\\n _ = self.get(key)\\n else:\\n curr = Node(val = value, key = key , left = self.end.left, right = self.end)\\n self.end.left.right = curr\\n self.end.left = curr\\n self.maps[key] = curr\\n if self.capacity == 0:\\n temp = self.head.right\\n self.head.right = self.head.right.right\\n self.head.right.left = self.head\\n self.maps.pop(temp.key)\\n else:\\n self.capacity -= 1\\n\"\n },\n {\n \"alpha_fraction\": 0.4146341383457184,\n \"alphanum_fraction\": 0.4471544623374939,\n \"avg_line_length\": 32.54545593261719,\n \"blob_id\": \"3bd4c39e69809d1f474ef8fe8cb66df480f14c42\",\n \"content_id\": \"01d55872801dc166f4bec243e7e50d4d2482a8c1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 369,\n \"license_type\": \"no_license\",\n \"max_line_length\": 89,\n \"num_lines\": 11,\n \"path\": \"/lc152_array.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxProduct(self, nums: List[int]) -> int:\\n if not nums: return 0\\n dp = None\\n for num in nums:\\n if dp is None:\\n dp = [num]*3\\n continue\\n dp[0], dp[1] = max(num, dp[0]*num, dp[1]*num), min(num, dp[1]*num, dp[0]*num)\\n dp[2] = max(dp[2], dp[0])\\n return dp[2]\\n\"\n },\n {\n \"alpha_fraction\": 0.5508885383605957,\n \"alphanum_fraction\": 0.5508885383605957,\n \"avg_line_length\": 43.14285659790039,\n \"blob_id\": \"76e0a522d38d5cfbb70789e4cd75a2716df22e90\",\n \"content_id\": \"a7f0c8e2a5afa635896f05034252287e0143c24d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 619,\n \"license_type\": \"no_license\",\n \"max_line_length\": 87,\n \"num_lines\": 14,\n \"path\": \"/lc113_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def pathSum(self, root: TreeNode, targetSum: int) -> List[List[int]]:\\n rslt = []\\n def backtracking(root, val, path):\\n if not root.left and not root.right and val == targetSum and path:\\n rslt.append(path)\\n else:\\n if root.left:\\n backtracking(root.left, val+root.left.val, path+[root.left.val])\\n if root.right:\\n backtracking(root.right, val+root.right.val, path+[root.right.val])\\n if not root: return rslt\\n backtracking(root, root.val, [root.val])\\n return rslt \\n\"\n },\n {\n \"alpha_fraction\": 0.48457351326942444,\n \"alphanum_fraction\": 0.49637022614479065,\n \"avg_line_length\": 38.35714340209961,\n \"blob_id\": \"a29711a19fc22172cd4e349f4dd8ded5ef90efe0\",\n \"content_id\": \"2f1735adcb52c36070c847d88c5287ec2fec3932\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1102,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 28,\n \"path\": \"/lc889_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def constructFromPrePost(self, pre: List[int], post: List[int]) -> TreeNode:\\n if not pre or not post: return None\\n root = TreeNode(pre[0])\\n if len(pre) > 1:\\n idx = post.index(pre[1])\\n root.left = self.constructFromPrePost(pre[1:idx+2], post[:idx+1])\\n root.right = self.constructFromPrePost(pre[idx+2:], post[idx+1:-1])\\n return root\\n \\nclass Solution:\\n def constructFromPrePost(self, pre: List[int], post: List[int]) -> TreeNode:\\n nodemap = {val:i for i, val in enumerate(post)}\\n stk, root = [], None\\n for val in pre:\\n if not root:\\n root = TreeNode(val)\\n stk.append(root)\\n else:\\n temp = TreeNode(val)\\n if nodemap[val] < nodemap[stk[-1].val]:\\n stk[-1].left = temp\\n else:\\n while stk and nodemap[val] > nodemap[stk[-1].val]:\\n last = stk.pop()\\n stk[-1].right = temp\\n stk.append(temp)\\n return root\\n\"\n },\n {\n \"alpha_fraction\": 0.48433613777160645,\n \"alphanum_fraction\": 0.4868224859237671,\n \"avg_line_length\": 40.04081726074219,\n \"blob_id\": \"25f87e19f55ad08b1c66e952747420e3a651e3eb\",\n \"content_id\": \"58b405ffa58f7825f049afe47cd70d004eed6d44\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2011,\n \"license_type\": \"no_license\",\n \"max_line_length\": 96,\n \"num_lines\": 49,\n \"path\": \"/lc126_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findLadders(self, beginWord: str, endWord: str, wordList: List[str]) -> List[List[str]]:\\n if not wordList or endWord not in wordList: return []\\n l, word_map, visited = len(beginWord), collections.defaultdict(list), set()\\n for word in wordList:\\n for i in range(l):\\n word_map[word[:i]+\\\"*\\\"+word[i+1:]].append(word)\\n dque = collections.deque([[beginWord]])\\n rslt = []\\n while dque:\\n size = len(dque)\\n for _ in range(size):\\n cands = dque.popleft()\\n curr = cands[-1]\\n visited.add(curr)\\n for i in range(l):\\n for word in word_map[curr[:i]+\\\"*\\\"+curr[i+1:]]:\\n if word == endWord:\\n rslt.append(cands+[word])\\n break\\n if word not in visited:\\n dque.append(cands+[word])\\n\\n if rslt:\\n break\\n return rslt\\n\\nclass Solution:\\n def findLadders(self, beginWord: str, endWord: str, wordList: List[str]) -> List[List[str]]:\\n words, wordmap, l = set(wordList), collections.defaultdict(list), len(beginWord)\\n if endWord not in words: return []\\n for word in wordList:\\n for i in range(l):\\n wordmap[word[:i]+\\\"*\\\"+word[i+1:]].append(word)\\n\\n layer, visited = collections.defaultdict(list), set()\\n layer[beginWord].append([beginWord])\\n while layer:\\n newlayer = collections.defaultdict(list)\\n for word in layer:\\n if word == endWord:\\n return layer[word]\\n for i in range(l):\\n for nxt in wordmap[word[:i]+\\\"*\\\"+word[i+1:]]:\\n if nxt not in visited:\\n newlayer[nxt] += [j + [nxt] for j in layer[word]]\\n visited |= set(newlayer.keys())\\n layer = newlayer\\n return []\\n\"\n },\n {\n \"alpha_fraction\": 0.47484663128852844,\n \"alphanum_fraction\": 0.48098158836364746,\n \"avg_line_length\": 37.80952453613281,\n \"blob_id\": \"414530349e049c03de42a498be1335666dd81e06\",\n \"content_id\": \"24dd22bc5bd1c02645e2ecd0b64c21b86333c4dc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 815,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 21,\n \"path\": \"/lc1192_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def criticalConnections(self, n: int, connections: List[List[int]]) -> List[List[int]]:\\n graph = collections.defaultdict(list)\\n for start, end in connections:\\n graph[start].append(end)\\n graph[end].append(start)\\n visited, rslt = {}, []\\n def dfs(cur, pre, d):\\n visited[cur] = d\\n for end in graph[cur]:\\n if end == pre:\\n continue\\n if end not in visited:\\n visited[cur] = min(visited[cur], dfs(end, cur, d+1))\\n else:\\n visited[cur] = min(visited[cur], visited[end])\\n if visited[cur] == d and pre != -1:\\n rslt.append([pre, cur])\\n return visited[cur]\\n dfs(0, -1, 0)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.3712121248245239,\n \"alphanum_fraction\": 0.40656566619873047,\n \"avg_line_length\": 32,\n \"blob_id\": \"e99a7a19391d4f9ec497bbe14ace34e90181c3bf\",\n \"content_id\": \"52ef043cf550ad7b944d5553b9945c42609ec5f7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1188,\n \"license_type\": \"no_license\",\n \"max_line_length\": 73,\n \"num_lines\": 36,\n \"path\": \"/lc1802_bs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxValue(self, n: int, index: int, maxSum: int) -> int:\\n left = 0\\n right = maxSum\\n while left < right:\\n mid = right - (right - left) // 2\\n extra1 = mid - 1 - index\\n extra1 = (extra1 + 1) * extra1 // 2 if extra1 > 0 else extra1\\n extra2 = mid - 1 - (n - index - 1)\\n extra2 = (extra2 + 1) * extra2 // 2 if extra2 > 0 else extra2\\n lower = (mid - 1) * mid // 2 - extra1\\n upper = (mid - 1) * mid // 2 - extra2\\n if lower + mid + upper > maxSum:\\n right = mid - 1\\n else:\\n left = mid\\n return left\\n\\n\\n def maxValue(self, n, index, maxSum):\\n def test(a):\\n b = max(a - index, 0)\\n res = (a + b) * (a - b + 1) / 2\\n b = max(a - ((n - 1) - index), 0)\\n res += (a + b) * (a - b + 1) / 2\\n return res - a\\n\\n maxSum -= n\\n left, right = 0, maxSum\\n while left < right:\\n mid = (left + right + 1) / 2\\n if test(mid) <= maxSum:\\n left = mid\\n else:\\n right = mid - 1\\n return left + 1\\n\"\n },\n {\n \"alpha_fraction\": 0.4907894730567932,\n \"alphanum_fraction\": 0.4934210479259491,\n \"avg_line_length\": 39,\n \"blob_id\": \"7eb69bc21e082423e0f1d3eb0029dc3b30d48fa0\",\n \"content_id\": \"f8ac6104cbe802eb4c73ecc45c56300ab55f0d20\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 760,\n \"license_type\": \"no_license\",\n \"max_line_length\": 129,\n \"num_lines\": 19,\n \"path\": \"/lc1311_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def watchedVideosByFriends(self, watchedVideos: List[List[str]], friends: List[List[int]], id: int, level: int) -> List[str]:\\n visited, dque = {id}, collections.deque([id])\\n while dque:\\n if level == 0:\\n break\\n size = len(dque)\\n for _ in range(size):\\n curr = dque.popleft()\\n for nt in friends[curr]:\\n if nt not in visited:\\n visited.add(nt)\\n dque.append(nt)\\n level -= 1\\n rslt = []\\n while dque:\\n rslt.extend(watchedVideos[dque.pop()])\\n summary = collections.Counter(rslt)\\n return sorted(summary.keys(), key = lambda x: (summary[x], x))\\n\"\n },\n {\n \"alpha_fraction\": 0.44111111760139465,\n \"alphanum_fraction\": 0.4511111080646515,\n \"avg_line_length\": 33.61538314819336,\n \"blob_id\": \"3d0102fef6868a1e1726eb52fb6478eb287a044f\",\n \"content_id\": \"c79ef07b9c226c7b8115f9f621a15380631c2eea\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 900,\n \"license_type\": \"no_license\",\n \"max_line_length\": 59,\n \"num_lines\": 26,\n \"path\": \"/lc22_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def generateParenthesis(self, n: int) -> List[str]:\\n rslt, m = [], 2*n\\n def backtracking(curr, left, right):\\n if left+right == m:\\n rslt.append(curr)\\n else:\\n if left < m -left:\\n backtracking(curr+\\\"(\\\", left + 1, right)\\n if right < left:\\n backtracking(curr+\\\")\\\", left, right+1)\\n backtracking(\\\"\\\", 0, 0)\\n return rslt\\n\\n def generateParenthesis(self, n: int) -> List[str]:\\n rslt = []\\n def backtracking(curr, left, right):\\n if left + right == 0:\\n rslt.append(curr)\\n else:\\n if left > 0:\\n backtracking(curr+\\\"(\\\", left -1, right)\\n if right > left:\\n backtracking(curr+\\\")\\\", left, right-1)\\n backtracking(\\\"\\\", n, n)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.509772002696991,\n \"alphanum_fraction\": 0.5146579742431641,\n \"avg_line_length\": 28.238094329833984,\n \"blob_id\": \"0a81011d76fa8df05473f1331aa26b3302917052\",\n \"content_id\": \"10483d4ff30618aae3099426692e782c412077a6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 614,\n \"license_type\": \"no_license\",\n \"max_line_length\": 73,\n \"num_lines\": 21,\n \"path\": \"/lc92_linkedlist.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Definition for singly-linked list.\\n# class ListNode:\\n# def __init__(self, val=0, next=None):\\n# self.val = val\\n# self.next = next\\nclass Solution:\\n def reverseBetween(self, head: ListNode, m: int, n: int) -> ListNode:\\n snode = ListNode(0, head)\\n left = snode\\n for _ in range(m-1):\\n left = left.next\\n right = left.next\\n for _ in range(n-m):\\n pre = right\\n right = right.next\\n\\n pre.next = right.next\\n right.next = left.next\\n left.next = right\\n right = pre\\n return snode.next\\n\"\n },\n {\n \"alpha_fraction\": 0.46863189339637756,\n \"alphanum_fraction\": 0.47543463110923767,\n \"avg_line_length\": 31.268293380737305,\n \"blob_id\": \"9ea4bf353096efe743e88cf6c80389ea5ffa91b1\",\n \"content_id\": \"36ea8443e1b8727476a4990f9996dcbd9d220b80\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1323,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 41,\n \"path\": \"/lc336_trie.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class TrieNode:\\n def __init__(self):\\n self.ch = {}\\n self.cands = []\\n self.idx = -1\\nclass Trie:\\n def __init__(self):\\n self.root = TrieNode()\\n def insert(self, word, idx):\\n curr = self.root\\n for i in range(len(word)-1, -1, -1):\\n if word[:i+1] == word[:i+1][::-1]:\\n curr.cands.append(idx)\\n if word[i] not in curr.ch:\\n curr.ch[word[i]] = TrieNode()\\n curr = curr.ch[word[i]]\\n curr.idx = idx\\n curr.cands.append(idx)\\n def search(self, word, idx):\\n curr = self.root\\n rslt = []\\n for i in range(len(word)):\\n if word[i:] == word[i:][::-1] and curr.idx != idx and curr.idx > -1:\\n rslt.append([idx, curr.idx])\\n ch = word[i]\\n if ch not in curr.ch:\\n return rslt\\n curr = curr.ch[ch]\\n for cand in curr.cands:\\n if cand != idx:\\n rslt.append([idx, cand])\\n return rslt\\nclass Solution:\\n def palindromePairs(self, words: List[str]) -> List[List[int]]:\\n paltrie = Trie()\\n rslt = []\\n for i, word in enumerate(words):\\n paltrie.insert(word, i)\\n for i, word in enumerate(words):\\n rslt += paltrie.search(word, i)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.39676111936569214,\n \"alphanum_fraction\": 0.4170040488243103,\n \"avg_line_length\": 34.21428680419922,\n \"blob_id\": \"da687103a560e098ee8c837d04c439b939f0a4d7\",\n \"content_id\": \"a6415665f2daa259d0a473d6ae89db6975112794\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 494,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 14,\n \"path\": \"/lc17_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def letterCombinations(self, digits: str) -> List[str]:\\n if not digits: return []\\n n, rslt = len(digits), []\\n cmap = {\\\"2\\\":\\\"abc\\\", \\\"3\\\":\\\"def\\\", \\\"4\\\":\\\"ghi\\\",\\n \\\"5\\\":\\\"jkl\\\", \\\"6\\\":\\\"mno\\\", \\\"7\\\":\\\"pqrs\\\", \\\"8\\\":\\\"tuv\\\", \\\"9\\\":\\\"wxyz\\\"}\\n def btrack(i, curr):\\n if i == n:\\n rslt.append(curr)\\n else:\\n for c in cmap[digits[i]]:\\n btrack(i+1, curr+c)\\n btrack(0, \\\"\\\")\\n return rslt \\n\"\n },\n {\n \"alpha_fraction\": 0.4620155096054077,\n \"alphanum_fraction\": 0.46666666865348816,\n \"avg_line_length\": 34.83333206176758,\n \"blob_id\": \"dc568d549e02e31d82081ef27dbf8405e7cba718\",\n \"content_id\": \"94ad041ed703e541c0ff4eacb1d92ed9c96c8e18\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 645,\n \"license_type\": \"no_license\",\n \"max_line_length\": 73,\n \"num_lines\": 18,\n \"path\": \"/lc1023_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def camelMatch(self, queries: List[str], pattern: str) -> List[bool]:\\n rslt = []\\n def backtracking(word, pattern):\\n if not pattern:\\n for c in word:\\n if c.isupper():\\n return False\\n return True\\n for i, c in enumerate(word):\\n if c == pattern[0]:\\n return backtracking(word[i+1:], pattern[1:])\\n if c.isupper():\\n return False\\n return False\\n for query in queries:\\n rslt.append(backtracking(query, pattern))\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.3623853325843811,\n \"alphanum_fraction\": 0.375,\n \"avg_line_length\": 29.068965911865234,\n \"blob_id\": \"e20f126f41d78c20c06400cfd9ab9c166128b43d\",\n \"content_id\": \"92b54e9cb2f33113585efda18424af3b3e4d33d8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 872,\n \"license_type\": \"no_license\",\n \"max_line_length\": 58,\n \"num_lines\": 29,\n \"path\": \"/lc331_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def isValidSerialization(self, preorder: str) -> bool:\\n stk = []\\n temp = preorder.split(\\\",\\\")\\n i = 0\\n while i < len(temp):\\n if temp[i] == \\\"#\\\":\\n if not stk or stk[-1] != \\\"#\\\":\\n stk.append(\\\"#\\\")\\n i += 1\\n elif len(stk) < 2:\\n return False\\n else:\\n stk.pop()\\n stk.pop()\\n else:\\n stk.append(temp[i])\\n i += 1\\n return stk == [\\\"#\\\"]\\n\\nclass Solution:\\n def isValidSerialization(self, preorder: str) -> bool:\\n slot = 1\\n for i, c in enumerate(preorder+\\\",\\\"):\\n if c == \\\",\\\":\\n slot -= 1\\n if slot < 0: return False\\n if preorder[i-1] != \\\"#\\\": slot += 2\\n return slot == 0\\n\"\n },\n {\n \"alpha_fraction\": 0.5209790468215942,\n \"alphanum_fraction\": 0.5314685106277466,\n \"avg_line_length\": 26.238094329833984,\n \"blob_id\": \"9cee2216bdb8480051a93154b4933e1d345249a7\",\n \"content_id\": \"95bca8f86ae8099305c1bb0999cbef9c4dcccd44\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 572,\n \"license_type\": \"no_license\",\n \"max_line_length\": 50,\n \"num_lines\": 21,\n \"path\": \"/lc1381_ood.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class CustomStack:\\n\\n def __init__(self, maxSize: int):\\n self.cap = maxSize\\n self.curSize = 0\\n self.stack = [0]*maxSize\\n\\n def push(self, x: int) -> None:\\n if self.curSize < self.cap:\\n self.stack[self.curSize] = x\\n self.curSize += 1\\n\\n def pop(self) -> int:\\n if not self.curSize: return -1\\n temp = self.stack[self.curSize-1]\\n self.curSize -= 1\\n return temp\\n\\n def increment(self, k: int, val: int) -> None:\\n for i in range(min(k, self.curSize)):\\n self.stack[i] += val\\n\"\n },\n {\n \"alpha_fraction\": 0.3575129508972168,\n \"alphanum_fraction\": 0.3911917209625244,\n \"avg_line_length\": 28,\n \"blob_id\": \"b31f357ea4dbe53e1eb0894fd50207c07a9f9e1c\",\n \"content_id\": \"41a4f9bfe468c4377a3ca09f3356ca173bd5cf7b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 386,\n \"license_type\": \"no_license\",\n \"max_line_length\": 48,\n \"num_lines\": 13,\n \"path\": \"/lc1567_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def getMaxLen(self, nums: List[int]) -> int:\\n cp, cn =0, 0\\n rslt = 0\\n for num in nums:\\n if num > 0:\\n cp, cn = cp+1, cn+1 if cn else 0\\n elif num < 0:\\n cp, cn = cn+1 if cn else 0, cp+1\\n else:\\n cp, cn = 0, 0\\n rslt = max(rslt, cp)\\n return rslt\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.5147679448127747,\n \"alphanum_fraction\": 0.5316455960273743,\n \"avg_line_length\": 38.5,\n \"blob_id\": \"83f13843e5166251ce754219354b7c667b69653e\",\n \"content_id\": \"67d56853e574d31f903109cd5f4ef1b3846960b6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 711,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 18,\n \"path\": \"/lc333_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Definition for a binary tree node.\\n# class TreeNode:\\n# def __init__(self, val=0, left=None, right=None):\\n# self.val = val\\n# self.left = left\\n# self.right = right\\nclass Solution:\\n def largestBSTSubtree(self, root: TreeNode) -> int:\\n def helper(root):\\n if not root: return 0, float(\\\"inf\\\"), -float(\\\"inf\\\")\\n left = helper(root.left)\\n right = helper(root.right)\\n if left[2] < root.val < right[1]:\\n return left[0]+right[0]+1, min(left[1], root.val), max(root.val, right[2])\\n else:\\n return max(left[0], right[0]), -float(\\\"inf\\\"), float(\\\"inf\\\")\\n temp = helper(root)\\n return temp[0]\\n\"\n },\n {\n \"alpha_fraction\": 0.4273972511291504,\n \"alphanum_fraction\": 0.45068493485450745,\n \"avg_line_length\": 35.5,\n \"blob_id\": \"02871df7e19a5ce14670f0ffd5358760b2d9ca9a\",\n \"content_id\": \"584a036bf8411a49e71d9b2a595608600b3b2ba7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 730,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 20,\n \"path\": \"/lc752_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def openLock(self, deadends: List[str], target: str) -> int:\\n def neighbors(node):\\n for i in range(4):\\n t = int(node[i])\\n for d in (-1, 1):\\n y = (t+d)%10\\n yield node[:i] + str(y) + node[i+1:]\\n\\n dd = set(deadends)\\n queue, seen = collections.deque([('0000', 0 )]), {'0000'}\\n while queue:\\n node, depth = queue.popleft()\\n if node == target: return depth\\n if node in dd: continue\\n for nei in neighbors(node):\\n if nei not in seen and nei not in dd:\\n seen.add(nei)\\n queue.append((nei, depth+1))\\n return -1\\n\"\n },\n {\n \"alpha_fraction\": 0.3912484049797058,\n \"alphanum_fraction\": 0.41312742233276367,\n \"avg_line_length\": 28.884614944458008,\n \"blob_id\": \"81fbade1841ef71c9c5791530a2b6d25625721a4\",\n \"content_id\": \"d41d3a96304d085f7434170cf4916e08baa6b8f1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 777,\n \"license_type\": \"no_license\",\n \"max_line_length\": 55,\n \"num_lines\": 26,\n \"path\": \"/lc279_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def numSquares(self, n: int) -> int:\\n cands = [i**2 for i in range(1, int(n**0.5)+1)]\\n @lru_cache(None)\\n def dfs(i):\\n if i in cands:\\n return 1\\n rslt = float(\\\"inf\\\")\\n for cand in cands:\\n if cand > i:\\n break\\n rslt = min(rslt, dfs(i-cand)+1)\\n return rslt\\n return dfs(n)\\n\\nclass Solution:\\n def numSquares(self, n: int) -> int:\\n cands = [i*i for i in range(int(n**0.5) + 1)]\\n dp = [float(\\\"inf\\\")]*(n+1)\\n dp[0] = 0\\n for i in range(1, n+1):\\n for cand in cands:\\n if i < cand:\\n break\\n dp[i] = min(dp[i], dp[i-cand] + 1)\\n return dp[-1]\\n\"\n },\n {\n \"alpha_fraction\": 0.48097825050354004,\n \"alphanum_fraction\": 0.4945652186870575,\n \"avg_line_length\": 35.79999923706055,\n \"blob_id\": \"6355945b7d8243b917170249cd00c36ef0110d69\",\n \"content_id\": \"bbf9eaaee2a080d32c45c43b9f49d12ad357f4f6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 368,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 10,\n \"path\": \"/lc503_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def nextGreaterElements(self, nums: List[int]) -> List[int]:\\n if not nums: return nums\\n rslt, stk = [-1]*len(nums), []\\n for i in range(len(nums)-1, -len(nums), -1):\\n while stk and stk[-1] <= nums[i]:\\n stk.pop()\\n if stk: rslt[i] = stk[-1]\\n stk.append(nums[i])\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4274061918258667,\n \"alphanum_fraction\": 0.43882545828819275,\n \"avg_line_length\": 29.649999618530273,\n \"blob_id\": \"2d32bf604a0501753dfdbe13e27ada3e5abb8b04\",\n \"content_id\": \"325228c05d02b45cabac575920cc6517b50fd401\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 613,\n \"license_type\": \"no_license\",\n \"max_line_length\": 55,\n \"num_lines\": 20,\n \"path\": \"/lc1675_heap.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minimumDeviation(self, nums: List[int]) -> int:\\n heap = []\\n left, right = float(\\\"inf\\\"), 0\\n rslt = float(\\\"inf\\\")\\n for num in nums:\\n if num%2:\\n num = num*2\\n heapq.heappush(heap, -num)\\n left = min(left, num)\\n while True:\\n right = -heapq.heappop(heap)\\n if right-left < rslt:\\n rslt = right-left\\n if right%2 == 0:\\n heapq.heappush(heap, -right//2)\\n left = min(left, right//2)\\n else:\\n break\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.44474393129348755,\n \"alphanum_fraction\": 0.4582210183143616,\n \"avg_line_length\": 32.727272033691406,\n \"blob_id\": \"d70f2c787bd2d48978472b123496c243bef44bca\",\n \"content_id\": \"c609b9025e264e349e4e17c56bd5cd286e006359\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 371,\n \"license_type\": \"no_license\",\n \"max_line_length\": 54,\n \"num_lines\": 11,\n \"path\": \"/lc962_stk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxWidthRamp(self, A: List[int]) -> int:\\n stk = []\\n for i, num in enumerate(A):\\n if not stk or A[stk[-1]] > num:\\n stk.append(i)\\n rslt = 0\\n for i, num in enumerate(A[::-1]):\\n while stk and A[stk[-1]] <= num:\\n rslt = max(rslt, len(A)-1-i-stk.pop())\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.3875739574432373,\n \"alphanum_fraction\": 0.40532544255256653,\n \"avg_line_length\": 29.727272033691406,\n \"blob_id\": \"3ff6bbeedf9cc51530a695f48270b10787abe679\",\n \"content_id\": \"3651a6895d55f13e12003ad9635ac82876e1356a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 338,\n \"license_type\": \"no_license\",\n \"max_line_length\": 48,\n \"num_lines\": 11,\n \"path\": \"/lc856_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def scoreOfParentheses(self, s: str) -> int:\\n count_right, rslt = 0, 0\\n for i, c in enumerate(s):\\n if c == \\\"(\\\":\\n count_right += 1\\n else:\\n count_right -= 1\\n if s[i-1] == '(':\\n rslt += 2**count_right\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.5015479922294617,\n \"alphanum_fraction\": 0.5139318704605103,\n \"avg_line_length\": 39.375,\n \"blob_id\": \"8477fff05f4e4a26d969cccd833cc821a7d6f155\",\n \"content_id\": \"cfc305b18cf6f970eab8d41796c17e1b3dc7c8e7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 323,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 8,\n \"path\": \"/lc1366_sort.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def rankTeams(self, votes: List[str]) -> str:\\n l = len(votes[0])\\n mapping = {k: [0]*l for k in votes[0]}\\n for vote in votes:\\n for i, v in enumerate(vote):\\n mapping[v][i] += 1\\n return \\\"\\\".join(sorted(mapping.keys(), key = lambda x: (-mapping[x], x)))\\n\"\n },\n {\n \"alpha_fraction\": 0.43018868565559387,\n \"alphanum_fraction\": 0.4377358555793762,\n \"avg_line_length\": 27.44444465637207,\n \"blob_id\": \"ec92e925ad6984866381237375653f9547dc315e\",\n \"content_id\": \"45af675bfc4fd8b2c942840ed97e0fc9358d50eb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 265,\n \"license_type\": \"no_license\",\n \"max_line_length\": 51,\n \"num_lines\": 9,\n \"path\": \"/lc1608_array.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def specialArray(self, nums: List[int]) -> int:\\n nums.sort()\\n last, n = 0, len(nums)\\n for i in range(n):\\n if nums[i] >= n-i > last:\\n return n-i\\n last = nums[i]\\n return -1\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.47907325625419617,\n \"alphanum_fraction\": 0.4805680215358734,\n \"avg_line_length\": 28.733333587646484,\n \"blob_id\": \"97c462a1af2ce3ac97450d18b275547d5b952513\",\n \"content_id\": \"4c6fd94cc5bdeb9a95044c8f874fc09e8a98d763\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1338,\n \"license_type\": \"no_license\",\n \"max_line_length\": 63,\n \"num_lines\": 45,\n \"path\": \"/lc642_tire.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Trie:\\n def __init__(self):\\n self.root = {}\\n def insert(self, word):\\n curr = self.root\\n for ch in word:\\n if ch not in curr:\\n curr[ch] = {}\\n curr = curr[ch]\\n curr[\\\"#\\\"] = word\\n def search(self, prefix):\\n curr = self.root\\n for ch in prefix:\\n if ch not in curr:\\n return []\\n curr = curr[ch]\\n return self.findall(curr)\\n def findall(self, curr):\\n rslt = []\\n for key in curr:\\n if key == \\\"#\\\":\\n rslt.append(curr[key])\\n else:\\n rslt += self.findall(curr[key])\\n return rslt\\nclass AutocompleteSystem:\\n\\n def __init__(self, sentences: List[str], times: List[int]):\\n self.trie = Trie()\\n self.hotmap = collections.defaultdict(int)\\n for sentence, time in zip(sentences, times):\\n self.trie.insert(sentence)\\n self.hotmap[sentence] += time\\n self.last = \\\"\\\"\\n\\n def input(self, c: str) -> List[str]:\\n if c == \\\"#\\\":\\n self.hotmap[self.last] += 1\\n self.trie.insert(self.last)\\n self.last = \\\"\\\"\\n return []\\n self.last += c\\n rslt = self.trie.search(self.last)\\n rslt.sort(key= lambda x: (-self.hotmap[x], x))\\n return rslt[:3]\\n\"\n },\n {\n \"alpha_fraction\": 0.4375,\n \"alphanum_fraction\": 0.5193014740943909,\n \"avg_line_length\": 40.846153259277344,\n \"blob_id\": \"b00eadd281928300a07f34f64b4c9cde10fc7f19\",\n \"content_id\": \"68da9221101520053ee69f2ffb3abaf3f1430abd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1088,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 26,\n \"path\": \"/lc4_dandc.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:\\n if not nums1 and not nums2:\\n return None\\n l1, l2 = len(nums1), len(nums2)\\n if (l1+l2)%2 ==1:\\n return self.findKth(nums1, nums2, 0, 0, (l1+l2)//2 + 1)\\n else:\\n left = self.findKth(nums1, nums2, 0, 0, (l1+l2)//2)\\n right = self.findKth(nums1, nums2, 0, 0, (l1+l2)//2+1)\\n return (left+right)/2\\n\\n def findKth(self, nums1, nums2, loc1, loc2, k):\\n if loc1 == len(nums1):\\n return nums2[loc2 + k -1]\\n if loc2 == len(nums2):\\n return nums1[loc1 + k -1]\\n if k == 1:\\n return min(nums1[loc1], nums2[loc2])\\n\\n p1 = nums1[loc1 + k//2 - 1] if loc1 + k//2 - 1 < len(nums1) else None\\n p2 = nums2[loc2 + k//2 - 1] if loc2 + k//2 - 1 < len(nums2) else None\\n\\n if p2 is None or (p1 is not None and p1 < p2):\\n return self.findKth(nums1, nums2, loc1+k//2, loc2, k - k//2)\\n return self.findKth(nums1, nums2, loc1, loc2 + k//2, k - k//2)\\n\"\n },\n {\n \"alpha_fraction\": 0.5185185074806213,\n \"alphanum_fraction\": 0.5303703546524048,\n \"avg_line_length\": 47.21428680419922,\n \"blob_id\": \"5063b47e51aa5c93941bb90b2ac09f6221990766\",\n \"content_id\": \"def42b414f709a874ceac87e6d3ef232e7be940f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 675,\n \"license_type\": \"no_license\",\n \"max_line_length\": 76,\n \"num_lines\": 14,\n \"path\": \"/lc1438_deque.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def longestSubarray(self, nums: List[int], limit: int) -> int:\\n dque_max, dque_min, l= collections.deque(), collections.deque(), 0\\n for num in nums:\\n while dque_max and dque_max[-1] < num: dque_max.pop()\\n while dque_min and dque_min[-1] > num: dque_min.pop()\\n dque_max.append(num)\\n dque_min.append(num)\\n if dque_max and dque_min and dque_max[0] - dque_min[0] > limit:\\n if dque_max[0] == nums[l]: dque_max.popleft()\\n if dque_min[0] == nums[l]: dque_min.popleft()\\n l += 1\\n # print(dque_max, dque_min, l)\\n return len(nums)-l\\n\"\n },\n {\n \"alpha_fraction\": 0.4505050480365753,\n \"alphanum_fraction\": 0.4727272689342499,\n \"avg_line_length\": 37.07692337036133,\n \"blob_id\": \"d470f372cf49088c7c349aa2086fbfbae301a5de\",\n \"content_id\": \"03cac9048932ec54fb22cfa4ad29099bea5afd41\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 495,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 13,\n \"path\": \"/lc1856_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxSumMinProduct(self, nums: List[int]) -> int:\\n n = len(nums)\\n presum = [0]*(n+1)\\n for i in range(n): presum[i+1] = presum[i] + nums[i]\\n stack, rslt = [], 0\\n for i, h in enumerate(nums+[0]):\\n start = i\\n while stack and stack[-1][1] >= h:\\n start, mh = stack.pop()\\n rslt = max(rslt, (presum[i] - presum[start])*mh)\\n stack.append((start, h))\\n return rslt %(10**9 + 7)\\n\"\n },\n {\n \"alpha_fraction\": 0.4228723347187042,\n \"alphanum_fraction\": 0.4441489279270172,\n \"avg_line_length\": 30.33333396911621,\n \"blob_id\": \"aa390039d86e6031c1c63c008938a9833d03151a\",\n \"content_id\": \"180cb839d7195c026df5339c2041c25cc16e81a4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 376,\n \"license_type\": \"no_license\",\n \"max_line_length\": 67,\n \"num_lines\": 12,\n \"path\": \"/lc56_array.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def merge(self, intervals: List[List[int]]) -> List[List[int]]:\\n intervals.sort()\\n rslt = []\\n for it in intervals:\\n if not rslt:\\n rslt.append(it)\\n elif it[0] <= rslt[-1][1]:\\n rslt[-1][1] = max(rslt[-1][1], it[1])\\n else:\\n rslt.append(it)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.44871795177459717,\n \"alphanum_fraction\": 0.4643874764442444,\n \"avg_line_length\": 30.909090042114258,\n \"blob_id\": \"0f93029dba7bd7071b745fa7c2accec0e54bc9ba\",\n \"content_id\": \"28aad7423f3c0a13939d188ab3e1119c8754ae13\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 702,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 22,\n \"path\": \"/lc452_array.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findMinArrowShots(self, points: List[List[int]]) -> int:\\n points.sort(key = lambda x: x[1])\\n rslt, last = 0, -float(\\\"inf\\\")\\n for point in points:\\n if point[0] <= last:\\n last = min(last, point[1])\\n else:\\n rslt += 1\\n last = point[1]\\n return rslt\\n\\n def findMinArrowShots(self, points: List[List[int]]) -> int:\\n points.sort(reverse = True)\\n rslt, last = 0, float(\\\"inf\\\")\\n for point in points:\\n if point[1] >= last:\\n last = max(last, point[0])\\n else:\\n rslt += 1\\n last = point[0]\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.5090609788894653,\n \"alphanum_fraction\": 0.5255354046821594,\n \"avg_line_length\": 42.35714340209961,\n \"blob_id\": \"58259b5781c4e63c44c91634ee3a4ba8a41115ed\",\n \"content_id\": \"a8f3befd8951ecfce7198a35fb72bf00edc1a84f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 607,\n \"license_type\": \"no_license\",\n \"max_line_length\": 111,\n \"num_lines\": 14,\n \"path\": \"/lc986_arrary.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def intervalIntersection(self, firstList: List[List[int]], secondList: List[List[int]]) -> List[List[int]]:\\n idxa, idxb, na, nb = 0, 0, len(firstList), len(secondList)\\n rslt = []\\n while idxa < na and idxb < nb:\\n leftb = max(firstList[idxa][0], secondList[idxb][0])\\n rightb = min(firstList[idxa][1], secondList[idxb][1])\\n if leftb <= rightb:\\n rslt.append([leftb, rightb])\\n if firstList[idxa][1] < secondList[idxb][1]:\\n idxa += 1\\n else:\\n idxb += 1\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.43906810879707336,\n \"alphanum_fraction\": 0.4534050226211548,\n \"avg_line_length\": 33.875,\n \"blob_id\": \"a1a48b84185efda7eb5f505f909f89c4d47f3e0b\",\n \"content_id\": \"e555e13e7e08b198d6aea0009293fbef6c458ef6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 558,\n \"license_type\": \"no_license\",\n \"max_line_length\": 69,\n \"num_lines\": 16,\n \"path\": \"/lc1406_minmax.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def stoneGameIII(self, stoneValue: List[int]) -> str:\\n memo = {}\\n def dfs(i):\\n if i in memo:\\n return memo[i]\\n if i >= len(stoneValue):\\n return 0\\n rslt = float(\\\"-inf\\\")\\n for j in range(3):\\n if i+j+1 > len(stoneValue): break\\n rslt = max(rslt, sum(stoneValue[i:i+j+1])-dfs(i+j+1))\\n memo[i] = rslt\\n return rslt\\n temp = dfs(0)\\n return \\\"Tie\\\" if temp == 0 else \\\"Alice\\\" if temp > 0 else \\\"Bob\\\"\\n\"\n },\n {\n \"alpha_fraction\": 0.4414893686771393,\n \"alphanum_fraction\": 0.4541223347187042,\n \"avg_line_length\": 29.693878173828125,\n \"blob_id\": \"be7d0a57575019ece4dba3df7b8e8143103491ed\",\n \"content_id\": \"054cae6ca38e1496bab6d9cd0583b465f9c7f0b2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1504,\n \"license_type\": \"no_license\",\n \"max_line_length\": 54,\n \"num_lines\": 49,\n \"path\": \"/lc421_trie.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Trie:\\n def __init__(self, L):\\n self.root = TrieNode()\\n self.maskL = L\\n def insert(self, num):\\n mask = 1 << self.maskL\\n curr = self.root\\n while mask:\\n key = 1 if num & mask else 0\\n if key not in curr.children:\\n curr.children[key] = TrieNode()\\n curr = curr.children[key]\\n mask >>= 1\\n def search(self, num):\\n mask = 1 << self.maskL\\n curr, rslt = self.root, 0\\n while mask:\\n key = 0 if num&mask else 1\\n if key in curr.children:\\n curr = curr.children[key]\\n rslt |= mask\\n else:\\n curr = curr.children[1-key]\\n mask >>= 1\\n return rslt\\nclass Solution:\\n def findMaximumXOR(self, nums: List[int]) -> int:\\n L = len(bin(max(nums)))-2\\n solTrie, rslt = Trie(L), 0\\n for num in nums:\\n solTrie.insert(num)\\n rslt = max(rslt, solTrie.search(num))\\n return rslt\\n\\nclass Solution:\\n def findMaximumXOR(self, nums: List[int]) -> int:\\n l = len(bin(max(nums)))-2\\n rslt = 0\\n for i in range(l-1, -1, -1):\\n mask = rslt | 1 << i\\n used = set()\\n for num in nums:\\n curr = num & mask\\n # curr ^ mask ^ curr = 0 ^ mask = mask\\n if (curr ^ mask) in used:\\n rslt = mask\\n break\\n used.add(curr)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4125683009624481,\n \"alphanum_fraction\": 0.43852460384368896,\n \"avg_line_length\": 32.272727966308594,\n \"blob_id\": \"28cbdd731543f10e8132d6e276f619fdc7ba781b\",\n \"content_id\": \"c6091e55701f1791736ebc81436bab21aad9fd0d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 732,\n \"license_type\": \"no_license\",\n \"max_line_length\": 70,\n \"num_lines\": 22,\n \"path\": \"/lc474_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findMaxForm(self, strs, m, n):\\n xy = [[s.count(\\\"0\\\"), s.count(\\\"1\\\")] for s in strs]\\n\\n @lru_cache(None)\\n\\n def dp(mm, nn, kk):\\n if mm < 0 or nn < 0: return -float(\\\"inf\\\")\\n if kk == len(strs): return 0\\n x, y = xy[kk]\\n return max(1 + dp(mm-x, nn-y, kk + 1), dp(mm, nn, kk + 1))\\n\\n return dp(m, n, 0)\\n\\n def findMaxForm(self, strs, m, n):\\n dp = [[0]*(m+1) for _ in range(n+1)]\\n for s in strs:\\n ones, zeros = s.count(\\\"1\\\"), s.count(\\\"0\\\")\\n for i in range(n, ones-1, -1):\\n for j in range(m, zeros-1, -1):\\n dp[i][j] = max(dp[i][j], dp[i-ones][j-zeros]+1)\\n return dp[n][m]\\n\"\n },\n {\n \"alpha_fraction\": 0.39358600974082947,\n \"alphanum_fraction\": 0.4169096350669861,\n \"avg_line_length\": 30.18181800842285,\n \"blob_id\": \"a5300bca372db952b2a9ec6f5a240a3005eda9f6\",\n \"content_id\": \"ee7c214669162040dd4f9992f8776ea2bc6f12c2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 343,\n \"license_type\": \"no_license\",\n \"max_line_length\": 55,\n \"num_lines\": 11,\n \"path\": \"/lc1004_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def longestOnes(self, A: List[int], K: int) -> int:\\n left, cnt, rslt = 0, 0, 0\\n for right, c in enumerate(A):\\n if c == 0:\\n cnt += 1\\n while cnt > K:\\n cnt -= 1-A[left]\\n left += 1\\n rslt = max(rslt, right-left + 1)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.47130435705184937,\n \"alphanum_fraction\": 0.47652173042297363,\n \"avg_line_length\": 34.9375,\n \"blob_id\": \"5e1d6f90636874be648e952568c697c72bc8b69c\",\n \"content_id\": \"14b61d5b26ac7c6a9d8ee49d68da64dae8419111\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 575,\n \"license_type\": \"no_license\",\n \"max_line_length\": 97,\n \"num_lines\": 16,\n \"path\": \"/lc1644_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def lowestCommonAncestor(self, root: 'TreeNode', p: 'TreeNode', q: 'TreeNode') -> 'TreeNode':\\n self.mark = 0\\n def dfs(root, p, q):\\n if not root: return None\\n left = dfs(root.left, p, q)\\n right = dfs(root.right, p, q)\\n if root == p or root == q:\\n self.mark += 1\\n return root\\n if left and right:\\n return root\\n else:\\n return left or right\\n rslt = dfs(root, p, q)\\n return rslt if self.mark == 2 else None\\n\"\n },\n {\n \"alpha_fraction\": 0.37877094745635986,\n \"alphanum_fraction\": 0.40223464369773865,\n \"avg_line_length\": 26.96875,\n \"blob_id\": \"66c6ba48da9a232facb5e2f7fdadfbd014835162\",\n \"content_id\": \"dd92699c3005642a22363c6addf9cd390530dbdb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 895,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 32,\n \"path\": \"/lc1248_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def numberOfSubarrays(self, nums: List[int], k: int) -> int:\\n left, rslt, last = 0, 0, 0\\n for right, num in enumerate(nums):\\n if num%2 == 1:\\n k -= 1\\n last = 0\\n while k == 0:\\n last += 1\\n k += nums[left]%2\\n left += 1\\n rslt += last\\n return rslt\\n\\n\\n\\nclass Solution:\\n def numberOfSubarrays(self, nums: List[int], k: int) -> int:\\n left, rslt, count = 0, 0, 0\\n for right, num in enumerate(nums):\\n count += num%2\\n while count > k:\\n count -= nums[left]%2\\n left += 1\\n temp = left\\n while count == k:\\n count -= nums[temp] % 2\\n rslt += 1\\n temp += 1\\n if temp > left:\\n count += 1\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.3758865296840668,\n \"alphanum_fraction\": 0.38770684599876404,\n \"avg_line_length\": 29.214284896850586,\n \"blob_id\": \"85047e8430feb409f98faf3bc3785967bd8c1eca\",\n \"content_id\": \"a917a2a12a045177fe957f1f63d5af9ceef79099\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 423,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 14,\n \"path\": \"/lc375_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def getMoneyAmount(self, n: int) -> int:\\n memo = {}\\n def dfs(i, j):\\n if (i, j) in memo:\\n return memo[(i, j)]\\n if i >= j:\\n return 0\\n rslt = n**n\\n for t in range(i, j+1):\\n rslt = min(rslt, t+max(dfs(i, t-1), dfs(t+1, j)))\\n memo[(i,j)] = rslt\\n return rslt\\n return dfs(1, n)\\n\"\n },\n {\n \"alpha_fraction\": 0.5829383730888367,\n \"alphanum_fraction\": 0.6050552725791931,\n \"avg_line_length\": 36.235294342041016,\n \"blob_id\": \"3172ee72ef6567947d4c1669eb18e242c9349728\",\n \"content_id\": \"c80d8bd6450cb9e7a74a30c2458c7dca7e539c83\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 633,\n \"license_type\": \"no_license\",\n \"max_line_length\": 111,\n \"num_lines\": 17,\n \"path\": \"/lc1348_hash.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class TweetCounts:\\n\\n def __init__(self):\\n self.maps = collections.defaultdict(list)\\n\\n def recordTweet(self, tweetName: str, time: int) -> None:\\n # dictionary of list\\n self.maps[tweetName].append(time)\\n\\n\\n def getTweetCountsPerFrequency(self, freq: str, tweetName: str, startTime: int, endTime: int) -> List[int]:\\n chunk_size = {\\\"minute\\\": 60, \\\"hour\\\":3600, \\\"day\\\":86400}\\n rslt = [0]*((endTime-startTime)//chunk_size[freq]+1)\\n for time in self.maps[tweetName]:\\n if startTime <= time <= endTime:\\n rslt[(time-startTime)//chunk_size[freq]] +=1\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.48439618945121765,\n \"alphanum_fraction\": 0.4993215799331665,\n \"avg_line_length\": 35.849998474121094,\n \"blob_id\": \"975cfcab95805c15b54eb657fac1abf17f6308ca\",\n \"content_id\": \"b1a99c6d90526fb4b0fc2b3e93cacbad5a106394\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1474,\n \"license_type\": \"no_license\",\n \"max_line_length\": 84,\n \"num_lines\": 40,\n \"path\": \"/lc1307_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def isSolvable(self, words: list[str], result: str) -> bool:\\n max_woerd_len = max(map(len, words))\\n if max_woerd_len < len(result) - 1:\\n return False\\n if max_woerd_len > len(result):\\n return False\\n\\n words = words + [result]\\n first_letters = set(word[0] for word in words if len(word) > 1)\\n cols, rows = len(result), len(words)\\n letter_to_digits = {}\\n\\n def search(col: int, row: int, carry: int) -> bool:\\n if col == cols:\\n return carry == 0\\n if row == rows:\\n quotient, reminder = divmod(carry, 10)\\n return reminder == 0 and search(col + 1, 0, quotient)\\n\\n word = words[row]\\n if col >= len(word):\\n return search(col, row + 1, carry)\\n\\n letter = word[~col]\\n sign = 1 if row < (rows - 1) else -1\\n\\n if letter in letter_to_digits:\\n return search(col, row + 1, carry + sign * letter_to_digits[letter])\\n\\n init_digit = 1 if letter in first_letters else 0\\n for digit in range(init_digit, 10):\\n if digit not in letter_to_digits.values():\\n letter_to_digits[letter] = digit\\n if search(col, row + 1, carry + sign * digit):\\n return True\\n del letter_to_digits[letter]\\n return False\\n\\n return search(0, 0, 0)\\n\"\n },\n {\n \"alpha_fraction\": 0.4791666567325592,\n \"alphanum_fraction\": 0.4886363744735718,\n \"avg_line_length\": 32,\n \"blob_id\": \"4feae26ee93c0ce1a97681ecc69c34e26bae911f\",\n \"content_id\": \"d0f6da833bb10b8a729cc491aef8a39dc3e064aa\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 528,\n \"license_type\": \"no_license\",\n \"max_line_length\": 58,\n \"num_lines\": 16,\n \"path\": \"/lc437_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def pathSum(self, root: TreeNode, sum: int) -> int:\\n def preorder(node, curr):\\n if not node:\\n return\\n curr += node.val\\n if curr == sum:\\n self.count += 1\\n self.count += maps[curr-sum]\\n maps[curr] += 1\\n preorder(node.left, curr)\\n preorder(node.right, curr)\\n maps[curr] -= 1\\n self.count, maps = 0, collections.defaultdict(int)\\n preorder(root, 0)\\n return self.count\\n\"\n },\n {\n \"alpha_fraction\": 0.43178170919418335,\n \"alphanum_fraction\": 0.4365971088409424,\n \"avg_line_length\": 27.31818199157715,\n \"blob_id\": \"73e48dbc0947267cbae8328175f6ef2bd7c269c4\",\n \"content_id\": \"cf5451be7a61a321d958436e28842d25e80b918f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1246,\n \"license_type\": \"no_license\",\n \"max_line_length\": 60,\n \"num_lines\": 44,\n \"path\": \"/lc297_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Codec:\\n\\n def serialize(self, root):\\n \\\"\\\"\\\"Encodes a tree to a single string.\\n\\n :type root: TreeNode\\n :rtype: str\\n \\\"\\\"\\\"\\n if not root: return \\\"\\\"\\n dqueue = collections.deque([root])\\n rslt = []\\n while dqueue:\\n temp = dqueue.popleft()\\n if temp:\\n rslt.append(str(temp.val))\\n dqueue.append(temp.left)\\n dqueue.append(temp.right)\\n else:\\n rslt.append(\\\"\\\")\\n return \\\",\\\".join(rslt)\\n\\n def deserialize(self, data):\\n \\\"\\\"\\\"Decodes your encoded data to tree.\\n\\n :type data: str\\n :rtype: TreeNode\\n \\\"\\\"\\\"\\n dqueue = collections.deque([])\\n root, check = None, 0\\n for val in data.split(\\\",\\\"):\\n temp = TreeNode(int(val)) if val != \\\"\\\" else None\\n if not root:\\n root = temp\\n dqueue.append(root)\\n else:\\n if check == 0:\\n dqueue[0].left = temp\\n check += 1\\n else:\\n dqueue[0].right = temp\\n dqueue.popleft()\\n check = 0\\n if temp: dqueue.append(temp)\\n return root\\n\"\n },\n {\n \"alpha_fraction\": 0.33888888359069824,\n \"alphanum_fraction\": 0.3611111044883728,\n \"avg_line_length\": 31.727272033691406,\n \"blob_id\": \"4d356af250a02c80ed821aef44c2d3499411bbf0\",\n \"content_id\": \"330c966139698896538bb30abf70c3717fa0ae77\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 720,\n \"license_type\": \"no_license\",\n \"max_line_length\": 68,\n \"num_lines\": 22,\n \"path\": \"/lc1593_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxUniqueSplit(self, s: str) -> int:\\n dp = [0]*(len(s)+1)\\n def backtracking(i, c, seen):\\n if dp[i] > 0 and dp[i] > c + 1 or len(s)-i + c < dp[-1]:\\n return\\n if i == len(s):\\n dp[-1] = max(dp[-1], c)\\n return\\n for j in range(i+1, len(s)+1):\\n if s[i:j] in seen:\\n dp[j-1] = c\\n backtracking(j, c, seen)\\n else:\\n dp[j-1] = c+1\\n seen.add(s[i:j])\\n backtracking(j,c+1, seen)\\n seen.remove(s[i:j])\\n return\\n backtracking(0, 0, set())\\n\\n return dp[-1]\\n\"\n },\n {\n \"alpha_fraction\": 0.4280821979045868,\n \"alphanum_fraction\": 0.4497717022895813,\n \"avg_line_length\": 35.29166793823242,\n \"blob_id\": \"495d14fdf06bc15e3a6ecaadf8caf9a4fa2df27d\",\n \"content_id\": \"2a7e23f2b73ca7c33d922b56313878bb4f3169d6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 876,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 24,\n \"path\": \"/lc216_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def combinationSum3(self, k: int, n: int) -> List[List[int]]:\\n if n < k*1 or n > k*9: return []\\n rslt = []\\n def backtracking(i, n, curr):\\n if n == 0 and len(curr) == k:\\n rslt.append(curr)\\n elif len(curr) < k and i < 10:\\n for j in range(i, 10):\\n backtracking(j+1, n-j, curr + [j])\\n backtracking(1, n, [])\\n return rslt\\n\\n def combinationSum3(self, k: int, n: int) -> List[List[int]]:\\n if n < k*1 or n > k*9: return []\\n rslt = []\\n def backtracking(i, n, curr):\\n if n == 0 and len(curr) == k:\\n rslt.append(curr)\\n elif len(curr) < k and i < 10:\\n backtracking(i+1, n, curr)\\n backtracking(i+1, n-i, curr + [i])\\n backtracking(1, n, [])\\n return rslt\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.4378320872783661,\n \"alphanum_fraction\": 0.4495217800140381,\n \"avg_line_length\": 39.91304397583008,\n \"blob_id\": \"a3935ff07d39e615b36be6adf55282b4bef44d84\",\n \"content_id\": \"4842307d1f8a705b6e1a8e65b8f6c709ed47a652\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 941,\n \"license_type\": \"no_license\",\n \"max_line_length\": 114,\n \"num_lines\": 23,\n \"path\": \"/lc417_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def pacificAtlantic(self, matrix: List[List[int]]) -> List[List[int]]:\\n if not matrix: return []\\n pdque, adque = collections.deque(), collections.deque()\\n n, m = len(matrix), len(matrix[0])\\n for i in range(n):\\n pdque.append((i, 0))\\n adque.append((i, m-1))\\n for j in range(m):\\n pdque.append((0, j))\\n adque.append((n-1, j))\\n def bfs(queue):\\n visited = set()\\n while queue:\\n x, y = queue.popleft()\\n if (x, y) in visited:\\n continue\\n visited.add((x,y))\\n for nx, ny in [(x-1, y), (x+1, y), (x, y+1), (x, y-1)]:\\n if 0 <= nx < n and 0 <= ny < m and matrix[nx][ny] >= matrix[x][y] and (nx, ny) not in visited:\\n queue.append((nx, ny))\\n return visited\\n return bfs(pdque) & bfs(adque)\\n\"\n },\n {\n \"alpha_fraction\": 0.4260089695453644,\n \"alphanum_fraction\": 0.439461886882782,\n \"avg_line_length\": 29.758621215820312,\n \"blob_id\": \"2d2bb1c9f98902a76478de501ac15c717470523c\",\n \"content_id\": \"6381975c9d644f53fd9a93bc712fb2212450908f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 892,\n \"license_type\": \"no_license\",\n \"max_line_length\": 70,\n \"num_lines\": 29,\n \"path\": \"/lc1283_bs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def smallestDivisor(self, nums: List[int], threshold: int) -> int:\\n left, right = 1, sum(nums)\\n while left <= right:\\n mid = (left + right)//2\\n rslt = sum(ceil(x/mid) for x in nums)\\n if rslt > threshold:\\n left = mid\\n else:\\n right = mid - 1\\n if mid == (left+right)//2:\\n left += 1\\n return left\\n\\nclass Solution:\\n def smallestDivisor(self, nums: List[int], threshold: int) -> int:\\n def check(i):\\n rslt = 0\\n for n in nums:\\n rslt += (n-1)//i + 1\\n return rslt\\n left, right = 1, max(nums)\\n while left <= right:\\n mid = (left + right)//2\\n if check(mid) <= threshold:\\n right = mid -1\\n else:\\n left = mid + 1\\n return left\\n\"\n },\n {\n \"alpha_fraction\": 0.4781420826911926,\n \"alphanum_fraction\": 0.4890710413455963,\n \"avg_line_length\": 35.599998474121094,\n \"blob_id\": \"5520e513fd3829848a2965f46447a64a2bbb895a\",\n \"content_id\": \"1b07c407d39f65d00926004e701111357e41bbb6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 366,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 10,\n \"path\": \"/lc84_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def largestRectangleArea(self, heights: List[int]) -> int:\\n stack, rslt = [], 0\\n for i, h in enumerate(heights+[0]):\\n start = i\\n while stack and stack[-1][1] >= h:\\n start, mh = stack.pop()\\n rslt = max(rslt, (i - start)*mh)\\n stack.append((start, h))\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.32870370149612427,\n \"alphanum_fraction\": 0.33217594027519226,\n \"avg_line_length\": 27.799999237060547,\n \"blob_id\": \"32ae75ff6c120b022c29300856f3cde3d08a99a9\",\n \"content_id\": \"0a9dfe9fdfc506fa76e85133e72d112deecf9667\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 864,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 30,\n \"path\": \"/lc227_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def calculate(self, s: str) -> int:\\n stk, last, op = [], \\\"\\\", None\\n for c in s+\\\"+\\\":\\n if c == \\\" \\\": continue\\n if c.isdigit():\\n last = last+c if last else c\\n else:\\n if op == \\\"*\\\":\\n stk.pop()\\n stk.append(int(stk.pop())*int(last))\\n elif op == \\\"/\\\":\\n stk.pop()\\n stk.append(int(stk.pop())//int(last))\\n else:\\n stk.append(last)\\n stk.append(c)\\n op = c\\n last = \\\"\\\"\\n rslt, last = 0, 0\\n\\n while stk:\\n temp = stk.pop()\\n if temp == \\\"-\\\":\\n rslt -= 2*last\\n elif temp != \\\"+\\\":\\n last = int(temp)\\n rslt += last\\n\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.37142857909202576,\n \"alphanum_fraction\": 0.37460318207740784,\n \"avg_line_length\": 30.5,\n \"blob_id\": \"417f0dd0a82ed1643aaf0bb45d2444235cd44235\",\n \"content_id\": \"0a7308dbcaf5aa87a0710548cd2ae8ea9a30b6e7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 630,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 20,\n \"path\": \"/lc385_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def deserialize(self, s: str) -> NestedInteger:\\n stk, last, curr = [], \\\"\\\", None\\n for c in s:\\n if c in \\\",]\\\":\\n if last:\\n stk[-1].add(NestedInteger(int(last)))\\n last = \\\"\\\"\\n if c == \\\"]\\\":\\n rslt = stk.pop()\\n elif c == \\\"[\\\":\\n temp = NestedInteger()\\n if stk:\\n stk[-1].add(temp)\\n stk.append(temp)\\n else:\\n last += c\\n if not stk and last:\\n return NestedInteger(int(last))\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.459940642118454,\n \"alphanum_fraction\": 0.4658753573894501,\n \"avg_line_length\": 32.70000076293945,\n \"blob_id\": \"96cfca0c76df156faaa61223b5cfd088279c77a6\",\n \"content_id\": \"0d188a1077520b500b42fc17ca958cd62a347313\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 337,\n \"license_type\": \"no_license\",\n \"max_line_length\": 59,\n \"num_lines\": 10,\n \"path\": \"/lc739_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def dailyTemperatures(self, T: List[int]) -> List[int]:\\n if not T: return T\\n stk, rslt = [], [0]*len(T)\\n for i, t in enumerate(T):\\n while stk and t > T[stk[-1]]:\\n pre_id = stk.pop()\\n rslt[pre_id] = i - pre_id\\n stk.append(i)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.35686779022216797,\n \"alphanum_fraction\": 0.3671373426914215,\n \"avg_line_length\": 29.31999969482422,\n \"blob_id\": \"10f20e02fcfbc38e0e0b7c0d759327803c56c16c\",\n \"content_id\": \"39b3e339b17e251353784620e6876aa0e9747fcb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 779,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 25,\n \"path\": \"/lc410_bs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def splitArray(self, nums: List[int], m: int) -> int:\\n left, right = 0, 0\\n n = len(nums)\\n for i in range(n):\\n right += nums[i]\\n if left < nums[i]: left = nums[i]\\n rslt = right\\n while left <= right:\\n mid = (left + right)//2\\n total = 0\\n count = 0\\n # check if is a viable mid\\n for i in range(n):\\n if total + nums[i] > mid:\\n count += 1\\n total = nums[i]\\n else:\\n total += nums[i]\\n if count < m:\\n rslt = min(rslt, mid)\\n right = mid - 1\\n else:\\n left = mid + 1\\n return rslt\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.3499627709388733,\n \"alphanum_fraction\": 0.3708116114139557,\n \"avg_line_length\": 26.40816307067871,\n \"blob_id\": \"a7125d6d2d838d2fe55cbe6e6877b85c9fb7fd95\",\n \"content_id\": \"be33ae9c95d1179b9fb4583a97f8ccd6f6b1166f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1343,\n \"license_type\": \"no_license\",\n \"max_line_length\": 56,\n \"num_lines\": 49,\n \"path\": \"/lc115_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#dfs+memo\\nclass Solution:\\n def numDistinct(self, s: str, t: str) -> int:\\n memo = {}\\n def dfs(i_s, j_t):\\n if (i_s, j_t) in memo:\\n return memo[(i_s,j_t)]\\n if j_t == len(t):\\n temp = 1\\n elif i_s == len(s):\\n temp = 0\\n elif s[i_s] == t[j_t]:\\n temp = dfs(i_s+1, j_t+1)+dfs(i_s+1, j_t)\\n else:\\n temp = dfs(i_s+1, j_t)\\n memo[(i_s, j_t)] = temp\\n return temp\\n return dfs(0, 0)\\n# 1-d dp\\n\\nclass Solution:\\n def numDistinct(self, s: str, t: str) -> int:\\n dp = [0]*(len(t)+1)\\n dp[0] = 1\\n for i in range(len(s)):\\n temp = [1]*(len(t)+1)\\n for j in range(len(t)):\\n temp[j+1] = dp[j+1]\\n if s[i] == t[j]:\\n temp[j+1] += dp[j]\\n dp = temp\\n return dp[-1]\\n\\n\\n\\nclass Solution:\\n def numDistinct(self, s: str, t: str) -> int:\\n self.rslt = 0\\n @lru_cache(None)\\n def dfs(i_s, j_t):\\n if j_t == len(t):\\n return 1\\n if i_s == len(s):\\n return 0\\n if s[i_s] == t[j_t]:\\n return dfs(i_s+1, j_t+1)+dfs(i_s+1, j_t)\\n else:\\n return dfs(i_s+1, j_t)\\n return dfs(0, 0)\\n\"\n },\n {\n \"alpha_fraction\": 0.4312354326248169,\n \"alphanum_fraction\": 0.4428904354572296,\n \"avg_line_length\": 34.75,\n \"blob_id\": \"6995247e91c8b96a6fae8cc5928f7c36b762ff20\",\n \"content_id\": \"8620617faafc1f84e3f1726650ffeb9e8c3b48b2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 429,\n \"license_type\": \"no_license\",\n \"max_line_length\": 53,\n \"num_lines\": 12,\n \"path\": \"/lc402_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def removeKdigits(self, num: str, k: int) -> str:\\n if k >= len(num): return \\\"0\\\"\\n stk, temp= [], k\\n for i, c in enumerate(num):\\n while stk and temp > 0 and stk[-1] > c:\\n stk.pop()\\n temp -= 1\\n if temp == 0:\\n return str(int(\\\"\\\".join(stk)+num[i:]))\\n stk.append(c)\\n return str(int(\\\"\\\".join(stk[:len(num)-k])))\\n\"\n },\n {\n \"alpha_fraction\": 0.4683684706687927,\n \"alphanum_fraction\": 0.4772475063800812,\n \"avg_line_length\": 28.064516067504883,\n \"blob_id\": \"09986d18f0df56a33440e8de3d634c990c6f3825\",\n \"content_id\": \"841b0a3b7a35abb85b77e802d416b303874f20bc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 901,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 31,\n \"path\": \"/lc23_linkedlist.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Definition for singly-linked list.\\n# class ListNode:\\n# def __init__(self, val=0, next=None):\\n# self.val = val\\n# self.next = next\\nclass Solution:\\n def mergeKLists(self, lists: List[ListNode]) -> ListNode:\\n if not lists: return None\\n interval = 1\\n n = len(lists)\\n while interval < n:\\n for i in range(0, n-interval, 2*interval):\\n lists[i] = self.merge2Lists(lists[i], lists[i+interval])\\n interval *= 2\\n return lists[0]\\n\\n def merge2Lists(self, a, b):\\n snode = curr = ListNode()\\n while a and b:\\n if a.val < b.val:\\n curr.next = a\\n a = a.next\\n else:\\n curr.next = b\\n b = b.next\\n curr = curr.next\\n if a:\\n curr.next = a\\n else:\\n curr.next = b\\n return snode.next\\n\"\n },\n {\n \"alpha_fraction\": 0.48367953300476074,\n \"alphanum_fraction\": 0.48961424827575684,\n \"avg_line_length\": 36.44444274902344,\n \"blob_id\": \"c7b85cf2f9b711163d8311b56e3f801f1337110b\",\n \"content_id\": \"d15b9da19a9ab6c89fa79a0541441cbf00e4bd7d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 674,\n \"license_type\": \"no_license\",\n \"max_line_length\": 87,\n \"num_lines\": 18,\n \"path\": \"/lc1606_sortedlist.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from sortedcontainers import SortedList\\nclass Solution:\\n def busiestServers(self, k: int, arrival: List[int], load: List[int]) -> List[int]:\\n freq = [0]*k\\n n = len(arrival)\\n busy = []\\n free = SortedList(range(k))\\n for i in range(n):\\n while busy and busy[0][0] <= arrival[i]:\\n t, si = heapq.heappop(busy)\\n free.add(si)\\n if free:\\n idx = free.bisect_left(i%k)%len(free)\\n s = free.pop(idx)\\n freq[s] += 1\\n heapq.heappush(busy, (arrival[i]+load[i], s))\\n mx = max(freq)\\n return [x for x in range(k) if freq[x] == mx]\\n\"\n },\n {\n \"alpha_fraction\": 0.3953045606613159,\n \"alphanum_fraction\": 0.4029187858104706,\n \"avg_line_length\": 27.654544830322266,\n \"blob_id\": \"c44d69ced6299e0c0ec1a1d131c7e5875b3785d0\",\n \"content_id\": \"582d17037b5737c41b6c9f36ee9d79fa8a0c71c2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1576,\n \"license_type\": \"no_license\",\n \"max_line_length\": 50,\n \"num_lines\": 55,\n \"path\": \"/lc300_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def lengthOfLIS(self, nums: List[int]) -> int:\\n @lru_cache(None)\\n def dfs(i):\\n temp = 1\\n for j in range(i+1, len(nums)):\\n if nums[j] > nums[i]:\\n temp = max(temp, dfs(j)+1)\\n return temp\\n rslt = 0\\n for i in range(len(nums)):\\n rslt = max(rslt, dfs(i))\\n return rslt\\n\\n def lengthOfLIS(self, nums: List[int]) -> int:\\n sub = []\\n for num in nums:\\n idx = bisect.bisect_left(sub, num)\\n if idx < len(sub):\\n sub[idx] = num\\n else:\\n sub.append(num)\\n return len(sub)\\n \\n def lengthOfLIS(self, nums:List[int]) -> int:\\n n = len(nums)\\n dp = [1]*n\\n for i in range(n):\\n for j in range(i):\\n if dp[j] < dp[i]:\\n dp[i] = max(dp[i], dp[j]+1)\\n return max(dp)\\n\\n\\n\\n def lengthOfLIS(self, nums: List[int]) -> int:\\n def bisearch(arr, target):\\n left, right = 0, len(arr)-1\\n while left <= right:\\n mid = (left + right)//2\\n if arr[mid] < target:\\n left = mid\\n else:\\n right = mid - 1\\n if (left+right)//2 == mid:\\n left += 1\\n return left\\n sub = []\\n for num in nums:\\n idx = bisearch(sub, num)\\n if idx < len(sub):\\n sub[idx] = num\\n else:\\n sub.append(num)\\n return len(sub)\\n\"\n },\n {\n \"alpha_fraction\": 0.5076400637626648,\n \"alphanum_fraction\": 0.5178267955780029,\n \"avg_line_length\": 31.72222137451172,\n \"blob_id\": \"e1f90d631bf0f52f203221e91bcd5f11405e558f\",\n \"content_id\": \"73a1b09813a7f1d70a3e71adededf0724a696bd6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 589,\n \"license_type\": \"no_license\",\n \"max_line_length\": 59,\n \"num_lines\": 18,\n \"path\": \"/lc1019_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Definition for singly-linked list.\\n# class ListNode:\\n# def __init__(self, val=0, next=None):\\n# self.val = val\\n# self.next = next\\n# maintain a monotonic decreasing stack\\nclass Solution:\\n def nextLargerNodes(self, head: ListNode) -> List[int]:\\n answer, stack, i = [], [], 0\\n while head:\\n while stack and stack[-1][0] < head.val:\\n _, j = stack.pop()\\n answer[j] = head.val\\n stack.append((head.val, i))\\n answer.append(0)\\n head = head.next\\n i = i+1\\n return answer\\n\"\n },\n {\n \"alpha_fraction\": 0.49862638115882874,\n \"alphanum_fraction\": 0.5096153616905212,\n \"avg_line_length\": 25,\n \"blob_id\": \"7a7472ba0c8549b02b9643c5fad90f8a83d38927\",\n \"content_id\": \"854a819fa2c1d67d3f25fa0aa2555ff12ecb778c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 728,\n \"license_type\": \"no_license\",\n \"max_line_length\": 63,\n \"num_lines\": 28,\n \"path\": \"/lc528_random.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n\\n def __init__(self, w: List[int]):\\n self.cumsum = []\\n for i, n in enumerate(w):\\n if i == 0: self.cumsum.append(n)\\n else:\\n self.cumsum.append(n + self.cumsum[-1])\\n\\n def pickIndex(self) -> int:\\n t = random.randint(1, self.cumsum[-1])\\n return self.binSearch(t)\\n\\n def binSearch(self, t):\\n left, right = 0, len(self.cumsum)\\n while left < right:\\n mid = (left + right)//2\\n if self.cumsum[mid] < t:\\n left = mid+1\\n else:\\n right = mid\\n return left\\n\\n\\n\\n# Your Solution object will be instantiated and called as such:\\n# obj = Solution(w)\\n# param_1 = obj.pickIndex()\\n\"\n },\n {\n \"alpha_fraction\": 0.4324324429035187,\n \"alphanum_fraction\": 0.45945945382118225,\n \"avg_line_length\": 29.272727966308594,\n \"blob_id\": \"fb95451b267c6f40d003a0ad9555b48801c455c2\",\n \"content_id\": \"64541f9488eb4ed5426bf4f63095bc729d037d6e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 333,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 11,\n \"path\": \"/lc238_array.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def productExceptSelf(self, nums: List[int]) -> List[int]:\\n n = len(nums)\\n rslt = [1]*n\\n for i in range(1, n):\\n rslt[i] = rslt[i-1]*nums[i-1]\\n curr = 1\\n for i in range(n-2, -1, -1):\\n curr *= nums[i+1]\\n rslt[i] = rslt[i]*curr\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.48226097226142883,\n \"alphanum_fraction\": 0.49789535999298096,\n \"avg_line_length\": 28.175437927246094,\n \"blob_id\": \"05d1bd89d45bcdcb2586c249640b0fe41992ab18\",\n \"content_id\": \"64a92e746be8d797a8b419264ef11ccf04fbcd20\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1663,\n \"license_type\": \"no_license\",\n \"max_line_length\": 113,\n \"num_lines\": 57,\n \"path\": \"/lc706_oop.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# hash function + collision handling(chaining)\\nclass MyHashMap:\\n\\n def __init__(self):\\n \\\"\\\"\\\"\\n Initialize your data structure here.\\n \\\"\\\"\\\"\\n self.table = [[] for _ in range(200)]\\n\\n def put(self, key: int, value: int) -> None:\\n \\\"\\\"\\\"\\n value will always be non-negative.\\n \\\"\\\"\\\"\\n hs = key % 200\\n if len(self.table[hs]) == 0:\\n self.table[hs].append((key, value))\\n return\\n for i, v in enumerate(self.table[hs]):\\n if v[0] == key:\\n self.table[hs][i] = (key, value)\\n return\\n self.table[hs].append((key, value))\\n\\n\\n def get(self, key: int) -> int:\\n \\\"\\\"\\\"\\n Returns the value to which the specified key is mapped, or -1 if this map contains no mapping for the key\\n \\\"\\\"\\\"\\n hs = key % 200\\n if len(self.table[hs]) == 0:\\n return -1\\n for i, v in enumerate(self.table[hs]):\\n if v[0] == key:\\n return v[1]\\n return -1\\n def remove(self, key: int) -> None:\\n \\\"\\\"\\\"\\n Removes the mapping of the specified value key if this map contains a mapping for the key\\n \\\"\\\"\\\"\\n hs = key % 200\\n if len(self.table[hs]) == 0:\\n return\\n check = -1\\n for i, v in enumerate(self.table[hs]):\\n if v[0] == key:\\n check = i\\n break\\n if check >= 0:\\n self.table[hs][check] = self.table[hs][-1]\\n self.table[hs].pop()\\n\\n\\n# Your MyHashMap object will be instantiated and called as such:\\n# obj = MyHashMap()\\n# obj.put(key,value)\\n# param_2 = obj.get(key)\\n# obj.remove(key)\\n\"\n },\n {\n \"alpha_fraction\": 0.47732996940612793,\n \"alphanum_fraction\": 0.48488664627075195,\n \"avg_line_length\": 43.11111068725586,\n \"blob_id\": \"c19adee3ec3909f52c04b7861084c56a2fa62b88\",\n \"content_id\": \"47f4cecf2131d5f5cd930918a908a6b9d5f8527c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 794,\n \"license_type\": \"no_license\",\n \"max_line_length\": 88,\n \"num_lines\": 18,\n \"path\": \"/lc241_str.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def diffWaysToCompute(self, expression: str) -> List[int]:\\n if not expression: return []\\n op = {\\\"+\\\": (lambda x, y: x+y), \\\"-\\\": (lambda x, y: x-y), \\\"*\\\": (lambda x, y: x*y)}\\n def helper(left, right):\\n if left >= right: return expression[right]\\n rslt = []\\n for i in range(left, right + 1):\\n if expression[i] in op:\\n leftr, rightr = helper(left, i-1), helper(i+1, right)\\n for l in leftr:\\n for r in rightr:\\n rslt.append(op[expression[i]](int(l), int(r)))\\n print(left, right)\\n if not rslt: rslt.append(int(expression[left:right+1]))\\n\\n return rslt\\n return helper(0, len(expression)-1)\\n\"\n },\n {\n \"alpha_fraction\": 0.48880597949028015,\n \"alphanum_fraction\": 0.49253731966018677,\n \"avg_line_length\": 30.52941131591797,\n \"blob_id\": \"d311d7640bff8212c7d86642b24eb763b19ce229\",\n \"content_id\": \"3b70125f5f4286ea3ea7b03e462e83f50f845f25\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 536,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 17,\n \"path\": \"/lc1171_linkedlist.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def removeZeroSumSublists(self, head: ListNode) -> ListNode:\\n presum = 0\\n snode = ListNode(0, head)\\n maps = collections.OrderedDict()\\n curr = snode\\n while curr:\\n presum += curr.val\\n prev = curr\\n if presum in maps:\\n prev = maps[presum]\\n prev.next = curr.next\\n while presum in maps:\\n maps.popitem()\\n maps[presum] = prev\\n curr = curr.next\\n return snode.next\\n\"\n },\n {\n \"alpha_fraction\": 0.5034722089767456,\n \"alphanum_fraction\": 0.5138888955116272,\n \"avg_line_length\": 31,\n \"blob_id\": \"75a349f856b09b915c285d69b7bd60116db8fe12\",\n \"content_id\": \"dae00d9e95811e7ba0d0e379795a7be018ed7519\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 288,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 9,\n \"path\": \"/lc253_hp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minMeetingRooms(self, intervals: List[List[int]]) -> int:\\n intervals.sort()\\n hq = []\\n for itv in intervals:\\n if hq and itv[0] >= hq[0]:\\n heapq.heappop(hq)\\n heapq.heappush(hq, itv[1])\\n return len(hq)\\n\"\n },\n {\n \"alpha_fraction\": 0.453125,\n \"alphanum_fraction\": 0.46875,\n \"avg_line_length\": 31,\n \"blob_id\": \"b8076718320e30aba6c68999bf948987c54af693\",\n \"content_id\": \"baba4646dc4ec108c72d44f432317eecb049c807\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 256,\n \"license_type\": \"no_license\",\n \"max_line_length\": 45,\n \"num_lines\": 8,\n \"path\": \"/lc338_bit.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def countBits(self, n: int) -> List[int]:\\n rslt = [0]\\n while len(rslt) < n+1:\\n for i in range(len(rslt)):\\n rslt.append(rslt[i]+1)\\n if len(rslt) > n: break\\n return rslt[:n+1]\\n\"\n },\n {\n \"alpha_fraction\": 0.38433516025543213,\n \"alphanum_fraction\": 0.39708560705184937,\n \"avg_line_length\": 27.894737243652344,\n \"blob_id\": \"c94b18a6ca334e641cd89577d8fcb95293f5e020\",\n \"content_id\": \"4d089d13fee8d85a7cd7fc7e88329bb3a3e7c936\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 549,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 19,\n \"path\": \"/lc1011_bs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def shipWithinDays(self, weights: List[int], D: int) -> int:\\n def check(t):\\n rslt, temp = 0, 0\\n for w in weights:\\n temp += w\\n if temp > t:\\n rslt += 1\\n temp = w\\n return rslt+1\\n\\n left, right = max(weights), sum(weights)\\n while left <= right:\\n mid = (left + right)//2\\n if check(mid) > D:\\n left = mid + 1\\n else:\\n right = mid -1\\n return left\\n\"\n },\n {\n \"alpha_fraction\": 0.3367496430873871,\n \"alphanum_fraction\": 0.4260614812374115,\n \"avg_line_length\": 36.38888931274414,\n \"blob_id\": \"17be8fb293371785b2340d350baff88d1657fa96\",\n \"content_id\": \"b2851df486cdcc7985df8856a3b5353ff4381120\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 683,\n \"license_type\": \"no_license\",\n \"max_line_length\": 73,\n \"num_lines\": 18,\n \"path\": \"/lc1537_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxSum(self, nums1: List[int], nums2: List[int]) -> int:\\n l1, l2 = len(nums1), len(nums2)\\n idx1, idx2 = 0, 0\\n max1, max2 = 0, 0\\n while idx1 < l1 or idx2 < l2:\\n if idx1 < l1 and (idx2 == l2 or nums1[idx1] < nums2[idx2]):\\n max1 += nums1[idx1]\\n idx1 += 1\\n elif idx2 < l2 and (idx1 == l1 or nums1[idx1] > nums2[idx2]):\\n max2 += nums2[idx2]\\n idx2 += 1\\n else:\\n max1 = max(max1, max2) + nums1[idx1]\\n max2 = max1\\n idx1 += 1\\n idx2 += 1\\n return max(max1, max2)%(10**9+7)\\n\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.3949275314807892,\n \"alphanum_fraction\": 0.4214975833892822,\n \"avg_line_length\": 42.578948974609375,\n \"blob_id\": \"9427790f75b92015a51140c001fdf9128cd37066\",\n \"content_id\": \"2736d4c9d728421e34bfcc447d0372b6cff14af3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 828,\n \"license_type\": \"no_license\",\n \"max_line_length\": 87,\n \"num_lines\": 19,\n \"path\": \"/lc980_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def uniquePathsIII(self, grid: List[List[int]]) -> int:\\n toCover, nrow, ncol = 0, len(grid), len(grid[0])\\n for i in range(nrow):\\n for j in range(ncol):\\n if grid[i][j] == 0: toCover += 1\\n if grid[i][j] == 1: start = (i,j)\\n self.rslt = 0\\n def backtracking(i, j, step):\\n for x, y in [(i+1, j), (i-1, j), (i, j+1), (i, j-1)]:\\n if x < 0 or x >= nrow or y < 0 or y >= ncol or grid[x][y] < 0: continue\\n if grid[x][y] == 0:\\n grid[x][y] = -2\\n backtracking(x, y, step + 1)\\n grid[x][y] = 0\\n elif grid[x][y] == 2 and step == toCover:\\n self.rslt += 1\\n backtracking(start[0], start[1], 0)\\n return self.rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.43216782808303833,\n \"alphanum_fraction\": 0.4475524425506592,\n \"avg_line_length\": 29.211267471313477,\n \"blob_id\": \"a33f3ad87ccb5e22d20d960ed07e1423f613df55\",\n \"content_id\": \"5e904a22a49e1b9420e8e857bff38e6b78ffe4c1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2145,\n \"license_type\": \"no_license\",\n \"max_line_length\": 100,\n \"num_lines\": 71,\n \"path\": \"/lc1707_trie.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Trie:\\n def __init__(self, L):\\n self.trie = {}\\n self.L = L\\n def insert(self, num):\\n curr = self.trie\\n mask = 1 << self.L\\n while mask:\\n key = 1 if num&mask else 0\\n if key not in curr:\\n curr[key] = {}\\n curr = curr[key]\\n mask >>= 1\\n def search(self, num):\\n curr = self.trie\\n mask = 1 << self.L\\n rslt = 0\\n while mask:\\n key = 0 if num&mask else 1\\n if key in curr:\\n curr = curr[key]\\n rslt |= mask\\n else:\\n curr = curr[1-key]\\n mask >>= 1\\n return rslt\\n\\nclass Solution:\\n def maximizeXor(self, nums: List[int], queries: List[List[int]]) -> List[int]:\\n nums.sort()\\n nn, nq = len(nums), len(queries)\\n idx = list(range(nq))\\n idx.sort(key = lambda x: queries[x][1])\\n idxn = 0\\n l = len(bin(max(nums[-1], max(x[0] for x in queries))))-3\\n trie, rslt = Trie(l), [-1]*nq\\n for idxq in idx:\\n x, m = queries[idxq]\\n while idxn < nn and nums[idxn] <= m:\\n trie.insert(nums[idxn])\\n idxn += 1\\n if idxn != 0:\\n rslt[idxq] = trie.search(x)\\n return rslt\\n\\n#https://leetcode.com/problems/maximum-xor-with-an-element-from-array/discuss/989454/Python-14-lines\\ndef maximizeXor(self, nums: List[int], queries: List[List[int]]) -> List[int]:\\n nums.sort()\\n answer = []\\n append = answer.append\\n for x, m in queries:\\n if nums[0] > m:\\n append(-1)\\n continue\\n start, stop = 0, bisect_right(nums, m)\\n num = 0\\n bit = 2 ** m.bit_length()\\n while bit:\\n plus = num + bit\\n if nums[start] >= plus:\\n num = plus\\n elif nums[stop-1] >= plus:\\n cut = bisect_left(nums, plus, start, stop)\\n if x & bit:\\n stop = cut\\n else:\\n start = cut\\n num = plus\\n bit //= 2\\n append(num ^ x)\\n return answer\\n\"\n },\n {\n \"alpha_fraction\": 0.44129031896591187,\n \"alphanum_fraction\": 0.4606451690196991,\n \"avg_line_length\": 35.904762268066406,\n \"blob_id\": \"56162f0a3a3e2a6b36ba578ebc940527dd06bfdc\",\n \"content_id\": \"bd3fb33bfef78d6d28d7079c3ce18569d658d809\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 775,\n \"license_type\": \"no_license\",\n \"max_line_length\": 88,\n \"num_lines\": 21,\n \"path\": \"/lc301_str.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def removeInvalidParentheses(self, s: str) -> List[str]:\\n self.rslt = []\\n self.helper(s, 0, 0, ('(', ')'))\\n return self.rslt\\n\\n\\n def helper(self, curr_s, s_check, s_modify, check):\\n count = 0\\n for i in range(s_check, len(curr_s)):\\n count += (curr_s[i] == check[0]) - (curr_s[i] == check[1])\\n if count >= 0: continue\\n for j in range(s_modify, i+1):\\n if curr_s[j] == check[1] and (j == s_modify or curr_s[j-1] != check[1]):\\n self.helper(curr_s[:j]+curr_s[j+1:], i, j, check)\\n return\\n curr_s = curr_s[::-1]\\n if check[0] == '(':\\n self.helper(curr_s, 0, 0, (')', '('))\\n else:\\n self.rslt.append(curr_s)\\n\"\n },\n {\n \"alpha_fraction\": 0.46507516503334045,\n \"alphanum_fraction\": 0.5083996653556824,\n \"avg_line_length\": 36.70000076293945,\n \"blob_id\": \"051b5dffd898dc5645f8c175c6a3b64fd71097d9\",\n \"content_id\": \"6f739cf40ae9835919706af84f9ab0bf7f1cbae4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1131,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 30,\n \"path\": \"/lc244_hash.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class WordDistance:\\n\\n def __init__(self, wordsDict: List[str]):\\n self.maps = collections.defaultdict(list)\\n self.cache = {}\\n for i, word in enumerate(wordsDict):\\n self.maps[word].append(i)\\n\\n def shortest(self, word1: str, word2: str) -> int:\\n if (word1, word2) in self.cache: return self.cache[(word1, word2)]\\n if (word2, word1) in self.cache: return self.cache[(word2, word1)]\\n cand1, cand2 = self.maps[word1], self.maps[word2]\\n # if len(cand1) > len(cand2): cand1, cand2 = cand2, cand1\\n # rslt = float(\\\"inf\\\")\\n # for x in cand1:\\n # y = bisect.bisect(cand2, x)\\n # if y < len(cand2):\\n # rslt = min(rslt, cand2[y]-x)\\n # if y > 0: rslt = min(rslt, x-cand2[y-1])\\n # return rslt\\n l1, l2 = 0, 0\\n rslt = float(\\\"inf\\\")\\n while l1 < len(cand1) and l2 < len(cand2):\\n rslt = min(rslt, abs(cand1[l1]-cand2[l2]))\\n if cand1[l1] < cand2[l2]:\\n l1 += 1\\n else:\\n l2 += 1\\n self.cache[(word1, word2)] = rslt\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.37906646728515625,\n \"alphanum_fraction\": 0.39745402336120605,\n \"avg_line_length\": 38.27777862548828,\n \"blob_id\": \"11a96639517bcc375870f986c63b46d72c29bf45\",\n \"content_id\": \"2e19cee85667f121f97ca241f9e02f4b324ed2b7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 707,\n \"license_type\": \"no_license\",\n \"max_line_length\": 101,\n \"num_lines\": 18,\n \"path\": \"/lc52_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def totalNQueens(self, n: int) -> int:\\n self.rslt, visited = 0, set()\\n def backtracking(i):\\n if i == n:\\n self.rslt += 1\\n else:\\n for j in range(n):\\n if (2, j) not in visited and (3, j-i) not in visited and (4, j+i) not in visited:\\n visited.add((2, j))\\n visited.add((3, j-i))\\n visited.add((4, j+i))\\n backtracking(i+1)\\n visited.remove((2, j))\\n visited.remove((3, j-i))\\n visited.remove((4, j+i))\\n backtracking(0)\\n return self.rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.47999998927116394,\n \"alphanum_fraction\": 0.49925926327705383,\n \"avg_line_length\": 28.34782600402832,\n \"blob_id\": \"7d292831e11bca4ad67c9d8cbc332b7ad87ec079\",\n \"content_id\": \"3571c970366dfa11e1c707f844dda58b390e1753\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 675,\n \"license_type\": \"no_license\",\n \"max_line_length\": 55,\n \"num_lines\": 23,\n \"path\": \"/lc1146_ood.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class SnapshotArray:\\n\\n def __init__(self, length: int):\\n self.array = [[(-1, 0)] for _ in range(length)]\\n self.snap_id = 0\\n\\n def set(self, index: int, val: int) -> None:\\n self.array[index].append((self.snap_id, val))\\n\\n\\n def snap(self) -> int:\\n self.snap_id += 1\\n return self.snap_id - 1\\n\\n def get(self, index: int, snap_id: int) -> int:\\n left, right = 0, len(self.array[index])-1\\n while left <= right:\\n mid = (left + right)//2\\n if self.array[index][mid][0] > snap_id:\\n right = mid -1\\n else:\\n left = mid + 1\\n return self.array[index][left-1][1]\\n\"\n },\n {\n \"alpha_fraction\": 0.4386363625526428,\n \"alphanum_fraction\": 0.4522727131843567,\n \"avg_line_length\": 30.428571701049805,\n \"blob_id\": \"86a18cf38959216af26744334a9038197ba2d7ba\",\n \"content_id\": \"75df68515c51f8e2706475253bfc821e28df9d55\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 440,\n \"license_type\": \"no_license\",\n \"max_line_length\": 68,\n \"num_lines\": 14,\n \"path\": \"/lc1641_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# dfs + memo\\nclass Solution:\\n def countVowelStrings(self, n: int) -> int:\\n memo = {}\\n def dfs(curr, rest):\\n if (curr, rest) in memo:\\n return memo[(curr, rest)]\\n if curr == 1:\\n return rest\\n if rest == 1:\\n return 1\\n memo[(curr, rest)] = dfs(curr-1, rest)+dfs(curr, rest-1)\\n return memo[(curr, rest)]\\n return dfs(n, 5)\\n\"\n },\n {\n \"alpha_fraction\": 0.39177101850509644,\n \"alphanum_fraction\": 0.40787118673324585,\n \"avg_line_length\": 30.05555534362793,\n \"blob_id\": \"fa4af0717539bc5ede49b83c12fb1023356cbc2d\",\n \"content_id\": \"d1e636329b5c95e9539d784a9404b75bb2967020\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 559,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 18,\n \"path\": \"/lc343_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def integerBreak(self, n: int) -> int:\\n @functools.lru_cache(None)\\n def dfs(j):\\n if j <= 2:\\n return 1\\n else:\\n rslt = 0\\n for i in range(1, j):\\n rslt = max(rslt, i*max(dfs(j-i),j-i))\\n return rslt\\n return dfs(n)\\n def integerBreak(self, n:int) -> int:\\n dp = [1]*(n+1)\\n for j in range(3, n+1):\\n for i in range(1, j):\\n dp[j] = max(dp[j], i*max(dp[j-i], j-i))\\n return dp[n]\\n\"\n },\n {\n \"alpha_fraction\": 0.40689656138420105,\n \"alphanum_fraction\": 0.4275861978530884,\n \"avg_line_length\": 32.46154022216797,\n \"blob_id\": \"c9b62d115af5de1899b2c03d7ee3f39a858e17f6\",\n \"content_id\": \"13470bea045eebf2acfe2d096f1271f60ab1aa4f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 435,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 13,\n \"path\": \"/lc1542_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def longestAwesome(self, s: str) -> int:\\n rslt, curr = 0, 0\\n seen = {0:-1}\\n for i, c in enumerate(s):\\n curr ^= 1 << int(c)\\n if curr not in seen:\\n seen[curr] = i\\n rslt = max(rslt, i-seen[curr])\\n for j in range(10):\\n if curr^(1 << j) in seen:\\n rslt = max(rslt, i-seen[curr^(1< int:\\n preset = self.TriePre.search(prefix)\\n postset = self.TriePost.search(suffix[::-1])\\n rslt = preset & postset\\n return max(rslt) if rslt else -1\\n\\n\\nclass TrieNode:\\n def __init__(self, idx = -1):\\n self.next = {}\\n self.idx = -1\\nclass Trie:\\n def __init__(self):\\n self.root = TrieNode()\\n def insert(self, word, idx):\\n curr = self.root\\n for ch in word:\\n if ch not in curr.next:\\n curr.next[ch] = TrieNode()\\n curr.next[ch].idx = idx\\n curr = curr.next[ch]\\n def search(self, word):\\n curr = self.root\\n for ch in word:\\n if ch not in curr.next:\\n return -1\\n curr = curr.next[ch]\\n return curr.idx\\nclass WordFilter:\\n\\n def __init__(self, words: List[str]):\\n self.trie = Trie()\\n for j, word in enumerate(words):\\n for i in range(len(word)):\\n self.trie.insert(word[i:]+\\\"#\\\"+word, j)\\n def f(self, prefix: str, suffix: str) -> int:\\n return self.trie.search(suffix+\\\"#\\\"+prefix)\\n\"\n },\n {\n \"alpha_fraction\": 0.4093078672885895,\n \"alphanum_fraction\": 0.4343675374984741,\n \"avg_line_length\": 43.105262756347656,\n \"blob_id\": \"c6d01abdf9916ef60ce0ca290b5fec47672cd3f1\",\n \"content_id\": \"dbdfa02bfc39a6f5586e77193406b1057ead5f72\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 838,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 19,\n \"path\": \"/lc282_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def addOperators(self, num: str, target: int) -> List[str]:\\n rslt, n = [], len(num)\\n def backtracking(i, curr, last, total):\\n if i == n and total == target:\\n rslt.append(curr)\\n else:\\n for j in range(i+1, n+1):\\n temp1, temp2 = int(num[i:j]), num[i:j]\\n if i == 0:\\n backtracking(j, temp2, temp1, temp1)\\n else:\\n backtracking(j, curr+\\\"+\\\"+temp2, temp1, total+temp1)\\n backtracking(j, curr+\\\"*\\\"+temp2, last*temp1,total-last + last*temp1)\\n backtracking(j, curr+\\\"-\\\"+temp2, -temp1,total-temp1)\\n if num[i] == \\\"0\\\":\\n break\\n backtracking(0, \\\"\\\",0, 0)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4397905766963959,\n \"alphanum_fraction\": 0.45724257826805115,\n \"avg_line_length\": 30.83333396911621,\n \"blob_id\": \"3c17bbf09088d16e9c2da8165d1f15aeb4ef688a\",\n \"content_id\": \"29f10a4bb8e54ff26874fe06d66a0cecc55534af\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 573,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 18,\n \"path\": \"/lc774_bs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minmaxGasDist(self, stations: List[int], k: int) -> float:\\n def check(p):\\n count = 0\\n for i in range(1, n):\\n count += int((stations[i]-stations[i-1])/p)\\n return count\\n\\n left, right, n =0, 0, len(stations)\\n for i in range(1, n):\\n right = max(right, stations[i]-stations[i-1])\\n while left < right - 1e-6:\\n mid = (left + right)/2\\n if check(mid) > k:\\n left = mid\\n else:\\n right = mid\\n return left\\n\"\n },\n {\n \"alpha_fraction\": 0.3363695442676544,\n \"alphanum_fraction\": 0.3604424297809601,\n \"avg_line_length\": 38.410255432128906,\n \"blob_id\": \"ec2ed0ab1598633b99db7ecffba579a35261aa02\",\n \"content_id\": \"98f924795a19380d1a76ca465745e0b35ab3f7c3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1537,\n \"license_type\": \"no_license\",\n \"max_line_length\": 104,\n \"num_lines\": 39,\n \"path\": \"/lc37_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def solveSudoku(self, board: List[List[str]]) -> None:\\n \\\"\\\"\\\"\\n Do not return anything, modify board in-place instead.\\n \\\"\\\"\\\"\\n memo = collections.defaultdict(set)\\n # store curr board:\\n for i, l in enumerate(board):\\n for j, t in enumerate(l):\\n if t !=\\\".\\\":\\n memo[(i, -1)].add(t)\\n memo[(-1, j)].add(t)\\n memo[(i//3, j//3)].add(t)\\n def backtracking(i, j):\\n if i == 9:\\n return True\\n if board[i][j] == \\\".\\\":\\n for t in \\\"123456789\\\":\\n if t not in memo[(i,-1)] and t not in memo[(-1, j)] and t not in memo[(i//3, j//3)]:\\n board[i][j] = t\\n memo[(i, -1)].add(t)\\n memo[(-1, j)].add(t)\\n memo[(i//3, j//3)].add(t)\\n if j < 8 and backtracking(i, j+1):\\n return True\\n if j == 8 and backtracking(i+1, 0):\\n return True\\n board[i][j] = \\\".\\\"\\n memo[(i, -1)].remove(t)\\n memo[(-1, j)].remove(t)\\n memo[(i//3, j//3)].remove(t)\\n return False\\n else:\\n if j < 8 :\\n return backtracking(i, j+1)\\n else:\\n return backtracking(i+1, 0)\\n backtracking(0, 0)\\n return board\\n\"\n },\n {\n \"alpha_fraction\": 0.5080645084381104,\n \"alphanum_fraction\": 0.5100806355476379,\n \"avg_line_length\": 32.06666564941406,\n \"blob_id\": \"4c3993c0e5de31ae692874596a2f08cb0c5406dd\",\n \"content_id\": \"0d5bcf29f35cfbe87012fe9f18888ffad53d08a8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 496,\n \"license_type\": \"no_license\",\n \"max_line_length\": 67,\n \"num_lines\": 15,\n \"path\": \"/lc332_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findItinerary(self, tickets: List[List[str]]) -> List[str]:\\n graph = collections.defaultdict(list)\\n for start, end in tickets:\\n graph[start].append(end)\\n for key, val in graph.items():\\n val.sort(reverse = True)\\n rslt = []\\n def dfs(start):\\n while graph[start]:\\n temp = graph[start].pop()\\n dfs(temp)\\n rslt.append(start)\\n dfs(\\\"JFK\\\")\\n return rslt[::-1]\\n\"\n },\n {\n \"alpha_fraction\": 0.46584802865982056,\n \"alphanum_fraction\": 0.4666154980659485,\n \"avg_line_length\": 28.613636016845703,\n \"blob_id\": \"c7d874b8d769fc6b9f7766baa3ce79ba59f719d8\",\n \"content_id\": \"d4ea5601a63cccfd9a680044fafce6bf59415510\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1303,\n \"license_type\": \"no_license\",\n \"max_line_length\": 55,\n \"num_lines\": 44,\n \"path\": \"/lc426_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def treeToDoublyList(self, root: 'Node') -> 'Node':\\n # inorder traversal\\n def inorder(root):\\n if not root:\\n return None, None\\n left, right = root.left, root.right\\n head, tail = inorder(left)\\n if head:\\n tail.right = root\\n root.left = tail\\n else:\\n head = root\\n nhead, ntail = inorder(right)\\n root.right = nhead\\n if nhead:\\n nhead.left = root\\n else:\\n ntail = root\\n return head, ntail\\n head, tail = inorder(root)\\n if not head: return None\\n head.left = tail\\n tail.right = head\\n return head\\nclass Solution:\\n def treeToDoublyList(self, root: 'Node') -> 'Node':\\n if not root:\\n return None\\n dummy = Node(0)\\n prev = dummy\\n stack, curr = [], root\\n while stack or curr:\\n while curr:\\n stack.append(curr)\\n curr = curr.left\\n curr = stack.pop()\\n curr.left = prev\\n prev.right = curr\\n prev = curr\\n curr = curr.right\\n dummy.right.left = prev\\n prev.right = dummy.right\\n return dummy.right\\n\"\n },\n {\n \"alpha_fraction\": 0.4612794518470764,\n \"alphanum_fraction\": 0.46464645862579346,\n \"avg_line_length\": 33.94117736816406,\n \"blob_id\": \"b53d050676e7267397e7f04832b98e518ec48398\",\n \"content_id\": \"820c0d4499a20fecf039a4827a2101255da50c0b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 594,\n \"license_type\": \"no_license\",\n \"max_line_length\": 58,\n \"num_lines\": 17,\n \"path\": \"/lc785_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def isBipartite(self, graph: List[List[int]]) -> bool:\\n set_map = {}\\n def dfs(node):\\n for snode in graph[node]:\\n if snode not in set_map:\\n set_map[snode] = 1 - set_map[node]\\n if not dfs(snode): return False\\n elif set_map[node] == set_map[snode]:\\n return False\\n return True\\n\\n for i in range(len(graph)):\\n if graph[i] and i not in set_map:\\n set_map[i] = 1\\n if not dfs(i): return False\\n return True\\n\"\n },\n {\n \"alpha_fraction\": 0.459090918302536,\n \"alphanum_fraction\": 0.4636363685131073,\n \"avg_line_length\": 32.33333206176758,\n \"blob_id\": \"9e1249a434b754a41dad5ba7cf40bcc6dfd280bb\",\n \"content_id\": \"f50dae2390fbaedc0e41ae1692c572b0d02285ea\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1100,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 33,\n \"path\": \"/lc1740_lca.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Definition for a binary tree node.\\n# class TreeNode:\\n# def __init__(self, val=0, left=None, right=None):\\n# self.val = val\\n# self.left = left\\n# self.right = right\\nclass Solution:\\n def findDistance(self, root: TreeNode, p: int, q: int) -> int:\\n\\n def lca(root, p, q):\\n if not root: return None\\n if root.val == p or root.val == q:\\n return root\\n left = lca(root.left, p, q)\\n right = lca(root.right, p, q)\\n if left and right:\\n return root\\n return left or right\\n pqlca = lca(root, p, q)\\n queue = collections.deque([pqlca])\\n lq, lp, level = 0, 0, 0\\n while queue:\\n l = len(queue)\\n for _ in range(l):\\n temp = queue.popleft()\\n if temp.val == q:\\n lq = level\\n if temp.val == p:\\n lp = level\\n if temp.left: queue.append(temp.left)\\n if temp.right: queue.append(temp.right)\\n level += 1\\n return lq+lp\\n\"\n },\n {\n \"alpha_fraction\": 0.4606741666793823,\n \"alphanum_fraction\": 0.47752809524536133,\n \"avg_line_length\": 31.363636016845703,\n \"blob_id\": \"39ca79cc975b9922d8490b9d9473c5527bc313e0\",\n \"content_id\": \"9ed447a87a2149232887c17ada750c48d6bde047\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 712,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 22,\n \"path\": \"/lc968_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Definition for a binary tree node.\\n# class TreeNode:\\n# def __init__(self, val=0, left=None, right=None):\\n# self.val = val\\n# self.left = left\\n# self.right = right\\nclass Solution:\\n def minCameraCover(self, root: TreeNode) -> int:\\n self.rslt = 0\\n def dfs(root):\\n if not root: return -1\\n left, right = dfs(root.left), dfs(root.right)\\n # leaf's parent, put carmera\\n if left == 0 or right == 0:\\n self.rslt += 1\\n return -2\\n if left == -2 or right == -2:\\n return -1\\n else:\\n return 0\\n temp = dfs(root)\\n return self.rslt + (temp == 0)\\n\"\n },\n {\n \"alpha_fraction\": 0.5391250848770142,\n \"alphanum_fraction\": 0.5408194661140442,\n \"avg_line_length\": 31.62311553955078,\n \"blob_id\": \"ce89cdc5c779a036a90aa5fc50fbea389ab5503c\",\n \"content_id\": \"18a8bda8ad7a5e31b99cbbcaf88d85d0400cc5cd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6492,\n \"license_type\": \"no_license\",\n \"max_line_length\": 125,\n \"num_lines\": 199,\n \"path\": \"/lc732_mcal.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class MyCalendar:\\n\\n def __init__(self):\\n self.booking = []\\n\\n def book(self, start: int, end: int) -> bool:\\n for s, e in self.booking:\\n if s < end and e > start:\\n return False\\n self.booking.append((start, end))\\n return True\\nclass Node:\\n def __init__(self, start, end):\\n self.start = start\\n self.end = end\\n self.left = self.right = None\\nclass MyCalendar:\\n\\n def __init__(self):\\n self.root = None\\n\\n def book(self, start: int, end: int) -> bool:\\n rslt, self.root = self.insert(self.root, start, end)\\n return rslt\\n\\n def insert(self, root, start, end):\\n if not root:\\n return True, Node(start, end)\\n if end <= root.start:\\n rslt, root.left = self.insert(root.left, start, end)\\n return rslt, root\\n if start >= root.end:\\n rslt, root.right = self.insert(root.right, start, end)\\n return rslt, root\\n return False, root\\nclass MyCalendar:\\n\\n def __init__(self):\\n self.starts = []\\n self.ends = []\\n\\n def book(self, start: int, end: int) -> bool:\\n s, e = bisect.bisect_right(self.ends, start), bisect.bisect_left(self.starts, end)\\n if s != e: return False\\n bisect.insort(self.starts, start, s, e)\\n bisect.insort(self.ends, end, s, e)\\n return True\\n\\nclass MyCalendarTwo:\\n\\n def __init__(self):\\n self.seen_once = []\\n self.seen_twice = []\\n\\n def book(self, start: int, end: int) -> bool:\\n for st, ed in self.seen_twice:\\n if st < end and ed > start:\\n return False\\n for st, ed in self.seen_once:\\n if st < end and ed > start:\\n self.seen_twice.append((max(st, start), min(ed, end)))\\n self.seen_once.append((start, end))\\n return True\\nclass Node:\\n def __init__(self, start, end):\\n self.start = start\\n self.end = end\\n self.left = self.right = None\\n self.seen_twice = False\\nclass MyCalendarTwo:\\n\\n def __init__(self):\\n self.root = None\\n\\n def book(self, start: int, end: int) -> bool:\\n if not self.check(self.root, start, end): return False\\n self.root = self.insert(self.root, start, end)\\n return True\\n\\n def check(self, root, start, end):\\n if not root:\\n return True\\n if end <= root.start:\\n return self.check(root.left, start, end)\\n if start >= root.end:\\n return self.check(root.right, start, end)\\n if root.seen_twice: return False\\n lefts, lefte, rights, righte = min(start, root.start), max(start, root.start), min(end, root.end), max(end, root.end)\\n leftc = self.check(root.left, start, end)\\n rightc = self.check(root.right, start, end)\\n return leftc and rightc\\n\\n def insert(self, root, start, end):\\n if start >= end:\\n return root\\n if not root:\\n return Node(start, end)\\n if end <= root.start:\\n root.left = self.insert(root.left, start, end)\\n return root\\n if start >= root.end:\\n root.right = self.insert(root.right, start, end)\\n return root\\n lefts, lefte, rights, righte = min(start, root.start), max(start, root.start), min(end, root.end), max(end, root.end)\\n root.left = self.insert(root.left, lefts, lefte)\\n root.right = self.insert(root.right, rights, righte)\\n root.seen_twice = True\\n root.start = lefte\\n root.end = rights\\n return root\\nclass MyCalendarTwo:\\n\\n def __init__(self):\\n self.starts = []\\n self.ends = []\\n\\n def book(self, start: int, end: int) -> bool:\\n s, e = bisect.bisect_right(self.ends, start), bisect.bisect_left(self.starts, end)\\n hp = []\\n for i in range(s, e):\\n while hp and hp[0] <= self.starts[i]:\\n heapq.heappop(hp)\\n heapq.heappush(hp, self.ends[i])\\n if len(hp) > 1:\\n return False\\n bisect.insort(self.starts, start, s, e)\\n bisect.insort(self.ends, end, s, e)\\n return True\\n\\n\\n\\nclass MyCalendarThree:\\n\\n def __init__(self):\\n self.booking = []\\n def book(self, start: int, end: int) -> int:\\n self.booking.append((start, 1))\\n self.booking.append((end, -1))\\n cnt, rslt = 0, 0\\n self.booking.sort()\\n for x, y in self.booking:\\n cnt += y\\n if cnt > rslt:\\n rslt = cnt\\n return rslt\\nclass Node:\\n def __init__(self, start, end, k):\\n self.start = start\\n self.end = end\\n self.k = k\\n self.right = self.left = None\\nclass MyCalendarThree:\\n\\n def __init__(self):\\n self.root = None\\n self.k = 0\\n def book(self, start: int, end: int) -> int:\\n self.root = self.insert(self.root, start, end, 1)\\n return self.k\\n\\n def insert(self, root, start, end, k):\\n if start >= end:\\n return root\\n if not root:\\n self.k = max(self.k, k)\\n return Node(start, end, k)\\n if end <= root.start:\\n root.left = self.insert(root.left, start, end, k)\\n return root\\n if start >= root.end:\\n root.right = self.insert(root.right, start, end, k)\\n return root\\n lefts, lefte, rights, righte = min(start, root.start), max(start, root.start), min(end, root.end), max(end, root.end)\\n root.left = self.insert(root.left, lefts, lefte, root.k if lefts == root.start else k)\\n root.right = self.insert(root.right, rights, righte, root.k if righte == root.end else k)\\n root.k += k\\n root.start = lefte\\n root.end = rights\\n self.k = max(self.k, root.k)\\n return root\\nclass MyCalendarThree:\\n def __init__(self):\\n self.starts = []\\n self.ends = []\\n self.booking = 0\\n\\n def book(self, start: int, end: int) -> int:\\n s, e = bisect.bisect_right(self.ends, start), bisect.bisect_left(self.starts, end)\\n bisect.insort(self.starts, start, s, e)\\n bisect.insort(self.ends, end, s, e)\\n # print(start, end, s, e, self.starts, self.ends)\\n hp = [] # store end points\\n for i in range(s, e + 1):\\n while hp and hp[0] <= self.starts[i]:\\n heapq.heappop(hp)\\n heapq.heappush(hp, self.ends[i])\\n if len(hp) > self.booking:\\n self.booking = len(hp)\\n return self.booking\\n\"\n },\n {\n \"alpha_fraction\": 0.5324568748474121,\n \"alphanum_fraction\": 0.5324568748474121,\n \"avg_line_length\": 32.80555725097656,\n \"blob_id\": \"e429d97fa273bded608775130cd2352ea0b4d14a\",\n \"content_id\": \"2ba1f03eebec6a17f5c7f20167cafa36069a1e9a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1217,\n \"license_type\": \"no_license\",\n \"max_line_length\": 97,\n \"num_lines\": 36,\n \"path\": \"/lc236_lca.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Definition for a binary tree node.\\n# class TreeNode:\\n# def __init__(self, x):\\n# self.val = x\\n# self.left = None\\n# self.right = None\\n\\nclass Solution:\\n def lowestCommonAncestor(self, root: 'TreeNode', p: 'TreeNode', q: 'TreeNode') -> 'TreeNode':\\n if not root: return None\\n if root == p or root == q:\\n return root\\n left = self.lowestCommonAncestor(root.left, p, q)\\n right = self.lowestCommonAncestor(root.right, p, q)\\n if left and right:\\n return root\\n return left or right\\n\\n def lowestCommonAncestor(self, root: 'TreeNode', p: 'TreeNode', q: 'TreeNode') -> 'TreeNode':\\n stack = [root]\\n seen, parents = set(), {root:None}\\n while p not in parents or q not in parents:\\n parent = stack.pop()\\n if parent.left:\\n parents[parent.left] = parent\\n stack.append(parent.left)\\n if parent.right:\\n parents[parent.right] = parent\\n stack.append(parent.right)\\n cands = set()\\n while p:\\n cands.add(p)\\n p = parents[p]\\n while q not in cands:\\n q = parents[q]\\n return q\\n\"\n },\n {\n \"alpha_fraction\": 0.48841893672943115,\n \"alphanum_fraction\": 0.503524661064148,\n \"avg_line_length\": 34.46428680419922,\n \"blob_id\": \"06853a34dd10c985abc182225c129cb4e7eeeb41\",\n \"content_id\": \"114e11e02f1db956c11da1ef9a4547a653556e07\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 993,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 28,\n \"path\": \"/lc320_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def generateAbbreviations(self, word: str) -> List[str]:\\n rslt, n = [], len(word)\\n def backtracking(i, curr):\\n if i == n:\\n rslt.append(\\\"\\\".join(curr))\\n else:\\n if not curr or curr[-1].isalpha():\\n backtracking(i+1, curr+['1'])\\n else:\\n backtracking(i+1, curr[:-1]+[str(int(curr[-1])+1)])\\n backtracking(i+1, curr+[word[i]])\\n backtracking(0, [])\\n return rslt\\n\\n\\nclass Solution:\\n def generateAbbreviations(self, word: str) -> List[str]:\\n self.rslt, self.n, self.word = [], len(word), word\\n self.backtracking(0, \\\"\\\", 0)\\n return self.rslt\\n\\n def backtracking(self, i, curr, cnt):\\n if i == self.n:\\n self.rslt.append(curr+(str(cnt) if cnt else \\\"\\\"))\\n else:\\n self.backtracking(i+1, curr, cnt+1)\\n self.backtracking(i+1, curr+(str(cnt) if cnt else \\\"\\\")+self.word[i], 0)\\n\"\n },\n {\n \"alpha_fraction\": 0.31351980566978455,\n \"alphanum_fraction\": 0.3170163035392761,\n \"avg_line_length\": 29.64285659790039,\n \"blob_id\": \"211856f58c810790c1ed95c34055ef2c6b348861\",\n \"content_id\": \"35cb21fb5609b49a06b0ba913df1e7e3f9cf41fe\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 858,\n \"license_type\": \"no_license\",\n \"max_line_length\": 52,\n \"num_lines\": 28,\n \"path\": \"/lc224_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def calculate(self, s: str) -> int:\\n stk, last = [\\\"(\\\"], False\\n for c in s+\\\")\\\":\\n if c == \\\" \\\":\\n continue\\n if c.isdigit() and last:\\n stk.append(stk.pop()+c)\\n elif c.isdigit():\\n stk.append(c)\\n last = True\\n elif c == \\\")\\\":\\n t_stk = []\\n while stk[-1]!=\\\"(\\\":\\n if stk[-1] == \\\"-\\\":\\n t_stk.append(-t_stk.pop())\\n stk.pop()\\n else:\\n t_stk.append(int(stk.pop()))\\n stk.pop()\\n stk.append(str(sum(t_stk)))\\n last = False\\n else:\\n if c != \\\"+\\\":\\n stk.append(c)\\n last = False\\n\\n return int(stk[0])\\n\"\n },\n {\n \"alpha_fraction\": 0.37771347165107727,\n \"alphanum_fraction\": 0.38784369826316833,\n \"avg_line_length\": 30.409090042114258,\n \"blob_id\": \"8701b7e8ba33ffcc80dc1a502693852064abe657\",\n \"content_id\": \"8116b93ec6066ddd6e4673a14a6a22a86e5479c2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 691,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 22,\n \"path\": \"/lc218_array.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def getSkyline(self, buildings: List[List[int]]) -> List[List[int]]:\\n lines = []\\n for l, r, h in buildings:\\n lines.append([l, h])\\n lines.append([r, -h])\\n lines.sort(key = lambda x: (x[0], -x[1]))\\n heap = [0]\\n rslt = []\\n for x, h in lines:\\n if h > 0:\\n # start\\n if h > -heap[0]:\\n rslt.append([x, h])\\n heapq.heappush(heap, -h)\\n else:\\n # end\\n heap.remove(h)\\n heapq.heapify(heap)\\n if -h > -heap[0]:\\n rslt.append([x, -heap[0]])\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4337349534034729,\n \"alphanum_fraction\": 0.45542168617248535,\n \"avg_line_length\": 32.83333206176758,\n \"blob_id\": \"f788bf18e8c5e6ac5086c8d0f71b8999a272b165\",\n \"content_id\": \"29d86f3bf77a18dacccba7b26c297bde817bc2a6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 415,\n \"license_type\": \"no_license\",\n \"max_line_length\": 51,\n \"num_lines\": 12,\n \"path\": \"/lc1124_hash.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def longestWPI(self, hours: List[int]) -> int:\\n maps = {}\\n curr, rslt = 0, 0\\n for i, hour in enumerate(hours):\\n curr = curr + 1 if hour > 8 else curr-1\\n if curr > 0:\\n rslt = i + 1\\n if curr not in maps: maps[curr] = i\\n if curr-1 in maps:\\n rslt = max(rslt, i-maps[curr-1])\\n return rslt\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.43801653385162354,\n \"alphanum_fraction\": 0.4573002755641937,\n \"avg_line_length\": 32,\n \"blob_id\": \"61fffc06f1265ddc15668c203971f538aaf8e81a\",\n \"content_id\": \"c6e97ccb6989edafdbf46bbcc1d829e467691599\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 363,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 11,\n \"path\": \"/lc128_array.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def longestConsecutive(self, nums: List[int]) -> int:\\n cands, curr, rslt= set(nums), 1, 0\\n for cand in cands:\\n curr = 1\\n if cand-1 not in cands:\\n while cand+1 in cands:\\n cand = cand+1\\n curr += 1\\n rslt = max(rslt, curr)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4289276897907257,\n \"alphanum_fraction\": 0.4463840425014496,\n \"avg_line_length\": 29.846153259277344,\n \"blob_id\": \"9a895ee2d74e4bb4128094da7eb972215dbcee1e\",\n \"content_id\": \"14812dbbbbca6fc384a3885acb5b7073e2e80633\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 401,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 13,\n \"path\": \"/lc435_array.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def eraseOverlapIntervals(self, intervals: List[List[int]]) -> int:\\n if len(intervals) <= 0: return 0\\n intervals.sort()\\n last, rslt = intervals[0][1], 0\\n for x, y in intervals[1:]:\\n if x < last:\\n rslt += 1\\n if y < last:\\n last = y\\n else:\\n last = y\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.37171053886413574,\n \"alphanum_fraction\": 0.40723684430122375,\n \"avg_line_length\": 30.66666603088379,\n \"blob_id\": \"a75c43eea1cafaca3afecc7d444007abf4c4c3a2\",\n \"content_id\": \"73c369906edda7ba6f1caab96646dc3d0b5222bb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1520,\n \"license_type\": \"no_license\",\n \"max_line_length\": 84,\n \"num_lines\": 48,\n \"path\": \"/lc308_segtree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class NumMatrix:\\n def __init__(self, matrix: List[List[int]]):\\n self.m, self.n = len(matrix), len(matrix[0])\\n self.seg_tree = [[0]*(2*self.n) for _ in range(2*self.m)]\\n for i in range(self.m):\\n for j in range(self.n):\\n self.update(i, j, matrix[i][j])\\n\\n def update(self, row: int, col: int, val: int) -> None:\\n m = row + self.m\\n n = col + self.n\\n self.seg_tree[m][n] = val\\n while m > 0:\\n self.seg_tree[m >> 1][n] = self.seg_tree[m][n]+self.seg_tree[m^1][n]\\n t = n\\n while t > 0:\\n self.seg_tree[m][t >> 1] = self.seg_tree[m][t]+self.seg_tree[m][t^1]\\n t >>= 1\\n m >>= 1\\n def sumRegion(self, row1: int, col1: int, row2: int, col2: int) -> int:\\n rslt = 0\\n r1 = self.m + row1\\n r2 = self.m + row2\\n while r1 <= r2:\\n if r1&1:\\n rslt += self.sumRange(r1, col1, col2)\\n r1 += 1\\n\\n if r2&1 == 0:\\n rslt += self.sumRange(r2, col1, col2)\\n r2 -= 1\\n r1 >>= 1\\n r2 >>= 1\\n return rslt\\n\\n def sumRange(self, row, col1, col2):\\n rslt = 0\\n l, r= self.n+col1, self.n+col2\\n while l <= r:\\n if l&1:\\n rslt += self.seg_tree[row][l]\\n l += 1\\n if r&1 == 0:\\n rslt += self.seg_tree[row][r]\\n r -= 1\\n l >>= 1\\n r >>= 1\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.7749999761581421,\n \"alphanum_fraction\": 0.7749999761581421,\n \"avg_line_length\": 19,\n \"blob_id\": \"14779852faf77b20122b42df1016088b6f05f078\",\n \"content_id\": \"637d94def0546a6622650b27b324c7fd4744aad3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 40,\n \"license_type\": \"no_license\",\n \"max_line_length\": 28,\n \"num_lines\": 2,\n \"path\": \"/README.md\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# lc_punch\\nRepo for LC practice logging\\n\"\n },\n {\n \"alpha_fraction\": 0.4428237974643707,\n \"alphanum_fraction\": 0.46674445271492004,\n \"avg_line_length\": 35.46808624267578,\n \"blob_id\": \"b10a64818b79e57ea4b8d23ae118081cdd7bc0ee\",\n \"content_id\": \"4b120c84a970eeb1514572ae368f643b0dc1d592\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1714,\n \"license_type\": \"no_license\",\n \"max_line_length\": 109,\n \"num_lines\": 47,\n \"path\": \"/lc992_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def subarraysWithKDistinct(self, A: List[int], K: int) -> int:\\n memo1, memo2, left, right, rslt = collections.defaultdict(int), collections.defaultdict(int), 0, 0, 0\\n for it in A:\\n memo1[it] += 1\\n memo2[it] += 1\\n while len(memo1) > K:\\n memo1[A[left]] -= 1\\n if memo1[A[left]] == 0: del memo1[A[left]]\\n left += 1\\n while len(memo2) >= K:\\n memo2[A[right]] -= 1\\n if memo2[A[right]] == 0: del memo2[A[right]]\\n right += 1\\n rslt += right-left\\n return rslt\\n\\nclass Solution:\\n def atMostK(self, A, K):\\n memo, left, rslt = collections.defaultdict(int), 0, 0\\n for right, it in enumerate(A):\\n memo[it] += 1\\n while len(memo) > K:\\n memo[A[left]] -= 1\\n if memo[A[left]] == 0: del memo[A[left]]\\n left += 1\\n rslt += right-left + 1\\n return rslt\\n def subarraysWithKDistinct(self, A: List[int], K: int) -> int:\\n if not A: return 0\\n return self.atMostK(A, K) - self.atMostK(A, K - 1)\\n\\nclass Solution:\\n def subarraysWithKDistinct(self, A: List[int], K: int) -> int:\\n memo, lefta, leftb, rslt = collections.defaultdict(int), 0, 0, 0\\n for right, num in enumerate(A):\\n memo[num] += 1\\n if len(memo) > K:\\n del memo[A[leftb]]\\n leftb += 1\\n lefta = leftb\\n if len(memo) == K:\\n while memo[A[leftb]] > 1:\\n memo[A[leftb]] -= 1\\n leftb += 1\\n rslt += leftb-lefta+1\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.43790850043296814,\n \"alphanum_fraction\": 0.4562091529369354,\n \"avg_line_length\": 46.8125,\n \"blob_id\": \"f47fd27d163848bf6b60d7d31d752c3cdb0afe2b\",\n \"content_id\": \"a0d3fc9105e20479b5de61be1078a8f3d434a73e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 765,\n \"license_type\": \"no_license\",\n \"max_line_length\": 95,\n \"num_lines\": 16,\n \"path\": \"/lc490_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def hasPath(self, maze: List[List[int]], start: List[int], destination: List[int]) -> bool:\\n dque, visited = collections.deque([tuple(start)]), {tuple(start)}\\n n, c = len(maze), len(maze[0])\\n while dque:\\n currow, curcol = dque.popleft()\\n if destination[0] == currow and destination[1] == curcol: return True\\n for x, y in [(1, 0), (-1, 0), (0, 1), (0, -1)]:\\n tx, ty = currow, curcol\\n while 0 <= tx+x < n and 0 <= ty+y < c and maze[tx+x][ty+y] == 0:\\n tx += x\\n ty += y\\n if (tx, ty) not in visited:\\n dque.append((tx, ty))\\n visited.add((tx, ty))\\n return False\\n\"\n },\n {\n \"alpha_fraction\": 0.5112107396125793,\n \"alphanum_fraction\": 0.5182574987411499,\n \"avg_line_length\": 32.212764739990234,\n \"blob_id\": \"80d1bd32e50135357db4f5bf8d9a5336965b5a6b\",\n \"content_id\": \"d0a5d599328ab78d633566ba26084cf118faea0a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1561,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 47,\n \"path\": \"/lc207_graph.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def canFinish(self, numCourses: int, prerequisites: List[List[int]]) -> bool:\\n if not prerequisites: return True\\n graph = collections.defaultdict(list)\\n visited = [0]*numCourses\\n for crs, pre in prerequisites:\\n graph[crs].append(pre)\\n\\n def dfs(crs):\\n # in cycle\\n if visited[crs] == 1:\\n return False\\n # not in cycle\\n if visited[crs] == 2:\\n return True\\n # new node: assume in cycle until approve not\\n visited[crs] = 1\\n if crs in graph:\\n for pre in graph[crs]:\\n if not dfs(pre): return False\\n visited[crs] = 2\\n return True\\n\\n for crs in graph:\\n if not dfs(crs): return False\\n return True\\n\\n\\nclass Solution:\\n def canFinish(self, numCourses: int, prerequisites: List[List[int]]) -> bool:\\n if not prerequisites: return True\\n graph, degree = collections.defaultdict(list), collections.defaultdict(int)\\n for crs, pre in prerequisites:\\n graph[pre].append(crs)\\n degree[crs] += 1\\n dque = collections.deque()\\n for i in range(numCourses):\\n if degree[i] == 0: dque.append(i)\\n count = 0\\n while dque:\\n curr = dque.popleft()\\n count += 1\\n for post in graph[curr]:\\n degree[post] -= 1\\n if degree[post] == 0:\\n dque.append(post)\\n return count == numCourses\\n\"\n },\n {\n \"alpha_fraction\": 0.28145694732666016,\n \"alphanum_fraction\": 0.3509933650493622,\n \"avg_line_length\": 32,\n \"blob_id\": \"8f5e1d284fd5be1560f16739996320b9c92727bd\",\n \"content_id\": \"eb61a90760defa7ac624754085ebd1de6ecb5205\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 604,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 18,\n \"path\": \"/lc1868_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxSum(self, nums1: List[int], nums2: List[int]) -> int:\\n l1, l2 = len(nums1), len(nums2)\\n x, y = 0, 0\\n r1, r2 = 0, 0\\n while x < l1 or y < l2:\\n if x < l1 and (y == l2 or nums1[x] < nums2[y]):\\n r1 += nums1[x]\\n x += 1\\n elif y < l2 and (x == l1 or nums1[x] > nums2[y]):\\n r2 += nums2[y]\\n y += 1\\n else:\\n r1 = max(r1, r2) + nums1[x]\\n r2 = r1\\n x += 1\\n y += 1\\n return max(r1, r2)%(10**9+7)\\n\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.4606427550315857,\n \"alphanum_fraction\": 0.4625058174133301,\n \"avg_line_length\": 32.546875,\n \"blob_id\": \"0f7c38e245450e4aebc0b799131c28617db65b7b\",\n \"content_id\": \"66d9d2a7c789f17319cc6e88a292bf6397aea021\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2147,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 64,\n \"path\": \"/lc99_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Definition for a binary tree node.\\n# class TreeNode:\\n# def __init__(self, val=0, left=None, right=None):\\n# self.val = val\\n# self.left = left\\n# self.right = right\\nclass Solution:\\n # def recoverTree(self, root: TreeNode) -> None:\\n # \\\"\\\"\\\"\\n # Do not return anything, modify root in-place instead.\\n # \\\"\\\"\\\"\\n # order = self.inorder(root)\\n # y, x = None, None\\n # for i in range(len(order)-1):\\n # if order[i+1] < order[i]:\\n # y = order[i+1]\\n # if x is None:\\n # x = order[i]\\n # else:\\n # break\\n # stack = [root]\\n # while stack:\\n # temp = stack.pop()\\n # if temp.val in (x, y):\\n # temp.val = x if temp.val == y else y\\n # if temp.left: stack.append(temp.left)\\n # if temp.right:stack.append(temp.right)\\n # def inorder(self, root):\\n # if not root: return []\\n # return self.inorder(root.left) +[root.val] + self.inorder(root.right)\\n\\n def recoverTree(self, root: TreeNode) -> None:\\n self.x, self.y, self.pre = None, None, None\\n def inorder(root):\\n if not root: return\\n inorder(root.left)\\n if self.pre and root.val < self.pre.val:\\n self.y = root\\n if not self.x:\\n self.x = self.pre\\n self.pre = root\\n inorder(root.right)\\n inorder(root)\\n self.x.val, self.y.val = self.y.val, self.x.val\\n\\n def recoverTree(self, root: TreeNode) -> None:\\n \\\"\\\"\\\"\\n Do not return anything, modify root in-place instead.\\n \\\"\\\"\\\"\\n curr, stack = root, []\\n x = y = pre = None\\n while curr or stack:\\n while curr:\\n stack.append(curr)\\n curr = curr.left\\n curr = stack.pop()\\n if pre and curr.val < pre.val:\\n y = curr\\n if x is None:\\n x = pre\\n else: break\\n pre = curr\\n curr = curr.right\\n x.val, y.val = y.val, x.val\\n\"\n },\n {\n \"alpha_fraction\": 0.40419161319732666,\n \"alphanum_fraction\": 0.41766467690467834,\n \"avg_line_length\": 36.11111068725586,\n \"blob_id\": \"ad52b2b89952074de5d01c46118208223bde891e\",\n \"content_id\": \"445cce9933958bff568461541b81303fc168eb42\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 668,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 18,\n \"path\": \"/lc847_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def shortestPathLength(self, graph: List[List[int]]) -> int:\\n visited, n = set(), len(graph)\\n final = (1 << n)-1\\n dque = collections.deque([(1 << i, i) for i in range(n)])\\n step = 0\\n while dque:\\n size = len(dque)\\n for _ in range(size):\\n state, x = dque.popleft()\\n if state == final:\\n return step\\n for y in graph[x]:\\n if (state|(1<< y), y) not in visited:\\n dque.append((state|(1<< y), y))\\n visited.add((state|(1<< y), y))\\n step += 1\\n return -1\\n\"\n },\n {\n \"alpha_fraction\": 0.42664670944213867,\n \"alphanum_fraction\": 0.450598806142807,\n \"avg_line_length\": 30.809524536132812,\n \"blob_id\": \"0163b2a49e94903b2c36c0e65a3c032f49d9a811\",\n \"content_id\": \"0985f933ebaeffc9d1540c55824d08de4ac3ffc3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 668,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 21,\n \"path\": \"/lc1574_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findLengthOfShortestSubarray(self, arr: List[int]) -> int:\\n n = len(arr)\\n left, right = 0, n-1\\n while left < n-1 and arr[left+1] >= arr[left]:\\n left += 1\\n if left == n-1:\\n return 0\\n while right > 0 and arr[right-1] <= arr[right]:\\n right -= 1\\n if arr[left] <= arr[right]:\\n return right-left-1\\n rslt = min(n-left-1, right)\\n for i in range(left+1):\\n if arr[i] <= arr[right]:\\n rslt = min(rslt, right-i-1)\\n elif right < n-1:\\n right += 1\\n else:\\n break\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.5125628113746643,\n \"alphanum_fraction\": 0.5326633453369141,\n \"avg_line_length\": 27.428571701049805,\n \"blob_id\": \"9386bae485636cce67c13e2c7467b362ead5e1bd\",\n \"content_id\": \"74dbcdcf1551500bb8414ecd71cb9d64c19bc724\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 398,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 14,\n \"path\": \"/lc901_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class StockSpanner:\\n\\n def __init__(self):\\n self.last = [(float('inf'), -1)]\\n self.curr = 0\\n\\n\\n def next(self, price: int) -> int:\\n while self.last and price >= self.last[-1][0]:\\n self.last.pop()\\n temp = self.curr if not self.last else self.last[-1][1]+1\\n self.last.append((price, self.curr))\\n self.curr += 1\\n return self.curr - temp\\n\"\n },\n {\n \"alpha_fraction\": 0.49317148327827454,\n \"alphanum_fraction\": 0.5022761821746826,\n \"avg_line_length\": 31.950000762939453,\n \"blob_id\": \"f825416afa8dbdae5db94b91dc946281e74d9ea6\",\n \"content_id\": \"6585741115370abd84324cd637729a9f2f8af8d3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 659,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 20,\n \"path\": \"/lc179_sort.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def largestNumber(self, nums):\\n self.quickSort(nums, 0, len(nums)-1)\\n return str(int(\\\"\\\".join(map(str, nums))))\\n\\n def quickSort(self, nums, l, r):\\n if l >= r: return\\n pos = self.partition(nums, l, r)\\n self.quickSort(nums, l, pos-1)\\n self.quickSort(nums, pos+1, r)\\n\\n def partition(self, nums, l, r):\\n pivot = l\\n while l < r:\\n if str(nums[l]) + str(nums[r]) > str(nums[r]) + str(nums[l]) :\\n nums[l], nums[pivot] = nums[pivot], nums[l]\\n pivot += 1\\n l += 1\\n nums[pivot], nums[r] = nums[r], nums[pivot]\\n return pivot\\n\"\n },\n {\n \"alpha_fraction\": 0.5072463750839233,\n \"alphanum_fraction\": 0.5144927501678467,\n \"avg_line_length\": 29.66666603088379,\n \"blob_id\": \"d909cdd55d1aed8881ee8173d78f6804a2014659\",\n \"content_id\": \"7f6395f4264a1cf095e204d79b822b685c8da0c1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 276,\n \"license_type\": \"no_license\",\n \"max_line_length\": 51,\n \"num_lines\": 9,\n \"path\": \"/lc493_sort.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def reversePairs(self, nums: List[int]) -> int:\\n cand = []\\n rslt = 0\\n for num in nums:\\n idx = bisect.bisect_right(cand, 2*num)\\n rslt = rslt + len(cand)-idx\\n bisect.insort(cand, num)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.5064575672149658,\n \"alphanum_fraction\": 0.5129151344299316,\n \"avg_line_length\": 30.882352828979492,\n \"blob_id\": \"76640f13e865ac66623f672d8617f84916bc4e48\",\n \"content_id\": \"a9ccc3edca5135da4c6a3fb517d7e6e29ce61bfd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1084,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 34,\n \"path\": \"/lc1038_bt.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# same as 538\\nclass Solution:\\n def bstToGst(self, root: TreeNode) -> TreeNode:\\n def helper(root, pre):\\n if not root: return pre\\n root.val += helper(root.right, pre)\\n return helper(root.left, root.val)\\n helper(root, 0)\\n return root\\nclass Solution:\\n def bstToGst(self, root: TreeNode) -> TreeNode:\\n def helper(root, pre):\\n if not root: return (0+pre, None)\\n valr, right = helper(root.right, pre)\\n vall, left = helper(root.left, valr+root.val)\\n root.val += valr\\n root.right =right\\n root.left = left\\n return (vall,root)\\n _, root = helper(root, 0)\\n return root\\n \\nclass Solution:\\n def convertBST(self, root: TreeNode) -> TreeNode:\\n curr, stk, last = root, [], 0\\n while stk or curr:\\n while curr:\\n stk.append(curr)\\n curr = curr.right\\n temp = stk.pop()\\n temp.val += last\\n last = temp.val\\n curr = temp.left\\n return root\\n\"\n },\n {\n \"alpha_fraction\": 0.4921700358390808,\n \"alphanum_fraction\": 0.501118540763855,\n \"avg_line_length\": 36.25,\n \"blob_id\": \"85c8796bc11817fe0ab5dfccc10ecfc3a1921aa6\",\n \"content_id\": \"00b228f037cded30bcb42714a82f8d7e7b214b08\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 447,\n \"license_type\": \"no_license\",\n \"max_line_length\": 69,\n \"num_lines\": 12,\n \"path\": \"/lc239_deque.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxSlidingWindow(self, nums: List[int], k: int) -> List[int]:\\n rslt, holder = [], collections.deque()\\n for idx, num in enumerate(nums):\\n while holder and nums[holder[-1]] < num:\\n holder.pop()\\n holder.append(idx)\\n if holder[0] <= idx-k:\\n holder.popleft()\\n if idx >= k-1:\\n rslt.append(nums[holder[0]])\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.5241488814353943,\n \"alphanum_fraction\": 0.5241488814353943,\n \"avg_line_length\": 29.071428298950195,\n \"blob_id\": \"6cb6618ce41afdeeccffd2f22daa31dd9414f153\",\n \"content_id\": \"ff6464a9c49dcb42bbc0a9f589c6345ef25f48fd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1263,\n \"license_type\": \"no_license\",\n \"max_line_length\": 75,\n \"num_lines\": 42,\n \"path\": \"/lc138_linkedlist.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\n# Definition for a Node.\\nclass Node:\\n def __init__(self, x: int, next: 'Node' = None, random: 'Node' = None):\\n self.val = int(x)\\n self.next = next\\n self.random = random\\n\\\"\\\"\\\"\\n\\nclass Solution:\\n def copyRandomList(self, head: 'Node') -> 'Node':\\n if not head: return None\\n curr = head\\n while curr:\\n copy = Node(curr.val, curr.next)\\n curr.next = copy\\n curr = copy.next\\n curr = head\\n while curr:\\n curr.next.random = curr.random.next if curr.random else None\\n curr = curr.next.next\\n curr, rslt = head, head.next\\n while curr:\\n temp = curr.next\\n curr.next = temp.next\\n temp.next = temp.next.next if temp.next else None\\n curr = curr.next\\n return rslt\\n\\nclass Solution:\\n def copyRandomList(self, head: 'Node') -> 'Node':\\n if not head: return None\\n nodemap = {}\\n def dfs(curr):\\n if not curr: return None\\n if curr in nodemap: return nodemap[curr]\\n copy = Node(curr.val)\\n nodemap[curr] = copy\\n copy.next = dfs(curr.next)\\n copy.random = dfs(curr.random)\\n return copy\\n return dfs(head)\\n\"\n },\n {\n \"alpha_fraction\": 0.3534635901451111,\n \"alphanum_fraction\": 0.42451155185699463,\n \"avg_line_length\": 39.28571319580078,\n \"blob_id\": \"290ba91a1b6623b754dc698d603326843ce44543\",\n \"content_id\": \"185d8fe1182a0993f4645eb493f59a3c6fcd2d51\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 563,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 14,\n \"path\": \"/lc583_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minDistance(self, word1: str, word2: str) -> int:\\n l_w1, l_w2 = len(word1), len(word2)\\n dp = [[0]*(l_w2+1) for _ in range(l_w1+1)]\\n for i in range(1, l_w1+1): dp[i][0] = dp[i-1][0]+1\\n for j in range(1, l_w2+1): dp[0][j] = dp[0][j-1]+1\\n \\n for i, c1 in enumerate(word1):\\n for j, c2 in enumerate(word2):\\n if c1 == c2:\\n dp[i+1][j+1] = dp[i][j]\\n else:\\n dp[i+1][j+1] = min(dp[i+1][j], dp[i][j+1])+1\\n return dp[-1][-1]\"\n },\n {\n \"alpha_fraction\": 0.525611162185669,\n \"alphanum_fraction\": 0.5296856760978699,\n \"avg_line_length\": 43.0512809753418,\n \"blob_id\": \"78b9ca965b2510187da0c3e02d8fd92eedf6079e\",\n \"content_id\": \"f69a31c9fe41db57f9d4eb659a12a45c0ec53342\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1718,\n \"license_type\": \"no_license\",\n \"max_line_length\": 101,\n \"num_lines\": 39,\n \"path\": \"/lc1203_toposort.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def sortItems(self, n: int, m: int, group: List[int], beforeItems: List[List[int]]) -> List[int]:\\n graph_i, graph_g = collections.defaultdict(list), collections.defaultdict(list)\\n degree_i, degree_g = collections.defaultdict(int), collections.defaultdict(int)\\n group_dict = collections.defaultdict(list)\\n # Add to new group for item belongs to no group\\n for i in range(n):\\n if group[i] == -1: group[i] = m\\n m += 1\\n group_dict[group[i]].append(i)\\n # Prepare for topo sort on groups then items\\n for curr in range(n):\\n for pre in beforeItems[curr]:\\n if group[curr] == group[pre]:\\n graph_i[pre].append(curr)\\n degree_i[curr] += 1\\n else:\\n graph_g[group[pre]].append(group[curr])\\n degree_g[group[curr]] += 1\\n # order between group\\n group_order, rslt = self.toposort(graph_g, degree_g, list(range(m))), []\\n # order within group\\n for i in group_order:\\n if group_dict[i]:\\n tt = self.toposort(graph_i, degree_i, group_dict[i])\\n if len(tt) != len(group_dict[i]): return []\\n rslt.extend(tt)\\n return rslt\\n\\n def toposort(self, graph, degree, cands):\\n dque = collections.deque([i for i in cands if degree[i] == 0])\\n rslt = []\\n while dque:\\n pre = dque.popleft()\\n rslt.append(pre)\\n for post in graph[pre]:\\n degree[post] -= 1\\n if degree[post] == 0 and post in cands: dque.append(post)\\n return rslt if len(rslt) == len(cands) else []\\n\"\n },\n {\n \"alpha_fraction\": 0.41511771082878113,\n \"alphanum_fraction\": 0.4696406424045563,\n \"avg_line_length\": 35.681819915771484,\n \"blob_id\": \"6082cee12bba36f479682c5e1d30f560ba47269b\",\n \"content_id\": \"8033dba5cfe841d849ed9342c7aa7285cd9e1e40\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1614,\n \"license_type\": \"no_license\",\n \"max_line_length\": 78,\n \"num_lines\": 44,\n \"path\": \"/lc1775_array.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minOperations(self, nums1: List[int], nums2: List[int]) -> int:\\n diff = sum(nums1) - sum(nums2)\\n if diff < 0:\\n nums1, nums2 = nums2, nums1\\n diff = -diff\\n quota = collections.Counter([x-1 for x in nums1]+[6-x for x in nums2])\\n count, change = 0, 5\\n while diff > 0:\\n if change == 0: return -1\\n if (diff-1)//change+1 <= quota[change]:\\n return count + (diff-1)//change + 1\\n else:\\n diff, count = diff-change*quota[change], count + quota[change]\\n change -= 1\\n return count\\n\\nclass Solution:\\n def minOperations(self, nums1: List[int], nums2: List[int]) -> int:\\n diff = sum(nums1) - sum(nums2)\\n if not diff: return 0\\n if diff < 0:\\n nums1, nums2 = nums2, nums1\\n diff = -diff\\n nums1.sort(reverse = True)\\n nums2.sort()\\n count, idx1, idx2, l1, l2 = 0, 0, 0, len(nums1), len(nums2)\\n while idx1 < l1 or idx2 < l2:\\n if diff <= 0:\\n return count\\n if idx1 < l1 and diff <= nums1[idx1]-1:\\n return count + 1\\n if idx2 < l2 and diff <= 6 - nums2[idx2]:\\n return count + 1\\n sub1 = (nums1[idx1]-1) if idx1 < l1 else 0\\n sub2 = (6 - nums2[idx2]) if idx2 < l2 else 0\\n if sub1 >= sub2 and idx1 < l1:\\n diff -= sub1\\n idx1 += 1\\n else:\\n diff -= sub2\\n idx2 += 1\\n count += 1\\n return count if diff <= 0 else -1\\n\"\n },\n {\n \"alpha_fraction\": 0.3754071593284607,\n \"alphanum_fraction\": 0.3965798020362854,\n \"avg_line_length\": 38.6129035949707,\n \"blob_id\": \"adf707973150ed1051b141f3101f3fb473934d1c\",\n \"content_id\": \"5923f3f0d02edea54c7fd56b62e35f801bcefe0c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1228,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 31,\n \"path\": \"/lc1778_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution(object):\\n def findShortestPath(self, master: 'GridMaster') -> int:\\n maps = {(0, 0)}\\n ops = {\\\"U\\\":\\\"D\\\", \\\"D\\\":\\\"U\\\", \\\"L\\\":\\\"R\\\", \\\"R\\\":\\\"L\\\"}\\n steps = {\\\"U\\\":(1, 0), \\\"D\\\":(-1, 0), \\\"L\\\":(0, -1), \\\"R\\\":(0, 1)}\\n self.target = None\\n def buildmap(x, y):\\n if master.isTarget(): self.target = (x, y)\\n for d in steps:\\n nx, ny = x + steps[d][0], y+steps[d][1]\\n if (nx, ny) in maps: continue\\n if master.canMove(d):\\n master.move(d)\\n maps.add((nx, ny))\\n buildmap(nx, ny)\\n master.move(ops[d])\\n buildmap(0, 0)\\n if self.target is None: return -1\\n queue = collections.deque([(0, 0)])\\n rslt = 0\\n seen = {(0, 0)}\\n while queue:\\n for _ in range(len(queue)):\\n x, y = queue.popleft()\\n if (x, y) == self.target: return rslt\\n for nx, ny in [(x-1, y), (x+1, y), (x, y-1), (x, y+1)]:\\n if (nx, ny) in maps and (nx, ny) not in seen:\\n seen.add((nx, ny))\\n queue.append((nx, ny))\\n rslt += 1\\n return -1\\n\"\n },\n {\n \"alpha_fraction\": 0.43800538778305054,\n \"alphanum_fraction\": 0.4501347839832306,\n \"avg_line_length\": 31.2608699798584,\n \"blob_id\": \"03316d1c16fc697bd9534a0de80a62cde149097d\",\n \"content_id\": \"b12012d544a479a2469a03a1a3756563162666d1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 742,\n \"license_type\": \"no_license\",\n \"max_line_length\": 53,\n \"num_lines\": 23,\n \"path\": \"/lc765_unionfind.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minSwapsCouples(self, row: List[int]) -> int:\\n parent = [x//2*2 for x in range(len(row))]\\n size = [1]*len(row)\\n def find(p):\\n while p != parent[p]:\\n parent[p] = parent[parent[p]]\\n p = parent[p]\\n return p\\n\\n def union(p, q):\\n rootp, rootq = find(p), find(q)\\n if rootp == rootq: return False\\n if size[rootp] < size[rootq]:\\n rootp, rootq = rootq, rootp\\n parent[rootq] = rootp\\n size[rootp] += size[rootq]\\n return True\\n rslt = 0\\n for i in range(len(row)//2):\\n if union(row[i*2], row[i*2+1]):\\n rslt += 1\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4419354796409607,\n \"alphanum_fraction\": 0.4580645263195038,\n \"avg_line_length\": 31.55555534362793,\n \"blob_id\": \"7cb580a9051349c7d006d5480a2286126673572b\",\n \"content_id\": \"aa9a3d42868df649627f4e7573340b04b519e8ea\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 310,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 9,\n \"path\": \"/lc1288_array.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def removeCoveredIntervals(self, intervals: List[List[int]]) -> int:\\n intervals.sort(key = lambda x:(x[0], -x[1]))\\n last, cnt = -1, 0\\n for x, y in intervals:\\n if y > last:\\n last = y\\n cnt += 1\\n return cnt\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.40978261828422546,\n \"alphanum_fraction\": 0.427173912525177,\n \"avg_line_length\": 47.421051025390625,\n \"blob_id\": \"af42ddaa7a83c8f55e324fc32c69b934714a54eb\",\n \"content_id\": \"d663955428e76e76e8f413a8acf8761a9fcf3153\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 920,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 19,\n \"path\": \"/lc679_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def judgePoint24(self, nums: List[int]) -> bool:\\n if not nums: return False\\n if len(nums) == 1: return 23.9 < nums[0] < 24.1\\n for i in range(len(nums)):\\n for j in range(i):\\n if i > j:\\n nums_next = nums[:j]+nums[j+1:i]+nums[i+1:]\\n for op in (truediv, mul, add, sub):\\n if op is not truediv or nums[j]:\\n nums_next.append(op(nums[i], nums[j]))\\n if self.judgePoint24(nums_next): return True\\n nums_next.pop()\\n if op in (add, mul): continue\\n if op is sub or nums[i]:\\n nums_next.append(op(nums[j], nums[i]))\\n if self.judgePoint24(nums_next): return True\\n nums_next.pop()\\n return False\\n\"\n },\n {\n \"alpha_fraction\": 0.5169811248779297,\n \"alphanum_fraction\": 0.5188679099082947,\n \"avg_line_length\": 36.78571319580078,\n \"blob_id\": \"ed1eede5df1882b3e2c9d63dc792b3cca6fff408\",\n \"content_id\": \"49edd37b88b2660a304fa1a250f77175ca6652b4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 530,\n \"license_type\": \"no_license\",\n \"max_line_length\": 73,\n \"num_lines\": 14,\n \"path\": \"/lc690_bfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def getImportance(self, employees: List['Employee'], id: int) -> int:\\n hold = {x.id: i for i, x in enumerate(employees)}\\n dque = collections.deque([id])\\n visited, rslt = {id}, 0\\n while dque:\\n curr = dque.popleft()\\n employee = employees[hold[curr]]\\n rslt += employee.importance\\n for n in employee.subordinates:\\n if n not in visited:\\n visited.add(n)\\n dque.append(n)\\n return rslt \\n\"\n },\n {\n \"alpha_fraction\": 0.5021833777427673,\n \"alphanum_fraction\": 0.5152838230133057,\n \"avg_line_length\": 31.714284896850586,\n \"blob_id\": \"139ffb3d3997d57287c12343a24e2178033ebc32\",\n \"content_id\": \"5c9b76464f7388f2b2f0adcf76ba9a76e755d699\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 229,\n \"license_type\": \"no_license\",\n \"max_line_length\": 50,\n \"num_lines\": 7,\n \"path\": \"/lc53_dp.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maxSubArray(self, nums: List[int]) -> int:\\n temp, rslt = nums[0], nums[0]\\n for num in nums[1:]:\\n temp = max(temp + num, num)\\n rslt = max(temp, rslt)\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.4598168730735779,\n \"alphanum_fraction\": 0.475076287984848,\n \"avg_line_length\": 32.89655303955078,\n \"blob_id\": \"124f6a52893b5c4f868edd6291e9e267f37657c3\",\n \"content_id\": \"83bf3ba55eecde7e10eed7b347c4f742129eb814\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 983,\n \"license_type\": \"no_license\",\n \"max_line_length\": 99,\n \"num_lines\": 29,\n \"path\": \"/lc930_hashtable.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def numSubarraysWithSum(self, A: List[int], S: int) -> int:\\n prefix, rslt, temp = 0, 0, {0:1}\\n for i, x in enumerate(A):\\n prefix += x\\n if prefix-S in temp:\\n rslt += temp[prefix-S]\\n temp[prefix] = temp.get(prefix, 0)+1\\n return rslt\\n \\nclass Solution:\\n def numSubarraysWithSum(self, nums: List[int], goal: int) -> int:\\n left_lo, left_hi = 0, 0\\n last_lo, last_hi = 0, 0\\n rslt = 0\\n for right, num in enumerate(nums):\\n last_lo += num\\n last_hi += num\\n\\n while left_lo < right and last_lo > goal:\\n last_lo -= nums[left_lo]\\n left_lo += 1\\n while left_hi < right and (last_hi > goal or (last_hi == goal and nums[left_hi] == 0)):\\n last_hi -= nums[left_hi]\\n left_hi += 1\\n\\n if last_lo == goal:\\n rslt += left_hi - left_lo + 1\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.35752686858177185,\n \"alphanum_fraction\": 0.3844085931777954,\n \"avg_line_length\": 30,\n \"blob_id\": \"60c0e4ded3229da88a777599bd70a476008fece2\",\n \"content_id\": \"96b0b08ceee364880033c0033d24fc925d25283d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 372,\n \"license_type\": \"no_license\",\n \"max_line_length\": 53,\n \"num_lines\": 12,\n \"path\": \"/lc1371_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findTheLongestSubstring(self, s: str) -> int:\\n mask = {\\\"a\\\":1, \\\"e\\\":2, \\\"i\\\":4, \\\"o\\\":8, \\\"u\\\":16}\\n d, n, res = {0:-1}, 0, 0\\n for i, c in enumerate(s):\\n if c in mask:\\n n ^= mask[c]\\n if n not in d:\\n d[n] = i\\n else:\\n res = max(res, i-d[n])\\n return res\\n\"\n },\n {\n \"alpha_fraction\": 0.3419540226459503,\n \"alphanum_fraction\": 0.37068966031074524,\n \"avg_line_length\": 30.636363983154297,\n \"blob_id\": \"7cbb9ca2fd6ea88216e0c07a5df05104ddeda1fd\",\n \"content_id\": \"18942add6c81db222d3e5a88bc2e244a2f6fcf9d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 348,\n \"license_type\": \"no_license\",\n \"max_line_length\": 54,\n \"num_lines\": 11,\n \"path\": \"/lc1209_stack.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def removeDuplicates(self, s: str, k: int) -> str:\\n stk = []\\n for c in s:\\n if stk and c == stk[-1][0]:\\n stk[-1][1] +=1\\n if stk[-1][1] == k:\\n stk.pop()\\n else:\\n stk.append([c, 1])\\n return \\\"\\\".join([x[0]*x[1] for x in stk])\\n\"\n },\n {\n \"alpha_fraction\": 0.4740259647369385,\n \"alphanum_fraction\": 0.47922077775001526,\n \"avg_line_length\": 37.04999923706055,\n \"blob_id\": \"33175cb998d50e782cdc9edc364d90f2bd00a42d\",\n \"content_id\": \"58f6743a29082f8f379a60d681e2a3c0caea7930\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 770,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 20,\n \"path\": \"/lc863_tree.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def distanceK(self, root: TreeNode, target: TreeNode, K: int) -> List[int]:\\n maps = {}\\n def get_parents(curr, prev):\\n if curr:\\n maps[curr] = prev\\n get_parents(curr.left, curr)\\n get_parents(curr.right, curr)\\n get_parents(root, None)\\n queue = collections.deque([(target, 0)])\\n visited = {target}\\n while queue:\\n if queue[0][1] == K:\\n return [x.val for x, y in queue]\\n node, dist = queue.popleft()\\n for nxt in [node.left, node.right, maps[node]]:\\n if nxt and nxt not in visited:\\n visited.add(nxt)\\n queue.append((nxt, dist + 1))\\n return []\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.4175824224948883,\n \"alphanum_fraction\": 0.4263736307621002,\n \"avg_line_length\": 34,\n \"blob_id\": \"50638d619415e82ffb1ee0a880920159c226add2\",\n \"content_id\": \"82fbe0e128699d1adc998601f68739ca708bcf89\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 455,\n \"license_type\": \"no_license\",\n \"max_line_length\": 54,\n \"num_lines\": 13,\n \"path\": \"/lc131_bk.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def partition(self, s: str) -> List[List[str]]:\\n rslt, n, checked = [], len(s), set()\\n def backtracking(left, curr):\\n if left == n:\\n rslt.append(curr)\\n else:\\n for j in range(left, n):\\n temp = s[left:j+1]\\n if temp == temp[::-1]:\\n backtracking(j+1, curr+[temp])\\n backtracking(0, [])\\n return rslt\\n\"\n },\n {\n \"alpha_fraction\": 0.493630588054657,\n \"alphanum_fraction\": 0.4968152940273285,\n \"avg_line_length\": 32.05263137817383,\n \"blob_id\": \"6c6f149f2db012aed4b0c871e6ff178323148bf0\",\n \"content_id\": \"73fefef61b6772d4b48c4a0fb1dca97ea9e6c8ef\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 628,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 19,\n \"path\": \"/lc1681_dfs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def minimumIncompatibility(self, nums: List[int], k: int) -> int:\\n d = len(nums)//k\\n count = collections.Counter(nums)\\n if max(count.values()) > k: return -1\\n\\n @lru_cache(None)\\n def dfs(curr):\\n if not curr:\\n return 0\\n rslt = float(\\\"inf\\\")\\n\\n for comb in itertools.combinations(set(curr), d):\\n remain = list(curr)\\n for t in comb:\\n remain.remove(t)\\n rslt = min(rslt, max(comb)-min(comb) + dfs(tuple(remain)))\\n return rslt\\n return dfs(tuple(nums))\\n\"\n },\n {\n \"alpha_fraction\": 0.5203996896743774,\n \"alphanum_fraction\": 0.5287260413169861,\n \"avg_line_length\": 35.39393997192383,\n \"blob_id\": \"9d6b3d6bb162a57ca82e9eee5a9993c32854649f\",\n \"content_id\": \"84c76162a90d9457f90d9cdf591f81f89896be78\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1201,\n \"license_type\": \"no_license\",\n \"max_line_length\": 75,\n \"num_lines\": 33,\n \"path\": \"/lc1825_sortedlist.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from sortedcontainers import SortedList\\nclass MKAverage:\\n\\n def __init__(self, m: int, k: int):\\n self.m, self.k = m, k\\n self.win = collections.deque([0]*m)\\n self.o_win = SortedList([0]*m)\\n self.sum_k, self.sum_m_k = 0, 0\\n\\n def addElement(self, num: int) -> None:\\n self.win.append(num)\\n # update out of window ele\\n oow = self.win.popleft()\\n i_oow = self.o_win.bisect_right(oow)\\n if i_oow <= self.k:\\n self.sum_k = self.sum_k - oow + self.o_win[self.k]\\n if i_oow <= self.m - self.k:\\n self.sum_m_k = self.sum_m_k - oow + self.o_win[self.m-self.k]\\n self.o_win.remove(oow)\\n # update new ele\\n i_new = self.o_win.bisect_right(num)\\n if i_new < self.k:\\n self.sum_k = self.sum_k - self.o_win[self.k-1] + num\\n if i_new < self.m-self.k:\\n self.sum_m_k = self.sum_m_k - self.o_win[self.m-self.k-1] + num\\n self.o_win.add(num)\\n return\\n\\n def calculateMKAverage(self) -> int:\\n # print(self.o_win, self.sum_m_k, self.sum_k)\\n if self.o_win[0] == 0:\\n return -1\\n return (self.sum_m_k - self.sum_k) // (self.m - self.k*2)\\n\"\n },\n {\n \"alpha_fraction\": 0.424119234085083,\n \"alphanum_fraction\": 0.4444444477558136,\n \"avg_line_length\": 35,\n \"blob_id\": \"1c2c7e16a4288925ad9584a7616fc695c10512c3\",\n \"content_id\": \"3cd6bb10b4cb762b901f860458c89106ea34a3fb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 738,\n \"license_type\": \"no_license\",\n \"max_line_length\": 77,\n \"num_lines\": 20,\n \"path\": \"/lc1793_twopointer.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def maximumScore(self, nums: List[int], k: int) -> int:\\n left, right = k, k\\n rslt = nums[k]\\n tempmin = nums[k]\\n n = len(nums)\\n while left > 0 or right < n-1:\\n if left == 0 or (right < n-1 and nums[left-1] < nums[right+1]):\\n right += 1\\n tempmin = min(nums[right], tempmin)\\n elif right == n-1 or (left > 0 and nums[left-1] > nums[right+1]):\\n left -= 1\\n tempmin = min(nums[left], tempmin)\\n else:\\n left -= 1\\n right += 1\\n tempmin = min(nums[right], tempmin)\\n\\n rslt = max(rslt, (right-left+1)*tempmin)\\n return rslt\\n\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.4253164529800415,\n \"alphanum_fraction\": 0.4430379867553711,\n \"avg_line_length\": 31.91666603088379,\n \"blob_id\": \"d8ce35bf62da923c4cb3b47ae81a143db439fe09\",\n \"content_id\": \"454fcd0c085081c6ce86c859d287a10b80795a96\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 395,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 12,\n \"path\": \"/lc162_bs.py\",\n \"repo_name\": \"Mela2014/lc_punch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def findPeakElement(self, nums: List[int]) -> int:\\n left, right = 0, len(nums)-1\\n while left <= right:\\n mid = (left + right)//2\\n if mid < len(nums)-1 and nums[mid] < nums[mid+1]:\\n left = mid+1\\n else:\\n right = mid\\n if (left + right) //2 == mid:\\n break\\n return left\\n\"\n }\n]"},"num_files":{"kind":"number","value":322,"string":"322"}}},{"rowIdx":1178,"cells":{"repo_name":{"kind":"string","value":"ogspeace/quotedreps"},"repo_url":{"kind":"string","value":"https://github.com/ogspeace/quotedreps"},"snapshot_id":{"kind":"string","value":"d5009267b3ec0f86441c52e70b14b7166f3f09b9"},"revision_id":{"kind":"string","value":"3810d3afc89632f68f6b27d683367a403e0d95b9"},"directory_id":{"kind":"string","value":"e0d93837a131717c8b3ef6fdc5d54d72778b593a"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"string","value":"2022-09-03T11:56:35.415820"},"revision_date":{"kind":"string","value":"2020-05-24T11:23:10"},"committer_date":{"kind":"string","value":"2020-05-24T11:23:10"},"github_id":{"kind":"number","value":266524169,"string":"266,524,169"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7558139562606812,"alphanum_fraction":0.7591361999511719,"avg_line_length":45.30769348144531,"blob_id":"3da401259d15195b409a3f3056b396eeb5354ab8","content_id":"bd37033ea60551d19effc5af3a11827eed083fd8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":602,"license_type":"no_license","max_line_length":110,"num_lines":13,"path":"/README.md","repo_name":"ogspeace/quotedreps","src_encoding":"UTF-8","text":"# quotedreps\nsimple command line python script that generates a URL string containing the quoted replies of a user's tweet.\n\n# requirements\n* Use python3.6+
\n* I use git bash to execute my script.\n\n# usage\n* look and click on a tweet you want to get the quoted replies of.
\n* on the address bar of your browser, copy the tweet URL.
\n* on your command line interface or terminal type ``python quotedreplies.py pastetheurloftweethere ``
\n* the script would then generate a URL string containing the full query of your quoted replies.
\n* copy this string and paste it on your browser\n"},{"alpha_fraction":0.703832745552063,"alphanum_fraction":0.7177700400352478,"avg_line_length":46.83333206176758,"blob_id":"2de12ca8d11498f6dba0fb19e0b96e9c5a01ad5a","content_id":"bd6f046b2403d46c7905e12bc90aa5e0d6ac4650","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":287,"license_type":"no_license","max_line_length":118,"num_lines":6,"path":"/quotedreplies.py","repo_name":"ogspeace/quotedreps","src_encoding":"UTF-8","text":"import sys\n\ntweet_id = sys.argv[1].split('/')[-1]\nurl_string = f\"https://twitter.com/search?q=-from:quotedreplies%20url:{tweet_id}&src=typed_query&f=live\"\nif tweet_id:\n print(f\"copy the following url to your browser to view all quoted replies of your chosen tweet: \\n\\n{url_string}\")\n"}],"string":"[\n {\n \"alpha_fraction\": 0.7558139562606812,\n \"alphanum_fraction\": 0.7591361999511719,\n \"avg_line_length\": 45.30769348144531,\n \"blob_id\": \"3da401259d15195b409a3f3056b396eeb5354ab8\",\n \"content_id\": \"bd37033ea60551d19effc5af3a11827eed083fd8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 602,\n \"license_type\": \"no_license\",\n \"max_line_length\": 110,\n \"num_lines\": 13,\n \"path\": \"/README.md\",\n \"repo_name\": \"ogspeace/quotedreps\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# quotedreps\\nsimple command line python script that generates a URL string containing the quoted replies of a user's tweet.\\n\\n# requirements\\n* Use python3.6+
\\n* I use git bash to execute my script.\\n\\n# usage\\n* look and click on a tweet you want to get the quoted replies of.
\\n* on the address bar of your browser, copy the tweet URL.
\\n* on your command line interface or terminal type ``python quotedreplies.py pastetheurloftweethere ``
\\n* the script would then generate a URL string containing the full query of your quoted replies.
\\n* copy this string and paste it on your browser\\n\"\n },\n {\n \"alpha_fraction\": 0.703832745552063,\n \"alphanum_fraction\": 0.7177700400352478,\n \"avg_line_length\": 46.83333206176758,\n \"blob_id\": \"2de12ca8d11498f6dba0fb19e0b96e9c5a01ad5a\",\n \"content_id\": \"bd6f046b2403d46c7905e12bc90aa5e0d6ac4650\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 287,\n \"license_type\": \"no_license\",\n \"max_line_length\": 118,\n \"num_lines\": 6,\n \"path\": \"/quotedreplies.py\",\n \"repo_name\": \"ogspeace/quotedreps\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\n\\ntweet_id = sys.argv[1].split('/')[-1]\\nurl_string = f\\\"https://twitter.com/search?q=-from:quotedreplies%20url:{tweet_id}&src=typed_query&f=live\\\"\\nif tweet_id:\\n print(f\\\"copy the following url to your browser to view all quoted replies of your chosen tweet: \\\\n\\\\n{url_string}\\\")\\n\"\n }\n]"},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":1179,"cells":{"repo_name":{"kind":"string","value":"GhostingRecon/Oblig1"},"repo_url":{"kind":"string","value":"https://github.com/GhostingRecon/Oblig1"},"snapshot_id":{"kind":"string","value":"93a3bc95ae892783582a800e19d6685ae6ed99d8"},"revision_id":{"kind":"string","value":"0277e07f4035608ec38c78d666fb8bf09a459b06"},"directory_id":{"kind":"string","value":"b5fb6683979705b9ff8c63c2446979e4b0f049e7"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"string","value":"2020-07-31T10:56:50.681797"},"revision_date":{"kind":"string","value":"2019-10-22T12:13:25"},"committer_date":{"kind":"string","value":"2019-10-22T12:13:25"},"github_id":{"kind":"number","value":210580691,"string":"210,580,691"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5264720320701599,"alphanum_fraction":0.5685304403305054,"avg_line_length":17.44748878479004,"blob_id":"bed8c5370bfb4face09192d37c3a6bd8c94f7022","content_id":"f425bf8612d581b3adc3458bc6f85a46ebc2f2f6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4053,"license_type":"no_license","max_line_length":84,"num_lines":219,"path":"/sem_1.py","repo_name":"GhostingRecon/Oblig1","src_encoding":"UTF-8","text":"#Oblig_1\n\n\n\n#1\n\ntall_1 = int(input(\"Tall nr 1: \"))\ntall_2 = int(input(\"Tall nr 2: \"))\ntall_3 = int(input(\"Tall nr 3: \"))\nprint()\n\nprint(\"Tall nr 1: \" + str(tall_1))\nprint(\"Tall nr 2: \" + str(tall_2))\nprint(\"Tall nr 2: \" + str(tall_3))\n\n\nif tall_1 < tall_2 < tall_3:\n print(\"Strengt stigende\")\nelif tall_1 > tall_2 > tall_3:\n print(\"Strengt synkende\")\nelse:\n print(\"Verken strengt stigende eller synkende\")\n\n#2\n\nprint()\n#2.a\n\n\n\nkort = input(\"Skriv inn \\\"kort tegn\\\" for konvertering (D, H, S, C): \").upper()\n\nif kort == \"D\":\n print(\"Ruter\")\nelif kort == \"H\":\n print(\"Hjerter\")\nelif kort == \"S\":\n print(\"Spar\")\nelif kort == \"C\":\n print(\"Kløver\")\nelse:\n print(\"ikke et tegn i kortstokken\")\n\nprint()\n\n#2.b\n\"\"\"lager en array med alle tall og tegn\"\"\"\n\ntegnDict = {\n \"A\": \"Ess\",\n \"2\": \"To\",\n \"3\": \"Tre\",\n \"4\": \"Fire\",\n \"5\": \"Fem\",\n \"6\": \"Seks\",\n \"7\": \"Syv\",\n \"8\": \"Åtte\",\n \"9\": \"Ni\",\n \"10\": \"Ti\",\n \"J\": \"Knekt\",\n \"Q\": \"Dame\",\n \"K\": \"Konge\"\n\n}\n\n\"\"\"Legger til upper her siden alle ord er med stor bokstav\"\"\"\n\nbruker = input(\"tast inn tegn eller tall for konvertering: \").upper()\n\n\"\"\"Printer så verdien\"\"\"\n\nprint(tegnDict[bruker])\n\n\nprint()\n\n#2.a og 2.b\n\nkort_konvertering = input(\"Skriv inn nummer og korttegn for konvertering: \").upper()\n\nliste = list(kort_konvertering)\n\n\n\ndef kort(type):\n\n\n kortNummer = {\n \"A\": \"Ess\",\n \"2\": \"To\",\n \"3\": \"Tre\",\n \"4\": \"Fire\",\n \"5\": \"Fem\",\n \"6\": \"Seks\",\n \"7\": \"Syv\",\n \"8\": \"Åtte\",\n \"9\": \"Ni\",\n \"10\": \"Ti\",\n \"J\": \"Knekt\",\n \"Q\": \"Dame\",\n \"K\": \"Konge\"\n}\n\n kortTegn = {\n \"D\": \"Ruter\",\n \"H\": \"Hjerter\",\n \"S\": \"Spar\",\n \"C\": \"Kløver\"\n}\n return kortNummer[type[0]] + \" \" + kortTegn[type[1]]\n\n\nprint(kort(liste))\n\n#3\n\nprint()\n\n#3.a\n\n\n\nvaluta = {\n \"EUR\": 9.68551,\n \"USD\": 8.50373,\n \"GBP\": 11.0134,\n \"SEK\": 0.92950,\n \"AUD\": 6.06501,\n \"NOK\": 1.00000\n}\nprint(\"valuttakalkulator til NOK\")\nverdiInput = float(input(\"Verdi for konvertering: \"))\nvalutaInput = input(\"Fra hvilke valutta: \").upper()\n\nprint(verdiInput * valuta[valutaInput], \"NOK\")\n\nprint()\n#3.b\n\"\"\"skal bare gå andre veien her så da kan jeg bruke samme dictionary som i a\"\"\"\n\nprint(\"valutakalkulator fra NOK\")\nverdiInputFra = float(input(\"Verdi for konvertering: \"))\nvalutaInputFra = input(\"Til hvilke valutta: \").upper()\n\nprint(verdiInputFra / valuta[valutaInputFra], valutaInputFra)\n\nprint()\n#4\n\"\"\"Printe ut alle heltall fra 0-9 i potens 3\"\"\"\n\nfor x in range(0, 10+1):\n print(str(x) + \"**\" + str(3), \"=\", str(x**3))\n\n#5.a\nprint()\n\ninputStart = int(input(\"Start: \"))\ninputStop = int(input(\"Stop: \"))\ninputN = int(input(\"n: \"))\n\nprint(\"Verdier mellom\", str(inputStart), \"og\", str(inputStop),\n \"som er delelige på\", str(inputN) + \":\")\n\nfor n in range(inputStart, inputStop+1):\n\n if n % inputN == 0:\n print(n)\n\nprint()\n#6\n\n#6.a\n\ndef tilFahrenheit(celsius):\n return int(celsius * 1.800)+32\nprint(\"Celsius Fahrenheit\")\nfor n in range(0, 100+1, 10):\n print(\"%7d %12d\" % (n, tilFahrenheit(n)))\n\n\nprint()\n#6.b\n\n\"\"\"def tilFahrenheit(celsius):\n return int(celsius * 1.800)+32\nprint(\"Celsius Fahrenheit Status\")\nfor n in range(0, 100+1, 10):\n\n if n >= 60:\n print(\"%7d %12d %s\" % (n, tilFahrenheit(n), str(\"Jeg svetter ihjel! \")))\n else:\n print(\"7d %12d %s\" % (n, tilFahrenheit(n) ,str(\"Jeg har det bra\")))\"\"\"\n\n\n\ndef tilFahrenheit(celsius):\n return int(celsius * 1.800) + 32\nprint(\"Celsius Fahrenheit Status\")\nfor n in range(0, 100+1, 10):\n if n >= 60:\n print(\"%7d %12d %23s\" % (n, tilFahrenheit(n), str(\"jeg svetter ihjel! \")))\n else:\n print(\"%7d %12d %21s\" % (n, tilFahrenheit(n), str(\"jeg har det bra. \")))\n\n#7\n\nprint()\n\nverdi = float(input(\"Hvor mye har du?: \"))\nfra = float(input(\"Fra år: \"))\ntil = float(input(\"Til år: \"))\n\ndef renteOkning(verdi, fra, til):\n\n tid = til - fra\n endring = verdiInputFra * (1.02 ** tid)\n return endring\nprint(round(renteOkning(verdi, fra, til), 2),\n \"Kroner med 2% inflasjonsøkning\")\n\n\n"},{"alpha_fraction":0.5002036094665527,"alphanum_fraction":0.5622963905334473,"avg_line_length":24.57291603088379,"blob_id":"77013bac7bc624bfe922f43fbe538e3478afffa7","content_id":"38c30e91bc8becb4f0649c02f1be1bc5c5f75fe2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4926,"license_type":"no_license","max_line_length":96,"num_lines":192,"path":"/semester_oppgave_2.py","repo_name":"GhostingRecon/Oblig1","src_encoding":"UTF-8","text":"\n\nimport matplotlib.pyplot as plt\nimport random\n\n\ndef oppgave1():\n \"\"\" Printer ut store tall i kort og lang form,\n ofte kalt amerikansk og britisk form.\n \"\"\"\n store_tall = [\n [\"Million\", \"10^6 \", \"10^6 \"],\n [\"Milliard\", \" \", \"10^9 \"],\n [\"Billion\", \"10^9 \", \"10^12\"],\n [\"Billiard\", \" \", \"10^15\"],\n [\"Trillion\", \"10^12\", \"10^18\"],\n [\"Quadrillion\",\"10^15\", \"10^24\"],\n [\"Quintillion\",\"10^18\", \"10^30\"],\n [\"Sextillion\", \"10^21\", \"10^36\"]\n ] # Hentet fra https://en.wikipedia.org/wiki/Names_of_large_numbers\n\n print(\"%-15s %9s %9s\" % (\"Navn\", \"lang\", \"Kort\"))\n for i in store_tall:\n print(\"%-15s %9s %9s\" % tuple(i))\n\n\n\n# I billion USD (kort form)\n\nmicrosoft_inntekt_dollar = [\n [2002, 28.37],\n [2003, 32.19],\n [2004, 36.84],\n [2005, 39.79],\n [2006, 44.28],\n [2007, 51.12],\n [2008, 60.42],\n [2009, 58.44],\n [2010, 62.48],\n [2011, 69.94],\n [2012, 73.12],\n [2013, 77.85],\n [2014, 86.83],\n [2015, 93.58],\n [2016, 85.32],\n [2017, 89.95],\n [2018, 110.36],\n [2019, 125.84]\n] # Hentet fra https://www.statista.com/statistics/267805/microsofts-global-revenue-since-2002/\n\n#legger inn kursen\nvaluta = float(8.6862)\n\n\ndef oppgave2a():\n \"\"\" Henter en rekke tall fra brukeren og ut hva som\n var medianen i listen\n \"\"\"\n\n tall_liste = []\n while True:\n bruker_input = input(\"Skriv inn tall (avslutt med ferdig): \")\n if bruker_input == \"ferdig\":\n break\n tall_liste.append(int(bruker_input))\n print(\"Dine tall:\", tall_liste)\n\n tall_liste.sort()\n if len(tall_liste) % 2 == 0:\n ny_1 = int(len(tall_liste) /2)\n ny_2 = ny_1 - 1\n median = (tall_liste[ny_1] + tall_liste[ny_2]) /2\n print(\"Medianen til tallene: \", median)\n else:\n print(\"Medianen til tallene: \", tall_liste[int(len(tall_liste)/2)])\n\n pass\n#kopierer listen\nmicrosoft_inntekt_kroner_ny = microsoft_inntekt_dollar.copy()\ndef oppgave3a():\n \"\"\" Konverterer inntektene fra dollar til kroner\n uten å gjøre endringer på originallisten.\n \"\"\"\n #multipliserer index i ny liste med valuta\n microsoft_inntekt_kroner = [valuta * i[-1] for i in microsoft_inntekt_kroner_ny]\n print(\"I NOK:\", [float(round(n, 2)) for n in microsoft_inntekt_kroner])\n print(\"Orginal:\", microsoft_inntekt_dollar)\n\n \"\"\" berge sin kommentar: for n in microsoft_inntekt_dollar:\n print(\"%-15s %-15s %10s\" %(n[0], n[1], n[1]*usdtilNOK))\"\"\"\n\n\n\ndef oppgave3b():\n \"\"\" Tegner et histogram over inntektene til Microsoft\n \"\"\"\n year = []\n inntekt = []\n for i in range(0, len(microsoft_inntekt_dollar)):\n year.append(microsoft_inntekt_dollar[i][0])\n inntekt.append(microsoft_inntekt_dollar[i][1])\n plt.bar(year, inntekt)\n plt.show()\n\n\n\ndef oppgave3c():\n \"\"\" Summerer opp inntektene til Microsoft\n \"\"\"\n inntekt = []\n for i in range(0, len(microsoft_inntekt_dollar)):\n inntekt.append(microsoft_inntekt_dollar[i][1])\n total_milliard = sum(inntekt)\n total_billion = int(total_milliard * 100)\n print(\"Microsoft tjente\", total_billion, \"billioner dollar i perioden 2002-2019\")\n\n\ndef print_kart(spiller, monster):\n x = 0\n while x <= 10:\n y = 0\n kart_liste = []\n while y <= 10:\n if x == spiller[1] and y == spiller[0]:\n kart_liste.append(\"S\")\n elif x == monster[1] and y == monster[0]:\n kart_liste.append(\"M\")\n else: kart_liste.append(\"* \")\n\n y = y + 1\n\n samlet = \" \".join(kart_liste)\n\n print(samlet)\n\n x = x + 1\n\nspiller = [2, 6]\nmonster = [4, 5]\n\ndef spiller_bevegelse():\n flytt = input()\n\n if flytt == \"W\":\n spiller[1] = spiller[1] - 1\n if flytt == \"a\":\n spiller[0] = spiller[0] - 1\n if flytt == \"s\":\n spiller[1] = spiller[1] + 1\n if flytt == \"d\":\n spiller[0] = spiller[0] + 1\n\n monster[0] = monster[0] + random.randint(-1, 1)\n monster[1] = monster[1] + random.randint(-1, 1)\n\ndef spill():\n\n print(\"wasd + enter for å bevege seg\")\n print_kart(spiller, monster)\n\n try:\n while True:\n spiller_bevegelse()\n print_kart(spiller, monster)\n except KeyboardInterrupt:\n exit()\n\n\n\n\n\ndef main():\n \"\"\" Dette er filens main-funksjon, det er denne funksjonen som kjører\n når hele filen blir kjørt. \n Hvis du vil kjøre en av oppgave-funksjonene nedenfor fjerner du #-tegnet \n foran oppgave-funksjonen slik at den blir \"skrudd på\".\n Før du leverer kan det være lurt å sjekke alle funksjonene. Dette gjør \n du ved å fjerne alle #-tegnene nedenfor.\n \"\"\"\n oppgave1()\n print()\n oppgave2a()\n print()\n print()\n oppgave3a()\n print()\n oppgave3b()\n print()\n oppgave3c()\n print()\n spill()\n print()\n\nmain()\n"},{"alpha_fraction":0.7777777910232544,"alphanum_fraction":0.8333333134651184,"avg_line_length":17,"blob_id":"cd1fd084005d7394e80f7aea08d4022221555946","content_id":"b52b4b1a1690bb9af5006113cedb6f665684296f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":36,"license_type":"no_license","max_line_length":26,"num_lines":2,"path":"/README.md","repo_name":"GhostingRecon/Oblig1","src_encoding":"UTF-8","text":"# Oblig1\nobligatorisk innlevering 1\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5264720320701599,\n \"alphanum_fraction\": 0.5685304403305054,\n \"avg_line_length\": 17.44748878479004,\n \"blob_id\": \"bed8c5370bfb4face09192d37c3a6bd8c94f7022\",\n \"content_id\": \"f425bf8612d581b3adc3458bc6f85a46ebc2f2f6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4053,\n \"license_type\": \"no_license\",\n \"max_line_length\": 84,\n \"num_lines\": 219,\n \"path\": \"/sem_1.py\",\n \"repo_name\": \"GhostingRecon/Oblig1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#Oblig_1\\n\\n\\n\\n#1\\n\\ntall_1 = int(input(\\\"Tall nr 1: \\\"))\\ntall_2 = int(input(\\\"Tall nr 2: \\\"))\\ntall_3 = int(input(\\\"Tall nr 3: \\\"))\\nprint()\\n\\nprint(\\\"Tall nr 1: \\\" + str(tall_1))\\nprint(\\\"Tall nr 2: \\\" + str(tall_2))\\nprint(\\\"Tall nr 2: \\\" + str(tall_3))\\n\\n\\nif tall_1 < tall_2 < tall_3:\\n print(\\\"Strengt stigende\\\")\\nelif tall_1 > tall_2 > tall_3:\\n print(\\\"Strengt synkende\\\")\\nelse:\\n print(\\\"Verken strengt stigende eller synkende\\\")\\n\\n#2\\n\\nprint()\\n#2.a\\n\\n\\n\\nkort = input(\\\"Skriv inn \\\\\\\"kort tegn\\\\\\\" for konvertering (D, H, S, C): \\\").upper()\\n\\nif kort == \\\"D\\\":\\n print(\\\"Ruter\\\")\\nelif kort == \\\"H\\\":\\n print(\\\"Hjerter\\\")\\nelif kort == \\\"S\\\":\\n print(\\\"Spar\\\")\\nelif kort == \\\"C\\\":\\n print(\\\"Kløver\\\")\\nelse:\\n print(\\\"ikke et tegn i kortstokken\\\")\\n\\nprint()\\n\\n#2.b\\n\\\"\\\"\\\"lager en array med alle tall og tegn\\\"\\\"\\\"\\n\\ntegnDict = {\\n \\\"A\\\": \\\"Ess\\\",\\n \\\"2\\\": \\\"To\\\",\\n \\\"3\\\": \\\"Tre\\\",\\n \\\"4\\\": \\\"Fire\\\",\\n \\\"5\\\": \\\"Fem\\\",\\n \\\"6\\\": \\\"Seks\\\",\\n \\\"7\\\": \\\"Syv\\\",\\n \\\"8\\\": \\\"Åtte\\\",\\n \\\"9\\\": \\\"Ni\\\",\\n \\\"10\\\": \\\"Ti\\\",\\n \\\"J\\\": \\\"Knekt\\\",\\n \\\"Q\\\": \\\"Dame\\\",\\n \\\"K\\\": \\\"Konge\\\"\\n\\n}\\n\\n\\\"\\\"\\\"Legger til upper her siden alle ord er med stor bokstav\\\"\\\"\\\"\\n\\nbruker = input(\\\"tast inn tegn eller tall for konvertering: \\\").upper()\\n\\n\\\"\\\"\\\"Printer så verdien\\\"\\\"\\\"\\n\\nprint(tegnDict[bruker])\\n\\n\\nprint()\\n\\n#2.a og 2.b\\n\\nkort_konvertering = input(\\\"Skriv inn nummer og korttegn for konvertering: \\\").upper()\\n\\nliste = list(kort_konvertering)\\n\\n\\n\\ndef kort(type):\\n\\n\\n kortNummer = {\\n \\\"A\\\": \\\"Ess\\\",\\n \\\"2\\\": \\\"To\\\",\\n \\\"3\\\": \\\"Tre\\\",\\n \\\"4\\\": \\\"Fire\\\",\\n \\\"5\\\": \\\"Fem\\\",\\n \\\"6\\\": \\\"Seks\\\",\\n \\\"7\\\": \\\"Syv\\\",\\n \\\"8\\\": \\\"Åtte\\\",\\n \\\"9\\\": \\\"Ni\\\",\\n \\\"10\\\": \\\"Ti\\\",\\n \\\"J\\\": \\\"Knekt\\\",\\n \\\"Q\\\": \\\"Dame\\\",\\n \\\"K\\\": \\\"Konge\\\"\\n}\\n\\n kortTegn = {\\n \\\"D\\\": \\\"Ruter\\\",\\n \\\"H\\\": \\\"Hjerter\\\",\\n \\\"S\\\": \\\"Spar\\\",\\n \\\"C\\\": \\\"Kløver\\\"\\n}\\n return kortNummer[type[0]] + \\\" \\\" + kortTegn[type[1]]\\n\\n\\nprint(kort(liste))\\n\\n#3\\n\\nprint()\\n\\n#3.a\\n\\n\\n\\nvaluta = {\\n \\\"EUR\\\": 9.68551,\\n \\\"USD\\\": 8.50373,\\n \\\"GBP\\\": 11.0134,\\n \\\"SEK\\\": 0.92950,\\n \\\"AUD\\\": 6.06501,\\n \\\"NOK\\\": 1.00000\\n}\\nprint(\\\"valuttakalkulator til NOK\\\")\\nverdiInput = float(input(\\\"Verdi for konvertering: \\\"))\\nvalutaInput = input(\\\"Fra hvilke valutta: \\\").upper()\\n\\nprint(verdiInput * valuta[valutaInput], \\\"NOK\\\")\\n\\nprint()\\n#3.b\\n\\\"\\\"\\\"skal bare gå andre veien her så da kan jeg bruke samme dictionary som i a\\\"\\\"\\\"\\n\\nprint(\\\"valutakalkulator fra NOK\\\")\\nverdiInputFra = float(input(\\\"Verdi for konvertering: \\\"))\\nvalutaInputFra = input(\\\"Til hvilke valutta: \\\").upper()\\n\\nprint(verdiInputFra / valuta[valutaInputFra], valutaInputFra)\\n\\nprint()\\n#4\\n\\\"\\\"\\\"Printe ut alle heltall fra 0-9 i potens 3\\\"\\\"\\\"\\n\\nfor x in range(0, 10+1):\\n print(str(x) + \\\"**\\\" + str(3), \\\"=\\\", str(x**3))\\n\\n#5.a\\nprint()\\n\\ninputStart = int(input(\\\"Start: \\\"))\\ninputStop = int(input(\\\"Stop: \\\"))\\ninputN = int(input(\\\"n: \\\"))\\n\\nprint(\\\"Verdier mellom\\\", str(inputStart), \\\"og\\\", str(inputStop),\\n \\\"som er delelige på\\\", str(inputN) + \\\":\\\")\\n\\nfor n in range(inputStart, inputStop+1):\\n\\n if n % inputN == 0:\\n print(n)\\n\\nprint()\\n#6\\n\\n#6.a\\n\\ndef tilFahrenheit(celsius):\\n return int(celsius * 1.800)+32\\nprint(\\\"Celsius Fahrenheit\\\")\\nfor n in range(0, 100+1, 10):\\n print(\\\"%7d %12d\\\" % (n, tilFahrenheit(n)))\\n\\n\\nprint()\\n#6.b\\n\\n\\\"\\\"\\\"def tilFahrenheit(celsius):\\n return int(celsius * 1.800)+32\\nprint(\\\"Celsius Fahrenheit Status\\\")\\nfor n in range(0, 100+1, 10):\\n\\n if n >= 60:\\n print(\\\"%7d %12d %s\\\" % (n, tilFahrenheit(n), str(\\\"Jeg svetter ihjel! \\\")))\\n else:\\n print(\\\"7d %12d %s\\\" % (n, tilFahrenheit(n) ,str(\\\"Jeg har det bra\\\")))\\\"\\\"\\\"\\n\\n\\n\\ndef tilFahrenheit(celsius):\\n return int(celsius * 1.800) + 32\\nprint(\\\"Celsius Fahrenheit Status\\\")\\nfor n in range(0, 100+1, 10):\\n if n >= 60:\\n print(\\\"%7d %12d %23s\\\" % (n, tilFahrenheit(n), str(\\\"jeg svetter ihjel! \\\")))\\n else:\\n print(\\\"%7d %12d %21s\\\" % (n, tilFahrenheit(n), str(\\\"jeg har det bra. \\\")))\\n\\n#7\\n\\nprint()\\n\\nverdi = float(input(\\\"Hvor mye har du?: \\\"))\\nfra = float(input(\\\"Fra år: \\\"))\\ntil = float(input(\\\"Til år: \\\"))\\n\\ndef renteOkning(verdi, fra, til):\\n\\n tid = til - fra\\n endring = verdiInputFra * (1.02 ** tid)\\n return endring\\nprint(round(renteOkning(verdi, fra, til), 2),\\n \\\"Kroner med 2% inflasjonsøkning\\\")\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5002036094665527,\n \"alphanum_fraction\": 0.5622963905334473,\n \"avg_line_length\": 24.57291603088379,\n \"blob_id\": \"77013bac7bc624bfe922f43fbe538e3478afffa7\",\n \"content_id\": \"38c30e91bc8becb4f0649c02f1be1bc5c5f75fe2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4926,\n \"license_type\": \"no_license\",\n \"max_line_length\": 96,\n \"num_lines\": 192,\n \"path\": \"/semester_oppgave_2.py\",\n \"repo_name\": \"GhostingRecon/Oblig1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\nimport matplotlib.pyplot as plt\\nimport random\\n\\n\\ndef oppgave1():\\n \\\"\\\"\\\" Printer ut store tall i kort og lang form,\\n ofte kalt amerikansk og britisk form.\\n \\\"\\\"\\\"\\n store_tall = [\\n [\\\"Million\\\", \\\"10^6 \\\", \\\"10^6 \\\"],\\n [\\\"Milliard\\\", \\\" \\\", \\\"10^9 \\\"],\\n [\\\"Billion\\\", \\\"10^9 \\\", \\\"10^12\\\"],\\n [\\\"Billiard\\\", \\\" \\\", \\\"10^15\\\"],\\n [\\\"Trillion\\\", \\\"10^12\\\", \\\"10^18\\\"],\\n [\\\"Quadrillion\\\",\\\"10^15\\\", \\\"10^24\\\"],\\n [\\\"Quintillion\\\",\\\"10^18\\\", \\\"10^30\\\"],\\n [\\\"Sextillion\\\", \\\"10^21\\\", \\\"10^36\\\"]\\n ] # Hentet fra https://en.wikipedia.org/wiki/Names_of_large_numbers\\n\\n print(\\\"%-15s %9s %9s\\\" % (\\\"Navn\\\", \\\"lang\\\", \\\"Kort\\\"))\\n for i in store_tall:\\n print(\\\"%-15s %9s %9s\\\" % tuple(i))\\n\\n\\n\\n# I billion USD (kort form)\\n\\nmicrosoft_inntekt_dollar = [\\n [2002, 28.37],\\n [2003, 32.19],\\n [2004, 36.84],\\n [2005, 39.79],\\n [2006, 44.28],\\n [2007, 51.12],\\n [2008, 60.42],\\n [2009, 58.44],\\n [2010, 62.48],\\n [2011, 69.94],\\n [2012, 73.12],\\n [2013, 77.85],\\n [2014, 86.83],\\n [2015, 93.58],\\n [2016, 85.32],\\n [2017, 89.95],\\n [2018, 110.36],\\n [2019, 125.84]\\n] # Hentet fra https://www.statista.com/statistics/267805/microsofts-global-revenue-since-2002/\\n\\n#legger inn kursen\\nvaluta = float(8.6862)\\n\\n\\ndef oppgave2a():\\n \\\"\\\"\\\" Henter en rekke tall fra brukeren og ut hva som\\n var medianen i listen\\n \\\"\\\"\\\"\\n\\n tall_liste = []\\n while True:\\n bruker_input = input(\\\"Skriv inn tall (avslutt med ferdig): \\\")\\n if bruker_input == \\\"ferdig\\\":\\n break\\n tall_liste.append(int(bruker_input))\\n print(\\\"Dine tall:\\\", tall_liste)\\n\\n tall_liste.sort()\\n if len(tall_liste) % 2 == 0:\\n ny_1 = int(len(tall_liste) /2)\\n ny_2 = ny_1 - 1\\n median = (tall_liste[ny_1] + tall_liste[ny_2]) /2\\n print(\\\"Medianen til tallene: \\\", median)\\n else:\\n print(\\\"Medianen til tallene: \\\", tall_liste[int(len(tall_liste)/2)])\\n\\n pass\\n#kopierer listen\\nmicrosoft_inntekt_kroner_ny = microsoft_inntekt_dollar.copy()\\ndef oppgave3a():\\n \\\"\\\"\\\" Konverterer inntektene fra dollar til kroner\\n uten å gjøre endringer på originallisten.\\n \\\"\\\"\\\"\\n #multipliserer index i ny liste med valuta\\n microsoft_inntekt_kroner = [valuta * i[-1] for i in microsoft_inntekt_kroner_ny]\\n print(\\\"I NOK:\\\", [float(round(n, 2)) for n in microsoft_inntekt_kroner])\\n print(\\\"Orginal:\\\", microsoft_inntekt_dollar)\\n\\n \\\"\\\"\\\" berge sin kommentar: for n in microsoft_inntekt_dollar:\\n print(\\\"%-15s %-15s %10s\\\" %(n[0], n[1], n[1]*usdtilNOK))\\\"\\\"\\\"\\n\\n\\n\\ndef oppgave3b():\\n \\\"\\\"\\\" Tegner et histogram over inntektene til Microsoft\\n \\\"\\\"\\\"\\n year = []\\n inntekt = []\\n for i in range(0, len(microsoft_inntekt_dollar)):\\n year.append(microsoft_inntekt_dollar[i][0])\\n inntekt.append(microsoft_inntekt_dollar[i][1])\\n plt.bar(year, inntekt)\\n plt.show()\\n\\n\\n\\ndef oppgave3c():\\n \\\"\\\"\\\" Summerer opp inntektene til Microsoft\\n \\\"\\\"\\\"\\n inntekt = []\\n for i in range(0, len(microsoft_inntekt_dollar)):\\n inntekt.append(microsoft_inntekt_dollar[i][1])\\n total_milliard = sum(inntekt)\\n total_billion = int(total_milliard * 100)\\n print(\\\"Microsoft tjente\\\", total_billion, \\\"billioner dollar i perioden 2002-2019\\\")\\n\\n\\ndef print_kart(spiller, monster):\\n x = 0\\n while x <= 10:\\n y = 0\\n kart_liste = []\\n while y <= 10:\\n if x == spiller[1] and y == spiller[0]:\\n kart_liste.append(\\\"S\\\")\\n elif x == monster[1] and y == monster[0]:\\n kart_liste.append(\\\"M\\\")\\n else: kart_liste.append(\\\"* \\\")\\n\\n y = y + 1\\n\\n samlet = \\\" \\\".join(kart_liste)\\n\\n print(samlet)\\n\\n x = x + 1\\n\\nspiller = [2, 6]\\nmonster = [4, 5]\\n\\ndef spiller_bevegelse():\\n flytt = input()\\n\\n if flytt == \\\"W\\\":\\n spiller[1] = spiller[1] - 1\\n if flytt == \\\"a\\\":\\n spiller[0] = spiller[0] - 1\\n if flytt == \\\"s\\\":\\n spiller[1] = spiller[1] + 1\\n if flytt == \\\"d\\\":\\n spiller[0] = spiller[0] + 1\\n\\n monster[0] = monster[0] + random.randint(-1, 1)\\n monster[1] = monster[1] + random.randint(-1, 1)\\n\\ndef spill():\\n\\n print(\\\"wasd + enter for å bevege seg\\\")\\n print_kart(spiller, monster)\\n\\n try:\\n while True:\\n spiller_bevegelse()\\n print_kart(spiller, monster)\\n except KeyboardInterrupt:\\n exit()\\n\\n\\n\\n\\n\\ndef main():\\n \\\"\\\"\\\" Dette er filens main-funksjon, det er denne funksjonen som kjører\\n når hele filen blir kjørt. \\n Hvis du vil kjøre en av oppgave-funksjonene nedenfor fjerner du #-tegnet \\n foran oppgave-funksjonen slik at den blir \\\"skrudd på\\\".\\n Før du leverer kan det være lurt å sjekke alle funksjonene. Dette gjør \\n du ved å fjerne alle #-tegnene nedenfor.\\n \\\"\\\"\\\"\\n oppgave1()\\n print()\\n oppgave2a()\\n print()\\n print()\\n oppgave3a()\\n print()\\n oppgave3b()\\n print()\\n oppgave3c()\\n print()\\n spill()\\n print()\\n\\nmain()\\n\"\n },\n {\n \"alpha_fraction\": 0.7777777910232544,\n \"alphanum_fraction\": 0.8333333134651184,\n \"avg_line_length\": 17,\n \"blob_id\": \"cd1fd084005d7394e80f7aea08d4022221555946\",\n \"content_id\": \"b52b4b1a1690bb9af5006113cedb6f665684296f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 36,\n \"license_type\": \"no_license\",\n \"max_line_length\": 26,\n \"num_lines\": 2,\n \"path\": \"/README.md\",\n \"repo_name\": \"GhostingRecon/Oblig1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Oblig1\\nobligatorisk innlevering 1\\n\"\n }\n]"},"num_files":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":1180,"cells":{"repo_name":{"kind":"string","value":"moritzhertler/Artemis"},"repo_url":{"kind":"string","value":"https://github.com/moritzhertler/Artemis"},"snapshot_id":{"kind":"string","value":"d6b063ffe7e4b664f073929fbd125858dcb59cf9"},"revision_id":{"kind":"string","value":"ae7f91c050c6db996cab78da9c2a1b675cab4e21"},"directory_id":{"kind":"string","value":"11bbc8931c047946a7ef58a9af7bb2bbafa7d993"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"string","value":"2023-04-06T06:32:25.071429"},"revision_date":{"kind":"string","value":"2021-04-09T22:12:08"},"committer_date":{"kind":"string","value":"2021-04-09T22:12:08"},"github_id":{"kind":"number","value":357192785,"string":"357,192,785"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"string","value":"MIT"},"gha_created_at":{"kind":"string","value":"2021-04-12T12:53:56"},"gha_updated_at":{"kind":"string","value":"2021-04-12T11:03:55"},"gha_pushed_at":{"kind":"string","value":"2021-04-12T12:21:15"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6124572157859802,"alphanum_fraction":0.6164271235466003,"avg_line_length":43.272727966308594,"blob_id":"dd536202cf5bae9f3d69472c3b2f38f3200efd6c","content_id":"e7cde517c75456c9b31a84e90dd70e5809f8fc59","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":7305,"license_type":"permissive","max_line_length":180,"num_lines":165,"path":"/src/test/javascript/spec/component/lecture-unit/video-unit/edit-video-unit.component.spec.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import * as chai from 'chai';\nimport * as sinonChai from 'sinon-chai';\nimport * as sinon from 'sinon';\nimport * as moment from 'moment';\n\nimport { VideoUnitFormData } from 'app/lecture/lecture-unit/lecture-unit-management/video-unit-form/video-unit-form.component';\nimport { VideoUnitService } from 'app/lecture/lecture-unit/lecture-unit-management/videoUnit.service';\nimport { MockRouter } from '../../../helpers/mocks/mock-router';\nimport { Component, EventEmitter, Input, Output } from '@angular/core';\nimport { ComponentFixture, TestBed } from '@angular/core/testing';\nimport { EditVideoUnitComponent } from 'app/lecture/lecture-unit/lecture-unit-management/edit-video-unit/edit-video-unit.component';\nimport { MockProvider } from 'ng-mocks';\nimport { JhiAlertService } from 'ng-jhipster';\nimport { ActivatedRoute, Router } from '@angular/router';\nimport { Observable, of } from 'rxjs';\nimport { VideoUnit } from 'app/entities/lecture-unit/videoUnit.model';\nimport { HttpResponse } from '@angular/common/http';\nimport { By } from '@angular/platform-browser';\n\nchai.use(sinonChai);\nconst expect = chai.expect;\n\n@Component({ selector: 'jhi-video-unit-form', template: '' })\nclass VideoUnitFormStubComponent {\n @Input() isEditMode = false;\n @Input() formData: VideoUnitFormData;\n @Output() formSubmitted: EventEmitter = new EventEmitter();\n}\n\n@Component({ selector: 'jhi-lecture-unit-layout', template: ';\n let editVideoUnitComponent: EditVideoUnitComponent;\n const sandbox = sinon.createSandbox();\n\n beforeEach(() => {\n TestBed.configureTestingModule({\n imports: [],\n declarations: [VideoUnitFormStubComponent, LectureUnitLayoutStubComponent, EditVideoUnitComponent],\n providers: [\n MockProvider(VideoUnitService),\n MockProvider(JhiAlertService),\n { provide: Router, useClass: MockRouter },\n {\n provide: ActivatedRoute,\n useValue: {\n paramMap: Observable.of({\n get: (key: string) => {\n switch (key) {\n case 'videoUnitId':\n return 1;\n }\n },\n }),\n parent: {\n parent: {\n paramMap: Observable.of({\n get: (key: string) => {\n switch (key) {\n case 'lectureId':\n return 1;\n }\n },\n }),\n },\n },\n },\n },\n ],\n schemas: [],\n })\n .compileComponents()\n .then(() => {\n editVideoUnitComponentFixture = TestBed.createComponent(EditVideoUnitComponent);\n editVideoUnitComponent = editVideoUnitComponentFixture.componentInstance;\n });\n });\n\n afterEach(function () {\n sandbox.restore();\n });\n\n it('should initialize', () => {\n editVideoUnitComponentFixture.detectChanges();\n expect(editVideoUnitComponent).to.be.ok;\n });\n\n it('should set form data correctly', () => {\n const videoUnitService = TestBed.inject(VideoUnitService);\n\n const videoUnitOfResponse = new VideoUnit();\n videoUnitOfResponse.id = 1;\n videoUnitOfResponse.name = 'test';\n videoUnitOfResponse.releaseDate = moment({ years: 2010, months: 3, date: 5 });\n videoUnitOfResponse.description = 'lorem ipsum';\n videoUnitOfResponse.source = 'https://www.youtube.com/embed/M7lc1UVf-VE';\n\n const response: HttpResponse = new HttpResponse({\n body: videoUnitOfResponse,\n status: 200,\n });\n\n const findByIdStub = sandbox.stub(videoUnitService, 'findById').returns(of(response));\n const videoUnitFormStubComponent: VideoUnitFormStubComponent = editVideoUnitComponentFixture.debugElement.query(By.directive(VideoUnitFormStubComponent)).componentInstance;\n editVideoUnitComponentFixture.detectChanges(); // onInit\n expect(editVideoUnitComponent.videoUnit).to.equal(videoUnitOfResponse);\n expect(findByIdStub).to.have.been.calledOnce;\n expect(editVideoUnitComponent.formData.name).to.equal(videoUnitOfResponse.name);\n expect(editVideoUnitComponent.formData.releaseDate).to.equal(videoUnitOfResponse.releaseDate);\n expect(editVideoUnitComponent.formData.description).to.equal(videoUnitOfResponse.description);\n expect(editVideoUnitComponent.formData.source).to.equal(videoUnitOfResponse.source);\n expect(videoUnitFormStubComponent.formData).to.equal(editVideoUnitComponent.formData);\n });\n\n it('should send PUT request upon form submission and navigate', () => {\n const router: Router = TestBed.inject(Router);\n const videoUnitService = TestBed.inject(VideoUnitService);\n\n const videoUnitInDatabase: VideoUnit = new VideoUnit();\n videoUnitInDatabase.id = 1;\n videoUnitInDatabase.name = 'test';\n videoUnitInDatabase.releaseDate = moment({ years: 2010, months: 3, date: 5 });\n videoUnitInDatabase.description = 'lorem ipsum';\n videoUnitInDatabase.source = 'https://www.youtube.com/embed/M7lc1UVf-VE';\n\n const findByIdResponse: HttpResponse = new HttpResponse({\n body: videoUnitInDatabase,\n status: 200,\n });\n const findByIdStub = sandbox.stub(videoUnitService, 'findById').returns(of(findByIdResponse));\n\n editVideoUnitComponentFixture.detectChanges(); // onInit\n expect(findByIdStub).to.have.been.calledOnce;\n expect(editVideoUnitComponent.videoUnit).to.equal(videoUnitInDatabase);\n\n const changedUnit: VideoUnit = {\n ...videoUnitInDatabase,\n name: 'Changed',\n };\n\n const updateResponse: HttpResponse = new HttpResponse({\n body: changedUnit,\n status: 200,\n });\n const updatedStub = sandbox.stub(videoUnitService, 'update').returns(of(updateResponse));\n const navigateSpy = sinon.spy(router, 'navigate');\n\n const textUnitForm: VideoUnitFormStubComponent = editVideoUnitComponentFixture.debugElement.query(By.directive(VideoUnitFormStubComponent)).componentInstance;\n textUnitForm.formSubmitted.emit({\n name: changedUnit.name,\n description: changedUnit.description,\n releaseDate: changedUnit.releaseDate,\n source: changedUnit.source,\n });\n\n expect(updatedStub).to.have.been.calledOnce;\n expect(navigateSpy).to.have.been.calledOnce;\n navigateSpy.restore();\n });\n});\n"},{"alpha_fraction":0.621839702129364,"alphanum_fraction":0.621839702129364,"avg_line_length":35.45098114013672,"blob_id":"df0bac7992c1089bbf5cd691d557f701956175a9","content_id":"2c98e80b0ad670a7b2204125920b3c52efaae589","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":3718,"license_type":"permissive","max_line_length":112,"num_lines":102,"path":"/src/main/webapp/app/course/manage/course-detail.component.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { Component, OnDestroy, OnInit } from '@angular/core';\nimport { ActivatedRoute, Router } from '@angular/router';\nimport { HttpErrorResponse, HttpResponse } from '@angular/common/http';\nimport { Subscription } from 'rxjs/Subscription';\nimport { JhiEventManager } from 'ng-jhipster';\nimport { Course } from 'app/entities/course.model';\nimport { CourseManagementService } from './course-management.service';\nimport { CachingStrategy } from 'app/shared/image/secured-image.component';\nimport { JhiAlertService } from 'ng-jhipster';\nimport { ActionType } from 'app/shared/delete-dialog/delete-dialog.model';\nimport { ButtonSize } from 'app/shared/components/button.component';\nimport { Subject } from 'rxjs';\n\n@Component({\n selector: 'jhi-course-detail',\n templateUrl: './course-detail.component.html',\n styleUrls: ['./course-detail.component.scss'],\n})\nexport class CourseDetailComponent implements OnInit, OnDestroy {\n ButtonSize = ButtonSize;\n ActionType = ActionType;\n CachingStrategy = CachingStrategy;\n course: Course;\n private eventSubscriber: Subscription;\n private dialogErrorSource = new Subject();\n dialogError$ = this.dialogErrorSource.asObservable();\n\n constructor(\n private eventManager: JhiEventManager,\n private courseService: CourseManagementService,\n private route: ActivatedRoute,\n private router: Router,\n private jhiAlertService: JhiAlertService,\n ) {}\n\n /**\n * On init load the course information and subscribe to listen for changes in courses.\n */\n ngOnInit() {\n this.route.data.subscribe(({ course }) => {\n this.course = course;\n\n this.registerChangeInCourses();\n });\n }\n\n /**\n * Subscribe to changes in courses and reload the course after a change.\n */\n registerChangeInCourses() {\n this.eventSubscriber = this.eventManager.subscribe('courseListModification', () => {\n this.courseService.find(this.course.id!).subscribe((courseResponse: HttpResponse) => {\n this.course = courseResponse.body!;\n });\n });\n }\n\n /**\n * Register for the currently loaded course.\n */\n registerForCourse() {\n this.courseService.registerForCourse(this.course.id!).subscribe(\n (userResponse) => {\n if (userResponse.body != undefined) {\n const message = 'Registered user for course ' + this.course.title;\n const jhiAlert = this.jhiAlertService.info(message);\n jhiAlert.msg = message;\n }\n },\n (error: HttpErrorResponse) => {\n const errorMessage = error.headers.get('X-artemisApp-message')!;\n // TODO: this is a workaround to avoid translation not found issues. Provide proper translations\n const jhiAlert = this.jhiAlertService.error(errorMessage);\n jhiAlert.msg = errorMessage;\n },\n );\n }\n\n /**\n * On destroy unsubscribe all subscriptions.\n */\n ngOnDestroy() {\n this.eventManager.destroy(this.eventSubscriber);\n }\n\n /**\n * Deletes the course\n * @param courseId id the course that will be deleted\n */\n deleteCourse(courseId: number) {\n this.courseService.delete(courseId).subscribe(\n () => {\n this.eventManager.broadcast({\n name: 'courseListModification',\n content: 'Deleted an course',\n });\n this.dialogErrorSource.next('');\n },\n (error: HttpErrorResponse) => this.dialogErrorSource.next(error.message),\n );\n }\n}\n"},{"alpha_fraction":0.6680992841720581,"alphanum_fraction":0.6683072447776794,"avg_line_length":48.06802749633789,"blob_id":"932d779a2ef8d9fe6fbd94a4a277d70e15f1fa27","content_id":"d2cbd44d39bdd966f00b5e23ac1b5ef9d01622a2","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":14426,"license_type":"permissive","max_line_length":176,"num_lines":294,"path":"/src/main/webapp/app/exercises/text/assess/text-assessment.service.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { Injectable } from '@angular/core';\nimport { SERVER_API_URL } from 'app/app.constants';\nimport { HttpClient, HttpParams, HttpResponse } from '@angular/common/http';\nimport { Observable } from 'rxjs';\nimport { map, tap } from 'rxjs/operators';\nimport { Result } from 'app/entities/result.model';\nimport * as moment from 'moment';\nimport { ComplaintResponse } from 'app/entities/complaint-response.model';\nimport { Feedback } from 'app/entities/feedback.model';\nimport { StudentParticipation } from 'app/entities/participation/student-participation.model';\nimport { TextBlock } from 'app/entities/text-block.model';\nimport { TextBlockRef } from 'app/entities/text-block-ref.model';\nimport { cloneDeep } from 'lodash';\nimport { TextSubmission } from 'app/entities/text-submission.model';\nimport { FeedbackConflict } from 'app/entities/feedback-conflict';\nimport { getLatestSubmissionResult, getSubmissionResultByCorrectionRound, getSubmissionResultById, setLatestSubmissionResult, Submission } from 'app/entities/submission.model';\nimport { Participation } from 'app/entities/participation/participation.model';\n\ntype EntityResponseType = HttpResponse;\ntype TextAssessmentDTO = { feedbacks: Feedback[]; textBlocks: TextBlock[] };\n\n@Injectable({\n providedIn: 'root',\n})\nexport class TextAssessmentService {\n private readonly resourceUrl = SERVER_API_URL + 'api/text-assessments';\n\n constructor(private http: HttpClient) {}\n\n /**\n * Saves the passed feedback items of the assessment.\n * @param exerciseId id of the exercise the assessed submission was made to of type {number}\n * @param resultId id of the corresponding result of type {number}\n * @param feedbacks list of feedback made during assessment of type {Feedback[]}\n * @param textBlocks list of text blocks of type {TextBlock[]}\n */\n public save(exerciseId: number, resultId: number, feedbacks: Feedback[], textBlocks: TextBlock[]): Observable {\n const body = TextAssessmentService.prepareFeedbacksAndTextblocksForRequest(feedbacks, textBlocks);\n return this.http\n .put(`${this.resourceUrl}/exercise/${exerciseId}/result/${resultId}`, body, { observe: 'response' })\n .map((res: EntityResponseType) => TextAssessmentService.convertResponse(res));\n }\n\n /**\n * Submits the passed feedback items of the assessment.\n * @param exerciseId id of the exercise the assessed submission was made to of type {number}\n * @param resultId id of the corresponding result of type {number}\n * @param feedbacks list of feedback made during assessment of type {Feedback[]}\n * @param textBlocks list of text blocks of type {TextBlock[]}\n */\n public submit(exerciseId: number, resultId: number, feedbacks: Feedback[], textBlocks: TextBlock[]): Observable {\n const body = TextAssessmentService.prepareFeedbacksAndTextblocksForRequest(feedbacks, textBlocks);\n return this.http\n .put(`${this.resourceUrl}/exercise/${exerciseId}/result/${resultId}/submit`, body, { observe: 'response' })\n .map((res: EntityResponseType) => TextAssessmentService.convertResponse(res));\n }\n\n /**\n * Updates an assessment after a complaint.\n * @param feedbacks list of feedback made during assessment of type {Feedback[]}\n * @param textBlocks list of text blocks of type {TextBlock[]}\n * @param complaintResponse response on the complaint of type {ComplaintResponse}\n * @param submissionId id of corresponding submission of type {number}\n */\n public updateAssessmentAfterComplaint(\n feedbacks: Feedback[],\n textBlocks: TextBlock[],\n complaintResponse: ComplaintResponse,\n submissionId: number,\n ): Observable {\n const url = `${this.resourceUrl}/text-submissions/${submissionId}/assessment-after-complaint`;\n const assessmentUpdate = {\n feedbacks,\n textBlocks,\n complaintResponse,\n };\n return this.http\n .put(url, assessmentUpdate, { observe: 'response' })\n .map((res: EntityResponseType) => TextAssessmentService.convertResponse(res));\n }\n\n saveExampleAssessment(exampleSubmissionId: number, feedbacks: Feedback[], textBlocks: TextBlock[]): Observable {\n const url = `${this.resourceUrl}/text-submissions/${exampleSubmissionId}/example-assessment`;\n const body = TextAssessmentService.prepareFeedbacksAndTextblocksForRequest(feedbacks, textBlocks);\n return this.http\n .put(url, body, { observe: 'response' })\n .map((res: EntityResponseType) => TextAssessmentService.convertResponse(res));\n }\n\n /**\n * Cancels an assessment.\n * @param exerciseId id of the exercise the assessed submission was made to of type {number}\n * @param submissionId id of corresponding submission of type {number}\n */\n public cancelAssessment(exerciseId: number, submissionId: number): Observable {\n return this.http.put(`${this.resourceUrl}/exercise/${exerciseId}/submission/${submissionId}/cancel-assessment`, null);\n }\n\n /**\n * Get all feedback items for a submission.\n * @param submissionId id of the submission for which the feedback items should be retrieved of type {number}\n * @param correctionRound\n * @param resultId instructors can results by id\n */\n public getFeedbackDataForExerciseSubmission(submissionId: number, correctionRound = 0, resultId?: number): Observable {\n let params = new HttpParams();\n if (resultId && resultId > 0) {\n // in case resultId is set, we do not need the correction round\n params = params.set('resultId', resultId!.toString());\n } else {\n params = params.set('correction-round', correctionRound.toString());\n }\n return this.http\n .get(`${this.resourceUrl}/submission/${submissionId}`, { observe: 'response', params })\n .pipe(\n // Wire up Result and Submission\n tap((response) => {\n const participation = response.body!;\n const submission = participation.submissions![0];\n let result;\n if (resultId) {\n result = getSubmissionResultById(submission, resultId);\n } else {\n result = getSubmissionResultByCorrectionRound(submission, correctionRound)!;\n }\n TextAssessmentService.reconnectResultsParticipation(participation, submission, result!);\n (submission as TextSubmission).atheneTextAssessmentTrackingToken = response.headers.get('x-athene-tracking-authorization') || undefined;\n }),\n map((response) => response.body!),\n );\n }\n\n /**\n * Gets an example result for defined exercise and submission.\n * @param exerciseId id of the exercise for which the example result should be retrieved of type {number}\n * @param submissionId id of the submission for which the example result should be retrieved of type {number}\n */\n public getExampleResult(exerciseId: number, submissionId: number): Observable {\n return this.http.get(`${this.resourceUrl}/exercise/${exerciseId}/submission/${submissionId}/example-result`);\n }\n\n public deleteExampleFeedback(exampleSubmissionId: number): Observable {\n return this.http.delete(`${this.resourceUrl}/text-submissions/${exampleSubmissionId}/example-assessment/feedback`);\n }\n\n /**\n * Gets an array of text submissions that contains conflicting feedback with the given feedback id.\n *\n * @param submissionId id of the submission feedback belongs to of type {number}\n * @param feedbackId id of the feedback to search for conflicts of type {number}\n */\n public getConflictingTextSubmissions(submissionId: number, feedbackId: number): Observable {\n return this.http.get(`${this.resourceUrl}/submission/${submissionId}/feedback/${feedbackId}/feedback-conflicts`);\n }\n\n /**\n * Set feedback conflict as solved. (Tutor decides it is not a conflict)\n *\n * @param exerciseId id of the exercise feedback conflict belongs to\n * @param feedbackConflictId id of the feedback conflict to be solved\n */\n public solveFeedbackConflict(exerciseId: number, feedbackConflictId: number): Observable {\n return this.http.get(`${this.resourceUrl}/exercise/${exerciseId}/feedbackConflict/${feedbackConflictId}/solve-feedback-conflict`);\n }\n\n private static prepareFeedbacksAndTextblocksForRequest(feedbacks: Feedback[], textBlocks: TextBlock[]): TextAssessmentDTO {\n feedbacks = feedbacks.map((feedback) => {\n feedback = Object.assign({}, feedback);\n delete feedback.result;\n delete feedback.conflictingTextAssessments;\n return feedback;\n });\n textBlocks = textBlocks.map((textBlock) => {\n textBlock = Object.assign({}, textBlock);\n textBlock.submission = undefined;\n return textBlock;\n });\n\n return { feedbacks, textBlocks };\n }\n\n private static convertResponse(res: EntityResponseType): EntityResponseType {\n const result = TextAssessmentService.convertItemFromServer(res.body!);\n\n if (result.completionDate) {\n result.completionDate = moment(result.completionDate);\n }\n if (result.submission && result.submission.submissionDate) {\n result.submission.submissionDate = moment(result.submission.submissionDate);\n }\n if (result.participation && result.participation.initializationDate) {\n result.participation.initializationDate = moment(result.participation.initializationDate);\n }\n\n return res.clone({ body: result });\n }\n\n private static convertItemFromServer(result: Result): Result {\n return Object.assign({}, result);\n }\n\n /**\n * Convert Feedback elements to use single array of FeedbackConflicts in the Feedback class.\n * It is stored with two references on the server side.\n *\n * @param feedbacks list of Feedback elements to convert.\n */\n private static convertFeedbackConflictsFromServer(feedbacks: Feedback[]): void {\n feedbacks.forEach((feedback) => {\n feedback.conflictingTextAssessments = [...(feedback['firstConflicts'] || []), ...(feedback['secondConflicts'] || [])];\n delete feedback['firstConflicts'];\n delete feedback['secondConflicts'];\n feedback.conflictingTextAssessments.forEach((textAssessmentConflict) => {\n textAssessmentConflict.conflictingFeedbackId =\n textAssessmentConflict['firstFeedback'].id === feedback.id ? textAssessmentConflict['secondFeedback'].id : textAssessmentConflict['firstFeedback'].id;\n });\n });\n }\n\n /**\n * Match given text blocks and feedback items by text block references.\n * @param blocks list of text blocks of type {TextBlock[]}\n * @param feedbacks list of feedback made during assessment of type {Feedback[]}\n */\n public static matchBlocksWithFeedbacks(blocks: TextBlock[], feedbacks: Feedback[]): TextBlockRef[] {\n return blocks.map(\n (block: TextBlock) =>\n new TextBlockRef(\n block,\n feedbacks.find(({ reference }) => block.id === reference),\n ),\n );\n }\n\n /**\n * Track the change of the Feedback in Athene.\n *\n * The routing to athene is done using nginx on the production server.\n *\n * @param submission - The submission object that holds the data that is tracked\n * @param origin - The method that calls the the tracking method\n */\n public trackAssessment(submission?: TextSubmission, origin?: string) {\n if (submission?.atheneTextAssessmentTrackingToken) {\n // clone submission and resolve circular json properties\n const submissionForSending = cloneDeep(submission);\n if (submissionForSending.participation) {\n submissionForSending.participation.submissions = [];\n if (submissionForSending.participation.exercise) {\n submissionForSending.participation.exercise.course = undefined;\n submissionForSending.participation.exercise.exerciseGroup = undefined;\n }\n }\n submissionForSending.atheneTextAssessmentTrackingToken = undefined;\n\n // eslint-disable-next-line chai-friendly/no-unused-expressions\n submissionForSending.participation?.results!.forEach((result) => {\n result.participation = undefined;\n result.submission = undefined;\n });\n\n const trackingObject = {\n origin,\n textBlocks: submissionForSending.blocks,\n participation: submissionForSending.participation,\n };\n\n // The request is directly routed to athene via nginx\n this.http\n .post(`${SERVER_API_URL}/athene-tracking/text-exercise-assessment`, trackingObject, {\n headers: { 'X-Athene-Tracking-Authorization': submission.atheneTextAssessmentTrackingToken },\n })\n .subscribe();\n }\n }\n\n /**\n * Connects the participation with the submission and result\n *\n * @param participation\n * @param submission\n * @param result\n */\n private static reconnectResultsParticipation(participation: Participation, submission: Submission, result: Result) {\n setLatestSubmissionResult(submission, getLatestSubmissionResult(submission));\n submission.participation = participation;\n participation.results = submission.results!;\n result.submission = submission;\n result.participation = participation;\n // Make sure Feedbacks Array is initialized\n result.feedbacks = result.feedbacks || [];\n TextAssessmentService.convertFeedbackConflictsFromServer(result.feedbacks);\n }\n}\n"},{"alpha_fraction":0.6572809219360352,"alphanum_fraction":0.6624458432197571,"avg_line_length":41.26760482788086,"blob_id":"2d7b8a620407084734bb68d07681c905aaeb8f00","content_id":"7402ac47e73c8724712176af260f18aacc071306","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":6002,"license_type":"permissive","max_line_length":139,"num_lines":142,"path":"/src/test/javascript/spec/component/text-submission-assessment/textblock-feedback-editor.component.spec.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { ComponentFixture, TestBed } from '@angular/core/testing';\nimport { ArtemisTestModule } from '../../test.module';\nimport { ArtemisSharedModule } from 'app/shared/shared.module';\nimport { TextblockFeedbackEditorComponent } from 'app/exercises/text/assess/textblock-feedback-editor/textblock-feedback-editor.component';\nimport { Feedback } from 'app/entities/feedback.model';\nimport { TextBlock } from 'app/entities/text-block.model';\nimport { ArtemisConfirmIconModule } from 'app/shared/confirm-icon/confirm-icon.module';\nimport { TranslateModule } from '@ngx-translate/core';\nimport { MockComponent } from 'ng-mocks';\nimport { FaIconComponent } from '@fortawesome/angular-fontawesome';\nimport { GradingInstruction } from 'app/exercises/shared/structured-grading-criterion/grading-instruction.model';\nimport { FeedbackConflict } from 'app/entities/feedback-conflict';\n\ndescribe('TextblockFeedbackEditorComponent', () => {\n let component: TextblockFeedbackEditorComponent;\n let fixture: ComponentFixture;\n let compiled: any;\n\n const textBlock = { id: 'f6773c4b3c2d057fd3ac11f02df31c0a3e75f800' } as TextBlock;\n\n beforeEach(async () => {\n TestBed.configureTestingModule({\n imports: [ArtemisTestModule, ArtemisSharedModule, TranslateModule.forRoot(), ArtemisConfirmIconModule],\n declarations: [TextblockFeedbackEditorComponent],\n })\n .overrideModule(ArtemisTestModule, {\n remove: {\n declarations: [MockComponent(FaIconComponent)],\n exports: [MockComponent(FaIconComponent)],\n },\n })\n .compileComponents();\n });\n\n beforeEach(() => {\n fixture = TestBed.createComponent(TextblockFeedbackEditorComponent);\n component = fixture.componentInstance;\n component.textBlock = textBlock;\n component.feedback = Feedback.forText(textBlock);\n component.feedback.gradingInstruction = new GradingInstruction();\n component.feedback.gradingInstruction.usageCount = 0;\n compiled = fixture.debugElement.nativeElement;\n fixture.detectChanges();\n });\n\n it('should create', () => {\n expect(component).toBeTruthy();\n\n const textarea = compiled.querySelector('textarea');\n expect(textarea).toBeTruthy();\n\n const input = compiled.querySelector('input');\n expect(input).toBeTruthy();\n });\n\n it('should show delete button for empty feedback only', () => {\n // fixture.debugElement.query(By.css('fa-icon.back-button'));\n let button = compiled.querySelector('.close fa-icon[icon=\"times\"]');\n let confirm = compiled.querySelector('.close jhi-confirm-icon');\n expect(button).toBeTruthy();\n expect(confirm).toBeFalsy();\n\n component.feedback.credits = 1;\n fixture.detectChanges();\n button = compiled.querySelector('.close fa-icon[icon=\"times\"]');\n confirm = compiled.querySelector('.close jhi-confirm-icon');\n expect(button).toBeFalsy();\n expect(confirm).toBeTruthy();\n\n component.feedback.detailText = 'Lorem Ipsum';\n fixture.detectChanges();\n button = compiled.querySelector('.close fa-icon[icon=\"times\"]');\n confirm = compiled.querySelector('.close jhi-confirm-icon');\n expect(button).toBeFalsy();\n expect(confirm).toBeTruthy();\n\n component.feedback.credits = 0;\n fixture.detectChanges();\n button = compiled.querySelector('.close fa-icon[icon=\"times\"]');\n confirm = compiled.querySelector('.close jhi-confirm-icon');\n expect(button).toBeFalsy();\n expect(confirm).toBeTruthy();\n\n component.feedback.detailText = '';\n fixture.detectChanges();\n\n button = compiled.querySelector('.close fa-icon[icon=\"times\"]');\n confirm = compiled.querySelector('.close jhi-confirm-icon');\n expect(button).toBeTruthy();\n expect(confirm).toBeFalsy();\n });\n\n it('should put the badge and the text correctly for feedback conflicts', () => {\n component.feedback.conflictingTextAssessments = [new FeedbackConflict()];\n fixture.detectChanges();\n const badge = compiled.querySelector('.badge-warning fa-icon[ng-reflect-icon=\"balance-scale-right\"]');\n expect(badge).toBeTruthy();\n const text = compiled.querySelector('[jhiTranslate$=conflictingAssessments]');\n expect(text).toBeTruthy();\n });\n\n it('should focus to the text area if it is left conflicting feedback', () => {\n component.feedback.credits = 0;\n component.feedback.detailText = 'Lorem Ipsum';\n component.conflictMode = true;\n component.isConflictingFeedback = true;\n component.isLeftConflictingFeedback = true;\n fixture.detectChanges();\n\n spyOn(component['textareaElement'], 'focus');\n component.focus();\n\n expect(component['textareaElement'].focus).toHaveBeenCalled();\n });\n\n it('should not focus to the text area if it is right conflicting feedback', () => {\n component.feedback.credits = 0;\n component.feedback.detailText = 'Lorem Ipsum';\n component.conflictMode = true;\n component.isConflictingFeedback = true;\n component.isLeftConflictingFeedback = false;\n fixture.detectChanges();\n\n spyOn(component['textareaElement'], 'focus');\n component.focus();\n\n expect(component['textareaElement'].focus).toHaveBeenCalledTimes(0);\n });\n\n it('should call escKeyup when keyEvent', () => {\n component.feedback.credits = 0;\n component.feedback.detailText = '';\n spyOn(component, 'escKeyup');\n const event = new KeyboardEvent('keydown', {\n key: 'Esc',\n });\n const textarea = fixture.nativeElement.querySelector('textarea');\n textarea.dispatchEvent(event);\n fixture.detectChanges();\n expect(component.escKeyup).toHaveBeenCalled();\n });\n});\n"},{"alpha_fraction":0.7081310749053955,"alphanum_fraction":0.7094963788986206,"avg_line_length":46.08571243286133,"blob_id":"b342fb128539a9b5182c5669c34bfebad3a78e0d","content_id":"fad31bff26c3a649f233f97f179ff3e5de24692e","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":6592,"license_type":"permissive","max_line_length":170,"num_lines":140,"path":"/src/test/javascript/spec/component/exam/participate/exercises/programming-exam-submission.component.spec.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { ComponentFixture, TestBed } from '@angular/core/testing';\nimport { Course } from 'app/entities/course.model';\nimport { ExerciseGroup } from 'app/entities/exercise-group.model';\nimport { ProgrammingExerciseStudentParticipation } from 'app/entities/participation/programming-exercise-student-participation.model';\nimport { ProgrammingExercise } from 'app/entities/programming-exercise.model';\nimport { ProgrammingSubmission } from 'app/entities/programming-submission.model';\nimport { ProgrammingExamSubmissionComponent } from 'app/exam/participate/exercises/programming/programming-exam-submission.component';\nimport { ModelingEditorComponent } from 'app/exercises/modeling/shared/modeling-editor.component';\nimport { ArtemisProgrammingExerciseActionsModule } from 'app/exercises/programming/shared/actions/programming-exercise-actions.module';\nimport { CodeEditorContainerComponent } from 'app/exercises/programming/shared/code-editor/container/code-editor-container.component';\nimport { CommitState } from 'app/exercises/programming/shared/code-editor/model/code-editor.model';\nimport { DomainService } from 'app/exercises/programming/shared/code-editor/service/code-editor-domain.service';\nimport { ArtemisProgrammingExerciseInstructionsRenderModule } from 'app/exercises/programming/shared/instructions-render/programming-exercise-instructions-render.module';\nimport { IncludedInScoreBadgeComponent } from 'app/exercises/shared/exercise-headers/included-in-score-badge.component';\nimport { ArtemisResultModule } from 'app/exercises/shared/result/result.module';\nimport { ArtemisExerciseButtonsModule } from 'app/overview/exercise-details/exercise-buttons.module';\nimport { OrionModule } from 'app/shared/orion/orion.module';\nimport { ArtemisTranslatePipe } from 'app/shared/pipes/artemis-translate.pipe.ts';\nimport * as chai from 'chai';\nimport * as moment from 'moment';\nimport { MockComponent, MockModule, MockPipe, MockProvider } from 'ng-mocks';\nimport * as sinon from 'sinon';\nimport * as sinonChai from 'sinon-chai';\n\nchai.use(sinonChai);\nconst expect = chai.expect;\n\ndescribe('ProgrammingExamSubmissionComponent', () => {\n let fixture: ComponentFixture;\n let component: ProgrammingExamSubmissionComponent;\n\n let studentParticipation: ProgrammingExerciseStudentParticipation;\n let exercise: ProgrammingExercise;\n let programmingSubmission: ProgrammingSubmission;\n\n let domainService: any;\n\n beforeEach(() => {\n programmingSubmission = { commitHash: 'Hash commit', buildFailed: false, buildArtifact: false };\n studentParticipation = { submissions: [programmingSubmission] };\n exercise = new ProgrammingExercise(new Course(), new ExerciseGroup());\n\n return TestBed.configureTestingModule({\n imports: [\n MockModule(ArtemisExerciseButtonsModule),\n MockModule(ArtemisProgrammingExerciseActionsModule),\n MockModule(OrionModule),\n MockModule(ArtemisResultModule),\n MockModule(ArtemisProgrammingExerciseInstructionsRenderModule),\n ],\n declarations: [\n ProgrammingExamSubmissionComponent,\n MockComponent(ModelingEditorComponent),\n MockComponent(CodeEditorContainerComponent),\n MockPipe(ArtemisTranslatePipe),\n MockComponent(IncludedInScoreBadgeComponent),\n ],\n providers: [MockProvider(DomainService)],\n })\n .compileComponents()\n .then(() => {\n fixture = TestBed.createComponent(ProgrammingExamSubmissionComponent);\n component = fixture.componentInstance;\n domainService = TestBed.inject(DomainService);\n });\n });\n afterEach(() => {\n sinon.restore();\n });\n\n it('should initialize with unlocked repository', () => {\n const domainServiceSpy = sinon.spy(domainService, 'setDomain');\n component.exercise = exercise;\n\n fixture.detectChanges();\n\n expect(fixture).to.be.ok;\n expect(domainServiceSpy.calledOnce);\n expect(component.repositoryIsLocked).to.equal(false);\n expect(component.getExercise()).to.equal(exercise);\n });\n\n it('should set the repositoryIsLocked value to true', () => {\n const programmingExercise = new ProgrammingExercise(new Course(), new ExerciseGroup());\n programmingExercise.dueDate = moment().subtract(10, 'seconds');\n programmingExercise.buildAndTestStudentSubmissionsAfterDueDate = moment().subtract(60, 'seconds');\n\n component.exercise = programmingExercise;\n fixture.detectChanges();\n\n expect(component.repositoryIsLocked).to.equal(true);\n });\n\n it('should change state on commit', () => {\n component.studentParticipation = studentParticipation;\n\n const undefinedCommitState = CommitState.UNDEFINED;\n component.onCommitStateChange(undefinedCommitState);\n expect(component.hasSubmittedOnce).to.equal(false);\n\n const cleanCommitState = CommitState.CLEAN;\n\n component.onCommitStateChange(cleanCommitState);\n expect(component.hasSubmittedOnce).to.equal(true);\n\n /**\n * After the first call with CommitState.CLEAN, component.hasSubmittedOnce must be now true\n */\n component.onCommitStateChange(cleanCommitState);\n if (component.studentParticipation.submissions) {\n expect(component.studentParticipation.submissions[0].submitted).to.equal(true);\n expect(component.studentParticipation.submissions[0].isSynced).to.equal(true);\n }\n });\n\n it('should desync on file change', () => {\n component.studentParticipation = studentParticipation;\n if (component.studentParticipation.submissions) {\n component.studentParticipation.submissions[0].isSynced = true;\n component.onFileChanged();\n\n expect(component.studentParticipation.submissions[0].isSynced).to.equal(false);\n }\n });\n\n it('should get submission', () => {\n component.studentParticipation = studentParticipation;\n if (studentParticipation.submissions) {\n expect(component.getSubmission()).to.equal(studentParticipation.submissions[0]);\n }\n });\n\n it('should return false if no unsaved changes', () => {\n exercise.allowOfflineIde = true;\n exercise.allowOnlineEditor = false;\n component.exercise = exercise;\n\n expect(component.hasUnsavedChanges()).to.equal(false);\n });\n});\n"},{"alpha_fraction":0.7992077469825745,"alphanum_fraction":0.8021787405014038,"avg_line_length":38.59803771972656,"blob_id":"7785fc768e1835bf91a2fd5e11237ad8ff2f72c7","content_id":"cf87492e6d6a9a764f295e72eeb0cfcc1002bc94","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":4039,"license_type":"permissive","max_line_length":166,"num_lines":102,"path":"/src/test/java/de/tum/in/www1/artemis/util/AbstractArtemisIntegrationTest.java","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"package de.tum.in.www1.artemis.util;\n\nimport static org.mockito.ArgumentMatchers.any;\nimport static org.mockito.Mockito.doReturn;\n\nimport org.mockito.Mockito;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.boot.test.mock.mockito.SpyBean;\nimport org.springframework.messaging.simp.SimpMessageSendingOperations;\n\nimport de.tum.in.www1.artemis.domain.VcsRepositoryUrl;\nimport de.tum.in.www1.artemis.programmingexercise.MockDelegate;\nimport de.tum.in.www1.artemis.service.*;\nimport de.tum.in.www1.artemis.service.connectors.GitService;\nimport de.tum.in.www1.artemis.service.connectors.LtiService;\nimport de.tum.in.www1.artemis.service.exam.ExamAccessService;\nimport de.tum.in.www1.artemis.service.messaging.InstanceMessageSendService;\nimport de.tum.in.www1.artemis.service.programming.ProgrammingExerciseGradingService;\nimport de.tum.in.www1.artemis.service.programming.ProgrammingExerciseParticipationService;\nimport de.tum.in.www1.artemis.service.programming.ProgrammingSubmissionService;\nimport de.tum.in.www1.artemis.service.scheduled.ProgrammingExerciseScheduleService;\n\n/**\n * this test should be completely independent of any profiles or configurations (e.g. VCS, CIS)\n */\npublic abstract class AbstractArtemisIntegrationTest implements MockDelegate {\n\n @Value(\"${server.url}\")\n protected String artemisServerUrl;\n\n // NOTE: we prefer SpyBean over MockBean, because it is more lightweight, we can mock method, but we can also invoke actual methods during testing\n @SpyBean\n protected LtiService ltiService;\n\n @SpyBean\n protected GitService gitService;\n\n @SpyBean\n protected GroupNotificationService groupNotificationService;\n\n @SpyBean\n protected WebsocketMessagingService websocketMessagingService;\n\n @SpyBean\n protected PlantUmlService plantUmlService;\n\n @SpyBean\n protected SimpMessageSendingOperations messagingTemplate;\n\n @SpyBean\n protected ProgrammingSubmissionService programmingSubmissionService;\n\n @SpyBean\n protected ProgrammingExerciseGradingService programmingExerciseGradingService;\n\n @SpyBean\n protected ExamAccessService examAccessService;\n\n @SpyBean\n protected InstanceMessageSendService instanceMessageSendService;\n\n @SpyBean\n protected ProgrammingExerciseScheduleService programmingExerciseScheduleService;\n\n @SpyBean\n protected ProgrammingExerciseParticipationService programmingExerciseParticipationService;\n\n @SpyBean\n protected ScoreService scoreService;\n\n @SpyBean\n protected UrlService urlService;\n\n @Autowired\n protected DatabaseUtilService database;\n\n @Autowired\n protected RequestUtilService request;\n\n public void resetSpyBeans() {\n Mockito.reset(ltiService, gitService, groupNotificationService, websocketMessagingService, plantUmlService, messagingTemplate, programmingSubmissionService,\n examAccessService, instanceMessageSendService, programmingExerciseScheduleService, programmingExerciseParticipationService, urlService, scoreService);\n }\n\n @Override\n public void mockGetRepositorySlugFromRepositoryUrl(String repositorySlug, VcsRepositoryUrl repositoryUrl) {\n // we convert this to URL to make sure the mock is properly hit, as there could be problems with objects such as VcsRepositoryUrl and its subclasses\n doReturn(repositorySlug).when(urlService).getRepositorySlugFromUrl(repositoryUrl.getURL());\n }\n\n @Override\n public void mockGetProjectKeyFromRepositoryUrl(String projectKey, VcsRepositoryUrl repositoryUrl) {\n // we convert this to URL to make sure the mock is properly hit, as there could be problems with objects such as VcsRepositoryUrl and its subclasses\n doReturn(projectKey).when(urlService).getProjectKeyFromUrl(repositoryUrl.getURL());\n }\n\n @Override\n public void mockGetProjectKeyFromAnyUrl(String projectKey) {\n doReturn(projectKey).when(urlService).getProjectKeyFromUrl(any());\n }\n}\n"},{"alpha_fraction":0.8153846263885498,"alphanum_fraction":0.8153846263885498,"avg_line_length":58.58333206176758,"blob_id":"9831448c7a102fed931a9ed73a8b1b149c9523a9","content_id":"56f6c14ad8b4c8f62f68a08cc930dc96702f66ad","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":715,"license_type":"permissive","max_line_length":125,"num_lines":12,"path":"/src/main/webapp/app/course/course-questions/course-questions.module.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { NgModule } from '@angular/core';\nimport { ArtemisSharedModule } from 'app/shared/shared.module';\nimport { CourseQuestionsComponent } from 'app/course/course-questions/course-questions.component';\nimport { ArtemisCourseQuestionsRoutingModule } from 'app/course/course-questions/course-questions-routing.module';\nimport { ArtemisSharedComponentModule } from 'app/shared/components/shared-component.module';\nimport { ArtemisMarkdownModule } from 'app/shared/markdown.module';\n\n@NgModule({\n imports: [ArtemisSharedModule, ArtemisCourseQuestionsRoutingModule, ArtemisSharedComponentModule, ArtemisMarkdownModule],\n declarations: [CourseQuestionsComponent],\n})\nexport class ArtemisCourseQuestionsModule {}\n"},{"alpha_fraction":0.7469496130943298,"alphanum_fraction":0.7485411167144775,"avg_line_length":31.5,"blob_id":"b4d32a923423cadb64e6d4357a8d967d2c57c33a","content_id":"83465b684cd3564ca1942189101e9b507e826bc6","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1885,"license_type":"permissive","max_line_length":108,"num_lines":58,"path":"/src/main/java/de/tum/in/www1/artemis/domain/quiz/QuizQuestionStatistic.java","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"package de.tum.in.www1.artemis.domain.quiz;\n\nimport javax.persistence.*;\n\nimport org.hibernate.annotations.DiscriminatorOptions;\n\nimport com.fasterxml.jackson.annotation.*;\n\n@Entity\n@DiscriminatorValue(value = \"Q\")\n@DiscriminatorOptions(force = true)\n@JsonTypeInfo(use = JsonTypeInfo.Id.NAME, property = \"type\")\n@JsonSubTypes({ @JsonSubTypes.Type(value = MultipleChoiceQuestionStatistic.class, name = \"multiple-choice\"),\n @JsonSubTypes.Type(value = DragAndDropQuestionStatistic.class, name = \"drag-and-drop\"),\n @JsonSubTypes.Type(value = ShortAnswerQuestionStatistic.class, name = \"short-answer\") })\n@JsonInclude(JsonInclude.Include.NON_EMPTY)\npublic abstract class QuizQuestionStatistic extends QuizStatistic {\n\n @Column(name = \"rated_correct_counter\")\n private Integer ratedCorrectCounter = 0;\n\n @Column(name = \"un_rated_correct_counter\")\n private Integer unRatedCorrectCounter = 0;\n\n @OneToOne(mappedBy = \"quizQuestionStatistic\")\n @JsonIgnore\n private QuizQuestion quizQuestion;\n\n public Integer getRatedCorrectCounter() {\n return ratedCorrectCounter;\n }\n\n public void setRatedCorrectCounter(Integer ratedCorrectCounter) {\n this.ratedCorrectCounter = ratedCorrectCounter;\n }\n\n public Integer getUnRatedCorrectCounter() {\n return unRatedCorrectCounter;\n }\n\n public void setUnRatedCorrectCounter(Integer unRatedCorrectCounter) {\n this.unRatedCorrectCounter = unRatedCorrectCounter;\n }\n\n public QuizQuestion getQuizQuestion() {\n return quizQuestion;\n }\n\n public void setQuizQuestion(QuizQuestion quizQuestion) {\n this.quizQuestion = quizQuestion;\n }\n\n public abstract void addResult(SubmittedAnswer submittedAnswer, boolean rated);\n\n public abstract void removeOldResult(SubmittedAnswer submittedAnswer, boolean rated);\n\n public abstract void resetStatistic();\n}\n"},{"alpha_fraction":0.617827296257019,"alphanum_fraction":0.6217269897460938,"avg_line_length":34.19607925415039,"blob_id":"6d104e1e029b72ac3eb536bc8ff10ffddfc755ba","content_id":"75cc306941afafd637ae65751a443c753a42fb05","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":1795,"license_type":"permissive","max_line_length":106,"num_lines":51,"path":"/src/test/javascript/spec/component/exercise-hint/exercise-hint.component.spec.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { ComponentFixture, TestBed } from '@angular/core/testing';\nimport { of } from 'rxjs';\nimport { HttpHeaders, HttpResponse } from '@angular/common/http';\n\nimport { ExerciseHintComponent } from 'app/exercises/shared/exercise-hint/manage/exercise-hint.component';\nimport { ExerciseHintService } from 'app/exercises/shared/exercise-hint/manage/exercise-hint.service';\nimport { ExerciseHint } from 'app/entities/exercise-hint.model';\nimport { ArtemisTestModule } from '../../test.module';\n\ndescribe('ExerciseHint Management Component', () => {\n let comp: ExerciseHintComponent;\n let fixture: ComponentFixture;\n let service: ExerciseHintService;\n\n beforeEach(() => {\n TestBed.configureTestingModule({\n imports: [ArtemisTestModule],\n declarations: [ExerciseHintComponent],\n providers: [],\n })\n .overrideTemplate(ExerciseHintComponent, '')\n .compileComponents();\n\n fixture = TestBed.createComponent(ExerciseHintComponent);\n comp = fixture.componentInstance;\n service = fixture.debugElement.injector.get(ExerciseHintService);\n });\n\n it('Should call load all on init', () => {\n // GIVEN\n const headers = new HttpHeaders().append('link', 'link;link');\n const hint = new ExerciseHint();\n hint.id = 123;\n\n spyOn(service, 'findByExerciseId').and.returnValue(\n of(\n new HttpResponse({\n body: [hint],\n headers,\n }),\n ),\n );\n\n // WHEN\n comp.ngOnInit();\n\n // THEN\n expect(service.findByExerciseId).toHaveBeenCalled();\n expect(comp.exerciseHints[0]).toEqual(expect.objectContaining({ id: 123 }));\n });\n});\n"},{"alpha_fraction":0.6829794049263,"alphanum_fraction":0.6851338744163513,"avg_line_length":29.08333396911621,"blob_id":"591794f677c9320e1aab86a8216ad910687aeb1c","content_id":"4ad1704688cf15879f2eb64f79b1f91e4057cd06","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":3249,"license_type":"permissive","max_line_length":105,"num_lines":108,"path":"/src/main/java/de/tum/in/www1/artemis/service/user/PasswordService.java","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"package de.tum.in.www1.artemis.service.user;\n\nimport java.util.Optional;\n\nimport org.jasypt.encryption.pbe.StandardPBEStringEncryptor;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.stereotype.Service;\n\nimport de.tum.in.www1.artemis.domain.User;\nimport de.tum.in.www1.artemis.repository.UserRepository;\nimport de.tum.in.www1.artemis.security.PBEPasswordEncoder;\nimport de.tum.in.www1.artemis.security.SecurityUtils;\n\n@Service\npublic class PasswordService {\n\n @Value(\"${artemis.encryption-password}\")\n private String encryptionPassword;\n\n private final UserRepository userRepository;\n\n private PBEPasswordEncoder passwordEncoder;\n\n private StandardPBEStringEncryptor encryptor;\n\n public PasswordService(UserRepository userRepository) {\n this.userRepository = userRepository;\n }\n\n /**\n * Get the encoder for password encryption\n *\n * @return existing password encoder or newly created password encryptor\n */\n private PBEPasswordEncoder passwordEncoder() {\n if (passwordEncoder != null) {\n return passwordEncoder;\n }\n passwordEncoder = new PBEPasswordEncoder(encryptor());\n return passwordEncoder;\n }\n\n /**\n * Get the the password encryptor with MD5 and DES encryption algorithm\n *\n * @return existing encryptor or newly created encryptor\n */\n private StandardPBEStringEncryptor encryptor() {\n if (encryptor != null) {\n return encryptor;\n }\n encryptor = new StandardPBEStringEncryptor();\n encryptor.setAlgorithm(\"PBEWithMD5AndDES\");\n encryptor.setPassword(encryptionPassword);\n return encryptor;\n }\n\n /**\n * Get decrypted password for the current user\n *\n * @return decrypted password or empty string\n */\n public String decryptPasswordOfCurrentUser() {\n User user = userRepository.findOneByLogin(SecurityUtils.getCurrentUserLogin().get()).get();\n try {\n return encryptor().decrypt(user.getPassword());\n }\n catch (Exception e) {\n return \"\";\n }\n }\n\n /**\n * Get decrypted password for given user\n *\n * @param user the user\n * @return decrypted password or empty string\n */\n public String decryptPassword(User user) {\n return encryptor().decrypt(user.getPassword());\n }\n\n public String decryptPassword(String encodedPassword) {\n return encryptor().decrypt(encodedPassword);\n }\n\n public String encryptPassword(String rawPassword) {\n return encryptor().encrypt(rawPassword);\n }\n\n public String encodePassword(CharSequence rawPassword) {\n return passwordEncoder().encode(rawPassword);\n }\n\n public boolean checkPasswordMatch(CharSequence rawPassword, String encodedPassword) {\n return passwordEncoder().matches(rawPassword, encodedPassword);\n }\n\n /**\n * Get decrypted password for given user login\n *\n * @param login of a user\n * @return decrypted password or empty string\n */\n public Optional decryptPasswordByLogin(String login) {\n return userRepository.findOneByLogin(login).map(user -> encryptor().decrypt(user.getPassword()));\n }\n}\n"},{"alpha_fraction":0.6555360555648804,"alphanum_fraction":0.6590509414672852,"avg_line_length":30.61111068725586,"blob_id":"4b16394b950ded65b12f9e227bebd7cad7aa73d8","content_id":"cf682bb1531a6aef7111aabc3133976f0ac1343f","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":569,"license_type":"permissive","max_line_length":92,"num_lines":18,"path":"/src/test/javascript/spec/helpers/mocks/service/mock-exercise.service.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { of, Observable } from 'rxjs';\nimport { HttpResponse } from '@angular/common/http';\nimport { Exercise } from 'app/entities/exercise.model';\n\nexport class MockExerciseService {\n find(exerciseId: number) {\n return MockExerciseService.response({ id: exerciseId } as Exercise);\n }\n\n getUpcomingExercises() {\n return MockExerciseService.response([{ id: 1 } as Exercise, { id: 2 } as Exercise]);\n }\n\n // helper method\n private static response(entity: T) {\n return of({ body: entity }) as Observable>;\n }\n}\n"},{"alpha_fraction":0.6834981441497803,"alphanum_fraction":0.6870291829109192,"avg_line_length":44.43315505981445,"blob_id":"c28dc831410ba7301da4330718acb4fdf583edd1","content_id":"8b5da11e2d3936ea182afd7f86d7c3b803159513","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":8496,"license_type":"permissive","max_line_length":169,"num_lines":187,"path":"/src/test/javascript/spec/component/short-answer-question/short-answer-question.component.spec.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import * as chai from 'chai';\nimport * as sinonChai from 'sinon-chai';\nimport * as sinon from 'sinon';\nimport { ComponentFixture, TestBed } from '@angular/core/testing';\nimport { ArtemisTestModule } from '../../test.module';\nimport { MockComponent, MockDirective, MockPipe } from 'ng-mocks';\nimport { ArtemisTranslatePipe } from 'app/shared/pipes/artemis-translate.pipe.ts';\nimport { FormsModule } from '@angular/forms';\nimport { NgbPopoverModule, NgbTooltip } from '@ng-bootstrap/ng-bootstrap';\nimport { ShortAnswerQuestionComponent } from 'app/exercises/quiz/shared/questions/short-answer-question/short-answer-question.component';\nimport { QuizScoringInfoStudentModalComponent } from 'app/exercises/quiz/shared/questions/quiz-scoring-infostudent-modal/quiz-scoring-info-student-modal.component';\nimport { ShortAnswerQuestion } from 'app/entities/quiz/short-answer-question.model';\nimport { ArtemisMarkdownEditorModule } from 'app/shared/markdown-editor/markdown-editor.module';\nimport { stub } from 'sinon';\nimport { ShortAnswerSpot } from 'app/entities/quiz/short-answer-spot.model';\nimport { ShortAnswerMapping } from 'app/entities/quiz/short-answer-mapping.model';\nimport { ShortAnswerSolution } from 'app/entities/quiz/short-answer-solution.model';\nimport { ShortAnswerSubmittedText } from 'app/entities/quiz/short-answer-submitted-text.model';\n\nchai.use(sinonChai);\nconst expect = chai.expect;\n\nconst question = new ShortAnswerQuestion();\nquestion.id = 1;\n\ndescribe('ShortAnswerQuestionComponent', () => {\n let fixture: ComponentFixture;\n let component: ShortAnswerQuestionComponent;\n\n beforeEach(() => {\n TestBed.configureTestingModule({\n imports: [ArtemisTestModule, FormsModule, NgbPopoverModule, ArtemisMarkdownEditorModule],\n declarations: [ShortAnswerQuestionComponent, MockPipe(ArtemisTranslatePipe), MockComponent(QuizScoringInfoStudentModalComponent), MockDirective(NgbTooltip)],\n providers: [],\n }).compileComponents();\n fixture = TestBed.createComponent(ShortAnswerQuestionComponent);\n component = fixture.componentInstance;\n });\n\n beforeEach(() => {\n component.submittedTexts = [];\n component.clickDisabled = false;\n component.showResult = true;\n component.questionIndex = 0;\n component.score = 0;\n component.shortAnswerQuestion = question;\n });\n\n afterEach(function () {\n sinon.restore();\n });\n\n it('should initialize', () => {\n const alternativeQuestion = new ShortAnswerQuestion();\n alternativeQuestion.id = 10;\n const text = 'Please explain this question about stuff';\n alternativeQuestion.text = text;\n const hint = 'new Hint!';\n alternativeQuestion.hint = hint;\n const explanation = 'This is a very good explanation!';\n alternativeQuestion.explanation = explanation;\n\n component.question = alternativeQuestion;\n\n expect(component.textParts).to.deep.equal([[` associatedParticipantScoreOptional;\n if (resultToBeDeleted.isRated() != null && resultToBeDeleted.isRated()) {\n associatedParticipantScoreOptional = participantScoreRepository.findParticipantScoreByLastRatedResult(resultToBeDeleted);\n }\n else {\n associatedParticipantScoreOptional = participantScoreRepository.findParticipantScoresByLastResult(resultToBeDeleted);\n }\n\n if (associatedParticipantScoreOptional.isEmpty()) {\n return;\n }\n\n // There is a participant score connected to the result that will be deleted\n ParticipantScore associatedParticipantScore = associatedParticipantScoreOptional.get();\n Exercise exercise = associatedParticipantScore.getExercise();\n String originalParticipantScoreStructure = associatedParticipantScore.toString();\n\n // There are two possibilities now:\n // A: Another result exists for the exercise and the student / team -> update participant score with the newest one\n // B: No other result exists for the exercise and the student / team -> remove participant score\n tryToFindNewLastResult(resultToBeDeleted, associatedParticipantScore, exercise);\n\n if (associatedParticipantScore.getLastResult() == null && associatedParticipantScore.getLastRatedResult() == null) {\n participantScoreRepository.deleteById(associatedParticipantScore.getId());\n logger.info(\"Deleted an existing participant score: \" + originalParticipantScoreStructure);\n }\n else {\n ParticipantScore updatedParticipantScore = participantScoreRepository.saveAndFlush(associatedParticipantScore);\n logger.info(\"Updated an existing participant score. Was: \" + originalParticipantScoreStructure + \". Is: \" + updatedParticipantScore.toString());\n }\n }\n\n private void tryToFindNewLastResult(Result resultToBeDeleted, ParticipantScore associatedParticipantScore, Exercise exercise) {\n if (resultToBeDeleted.equals(associatedParticipantScore.getLastRatedResult())) {\n Optional newLastRatedResultOptional = getNewLastRatedResultForParticipantScore(associatedParticipantScore);\n if (newLastRatedResultOptional.isPresent()) {\n Result newLastRatedResult = newLastRatedResultOptional.get();\n setLastRatedAttributes(associatedParticipantScore, newLastRatedResult, exercise);\n }\n else {\n setLastRatedAttributes(associatedParticipantScore, null, exercise);\n }\n }\n\n if (resultToBeDeleted.equals(associatedParticipantScore.getLastResult())) {\n Optional newLastResultOptional = getNewLastResultForParticipantScore(associatedParticipantScore);\n if (newLastResultOptional.isPresent()) {\n Result newLastResult = newLastResultOptional.get();\n setLastAttributes(associatedParticipantScore, newLastResult, exercise);\n }\n else {\n setLastAttributes(associatedParticipantScore, null, exercise);\n }\n }\n }\n\n /**\n * Either updates an existing participant score or creates a new participant score if a new result comes in\n * The annotation \"@Transactional\" is ok because it means that this method does not support run in an outer transactional context, instead the outer transaction is paused\n *\n * @param createdOrUpdatedResult newly created or updated result\n */\n @Transactional(propagation = Propagation.NOT_SUPPORTED) // ok (see JavaDoc)\n public void updateOrCreateParticipantScore(Result createdOrUpdatedResult) {\n if (createdOrUpdatedResult.getScore() == null || createdOrUpdatedResult.getCompletionDate() == null) {\n return;\n }\n // There is a deadlock problem with programming exercises here if we use the participation from the result (reason unknown at the moment)\n // therefore we get the participation from the database\n Optional studentParticipationOptional = getStudentParticipationForResult(createdOrUpdatedResult);\n if (studentParticipationOptional.isEmpty()) {\n return;\n }\n StudentParticipation studentParticipation = studentParticipationOptional.get();\n // we ignore test runs of exams\n if (studentParticipation.isTestRun()) {\n return;\n }\n Exercise exercise = studentParticipation.getExercise();\n ParticipantScore existingParticipationScoreForExerciseAndParticipant = getExistingParticipationScore(studentParticipation, exercise);\n // there already exists a participant score -> we need to update it\n if (existingParticipationScoreForExerciseAndParticipant != null) {\n updateExistingParticipantScore(existingParticipationScoreForExerciseAndParticipant, createdOrUpdatedResult, exercise);\n }\n else { // there does not already exists a participant score -> we need to create it\n createNewParticipantScore(createdOrUpdatedResult, studentParticipation, exercise);\n }\n }\n\n /**\n * Gets the student participation for a result from the database\n *\n * @param result result for which to get the student participation\n * @return student participation optional\n */\n private Optional getStudentParticipationForResult(Result result) {\n Optional participationOptional = participationRepository.findByResults(result);\n if (participationOptional.isEmpty()) {\n return Optional.empty();\n }\n Participation participation = participationOptional.get();\n\n if (!(participation instanceof StudentParticipation)) {\n return Optional.empty();\n }\n return Optional.of((StudentParticipation) participation);\n }\n\n /**\n * Gets the existing participation score for an exercise and a participant or null if none can be found\n *\n * @param studentParticipation participation containing the information about the participant\n * @param exercise exercise for which to find the participation score of the participant\n * @return existing participation score or null if none can be found\n */\n private ParticipantScore getExistingParticipationScore(StudentParticipation studentParticipation, Exercise exercise) {\n ParticipantScore existingParticipationScoreForExerciseAndParticipant = null;\n if (exercise.isTeamMode()) {\n Team team = studentParticipation.getTeam().get();\n Optional teamScoreOptional = teamScoreRepository.findTeamScoreByExerciseAndTeam(exercise, team);\n if (teamScoreOptional.isPresent()) {\n existingParticipationScoreForExerciseAndParticipant = teamScoreOptional.get();\n }\n }\n else {\n User user = studentParticipation.getStudent().get();\n Optional studentScoreOptional = studentScoreRepository.findStudentScoreByExerciseAndUser(exercise, user);\n if (studentScoreOptional.isPresent()) {\n existingParticipationScoreForExerciseAndParticipant = studentScoreOptional.get();\n }\n }\n return existingParticipationScoreForExerciseAndParticipant;\n }\n\n /**\n * Create a new Participant Score\n *\n * @param newResult result containing the information about the score achieved\n * @param studentParticipation participation containing the information about the participant\n * @param exercise exercise for which to create participant score\n */\n private void createNewParticipantScore(Result newResult, StudentParticipation studentParticipation, Exercise exercise) {\n if (exercise.isTeamMode()) {\n createNewTeamScore(newResult, studentParticipation, exercise);\n }\n else {\n createNewStudentScore(newResult, studentParticipation, exercise);\n }\n }\n\n private void createNewStudentScore(Result newResult, StudentParticipation studentParticipation, Exercise exercise) {\n StudentScore newStudentScore = new StudentScore();\n newStudentScore.setExercise(exercise);\n newStudentScore.setUser(studentParticipation.getStudent().get());\n setLastAttributes(newStudentScore, newResult, exercise);\n if (newResult.isRated() != null && newResult.isRated()) {\n setLastRatedAttributes(newStudentScore, newResult, exercise);\n }\n StudentScore studentScore = studentScoreRepository.saveAndFlush(newStudentScore);\n logger.info(\"Saved a new student score: \" + studentScore.toString());\n }\n\n private void createNewTeamScore(Result newResult, StudentParticipation studentParticipation, Exercise exercise) {\n TeamScore newTeamScore = new TeamScore();\n newTeamScore.setExercise(exercise);\n newTeamScore.setTeam(studentParticipation.getTeam().get());\n setLastAttributes(newTeamScore, newResult, exercise);\n if (newResult.isRated() != null && newResult.isRated()) {\n setLastRatedAttributes(newTeamScore, newResult, exercise);\n }\n TeamScore teamScore = teamScoreRepository.saveAndFlush(newTeamScore);\n logger.info(\"Saved a new team score: \" + teamScore.toString());\n }\n\n /**\n * Update an existing participant score when a new or updated result comes in\n *\n * @param participantScore existing participant score that refers to the same exercise and participant as the result\n * @param exercise the exercise to which the participant score belong\n * @param updatedOrNewlyCreatedResult updated or new result\n */\n private void updateExistingParticipantScore(ParticipantScore participantScore, Result updatedOrNewlyCreatedResult, Exercise exercise) {\n String originalParticipantScoreStructure = participantScore.toString();\n\n ParticipantScore participantScoreToSave = participantScore;\n // update the last result and last score if either it has not been set previously or new result is either the old one (=) or newer (>)\n if (participantScoreToSave.getLastResult() == null || updatedOrNewlyCreatedResult.getId() >= participantScoreToSave.getLastResult().getId()) {\n setLastAttributes(participantScoreToSave, updatedOrNewlyCreatedResult, exercise);\n }\n // update the last rated result and last rated score if either it has not been set previously or new rated result is either the old one (=) or newer (>)\n if (updatedOrNewlyCreatedResult.isRated() != null && updatedOrNewlyCreatedResult.isRated()\n && (participantScoreToSave.getLastRatedResult() == null || updatedOrNewlyCreatedResult.getId() >= participantScoreToSave.getLastRatedResult().getId())) {\n setLastRatedAttributes(participantScoreToSave, updatedOrNewlyCreatedResult, exercise);\n }\n // Edge Case: if the result is now unrated but is equal to the current last rated result we have to set these to null (result was switched from rated to unrated)\n if ((updatedOrNewlyCreatedResult.isRated() == null || !updatedOrNewlyCreatedResult.isRated())\n && updatedOrNewlyCreatedResult.equals(participantScoreToSave.getLastRatedResult())) {\n setLastRatedAttributes(participantScoreToSave, null, exercise);\n }\n participantScoreRepository.saveAndFlush(participantScoreToSave);\n logger.info(\"Updated an existing participant score. Was: \" + originalParticipantScoreStructure + \". Is: \" + participantScoreToSave.toString());\n }\n\n /**\n * Get the result that can replace the currently set last result for a participant score\n *\n * @param participantScore participant score\n * @return optional of new result\n */\n private Optional getNewLastResultForParticipantScore(ParticipantScore participantScore) {\n List resultOrdered;\n if (participantScore.getClass().equals(StudentScore.class)) {\n StudentScore studentScore = (StudentScore) participantScore;\n resultOrdered = resultRepository\n .getResultsOrderedByParticipationIdLegalSubmissionIdResultIdDescForStudent(participantScore.getExercise().getId(), studentScore.getUser().getId()).stream()\n .filter(r -> !participantScore.getLastResult().equals(r)).collect(Collectors.toList());\n }\n else {\n TeamScore teamScore = (TeamScore) participantScore;\n resultOrdered = resultRepository\n .getResultsOrderedByParticipationIdLegalSubmissionIdResultIdDescForTeam(participantScore.getExercise().getId(), teamScore.getTeam().getId()).stream()\n .filter(r -> !participantScore.getLastResult().equals(r)).collect(Collectors.toList());\n }\n // the new last result (result with highest id of submission with highest id) will be at the beginning of the list\n return resultOrdered.isEmpty() ? Optional.empty() : Optional.of(resultOrdered.get(0));\n\n }\n\n /**\n * Get the result that can replace the currently set last rated result for a participant score\n *\n * @param participantScore participant score\n * @return optional of new result\n */\n private Optional getNewLastRatedResultForParticipantScore(ParticipantScore participantScore) {\n List ratedResultsOrdered;\n if (participantScore.getClass().equals(StudentScore.class)) {\n StudentScore studentScore = (StudentScore) participantScore;\n ratedResultsOrdered = resultRepository\n .getRatedResultsOrderedByParticipationIdLegalSubmissionIdResultIdDescForStudent(participantScore.getExercise().getId(), studentScore.getUser().getId()).stream()\n .filter(r -> !participantScore.getLastRatedResult().equals(r)).collect(Collectors.toList());\n }\n else {\n TeamScore teamScore = (TeamScore) participantScore;\n ratedResultsOrdered = resultRepository\n .getRatedResultsOrderedByParticipationIdLegalSubmissionIdResultIdDescForTeam(participantScore.getExercise().getId(), teamScore.getTeam().getId()).stream()\n .filter(r -> !participantScore.getLastRatedResult().equals(r)).collect(Collectors.toList());\n }\n // the new last rated result (rated result with highest id of submission with highest id) will be at the beginning of the list\n return ratedResultsOrdered.isEmpty() ? Optional.empty() : Optional.of(ratedResultsOrdered.get(0));\n\n }\n\n private void setLastAttributes(ParticipantScore associatedParticipantScore, Result newLastResult, Exercise exercise) {\n associatedParticipantScore.setLastResult(newLastResult);\n if (newLastResult == null) {\n associatedParticipantScore.setLastScore(null);\n associatedParticipantScore.setLastPoints(null);\n }\n else {\n associatedParticipantScore.setLastScore(newLastResult.getScore());\n associatedParticipantScore.setLastPoints(round(newLastResult.getScore() * 0.01 * exercise.getMaxPoints()));\n\n }\n }\n\n private void setLastRatedAttributes(ParticipantScore associatedParticipantScore, Result newLastRatedResult, Exercise exercise) {\n associatedParticipantScore.setLastRatedResult(newLastRatedResult);\n if (newLastRatedResult == null) {\n associatedParticipantScore.setLastRatedScore(null);\n associatedParticipantScore.setLastRatedPoints(null);\n }\n else {\n associatedParticipantScore.setLastRatedScore(newLastRatedResult.getScore());\n associatedParticipantScore.setLastRatedPoints(round(newLastRatedResult.getScore() * 0.01 * exercise.getMaxPoints()));\n }\n }\n\n}\n"},{"alpha_fraction":0.8333333134651184,"alphanum_fraction":0.8376068472862244,"avg_line_length":30.200000762939453,"blob_id":"12d91da19cdc47b12023217b47cb66797cdcd89e","content_id":"594b21ada96b3456c0da424ab99865d52ef3a385","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":468,"license_type":"permissive","max_line_length":115,"num_lines":15,"path":"/src/main/java/de/tum/in/www1/artemis/repository/ShortAnswerQuestionStatisticRepository.java","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"package de.tum.in.www1.artemis.repository;\n\nimport org.springframework.data.jpa.repository.JpaRepository;\nimport org.springframework.stereotype.Repository;\n\nimport de.tum.in.www1.artemis.domain.quiz.ShortAnswerQuestionStatistic;\n\n/**\n * Spring Data repository for the ShortAnswerQuestionStatistic entity.\n */\n@SuppressWarnings(\"unused\")\n@Repository\npublic interface ShortAnswerQuestionStatisticRepository extends JpaRepository {\n\n}\n"},{"alpha_fraction":0.6129742860794067,"alphanum_fraction":0.6182374358177185,"avg_line_length":39.646766662597656,"blob_id":"7ce8a382020d2c05403e68cd9032ccc89cf86238","content_id":"6f8a4c2ed5ea6691eadae1424f615eaa6113cb3f","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":8170,"license_type":"permissive","max_line_length":179,"num_lines":201,"path":"/src/main/webapp/app/exam/manage/student-exams/student-exam-detail.component.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { Component, OnInit } from '@angular/core';\nimport { FormControl, FormGroup, Validators } from '@angular/forms';\nimport { ActivatedRoute } from '@angular/router';\nimport { StudentExam } from 'app/entities/student-exam.model';\nimport { StudentExamService } from 'app/exam/manage/student-exams/student-exam.service';\nimport { Course } from 'app/entities/course.model';\nimport { CourseManagementService } from 'app/course/manage/course-management.service';\nimport { User } from 'app/core/user/user.model';\nimport { ArtemisDurationFromSecondsPipe } from 'app/shared/pipes/artemis-duration-from-seconds.pipe';\nimport { JhiAlertService } from 'ng-jhipster';\nimport { round } from 'app/shared/util/utils';\nimport * as moment from 'moment';\nimport { NgbModal } from '@ng-bootstrap/ng-bootstrap';\nimport { getLatestSubmissionResult, setLatestSubmissionResult } from 'app/entities/submission.model';\n\n@Component({\n selector: 'jhi-student-exam-detail',\n templateUrl: './student-exam-detail.component.html',\n providers: [ArtemisDurationFromSecondsPipe],\n})\nexport class StudentExamDetailComponent implements OnInit {\n courseId: number;\n studentExam: StudentExam;\n course: Course;\n student: User;\n workingTimeForm: FormGroup;\n isSavingWorkingTime = false;\n isTestRun = false;\n maxTotalPoints = 0;\n achievedTotalPoints = 0;\n bonusTotalPoints = 0;\n busy = false;\n\n examId: number;\n\n constructor(\n private route: ActivatedRoute,\n private studentExamService: StudentExamService,\n private courseService: CourseManagementService,\n private artemisDurationFromSecondsPipe: ArtemisDurationFromSecondsPipe,\n private alertService: JhiAlertService,\n private modalService: NgbModal,\n ) {}\n\n /**\n * Initialize the courseId and studentExam\n */\n ngOnInit(): void {\n this.isTestRun = this.route.snapshot.url[1]?.toString() === 'test-runs';\n this.loadStudentExam();\n }\n\n /**\n * Load the course and the student exam\n */\n loadStudentExam() {\n this.courseId = Number(this.route.snapshot.paramMap.get('courseId'));\n this.examId = Number(this.route.snapshot.paramMap.get('examId'));\n this.route.data.subscribe(({ studentExam }) => this.setStudentExam(studentExam));\n\n this.courseService.find(this.courseId).subscribe((courseResponse) => {\n this.course = courseResponse.body!;\n });\n this.student = this.studentExam.user!;\n }\n\n /**\n * Save the defined working time\n */\n saveWorkingTime() {\n this.isSavingWorkingTime = true;\n const seconds = this.workingTimeForm.controls.minutes.value * 60 + this.workingTimeForm.controls.seconds.value;\n this.studentExamService.updateWorkingTime(this.courseId, this.studentExam.exam!.id!, this.studentExam.id!, seconds).subscribe(\n (res) => {\n if (res.body) {\n this.setStudentExam(res.body);\n }\n this.isSavingWorkingTime = false;\n this.alertService.success('artemisApp.studentExamDetail.saveWorkingTimeSuccessful');\n },\n () => {\n this.alertService.error('artemisApp.studentExamDetail.workingTimeCouldNotBeSaved');\n this.isSavingWorkingTime = false;\n },\n );\n }\n\n /**\n * Sets the student exam, initialised the component which allows changing the working time and sets the score of the student.\n * @param studentExam\n */\n private setStudentExam(studentExam: StudentExam) {\n this.studentExam = studentExam;\n this.initWorkingTimeForm();\n this.maxTotalPoints = 0;\n this.achievedTotalPoints = 0;\n this.bonusTotalPoints = 0;\n studentExam.exercises!.forEach((exercise) => {\n this.maxTotalPoints += exercise.maxPoints!;\n this.bonusTotalPoints += exercise.bonusPoints!;\n if (\n exercise.studentParticipations?.length &&\n exercise.studentParticipations.length > 0 &&\n exercise.studentParticipations[0].results?.length &&\n exercise.studentParticipations[0].results!.length > 0\n ) {\n if (exercise!.studentParticipations[0].submissions && exercise!.studentParticipations[0].submissions!.length > 0) {\n exercise!.studentParticipations[0].submissions![0].results! = exercise.studentParticipations[0].results;\n setLatestSubmissionResult(exercise?.studentParticipations[0].submissions?.[0], getLatestSubmissionResult(exercise?.studentParticipations[0].submissions?.[0]));\n }\n\n this.achievedTotalPoints += this.rounding((exercise.studentParticipations[0].results[0].score! * exercise.maxPoints!) / 100);\n }\n });\n }\n\n private initWorkingTimeForm() {\n const workingTime = this.artemisDurationFromSecondsPipe.transform(this.studentExam.workingTime!);\n const workingTimeParts = workingTime.split(':');\n this.workingTimeForm = new FormGroup({\n minutes: new FormControl({ value: parseInt(workingTimeParts[0] ? workingTimeParts[0] : '0', 10), disabled: this.examIsVisible() }, [\n Validators.min(0),\n Validators.required,\n ]),\n seconds: new FormControl({ value: parseInt(workingTimeParts[1] ? workingTimeParts[1] : '0', 10), disabled: this.examIsVisible() }, [\n Validators.min(0),\n Validators.max(59),\n Validators.required,\n ]),\n });\n }\n\n examIsVisible(): boolean {\n if (this.isTestRun) {\n // for test runs we always want to be able to change the working time\n return !!this.studentExam.submitted;\n } else if (this.studentExam.exam) {\n // Disable the form to edit the working time if the exam is already visible\n return moment(this.studentExam.exam.visibleDate).isBefore(moment());\n }\n // if exam is undefined, the form to edit the working time is disabled\n return true;\n }\n\n examIsOver(): boolean {\n if (this.studentExam.exam) {\n // only show the button when the exam is over\n return moment(this.studentExam.exam.endDate).add(this.studentExam.exam.gracePeriod, 'seconds').isBefore(moment());\n }\n // if exam is undefined, we do not want to show the button\n return false;\n }\n\n getWorkingTimeToolTip(): string {\n return this.examIsVisible()\n ? 'You cannot change the individual working time after the exam has become visible.'\n : 'You can change the individual working time of the student here.';\n }\n\n rounding(number: number) {\n return round(number, 1);\n }\n\n /**\n * switch the 'submitted' state of the studentExam.\n */\n toggle() {\n this.busy = true;\n if (this.studentExam.exam && this.studentExam.exam.id) {\n this.studentExamService.toggleSubmittedState(this.courseId, this.studentExam.exam!.id!, this.studentExam.id!, this.studentExam!.submitted!).subscribe(\n (res) => {\n if (res.body) {\n this.studentExam.submissionDate = res.body.submissionDate;\n this.studentExam.submitted = res.body.submitted;\n }\n this.alertService.success('artemisApp.studentExamDetail.toggleSuccessful');\n this.busy = false;\n },\n () => {\n this.alertService.error('artemisApp.studentExamDetail.togglefailed');\n this.busy = false;\n },\n );\n }\n }\n\n /**\n * Open a modal that requires the user's confirmation.\n * @param content the modal content\n */\n openConfirmationModal(content: any) {\n this.modalService.open(content).result.then(\n (result: string) => {\n if (result === 'confirm') {\n this.toggle();\n }\n },\n () => {},\n );\n }\n}\n"},{"alpha_fraction":0.6897590160369873,"alphanum_fraction":0.6897590160369873,"avg_line_length":26.66666603088379,"blob_id":"3df2cdcce42c2d0725d376ef32cfcc11bc2beaf2","content_id":"8951f43e32881629fafa4a9be17ee9a43732ccc6","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":664,"license_type":"permissive","max_line_length":63,"num_lines":24,"path":"/src/main/webapp/app/entities/notification.model.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { Moment } from 'moment';\nimport { BaseEntity } from 'app/shared/model/base-entity';\nimport { User } from 'app/core/user/user.model';\n\nexport enum NotificationType {\n SYSTEM = 'system',\n CONNECTION = 'connection',\n GROUP = 'group',\n SINGLE = 'single',\n}\n\nexport class Notification implements BaseEntity {\n public id?: number;\n public notificationType?: NotificationType;\n public title?: string;\n public text?: string;\n public notificationDate?: Moment;\n public target?: string;\n public author?: User;\n\n protected constructor(notificationType: NotificationType) {\n this.notificationType = notificationType;\n }\n}\n"},{"alpha_fraction":0.6244486570358276,"alphanum_fraction":0.6244486570358276,"avg_line_length":32.0625,"blob_id":"47eb6554c04eaaf3832af2a256152fc958ab8197","content_id":"62d147e756e8e84be83ba12e53c9c671f28901d4","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":1587,"license_type":"permissive","max_line_length":96,"num_lines":48,"path":"/src/main/webapp/app/shared/connection-status/connection-status.component.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { Component, OnInit, OnDestroy, ContentChild, ElementRef } from '@angular/core';\nimport { JhiWebsocketService } from 'app/core/websocket/websocket.service';\n\n@Component({\n selector: 'jhi-connection-status',\n templateUrl: './connection-status.component.html',\n styleUrls: ['./connection-status.component.scss'],\n})\nexport class JhiConnectionStatusComponent implements OnInit, OnDestroy {\n @ContentChild('innerContent', { static: false }) innerContent: ElementRef;\n\n disconnected = true;\n onConnected: () => void;\n onDisconnected: () => void;\n\n constructor(private jhiWebsocketService: JhiWebsocketService) {}\n\n /**\n * Life cycle hook called by Angular to indicate that Angular is done creating the component\n */\n ngOnInit() {\n // listen to connect / disconnect events\n this.onConnected = () => {\n this.disconnected = false;\n };\n this.jhiWebsocketService.bind('connect', () => {\n this.onConnected();\n });\n this.onDisconnected = () => {\n this.disconnected = true;\n };\n this.jhiWebsocketService.bind('disconnect', () => {\n this.onDisconnected();\n });\n }\n\n /**\n * Life cycle hook called by Angular for cleanup just before Angular destroys the component\n */\n ngOnDestroy() {\n if (this.onConnected) {\n this.jhiWebsocketService.unbind('connect', this.onConnected);\n }\n if (this.onDisconnected) {\n this.jhiWebsocketService.unbind('disconnect', this.onDisconnected);\n }\n }\n}\n"},{"alpha_fraction":0.7029520273208618,"alphanum_fraction":0.7195571660995483,"avg_line_length":27.526315689086914,"blob_id":"59f0a7b7711de49e670e991fe38d9af9bc1e8806","content_id":"46c6a1f103338bb85ce0fa9c373d8b6255d81453","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":542,"license_type":"permissive","max_line_length":147,"num_lines":19,"path":"/src/main/java/de/tum/in/www1/artemis/domain/enumeration/InitializationState.java","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"package de.tum.in.www1.artemis.domain.enumeration;\n\n/**\n * The InitializationState enumeration.\n */\npublic enum InitializationState {\n\n UNINITIALIZED(0), REPO_COPIED(1), REPO_CONFIGURED(2), INACTIVE(3), BUILD_PLAN_COPIED(4), BUILD_PLAN_CONFIGURED(5), INITIALIZED(6), FINISHED(7);\n\n private final Integer stateNumber;\n\n InitializationState(int stateNumber) {\n this.stateNumber = stateNumber;\n }\n\n public boolean hasCompletedState(InitializationState state) {\n return this.stateNumber >= state.stateNumber;\n }\n}\n"},{"alpha_fraction":0.644212543964386,"alphanum_fraction":0.6486400961875916,"avg_line_length":39.53845977783203,"blob_id":"50ddfd3e22e6ab57d55fe7e932c97808002b0307","content_id":"611d1512d89b12787797b1494cb8bcdc5369b021","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":3162,"license_type":"permissive","max_line_length":120,"num_lines":78,"path":"/src/test/javascript/spec/component/student-questions/student-votes.component.spec.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import * as chai from 'chai';\nimport * as sinonChai from 'sinon-chai';\nimport { ActivatedRoute } from '@angular/router';\nimport { ComponentFixture, TestBed } from '@angular/core/testing';\nimport { User } from 'app/core/user/user.model';\nimport { ArtemisTestModule } from '../../test.module';\nimport { ArtemisSharedModule } from 'app/shared/shared.module';\nimport { StudentVotesComponent } from 'app/overview/student-questions/student-votes/student-votes.component';\nimport { MockSyncStorage } from '../../helpers/mocks/service/mock-sync-storage.service';\nimport { LocalStorageService } from 'ngx-webstorage';\nimport { MockActivatedRouteWithSubjects } from '../../helpers/mocks/activated-route/mock-activated-route-with-subjects';\n\nchai.use(sinonChai);\nconst expect = chai.expect;\n\ndescribe('StudentVotesComponent', () => {\n let component: StudentVotesComponent;\n let componentFixture: ComponentFixture;\n\n const user1 = {\n id: 1,\n } as User;\n\n beforeEach(async () => {\n return TestBed.configureTestingModule({\n imports: [ArtemisTestModule, ArtemisSharedModule],\n providers: [\n { provide: LocalStorageService, useClass: MockSyncStorage },\n { provide: ActivatedRoute, useClass: MockActivatedRouteWithSubjects },\n ],\n declarations: [StudentVotesComponent],\n })\n .overrideTemplate(StudentVotesComponent, '')\n .compileComponents()\n .then(() => {\n componentFixture = TestBed.createComponent(StudentVotesComponent);\n component = componentFixture.componentInstance;\n });\n });\n\n it('should toggle upvote', () => {\n component.user = user1;\n component.votes = 42;\n component.userVote = null;\n // if not yet voted\n component.toggleUpVote();\n expect(component.voteValueChange).to.deep.equal(1);\n expect(component.userVote!.isPositive).to.be.true;\n // if already upvoted\n component.toggleUpVote();\n expect(component.voteValueChange).to.deep.equal(-1);\n expect(component.userVote).to.be.null;\n // if already downvoted\n component.userVote = { isPositive: false };\n component.toggleUpVote();\n expect(component.voteValueChange).to.deep.equal(2);\n expect(component.userVote!.isPositive).to.be.true;\n });\n\n it('should toggle downvote', () => {\n component.user = user1;\n component.votes = 42;\n component.userVote = null;\n // if not yet voted\n component.toggleDownVote();\n expect(component.voteValueChange).to.deep.equal(-1);\n expect(component.userVote!.isPositive).to.be.false;\n // if already downvoted\n component.toggleDownVote();\n expect(component.voteValueChange).to.deep.equal(+1);\n expect(component.userVote).to.be.null;\n // if already upvoted\n component.userVote = { isPositive: true };\n component.toggleDownVote();\n expect(component.voteValueChange).to.deep.equal(-2);\n expect(component.userVote!.isPositive).to.be.false;\n });\n});\n"},{"alpha_fraction":0.6675077676773071,"alphanum_fraction":0.6675077676773071,"avg_line_length":44.59292221069336,"blob_id":"ac432cbf9810c742c9f13a1dd5fc8a697e72e1de","content_id":"6a3980b5037bfb6ae1670aa0e77191573ce99cb7","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":5152,"license_type":"permissive","max_line_length":176,"num_lines":113,"path":"/src/main/webapp/app/exercises/modeling/manage/modeling-exercise.route.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { Injectable, NgModule } from '@angular/core';\nimport { ActivatedRouteSnapshot, Resolve, RouterModule, Routes } from '@angular/router';\nimport { UserRouteAccessService } from 'app/core/auth/user-route-access-service';\nimport { ModelingExerciseDetailComponent } from './modeling-exercise-detail.component';\nimport { ModelingExerciseUpdateComponent } from 'app/exercises/modeling/manage/modeling-exercise-update.component';\nimport { CourseManagementService } from 'app/course/manage/course-management.service';\nimport { ModelingExerciseService } from 'app/exercises/modeling/manage/modeling-exercise.service';\nimport { ModelingExercise, UMLDiagramType } from 'app/entities/modeling-exercise.model';\nimport { HttpResponse } from '@angular/common/http';\nimport { filter, map } from 'rxjs/operators';\nimport { Observable } from 'rxjs';\nimport { Course } from 'app/entities/course.model';\nimport { ExerciseGroup } from 'app/entities/exercise-group.model';\nimport { ExerciseGroupService } from 'app/exam/manage/exercise-groups/exercise-group.service';\nimport { PlagiarismInspectorComponent } from 'app/exercises/shared/plagiarism/plagiarism-inspector/plagiarism-inspector.component';\nimport { Authority } from 'app/shared/constants/authority.constants';\n\n@Injectable({ providedIn: 'root' })\nexport class ModelingExerciseResolver implements Resolve {\n constructor(private modelingExerciseService: ModelingExerciseService, private courseService: CourseManagementService, private exerciseGroupService: ExerciseGroupService) {}\n\n resolve(route: ActivatedRouteSnapshot) {\n if (route.params['exerciseId']) {\n return this.modelingExerciseService.find(route.params['exerciseId']).pipe(\n filter((res) => !!res.body),\n map((modelingExercise: HttpResponse) => modelingExercise.body!),\n );\n } else if (route.params['courseId']) {\n if (route.params['examId'] && route.params['groupId']) {\n return this.exerciseGroupService.find(route.params['courseId'], route.params['examId'], route.params['groupId']).pipe(\n filter((res) => !!res.body),\n map((exerciseGroup: HttpResponse) => new ModelingExercise(UMLDiagramType.ClassDiagram, undefined, exerciseGroup.body || undefined)),\n );\n } else {\n return this.courseService.find(route.params['courseId']).pipe(\n filter((res) => !!res.body),\n map((course: HttpResponse) => new ModelingExercise(UMLDiagramType.ClassDiagram, course.body || undefined, undefined)),\n );\n }\n }\n return Observable.of(new ModelingExercise(UMLDiagramType.ClassDiagram, undefined, undefined));\n }\n}\n\nexport const routes: Routes = [\n {\n path: ':courseId/modeling-exercises/new',\n component: ModelingExerciseUpdateComponent,\n resolve: {\n modelingExercise: ModelingExerciseResolver,\n },\n data: {\n authorities: [Authority.TA, Authority.INSTRUCTOR, Authority.ADMIN],\n pageTitle: 'artemisApp.modelingExercise.home.title',\n },\n canActivate: [UserRouteAccessService],\n },\n {\n path: ':courseId/modeling-exercises/:exerciseId/edit',\n component: ModelingExerciseUpdateComponent,\n resolve: {\n modelingExercise: ModelingExerciseResolver,\n },\n data: {\n authorities: [Authority.TA, Authority.INSTRUCTOR, Authority.ADMIN],\n pageTitle: 'artemisApp.modelingExercise.home.title',\n },\n canActivate: [UserRouteAccessService],\n },\n {\n path: ':courseId/modeling-exercises/:exerciseId/import',\n component: ModelingExerciseUpdateComponent,\n resolve: {\n modelingExercise: ModelingExerciseResolver,\n },\n data: {\n authorities: [Authority.INSTRUCTOR, Authority.ADMIN],\n pageTitle: 'artemisApp.modelingExercise.home.importLabel',\n },\n canActivate: [UserRouteAccessService],\n },\n {\n path: ':courseId/modeling-exercises/:exerciseId',\n component: ModelingExerciseDetailComponent,\n data: {\n authorities: [Authority.TA, Authority.INSTRUCTOR, Authority.ADMIN],\n pageTitle: 'artemisApp.modelingExercise.home.title',\n },\n canActivate: [UserRouteAccessService],\n },\n {\n path: ':courseId/modeling-exercises/:exerciseId/plagiarism',\n component: PlagiarismInspectorComponent,\n resolve: {\n exercise: ModelingExerciseResolver,\n },\n data: {\n authorities: [Authority.TA, Authority.INSTRUCTOR, Authority.ADMIN],\n pageTitle: 'artemisApp.plagiarism.plagiarism-detection',\n },\n canActivate: [UserRouteAccessService],\n },\n {\n path: ':courseId/modeling-exercises',\n redirectTo: ':courseId/exercises',\n },\n];\n\n@NgModule({\n imports: [RouterModule.forChild(routes)],\n exports: [RouterModule],\n})\nexport class ArtemisModelingExerciseRoutingModule {}\n"},{"alpha_fraction":0.7366254925727844,"alphanum_fraction":0.7481873631477356,"avg_line_length":51.07143020629883,"blob_id":"427fa6380f7ccd00ec6c2787d8891b72b7422234","content_id":"da74792afd2f6d81b39e3326055047af66392125","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":5103,"license_type":"permissive","max_line_length":178,"num_lines":98,"path":"/src/main/java/de/tum/in/www1/artemis/service/compass/utils/CompassConfiguration.java","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"package de.tum.in.www1.artemis.service.compass.utils;\n\n/**\n * All similarity related parameters\n */\npublic class CompassConfiguration {\n\n /*\n * Similarity related parameters\n */\n // Amount that is subtracted from the similarity of two UML elements/diagrams for every missing element\n public static final double MISSING_ELEMENT_PENALTY = 0.05;\n\n // Weight of the relationship type when calculating the similarity of two UML relationships\n public static final double RELATION_TYPE_WEIGHT = 0.3;\n\n // Weight of the source and destination elements when calculating the similarity of two UML relationships (e.g. in class diagrams)\n public static final double RELATION_ELEMENT_WEIGHT = 0.25;\n\n // Weight of the source and destination elements when calculating the similarity of two UML component relationships\n public static final double COMPONENT_RELATION_ELEMENT_WEIGHT = 0.35;\n\n // Weight of the source and destination elements when calculating the similarity of two UML use case associations\n public static final double USE_CASE_ASSOCIATION_ELEMENT_WEIGHT = 0.3;\n\n // Weight of the multiplicity when calculating the similarity of two UML relationships\n public static final double RELATION_MULTIPLICITY_WEIGHT = 0.05;\n\n // Weight of the roles when calculating the similarity of two UML relationships\n public static final double RELATION_ROLE_WEIGHT = 0.05;\n\n // Weight of the class type when calculating the similarity of two UML classes\n public static final double CLASS_TYPE_WEIGHT = 0.3;\n\n // Weight of the class name when calculating the similarity of two UML classes\n public static final double CLASS_NAME_WEIGHT = 0.7;\n\n public static final double USE_CASE_ASSOCIATION_NAME_WEIGHT = 0.1;\n\n public static final double NODE_NAME_WEIGHT = 0.5;\n\n public static final double NODE_STEREOTYPE_WEIGHT = 0.2;\n\n public static final double NODE_PARENT_WEIGHT = 0.3;\n\n public static final double COMPONENT_NAME_WEIGHT = 0.6;\n\n public static final double COMPONENT_PARENT_WEIGHT = 0.4;\n\n // Weight of the name similarity of their attributes when calculating the similarity of two UML classes\n public static final double ATTRIBUTE_NAME_WEIGHT = 0.7;\n\n // Weight of the type similarity of their attributes when calculating the similarity of two UML classes\n public static final double ATTRIBUTE_TYPE_WEIGHT = 0.3;\n\n // Threshold for the similarity of child elements (attributes/methods in UML classes, classes and relationships in UML diagrams) when calculation the similarity of two UML\n // classes/diagrams. If the similarity for specific child elements is smaller than the threshold, the elements will not be considered similar at all.\n public static final double NO_MATCH_THRESHOLD = 0.1;\n\n // Threshold used for building similarity sets. If the similarity for two UML elements is smaller than this threshold, they will not be put into the same similarity set.\n // TODO CZ: decrease equality threshold again in the future\n public static final double EQUALITY_THRESHOLD = 0.95;\n\n // Threshold used for re-assessing poorly assessed models. If the confidence for a model is smaller than this threshold the model is send to the client for re-assessment.\n /* CURRENTLY DISABLED */\n public static final double POORLY_ASSESSED_MODEL_THRESHOLD = 0.8;\n\n /*\n * Confidence and coverage parameters\n */\n // Threshold of the coverage of a UML diagram. If the coverage for a specific diagram is smaller than this threshold, no automatic result will be created for the diagram.\n /* CURRENTLY DISABLED */\n public static final double COVERAGE_THRESHOLD = 0.8;\n\n // Threshold of the confidence of a UML diagram. If the confidence for a specific diagram is smaller than this threshold, no automatic result will be created for the diagram.\n /* CURRENTLY DISABLED */\n public static final double CONFIDENCE_THRESHOLD = 0.75;\n\n // Threshold of the confidence of an element in a UML diagram. If the confidence for a specific model element is smaller than this threshold, no automatic feedback will be\n // created for this element.\n public static final double ELEMENT_CONFIDENCE_THRESHOLD = 0.8;\n\n /*\n * Calculation engine cleanup parameters\n */\n // Number of days to keep unused calculation engines in memory. If an engine is unused for a longer time, it will be removed in the cleanup job running every night.\n public static final int DAYS_TO_KEEP_UNUSED_ENGINE = 1;\n\n /*\n * Optimal model parameters\n */\n // Minimal number of optimal models that should be in cache. If the cache consists of less models, NUMBER_OF_NEW_OPTIMAL_MODELS new optimal models will be loaded.\n public static final int OPTIMAL_MODEL_THRESHOLD = 10;\n\n // Number of optimal models that should be generated and loaded into the cache if the number of models in the cache is smaller than OPTIMAL_MODEL_THRESHOLD.\n // NUMBER_OF_NEW_OPTIMAL_MODELS should be greater or equal to OPTIMAL_MODEL_THRESHOLD.\n public static final int NUMBER_OF_NEW_OPTIMAL_MODELS = 10;\n}\n"},{"alpha_fraction":0.5893304944038391,"alphanum_fraction":0.5921947956085205,"avg_line_length":33.48147964477539,"blob_id":"cd53a21f4ad21163ad335953888194576bb193b9","content_id":"ac1cab31747060beb3c59da9ba698bc971f95296","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2793,"license_type":"permissive","max_line_length":121,"num_lines":81,"path":"/src/main/resources/templates/c/test/testUtils/Tester.py","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"from typing import Dict\nfrom testUtils.AbstractTest import AbstractTest\nfrom testUtils.Utils import printTester, getTesterOutput, clearTesterOutputCache, resetStdoutLimit, setStdoutLimitEnabled\nfrom testUtils.junit.Junit import Junit\nfrom testUtils.junit.TestSuite import TestSuite\n\n\nclass Tester:\n name: str\n suite: TestSuite\n tests: Dict[str, AbstractTest]\n\n def __init__(self, name: str = \"GBS-Tester-1.31\"):\n self.name = name\n self.suite = TestSuite(name)\n self.tests = dict()\n\n def run(self):\n \"\"\"\n Starts the tester and runs all tests added via \"addTest(test: AbstractTest)\".\n \"\"\"\n\n setStdoutLimitEnabled(False)\n printTester(f\"Running: {self.name}\")\n\n # A dictionary of test results:\n # Test name -> result\n testResults: Dict[str, Result] = dict()\n\n for name, test in self.tests.items():\n if test.timeoutSec >= 0:\n printTester(\"Running test case '{}' with a {} second timeout...\".format(\n name, test.timeoutSec))\n else:\n printTester(\n f\"Running test case '{name}' with no timeout...\")\n\n # Reset the tester output cache:\n resetStdoutLimit()\n setStdoutLimitEnabled(True)\n clearTesterOutputCache()\n\n test.start(testResults, self.suite)\n\n setStdoutLimitEnabled(False)\n printTester(\"Finished test case '{}' in {} seconds.\".format(\n name, test.case.time.total_seconds()))\n\n # Store the tester output in the test case:\n test.case.testerOutput = self.name + \"\\n\" + getTesterOutput()\n # Update test results:\n testResults[name] = test.case.result\n self.__printResult()\n\n def addTest(self, test: AbstractTest):\n \"\"\"\n Adds a new test that will be run once \"run()\" is invoked.\n \"\"\"\n\n if test.name in self.tests:\n raise NameError(\n f\"Test '{test.name}' already registered. Test names should be unique!\")\n self.tests[test.name] = test\n\n def __printResult(self):\n print(\"Result\".center(50, \"=\"))\n print(\"{} finished {} test cases in {} seconds.\".format(\n self.name, len(self.tests), self.suite.time.total_seconds()))\n print(f\"SUCCESS: {self.suite.successful}\")\n print(f\"FAILED: {self.suite.failures}\")\n print(f\"ERROR: {self.suite.errors}\")\n print(f\"SKIPPED: {self.suite.skipped}\")\n print(\"\".center(50, \"=\"))\n\n def exportResult(self, outputPath: str):\n \"\"\"\n Exports the test results into a JUnit format and stores it at the given outputPath.\n \"\"\"\n\n junit: Junit = Junit(self.suite)\n junit.toXml(outputPath)\n"},{"alpha_fraction":0.620317816734314,"alphanum_fraction":0.6214528679847717,"avg_line_length":39.97674560546875,"blob_id":"155aece644358782ee89a75e1a656639d43f471c","content_id":"c559955593601651eb9376276a97185073d77957","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":1762,"license_type":"permissive","max_line_length":160,"num_lines":43,"path":"/src/main/webapp/app/shared/orion/outdated-plugin-warning/orion-outdated.component.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { Component, OnInit } from '@angular/core';\nimport { ActivatedRoute } from '@angular/router';\nimport { ProfileService } from 'app/shared/layouts/profiles/profile.service';\nimport { filter, tap } from 'rxjs/operators';\nimport { ProfileInfo } from 'app/shared/layouts/profiles/profile-info.model';\n\n@Component({\n selector: 'jhi-orion-outdated',\n template: `\n ();\n dialogError$ = this.dialogErrorSource.asObservable();\n\n paramSub: Subscription;\n\n constructor(\n private route: ActivatedRoute,\n protected exerciseHintService: ExerciseHintService,\n private jhiAlertService: JhiAlertService,\n protected eventManager: JhiEventManager,\n ) {}\n\n /**\n * Subscribes to the route params to act on the currently selected exercise.\n */\n ngOnInit() {\n this.paramSub = this.route.params.subscribe((params) => {\n this.exerciseId = params['exerciseId'];\n this.loadAllByExerciseId();\n this.registerChangeInExerciseHints();\n });\n }\n\n /**\n * Unsubscribe from subscriptions\n */\n ngOnDestroy() {\n if (this.paramSub) {\n this.paramSub.unsubscribe();\n }\n this.eventManager.destroy(this.eventSubscriber);\n this.dialogErrorSource.unsubscribe();\n }\n\n /**\n * Load all exercise hints with the currently selected exerciseId (taken from route params).\n */\n loadAllByExerciseId() {\n this.exerciseHintService\n .findByExerciseId(this.exerciseId)\n .pipe(\n filter((res: HttpResponse) => res.ok),\n map((res: HttpResponse) => res.body),\n )\n .subscribe(\n (res: ExerciseHint[]) => {\n this.exerciseHints = res;\n },\n (res: HttpErrorResponse) => onError(this.jhiAlertService, res),\n );\n }\n\n /**\n * Returns the track id of an exercise hint\n * @param index Index of the item\n * @param item Item for which to get the id\n */\n trackId(index: number, item: ExerciseHint) {\n return item.id;\n }\n\n /**\n * (Re-)subscribe to the exercise hint list modification subscription\n */\n registerChangeInExerciseHints() {\n if (this.eventSubscriber) {\n this.eventSubscriber.unsubscribe();\n }\n this.eventSubscriber = this.eventManager.subscribe('exerciseHintListModification', () => this.loadAllByExerciseId());\n }\n\n /**\n * Deletes exercise hint\n * @param exerciseHintId the id of the exercise hint that we want to delete\n */\n deleteExerciseHint(exerciseHintId: number) {\n this.exerciseHintService.delete(exerciseHintId).subscribe(\n () => {\n this.eventManager.broadcast({\n name: 'exerciseHintListModification',\n content: 'Deleted an exerciseHint',\n });\n this.dialogErrorSource.next('');\n },\n (error: HttpErrorResponse) => this.dialogErrorSource.next(error.message),\n );\n }\n}\n"},{"alpha_fraction":0.6415464878082275,"alphanum_fraction":0.6494461894035339,"avg_line_length":46.75448989868164,"blob_id":"de4a24d42638bedea96cea8d594b7bbbb6504329","content_id":"9964357ad769a94842e976496818b5367a5a0fdc","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":23925,"license_type":"permissive","max_line_length":179,"num_lines":501,"path":"/src/test/javascript/spec/component/shared/navbar.component.spec.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { HttpResponse } from '@angular/common/http';\nimport { Directive, Input } from '@angular/core';\nimport { ComponentFixture, TestBed } from '@angular/core/testing';\nimport { Router } from '@angular/router';\nimport { NgbCollapse, NgbDropdown } from '@ng-bootstrap/ng-bootstrap';\nimport { TranslateService } from '@ngx-translate/core';\nimport { CourseManagementService } from 'app/course/manage/course-management.service';\nimport { ExamManagementService } from 'app/exam/manage/exam-management.service';\nimport { ApollonDiagramService } from 'app/exercises/quiz/manage/apollon-diagrams/apollon-diagram.service';\nimport { ExerciseHintService } from 'app/exercises/shared/exercise-hint/manage/exercise-hint.service';\nimport { ExerciseService } from 'app/exercises/shared/exercise/exercise.service';\nimport { GuidedTourComponent } from 'app/guided-tour/guided-tour.component';\nimport { LectureService } from 'app/lecture/lecture.service';\nimport { HasAnyAuthorityDirective } from 'app/shared/auth/has-any-authority.directive';\nimport { FindLanguageFromKeyPipe } from 'app/shared/language/find-language-from-key.pipe';\nimport { ActiveMenuDirective } from 'app/shared/layouts/navbar/active-menu.directive';\nimport { NavbarComponent } from 'app/shared/layouts/navbar/navbar.component';\nimport { LoadingNotificationComponent } from 'app/shared/notification/loading-notification/loading-notification.component';\nimport { NotificationSidebarComponent } from 'app/shared/notification/notification-sidebar/notification-sidebar.component';\nimport { ArtemisTranslatePipe } from 'app/shared/pipes/artemis-translate.pipe';\nimport * as chai from 'chai';\nimport { JhiTranslateDirective } from 'ng-jhipster';\nimport { MockComponent, MockDirective, MockPipe, MockProvider } from 'ng-mocks';\nimport { ChartsModule } from 'ng2-charts';\nimport { LocalStorageService, SessionStorageService } from 'ngx-webstorage';\nimport { of } from 'rxjs/internal/observable/of';\nimport * as sinon from 'sinon';\nimport * as sinonChai from 'sinon-chai';\nimport { MockRouter } from '../../helpers/mocks/mock-router';\nimport { MockSyncStorage } from '../../helpers/mocks/service/mock-sync-storage.service';\nimport { ArtemisTestModule } from '../../test.module';\n\nchai.use(sinonChai);\nconst expect = chai.expect;\n\n// tslint:disable-next-line:directive-selector\n@Directive({ selector: '[routerLink]' })\nexport class MockRouterLinkDirective {\n @Input('routerLink') data: any;\n}\n\n// tslint:disable-next-line:directive-selector\n@Directive({ selector: '[routerLinkActiveOptions]' })\nexport class MockRouterLinkActiveOptionsDirective {\n @Input('routerLinkActiveOptions') data: any;\n}\n\nclass MockBreadcrumb {\n label: string;\n uri: string;\n translate: boolean;\n}\n\ndescribe('NavbarComponent', () => {\n let fixture: ComponentFixture;\n let component: NavbarComponent;\n let courseManagementStub: sinon.SinonStub;\n let exerciseStub: sinon.SinonStub;\n\n const router = new MockRouter();\n router.setUrl('');\n\n const courseManagementCrumb = {\n label: 'global.menu.course',\n translate: true,\n uri: '/course-management/',\n } as MockBreadcrumb;\n\n const testCourseCrumb = {\n label: 'Test Course',\n translate: false,\n uri: '/course-management/1/',\n } as MockBreadcrumb;\n\n const programmingExercisesCrumb = {\n label: 'artemisApp.course.exercises',\n translate: true,\n uri: '/course-management/1/programming-exercises/',\n } as MockBreadcrumb;\n\n beforeEach(() => {\n TestBed.configureTestingModule({\n imports: [ArtemisTestModule, ChartsModule],\n declarations: [\n NavbarComponent,\n MockDirective(NgbCollapse),\n MockDirective(HasAnyAuthorityDirective),\n MockDirective(NgbDropdown),\n MockDirective(ActiveMenuDirective),\n MockDirective(JhiTranslateDirective),\n MockDirective(MockRouterLinkDirective),\n MockDirective(MockRouterLinkActiveOptionsDirective),\n MockPipe(ArtemisTranslatePipe),\n MockPipe(FindLanguageFromKeyPipe),\n MockComponent(NotificationSidebarComponent),\n MockComponent(GuidedTourComponent),\n MockComponent(LoadingNotificationComponent),\n ],\n providers: [\n { provide: LocalStorageService, useClass: MockSyncStorage },\n { provide: SessionStorageService, useClass: MockSyncStorage },\n MockProvider(TranslateService),\n { provide: Router, useValue: router },\n ],\n })\n .compileComponents()\n .then(() => {\n fixture = TestBed.createComponent(NavbarComponent);\n component = fixture.componentInstance;\n\n const courseManagementService = fixture.debugElement.injector.get(CourseManagementService);\n courseManagementStub = sinon.stub(courseManagementService, 'getTitle').returns(of({ body: 'Test Course' } as HttpResponse));\n\n const exerciseService = fixture.debugElement.injector.get(ExerciseService);\n exerciseStub = sinon.stub(exerciseService, 'getTitle').returns(of({ body: 'Test Exercise' } as HttpResponse));\n });\n });\n\n afterEach(function () {\n sinon.restore();\n });\n\n it('should initialize component', () => {\n fixture.detectChanges();\n expect(component).to.be.ok;\n });\n\n it('should not build breadcrumbs for students', () => {\n const testUrl = '/courses/1/exercises';\n router.setUrl(testUrl);\n\n fixture.detectChanges();\n\n expect(component.breadcrumbs.length).to.equal(0);\n });\n\n it('should build breadcrumbs for course management', () => {\n const testUrl = '/course-management';\n router.setUrl(testUrl);\n\n fixture.detectChanges();\n\n expect(component.breadcrumbs.length).to.equal(1);\n\n // Use matching here to ignore non-semantic differences between objects\n sinon.assert.match(component.breadcrumbs[0], courseManagementCrumb);\n });\n\n it('should ignore query parameters', () => {\n const testUrl = '/course-management?query=param';\n router.setUrl(testUrl);\n\n fixture.detectChanges();\n\n expect(component.breadcrumbs.length).to.equal(1);\n\n // Use matching here to ignore non-semantic differences between objects\n sinon.assert.match(component.breadcrumbs[0], courseManagementCrumb);\n });\n\n it('should build breadcrumbs for system notification management', () => {\n const testUrl = '/admin/system-notification-management/1/edit';\n router.setUrl(testUrl);\n\n fixture.detectChanges();\n\n expect(component.breadcrumbs.length).to.equal(3);\n\n // Use matching here to ignore non-semantic differences between objects\n const systemBreadcrumb = { label: 'artemisApp.systemNotification.systemNotifications', translate: true, uri: '/admin/system-notification-management/' } as MockBreadcrumb;\n sinon.assert.match(component.breadcrumbs[0], systemBreadcrumb);\n sinon.assert.match(component.breadcrumbs[1], { label: '1', translate: false, uri: '/admin/system-notification-management/1/' } as MockBreadcrumb);\n sinon.assert.match(component.breadcrumbs[2], { label: 'global.generic.edit', translate: true, uri: '/admin/system-notification-management/1/edit/' } as MockBreadcrumb);\n });\n\n it('should build breadcrumbs for user management', () => {\n const testUrl = '/admin/user-management/test_user';\n router.setUrl(testUrl);\n\n fixture.detectChanges();\n\n expect(component.breadcrumbs.length).to.equal(2);\n\n // Use matching here to ignore non-semantic differences between objects\n sinon.assert.match(component.breadcrumbs[0], { label: 'userManagement.home.title', translate: true, uri: '/admin/user-management/' } as MockBreadcrumb);\n sinon.assert.match(component.breadcrumbs[1], { label: 'test_user', translate: false, uri: '/admin/user-management/test_user/' } as MockBreadcrumb);\n });\n\n it('should not error without translation', () => {\n const testUrl = '/admin/route-without-translation';\n router.setUrl(testUrl);\n\n fixture.detectChanges();\n\n expect(component.breadcrumbs.length).to.equal(1);\n\n // Use matching here to ignore non-semantic differences between objects\n sinon.assert.match(component.breadcrumbs[0], { label: 'route-without-translation', translate: false, uri: '/admin/route-without-translation/' } as MockBreadcrumb);\n });\n\n describe('Special Cases for Breadcrumbs', function () {\n it('programming exercise import', () => {\n const testUrl = '/course-management/1/programming-exercises/import/2';\n router.setUrl(testUrl);\n\n fixture.detectChanges();\n\n expect(courseManagementStub).to.have.been.calledWith(1);\n\n const importCrumb = {\n label: 'artemisApp.exercise.import.table.doImport',\n translate: true,\n uri: '/course-management/1/programming-exercises/import/2/',\n } as MockBreadcrumb;\n\n expect(component.breadcrumbs.length).to.equal(4);\n\n // Use matching here to ignore non-semantic differences between objects\n sinon.assert.match(component.breadcrumbs[0], courseManagementCrumb);\n sinon.assert.match(component.breadcrumbs[1], testCourseCrumb);\n sinon.assert.match(component.breadcrumbs[2], programmingExercisesCrumb);\n sinon.assert.match(component.breadcrumbs[3], importCrumb);\n });\n\n it('programming exercise grading', () => {\n const testUrl = '/course-management/1/programming-exercises/2/grading/test-cases';\n router.setUrl(testUrl);\n\n fixture.detectChanges();\n\n expect(courseManagementStub).to.have.been.calledWith(1);\n expect(exerciseStub).to.have.been.calledWith(2);\n\n const gradingCrumb = {\n label: 'artemisApp.programmingExercise.configureGrading.shortTitle',\n translate: true,\n uri: '/course-management/1/programming-exercises/2/grading/test-cases/',\n } as MockBreadcrumb;\n\n expect(component.breadcrumbs.length).to.equal(5);\n\n // Use matching here to ignore non-semantic differences between objects\n sinon.assert.match(component.breadcrumbs[0], courseManagementCrumb);\n sinon.assert.match(component.breadcrumbs[1], testCourseCrumb);\n sinon.assert.match(component.breadcrumbs[2], programmingExercisesCrumb);\n sinon.assert.match(component.breadcrumbs[3], { label: 'Test Exercise', translate: false, uri: '/course-management/1/programming-exercises/2/' } as MockBreadcrumb);\n sinon.assert.match(component.breadcrumbs[4], gradingCrumb);\n });\n\n it('programming exercise new assessment', () => {\n const testUrl = '/course-management/1/programming-exercises/2/code-editor/new/assessment';\n router.setUrl(testUrl);\n\n fixture.detectChanges();\n\n expect(courseManagementStub).to.have.been.calledWith(1);\n expect(exerciseStub).to.have.been.calledWith(2);\n\n const assessmentCrumb = {\n label: 'artemisApp.assessment.assessment',\n translate: true,\n uri: '/course-management/1/programming-exercises/2/code-editor/new/assessment/',\n } as MockBreadcrumb;\n\n expect(component.breadcrumbs.length).to.equal(5);\n\n // Use matching here to ignore non-semantic differences between objects\n sinon.assert.match(component.breadcrumbs[0], courseManagementCrumb);\n sinon.assert.match(component.breadcrumbs[1], testCourseCrumb);\n sinon.assert.match(component.breadcrumbs[2], programmingExercisesCrumb);\n sinon.assert.match(component.breadcrumbs[3], { label: 'Test Exercise', translate: false, uri: '/course-management/1/programming-exercises/2/' } as MockBreadcrumb);\n sinon.assert.match(component.breadcrumbs[4], assessmentCrumb);\n });\n\n it('programming exercise hints', () => {\n const testUrl = '/course-management/1/exercises/2/hints/3';\n router.setUrl(testUrl);\n\n const hintService = fixture.debugElement.injector.get(ExerciseHintService);\n const hintsStub = sinon.stub(hintService, 'getTitle').returns(of({ body: 'Exercise Hint' } as HttpResponse));\n\n fixture.detectChanges();\n\n expect(courseManagementStub).to.have.been.calledWith(1);\n expect(exerciseStub).to.have.been.calledWith(2);\n expect(hintsStub).to.have.been.calledWith(3);\n\n const hintsCrumb = {\n label: 'artemisApp.exerciseHint.home.title',\n translate: true,\n uri: '/course-management/1/exercises/2/hints/',\n } as MockBreadcrumb;\n\n const hintCrumb = {\n label: 'Exercise Hint',\n translate: false,\n uri: '/course-management/1/exercises/2/hints/3/',\n } as MockBreadcrumb;\n\n expect(component.breadcrumbs.length).to.equal(6);\n\n // Use matching here to ignore non-semantic differences between objects\n sinon.assert.match(component.breadcrumbs[0], courseManagementCrumb);\n sinon.assert.match(component.breadcrumbs[1], testCourseCrumb);\n sinon.assert.match(component.breadcrumbs[2], { label: 'artemisApp.course.exercises', translate: true, uri: '/course-management/1/exercises/' } as MockBreadcrumb);\n sinon.assert.match(component.breadcrumbs[3], { label: 'Test Exercise', translate: false, uri: '/course-management/1/exercises/2/' } as MockBreadcrumb);\n sinon.assert.match(component.breadcrumbs[4], hintsCrumb);\n sinon.assert.match(component.breadcrumbs[5], hintCrumb);\n });\n\n it('text exercise feedback conflict', () => {\n const testUrl = '/course-management/1/text-exercises/2/submissions/3/text-feedback-conflict/4';\n router.setUrl(testUrl);\n\n fixture.detectChanges();\n\n expect(courseManagementStub).to.have.been.calledWith(1);\n expect(exerciseStub).to.have.been.calledWith(2);\n\n const submissionsCrumb = {\n label: 'artemisApp.exercise.submissions',\n translate: true,\n uri: '/course-management/1/text-exercises/2/submissions/',\n } as MockBreadcrumb;\n\n const conflictCrumb = {\n label: 'artemisApp.textAssessment.title',\n translate: true,\n uri: '/course-management/1/text-exercises/2/submissions/3/text-feedback-conflict/4/',\n } as MockBreadcrumb;\n\n expect(component.breadcrumbs.length).to.equal(6);\n\n // Use matching here to ignore non-semantic differences between objects\n sinon.assert.match(component.breadcrumbs[0], courseManagementCrumb);\n sinon.assert.match(component.breadcrumbs[1], testCourseCrumb);\n sinon.assert.match(component.breadcrumbs[2], { label: 'artemisApp.course.exercises', translate: true, uri: '/course-management/1/text-exercises/' } as MockBreadcrumb);\n sinon.assert.match(component.breadcrumbs[3], { label: 'Test Exercise', translate: false, uri: '/course-management/1/text-exercises/2/' } as MockBreadcrumb);\n sinon.assert.match(component.breadcrumbs[4], submissionsCrumb);\n sinon.assert.match(component.breadcrumbs[5], conflictCrumb);\n });\n\n it('modeling exercise example submission', () => {\n const testUrl = '/course-management/1/modeling-exercises/2/example-submissions/new';\n router.setUrl(testUrl);\n\n fixture.detectChanges();\n\n expect(courseManagementStub).to.have.been.calledWith(1);\n expect(exerciseStub).to.have.been.calledWith(2);\n\n const submissionCrumb = {\n label: 'artemisApp.exampleSubmission.home.title',\n translate: true,\n uri: '/course-management/1/modeling-exercises/2/example-submissions/new/',\n } as MockBreadcrumb;\n\n expect(component.breadcrumbs.length).to.equal(5);\n\n // Use matching here to ignore non-semantic differences between objects\n sinon.assert.match(component.breadcrumbs[0], courseManagementCrumb);\n sinon.assert.match(component.breadcrumbs[1], testCourseCrumb);\n sinon.assert.match(component.breadcrumbs[2], {\n label: 'artemisApp.course.exercises',\n translate: true,\n uri: '/course-management/1/modeling-exercises/',\n } as MockBreadcrumb);\n sinon.assert.match(component.breadcrumbs[3], { label: 'Test Exercise', translate: false, uri: '/course-management/1/modeling-exercises/2/' } as MockBreadcrumb);\n sinon.assert.match(component.breadcrumbs[4], submissionCrumb);\n });\n\n it('modeling exercise example submission', () => {\n const testUrl = '/course-management/1/modeling-exercises/2/example-submissions/3';\n router.setUrl(testUrl);\n\n fixture.detectChanges();\n\n expect(courseManagementStub).to.have.been.calledWith(1);\n expect(exerciseStub).to.have.been.calledWith(2);\n\n const submissionCrumb = {\n label: 'artemisApp.exampleSubmission.home.title',\n translate: true,\n uri: '/course-management/1/modeling-exercises/2/example-submissions/3/',\n } as MockBreadcrumb;\n\n expect(component.breadcrumbs.length).to.equal(5);\n\n // Use matching here to ignore non-semantic differences between objects\n sinon.assert.match(component.breadcrumbs[0], courseManagementCrumb);\n sinon.assert.match(component.breadcrumbs[1], testCourseCrumb);\n sinon.assert.match(component.breadcrumbs[2], {\n label: 'artemisApp.course.exercises',\n translate: true,\n uri: '/course-management/1/modeling-exercises/',\n } as MockBreadcrumb);\n sinon.assert.match(component.breadcrumbs[3], { label: 'Test Exercise', translate: false, uri: '/course-management/1/modeling-exercises/2/' } as MockBreadcrumb);\n sinon.assert.match(component.breadcrumbs[4], submissionCrumb);\n });\n\n it('lecture units', () => {\n const testUrl = '/course-management/1/lectures/2/unit-management/text-units/create';\n router.setUrl(testUrl);\n\n const lectureService = fixture.debugElement.injector.get(LectureService);\n const lectureStub = sinon.stub(lectureService, 'getTitle').returns(of({ body: 'Test Lecture' } as HttpResponse));\n\n fixture.detectChanges();\n\n expect(courseManagementStub).to.have.been.calledWith(1);\n expect(lectureStub).to.have.been.calledWith(2);\n\n const unitManagementCrumb = {\n label: 'artemisApp.lectureUnit.home.title',\n translate: true,\n uri: '/course-management/1/lectures/2/unit-management/',\n } as MockBreadcrumb;\n\n const createCrumb = {\n label: 'global.generic.create',\n translate: true,\n uri: '/course-management/1/lectures/2/unit-management/text-units/create/',\n };\n\n expect(component.breadcrumbs.length).to.equal(6);\n\n // Use matching here to ignore non-semantic differences between objects\n sinon.assert.match(component.breadcrumbs[0], courseManagementCrumb);\n sinon.assert.match(component.breadcrumbs[1], testCourseCrumb);\n sinon.assert.match(component.breadcrumbs[2], { label: 'artemisApp.lecture.home.title', translate: true, uri: '/course-management/1/lectures/' } as MockBreadcrumb);\n sinon.assert.match(component.breadcrumbs[3], { label: 'Test Lecture', translate: false, uri: '/course-management/1/lectures/2/' } as MockBreadcrumb);\n sinon.assert.match(component.breadcrumbs[4], unitManagementCrumb);\n sinon.assert.match(component.breadcrumbs[5], createCrumb);\n });\n\n it('apollon diagrams', () => {\n const testUrl = '/course-management/1/apollon-diagrams/2';\n router.setUrl(testUrl);\n\n const apollonDiagramService = fixture.debugElement.injector.get(ApollonDiagramService);\n const apollonStub = sinon.stub(apollonDiagramService, 'getTitle').returns(of({ body: 'Apollon Diagram' } as HttpResponse));\n\n fixture.detectChanges();\n\n expect(courseManagementStub).to.have.been.calledWith(1);\n expect(apollonStub).to.have.been.calledWith(2);\n\n expect(component.breadcrumbs.length).to.equal(4);\n\n // Use matching here to ignore non-semantic differences between objects\n sinon.assert.match(component.breadcrumbs[0], courseManagementCrumb);\n sinon.assert.match(component.breadcrumbs[1], testCourseCrumb);\n sinon.assert.match(component.breadcrumbs[2], {\n label: 'artemisApp.apollonDiagram.home.title',\n translate: true,\n uri: '/course-management/1/apollon-diagrams/',\n } as MockBreadcrumb);\n sinon.assert.match(component.breadcrumbs[3], { label: 'Apollon Diagram', translate: false, uri: '/course-management/1/apollon-diagrams/2/' } as MockBreadcrumb);\n });\n\n it('exam exercise groups', () => {\n const testUrl = '/course-management/1/exams/2/exercise-groups/3/quiz-exercises/new';\n router.setUrl(testUrl);\n\n const examService = fixture.debugElement.injector.get(ExamManagementService);\n const examStub = sinon.stub(examService, 'getTitle').returns(of({ body: 'Test Exam' } as HttpResponse));\n\n fixture.detectChanges();\n\n expect(courseManagementStub).to.have.been.calledWith(1);\n expect(examStub).to.have.been.calledWith(2);\n\n const exerciseGroupsCrumb = {\n label: 'artemisApp.examManagement.exerciseGroups',\n translate: true,\n uri: '/course-management/1/exams/2/exercise-groups/',\n };\n const exercisesCrumb = {\n label: 'artemisApp.course.exercises',\n translate: true,\n uri: '/course-management/1/exams/2/exercise-groups/3/quiz-exercises/',\n };\n const createCrumb = {\n label: 'global.generic.create',\n translate: true,\n uri: '/course-management/1/exams/2/exercise-groups/3/quiz-exercises/new/',\n };\n\n expect(component.breadcrumbs.length).to.equal(7);\n\n // Use matching here to ignore non-semantic differences between objects\n sinon.assert.match(component.breadcrumbs[0], courseManagementCrumb);\n sinon.assert.match(component.breadcrumbs[1], testCourseCrumb);\n sinon.assert.match(component.breadcrumbs[2], { label: 'artemisApp.examManagement.title', translate: true, uri: '/course-management/1/exams/' } as MockBreadcrumb);\n sinon.assert.match(component.breadcrumbs[3], { label: 'Test Exam', translate: false, uri: '/course-management/1/exams/2/' } as MockBreadcrumb);\n sinon.assert.match(component.breadcrumbs[4], exerciseGroupsCrumb);\n sinon.assert.match(component.breadcrumbs[5], exercisesCrumb);\n sinon.assert.match(component.breadcrumbs[6], createCrumb);\n });\n });\n});\n"},{"alpha_fraction":0.6209262609481812,"alphanum_fraction":0.6209262609481812,"avg_line_length":29.6842098236084,"blob_id":"686a15d7b4be2e4040c53b18503f6a1c61ce0f66","content_id":"7e9d04783e417c3b90fc48e0767550d0bf3a7ca2","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":1749,"license_type":"permissive","max_line_length":92,"num_lines":57,"path":"/src/main/webapp/app/admin/metrics/metrics.component.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { Component, OnInit } from '@angular/core';\nimport { NgbModal } from '@ng-bootstrap/ng-bootstrap';\nimport { JhiMetricsService } from 'app/admin/metrics/metrics.service';\n\n@Component({\n selector: 'jhi-metrics',\n templateUrl: './metrics.component.html',\n})\nexport class JhiMetricsMonitoringComponent implements OnInit {\n metrics: any = {};\n threadData: any = {};\n updatingMetrics = true;\n JCACHE_KEY: string;\n\n constructor(private modalService: NgbModal, private metricsService: JhiMetricsService) {\n this.JCACHE_KEY = 'jcache.statistics';\n }\n\n /**\n * Calls the refresh method on init\n */\n ngOnInit() {\n this.refresh();\n }\n\n /**\n * Refreshes the metrics by retrieving all metrics and thread dumps\n */\n refresh() {\n this.updatingMetrics = true;\n this.metricsService.getMetrics().subscribe((metrics) => {\n this.metrics = metrics;\n this.metricsService.threadDump().subscribe((data) => {\n this.threadData = data.threads;\n this.updatingMetrics = false;\n });\n });\n }\n\n /**\n * Checks if the metric with the key {@param key} exists\n * @param metrics json with metrics objects\n * @param key string identifier of a metric\n */\n isObjectExisting(metrics: any, key: string) {\n return metrics && metrics[key];\n }\n\n /**\n * Checks if the metric with the key {@param key} exists and is not empty\n * @param metrics json with metrics objects\n * @param key key string identifier of a metric\n */\n isObjectExistingAndNotEmpty(metrics: any, key: string) {\n return this.isObjectExisting(metrics, key) && JSON.stringify(metrics[key]) !== '{}';\n }\n}\n"},{"alpha_fraction":0.6775928139686584,"alphanum_fraction":0.6801536679267883,"avg_line_length":38.04999923706055,"blob_id":"f7c8498a0eb8c59442bdce5185eac8c36fc974be","content_id":"ef82774ad989fe8b7df7ba2b0579b7b7d753db31","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":3905,"license_type":"permissive","max_line_length":138,"num_lines":100,"path":"/src/test/javascript/spec/component/student-questions/student-question-answer.component.spec.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import * as chai from 'chai';\nimport * as sinonChai from 'sinon-chai';\nimport { ActivatedRoute } from '@angular/router';\nimport { ComponentFixture, TestBed } from '@angular/core/testing';\nimport { TranslateModule } from '@ngx-translate/core';\nimport { StudentQuestionAnswerComponent } from 'app/overview/student-questions/student-question-answer/student-question-answer.component';\nimport { StudentQuestionAnswer } from 'app/entities/student-question-answer.model';\nimport { User } from 'app/core/user/user.model';\nimport { ArtemisTestModule } from '../../test.module';\nimport { ArtemisSharedModule } from 'app/shared/shared.module';\nimport { MockActivatedRouteWithSubjects } from '../../helpers/mocks/activated-route/mock-activated-route-with-subjects';\n\nchai.use(sinonChai);\nconst expect = chai.expect;\n\ndescribe('StudentQuestionAnswerComponent', () => {\n let component: StudentQuestionAnswerComponent;\n let componentFixture: ComponentFixture;\n\n const user1 = {\n id: 1,\n } as User;\n\n const user2 = {\n id: 2,\n } as User;\n\n const unApprovedStudentQuestionAnswer = {\n id: 1,\n answerDate: undefined,\n answerText: 'not approved',\n tutorApproved: false,\n author: user1,\n } as StudentQuestionAnswer;\n\n const approvedStudentQuestionAnswer = {\n id: 2,\n answerDate: undefined,\n answerText: 'approved',\n tutorApproved: true,\n author: user2,\n } as StudentQuestionAnswer;\n\n beforeEach(async () => {\n return TestBed.configureTestingModule({\n imports: [TranslateModule.forRoot(), ArtemisTestModule, ArtemisSharedModule],\n declarations: [StudentQuestionAnswerComponent],\n providers: [{ provide: ActivatedRoute, useClass: MockActivatedRouteWithSubjects }],\n })\n .overrideTemplate(StudentQuestionAnswerComponent, '')\n .compileComponents()\n .then(() => {\n componentFixture = TestBed.createComponent(StudentQuestionAnswerComponent);\n component = componentFixture.componentInstance;\n });\n });\n\n it('should be author of answer', () => {\n component.studentQuestionAnswer = approvedStudentQuestionAnswer;\n component.user = user2;\n expect(component.isAuthorOfAnswer(approvedStudentQuestionAnswer)).to.be.true;\n });\n\n it('should not be author of answer', () => {\n component.studentQuestionAnswer = approvedStudentQuestionAnswer;\n component.user = user2;\n expect(component.isAuthorOfAnswer(unApprovedStudentQuestionAnswer)).to.be.false;\n });\n\n it('should approve answer', () => {\n component.studentQuestionAnswer = unApprovedStudentQuestionAnswer;\n component.toggleAnswerTutorApproved();\n expect(component.studentQuestionAnswer.tutorApproved).to.be.true;\n });\n\n it('should unapprove answer', () => {\n component.studentQuestionAnswer = approvedStudentQuestionAnswer;\n component.toggleAnswerTutorApproved();\n expect(component.studentQuestionAnswer.tutorApproved).to.be.false;\n });\n\n it('should toggle edit mode and reset editor Text', () => {\n component.studentQuestionAnswer = approvedStudentQuestionAnswer;\n component.isEditMode = true;\n component.editText = 'test';\n component.toggleEditMode();\n expect(component.editText).to.deep.equal('approved');\n expect(component.isEditMode).to.be.false;\n component.toggleEditMode();\n expect(component.isEditMode).to.be.true;\n });\n\n it('should update answerText', () => {\n component.studentQuestionAnswer = approvedStudentQuestionAnswer;\n component.isEditMode = true;\n component.editText = 'test';\n component.saveAnswer();\n expect(component.studentQuestionAnswer.answerText).to.deep.equal('test');\n });\n});\n"},{"alpha_fraction":0.667067289352417,"alphanum_fraction":0.6674679517745972,"avg_line_length":35.173912048339844,"blob_id":"12184e9073699244cd2180820eb0f135a4a0124e","content_id":"f8c540e0e3af19a302e6befd6a2de581559170aa","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":2496,"license_type":"permissive","max_line_length":145,"num_lines":69,"path":"/src/main/webapp/app/exercises/programming/shared/utils/build-plan-link.directive.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { Directive, HostBinding, HostListener, Input, OnInit } from '@angular/core';\nimport { take, tap } from 'rxjs/operators';\nimport { ProfileService } from 'app/shared/layouts/profiles/profile.service';\nimport { ProfileInfo } from 'app/shared/layouts/profiles/profile-info.model';\n\n/**\n * Creates the build pan URL for the given template URL.\n *\n * @param template The URL to the CI with placeholders for the plan ID and project key\n * @param projectKey The project key of the programming exercise\n * @param buildPlanId The ID of the build plan for which to construct the URL\n */\nexport const createBuildPlanUrl = (template: string, projectKey: string, buildPlanId: string) => {\n if (template && projectKey && buildPlanId) {\n return template.replace('{buildPlanId}', buildPlanId).replace('{projectKey}', projectKey);\n }\n};\n\n@Directive({ selector: 'a[jhiBuildPlanLink]' })\nexport class BuildPlanLinkDirective implements OnInit {\n @HostBinding('attr.href')\n readonly href = '';\n\n private participationBuildPlanId: string;\n private exerciseProjectKey: string;\n private templateLink: string;\n private linkToBuildPlan?: string;\n\n constructor(private profileService: ProfileService) {}\n\n /**\n * Life cycle hook called by Angular to indicate that Angular is done creating the component\n */\n ngOnInit(): void {\n this.profileService\n .getProfileInfo()\n .pipe(\n take(1),\n tap((info: ProfileInfo) => {\n this.templateLink = info.buildPlanURLTemplate;\n this.linkToBuildPlan = createBuildPlanUrl(info.buildPlanURLTemplate, this.exerciseProjectKey, this.participationBuildPlanId);\n }),\n )\n .subscribe();\n }\n\n @Input()\n set projectKey(key: string) {\n this.exerciseProjectKey = key;\n this.linkToBuildPlan = createBuildPlanUrl(this.templateLink, this.exerciseProjectKey, this.participationBuildPlanId);\n }\n\n @Input()\n set buildPlanId(planId: string) {\n this.participationBuildPlanId = planId;\n this.linkToBuildPlan = createBuildPlanUrl(this.templateLink, this.exerciseProjectKey, this.participationBuildPlanId);\n }\n\n /**\n * Opens build plan link window.\n */\n @HostListener('click', ['$event'])\n openBuildPlanLink($event: any) {\n $event.preventDefault();\n if (this.linkToBuildPlan) {\n window.open(this.linkToBuildPlan);\n }\n }\n}\n"},{"alpha_fraction":0.6927047371864319,"alphanum_fraction":0.696377158164978,"avg_line_length":46.07944107055664,"blob_id":"2eadfd6e6ab46d10d4616744c77bfed136e1e966","content_id":"625a9430009a03c0990ef43846ca8e0a5845c4ae","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":10075,"license_type":"permissive","max_line_length":138,"num_lines":214,"path":"/src/test/javascript/spec/component/shared/clone-repo-button.component.spec.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { ComponentFixture, fakeAsync, TestBed, tick } from '@angular/core/testing';\n\nimport { CloneRepoButtonComponent } from 'app/shared/components/clone-repo-button/clone-repo-button.component';\nimport { TranslateModule } from '@ngx-translate/core';\nimport { MockComponent, MockProvider } from 'ng-mocks';\nimport { ProfileService } from 'app/shared/layouts/profiles/profile.service';\nimport { SourceTreeService } from 'app/exercises/programming/shared/service/sourceTree.service';\nimport { MockProfileService } from '../../helpers/mocks/service/mock-profile.service';\nimport { AccountService } from 'app/core/auth/account.service';\nimport { MockAccountService } from '../../helpers/mocks/service/mock-account.service';\nimport * as sinon from 'sinon';\nimport * as chai from 'chai';\nimport * as sinonChai from 'sinon-chai';\nimport { ExerciseActionButtonComponent } from 'app/shared/components/exercise-action-button.component';\nimport { NgbModule } from '@ng-bootstrap/ng-bootstrap';\nimport { ClipboardModule } from 'ngx-clipboard';\nimport { JhiAlertService } from 'ng-jhipster';\nimport { of, BehaviorSubject, Subject } from 'rxjs';\nimport { ProfileInfo } from 'app/shared/layouts/profiles/profile-info.model';\nimport { SinonStub, stub } from 'sinon';\nimport { MockSyncStorage } from '../../helpers/mocks/service/mock-sync-storage.service';\nimport { LocalStorageService } from 'ngx-webstorage';\nimport { By } from '@angular/platform-browser';\nimport { ArtemisTestModule } from '../../test.module';\nimport { ArtemisSharedModule } from 'app/shared/shared.module';\nimport { FeatureToggleModule } from 'app/shared/feature-toggle/feature-toggle.module';\nimport { MockAlertService } from '../../helpers/mocks/service/mock-alert.service';\nimport { MockFeatureToggleService } from '../../helpers/mocks/service/mock-feature-toggle.service';\nimport { FeatureToggleService } from 'app/shared/feature-toggle/feature-toggle.service';\n\nchai.use(sinonChai);\nconst expect = chai.expect;\n\ndescribe('JhiCloneRepoButtonComponent', () => {\n let component: CloneRepoButtonComponent;\n let fixture: ComponentFixture;\n let profileService: ProfileService;\n let sourceTreeService: SourceTreeService;\n let accountService: AccountService;\n\n let localStorageUseSshRetrieveStub: SinonStub;\n let localStorageUseSshObserveStub: SinonStub;\n let localStorageUseSshObserveStubSubject: Subject;\n let localStorageUseSshStoreStub: SinonStub;\n\n const info: ProfileInfo = {\n activeProfiles: [],\n allowedMinimumOrionVersion: '',\n buildPlanURLTemplate: '',\n commitHashURLTemplate: '',\n contact: '',\n externalUserManagementName: '',\n externalUserManagementURL: '',\n features: [],\n imprint: '',\n inProduction: false,\n programmingLanguageFeatures: [],\n ribbonEnv: '',\n sshCloneURLTemplate: 'ssh://git@bitbucket.ase.in.tum.de:7999/',\n sshKeysURL: 'sshKeysURL',\n testServer: false,\n versionControlUrl: 'https://bitbucket.ase.in.tum.de/scm/ITCPLEASE1/itcplease1-exercise-team1.git',\n };\n\n beforeEach(() => {\n TestBed.configureTestingModule({\n imports: [ArtemisTestModule, TranslateModule.forRoot(), NgbModule, ArtemisSharedModule, FeatureToggleModule, ClipboardModule],\n declarations: [CloneRepoButtonComponent, MockComponent(ExerciseActionButtonComponent)],\n providers: [\n { provide: JhiAlertService, useClass: MockAlertService },\n { provide: FeatureToggleService, useClass: MockFeatureToggleService },\n { provide: AccountService, useClass: MockAccountService },\n { provide: ProfileService, useClass: MockProfileService },\n { provide: LocalStorageService, useClass: MockSyncStorage },\n MockProvider(SourceTreeService, {}),\n ],\n }).compileComponents();\n\n fixture = TestBed.createComponent(CloneRepoButtonComponent);\n component = fixture.componentInstance;\n profileService = TestBed.inject(ProfileService);\n sourceTreeService = TestBed.inject(SourceTreeService);\n accountService = TestBed.inject(AccountService);\n\n const localStorageMock = fixture.debugElement.injector.get(LocalStorageService);\n localStorageUseSshRetrieveStub = stub(localStorageMock, 'retrieve');\n localStorageUseSshObserveStub = stub(localStorageMock, 'observe');\n localStorageUseSshStoreStub = stub(localStorageMock, 'store');\n localStorageUseSshObserveStubSubject = new Subject();\n localStorageUseSshObserveStub.returns(localStorageUseSshObserveStubSubject);\n });\n\n afterEach(function () {\n // completely restore all fakes created through the sandbox\n sinon.restore();\n });\n\n it('should initialize', fakeAsync(() => {\n stubServices();\n\n component.ngOnInit();\n tick();\n expect(component.sshKeysUrl).to.equal(info.sshKeysURL);\n expect(component.sshTemplateUrl).to.equal(info.sshCloneURLTemplate);\n expect(component.sshEnabled).to.equal(!!info.sshCloneURLTemplate);\n expect(component.repositoryPassword).to.equal('repository_password');\n expect(component.versionControlUrl).to.equal(info.versionControlUrl);\n }));\n\n it('should save repository password if SourceTree returns one', () => {\n const fakeSourceTreeResponse = { password: 'repository_password' };\n sinon.replace(sourceTreeService, 'getRepositoryPassword', sinon.fake.returns(of(fakeSourceTreeResponse)));\n\n component.getRepositoryPassword();\n expect(component.repositoryPassword).to.equal('repository_password');\n });\n\n it('should not save repository password if SourceTree doesnt return one', () => {\n const fakeSourceTreeResponse = { error: 'Some password not found error' };\n sinon.replace(sourceTreeService, 'getRepositoryPassword', sinon.fake.returns(of(fakeSourceTreeResponse)));\n\n component.repositoryPassword = 'password';\n component.getRepositoryPassword();\n expect(component.repositoryPassword).to.equal('password');\n });\n\n it('should get ssh url (same url for team and individual participation)', () => {\n component.repositoryUrl = 'https://bitbucket.ase.in.tum.de/scm/ITCPLEASE1/itcplease1-exercise.git';\n component.sshTemplateUrl = 'ssh://git@bitbucket.ase.in.tum.de:7999/';\n component.useSsh = true;\n\n component.isTeamParticipation = true;\n let url = component.getHttpOrSshRepositoryUrl();\n expect(url).to.equal('ssh://git@bitbucket.ase.in.tum.de:7999/ITCPLEASE1/itcplease1-exercise.git');\n\n component.isTeamParticipation = false;\n url = component.getHttpOrSshRepositoryUrl();\n expect(url).to.equal('ssh://git@bitbucket.ase.in.tum.de:7999/ITCPLEASE1/itcplease1-exercise.git');\n });\n\n it('should get html url (not the same url for team and individual participation)', () => {\n component.repositoryUrl = info.versionControlUrl!;\n component.sshTemplateUrl = 'ssh://git@bitbucket.ase.in.tum.de:7999/';\n component.useSsh = false;\n\n component.user = { login: 'user1', guidedTourSettings: [] };\n component.isTeamParticipation = true;\n let url = component.getHttpOrSshRepositoryUrl();\n expect(url).to.equal(`https://${component.user.login}@bitbucket.ase.in.tum.de/scm/ITCPLEASE1/itcplease1-exercise-team1.git`);\n\n component.isTeamParticipation = false;\n url = component.getHttpOrSshRepositoryUrl();\n expect(url).to.equal(info.versionControlUrl!);\n });\n\n it('should get copy the repository url', () => {\n component.repositoryUrl = info.versionControlUrl!;\n component.useSsh = false;\n\n component.user = { login: 'user1', guidedTourSettings: [] };\n component.isTeamParticipation = true;\n let url = component.getHttpOrSshRepositoryUrl();\n expect(url).to.equal(`https://${component.user.login}@bitbucket.ase.in.tum.de/scm/ITCPLEASE1/itcplease1-exercise-team1.git`);\n\n component.isTeamParticipation = false;\n url = component.getHttpOrSshRepositoryUrl();\n expect(url).to.equal(info.versionControlUrl!);\n });\n\n it('should fetch and store ssh preference', fakeAsync(() => {\n stubServices();\n\n component.sshEnabled = true;\n\n fixture.detectChanges();\n tick();\n\n expect(localStorageUseSshRetrieveStub).to.have.been.calledOnceWithExactly('useSsh');\n expect(localStorageUseSshObserveStub).to.have.been.calledOnceWithExactly('useSsh');\n expect(component.useSsh).to.be.false;\n\n fixture.debugElement.query(By.css('.clone-repository')).nativeElement.click();\n tick();\n fixture.debugElement.query(By.css('.use-ssh'));\n fixture.debugElement.query(By.css('.use-ssh')).nativeElement.click();\n tick();\n expect(localStorageUseSshStoreStub).to.have.been.calledOnceWithExactly('useSsh', true);\n expect(component.useSsh).to.be.true;\n\n fixture.debugElement.query(By.css('.use-ssh')).nativeElement.click();\n tick();\n expect(localStorageUseSshStoreStub).to.have.been.calledWithExactly('useSsh', false);\n expect(component.useSsh).to.be.false;\n\n localStorageUseSshObserveStubSubject.next(true);\n tick();\n expect(component.useSsh).to.be.true;\n\n localStorageUseSshObserveStubSubject.next(false);\n tick();\n expect(component.useSsh).to.be.false;\n }));\n\n function stubServices() {\n const identityStub = sinon.stub(accountService, 'identity');\n identityStub.returns(Promise.resolve({ guidedTourSettings: [], login: 'edx_userLogin' }));\n\n const getRepositoryPasswordStub = sinon.stub(sourceTreeService, 'getRepositoryPassword');\n getRepositoryPasswordStub.returns(of({ password: 'repository_password' }));\n\n const getProfileInfoStub = sinon.stub(profileService, 'getProfileInfo');\n getProfileInfoStub.returns(new BehaviorSubject(info));\n }\n});\n"},{"alpha_fraction":0.6298342347145081,"alphanum_fraction":0.6306235194206238,"avg_line_length":18.796875,"blob_id":"56b22df4789d31008c2849ca68fbf443e9827e03","content_id":"e5d33ce7b5c9dc8ebdebb0700b648d4cca4f95b1","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1267,"license_type":"permissive","max_line_length":78,"num_lines":64,"path":"/src/main/java/de/tum/in/www1/artemis/service/connectors/bitbucket/dto/BitbucketWebHookDTO.java","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"package de.tum.in.www1.artemis.service.connectors.bitbucket.dto;\n\nimport java.util.ArrayList;\nimport java.util.List;\n\nimport com.fasterxml.jackson.annotation.JsonIgnoreProperties;\nimport com.fasterxml.jackson.annotation.JsonInclude;\n\n@JsonIgnoreProperties(ignoreUnknown = true)\n@JsonInclude(JsonInclude.Include.NON_EMPTY)\npublic class BitbucketWebHookDTO {\n\n private Integer id;\n\n private String name;\n\n private String url;\n\n private List events = new ArrayList<>();\n\n public Integer getId() {\n return id;\n }\n\n public void setId(Integer id) {\n this.id = id;\n }\n\n public String getName() {\n return name;\n }\n\n public void setName(String name) {\n this.name = name;\n }\n\n public String getUrl() {\n return url;\n }\n\n public void setUrl(String url) {\n this.url = url;\n }\n\n public List getEvents() {\n return events;\n }\n\n public void setEvents(List events) {\n this.events = events;\n }\n\n /**\n * needed for Jackson\n */\n public BitbucketWebHookDTO() {\n }\n\n public BitbucketWebHookDTO(String name, String url, List events) {\n this.name = name;\n this.url = url;\n this.events = events;\n }\n}\n"},{"alpha_fraction":0.6220588088035583,"alphanum_fraction":0.6235294342041016,"avg_line_length":20.935483932495117,"blob_id":"3a7d106cbd7970d0edb0c81ca558b64823edff78","content_id":"1212529f9680eaef74abcb7b31885361c2ad0c00","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":680,"license_type":"permissive","max_line_length":112,"num_lines":31,"path":"/src/main/java/de/tum/in/www1/artemis/security/Role.java","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"package de.tum.in.www1.artemis.security;\n\n/**\n * Constants for Spring Security authorities.\n */\npublic enum Role {\n\n // NOTE: we will soon rename \"USER\" to \"STUDENT\" in the database and add a new role \"EDITOR\"\n ADMIN(\"ADMIN\"), INSTRUCTOR(\"INSTRUCTOR\"), TEACHING_ASSISTANT(\"TA\"), STUDENT(\"USER\"), ANONYMOUS(\"ANONYMOUS\");\n\n public static final String ROLE_PREFIX = \"ROLE_\";\n\n private final String role;\n\n Role(String role) {\n this.role = role;\n }\n\n public String getAuthority() {\n return ROLE_PREFIX + role;\n }\n\n public String getRole() {\n return role;\n }\n\n @Override\n public String toString() {\n return role;\n }\n}\n"},{"alpha_fraction":0.6048644185066223,"alphanum_fraction":0.6063264012336731,"avg_line_length":43.25882339477539,"blob_id":"662796fb5382e2ee9d1fdbcba4b660e0079cd057","content_id":"8a2d6ffd41dcfaf812a3d306581044eca66abaae","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":7524,"license_type":"permissive","max_line_length":153,"num_lines":170,"path":"/src/test/javascript/spec/component/team/team.component.spec.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { HTTP_INTERCEPTORS } from '@angular/common/http';\nimport { ComponentFixture, TestBed, waitForAsync } from '@angular/core/testing';\nimport { ActivatedRoute, Router, RouterModule } from '@angular/router';\nimport { NgbModule } from '@ng-bootstrap/ng-bootstrap';\nimport { TranslateService } from '@ngx-translate/core';\nimport { NgxDatatableModule } from '@swimlane/ngx-datatable';\nimport { AccountService } from 'app/core/auth/account.service';\nimport { User } from 'app/core/user/user.model';\nimport { ExerciseService } from 'app/exercises/shared/exercise/exercise.service';\nimport { TeamParticipationTableComponent } from 'app/exercises/shared/team/team-participation-table/team-participation-table.component';\nimport { TeamDeleteButtonComponent } from 'app/exercises/shared/team/team-update-dialog/team-delete-button.component';\nimport { TeamUpdateButtonComponent } from 'app/exercises/shared/team/team-update-dialog/team-update-button.component';\nimport { TeamComponent } from 'app/exercises/shared/team/team.component.ts';\nimport { TeamService } from 'app/exercises/shared/team/team.service';\nimport { AlertComponent } from 'app/shared/alert/alert.component';\nimport { DataTableComponent } from 'app/shared/data-table/data-table.component';\nimport { FeatureToggleModule } from 'app/shared/feature-toggle/feature-toggle.module';\nimport { ArtemisDatePipe } from 'app/shared/pipes/artemis-date.pipe.ts';\nimport { ArtemisTranslatePipe } from 'app/shared/pipes/artemis-translate.pipe.ts';\nimport * as chai from 'chai';\nimport { JhiAlertService, NgJhipsterModule } from 'ng-jhipster';\nimport { MockComponent, MockModule, MockPipe, MockProvider } from 'ng-mocks';\nimport { LocalStorageService, SessionStorageService } from 'ngx-webstorage';\nimport { of, throwError } from 'rxjs';\nimport { restore, SinonStub, spy, stub } from 'sinon';\nimport * as sinonChai from 'sinon-chai';\nimport { mockExercise, mockTeam, mockTeams, TeamRequestInterceptorMock } from '../../helpers/mocks/service/mock-team.service';\nimport { ArtemisTestModule } from '../../test.module';\n\nchai.use(sinonChai);\nconst expect = chai.expect;\n\ndescribe('TeamComponent', () => {\n let comp: TeamComponent;\n let fixture: ComponentFixture;\n let router: Router;\n const user = new User(99, 'newUser', 'UserFirstName', 'UserLastName');\n let accountService: AccountService;\n let identityStub: SinonStub;\n let exerciseService: ExerciseService;\n let teamService: TeamService;\n let alertService: JhiAlertService;\n\n beforeEach(() => {\n TestBed.configureTestingModule({\n imports: [\n ArtemisTestModule,\n MockModule(NgbModule),\n MockModule(NgJhipsterModule),\n MockModule(FeatureToggleModule),\n MockModule(NgxDatatableModule),\n MockModule(RouterModule),\n ],\n declarations: [\n TeamComponent,\n MockComponent(TeamUpdateButtonComponent),\n MockComponent(TeamDeleteButtonComponent),\n MockPipe(ArtemisTranslatePipe),\n MockPipe(ArtemisDatePipe),\n MockComponent(TeamParticipationTableComponent),\n MockComponent(DataTableComponent),\n MockComponent(AlertComponent),\n ],\n providers: [\n MockProvider(SessionStorageService),\n MockProvider(LocalStorageService),\n MockProvider(AccountService),\n TeamService,\n MockProvider(TranslateService),\n ExerciseService,\n {\n provide: HTTP_INTERCEPTORS,\n useClass: TeamRequestInterceptorMock,\n multi: true,\n },\n MockProvider(Router),\n {\n provide: ActivatedRoute,\n useValue: {\n params: of({ teamId: mockTeam.id, exerciseId: mockExercise.id }),\n },\n },\n ],\n })\n .compileComponents()\n .then(() => {\n accountService = TestBed.inject(AccountService);\n identityStub = stub(accountService, 'identity').returns(Promise.resolve(user));\n stub(accountService, 'isAtLeastTutorInCourse').returns(true);\n stub(accountService, 'isAtLeastInstructorInCourse').returns(true);\n fixture = TestBed.createComponent(TeamComponent);\n comp = fixture.componentInstance;\n alertService = TestBed.inject(JhiAlertService);\n router = TestBed.inject(Router);\n teamService = TestBed.inject(TeamService);\n exerciseService = TestBed.inject(ExerciseService);\n });\n });\n\n afterEach(() => {\n restore();\n });\n\n describe('ngOnInit', () => {\n let alertServiceStub: SinonStub;\n\n afterEach(() => {\n restore();\n });\n\n it('should set team and exercise from services', () => {\n comp.ngOnInit();\n expect(comp.exercise).to.deep.equal(mockExercise);\n expect(comp.team).to.deep.equal(mockTeam);\n expect(comp.exercise.isAtLeastTutor).equal(true);\n expect(comp.exercise.isAtLeastInstructor).equal(true);\n expect(comp.isTeamOwner).to.equal(false);\n });\n\n it('should call alert service error when exercise service fails', () => {\n const exerciseStub = stub(exerciseService, 'find').returns(throwError({ status: 404 }));\n alertServiceStub = stub(alertService, 'error');\n waitForAsync(() => {\n comp.ngOnInit();\n expect(exerciseStub).to.have.been.called;\n expect(alertServiceStub).to.have.been.called;\n expect(comp.isLoading).to.equal(false);\n });\n });\n\n it('should call alert service error when team service fails', () => {\n const teamStub = stub(teamService, 'find').returns(throwError({ status: 404 }));\n alertServiceStub = stub(alertService, 'error');\n waitForAsync(() => {\n comp.ngOnInit();\n expect(teamStub).to.have.been.called;\n expect(alertServiceStub).to.have.been.called;\n expect(comp.isLoading).to.equal(false);\n });\n });\n });\n\n describe('ngOnInit with team owner', () => {\n it('should set team owner true if user is team owner', () => {\n waitForAsync(() => {\n identityStub.returns(Promise.resolve({ ...user, id: 1 }));\n fixture = TestBed.createComponent(TeamComponent);\n comp = fixture.componentInstance;\n comp.ngOnInit();\n expect(comp.isTeamOwner).to.equal(true);\n });\n });\n });\n\n describe('onTeamUpdate', () => {\n it('should update team to given team', () => {\n comp.onTeamUpdate(mockTeams[1]);\n expect(comp.team).to.deep.equal(mockTeams[1]);\n });\n });\n\n describe('onTeamDelete', () => {\n it('should go to teams overview on delete', () => {\n comp.ngOnInit();\n const routerSpy = spy(router, 'navigate');\n comp.onTeamDelete();\n expect(routerSpy).to.have.been.calledOnceWithExactly(['/course-management', mockExercise.course?.id, 'exercises', mockExercise.id, 'teams']);\n });\n });\n});\n"},{"alpha_fraction":0.5307376980781555,"alphanum_fraction":0.5420082211494446,"avg_line_length":39.66666793823242,"blob_id":"6ebcbc4d4fb53e29ebef4372a1e571e69ee4bdf7","content_id":"0cdb99b743b45fdb5b2cd605bded90670c12a76a","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":1952,"license_type":"permissive","max_line_length":114,"num_lines":48,"path":"/src/test/javascript/jest.config.js","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"const esModules = ['ngx-treeview', 'lodash-es'].join('|');\nmodule.exports = {\n globals: {\n 'ts-jest': {\n tsconfig: '/tsconfig.spec.json',\n stringifyContentPathRegex: '\\\\.html$',\n astTransformers: {\n before: [require.resolve('./InlineHtmlStripStylesTransformer')],\n },\n diagnostics: {\n ignoreCodes: [151001],\n },\n },\n },\n collectCoverageFrom: ['src/main/webapp/**/*.{js,jsx,ts,tsx}', '!src/main/webapp/**/*.module.{js,jsx,ts,tsx}'],\n coverageThreshold: {\n global: {\n // TODO: in the future, the following values should be increase to at least 80%\n statements: 75,\n branches: 56,\n functions: 64,\n lines: 74,\n },\n },\n preset: 'jest-preset-angular',\n setupFilesAfterEnv: ['/src/test/javascript/jest.ts', 'jest-sinon'],\n modulePaths: ['/src/main/webapp/'],\n transformIgnorePatterns: [`/node_modules/(?!${esModules})`],\n rootDir: '../../../',\n modulePathIgnorePatterns: [],\n testMatch: [\n '/src/test/javascript/spec/component/**/*.ts',\n '/src/test/javascript/spec/directive/**/*.ts',\n '/src/test/javascript/spec/integration/**/*.ts',\n '/src/test/javascript/spec/pipe/**/*.ts',\n '/src/test/javascript/spec/service/**/*.ts',\n '/src/test/javascript/spec/util/**/*.ts',\n ],\n moduleNameMapper: {\n '^app/(.*)': '/src/main/webapp/app/$1',\n 'test/(.*)': '/src/test/javascript/spec/$1',\n '@assets/(.*)': '/src/main/webapp/assets/$1',\n '@core/(.*)': '/src/main/webapp/app/core/$1',\n '@env': '/src/main/webapp/environments/environment',\n '@src/(.*)': '/src/src/$1',\n '@state/(.*)': '/src/app/state/$1',\n },\n};\n"},{"alpha_fraction":0.7087198495864868,"alphanum_fraction":0.7087198495864868,"avg_line_length":36.17241287231445,"blob_id":"2a4625295d381690c198daf96c8e99273bf0a26d","content_id":"09084e2a4fcb7931562f6df8de774a94f9e2cabc","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":2156,"license_type":"permissive","max_line_length":141,"num_lines":58,"path":"/src/main/webapp/app/exercises/shared/plagiarism/plagiarism-header/plagiarism-header.component.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { Component, Input } from '@angular/core';\nimport { HttpClient } from '@angular/common/http';\nimport { Subject } from 'rxjs';\nimport { PlagiarismStatus } from 'app/exercises/shared/plagiarism/types/PlagiarismStatus';\nimport { PlagiarismComparison } from 'app/exercises/shared/plagiarism/types/PlagiarismComparison';\n\n// False-positives:\n// eslint-disable-next-line @typescript-eslint/no-unused-vars\nimport { TextSubmissionElement } from 'app/exercises/shared/plagiarism/types/text/TextSubmissionElement';\n// eslint-disable-next-line @typescript-eslint/no-unused-vars\nimport { ModelingSubmissionElement } from 'app/exercises/shared/plagiarism/types/modeling/ModelingSubmissionElement';\nimport { SERVER_API_URL } from 'app/app.constants';\n\n@Component({\n selector: 'jhi-plagiarism-header',\n styleUrls: ['./plagiarism-header.component.scss'],\n templateUrl: './plagiarism-header.component.html',\n})\nexport class PlagiarismHeaderComponent {\n @Input() comparison: PlagiarismComparison;\n @Input() splitControlSubject: Subject;\n\n private resourceUrl = SERVER_API_URL + 'api/plagiarism-comparisons';\n\n constructor(public http: HttpClient) {}\n\n /**\n * Set the status of the currently selected comparison to CONFIRMED.\n */\n confirmPlagiarism() {\n this.updatePlagiarismStatus(PlagiarismStatus.CONFIRMED);\n }\n\n /**\n * Set the status of the currently selected comparison to DENIED.\n */\n denyPlagiarism() {\n this.updatePlagiarismStatus(PlagiarismStatus.DENIED);\n }\n\n /**\n * Update the status of the currently selected comparison.\n * @param status the new status of the comparison\n */\n updatePlagiarismStatus(status: PlagiarismStatus) {\n return this.http.put(`${this.resourceUrl}/${this.comparison.id}/status`, { status }, { observe: 'response' }).subscribe(() => {\n this.comparison.status = status;\n });\n }\n\n expandSplitPane(pane: 'left' | 'right') {\n this.splitControlSubject.next(pane);\n }\n\n resetSplitPanes() {\n this.splitControlSubject.next('even');\n }\n}\n"},{"alpha_fraction":0.6662269234657288,"alphanum_fraction":0.6662269234657288,"avg_line_length":37.37974548339844,"blob_id":"8c31a33bbde2e551c26be0d1756e0d3ff746a1d2","content_id":"817ea66628e43bcab7d90ea3940fe85daf41e331","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":3032,"license_type":"permissive","max_line_length":143,"num_lines":79,"path":"/src/main/webapp/app/exercises/file-upload/manage/file-upload-exercise-detail.component.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { Component, OnDestroy, OnInit } from '@angular/core';\nimport { ActivatedRoute } from '@angular/router';\nimport { HttpErrorResponse, HttpResponse } from '@angular/common/http';\nimport { Subscription } from 'rxjs/Subscription';\nimport { JhiEventManager } from 'ng-jhipster';\n\nimport { FileUploadExercise } from 'app/entities/file-upload-exercise.model';\nimport { FileUploadExerciseService } from './file-upload-exercise.service';\nimport { filter } from 'rxjs/operators';\nimport { JhiAlertService } from 'ng-jhipster';\n\n@Component({\n selector: 'jhi-file-upload-exercise-detail',\n templateUrl: './file-upload-exercise-detail.component.html',\n})\nexport class FileUploadExerciseDetailComponent implements OnInit, OnDestroy {\n fileUploadExercise: FileUploadExercise;\n isExamExercise: boolean;\n private subscription: Subscription;\n private eventSubscriber: Subscription;\n\n constructor(\n private eventManager: JhiEventManager,\n private fileUploadExerciseService: FileUploadExerciseService,\n private route: ActivatedRoute,\n private jhiAlertService: JhiAlertService,\n ) {}\n\n /**\n * Initializes subscription for file upload exercise\n */\n ngOnInit() {\n // TODO: route determines whether the component is in exam mode\n this.subscription = this.route.params.subscribe((params) => {\n this.load(params['exerciseId']);\n });\n this.registerChangeInFileUploadExercises();\n }\n\n /**\n * Loads file upload exercise from the server\n * @param exerciseId the id of the file upload exercise\n */\n load(exerciseId: number) {\n // TODO: Use a separate find method for exam exercises containing course, exam, exerciseGroup and exercise id\n this.fileUploadExerciseService\n .find(exerciseId)\n .pipe(filter((res) => !!res.body))\n .subscribe(\n (fileUploadExerciseResponse: HttpResponse) => {\n this.fileUploadExercise = fileUploadExerciseResponse.body!;\n this.isExamExercise = this.fileUploadExercise.exerciseGroup !== undefined;\n if (this.fileUploadExercise.categories) {\n this.fileUploadExercise.categories = this.fileUploadExercise.categories.map((category) => JSON.parse(category));\n }\n },\n (res: HttpErrorResponse) => this.onError(res),\n );\n }\n\n /**\n * Unsubscribes on component destruction\n */\n ngOnDestroy() {\n this.subscription.unsubscribe();\n this.eventManager.destroy(this.eventSubscriber);\n }\n\n /**\n * Listens to file upload exercise list modifications\n */\n registerChangeInFileUploadExercises() {\n this.eventSubscriber = this.eventManager.subscribe('fileUploadExerciseListModification', () => this.load(this.fileUploadExercise.id!));\n }\n\n private onError(error: HttpErrorResponse) {\n this.jhiAlertService.error(error.message);\n }\n}\n"},{"alpha_fraction":0.6126618385314941,"alphanum_fraction":0.6154935359954834,"avg_line_length":37.03076934814453,"blob_id":"7fd1471c17aeafbae5cdc65dc245f7d5239a2a9b","content_id":"8e95e9b93f5c5917014e0a4f4dea70e42be9dbd3","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":4944,"license_type":"permissive","max_line_length":118,"num_lines":130,"path":"/src/test/javascript/spec/component/student-questions/student-question.component.spec.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import * as chai from 'chai';\nimport * as sinonChai from 'sinon-chai';\nimport { ActivatedRoute } from '@angular/router';\nimport { ComponentFixture, TestBed } from '@angular/core/testing';\nimport { TranslateModule } from '@ngx-translate/core';\nimport { StudentQuestionComponent } from 'app/overview/student-questions/student-question/student-question.component';\nimport { StudentQuestionAnswer } from 'app/entities/student-question-answer.model';\nimport { StudentQuestion } from 'app/entities/student-question.model';\nimport { User } from 'app/core/user/user.model';\nimport { ArtemisTestModule } from '../../test.module';\nimport { MarkdownEditorComponent } from 'app/shared/markdown-editor/markdown-editor.component';\nimport { MockDirective, MockPipe } from 'ng-mocks';\nimport { ConfirmIconComponent } from 'app/shared/confirm-icon/confirm-icon.component';\nimport { StudentVotesComponent } from 'app/overview/student-questions/student-votes/student-votes.component';\nimport { NgbTooltip } from '@ng-bootstrap/ng-bootstrap';\nimport { HtmlForMarkdownPipe } from 'app/shared/pipes/html-for-markdown.pipe';\nimport { ArtemisDatePipe } from 'app/shared/pipes/artemis-date.pipe';\nimport { ArtemisTranslatePipe } from 'app/shared/pipes/artemis-translate.pipe.ts';\n\nchai.use(sinonChai);\nconst expect = chai.expect;\n\ndescribe('StudentQuestionComponent', () => {\n let component: StudentQuestionComponent;\n let componentFixture: ComponentFixture;\n\n const user1 = {\n id: 1,\n } as User;\n\n const user2 = {\n id: 2,\n } as User;\n\n const unApprovedStudentQuestionAnswer = {\n id: 1,\n answerDate: undefined,\n answerText: 'not approved',\n tutorApproved: false,\n author: user1,\n } as StudentQuestionAnswer;\n\n const approvedStudentQuestionAnswer = {\n id: 2,\n answerDate: undefined,\n answerText: 'approved',\n tutorApproved: true,\n author: user2,\n } as StudentQuestionAnswer;\n\n const studentQuestion = {\n id: 1,\n questionText: 'question',\n creationDate: undefined,\n author: user1,\n answers: [unApprovedStudentQuestionAnswer, approvedStudentQuestionAnswer],\n } as StudentQuestion;\n\n const maliciousStudentQuestion = {\n id: 2,\n questionText: ';\n let notificationService: NotificationService;\n let accountService: AccountService;\n let router: Router;\n\n const generateNotification = (id: number) => {\n const generatedNotification = { id, title: 'Quiz started', text: 'Quiz \"Proxy pattern\" just started.' } as Notification;\n generatedNotification.target = JSON.stringify({ mainPage: 'courses', course: 1, entity: 'exercise', id: 1 });\n return generatedNotification;\n };\n const notification = generateNotification(1);\n\n beforeEach(() => {\n TestBed.configureTestingModule({\n imports: [ArtemisTestModule, RouterTestingModule.withRoutes([])],\n declarations: [NotificationPopupComponent],\n providers: [\n { provide: LocalStorageService, useClass: MockSyncStorage },\n { provide: SessionStorageService, useClass: MockSyncStorage },\n { provide: NotificationService, useClass: MockNotificationService },\n { provide: TranslateService, useClass: MockTranslateService },\n { provide: AccountService, useClass: MockAccountService },\n ],\n })\n .compileComponents()\n .then(() => {\n notificationPopupComponentFixture = TestBed.createComponent(NotificationPopupComponent);\n notificationPopupComponent = notificationPopupComponentFixture.componentInstance;\n notificationService = TestBed.inject(NotificationService);\n accountService = TestBed.inject(AccountService);\n router = TestBed.get(Router);\n });\n });\n\n describe('Initialization', () => {\n it('should get authentication state', () => {\n sinon.spy(accountService, 'getAuthenticationState');\n notificationPopupComponent.ngOnInit();\n expect(accountService.getAuthenticationState).to.have.been.calledOnce;\n });\n\n it('should subscribe to notification updates', () => {\n sinon.spy(notificationService, 'subscribeToNotificationUpdates');\n notificationPopupComponent.ngOnInit();\n expect(notificationService.subscribeToNotificationUpdates).to.have.been.calledOnce;\n });\n });\n\n describe('Click', () => {\n beforeEach(() => {\n notificationPopupComponent.notifications.push(notification);\n notificationPopupComponentFixture.detectChanges();\n });\n\n it('should remove notification from component state and navigate to target when notification is clicked', () => {\n sinon.spy(notificationPopupComponent, 'removeNotification');\n sinon.replace(notificationPopupComponent, 'navigateToTarget', sinon.fake());\n const notificationElement = notificationPopupComponentFixture.debugElement.query(By.css('.notification-popup-container > div'));\n notificationElement.nativeElement.click();\n expect(notificationPopupComponent.removeNotification).to.have.been.calledOnce;\n expect(notificationPopupComponent.notifications).to.be.empty;\n expect(notificationPopupComponent.navigateToTarget).to.have.been.calledOnce;\n });\n\n it('should remove notification from component state when notification is closed', () => {\n sinon.spy(notificationPopupComponent, 'removeNotification');\n sinon.replace(notificationPopupComponent, 'navigateToTarget', sinon.fake());\n const button = notificationPopupComponentFixture.debugElement.query(By.css('.notification-popup-container > div button'));\n button.nativeElement.click();\n expect(notificationPopupComponent.removeNotification).to.have.been.called;\n expect(notificationPopupComponent.notifications).to.be.empty;\n });\n\n it('should navigate to target when notification is clicked', () => {\n sinon.spy(notificationPopupComponent, 'navigateToTarget');\n sinon.replace(router, 'navigateByUrl', sinon.fake());\n const button = notificationPopupComponentFixture.debugElement.query(By.css('.notification-popup-container > div button'));\n button.nativeElement.click();\n expect(notificationPopupComponent.navigateToTarget).to.have.been.calledOnce;\n expect(router.navigateByUrl).to.have.been.calledOnce;\n });\n });\n\n describe('Webscoket receive', () => {\n const replaceSubscribeToNotificationUpdates = () => {\n const fake = sinon.fake.returns(new BehaviorSubject(notification));\n sinon.replace(notificationService, 'subscribeToNotificationUpdates', fake);\n };\n\n it('should prepend received quiz notification', fakeAsync(() => {\n sinon.replace(router, 'isActive', sinon.fake.returns(false));\n replaceSubscribeToNotificationUpdates();\n const otherNotification = generateNotification(2);\n notificationPopupComponent.notifications = [otherNotification];\n notificationPopupComponent.ngOnInit();\n expect(notificationPopupComponent.notifications.length).to.be.equal(2);\n expect(notificationPopupComponent.notifications[0]).to.be.equal(notification);\n tick(30000);\n expect(notificationPopupComponent.notifications.length).to.be.equal(1);\n expect(notificationPopupComponent.notifications[0]).to.be.equal(otherNotification);\n }));\n\n it('should not add received notification if user is already on quiz page', () => {\n sinon.replace(router, 'isActive', sinon.fake.returns(true));\n replaceSubscribeToNotificationUpdates();\n notificationPopupComponent.ngOnInit();\n expect(notificationPopupComponent.notifications).to.be.empty;\n });\n });\n});\n"},{"alpha_fraction":0.5890411138534546,"alphanum_fraction":0.5942371487617493,"avg_line_length":37.490909576416016,"blob_id":"1c2ebfa6af95cbe68ee8ded3d3a79c3a7b3036a8","content_id":"6a62ab7f3bd564ac93c36c0fb8bc3d8a0b82b861","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":2117,"license_type":"permissive","max_line_length":122,"num_lines":55,"path":"/src/main/webapp/app/shared/markdown-editor/commands/unorderedListCommand.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { Command } from './command';\n\nexport class UnorderedListCommand extends Command {\n buttonIcon = 'list-ul';\n buttonTranslationString = 'artemisApp.multipleChoiceQuestion.editor.unorderedList';\n\n /**\n * @function execute\n * @desc Use the markdown language for creating an unordered list\n */\n execute(): void {\n const selectedText = this.getSelectedText();\n this.splitText(selectedText);\n }\n\n /**\n * @function splitText\n * @desc 1. Split the text at the line break into an array\n * 2. Call for each textline the replaceText method\n * @param {string} the selected text by the cursor\n */\n splitText(selectedText: string): void {\n const parseArray = selectedText.split('\\n');\n parseArray.forEach((element) => this.replaceText(element));\n }\n\n /**\n * @function replaceText\n * @desc 1. Check if the selected text includes (-)\n * 2. If included reduce the selected text by 2 (-, whitespace) and replace the selected text by textToAdd\n * 3. If not included combine (-) with the selected text and insert into the editor\n * 4. An unordered list in markdown appears\n * @param element {string} extracted textLine from the {array} selectedText\n */\n replaceText(element: string): void {\n /** case 1: text is formed in as an unordered list and the list should be unformed by deleting (-) + whitespace */\n if (element.includes('-')) {\n const textToAdd = element.slice(2);\n const text = `${textToAdd}\\n`;\n this.insertText(text);\n /** case 2: start a new unordered list from scratch */\n } else if (element === '') {\n const range = this.getRange();\n element = `- ${element}`;\n this.replace(range, element);\n this.focus();\n } else {\n /** case 3: formate existing text into an unformed list */\n const range = this.getRange();\n element = `- ${element}\\n`;\n this.replace(range, element);\n this.focus();\n }\n }\n}\n"},{"alpha_fraction":0.5399497747421265,"alphanum_fraction":0.5467336773872375,"avg_line_length":38.405941009521484,"blob_id":"8d546203f65c9ff6fb63754f62591e4d368a53de","content_id":"8e48fa38b8d462334411bcc49435e2b8bbc75a9c","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":3980,"license_type":"permissive","max_line_length":109,"num_lines":101,"path":"/src/test/javascript/spec/component/admin/user-management.component.spec.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { ComponentFixture, TestBed, fakeAsync, tick, inject } from '@angular/core/testing';\nimport { TranslateModule } from '@ngx-translate/core';\nimport { ArtemisTestModule } from '../../test.module';\nimport { ArtemisAdminModule } from 'app/admin/admin.module';\nimport { UserManagementComponent } from 'app/admin/user-management/user-management.component';\nimport { UserService } from 'app/core/user/user.service';\nimport { AccountService } from 'app/core/auth/account.service';\nimport { MockAccountService } from '../../helpers/mocks/service/mock-account.service';\nimport { ActivatedRoute } from '@angular/router';\nimport { HttpHeaders, HttpResponse } from '@angular/common/http';\nimport { User } from 'app/core/user/user.model';\nimport { of } from 'rxjs';\nimport { RouterTestingModule } from '@angular/router/testing';\n\ndescribe('UserManagementComponent', () => {\n let comp: UserManagementComponent;\n let fixture: ComponentFixture;\n let service: UserService;\n\n const route = ({\n params: of({ courseId: 123, sort: 'id,desc' }),\n children: [],\n } as any) as ActivatedRoute;\n\n beforeEach(async () => {\n return TestBed.configureTestingModule({\n imports: [TranslateModule.forRoot(), ArtemisTestModule, ArtemisAdminModule, RouterTestingModule],\n providers: [\n {\n provide: ActivatedRoute,\n useValue: route,\n },\n { provide: AccountService, useClass: MockAccountService },\n ],\n })\n .compileComponents()\n .then(() => {\n fixture = TestBed.createComponent(UserManagementComponent);\n comp = fixture.componentInstance;\n service = fixture.debugElement.injector.get(UserService);\n });\n });\n\n // The admin module is lazy loaded - we therefore need a dummy test to load\n // the module and verify that there are no dependency related issues.\n it('should render a component from the admin module', () => {\n expect(comp).toBeDefined();\n });\n\n it('should parse the user search result into the correct component state', inject(\n [],\n fakeAsync(() => {\n const headers = new HttpHeaders().append('link', 'link;link').append('X-Total-Count', '1');\n spyOn(service, 'query').and.returnValue(\n of(\n new HttpResponse({\n body: [new User(1)],\n headers,\n }),\n ),\n );\n\n comp.loadAll();\n // 1 sec of pause, because of the debounce time\n tick(1000);\n\n expect(comp.users && comp.users[0].id).toEqual(1);\n expect(comp.totalItems).toEqual(1);\n expect(comp.loadingSearchResult).toEqual(false);\n }),\n ));\n\n describe('setActive', () => {\n it('Should update user and call load all', inject(\n [],\n fakeAsync(() => {\n // GIVEN\n const headers = new HttpHeaders().append('link', 'link;link');\n const user = new User(123);\n spyOn(service, 'query').and.returnValue(\n of(\n new HttpResponse({\n body: [user],\n headers,\n }),\n ),\n );\n spyOn(service, 'update').and.returnValue(of(new HttpResponse({ status: 200 })));\n\n // WHEN\n comp.setActive(user, true);\n tick(1000); // simulate async\n\n // THEN\n expect(service.update).toHaveBeenCalledWith({ ...user, activated: true });\n expect(service.query).toHaveBeenCalled();\n expect(comp.users && comp.users[0]).toEqual(expect.objectContaining({ id: 123 }));\n }),\n ));\n });\n});\n"},{"alpha_fraction":0.7582612633705139,"alphanum_fraction":0.7638057470321655,"avg_line_length":44.09000015258789,"blob_id":"5bf82ce9174fef43db2593b536a82ca77f63f1ff","content_id":"e406b66504fe1927b810336746b705caff3a0129","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":4509,"license_type":"permissive","max_line_length":180,"num_lines":100,"path":"/src/test/java/de/tum/in/www1/artemis/programmingexercise/simulation/ProgrammingExerciseSubmissionAndResultSimulationIntegrationTest.java","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"package de.tum.in.www1.artemis.programmingexercise.simulation;\n\nimport static de.tum.in.www1.artemis.web.rest.ProgrammingSubmissionResultSimulationResource.Endpoints.*;\nimport static org.assertj.core.api.Assertions.assertThat;\n\nimport java.util.List;\nimport java.util.stream.Collectors;\n\nimport org.junit.jupiter.api.AfterEach;\nimport org.junit.jupiter.api.BeforeEach;\nimport org.junit.jupiter.api.Test;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.http.HttpStatus;\nimport org.springframework.security.test.context.support.WithMockUser;\n\nimport de.tum.in.www1.artemis.AbstractSpringDevelopmentTest;\nimport de.tum.in.www1.artemis.domain.ProgrammingExercise;\nimport de.tum.in.www1.artemis.domain.ProgrammingSubmission;\nimport de.tum.in.www1.artemis.domain.Result;\nimport de.tum.in.www1.artemis.domain.enumeration.SubmissionType;\nimport de.tum.in.www1.artemis.domain.participation.Participation;\nimport de.tum.in.www1.artemis.repository.*;\n\npublic class ProgrammingExerciseSubmissionAndResultSimulationIntegrationTest extends AbstractSpringDevelopmentTest {\n\n @Autowired\n ProgrammingSubmissionRepository submissionRepository;\n\n @Autowired\n ParticipationRepository participationRepository;\n\n @Autowired\n ProgrammingExerciseRepository programmingExerciseRepository;\n\n @Autowired\n ResultRepository resultRepository;\n\n private List participationIds;\n\n private Long exerciseId;\n\n private ProgrammingExercise exercise;\n\n @BeforeEach\n void setUp() {\n database.addUsers(3, 2, 2);\n database.addCourseWithOneProgrammingExerciseAndTestCases();\n\n exercise = programmingExerciseRepository.findAllWithEagerParticipationsAndLegalSubmissions().get(0);\n database.addStudentParticipationForProgrammingExercise(exercise, \"student1\");\n database.addStudentParticipationForProgrammingExercise(exercise, \"student2\");\n\n exerciseId = exercise.getId();\n exercise = programmingExerciseRepository.findAllWithEagerParticipationsAndLegalSubmissions().get(0);\n participationIds = exercise.getStudentParticipations().stream().map(Participation::getId).collect(Collectors.toList());\n }\n\n @AfterEach\n public void tearDown() {\n database.resetDatabase();\n }\n\n /**\n * This tests if the submission is created for programming exercises without local setup\n */\n\n @Test\n @WithMockUser(username = \"instructor1\", roles = \"INSTRUCTOR\")\n void shouldCreateSubmissionWithoutConnectionToVCSandCI() throws Exception {\n assertThat(submissionRepository.findAll()).hasSize(0);\n final var returnedSubmission = request.postWithResponseBody(ROOT + SUBMISSIONS_SIMULATION.replace(\"{exerciseId}\", String.valueOf(exerciseId)), null,\n ProgrammingSubmission.class, HttpStatus.CREATED);\n assertThat(submissionRepository.findAll()).hasSize(1);\n ProgrammingSubmission submission = submissionRepository.findAll().get(0);\n assertThat(returnedSubmission).isEqualTo(submission);\n assertThat(participationRepository.findById(submission.getParticipation().getId()));\n assertThat(participationIds.contains(submission.getParticipation().getId()));\n assertThat(submission.getType()).isEqualTo(SubmissionType.MANUAL);\n assertThat(submission.isSubmitted()).isTrue();\n }\n\n /**\n * This tests if the result is created for programming exercises without local setup\n */\n\n @Test\n @WithMockUser(username = \"instructor1\", roles = \"INSTRUCTOR\")\n void shouldCreateResultWithoutConnectionToVCSandCI() throws Exception {\n final var returnedSubmission = request.postWithResponseBody(ROOT + SUBMISSIONS_SIMULATION.replace(\"{exerciseId}\", String.valueOf(exerciseId)), null,\n ProgrammingSubmission.class, HttpStatus.CREATED);\n assertThat(resultRepository.findAll()).hasSize(0);\n Result returnedResult = request.postWithResponseBody(ROOT + RESULTS_SIMULATION.replace(\"{exerciseId}\", String.valueOf(exerciseId)), null, Result.class, HttpStatus.CREATED);\n ProgrammingSubmission submission = submissionRepository.findAll().get(0);\n assertThat(returnedSubmission).isEqualTo(submission);\n assertThat(resultRepository.findAll()).hasSize(1);\n Result result = resultRepository.findAll().get(0);\n assertThat(returnedResult).isEqualTo(result);\n assertThat(result.getParticipation().getId()).isEqualTo(submission.getParticipation().getId());\n }\n}\n"},{"alpha_fraction":0.6871333718299866,"alphanum_fraction":0.6901642680168152,"avg_line_length":58.12138748168945,"blob_id":"924c1bacdca1d0ebe1c278d632ecdf3d158c17b8","content_id":"88f077e6d7e67e6e17e00ce520b792c220eed9a2","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":10228,"license_type":"permissive","max_line_length":120,"num_lines":173,"path":"/src/test/javascript/spec/component/shared/notification/system-notification.component.spec.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import * as chai from 'chai';\nimport * as sinonChai from 'sinon-chai';\nimport * as sinon from 'sinon';\nimport * as moment from 'moment';\nimport { of } from 'rxjs';\nimport { ComponentFixture, fakeAsync, TestBed, tick } from '@angular/core/testing';\nimport { LocalStorageService, SessionStorageService } from 'ngx-webstorage';\nimport { SystemNotificationComponent } from 'app/shared/notification/system-notification/system-notification.component';\nimport { SystemNotificationService } from 'app/shared/notification/system-notification/system-notification.service';\nimport { AccountService } from 'app/core/auth/account.service';\nimport { ArtemisTestModule } from '../../../test.module';\nimport { ArtemisSharedModule } from 'app/shared/shared.module';\nimport { MockSyncStorage } from '../../../helpers/mocks/service/mock-sync-storage.service';\nimport { MockAccountService } from '../../../helpers/mocks/service/mock-account.service';\nimport { SystemNotification, SystemNotificationType } from 'app/entities/system-notification.model';\nimport { JhiWebsocketService } from 'app/core/websocket/websocket.service';\n\nchai.use(sinonChai);\nconst expect = chai.expect;\n\ndescribe('System Notification Component', () => {\n let systemNotificationComponent: SystemNotificationComponent;\n let systemNotificationComponentFixture: ComponentFixture;\n let systemNotificationService: SystemNotificationService;\n let jhiWebsocketService: JhiWebsocketService;\n\n const createActiveNotification = (type: SystemNotificationType) => {\n return {\n id: 1,\n title: 'Maintenance',\n text: 'Artemis will be unavailable',\n type,\n notificationDate: moment().subtract(1, 'days'),\n expireDate: moment().add(1, 'days'),\n } as SystemNotification;\n };\n\n beforeEach(() => {\n TestBed.configureTestingModule({\n imports: [ArtemisTestModule, ArtemisSharedModule],\n declarations: [SystemNotificationComponent],\n providers: [\n { provide: LocalStorageService, useClass: MockSyncStorage },\n { provide: SessionStorageService, useClass: MockSyncStorage },\n { provide: AccountService, useClass: MockAccountService },\n { provide: jhiWebsocketService, useClass: JhiWebsocketService },\n ],\n })\n .overrideModule(ArtemisTestModule, { set: { declarations: [], exports: [] } })\n .compileComponents()\n .then(() => {\n systemNotificationComponentFixture = TestBed.createComponent(SystemNotificationComponent);\n systemNotificationComponent = systemNotificationComponentFixture.componentInstance;\n systemNotificationService = TestBed.inject(SystemNotificationService);\n jhiWebsocketService = TestBed.inject(JhiWebsocketService);\n });\n });\n\n describe('Load active notification', () => {\n it('should get active system notification with system notification type warning', fakeAsync(() => {\n const notification = createActiveNotification(SystemNotificationType.WARNING);\n const fake = sinon.fake.returns(of(notification));\n sinon.replace(systemNotificationService, 'getActiveNotification', fake);\n systemNotificationComponent.ngOnInit();\n tick(500);\n expect(systemNotificationService.getActiveNotification).to.have.been.calledOnce;\n expect(systemNotificationComponent.notification).to.equal(notification);\n expect(systemNotificationComponent.alertClass).to.equal('alert-warning');\n expect(systemNotificationComponent.alertIcon).to.equal('exclamation-triangle');\n }));\n\n it('should get active system notification with system notification type info', fakeAsync(() => {\n const notification = createActiveNotification(SystemNotificationType.INFO);\n const fake = sinon.fake.returns(of(notification));\n sinon.replace(systemNotificationService, 'getActiveNotification', fake);\n systemNotificationComponent.ngOnInit();\n tick(500);\n expect(systemNotificationService.getActiveNotification).to.have.been.calledOnce;\n expect(systemNotificationComponent.notification).to.equal(notification);\n expect(systemNotificationComponent.alertClass).to.equal('alert-info');\n expect(systemNotificationComponent.alertIcon).to.equal('info-circle');\n }));\n });\n\n describe('Websocket', () => {\n beforeEach(() => {\n const fake = sinon.fake.yields('event');\n sinon.replace(jhiWebsocketService, 'bind', fake);\n });\n\n it('should subscribe to socket messages on init', fakeAsync(() => {\n sinon.spy(jhiWebsocketService, 'subscribe');\n sinon.spy(jhiWebsocketService, 'receive');\n systemNotificationComponent.ngOnInit();\n tick(500);\n expect(jhiWebsocketService.bind).to.have.been.calledOnce;\n expect(systemNotificationComponent.websocketChannel).to.equal('/topic/system-notification');\n expect(jhiWebsocketService.subscribe).to.have.been.calledOnce;\n expect(jhiWebsocketService.receive).to.have.been.calledOnce;\n }));\n\n it('should delete notification when \"deleted\" is received via websocket', fakeAsync(() => {\n sinon.replace(jhiWebsocketService, 'receive', sinon.fake.returns(of('deleted')));\n sinon.replace(systemNotificationService, 'getActiveNotification', sinon.fake.returns(of(null)));\n systemNotificationComponent.notification = createActiveNotification(SystemNotificationType.WARNING);\n systemNotificationComponent.ngOnInit();\n tick(500);\n expect(jhiWebsocketService.receive).to.have.been.calledOnce;\n expect(systemNotificationComponent.notification).to.equal(undefined);\n expect(systemNotificationService.getActiveNotification).to.have.been.calledTwice;\n }));\n\n it('should add notification when active notification is received via websocket', fakeAsync(() => {\n const notification = createActiveNotification(SystemNotificationType.WARNING);\n sinon.spy(jhiWebsocketService, 'subscribe');\n sinon.replace(jhiWebsocketService, 'receive', sinon.fake.returns(of(notification)));\n systemNotificationComponent.ngOnInit();\n tick(500);\n expect(jhiWebsocketService.subscribe).to.have.been.calledOnce;\n expect(jhiWebsocketService.receive).to.have.been.calledOnce;\n expect(systemNotificationComponent.notification).to.equal(notification);\n expect(systemNotificationComponent.alertClass).to.equal('alert-warning');\n expect(systemNotificationComponent.alertIcon).to.equal('exclamation-triangle');\n }));\n\n it('should not add notification when non-active notification is received via websocket', fakeAsync(() => {\n const notification = createActiveNotification(SystemNotificationType.WARNING);\n notification.expireDate = moment().subtract(5, 'minutes');\n sinon.spy(jhiWebsocketService, 'subscribe');\n sinon.replace(jhiWebsocketService, 'receive', sinon.fake.returns(of(notification)));\n sinon.replace(systemNotificationService, 'getActiveNotification', sinon.fake.returns(of()));\n systemNotificationComponent.ngOnInit();\n tick(500);\n expect(jhiWebsocketService.subscribe).to.have.been.calledOnce;\n expect(jhiWebsocketService.receive).to.have.been.calledOnce;\n expect(systemNotificationService.getActiveNotification).to.have.been.calledTwice;\n expect(systemNotificationComponent.notification).to.equal(undefined);\n expect(systemNotificationComponent.alertClass).to.equal(undefined);\n expect(systemNotificationComponent.alertIcon).to.equal(undefined);\n }));\n\n it('should update notification when same notification is received via websocket', fakeAsync(() => {\n const notification = createActiveNotification(SystemNotificationType.WARNING);\n const changedNotification = createActiveNotification(SystemNotificationType.INFO);\n sinon.spy(jhiWebsocketService, 'subscribe');\n sinon.replace(jhiWebsocketService, 'receive', sinon.fake.returns(of(changedNotification)));\n sinon.replace(systemNotificationService, 'getActiveNotification', sinon.fake.returns(of(notification)));\n systemNotificationComponent.ngOnInit();\n tick(500);\n expect(jhiWebsocketService.subscribe).to.have.been.calledOnce;\n expect(jhiWebsocketService.receive).to.have.been.calledOnce;\n expect(systemNotificationService.getActiveNotification).to.have.been.calledOnce;\n expect(systemNotificationComponent.notification).to.equal(changedNotification);\n }));\n\n it('should update notification when new active notification is received via websocket', fakeAsync(() => {\n const notification = createActiveNotification(SystemNotificationType.WARNING);\n const newNotification = createActiveNotification(SystemNotificationType.INFO);\n newNotification.id = 2;\n newNotification.notificationDate = moment().subtract(2, 'days');\n newNotification.expireDate = moment().add(2, 'days');\n sinon.spy(jhiWebsocketService, 'subscribe');\n sinon.replace(jhiWebsocketService, 'receive', sinon.fake.returns(of(newNotification)));\n sinon.replace(systemNotificationService, 'getActiveNotification', sinon.fake.returns(of(notification)));\n systemNotificationComponent.ngOnInit();\n tick(500);\n expect(jhiWebsocketService.subscribe).to.have.been.calledOnce;\n expect(jhiWebsocketService.receive).to.have.been.calledOnce;\n expect(systemNotificationService.getActiveNotification).to.have.been.calledOnce;\n expect(systemNotificationComponent.notification).to.equal(newNotification);\n }));\n });\n});\n"},{"alpha_fraction":0.6178403496742249,"alphanum_fraction":0.6413145661354065,"avg_line_length":24.975608825683594,"blob_id":"b02c6599d889dad31f1be0f424599d7554b7e51e","content_id":"3fdf984a145dd90e28ce237673644f5bed0b15dc","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":1065,"license_type":"permissive","max_line_length":48,"num_lines":41,"path":"/src/main/webapp/app/entities/statistics.model.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"export enum SpanType {\n DAY = 'DAY',\n WEEK = 'WEEK',\n MONTH = 'MONTH',\n QUARTER = 'QUARTER',\n YEAR = 'YEAR',\n}\n\nexport enum Graphs {\n // User statistics\n SUBMISSIONS = 'SUBMISSIONS',\n ACTIVE_USERS = 'ACTIVE_USERS',\n LOGGED_IN_USERS = 'LOGGED_IN_USERS',\n RELEASED_EXERCISES = 'RELEASED_EXERCISES',\n EXERCISES_DUE = 'EXERCISES_DUE',\n CONDUCTED_EXAMS = 'CONDUCTED_EXAMS',\n EXAM_PARTICIPATIONS = 'EXAM_PARTICIPATIONS',\n EXAM_REGISTRATIONS = 'EXAM_REGISTRATIONS',\n ACTIVE_TUTORS = 'ACTIVE_TUTORS',\n CREATED_RESULTS = 'CREATED_RESULTS',\n CREATED_FEEDBACKS = 'CREATED_FEEDBACKS',\n\n // Course overview\n ACTIVE_STUDENTS = 'ACTIVE_STUDENTS',\n\n // Course Statistics\n AVERAGE_SCORE = 'AVERAGE_SCORE',\n QUESTIONS_ASKED = 'QUESTIONS_ASKED',\n QUESTIONS_ANSWERED = 'QUESTIONS_ANSWERED',\n}\n\nexport enum StatisticsView {\n ARTEMIS = 'ARTEMIS',\n COURSE = 'COURSE',\n}\n\nexport enum GraphColors {\n DARK_BLUE = 'rgba(53,61,71,1)',\n BLUE = 'rgba(93,138,201,1)',\n BLUE_TRANSPARENT = 'rgba(93,138,201,0)',\n}\n"},{"alpha_fraction":0.7132353186607361,"alphanum_fraction":0.7132353186607361,"avg_line_length":39.79999923706055,"blob_id":"d8804c6029c367e763eafa8a84348d05134a68a7","content_id":"9af95fb054db943a35f7aa1276b8695be9a4fea5","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":2040,"license_type":"permissive","max_line_length":121,"num_lines":50,"path":"/src/main/webapp/app/entities/quiz/quiz-exercise.model.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { Moment } from 'moment';\nimport { Exercise, ExerciseType } from 'app/entities/exercise.model';\nimport { QuizPointStatistic } from 'app/entities/quiz/quiz-point-statistic.model';\nimport { QuizQuestion } from 'app/entities/quiz/quiz-question.model';\nimport { Course } from 'app/entities/course.model';\nimport { ExerciseGroup } from 'app/entities/exercise-group.model';\n\nexport enum QuizStatus {\n CLOSED,\n OPEN_FOR_PRACTICE,\n ACTIVE,\n VISIBLE,\n HIDDEN,\n}\n\nexport class QuizExercise extends Exercise {\n public id?: number;\n public remainingTime?: number; // (computed by server)\n public timeUntilPlannedStart?: number; // (computed by server)\n public visibleToStudents?: boolean; // (computed by server)\n public randomizeQuestionOrder?: boolean;\n public isVisibleBeforeStart?: boolean;\n public isOpenForPractice?: boolean;\n public isPlannedToStart?: boolean;\n public duration?: number;\n public quizPointStatistic?: QuizPointStatistic;\n public quizQuestions?: QuizQuestion[];\n public status?: QuizStatus;\n\n // helper attributes\n public adjustedDueDate?: Moment;\n public adjustedReleaseDate?: Moment;\n public ended?: boolean;\n public started?: boolean;\n\n public isActiveQuiz?: boolean;\n public isPracticeModeAvailable?: boolean;\n\n constructor(course: Course | undefined, exerciseGroup: ExerciseGroup | undefined) {\n super(ExerciseType.QUIZ);\n this.course = course;\n this.exerciseGroup = exerciseGroup;\n this.randomizeQuestionOrder = true; // default value (set by server)\n this.isVisibleBeforeStart = false; // default value (set by server)\n this.isOpenForPractice = false; // default value (set by server)\n this.isPlannedToStart = false; // default value (set by server)\n this.isActiveQuiz = false; // default value (set by client, might need to be computed before evaluated)\n this.isPracticeModeAvailable = true; // default value (set by client, might need to be computed before evaluated)\n }\n}\n"},{"alpha_fraction":0.6481686234474182,"alphanum_fraction":0.6490184664726257,"avg_line_length":44.08428955078125,"blob_id":"2db702f65f0f1df94f0238e4a74a1f1b4498131c","content_id":"6a4de1077336f089ed645cd89774ef9cf1b12bff","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":11767,"license_type":"permissive","max_line_length":158,"num_lines":261,"path":"/src/main/webapp/app/exercises/programming/manage/programming-exercise-detail.component.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { Component, OnDestroy, OnInit, ViewEncapsulation } from '@angular/core';\nimport { ActivatedRoute } from '@angular/router';\nimport { Observable, of, Subject } from 'rxjs';\nimport { catchError, map } from 'rxjs/operators';\nimport { ProgrammingExercise, ProgrammingLanguage } from 'app/entities/programming-exercise.model';\nimport { ProgrammingExerciseService } from 'app/exercises/programming/manage/services/programming-exercise.service';\nimport { Result } from 'app/entities/result.model';\nimport { JhiAlertService } from 'ng-jhipster';\nimport { ProgrammingExerciseParticipationType } from 'app/entities/programming-exercise-participation.model';\nimport { ProgrammingExerciseParticipationService } from 'app/exercises/programming/manage/services/programming-exercise-participation.service';\nimport { AccountService } from 'app/core/auth/account.service';\nimport { HttpErrorResponse } from '@angular/common/http';\nimport { ActionType } from 'app/shared/delete-dialog/delete-dialog.model';\nimport { FeatureToggle } from 'app/shared/feature-toggle/feature-toggle.service';\nimport { ExerciseService } from 'app/exercises/shared/exercise/exercise.service';\nimport { ExerciseType } from 'app/entities/exercise.model';\nimport { JhiEventManager } from 'ng-jhipster';\nimport { NgbModal } from '@ng-bootstrap/ng-bootstrap';\nimport { ConfirmAutofocusModalComponent } from 'app/shared/components/confirm-autofocus-button.component';\nimport { TranslateService } from '@ngx-translate/core';\n\n@Component({\n selector: 'jhi-programming-exercise-detail',\n templateUrl: './programming-exercise-detail.component.html',\n styleUrls: ['./programming-exercise-detail.component.scss'],\n encapsulation: ViewEncapsulation.None,\n})\nexport class ProgrammingExerciseDetailComponent implements OnInit, OnDestroy {\n readonly ActionType = ActionType;\n readonly ProgrammingExerciseParticipationType = ProgrammingExerciseParticipationType;\n readonly FeatureToggle = FeatureToggle;\n readonly ProgrammingLanguage = ProgrammingLanguage;\n readonly PROGRAMMING = ExerciseType.PROGRAMMING;\n\n programmingExercise: ProgrammingExercise;\n isExamExercise: boolean;\n\n loadingTemplateParticipationResults = true;\n loadingSolutionParticipationResults = true;\n lockingOrUnlockingRepositories = false;\n\n private dialogErrorSource = new Subject();\n dialogError$ = this.dialogErrorSource.asObservable();\n\n constructor(\n private activatedRoute: ActivatedRoute,\n private accountService: AccountService,\n private programmingExerciseService: ProgrammingExerciseService,\n private exerciseService: ExerciseService,\n private jhiAlertService: JhiAlertService,\n private programmingExerciseParticipationService: ProgrammingExerciseParticipationService,\n private eventManager: JhiEventManager,\n private modalService: NgbModal,\n private translateService: TranslateService,\n ) {}\n\n ngOnInit() {\n this.activatedRoute.data.subscribe(({ programmingExercise }) => {\n this.programmingExercise = programmingExercise;\n this.isExamExercise = !!this.programmingExercise.exerciseGroup;\n\n this.programmingExercise.isAtLeastTutor = this.accountService.isAtLeastTutorForExercise(this.programmingExercise);\n this.programmingExercise.isAtLeastInstructor = this.accountService.isAtLeastInstructorForExercise(this.programmingExercise);\n\n if (this.programmingExercise.categories) {\n this.programmingExercise.categories = this.programmingExercise.categories.map((category) => JSON.parse(category));\n }\n\n if (this.programmingExercise.templateParticipation) {\n this.programmingExercise.templateParticipation.programmingExercise = this.programmingExercise;\n this.loadLatestResultWithFeedbackAndSubmission(this.programmingExercise.templateParticipation.id!).subscribe((results) => {\n this.programmingExercise.templateParticipation!.results = results;\n this.loadingTemplateParticipationResults = false;\n });\n }\n\n if (this.programmingExercise.solutionParticipation) {\n this.programmingExercise.solutionParticipation.programmingExercise = this.programmingExercise;\n\n this.loadLatestResultWithFeedbackAndSubmission(this.programmingExercise.solutionParticipation.id!).subscribe((results) => {\n this.programmingExercise.solutionParticipation!.results = results;\n this.loadingSolutionParticipationResults = false;\n });\n }\n });\n }\n\n ngOnDestroy(): void {\n this.dialogErrorSource.unsubscribe();\n }\n\n /**\n * Load the latest result for the given participation. Will return [result] if there is a result, [] if not.\n * @param participationId of the given participation.\n * @return an empty array if there is no result or an array with the single latest result.\n */\n private loadLatestResultWithFeedbackAndSubmission(participationId: number): Observable {\n return this.programmingExerciseParticipationService.getLatestResultWithFeedback(participationId, true).pipe(\n catchError(() => of(null)),\n map((result: Result | null) => {\n return result ? [result] : [];\n }),\n );\n }\n\n /**\n * Returns the route for editing the exercise. Exam and course exercises have different routes.\n */\n getEditRoute() {\n if (this.isExamExercise) {\n return [\n '/course-management',\n this.programmingExercise.exerciseGroup?.exam?.course?.id,\n 'exams',\n this.programmingExercise.exerciseGroup?.exam?.id,\n 'exercise-groups',\n this.programmingExercise.exerciseGroup?.id,\n 'programming-exercises',\n this.programmingExercise.id,\n 'edit',\n ];\n } else {\n return ['/course-management', this.programmingExercise.course?.id, 'programming-exercises', this.programmingExercise.id, 'edit'];\n }\n }\n\n combineTemplateCommits() {\n this.programmingExerciseService.combineTemplateRepositoryCommits(this.programmingExercise.id!).subscribe(\n () => {\n this.jhiAlertService.success('artemisApp.programmingExercise.combineTemplateCommitsSuccess');\n },\n () => {\n this.jhiAlertService.error('artemisApp.programmingExercise.combineTemplateCommitsError');\n },\n );\n }\n\n generateStructureOracle() {\n this.programmingExerciseService.generateStructureOracle(this.programmingExercise.id!).subscribe(\n (res) => {\n const jhiAlert = this.jhiAlertService.success(res);\n jhiAlert.msg = res;\n },\n (error) => {\n const errorMessage = error.headers.get('X-artemisApp-alert');\n // TODO: this is a workaround to avoid translation not found issues. Provide proper translations\n const jhiAlert = this.jhiAlertService.error(errorMessage);\n jhiAlert.msg = errorMessage;\n },\n );\n }\n\n /**\n * Cleans up programming exercise\n * @param $event contains additional checks from the dialog\n */\n cleanupProgrammingExercise($event: { [key: string]: boolean }) {\n return this.exerciseService.cleanup(this.programmingExercise.id!, $event.deleteRepositories).subscribe(\n () => {\n if ($event.deleteRepositories) {\n this.jhiAlertService.success('artemisApp.programmingExercise.cleanup.successMessageWithRepositories');\n } else {\n this.jhiAlertService.success('artemisApp.programmingExercise.cleanup.successMessage');\n }\n this.dialogErrorSource.next('');\n },\n (error: HttpErrorResponse) => this.dialogErrorSource.next(error.message),\n );\n }\n\n public deleteProgrammingExercise($event: { [key: string]: boolean }) {\n this.programmingExerciseService.delete(this.programmingExercise.id!, $event.deleteStudentReposBuildPlans, $event.deleteBaseReposBuildPlans).subscribe(\n () => {\n this.eventManager.broadcast({\n name: 'programmingExerciseListModification',\n content: 'Deleted a programming exercise',\n });\n this.dialogErrorSource.next('');\n },\n (error: HttpErrorResponse) => this.dialogErrorSource.next(error.message),\n );\n }\n\n /**\n * Unlock all repositories immediately. Asks for confirmation.\n */\n handleUnlockAllRepositories() {\n const modalRef = this.modalService.open(ConfirmAutofocusModalComponent, { keyboard: true, size: 'lg' });\n modalRef.componentInstance.title = 'artemisApp.programmingExercise.unlockAllRepositories';\n modalRef.componentInstance.text = this.translateService.instant('artemisApp.programmingExercise.unlockAllRepositoriesModalText');\n modalRef.result.then(() => {\n this.unlockAllRepositories();\n });\n }\n\n /**\n * Unlocks all repositories that belong to the exercise\n */\n private unlockAllRepositories() {\n this.lockingOrUnlockingRepositories = true;\n this.programmingExerciseService.unlockAllRepositories(this.programmingExercise.id!).subscribe(\n (res) => {\n this.jhiAlertService.addAlert(\n {\n type: 'success',\n msg: 'artemisApp.programmingExercise.unlockAllRepositoriesSuccess',\n params: { number: res?.body },\n timeout: 10000,\n },\n [],\n );\n this.lockingOrUnlockingRepositories = false;\n },\n (err: HttpErrorResponse) => {\n this.lockingOrUnlockingRepositories = false;\n this.onError(err);\n },\n );\n }\n\n /**\n * Lock all repositories immediately. Asks for confirmation.\n */\n handleLockAllRepositories() {\n const modalRef = this.modalService.open(ConfirmAutofocusModalComponent, { keyboard: true, size: 'lg' });\n modalRef.componentInstance.title = 'artemisApp.programmingExercise.lockAllRepositories';\n modalRef.componentInstance.text = this.translateService.instant('artemisApp.programmingExercise.lockAllRepositoriesModalText');\n modalRef.result.then(() => {\n this.lockAllRepositories();\n });\n }\n\n /**\n * Locks all repositories that belong to the exercise\n */\n private lockAllRepositories() {\n this.lockingOrUnlockingRepositories = true;\n this.programmingExerciseService.lockAllRepositories(this.programmingExercise.id!).subscribe(\n (res) => {\n this.jhiAlertService.addAlert(\n {\n type: 'success',\n msg: 'artemisApp.programmingExercise.lockAllRepositoriesSuccess',\n params: { number: res?.body },\n timeout: 10000,\n },\n [],\n );\n this.lockingOrUnlockingRepositories = false;\n },\n (err: HttpErrorResponse) => {\n this.lockingOrUnlockingRepositories = false;\n this.onError(err);\n },\n );\n }\n\n private onError(error: HttpErrorResponse) {\n this.jhiAlertService.error(error.message);\n }\n}\n"},{"alpha_fraction":0.6687622666358948,"alphanum_fraction":0.670333981513977,"avg_line_length":34.34722137451172,"blob_id":"35b049bc614cd0dc2f633c39f891386b73d075ee","content_id":"4dc98cc2ce9aa2d32ac3630fbe051e19847c98da","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":5090,"license_type":"permissive","max_line_length":139,"num_lines":144,"path":"/src/main/webapp/app/exercises/text/assess/textblock-feedback-editor/textblock-feedback-editor.component.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { AfterViewInit, Component, ElementRef, EventEmitter, HostBinding, Input, Output, ViewChild } from '@angular/core';\nimport { TextBlock } from 'app/entities/text-block.model';\nimport { Feedback, FeedbackType } from 'app/entities/feedback.model';\nimport { ConfirmIconComponent } from 'app/shared/confirm-icon/confirm-icon.component';\nimport { StructuredGradingCriterionService } from 'app/exercises/shared/structured-grading-criterion/structured-grading-criterion.service';\nimport { FeedbackConflictType } from 'app/entities/feedback-conflict';\n\n@Component({\n selector: 'jhi-textblock-feedback-editor',\n templateUrl: './textblock-feedback-editor.component.html',\n styleUrls: ['./textblock-feedback-editor.component.scss'],\n})\nexport class TextblockFeedbackEditorComponent implements AfterViewInit {\n readonly FeedbackType = FeedbackType;\n\n @Input() textBlock: TextBlock;\n @Input() feedback: Feedback = new Feedback();\n @Output() feedbackChange = new EventEmitter();\n @Output() close = new EventEmitter();\n @Output() onFocus = new EventEmitter();\n @Output() onConflictsClicked = new EventEmitter();\n @ViewChild('detailText') textareaRef: ElementRef;\n @ViewChild(ConfirmIconComponent) confirmIconComponent: ConfirmIconComponent;\n @Input() disableEditScore = false;\n @Input() readOnly: boolean;\n @Input() isConflictingFeedback: boolean;\n @Input() conflictMode: boolean;\n @Input() conflictType?: FeedbackConflictType;\n @Input() isLeftConflictingFeedback: boolean;\n @Input() isSelectedConflict: boolean;\n private textareaElement: HTMLTextAreaElement;\n\n @HostBinding('class.alert') @HostBinding('class.alert-dismissible') readonly classes = true;\n\n @HostBinding('class.alert-secondary') get neutralFeedbackClass(): boolean {\n return !this.conflictMode ? this.feedback.credits === 0 : !this.isConflictingFeedback;\n }\n\n @HostBinding('class.alert-success') get positiveFeedbackClass(): boolean {\n return this.feedback.credits! > 0 && !this.conflictMode;\n }\n\n @HostBinding('class.alert-danger') get negativeFeedbackClass(): boolean {\n return this.feedback.credits! < 0 && !this.conflictMode;\n }\n\n @HostBinding('class.alert-warning') get conflictingFeedbackClass(): boolean {\n return this.isConflictingFeedback && this.conflictMode && !this.isSelectedConflict;\n }\n\n @HostBinding('class.alert-info') get selectedConflictingFeedbackClass(): boolean {\n return this.isSelectedConflict;\n }\n\n constructor(public structuredGradingCriterionService: StructuredGradingCriterionService) {}\n\n /**\n * Life cycle hook to indicate component initialization is done\n */\n ngAfterViewInit(): void {\n this.textareaElement = this.textareaRef.nativeElement as HTMLTextAreaElement;\n setTimeout(() => this.textareaAutogrow());\n if (this.feedback.gradingInstruction && this.feedback.gradingInstruction.usageCount !== 0) {\n this.disableEditScore = true;\n } else {\n this.disableEditScore = false;\n }\n }\n /**\n * Increase size of text area automatically\n */\n textareaAutogrow(): void {\n this.textareaElement.style.height = '0px';\n this.textareaElement.style.height = `${this.textareaElement.scrollHeight}px`;\n }\n\n get canDismiss(): boolean {\n return this.feedback.credits === 0 && (this.feedback.detailText || '').length === 0;\n }\n\n get isConflictingFeedbackEditable(): boolean {\n return this.conflictMode && this.isLeftConflictingFeedback && this.isConflictingFeedback;\n }\n\n /**\n * Set focus to feedback editor\n */\n inFocus(): void {\n this.onFocus.emit();\n }\n\n /**\n * Dismiss changes in feedback editor\n */\n dismiss(): void {\n this.close.emit();\n }\n\n /**\n * Hook to indicate pressed Escape key\n */\n escKeyup(): void {\n if (this.canDismiss) {\n this.dismiss();\n } else {\n this.confirmIconComponent.toggle();\n }\n }\n\n /**\n * Set focus to the text area\n */\n focus(): void {\n if (this.conflictMode && !this.isLeftConflictingFeedback && this.isConflictingFeedback) {\n return;\n }\n this.textareaElement.focus();\n }\n\n /**\n * Hook to indicate a score click\n */\n onScoreClick(event: MouseEvent): void {\n event.preventDefault();\n }\n\n /**\n * Hook to indicate changes in the feedback editor\n */\n didChange(): void {\n Feedback.updateFeedbackTypeOnChange(this.feedback);\n this.feedbackChange.emit(this.feedback);\n }\n\n connectFeedbackWithInstruction(event: Event) {\n this.structuredGradingCriterionService.updateFeedbackWithStructuredGradingInstructionEvent(this.feedback, event);\n if (this.feedback.gradingInstruction && this.feedback.gradingInstruction.usageCount !== 0) {\n this.disableEditScore = true;\n } else {\n this.disableEditScore = false;\n }\n this.didChange();\n }\n}\n"},{"alpha_fraction":0.6684829592704773,"alphanum_fraction":0.6688606142997742,"avg_line_length":42.51173782348633,"blob_id":"71f56b57e1127f765543689527e09a7271ce9d16","content_id":"74d17ae7d37216b43ecdaf0afd753c551f004b52","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":18536,"license_type":"permissive","max_line_length":175,"num_lines":426,"path":"/src/main/webapp/app/exercises/text/assess/text-submission-assessment.component.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { Component, OnInit } from '@angular/core';\nimport { Location } from '@angular/common';\nimport { HttpErrorResponse, HttpResponse } from '@angular/common/http';\nimport { ActivatedRoute, NavigationExtras, Router } from '@angular/router';\nimport { JhiAlertService } from 'ng-jhipster';\nimport * as moment from 'moment';\nimport { now } from 'moment';\nimport { AccountService } from 'app/core/auth/account.service';\nimport { StudentParticipation } from 'app/entities/participation/student-participation.model';\nimport { TextSubmission } from 'app/entities/text-submission.model';\nimport { TextExercise } from 'app/entities/text-exercise.model';\nimport { Result } from 'app/entities/result.model';\nimport { Complaint } from 'app/entities/complaint.model';\nimport { ComplaintResponse } from 'app/entities/complaint-response.model';\nimport { ComplaintService } from 'app/complaints/complaint.service';\nimport { TextAssessmentService } from 'app/exercises/text/assess/text-assessment.service';\nimport { Feedback, FeedbackType } from 'app/entities/feedback.model';\nimport { notUndefined } from 'app/shared/util/global.utils';\nimport { TranslateService } from '@ngx-translate/core';\nimport { NEW_ASSESSMENT_PATH } from 'app/exercises/text/assess/text-submission-assessment.route';\nimport { StructuredGradingCriterionService } from 'app/exercises/shared/structured-grading-criterion/structured-grading-criterion.service';\nimport { assessmentNavigateBack } from 'app/exercises/shared/navigate-back.util';\nimport {\n getLatestSubmissionResult,\n getSubmissionResultByCorrectionRound,\n getSubmissionResultById,\n setLatestSubmissionResult,\n setSubmissionResultByCorrectionRound,\n} from 'app/entities/submission.model';\nimport { TextAssessmentBaseComponent } from 'app/exercises/text/assess/text-assessment-base.component';\nimport { getExerciseDashboardLink, getLinkToSubmissionAssessment } from 'app/utils/navigation.utils';\nimport { ExerciseType } from 'app/entities/exercise.model';\n\n@Component({\n selector: 'jhi-text-submission-assessment',\n templateUrl: './text-submission-assessment.component.html',\n styleUrls: ['./text-submission-assessment.component.scss'],\n})\nexport class TextSubmissionAssessmentComponent extends TextAssessmentBaseComponent implements OnInit {\n /*\n * The instance of this component is REUSED for multiple assessments if using the \"Assess Next\" button!\n * All properties must be initialized with a default value (or null) in the resetComponent() method.\n * For traceability: Keep order in resetComponent() consistent with declaration.\n */\n\n participation?: StudentParticipation;\n result?: Result;\n unreferencedFeedback: Feedback[];\n complaint?: Complaint;\n totalScore: number;\n isTestRun = false;\n isLoading: boolean;\n saveBusy: boolean;\n submitBusy: boolean;\n cancelBusy: boolean;\n nextSubmissionBusy: boolean;\n isAssessor: boolean;\n isAtLeastInstructor: boolean;\n assessmentsAreValid: boolean;\n noNewSubmissions: boolean;\n hasAssessmentDueDatePassed: boolean;\n correctionRound: number;\n resultId: number;\n loadingInitialSubmission = true;\n\n /*\n * Non-resetted properties:\n * These properties are not resetted on purpose, as they cannot change between assessments.\n */\n private cancelConfirmationText: string;\n\n // ExerciseId is updated from Route Subscription directly.\n exerciseId: number;\n courseId: number;\n examId = 0;\n exerciseGroupId: number;\n exerciseDashboardLink: string[];\n isExamMode = false;\n\n private get referencedFeedback(): Feedback[] {\n return this.textBlockRefs.map(({ feedback }) => feedback).filter(notUndefined) as Feedback[];\n }\n\n private get assessments(): Feedback[] {\n return [...this.referencedFeedback, ...this.unreferencedFeedback];\n }\n\n constructor(\n private activatedRoute: ActivatedRoute,\n private router: Router,\n private location: Location,\n private route: ActivatedRoute,\n protected jhiAlertService: JhiAlertService,\n protected accountService: AccountService,\n protected assessmentsService: TextAssessmentService,\n private complaintService: ComplaintService,\n translateService: TranslateService,\n protected structuredGradingCriterionService: StructuredGradingCriterionService,\n ) {\n super(jhiAlertService, accountService, assessmentsService, structuredGradingCriterionService);\n translateService.get('artemisApp.textAssessment.confirmCancel').subscribe((text) => (this.cancelConfirmationText = text));\n this.correctionRound = 0;\n this.resetComponent();\n }\n\n /**\n * This method is called before the component is REUSED!\n * All properties MUST be set to a default value (e.g. null) to prevent data corruption by state leaking into following new assessments.\n */\n private resetComponent(): void {\n this.participation = undefined;\n this.submission = undefined;\n this.exercise = undefined;\n this.result = undefined;\n this.unreferencedFeedback = [];\n this.textBlockRefs = [];\n this.unusedTextBlockRefs = [];\n this.complaint = undefined;\n this.totalScore = 0;\n\n this.isLoading = true;\n this.saveBusy = false;\n this.submitBusy = false;\n this.cancelBusy = false;\n this.nextSubmissionBusy = false;\n this.isAssessor = false;\n this.isAtLeastInstructor = false;\n this.assessmentsAreValid = false;\n this.noNewSubmissions = false;\n }\n\n /**\n * Life cycle hook to indicate component creation is done\n */\n async ngOnInit(): Promise {\n await super.ngOnInit();\n this.route.queryParamMap.subscribe((queryParams) => {\n this.isTestRun = queryParams.get('testRun') === 'true';\n this.correctionRound = Number(queryParams.get('correction-round'));\n });\n\n this.activatedRoute.paramMap.subscribe((paramMap) => {\n this.exerciseId = Number(paramMap.get('exerciseId'));\n this.resultId = Number(paramMap.get('resultId')) ?? 0;\n this.courseId = Number(paramMap.get('courseId'));\n if (paramMap.has('examId')) {\n this.examId = Number(paramMap.get('examId'));\n this.exerciseGroupId = Number(paramMap.get('exerciseGroupId'));\n this.isExamMode = true;\n }\n this.exerciseDashboardLink = getExerciseDashboardLink(this.courseId, this.exerciseId, this.examId, this.isTestRun);\n });\n this.activatedRoute.data.subscribe(({ studentParticipation }) => this.setPropertiesFromServerResponse(studentParticipation));\n }\n\n private setPropertiesFromServerResponse(studentParticipation: StudentParticipation) {\n this.resetComponent();\n this.loadingInitialSubmission = false;\n\n if (studentParticipation == undefined) {\n // Show \"No New Submission\" banner on .../submissions/new/assessment route\n this.noNewSubmissions = this.isNewAssessmentRoute;\n return;\n }\n\n this.participation = studentParticipation;\n this.submission = this.participation!.submissions![0] as TextSubmission;\n this.exercise = this.participation?.exercise as TextExercise;\n setLatestSubmissionResult(this.submission, getLatestSubmissionResult(this.submission));\n\n if (this.resultId > 0) {\n this.result = getSubmissionResultById(this.submission, this.resultId);\n this.correctionRound = this.submission.results?.findIndex((result) => result.id === this.resultId)!;\n } else {\n this.result = getSubmissionResultByCorrectionRound(this.submission, this.correctionRound);\n }\n\n this.hasAssessmentDueDatePassed = !!this.exercise!.assessmentDueDate && moment(this.exercise!.assessmentDueDate).isBefore(now());\n\n this.prepareTextBlocksAndFeedbacks();\n this.getComplaint();\n this.updateUrlIfNeeded();\n\n this.checkPermissions(this.result);\n this.totalScore = this.computeTotalScore(this.assessments);\n this.isLoading = false;\n\n // track feedback in athene\n this.assessmentsService.trackAssessment(this.submission, 'start');\n }\n\n private updateUrlIfNeeded() {\n if (this.isNewAssessmentRoute) {\n // Update the url with the new id, without reloading the page, to make the history consistent\n const newUrl = this.router\n .createUrlTree(getLinkToSubmissionAssessment(ExerciseType.TEXT, this.courseId, this.exerciseId, this.submission!.id!, this.examId, this.exerciseGroupId))\n .toString();\n this.location.go(newUrl);\n }\n }\n\n private get isNewAssessmentRoute(): boolean {\n return this.activatedRoute.routeConfig?.path === NEW_ASSESSMENT_PATH;\n }\n\n private checkPermissions(result?: Result): void {\n this.isAssessor = result?.assessor?.id === this.userId;\n // case distinction for exam mode\n this.isAtLeastInstructor = this.accountService.isAtLeastInstructorInCourse(this.course!);\n }\n\n /**\n * Save the assessment\n */\n save(): void {\n if (!this.assessmentsAreValid) {\n this.jhiAlertService.error('artemisApp.textAssessment.error.invalidAssessments');\n return;\n }\n\n // track feedback in athene\n this.assessmentsService.trackAssessment(this.submission, 'save');\n\n this.saveBusy = true;\n this.assessmentsService.save(this.exercise!.id!, this.result!.id!, this.assessments, this.textBlocksWithFeedback).subscribe(\n (response) => this.handleSaveOrSubmitSuccessWithAlert(response, 'artemisApp.textAssessment.saveSuccessful'),\n (error: HttpErrorResponse) => this.handleError(error),\n );\n }\n\n /**\n * Submit the assessment\n */\n submit(): void {\n if (!this.result?.id) {\n return; // We need to have saved the result before\n }\n\n if (!this.assessmentsAreValid) {\n this.jhiAlertService.error('artemisApp.textAssessment.error.invalidAssessments');\n return;\n }\n\n // track feedback in athene\n this.assessmentsService.trackAssessment(this.submission, 'submit');\n\n this.submitBusy = true;\n this.assessmentsService.submit(this.exercise!.id!, this.result!.id!, this.assessments, this.textBlocksWithFeedback).subscribe(\n (response) => this.handleSaveOrSubmitSuccessWithAlert(response, 'artemisApp.textAssessment.submitSuccessful'),\n (error: HttpErrorResponse) => this.handleError(error),\n );\n }\n\n protected handleSaveOrSubmitSuccessWithAlert(response: HttpResponse, translationKey: string): void {\n super.handleSaveOrSubmitSuccessWithAlert(response, translationKey);\n // eslint-disable-next-line chai-friendly/no-unused-expressions\n response.body!.feedbacks?.forEach((newFeedback) => {\n newFeedback.conflictingTextAssessments = this.result?.feedbacks?.find((feedback) => feedback.id === newFeedback.id)?.conflictingTextAssessments;\n });\n this.result = response.body!;\n setSubmissionResultByCorrectionRound(this.submission!, this.result, this.correctionRound);\n this.saveBusy = this.submitBusy = false;\n }\n\n /**\n * Cancel the assessment\n */\n cancel(): void {\n const confirmCancel = window.confirm(this.cancelConfirmationText);\n this.cancelBusy = true;\n if (confirmCancel && this.exercise && this.submission) {\n this.assessmentsService.cancelAssessment(this.exercise!.id!, this.submission!.id!).subscribe(() => this.navigateBack());\n }\n }\n\n /**\n * Go to next submission\n */\n async nextSubmission(): Promise {\n const url = getLinkToSubmissionAssessment(ExerciseType.TEXT, this.courseId, this.exerciseId, 'new', this.examId, this.exerciseGroupId);\n this.nextSubmissionBusy = true;\n await this.router.navigate(url, { queryParams: { 'correction-round': this.correctionRound } });\n }\n\n /**\n * if the conflict badge is clicked, navigate to conflict page and add the submission to the extras.\n * @param feedbackId - selected feedback id with conflicts.\n */\n async navigateToConflictingSubmissions(feedbackId: number): Promise {\n const tempSubmission = this.submission!;\n const latestSubmissionResult = getLatestSubmissionResult(tempSubmission)!;\n latestSubmissionResult.completionDate = undefined;\n latestSubmissionResult.submission = undefined;\n latestSubmissionResult.participation = undefined;\n\n const url = !this.isExamMode\n ? ['/course-management', this.courseId, 'text-exercises', this.exerciseId, 'submissions', this.submission!.id, 'text-feedback-conflict', feedbackId]\n : [\n '/course-management',\n this.courseId,\n 'exams',\n this.examId,\n 'exercise-groups',\n this.exerciseGroupId,\n 'text-exercises',\n this.exerciseId,\n 'submissions',\n this.submission!.id,\n 'text-feedback-conflict',\n feedbackId,\n ];\n const navigationExtras: NavigationExtras = { state: { submission: tempSubmission } };\n await this.router.navigate(url, navigationExtras);\n }\n\n /**\n * Sends the current (updated) assessment to the server to update the original assessment after a complaint was accepted.\n * The corresponding complaint response is sent along with the updated assessment to prevent additional requests.\n *\n * @param complaintResponse the response to the complaint that is sent to the server along with the assessment update\n */\n updateAssessmentAfterComplaint(complaintResponse: ComplaintResponse): void {\n this.validateFeedback();\n if (!this.assessmentsAreValid) {\n this.jhiAlertService.error('artemisApp.textAssessment.error.invalidAssessments');\n return;\n }\n\n this.assessmentsService.updateAssessmentAfterComplaint(this.assessments, this.textBlocksWithFeedback, complaintResponse, this.submission?.id!).subscribe(\n (response) => this.handleSaveOrSubmitSuccessWithAlert(response, 'artemisApp.textAssessment.updateAfterComplaintSuccessful'),\n (httpErrorResponse: HttpErrorResponse) => {\n this.jhiAlertService.clear();\n const error = httpErrorResponse.error;\n if (error && error.errorKey && error.errorKey === 'complaintLock') {\n this.jhiAlertService.error(error.message, error.params);\n } else {\n this.jhiAlertService.error('artemisApp.textAssessment.updateAfterComplaintFailed');\n }\n },\n );\n }\n\n navigateBack() {\n assessmentNavigateBack(this.location, this.router, this.exercise!, this.submission!, this.isTestRun);\n }\n\n /**\n * Validate the feedback of the assessment\n */\n validateFeedback(): void {\n const hasReferencedFeedback = Feedback.haveCredits(this.referencedFeedback);\n const hasUnreferencedFeedback = Feedback.haveCreditsAndComments(this.unreferencedFeedback);\n // When unreferenced feedback is set, it has to be valid (score + detailed text)\n this.assessmentsAreValid = (hasReferencedFeedback && this.unreferencedFeedback.length === 0) || hasUnreferencedFeedback;\n\n this.totalScore = this.computeTotalScore(this.assessments);\n }\n\n private prepareTextBlocksAndFeedbacks(): void {\n if (!this.result) {\n return;\n }\n const feedbacks = this.result.feedbacks || [];\n this.unreferencedFeedback = feedbacks.filter((feedbackElement) => feedbackElement.reference == undefined && feedbackElement.type === FeedbackType.MANUAL_UNREFERENCED);\n\n const matchBlocksWithFeedbacks = TextAssessmentService.matchBlocksWithFeedbacks(this.submission?.blocks || [], feedbacks);\n this.sortAndSetTextBlockRefs(matchBlocksWithFeedbacks, this.textBlockRefs, this.unusedTextBlockRefs, this.submission);\n }\n\n private getComplaint(): void {\n if (!this.result?.hasComplaint) {\n return;\n }\n\n this.isLoading = true;\n this.complaintService.findByResultId(this.result!.id!).subscribe(\n (res) => {\n if (!res.body) {\n return;\n }\n this.complaint = res.body;\n this.isLoading = false;\n },\n (err: HttpErrorResponse) => {\n this.handleError(err.error);\n },\n );\n }\n\n /**\n * Boolean which determines whether the user can override a result.\n * If no exercise is loaded, for example during loading between exercises, we return false.\n * Instructors can always override a result.\n * Tutors can override their own results within the assessment due date, if there is no complaint about their assessment.\n * They cannot override a result anymore, if there is a complaint. Another tutor must handle the complaint.\n */\n get canOverride(): boolean {\n if (this.exercise) {\n if (this.isAtLeastInstructor) {\n // Instructors can override any assessment at any time.\n return true;\n }\n if (this.complaint && this.isAssessor) {\n // If there is a complaint, the original assessor cannot override the result anymore.\n return false;\n }\n let isBeforeAssessmentDueDate = true;\n // Add check as the assessmentDueDate must not be set for exercises\n if (this.exercise.assessmentDueDate) {\n isBeforeAssessmentDueDate = moment().isBefore(this.exercise.assessmentDueDate!);\n }\n // tutors are allowed to override one of their assessments before the assessment due date.\n return this.isAssessor && isBeforeAssessmentDueDate;\n }\n return false;\n }\n\n get readOnly(): boolean {\n return !this.isAtLeastInstructor && !!this.complaint && this.isAssessor;\n }\n\n protected handleError(error: HttpErrorResponse): void {\n super.handleError(error);\n this.saveBusy = this.submitBusy = false;\n }\n}\n"},{"alpha_fraction":0.7965796589851379,"alphanum_fraction":0.8001800179481506,"avg_line_length":40.14814758300781,"blob_id":"49dbf9276d78739a1b3e46eac674b5e404965886","content_id":"0522a52c34eeb364ad171c04a975eba44f3ea198","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1111,"license_type":"permissive","max_line_length":163,"num_lines":27,"path":"/src/main/java/de/tum/in/www1/artemis/repository/LtiOutcomeUrlRepository.java","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"package de.tum.in.www1.artemis.repository;\n\nimport java.util.List;\nimport java.util.Optional;\n\nimport org.springframework.data.jpa.repository.JpaRepository;\nimport org.springframework.data.jpa.repository.Query;\nimport org.springframework.data.repository.query.Param;\n\nimport de.tum.in.www1.artemis.domain.Exercise;\nimport de.tum.in.www1.artemis.domain.LtiOutcomeUrl;\nimport de.tum.in.www1.artemis.domain.User;\n\n/**\n * Spring Data JPA repository for the LtiOutcomeUrl entity.\n */\n@SuppressWarnings(\"unused\")\npublic interface LtiOutcomeUrlRepository extends JpaRepository {\n\n @Query(\"select ltiOutcomeUrl from LtiOutcomeUrl ltiOutcomeUrl where ltiOutcomeUrl.user.login = ?#{principal}\")\n List findByUserIsCurrentUser();\n\n @Query(\"select ltiOutcomeUrl from LtiOutcomeUrl ltiOutcomeUrl where ltiOutcomeUrl.user.login = ?#{principal} and ltiOutcomeUrl.exercise.id = :#{#exercise.id}\")\n Optional findByUserIsCurrentUserAndExercise(@Param(\"exercise\") Exercise exercise);\n\n Optional findByUserAndExercise(User user, Exercise exercise);\n}\n"},{"alpha_fraction":0.656148374080658,"alphanum_fraction":0.6601330041885376,"avg_line_length":48.89912414550781,"blob_id":"041edae2ffa399f9fff627147d5617d7ee004a32","content_id":"6bd4d2dced95ffe01215de5700f3346b75d195db","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":34131,"license_type":"permissive","max_line_length":164,"num_lines":684,"path":"/src/test/javascript/spec/component/code-editor/code-editor-file-browser.component.spec.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { ComponentFixture, fakeAsync, TestBed, tick } from '@angular/core/testing';\nimport { LocalStorageService, SessionStorageService } from 'ngx-webstorage';\nimport { MockComponent, MockPipe } from 'ng-mocks';\nimport { CookieService } from 'ngx-cookie-service';\nimport { By } from '@angular/platform-browser';\nimport { TranslateModule } from '@ngx-translate/core';\nimport { DebugElement } from '@angular/core';\nimport * as chai from 'chai';\nimport * as sinonChai from 'sinon-chai';\nimport { AceEditorModule } from 'ng2-ace-editor';\nimport { TreeviewItem, TreeviewModule } from 'ngx-treeview';\nimport { SinonStub, spy, stub } from 'sinon';\nimport { Observable, Subject } from 'rxjs';\nimport { ArtemisTestModule } from '../../test.module';\nimport { CommitState, FileType, GitConflictState } from 'app/exercises/programming/shared/code-editor/model/code-editor.model';\nimport { triggerChanges } from '../../helpers/utils/general.utils';\nimport { DeviceDetectorService } from 'ngx-device-detector';\nimport { CodeEditorRepositoryFileService, CodeEditorRepositoryService } from 'app/exercises/programming/shared/code-editor/service/code-editor-repository.service';\nimport { CodeEditorConflictStateService } from 'app/exercises/programming/shared/code-editor/service/code-editor-conflict-state.service';\nimport { CodeEditorFileService } from 'app/exercises/programming/shared/code-editor/service/code-editor-file.service';\nimport { CodeEditorFileBrowserFolderComponent } from 'app/exercises/programming/shared/code-editor/file-browser/code-editor-file-browser-folder.component';\nimport { CodeEditorFileBrowserCreateNodeComponent } from 'app/exercises/programming/shared/code-editor/file-browser/code-editor-file-browser-create-node.component';\nimport { CodeEditorFileBrowserFileComponent } from 'app/exercises/programming/shared/code-editor/file-browser/code-editor-file-browser-file.component';\nimport { CodeEditorFileBrowserComponent } from 'app/exercises/programming/shared/code-editor/file-browser/code-editor-file-browser.component';\nimport { CodeEditorStatusComponent } from 'app/exercises/programming/shared/code-editor/status/code-editor-status.component';\nimport { MockCodeEditorRepositoryService } from '../../helpers/mocks/service/mock-code-editor-repository.service';\nimport { MockCodeEditorRepositoryFileService } from '../../helpers/mocks/service/mock-code-editor-repository-file.service';\nimport { MockCodeEditorConflictStateService } from '../../helpers/mocks/service/mock-code-editor-conflict-state.service';\nimport { MockSyncStorage } from '../../helpers/mocks/service/mock-sync-storage.service';\nimport { MockCookieService } from '../../helpers/mocks/service/mock-cookie.service';\nimport { NgbModule } from '@ng-bootstrap/ng-bootstrap';\nimport { ArtemisTranslatePipe } from 'app/shared/pipes/artemis-translate.pipe.ts';\n\nchai.use(sinonChai);\nconst expect = chai.expect;\n\ndescribe('CodeEditorFileBrowserComponent', () => {\n let comp: CodeEditorFileBrowserComponent;\n let fixture: ComponentFixture;\n let debugElement: DebugElement;\n let codeEditorRepositoryFileService: CodeEditorRepositoryFileService;\n let codeEditorRepositoryService: CodeEditorRepositoryService;\n let conflictService: CodeEditorConflictStateService;\n let getRepositoryContentStub: SinonStub;\n let isCleanStub: SinonStub;\n let createFileStub: SinonStub;\n let createFolderStub: SinonStub;\n let renameFileStub: SinonStub;\n\n const createFileRoot = '#create_file_root';\n const createFolderRoot = '#create_folder_root';\n const compressTree = '#compress_tree';\n\n beforeEach(async () => {\n return TestBed.configureTestingModule({\n imports: [TranslateModule.forRoot(), ArtemisTestModule, AceEditorModule, TreeviewModule.forRoot(), NgbModule],\n declarations: [\n CodeEditorFileBrowserComponent,\n MockComponent(CodeEditorStatusComponent),\n CodeEditorFileBrowserFileComponent,\n CodeEditorFileBrowserFolderComponent,\n CodeEditorFileBrowserCreateNodeComponent,\n MockPipe(ArtemisTranslatePipe),\n ],\n providers: [\n CodeEditorFileService,\n DeviceDetectorService,\n { provide: CodeEditorRepositoryService, useClass: MockCodeEditorRepositoryService },\n { provide: CodeEditorRepositoryFileService, useClass: MockCodeEditorRepositoryFileService },\n { provide: CodeEditorConflictStateService, useClass: MockCodeEditorConflictStateService },\n { provide: LocalStorageService, useClass: MockSyncStorage },\n { provide: SessionStorageService, useClass: MockSyncStorage },\n { provide: CookieService, useClass: MockCookieService },\n ],\n })\n .compileComponents()\n .then(() => {\n fixture = TestBed.createComponent(CodeEditorFileBrowserComponent);\n comp = fixture.componentInstance;\n debugElement = fixture.debugElement;\n codeEditorRepositoryFileService = debugElement.injector.get(CodeEditorRepositoryFileService);\n codeEditorRepositoryService = debugElement.injector.get(CodeEditorRepositoryService);\n conflictService = debugElement.injector.get(CodeEditorConflictStateService);\n isCleanStub = stub(codeEditorRepositoryService, 'getStatus');\n getRepositoryContentStub = stub(codeEditorRepositoryFileService, 'getRepositoryContent');\n createFileStub = stub(codeEditorRepositoryFileService, 'createFile');\n createFolderStub = stub(codeEditorRepositoryFileService, 'createFolder');\n renameFileStub = stub(codeEditorRepositoryFileService, 'renameFile');\n });\n });\n\n afterEach(() => {\n isCleanStub.restore();\n getRepositoryContentStub.restore();\n createFileStub.restore();\n createFolderStub.restore();\n });\n\n it('should create no treeviewItems if getRepositoryContent returns an empty result', () => {\n const repositoryContent: { [fileName: string]: string } = {};\n const expectedFileTreeItems: TreeviewItem[] = [];\n getRepositoryContentStub.returns(Observable.of(repositoryContent));\n isCleanStub.returns(Observable.of({ repositoryStatus: CommitState.CLEAN }));\n comp.commitState = CommitState.UNDEFINED;\n\n triggerChanges(comp, { property: 'commitState', currentValue: CommitState.UNDEFINED });\n fixture.detectChanges();\n\n expect(comp.isLoadingFiles).to.equal(false);\n expect(comp.repositoryFiles).to.deep.equal(repositoryContent);\n expect(comp.filesTreeViewItem).to.deep.equal(expectedFileTreeItems);\n const renderedFolders = debugElement.queryAll(By.css('jhi-code-editor-file-browser-folder'));\n const renderedFiles = debugElement.queryAll(By.css('jhi-code-editor-file-browser-file'));\n expect(renderedFolders).to.have.lengthOf(0);\n expect(renderedFiles).to.have.lengthOf(0);\n });\n\n it('should create treeviewItems if getRepositoryContent returns files', () => {\n const repositoryContent: { [fileName: string]: string } = { file: 'FILE', folder: 'FOLDER' };\n getRepositoryContentStub.returns(Observable.of(repositoryContent));\n isCleanStub.returns(Observable.of({ repositoryStatus: CommitState.CLEAN }));\n comp.commitState = CommitState.UNDEFINED;\n\n triggerChanges(comp, { property: 'commitState', currentValue: CommitState.UNDEFINED });\n fixture.detectChanges();\n expect(comp.isLoadingFiles).to.equal(false);\n expect(comp.repositoryFiles).to.deep.equal(repositoryContent);\n const renderedFolders = debugElement.queryAll(By.css('jhi-code-editor-file-browser-folder'));\n const renderedFiles = debugElement.queryAll(By.css('jhi-code-editor-file-browser-file'));\n expect(renderedFolders).to.have.lengthOf(1);\n expect(renderedFiles).to.have.lengthOf(1);\n });\n\n it('should create treeviewItems with nested folder structure', () => {\n const repositoryFiles = {\n folder: FileType.FOLDER,\n 'folder/file1': FileType.FILE,\n folder2: FileType.FOLDER,\n 'folder2/file2': FileType.FILE,\n 'folder2/folder3': FileType.FOLDER,\n 'folder2/folder3/file3': FileType.FILE,\n };\n comp.repositoryFiles = repositoryFiles;\n comp.setupTreeview();\n fixture.detectChanges();\n const folder = comp.filesTreeViewItem.find(({ value }) => value === 'folder')!;\n expect(folder).to.exist;\n expect(folder.children).to.have.lengthOf(1);\n const file1 = folder.children.find(({ value }) => value === 'folder/file1')!;\n expect(file1).to.exist;\n expect(file1.children).to.be.undefined;\n const folder2 = comp.filesTreeViewItem.find(({ value }) => value === 'folder2')!;\n expect(folder2).to.exist;\n expect(folder2.children).to.have.lengthOf(2);\n const file2 = folder2.children.find(({ value }) => value === 'folder2/file2')!;\n expect(file2).to.exist;\n expect(file2.children).to.be.undefined;\n const folder3 = folder2.children.find(({ value }) => value === 'folder2/folder3')!;\n expect(folder3).to.exist;\n expect(folder3.children).to.have.lengthOf(1);\n const file3 = folder3.children.find(({ value }) => value === 'folder2/folder3/file3')!;\n expect(file3).to.exist;\n expect(file3.children).to.be.undefined;\n const renderedFolders = debugElement.queryAll(By.css('jhi-code-editor-file-browser-folder'));\n const renderedFiles = debugElement.queryAll(By.css('jhi-code-editor-file-browser-file'));\n expect(renderedFolders).to.have.lengthOf(3);\n expect(renderedFiles).to.have.lengthOf(3);\n });\n\n it('should filter forbidden files from getRepositoryContent response', () => {\n const allowedFiles = {\n 'allowedFile.java': FileType.FILE,\n };\n const forbiddenFiles = {\n 'danger.bin': FileType.FOLDER,\n 'README.md': FileType.FILE,\n '.hidden': FileType.FILE,\n '.': FileType.FOLDER,\n };\n const repositoryContent = {\n ...allowedFiles,\n ...forbiddenFiles,\n };\n const expectedFileTreeItems = [\n new TreeviewItem({\n internalDisabled: false,\n internalChecked: false,\n internalCollapsed: false,\n text: 'file1',\n value: 'file1',\n } as any),\n ].map((x) => x.toString());\n getRepositoryContentStub.returns(Observable.of(repositoryContent));\n isCleanStub.returns(Observable.of({ repositoryStatus: CommitState.CLEAN }));\n comp.commitState = CommitState.UNDEFINED;\n triggerChanges(comp, { property: 'commitState', currentValue: CommitState.UNDEFINED });\n fixture.detectChanges();\n expect(comp.isLoadingFiles).to.equal(false);\n expect(comp.repositoryFiles).to.deep.equal(allowedFiles);\n expect(comp.filesTreeViewItem.map((x) => x.toString())).to.deep.equal(expectedFileTreeItems);\n const renderedFolders = debugElement.queryAll(By.css('jhi-code-editor-file-browser-folder'));\n const renderedFiles = debugElement.queryAll(By.css('jhi-code-editor-file-browser-file'));\n expect(renderedFolders).to.have.lengthOf(0);\n expect(renderedFiles).to.have.lengthOf(1);\n });\n\n it('should not load files if commitState could not be retrieved (possibly corrupt repository or server error)', () => {\n const isCleanSubject = new Subject<{ isClean: boolean }>();\n const onErrorSpy = spy(comp.onError, 'emit');\n const loadFilesSpy = spy(comp, 'loadFiles');\n isCleanStub.returns(isCleanSubject);\n comp.commitState = CommitState.UNDEFINED;\n triggerChanges(comp, { property: 'commitState', currentValue: CommitState.UNDEFINED });\n fixture.detectChanges();\n expect(comp.isLoadingFiles).to.equal(true);\n expect(comp.commitState).to.equal(CommitState.UNDEFINED);\n isCleanSubject.error('fatal error');\n\n fixture.detectChanges();\n expect(comp.commitState).to.equal(CommitState.COULD_NOT_BE_RETRIEVED);\n expect(comp.isLoadingFiles).to.equal(false);\n expect(comp.repositoryFiles).to.be.undefined;\n expect(comp.filesTreeViewItem).to.be.undefined;\n expect(onErrorSpy).to.have.been.calledOnce;\n expect(loadFilesSpy).to.not.have.been.called;\n const renderedFolders = debugElement.queryAll(By.css('jhi-code-editor-file-browser-folder'));\n const renderedFiles = debugElement.queryAll(By.css('jhi-code-editor-file-browser-file'));\n expect(renderedFolders).to.have.lengthOf(0);\n expect(renderedFiles).to.have.lengthOf(0);\n });\n\n it('should set isLoading false and emit an error if loadFiles fails', () => {\n const isCleanSubject = new Subject<{ isClean: boolean }>();\n const getRepositoryContentSubject = new Subject<{ [fileName: string]: FileType }>();\n const onErrorSpy = spy(comp.onError, 'emit');\n isCleanStub.returns(isCleanSubject);\n getRepositoryContentStub.returns(getRepositoryContentSubject);\n comp.commitState = CommitState.UNDEFINED;\n triggerChanges(comp, { property: 'commitState', currentValue: CommitState.UNDEFINED });\n fixture.detectChanges();\n expect(comp.isLoadingFiles).to.equal(true);\n expect(comp.commitState).to.equal(CommitState.UNDEFINED);\n isCleanSubject.next({ isClean: true });\n getRepositoryContentSubject.error('fatal error');\n\n fixture.detectChanges();\n expect(comp.isLoadingFiles).to.equal(false);\n expect(comp.repositoryFiles).to.be.undefined;\n expect(comp.filesTreeViewItem).to.be.undefined;\n expect(onErrorSpy).to.have.been.calledOnce;\n const renderedFolders = debugElement.queryAll(By.css('jhi-code-editor-file-browser-folder'));\n const renderedFiles = debugElement.queryAll(By.css('jhi-code-editor-file-browser-file'));\n expect(renderedFolders).to.have.lengthOf(0);\n expect(renderedFiles).to.have.lengthOf(0);\n });\n\n it('should set node to checked if its file gets selected and update ui', () => {\n const selectedFile = 'folder/file1';\n const repositoryFiles = {\n folder: FileType.FOLDER,\n 'folder/file1': FileType.FILE,\n folder2: FileType.FOLDER,\n 'folder/file2': FileType.FILE,\n };\n comp.filesTreeViewItem = [\n new TreeviewItem({\n internalDisabled: false,\n internalChecked: false,\n internalCollapsed: false,\n text: selectedFile,\n value: 'file1',\n } as any),\n new TreeviewItem({\n internalDisabled: false,\n internalChecked: false,\n internalCollapsed: false,\n text: 'folder2/file2',\n value: 'file2',\n } as any),\n ];\n comp.repositoryFiles = repositoryFiles;\n comp.selectedFile = selectedFile;\n triggerChanges(comp, { property: 'selectedFile', currentValue: 'folder/file2', firstChange: false });\n fixture.detectChanges();\n expect(comp.selectedFile).to.equal(selectedFile);\n const selectedTreeItem = comp.filesTreeViewItem.find(({ value }) => value === 'folder')!.children.find(({ value }) => value === selectedFile)!;\n expect(selectedTreeItem).to.exist;\n expect(selectedTreeItem.checked).to.be.true;\n const renderedFolders = debugElement.queryAll(By.css('jhi-code-editor-file-browser-folder'));\n const renderedFiles = debugElement.queryAll(By.css('jhi-code-editor-file-browser-file'));\n expect(renderedFolders).to.have.lengthOf(2);\n expect(renderedFiles).to.have.lengthOf(2);\n\n const selectedFileHtml = renderedFiles[0];\n const isSelected = !!selectedFileHtml.query(By.css('.node-selected'));\n expect(isSelected).to.be.true;\n const notSelectedFilesHtml = [renderedFiles[1], ...renderedFolders];\n const areUnSelected = !notSelectedFilesHtml.some((el) => !!el.query(By.css('.node-selected')));\n expect(areUnSelected).to.be.true;\n });\n\n it('should add file to node tree if created', fakeAsync(() => {\n const fileName = 'file2';\n const filePath = 'folder2/file2';\n const repositoryFiles = { file1: FileType.FILE, folder2: FileType.FOLDER };\n const treeItems = [\n new TreeviewItem({\n internalDisabled: false,\n internalChecked: false,\n internalCollapsed: false,\n text: 'file1',\n value: 'file1',\n } as any),\n new TreeviewItem({\n internalDisabled: false,\n internalChecked: false,\n internalCollapsed: false,\n text: 'folder2',\n value: 'folder2',\n } as any),\n ];\n const onFileChangeSpy = spy(comp.onFileChange, 'emit');\n const setupTreeviewStub = stub(comp, 'setupTreeview');\n createFileStub.returns(Observable.of(undefined));\n comp.repositoryFiles = repositoryFiles;\n comp.filesTreeViewItem = treeItems;\n comp.creatingFile = ['folder2', FileType.FILE];\n fixture.detectChanges();\n\n let creatingElement = debugElement.query(By.css('jhi-code-editor-file-browser-create-node'));\n expect(creatingElement).to.exist;\n const creatingInput = creatingElement.query(By.css('input'));\n expect(creatingInput).to.exist;\n\n tick();\n const focusedElement = debugElement.query(By.css(':focus')).nativeElement;\n expect(creatingInput.nativeElement).to.deep.equal(focusedElement);\n\n comp.onCreateFile(fileName);\n fixture.detectChanges();\n\n expect(createFileStub).to.have.been.calledOnceWithExactly(filePath);\n expect(comp.creatingFile).to.be.undefined;\n expect(setupTreeviewStub).to.have.been.calledOnceWithExactly();\n expect(onFileChangeSpy).to.have.been.calledOnce;\n expect(comp.repositoryFiles).to.deep.equal({ ...repositoryFiles, [filePath]: FileType.FILE });\n creatingElement = debugElement.query(By.css('jhi-code-editor-file-browser-create-node'));\n expect(creatingElement).not.to.exist;\n }));\n\n it('should add folder to node tree if created', fakeAsync(() => {\n const fileName = 'folder3';\n const filePath = 'folder2/folder3';\n const repositoryFiles = { file1: FileType.FILE, folder2: FileType.FOLDER };\n const treeItems = [\n new TreeviewItem({\n internalDisabled: false,\n internalChecked: false,\n internalCollapsed: false,\n text: 'file1',\n value: 'file1',\n } as any),\n new TreeviewItem({\n internalDisabled: false,\n internalChecked: false,\n internalCollapsed: false,\n text: 'folder2',\n value: 'folder2',\n } as any),\n ];\n const onFileChangeSpy = spy(comp.onFileChange, 'emit');\n const setupTreeviewStub = stub(comp, 'setupTreeview');\n createFolderStub.returns(Observable.of(undefined));\n comp.repositoryFiles = repositoryFiles;\n comp.filesTreeViewItem = treeItems;\n comp.creatingFile = ['folder2', FileType.FOLDER];\n fixture.detectChanges();\n\n let creatingElement = debugElement.query(By.css('jhi-code-editor-file-browser-create-node'));\n expect(creatingElement).to.exist;\n const creatingInput = creatingElement.query(By.css('input'));\n expect(creatingInput).to.exist;\n\n tick();\n const focusedElement = debugElement.query(By.css(':focus')).nativeElement;\n expect(creatingInput.nativeElement).to.deep.equal(focusedElement);\n\n comp.onCreateFile(fileName);\n\n fixture.detectChanges();\n expect(createFolderStub).to.have.been.calledOnceWithExactly(filePath);\n expect(comp.creatingFile).to.be.undefined;\n expect(setupTreeviewStub).to.have.been.calledOnceWithExactly();\n expect(onFileChangeSpy).to.have.been.calledOnce;\n expect(comp.repositoryFiles).to.deep.equal({ ...repositoryFiles, [filePath]: FileType.FOLDER });\n creatingElement = debugElement.query(By.css('jhi-code-editor-file-browser-create-node'));\n expect(creatingElement).not.to.exist;\n }));\n\n it('should not be able to create binary file', () => {\n const fileName = 'danger.bin';\n const onErrorSpy = spy(comp.onError, 'emit');\n comp.creatingFile = ['', FileType.FILE];\n comp.onCreateFile(fileName);\n fixture.detectChanges();\n expect(onErrorSpy).to.have.been.calledOnce;\n expect(createFileStub).not.to.have.been.called;\n });\n\n it('should not be able to create node that already exists', () => {\n const fileName = 'file1';\n const repositoryFiles = { 'folder2/file1': FileType.FILE, folder2: FileType.FOLDER };\n const onErrorSpy = spy(comp.onError, 'emit');\n comp.creatingFile = ['folder2', FileType.FILE];\n comp.repositoryFiles = repositoryFiles;\n comp.onCreateFile(fileName);\n fixture.detectChanges();\n expect(onErrorSpy).to.have.been.calledOnce;\n expect(createFileStub).not.to.have.been.called;\n });\n\n it('should update repository file entry on rename', fakeAsync(() => {\n const fileName = 'file1';\n const afterRename = 'newFileName';\n const treeItems = [\n new TreeviewItem({\n internalDisabled: false,\n internalChecked: false,\n internalCollapsed: false,\n text: 'file1',\n value: 'file1',\n } as any),\n new TreeviewItem({\n internalDisabled: false,\n internalChecked: false,\n internalCollapsed: false,\n text: 'folder2',\n value: 'folder2',\n } as any),\n ];\n const repositoryFiles = { file1: FileType.FILE, folder2: FileType.FOLDER };\n const onFileChangeSpy = spy(comp.onFileChange, 'emit');\n renameFileStub.returns(Observable.of(undefined));\n comp.repositoryFiles = repositoryFiles;\n comp.renamingFile = [fileName, fileName, FileType.FILE];\n comp.filesTreeViewItem = treeItems;\n fixture.detectChanges();\n\n let filesInTreeHtml = debugElement.queryAll(By.css('jhi-code-editor-file-browser-file'));\n expect(filesInTreeHtml).to.have.lengthOf(1);\n let foldersInTreeHtml = debugElement.queryAll(By.css('jhi-code-editor-file-browser-folder'));\n expect(foldersInTreeHtml).to.have.lengthOf(1);\n let renamingInput = filesInTreeHtml[0].query(By.css('input'));\n expect(renamingInput).to.exist;\n\n // Wait for focus of input element\n tick();\n const focusedElement = debugElement.query(By.css(':focus')).nativeElement;\n expect(renamingInput.nativeElement).to.deep.equal(focusedElement);\n\n renamingInput.nativeElement.value = afterRename;\n renamingInput.nativeElement.dispatchEvent(new Event('input'));\n renamingInput.nativeElement.dispatchEvent(new Event('focusout'));\n fixture.detectChanges();\n\n expect(renameFileStub).to.have.been.calledOnceWithExactly(fileName, afterRename);\n expect(comp.renamingFile).to.be.undefined;\n expect(onFileChangeSpy).to.have.been.calledOnce;\n expect(comp.repositoryFiles).to.deep.equal({ folder2: FileType.FOLDER, [afterRename]: FileType.FILE });\n\n filesInTreeHtml = debugElement.queryAll(By.css('jhi-code-editor-file-browser-file'));\n expect(filesInTreeHtml).to.have.lengthOf(1);\n foldersInTreeHtml = debugElement.queryAll(By.css('jhi-code-editor-file-browser-folder'));\n expect(foldersInTreeHtml).to.have.lengthOf(1);\n renamingInput = filesInTreeHtml[0].query(By.css('input'));\n expect(renamingInput).not.to.exist;\n\n // Wait for focus of input element\n tick();\n }));\n\n it('should rename all paths concerned if a folder is renamed', fakeAsync(() => {\n const folderName = 'folder';\n const afterRename = 'newFolderName';\n const treeItems = [\n new TreeviewItem({\n internalDisabled: false,\n internalChecked: false,\n internalCollapsed: false,\n text: 'file1',\n value: 'folder/file1',\n } as any),\n new TreeviewItem({\n internalDisabled: false,\n internalChecked: false,\n internalCollapsed: false,\n text: 'file2',\n value: 'folder/file2',\n } as any),\n new TreeviewItem({\n internalDisabled: false,\n internalChecked: false,\n internalCollapsed: false,\n text: 'folder',\n value: 'folder',\n } as any),\n new TreeviewItem({\n internalDisabled: false,\n internalChecked: false,\n internalCollapsed: false,\n text: 'folder2',\n value: 'folder2',\n } as any),\n ];\n const repositoryFiles = { 'folder/file1': FileType.FILE, 'folder/file2': FileType.FILE, folder: FileType.FOLDER, folder2: FileType.FOLDER };\n const onFileChangeSpy = spy(comp.onFileChange, 'emit');\n renameFileStub.returns(Observable.of(undefined));\n comp.repositoryFiles = repositoryFiles;\n comp.renamingFile = [folderName, folderName, FileType.FILE];\n comp.filesTreeViewItem = treeItems;\n fixture.detectChanges();\n\n let filesInTreeHtml = debugElement.queryAll(By.css('jhi-code-editor-file-browser-file'));\n expect(filesInTreeHtml).to.have.lengthOf(2);\n let foldersInTreeHtml = debugElement.queryAll(By.css('jhi-code-editor-file-browser-folder'));\n expect(foldersInTreeHtml).to.have.lengthOf(2);\n let renamingInput = foldersInTreeHtml[0].query(By.css('input'));\n expect(renamingInput).to.exist;\n\n // Wait for focus of input element\n tick();\n const focusedElement = debugElement.query(By.css(':focus')).nativeElement;\n expect(renamingInput.nativeElement).to.deep.equal(focusedElement);\n\n renamingInput.nativeElement.value = afterRename;\n renamingInput.nativeElement.dispatchEvent(new Event('input'));\n renamingInput.nativeElement.dispatchEvent(new Event('focusout'));\n\n expect(renameFileStub).to.have.been.calledOnceWithExactly(folderName, afterRename);\n expect(comp.renamingFile).to.be.undefined;\n expect(onFileChangeSpy).to.have.been.calledOnce;\n expect(comp.repositoryFiles).to.deep.equal({\n [[afterRename, 'file1'].join('/')]: FileType.FILE,\n [[afterRename, 'file2'].join('/')]: FileType.FILE,\n [afterRename]: FileType.FOLDER,\n folder2: FileType.FOLDER,\n });\n\n filesInTreeHtml = debugElement.queryAll(By.css('jhi-code-editor-file-browser-file'));\n expect(filesInTreeHtml).to.have.lengthOf(2);\n foldersInTreeHtml = debugElement.queryAll(By.css('jhi-code-editor-file-browser-folder'));\n expect(foldersInTreeHtml).to.have.lengthOf(2);\n renamingInput = filesInTreeHtml[0].query(By.css('input'));\n expect(renamingInput).not.to.exist;\n }));\n\n it('should not rename a file if its new fileName already exists in the repository', fakeAsync(() => {\n const fileName = 'file1';\n const afterRename = 'newFileName';\n const repositoryFiles = { file1: FileType.FILE, newFileName: FileType.FILE };\n comp.repositoryFiles = repositoryFiles;\n comp.setupTreeview();\n fixture.detectChanges();\n comp.renamingFile = [fileName, fileName, FileType.FILE];\n fixture.detectChanges();\n\n let renamingInput = debugElement.queryAll(By.css('jhi-code-editor-file-browser-file'))[0].query(By.css('input'));\n expect(renamingInput).to.exist;\n\n // Wait for focus of input element\n tick();\n let focusedElement = debugElement.query(By.css(':focus')).nativeElement;\n expect(renamingInput.nativeElement).to.deep.equal(focusedElement);\n\n renamingInput.nativeElement.value = afterRename;\n renamingInput.nativeElement.dispatchEvent(new Event('input'));\n renamingInput.nativeElement.dispatchEvent(new Event('focusout'));\n fixture.detectChanges();\n\n expect(renameFileStub).not.to.have.been.called;\n expect(comp.repositoryFiles).to.deep.equal(repositoryFiles);\n\n // When renaming failed, the input should not be closed, because the user probably still wants to rename\n renamingInput = debugElement.queryAll(By.css('jhi-code-editor-file-browser-file'))[0].query(By.css('input'));\n expect(renamingInput).to.exist;\n focusedElement = debugElement.query(By.css(':focus')).nativeElement;\n expect(renamingInput.nativeElement).to.deep.equal(focusedElement);\n }));\n\n it('should not rename a file if its new fileName indicates a binary file', fakeAsync(() => {\n const fileName = 'file1';\n const afterRename = 'newFileName.bin';\n const repositoryFiles = { file1: FileType.FILE, newFileName: FileType.FILE };\n comp.repositoryFiles = repositoryFiles;\n comp.setupTreeview();\n fixture.detectChanges();\n comp.renamingFile = [fileName, fileName, FileType.FILE];\n fixture.detectChanges();\n\n let renamingInput = debugElement.queryAll(By.css('jhi-code-editor-file-browser-file'))[0].query(By.css('input'));\n expect(renamingInput).to.exist;\n\n // Wait for focus of input element\n tick();\n let focusedElement = debugElement.query(By.css(':focus')).nativeElement;\n expect(renamingInput.nativeElement).to.deep.equal(focusedElement);\n\n renamingInput.nativeElement.value = afterRename;\n renamingInput.nativeElement.dispatchEvent(new Event('input'));\n renamingInput.nativeElement.dispatchEvent(new Event('focusout'));\n fixture.detectChanges();\n\n expect(renameFileStub).not.to.have.been.called;\n expect(comp.repositoryFiles).to.deep.equal(repositoryFiles);\n\n // When renaming failed, the input should not be closed, because the user probably still wants to rename\n renamingInput = debugElement.queryAll(By.css('jhi-code-editor-file-browser-file'))[0].query(By.css('input'));\n expect(renamingInput).to.exist;\n focusedElement = debugElement.query(By.css(':focus')).nativeElement;\n expect(renamingInput.nativeElement).to.deep.equal(focusedElement);\n }));\n\n it('should leave rename state if renaming a file to the same file name', fakeAsync(() => {\n const fileName = 'file1';\n const repositoryFiles = { file1: FileType.FILE, newFileName: FileType.FILE };\n comp.repositoryFiles = repositoryFiles;\n comp.setupTreeview();\n fixture.detectChanges();\n comp.renamingFile = [fileName, fileName, FileType.FILE];\n fixture.detectChanges();\n\n let renamingInput = debugElement.queryAll(By.css('jhi-code-editor-file-browser-file'))[0].query(By.css('input'));\n expect(renamingInput).to.exist;\n\n // Wait for focus of input element\n tick();\n const focusedElement = debugElement.query(By.css(':focus')).nativeElement;\n expect(renamingInput.nativeElement).to.deep.equal(focusedElement);\n\n renamingInput.nativeElement.dispatchEvent(new Event('focusout'));\n fixture.detectChanges();\n\n expect(renameFileStub).not.to.have.been.called;\n expect(comp.repositoryFiles).to.deep.equal(repositoryFiles);\n\n // When renaming failed, the input should not be closed, because the user probably still wants to rename\n renamingInput = debugElement.queryAll(By.css('jhi-code-editor-file-browser-file'))[0].query(By.css('input'));\n expect(renamingInput).not.to.exist;\n }));\n\n it('should disable action buttons if there is a git conflict', () => {\n const repositoryContent: { [fileName: string]: string } = {};\n isCleanStub.returns(Observable.of({ repositoryStatus: CommitState.CONFLICT }));\n getRepositoryContentStub.returns(Observable.of(repositoryContent));\n comp.commitState = CommitState.UNDEFINED;\n\n triggerChanges(comp, { property: 'commitState', currentValue: CommitState.UNDEFINED });\n fixture.detectChanges();\n\n expect(comp.commitState).to.equal(CommitState.CONFLICT);\n\n expect(debugElement.query(By.css(createFileRoot)).nativeElement.disabled).to.be.true;\n expect(debugElement.query(By.css(createFolderRoot)).nativeElement.disabled).to.be.true;\n expect(debugElement.query(By.css(compressTree)).nativeElement.disabled).to.be.false;\n\n // Resolve conflict.\n conflictService.notifyConflictState(GitConflictState.OK);\n isCleanStub.returns(Observable.of({ repositoryStatus: CommitState.CLEAN }));\n\n comp.commitState = CommitState.UNDEFINED;\n\n triggerChanges(comp, { property: 'commitState', currentValue: CommitState.UNDEFINED });\n fixture.detectChanges();\n\n expect(comp.commitState).to.equal(CommitState.CLEAN);\n\n expect(debugElement.query(By.css(createFileRoot)).nativeElement.disabled).to.be.false;\n expect(debugElement.query(By.css(createFolderRoot)).nativeElement.disabled).to.be.false;\n expect(debugElement.query(By.css(compressTree)).nativeElement.disabled).to.be.false;\n\n expect(getRepositoryContentStub).to.have.been.calledOnce;\n expect(comp.selectedFile).to.be.undefined;\n });\n});\n"},{"alpha_fraction":0.7511365413665771,"alphanum_fraction":0.7538107633590698,"avg_line_length":67.4871826171875,"blob_id":"9ac19734db1143b1d1c430b204da12fc68bc1ed8","content_id":"e7d463dd91881e910ff26b64bda316a202810200","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":18697,"license_type":"permissive","max_line_length":180,"num_lines":273,"path":"/src/main/java/de/tum/in/www1/artemis/service/LearningGoalService.java","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"package de.tum.in.www1.artemis.service;\n\nimport java.util.*;\nimport java.util.stream.Collectors;\nimport java.util.stream.Stream;\n\nimport org.springframework.stereotype.Service;\n\nimport de.tum.in.www1.artemis.domain.*;\nimport de.tum.in.www1.artemis.domain.lecture.ExerciseUnit;\nimport de.tum.in.www1.artemis.domain.lecture.LectureUnit;\nimport de.tum.in.www1.artemis.domain.participation.StudentParticipation;\nimport de.tum.in.www1.artemis.domain.scores.StudentScore;\nimport de.tum.in.www1.artemis.domain.scores.TeamScore;\nimport de.tum.in.www1.artemis.repository.*;\nimport de.tum.in.www1.artemis.web.rest.dto.CourseExerciseStatisticsDTO;\nimport de.tum.in.www1.artemis.web.rest.dto.CourseLearningGoalProgress;\nimport de.tum.in.www1.artemis.web.rest.dto.IndividualLearningGoalProgress;\n\n@Service\npublic class LearningGoalService {\n\n private final StudentParticipationRepository studentParticipationRepository;\n\n private final ExerciseRepository exerciseRepository;\n\n private final StudentScoreRepository studentScoreRepository;\n\n private final TeamScoreRepository teamScoreRepository;\n\n public LearningGoalService(StudentParticipationRepository studentParticipationRepository, ExerciseRepository exerciseRepository, StudentScoreRepository studentScoreRepository,\n TeamScoreRepository teamScoreRepository) {\n this.exerciseRepository = exerciseRepository;\n this.studentParticipationRepository = studentParticipationRepository;\n this.studentScoreRepository = studentScoreRepository;\n this.teamScoreRepository = teamScoreRepository;\n }\n\n /**\n * Calculates the progress of the given user in the given exercise units\n *
\n\n# IMPORTANT: Upgrading from v0.1 with an existing database? \nThe database table has been modified to accomodate tracking of minimum keyword strings triggering \ncensorship. If you used blockedonweibo v0.1 and you used a database to store results, you will \nneed to update your database file. \n\n**To migrate your older database file**\n1. Move `update_db.py` to the same file directory as your database file (\"results.sqlite\" if you followed\nthis setup guide)\n2. In terminal, run `python update_db.py` and confirm database file. \n\n### Version 0.2 changes \n* now includes a feature to find canonical censored keywords (minimum set of keywords required to trigger explicit censorship message)\n * to run, pass `get_canonical=True` with rest of variables into `run()`\n * see section 4.8\n\n\n# Table of Contents\n \n\n# Install the blockedonweibo package\n\nThe github repo for this Weibo keyword testing script is located at https://github.com/jasonqng/blocked-on-weibo.\n\nTo begin using this python package, inside your terminal, run\n\n```\npip install blockedonweibo\n```\n\nAlternatively, you can clone this repo, `cd` into the repo directory and manually install the requirements and package:\n\n```\npip install -r requirements.txt\npython setup.py install\n```\n\nTo confirm the installation works, in a python shell (you can start by running `python` from terminal), try importing the package:\n\n```\nimport blockedonweibo\n```\n\nIf you don't get any errors, things have installed successfully. If not, you may need to fiddle with your python paths and settings to ensure it's being installed to the correct location.\n\n# Adjust your settings\n\nYour python script only requires the following. All other imports are handled by the package.\n\n\n```python\nfrom blockedonweibo import weibo\nimport pandas as pd\n```\n\nYou have the option of saving your test results to a file. You'll need to pass a path to to a file which will store the results in sqlite format. It can be helpful to set this at the top of your script and pass the variable each time you run the test.\n\n\n```python\nsqlite_file = 'results.sqlite' # name of sqlite file to read from/write to\n```\n\nIf you want to erase any existing data you have in the sqlite file defined above, just pass overwrite=True to the `create_database` function. Otherwise any new results will be appended to the end of the database.\n\n\n```python\nweibo.create_database(sqlite_file, overwrite=True)\n```\n\nThis testing script is enhanced if you allow it to log into Weibo, which increases your rate limit threshold as well as returns the number of results a search says it has. This script will work without your supplying credentials, but it is highly recommended. To do so, edit the `weibo_credentials.py` with your email address and password. The file is ignored and will not be uploaded by default when you push commits to github. You can inspect the code to verify that the credentials don't go anywhere except to weibo.\n\nUsing those credentials, the script logs you in and fetches a cookie for the user session you create. This cookie can be saved to a file by passing the `write_cookie` parameter in the `user_login` function.\n\n\n```python\nsession = weibo.user_login(write_cookie=True)\n```\n\nThere is a helper function to verify that the cookie actually works\n\n\n```python\ncookie = session.cookies.get_dict()\nprint(weibo.verify_cookies_work(cookie))\n```\n\n True\n\n\nIf you have the cookie already written to disk, you don't need to perform another `user_login` and instead, you can just use the `load_cookies` function to fetch the cookie from the file. Again, you can verify that it works. Just store the cookie's contents (a dictionary) to a variable and pass that to the `run` function below if you want to test as if you were logged in. Otherwise, it will emulate a search by a logged out user.\n\n\n```python\ncookie = weibo.load_cookies()\nprint(weibo.verify_cookies_work(cookie))\n```\n\n True\n\n\n# Let's start testing!\n\n## Pass a dictionary of keywords to start testing\n\n\n```python\nsample_keywords_df = pd.DataFrame(\n [{'keyword':'hello','source':'my dataframe'},\n {'keyword':'lxb','source':'my dataframe'},\n {'keyword':u'习胞子','source':'my dataframe'}\n ])\n```\n\n\n```python\nsample_keywords_df\n```\n\n\n\n\n\n\n \n
\n
\n\n\n\n\n```python\nweibo.run(sample_keywords_df,insert=False,return_df=True)\n```\n\n (0, u'hello', 'has_results')\n (1, u'lxb', 'censored')\n (2, u'\\u4e60\\u80de\\u5b50', 'no_results')\n\n\n\n\n\n| \n | keyword | \nsource | \n
|---|---|---|
| 0 | \nhello | \nmy dataframe | \n
| 1 | \nlxb | \nmy dataframe | \n
| 2 | \n习胞子 | \nmy dataframe | \n
\n\n \n
\n
\n\n\n\n## Pass in cookies so you can also get the number of results. Pass in sqlite_file to save the results to disk so you can load it later\n\n\n```python\nweibo.run(sample_keywords_df,sqlite_file=sqlite_file,cookies=cookie)\n```\n\n (0, u'hello', 'has_results')\n (1, u'lxb', 'censored')\n (2, u'\\u4e60\\u80de\\u5b50', 'no_results')\n\n\n\n```python\nweibo.sqlite_to_df(sqlite_file)\n```\n\n\n\n\n| \n | date | \ndatetime | \nis_canonical | \nkeyword | \nnum_results | \norig_keyword | \nresult | \nsource | \ntest_number | \n
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n2017-09-25 | \n2017-09-25 10:12:45.280812 | \nFalse | \nhello | \n[] | \nNone | \nhas_results | \nmy dataframe | \n1 | \n
| 0 | \n2017-09-25 | \n2017-09-25 10:13:00.191900 | \nFalse | \nlxb | \nNone | \nNone | \ncensored | \nmy dataframe | \n1 | \n
| 0 | \n2017-09-25 | \n2017-09-25 10:13:16.356805 | \nFalse | \n习胞子 | \nNone | \nNone | \nno_results | \nmy dataframe | \n1 | \n
\n\n \n
\n
\n\n\n\n## If your test gets interrupted or you add more keywords, you can pick up where you left off\n\nLet's pretend I wanted to test four total keywords, but I was only able to complete the first three above. I'll go ahead and add one more keyword to the test list to replicate an unfinished keyword.\n\n\n```python\nsample_keywords_df.loc[len(sample_keywords_df.index)] = ['刘晓波','my dataframe']\n```\n\n\n```python\nsample_keywords_df\n```\n\n\n\n\n| \n | id | \ndate | \ndatetime_logged | \ntest_number | \nkeyword | \ncensored | \nno_results | \nreset | \nis_canonical | \nresult | \nsource | \norig_keyword | \nnum_results | \nnotes | \n
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n0 | \n2017-09-25 | \n2017-09-25 10:13:37.816720 | \n1 | \nhello | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n80454701.0 | \nNone | \n
| 1 | \n1 | \n2017-09-25 | \n2017-09-25 10:13:54.356722 | \n1 | \nlxb | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 2 | \n2 | \n2017-09-25 | \n2017-09-25 10:14:11.489530 | \n1 | \n习胞子 | \n0 | \n0 | \n0 | \n0 | \nno_results | \nmy dataframe | \nNone | \nNaN | \nNone | \n
\n\n \n
\n
\n\n\n\n\n```python\nweibo.run(sample_keywords_df,sqlite_file=sqlite_file,cookies=cookie)\n```\n\n (3, u'\\u5218\\u6653\\u6ce2', 'censored')\n\n\nNeat-o, it was smart enough to start right at that new keyword and not start all over again!\n\n\n```python\nweibo.sqlite_to_df(sqlite_file)\n```\n\n\n\n\n| \n | keyword | \nsource | \n
|---|---|---|
| 0 | \nhello | \nmy dataframe | \n
| 1 | \nlxb | \nmy dataframe | \n
| 2 | \n习胞子 | \nmy dataframe | \n
| 3 | \n刘晓波 | \nmy dataframe | \n
\n\n \n
\n
\n\n\n\n## You can attach notes or categorizations to your keywords for easy querying and analysis later\n\n\n```python\nnew_keywords_df = pd.DataFrame(\n [{'keyword':'pokemon','source':'my dataframe',\"notes\":\"pop culture\"},\n {'keyword':'jay chou','source':'my dataframe',\"notes\":\"pop culture\"},\n {'keyword':u'weibo','source':'my dataframe',\"notes\":\"social media\"}\n ])\nmerged_keywords_df = pd.concat([sample_keywords_df,new_keywords_df]).reset_index(drop=True)\nmerged_keywords_df\n```\n\n\n\n\n| \n | id | \ndate | \ndatetime_logged | \ntest_number | \nkeyword | \ncensored | \nno_results | \nreset | \nis_canonical | \nresult | \nsource | \norig_keyword | \nnum_results | \nnotes | \n
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n0 | \n2017-09-25 | \n2017-09-25 10:13:37.816720 | \n1 | \nhello | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n80454701.0 | \nNone | \n
| 1 | \n1 | \n2017-09-25 | \n2017-09-25 10:13:54.356722 | \n1 | \nlxb | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 2 | \n2 | \n2017-09-25 | \n2017-09-25 10:14:11.489530 | \n1 | \n习胞子 | \n0 | \n0 | \n0 | \n0 | \nno_results | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 3 | \n3 | \n2017-09-25 | \n2017-09-25 10:14:29.667395 | \n1 | \n刘晓波 | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
\n\n \n
\n
\n\n\n\n\n```python\nweibo.run(merged_keywords_df,sqlite_file=sqlite_file,cookies=cookie)\n```\n\n (4, u'pokemon', 'has_results')\n (5, u'jay chou', 'has_results')\n (6, u'weibo', 'has_results')\n\n\n\n```python\nweibo.sqlite_to_df(sqlite_file)\n```\n\n\n\n\n| \n | keyword | \nnotes | \nsource | \n
|---|---|---|---|
| 0 | \nhello | \nNaN | \nmy dataframe | \n
| 1 | \nlxb | \nNaN | \nmy dataframe | \n
| 2 | \n习胞子 | \nNaN | \nmy dataframe | \n
| 3 | \n刘晓波 | \nNaN | \nmy dataframe | \n
| 4 | \npokemon | \npop culture | \nmy dataframe | \n
| 5 | \njay chou | \npop culture | \nmy dataframe | \n
| 6 | \nsocial media | \nmy dataframe | \n
\n\n \n
\n
\n\n\n\n\n```python\nresults = weibo.sqlite_to_df(sqlite_file)\nresults.query(\"notes=='pop culture'\")\n```\n\n\n\n\n| \n | id | \ndate | \ndatetime_logged | \ntest_number | \nkeyword | \ncensored | \nno_results | \nreset | \nis_canonical | \nresult | \nsource | \norig_keyword | \nnum_results | \nnotes | \n
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n0 | \n2017-09-25 | \n2017-09-25 10:13:37.816720 | \n1 | \nhello | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n80454701.0 | \nNone | \n
| 1 | \n1 | \n2017-09-25 | \n2017-09-25 10:13:54.356722 | \n1 | \nlxb | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 2 | \n2 | \n2017-09-25 | \n2017-09-25 10:14:11.489530 | \n1 | \n习胞子 | \n0 | \n0 | \n0 | \n0 | \nno_results | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 3 | \n3 | \n2017-09-25 | \n2017-09-25 10:14:29.667395 | \n1 | \n刘晓波 | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 4 | \n4 | \n2017-09-25 | \n2017-09-25 10:14:49.107078 | \n1 | \npokemon | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n5705260.0 | \npop culture | \n
| 5 | \n5 | \n2017-09-25 | \n2017-09-25 10:15:09.762484 | \n1 | \njay chou | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n881.0 | \npop culture | \n
| 6 | \n6 | \n2017-09-25 | \n2017-09-25 10:15:28.100418 | \n1 | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n63401495.0 | \nsocial media | \n
\n\n \n
\n
\n\n\n\n\n```python\nresults.query(\"notes=='pop culture'\").num_results.mean()\n```\n\n\n\n\n 2853070.5\n\n\n\n## If you want to test multiple times a day, just pass in the `test_number` param\n\nYou can off `verbose` output in case you don't need to troubleshoot anything...\n\n\n```python\nweibo.run(sample_keywords_df,sqlite_file=sqlite_file,cookies=cookie,verbose='none',test_number=2)\n```\n\n\n```python\nweibo.sqlite_to_df(sqlite_file)\n```\n\n\n\n\n| \n | id | \ndate | \ndatetime_logged | \ntest_number | \nkeyword | \ncensored | \nno_results | \nreset | \nis_canonical | \nresult | \nsource | \norig_keyword | \nnum_results | \nnotes | \n
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | \n4 | \n2017-09-25 | \n2017-09-25 10:14:49.107078 | \n1 | \npokemon | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n5705260.0 | \npop culture | \n
| 5 | \n5 | \n2017-09-25 | \n2017-09-25 10:15:09.762484 | \n1 | \njay chou | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n881.0 | \npop culture | \n
\n\n \n
\n
\n\n\n\n## It can skip redundant keywords\n\n\n```python\nmore_keywords_df = pd.DataFrame(\n [{'keyword':'zhongnanhai','source':'my dataframe2',\"notes\":\"location\"},\n {'keyword':'cats','source':'my dataframe2',\"notes\":\"pop culture\"},\n {'keyword':'zhongnanhai','source':'my dataframe2',\"notes\":\"location\"}\n ])\n```\n\n\n```python\nmore_keywords_df\n```\n\n\n\n\n| \n | id | \ndate | \ndatetime_logged | \ntest_number | \nkeyword | \ncensored | \nno_results | \nreset | \nis_canonical | \nresult | \nsource | \norig_keyword | \nnum_results | \nnotes | \n
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n0 | \n2017-09-25 | \n2017-09-25 10:13:37.816720 | \n1 | \nhello | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n80454701.0 | \nNone | \n
| 1 | \n1 | \n2017-09-25 | \n2017-09-25 10:13:54.356722 | \n1 | \nlxb | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 2 | \n2 | \n2017-09-25 | \n2017-09-25 10:14:11.489530 | \n1 | \n习胞子 | \n0 | \n0 | \n0 | \n0 | \nno_results | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 3 | \n3 | \n2017-09-25 | \n2017-09-25 10:14:29.667395 | \n1 | \n刘晓波 | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 4 | \n4 | \n2017-09-25 | \n2017-09-25 10:14:49.107078 | \n1 | \npokemon | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n5705260.0 | \npop culture | \n
| 5 | \n5 | \n2017-09-25 | \n2017-09-25 10:15:09.762484 | \n1 | \njay chou | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n881.0 | \npop culture | \n
| 6 | \n6 | \n2017-09-25 | \n2017-09-25 10:15:28.100418 | \n1 | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n63401495.0 | \nsocial media | \n|
| 7 | \n7 | \n2017-09-25 | \n2017-09-25 10:15:46.214464 | \n2 | \nhello | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n80454634.0 | \nNone | \n
| 8 | \n8 | \n2017-09-25 | \n2017-09-25 10:16:03.274804 | \n2 | \nlxb | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 9 | \n9 | \n2017-09-25 | \n2017-09-25 10:16:19.035805 | \n2 | \n习胞子 | \n0 | \n0 | \n0 | \n0 | \nno_results | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 10 | \n10 | \n2017-09-25 | \n2017-09-25 10:16:36.021837 | \n2 | \n刘晓波 | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
\n\n \n
\n
\n\n\n\n\n```python\nweibo.run(more_keywords_df,sqlite_file=sqlite_file,cookies=cookie)\n```\n\n (0, u'zhongnanhai', 'has_results')\n (1, u'cats', 'has_results')\n\n\n\n```python\nweibo.sqlite_to_df(sqlite_file)\n```\n\n\n\n\n| \n | keyword | \nnotes | \nsource | \n
|---|---|---|---|
| 0 | \nzhongnanhai | \nlocation | \nmy dataframe2 | \n
| 1 | \ncats | \npop culture | \nmy dataframe2 | \n
| 2 | \nzhongnanhai | \nlocation | \nmy dataframe2 | \n
\n\n \n
\n
\n\n\n## You can also pass in lists if you prefer (though you can't include the source or notes)\n\n\n```python\nsample_keywords_list = [\"cats\",'yes','自由亚洲电台','刘晓波','dhfjkdashfjkasdhf']\n```\n\n**See below how it handles connection reset errors (it waits a little extra to make sure your connection clears before continuing testing)**\n\n\n```python\nweibo.run(sample_keywords_list,sqlite_file=sqlite_file,cookies=cookie)\n```\n\n (0, u'cats', 'has_results')\n (1, u'yes', 'has_results')\n 自由亚洲电台 caused connection reset, waiting 95\n (2, u'\\u81ea\\u7531\\u4e9a\\u6d32\\u7535\\u53f0', 'reset')\n (3, u'\\u5218\\u6653\\u6ce2', 'censored')\n (4, u'dhfjkdashfjkasdsf87', 'no_results')\n\n\n\n```python\nweibo.sqlite_to_df(sqlite_file)\n```\n\n\n\n\n| \n | id | \ndate | \ndatetime_logged | \ntest_number | \nkeyword | \ncensored | \nno_results | \nreset | \nis_canonical | \nresult | \nsource | \norig_keyword | \nnum_results | \nnotes | \n
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n0 | \n2017-09-25 | \n2017-09-25 10:13:37.816720 | \n1 | \nhello | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n80454701.0 | \nNone | \n
| 1 | \n1 | \n2017-09-25 | \n2017-09-25 10:13:54.356722 | \n1 | \nlxb | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 2 | \n2 | \n2017-09-25 | \n2017-09-25 10:14:11.489530 | \n1 | \n习胞子 | \n0 | \n0 | \n0 | \n0 | \nno_results | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 3 | \n3 | \n2017-09-25 | \n2017-09-25 10:14:29.667395 | \n1 | \n刘晓波 | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 4 | \n4 | \n2017-09-25 | \n2017-09-25 10:14:49.107078 | \n1 | \npokemon | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n5705260.0 | \npop culture | \n
| 5 | \n5 | \n2017-09-25 | \n2017-09-25 10:15:09.762484 | \n1 | \njay chou | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n881.0 | \npop culture | \n
| 6 | \n6 | \n2017-09-25 | \n2017-09-25 10:15:28.100418 | \n1 | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n63401495.0 | \nsocial media | \n|
| 7 | \n7 | \n2017-09-25 | \n2017-09-25 10:15:46.214464 | \n2 | \nhello | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n80454634.0 | \nNone | \n
| 8 | \n8 | \n2017-09-25 | \n2017-09-25 10:16:03.274804 | \n2 | \nlxb | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 9 | \n9 | \n2017-09-25 | \n2017-09-25 10:16:19.035805 | \n2 | \n习胞子 | \n0 | \n0 | \n0 | \n0 | \nno_results | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 10 | \n10 | \n2017-09-25 | \n2017-09-25 10:16:36.021837 | \n2 | \n刘晓波 | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 11 | \n11 | \n2017-09-25 | \n2017-09-25 10:16:53.766351 | \n1 | \nzhongnanhai | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe2 | \nNone | \n109.0 | \nlocation | \n
| 12 | \n12 | \n2017-09-25 | \n2017-09-25 10:17:14.124440 | \n1 | \ncats | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe2 | \nNone | \n648313.0 | \npop culture | \n
\n\n \n
\n
\n\n\n## It can detect the canonical (minimum) set of characters in the search query triggering censorship\n\nSet `get_canonical=True` when running to find which part of a censored search query is actually triggering the censorship. Note: this will only work on explicitly censored search queries.\n\nFinding canonical censored keywords can take a large number of search cycles, especially with larger original queries. \n\n```python\nweibo.run(['江蛤','江泽民江蛤蟆'],sqlite_file=sqlite_file,cookies=cookie,continue_interruptions=False,get_canonical=True)\n```\n\nIf we find a minimum keyword component, we'll record it as a keyword, set column `is_canonical` to True, and record our full search query in `orig_keyword`. For completeness, we'll also include the original keyword as its own entry with `is_canonical=False`\n\n```python\nweibo.sqlite_to_df(sqlite_file)\n```\n\n\n| \n | id | \ndate | \ndatetime_logged | \ntest_number | \nkeyword | \ncensored | \nno_results | \nreset | \nis_canonical | \nresult | \nsource | \norig_keyword | \nnum_results | \nnotes | \n
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n0 | \n2017-09-25 | \n2017-09-25 10:13:37.816720 | \n1 | \nhello | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n80454701.0 | \nNone | \n
| 1 | \n1 | \n2017-09-25 | \n2017-09-25 10:13:54.356722 | \n1 | \nlxb | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 2 | \n2 | \n2017-09-25 | \n2017-09-25 10:14:11.489530 | \n1 | \n习胞子 | \n0 | \n0 | \n0 | \n0 | \nno_results | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 3 | \n3 | \n2017-09-25 | \n2017-09-25 10:14:29.667395 | \n1 | \n刘晓波 | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 4 | \n4 | \n2017-09-25 | \n2017-09-25 10:14:49.107078 | \n1 | \npokemon | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n5705260.0 | \npop culture | \n
| 5 | \n5 | \n2017-09-25 | \n2017-09-25 10:15:09.762484 | \n1 | \njay chou | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n881.0 | \npop culture | \n
| 6 | \n6 | \n2017-09-25 | \n2017-09-25 10:15:28.100418 | \n1 | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n63401495.0 | \nsocial media | \n|
| 7 | \n7 | \n2017-09-25 | \n2017-09-25 10:15:46.214464 | \n2 | \nhello | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n80454634.0 | \nNone | \n
| 8 | \n8 | \n2017-09-25 | \n2017-09-25 10:16:03.274804 | \n2 | \nlxb | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 9 | \n9 | \n2017-09-25 | \n2017-09-25 10:16:19.035805 | \n2 | \n习胞子 | \n0 | \n0 | \n0 | \n0 | \nno_results | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 10 | \n10 | \n2017-09-25 | \n2017-09-25 10:16:36.021837 | \n2 | \n刘晓波 | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 11 | \n11 | \n2017-09-25 | \n2017-09-25 10:16:53.766351 | \n1 | \nzhongnanhai | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe2 | \nNone | \n109.0 | \nlocation | \n
| 12 | \n12 | \n2017-09-25 | \n2017-09-25 10:17:14.124440 | \n1 | \ncats | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe2 | \nNone | \n648313.0 | \npop culture | \n
| 13 | \n13 | \n2017-09-25 | \n2017-09-25 10:17:36.205255 | \n1 | \ncats | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nlist | \nNone | \n648313.0 | \nNone | \n
| 14 | \n14 | \n2017-09-25 | \n2017-09-25 10:17:54.330039 | \n1 | \nyes | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nlist | \nNone | \n28413048.0 | \nNone | \n
| 15 | \n15 | \n2017-09-25 | \n2017-09-25 10:19:47.007930 | \n1 | \n自由亚洲电台 | \n0 | \n0 | \n0 | \n0 | \nreset | \nlist | \nNone | \nNaN | \nNone | \n
| 16 | \n16 | \n2017-09-25 | \n2017-09-25 10:20:03.491231 | \n1 | \n刘晓波 | \n0 | \n0 | \n0 | \n0 | \ncensored | \nlist | \nNone | \nNaN | \nNone | \n
| 17 | \n17 | \n2017-09-25 | \n2017-09-25 10:20:18.747414 | \n1 | \ndhfjkdashfjkasdsf87 | \n0 | \n0 | \n0 | \n0 | \nno_results | \nlist | \nNone | \nNaN | \nNone | \n
\n\n \n
\n
\n\n \n"},{"alpha_fraction":0.6037599444389343,"alphanum_fraction":0.6088213920593262,"avg_line_length":39.10144805908203,"blob_id":"2418231d5e29f78ec21bffba7bdc461b21f652fd","content_id":"ecfe6322ff0def9a7bcecc48feff51a2fbae643f","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2766,"license_type":"permissive","max_line_length":122,"num_lines":69,"path":"/update_db.py","repo_name":"jasonqng/blocked-on-weibo","src_encoding":"UTF-8","text":"import sqlite3\nimport os, sys\n\n\ndef find_db():\n \"\"\"\n Search for database files in the current directory of this script file. Gets user input for confirmation of db file\n if only 1 match; user input for selection if >1 db file present.\n \"\"\"\n\n print(\"Finding database files...\")\n files = list(os.walk('.'))[0][2]\n db_files = [file for file in files if \".db\" in file or \".sqlite\" in file]\n if len(db_files)>0:\n if len(db_files) == 1:\n user_input = raw_input(\"Is database file %s/%s? (y/N) \" %(os.getcwd(), db_files[0]))\n if user_input.lower() == 'y':\n migrate_db(db_files[0])\n else:\n break_script()\n else:\n print(\"%d database files found in dir\" %(len(db_files)))\n for i in range(len(db_files)):\n print(\"%d. %s\" %(i+1, db_files[i]))\n print(\"**************\")\n user_input = raw_input(\"Which db file to upgrade? (Enter NUMBER) \")\n if user_input.isdigit() and 0 < int(user_input) < len(db_files):\n migrate_db(db_files[int(user_input) - 1])\n else:\n print(\"Invalid input.\")\n break_script()\n else:\n break_script()\n\n\ndef migrate_db(db_location):\n \"\"\"\n sqlite3 code that conducts the actual database modifications. Renames the previous table, creates a new table\n with new columns, moves contents of previous table over, then deletes the previous table.\n :param db_location: filename of database file.\n \"\"\"\n conn = sqlite3.connect(db_location)\n c = conn.cursor()\n c.execute('ALTER TABLE results RENAME TO resultsOld')\n c.execute('''CREATE TABLE results (id INT, date DATE, datetime_logged DATETIME, test_number INT, keyword string, \n censored bool, no_results bool, reset bool, is_canonical bool, result string, source string, orig_keyword string, \n num_results INT, notes string, PRIMARY KEY(date,source,test_number,keyword,orig_keyword))''')\n c.execute('''INSERT INTO results (id, date, datetime_logged, test_number, keyword, censored, no_results, reset,\n result, source, num_results, notes)\n SELECT id, date, datetime_logged, test_number, keyword, censored, no_results, reset, result, source,\n num_results, notes FROM resultsOld\n ''')\n c.execute('DROP TABLE resultsOld')\n conn.commit()\n conn.close()\n print(\"Updates completed successfully\")\n sys.exit()\n\n\ndef break_script():\n \"\"\"\n Print an error message to console before triggering sys.exit().\n \"\"\"\n print(\"Please place this .py script in the same directory as your database file, then re-run. No changes were made\")\n sys.exit()\n\n\nif __name__ == \"__main__\":\n find_db()"},{"alpha_fraction":0.6517150402069092,"alphanum_fraction":0.6569920778274536,"avg_line_length":33.45454406738281,"blob_id":"191344d189d160fd1356d8beeb8c3e9d276561e6","content_id":"11acae76c58eb9f4ce733b18035ef783d00460df","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":379,"license_type":"permissive","max_line_length":74,"num_lines":11,"path":"/setup.py","repo_name":"jasonqng/blocked-on-weibo","src_encoding":"UTF-8","text":"from setuptools import setup, find_packages\n\nsetup(name='blockedonweibo',\n version='0.2',\n description='Testing if search keywords are censored on Sina Weibo',\n url='http://github.com/jasonqng/blocked-on-weibo',\n author='Jason Q. Ng',\n author_email='jason.q.ng@gmail.com',\n license='BSD License',\n packages=find_packages(exclude=['docs']),\n zip_safe=False)\n"},{"alpha_fraction":0.5565217137336731,"alphanum_fraction":0.5565217137336731,"avg_line_length":28,"blob_id":"b71817df477d628a1f890e95e4de6baaa2a155e0","content_id":"17db01e6a064484fe84f2e5822bc2b6a6f6227da","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":115,"license_type":"permissive","max_line_length":36,"num_lines":4,"path":"/blockedonweibo/weibo_credentials.py","repo_name":"jasonqng/blocked-on-weibo","src_encoding":"UTF-8","text":"class Creds(object):\n def __init__(self):\n self.username = 'your_email_address_you_use_to_log_into_weibo@domain.com'\n self.password = 'weibo_password'"}],"string":"[\n {\n \"alpha_fraction\": 0.5595763921737671,\n \"alphanum_fraction\": 0.5659759044647217,\n \"avg_line_length\": 40.5379753112793,\n \"blob_id\": \"52258cebb06ea2d8c0d3bdf569d039396f98bd43\",\n \"content_id\": \"8a08910ae9c0ae45d8fd5ad5ceea970c6286e32d\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 13176,\n \"license_type\": \"permissive\",\n \"max_line_length\": 207,\n \"num_lines\": 316,\n \"path\": \"/blockedonweibo/weibo.py\",\n \"repo_name\": \"jasonqng/blocked-on-weibo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from bs4 import BeautifulSoup\\nimport urllib\\nimport codecs\\nimport time\\nimport random\\nimport sqlite3\\nfrom datetime import datetime\\nimport pandas as pd\\nimport os\\nimport io\\nimport requests\\nimport base64 \\nimport re\\nimport ast\\nimport sys\\ntry:\\n from urllib.parse import urlparse\\nexcept ImportError:\\n from urlparse import urlparse\\nimport rsa \\nimport json \\nimport binascii \\nimport math\\ntry:\\n from . import weibo_credentials\\nexcept ValueError:\\n import weibo_credentials\\n### SETTINGS\\n\\nCENSORSHIP_PHRASE = u'根据相关法律法规和政策'\\nCAPTCHA_PHRASE = u'你的行为有些异常'\\nNO_RESULTS_PHRASE = u'抱歉，未找到'\\n\\ndef has_censorship(keyword_encoded,\\n cookie=None):\\n\\n \\\"\\\"\\\"\\n Function which actually looks up whether a search for the given keyword returns text\\n which is displayed during censorship.\\n Can handle unicode and strings\\n Currently no CAPTCHA handling, though it is detected\\n Returns string of 'censored','no_results','reset',or 'has_results'\\n ('has_results' is actually not a garuantee; it's merely a lack of other censorship indicators)\\n \\\"\\\"\\\"\\n url = (f'https://s.weibo.com/weibo?q={keyword_encoded}')\\n cookies = {'required_cookie': cookie}\\n headers = {'User-Agent': 'Mozilla/5.0'}\\n try:\\n r = requests.get(url,cookies=cookies, headers=headers).text\\n i = 1\\n while True:\\n if CAPTCHA_PHRASE not in r:\\n break\\n else:\\n print(\\\"CAPTCHA\\\", keyword_encoded, \\\"sleeping for %s seconds\\\" % 300*i)\\n time.sleep(300*i)\\n i+=1\\n if i==50:\\n print(\\\"Can't break out of CAPTCHA, aborting\\\")\\n sys.exit(1)\\n except IOError:\\n wait_seconds = random.randint(90, 100)\\n print(u\\\"%s caused connection reset, waiting %s\\\" % (keyword_encoded,wait_seconds))\\n time.sleep(wait_seconds)\\n return (\\\"reset\\\",None)\\n\\n num_results = re.findall(r'search_rese clearfix\\\\\\\\\\\">\\\\\\\\n \\\\\\\\u627e\\\\\\\\u5230(\\\\d*)',r)\\n if num_results:\\n num_results = int(num_results[0])\\n else:\\n None\\n if CENSORSHIP_PHRASE in r:\\n return (\\\"censored\\\",None)\\n elif NO_RESULTS_PHRASE in r:\\n return (\\\"no_results\\\",None)\\n else:\\n return (\\\"has_results\\\",num_results)\\n\\ndef create_database(sqlite_file, overwrite=False):\\n \\\"\\\"\\\"\\n Generating a sqlite file for storing results\\n Multi-index primary key on id, date, and source\\n Set new_database to True in order to remove any existing file\\n \\\"\\\"\\\"\\n if overwrite and os.path.isfile(sqlite_file):\\n os.remove(sqlite_file)\\n if not os.path.isfile(sqlite_file):\\n conn = sqlite3.connect(sqlite_file)\\n c = conn.cursor()\\n c.execute('''CREATE TABLE results (id int, date date, datetime_logged datetime, test_number int, keyword string, \\n censored bool, no_results bool, reset bool, is_canonical bool, result string, source string, orig_keyword string, \\n num_results int, notes string, PRIMARY KEY(date,source,test_number,keyword,orig_keyword))''')\\n conn.commit()\\n conn.close()\\n\\n\\ndef insert_into_database(record_id,\\n keyword_encoded,\\n result,\\n date=datetime.now().date(),\\n source=\\\"default\\\",\\n num_results=None,\\n notes=None,\\n sqlite_file=None,\\n test_number=1,\\n is_canonical=None,\\n orig_keyword=None):\\n \\\"\\\"\\\"\\n Writing the results to the sqlite database file\\n \\\"\\\"\\\"\\n conn = sqlite3.connect(sqlite_file)\\n conn.text_factory = str\\n conn.execute(\\\"PRAGMA busy_timeout = 5000\\\")\\n c = conn.cursor()\\n \\n dt_logged = datetime.now()\\n if isinstance(notes, list):\\n notes = str(notes)\\n if isinstance(num_results, list):\\n num_results = None\\n \\n if result == \\\"censored\\\":\\n censored = True\\n else:\\n censored = False\\n \\n if result == \\\"no_results\\\":\\n no_results = True\\n else:\\n no_results = False\\n \\n if result == \\\"reset\\\":\\n reset = True\\n else:\\n reset = False\\n\\n query = u\\\"\\\"\\\"INSERT OR REPLACE INTO results (id, date, datetime_logged, test_number, keyword, censored, no_results, reset, is_canonical, result, source, orig_keyword, num_results, notes) \\n VALUES (\\n coalesce(\\n (select id from results where date=date('{date}') and keyword='{keyword}' and test_number={test_number} and source='{source}'),\\n ?),\\n ?,?,?,?,?,?,?,?,?,?,?,?,?\\n );\\\"\\\"\\\".format(date=date,keyword=keyword_encoded,test_number=test_number,source=source)\\n c.execute(query, (record_id, date, dt_logged, test_number, keyword_encoded, censored, no_results, reset, is_canonical, result, source, orig_keyword, num_results, notes))\\n\\n conn.commit()\\n conn.close()\\n \\ndef sqlite_to_df(sqlite_file,\\n query=\\\"select * from results where source!='_meta_' or source is NULL;\\\"):\\n conn = sqlite3.connect(sqlite_file)\\n df = pd.read_sql_query(query, conn)\\n return df\\n\\ndef verify_cookies_work(cookie,\\n return_full_response_text=False):\\n \\\"\\\"\\\"\\n Returns True if cookies return profile indicator\\n If no cookie or bad cookie is passed, you get a generic login page which doesn't have the indicator\\n \\\"\\\"\\\"\\n cookies = {'required_cookie': cookie}\\n headers = {'User-Agent': 'Mozilla/5.0'}\\n r = requests.get('https://s.weibo.com/weibo?q=test',cookies=cookies, headers=headers).text\\n if return_full_response_text:\\n return r\\n if \\\"onick\\\" in r:\\n return True\\n else:\\n return False\\n\\ndef load_cookies(cookie_file=\\\"_cookie.txt\\\"):\\n with open(cookie_file, 'r') as f:\\n #cookie = ast.literal_eval(f.read())\\n cookie = f.read()\\n return cookie\\n\\ndef run(keywords,\\n verbose='all',\\n insert=True,\\n sqlite_file=None,\\n return_df=False,\\n sleep=True,\\n cookie=None,\\n sleep_secs=15,\\n continue_interruptions=True,\\n date=datetime.now().strftime('%Y-%m-%d'),\\n test_number=1,\\n list_source=\\\"list\\\",\\n get_canonical=False\\n ):\\n \\\"\\\"\\\"\\n Iterating through the keyword list and testing one at a time\\n Handles when script or connection breaks; will pick up where it left off\\n Set return_df to append each new result to a df in memory, which is returned at end of function\\n verbose = 'some','all',or 'none'(technically anything besides 'some' or 'all' will not show anything)\\n sleep = time in seconds to sleep between searches\\n \\\"\\\"\\\"\\n if sqlite_file:\\n create_database(sqlite_file)\\n\\n count=0\\n if return_df:\\n results_df = pd.DataFrame()\\n\\n if isinstance(keywords, list):\\n keywords = pd.DataFrame(keywords,columns=[\\\"keyword\\\"])\\n keywords['source'] = list_source\\n\\n test_keywords = keywords.copy()\\n\\n if \\\"Index\\\" not in test_keywords.columns:\\n test_keywords['Index'] = test_keywords.index\\n if \\\"notes\\\" not in test_keywords.columns:\\n test_keywords['notes'] = None\\n if \\\"source\\\" not in test_keywords.columns:\\n test_keywords['source'] = None\\n source=test_keywords['source'][0]\\n\\n for r in test_keywords.itertuples():\\n if isinstance(r.keyword, str):\\n keyword_encoded = r.keyword\\n\\n if sqlite_file:\\n if r.Index < len(sqlite_to_df(sqlite_file).query(\\\"date=='%s' & source=='%s' & test_number==%s & is_canonical!=1\\\" % (date,source,test_number))) and continue_interruptions:\\n continue\\n if len(sqlite_to_df(sqlite_file).query(u\\\"date=='%s' & source=='%s' & test_number==%s & keyword=='%s' & is_canonical!=1\\\" % (date,source,test_number,keyword_encoded)))>0 and continue_interruptions:\\n continue\\n result,num_results = has_censorship(keyword_encoded,cookie)\\n if verbose==\\\"all\\\":\\n print(r.Index,keyword_encoded, result)\\n elif verbose==\\\"some\\\" and (count%10==0 or count==0):\\n print(r.Index,keyword_encoded, result)\\n\\n min_str = None\\n if get_canonical and result == \\\"censored\\\":\\n if verbose==\\\"some\\\" or verbose==\\\"all\\\":\\n print(\\\"Found censored search phrase; determining canonical censored keyword set\\\")\\n sleep_recursive = sleep_secs if sleep else 0\\n potential_kws = split_search_query(keyword_encoded, cookie, sleep_recursive, res_rtn=[], known_blocked=True, verbose=verbose)\\n if verbose==\\\"all\\\":\\n print(potential_kws)\\n\\n for kw in potential_kws:\\n test_list = [kw[:i] + kw[i + 1:] for i in range(len(kw))]\\n min_str = \\\"\\\"\\n for i in range(len(test_list)):\\n if kw[i].isspace():\\n continue\\n if verbose==\\\"all\\\":\\n print(\\\"Testing %d of %d: omitting character %s\\\" %(i+1, len(test_list), kw[i]))\\n if sleep:\\n time.sleep(random.randint(math.ceil(sleep_secs * .90), math.ceil(sleep_secs * 1.10)))\\n if has_censorship(test_list[i], cookie)[0] != \\\"censored\\\":\\n min_str += (kw[i])\\n result_min_str, num_results_min_str = has_censorship(min_str, cookie)\\n if result_min_str == \\\"censored\\\": # minStr found properly\\n if verbose==\\\"all\\\" or verbose==\\\"some\\\":\\n print(\\\"the minimum phrase from '%s' is: '%s'\\\" % (kw, min_str))\\n if insert:\\n insert_into_database(len(sqlite_to_df(sqlite_file)), min_str, date=date, result=result_min_str,\\n source=r.source, num_results=num_results_min_str, notes=r.notes,\\n sqlite_file=sqlite_file, test_number=test_number, is_canonical=True,\\n orig_keyword=keyword_encoded)\\n else:\\n print(\\\"Failed to find canonical phrase\\\")\\n\\n if insert:\\n insert_into_database(len(sqlite_to_df(sqlite_file)), keyword_encoded, date=date, result=result, source=r.source,\\n num_results=num_results, notes=r.notes, sqlite_file=sqlite_file, test_number=test_number,\\n is_canonical=False)\\n if return_df:\\n results_df = pd.concat([results_df,\\n pd.DataFrame([{\\\"date\\\":date,\\n \\\"datetime\\\":datetime.now(),\\n \\\"keyword\\\":min_str if min_str is not None else r.keyword,\\n \\\"result\\\":result,\\n \\\"source\\\":r.source,\\n \\\"num_results\\\":num_results,\\n \\\"test_number\\\":test_number,\\n \\\"is_canonical\\\": True if min_str is not None else False,\\n \\\"orig_keyword\\\":r.keyword if min_str is not None else None\\n }])\\n ])\\n count+=1\\n if sleep:\\n time.sleep(random.randint(math.ceil(sleep_secs*.90), math.ceil(sleep_secs*1.10)))\\n if insert:\\n insert_into_database(int(test_keywords.index.max())+1,None,date=date,result=\\\"finished\\\",source=\\\"_meta_\\\",sqlite_file=sqlite_file,test_number=test_number)\\n if return_df:\\n return results_df\\n\\n\\ndef split_search_query(query, cookie, sleep_secs=0, res_rtn=[], known_blocked=False, verbose=\\\"\\\"):\\n \\\"\\\"\\\"\\n Recursively halves a query and returns portions with blocked keywords as a list of strings.\\n :param res_rtn: internal list holding found min keywords during recursive search, DO NOT SPECIFY.\\n :param known_blocked: set to True to skip a redundant first-check if you know your query is blocked.\\n :return: a list of one or more shortened keyword segments that trigger censorship\\n \\\"\\\"\\\"\\n if len(query) <= 1:\\n return [-1]\\n if sleep_secs:\\n time.sleep(random.randint(math.ceil(sleep_secs * .90), math.ceil(sleep_secs * 1.10)))\\n if (not known_blocked) and verbose=='all':\\n print('Recursively shortening... testing query: \\\"%s\\\"' %(query))\\n if (not known_blocked) and has_censorship(query, cookie)[0] != \\\"censored\\\": # known_blocked=True skips 1st check\\n return [-1]\\n else:\\n mid = len(query) // 2\\n left_half = query[:mid]\\n right_half = query[mid:]\\n left_res = split_search_query(left_half, cookie, sleep_secs, res_rtn, False, verbose)\\n right_res = split_search_query(right_half, cookie, sleep_secs, res_rtn, False, verbose)\\n if (left_res[0] == -1) and (right_res[0] == -1):\\n res_rtn.append(query)\\n return res_rtn\\n\"\n },\n {\n \"alpha_fraction\": 0.8666666746139526,\n \"alphanum_fraction\": 0.9111111164093018,\n \"avg_line_length\": 8.199999809265137,\n \"blob_id\": \"5fcbb04a146dcb0c6bf8c3437d83de500bac1a54\",\n \"content_id\": \"a84514617e7d66d4648fc3242269b066fb9653b6\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 45,\n \"license_type\": \"permissive\",\n \"max_line_length\": 14,\n \"num_lines\": 5,\n \"path\": \"/requirements.txt\",\n \"repo_name\": \"jasonqng/blocked-on-weibo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"beautifulsoup4\\npandas\\nrequests\\nrsa\\ndf2gspread\"\n },\n {\n \"alpha_fraction\": 0.4322457015514374,\n \"alphanum_fraction\": 0.5026606917381287,\n \"avg_line_length\": 21.568435668945312,\n \"blob_id\": \"82e59b6e8b7d3647ae6963474f512d0bb25a24ef\",\n \"content_id\": \"41c7494f177351362b32fd7c326e0dbfe3b8b57f\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 48780,\n \"license_type\": \"permissive\",\n \"max_line_length\": 3746,\n \"num_lines\": 2148,\n \"path\": \"/README.md\",\n \"repo_name\": \"jasonqng/blocked-on-weibo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# What is blockedonweibo?\\n\\nThis python package allows you to automate tests to check if a keyword is censored or not on Sina Weibo, a Chinese social media site. It is an updated version of the script which was used to detect keywords on the site, http://blockedonweibo.com. It handles interrupted tests, multiple tests per day, storing results to a database, and a number of other features which simplify testing Weibo censorship at scale. The researcher merely has to feed the script a list of words for one-off tests. For recurring tests, simply wrap the script with a scheduler.\\n\\n| \n | id | \ndate | \ndatetime_logged | \ntest_number | \nkeyword | \ncensored | \nno_results | \nreset | \nis_canonical | \nresult | \nsource | \norig_keyword | \nnum_results | \nnotes | \n
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n0 | \n2017-09-25 | \n2017-09-25 10:13:37.816720 | \n1 | \nhello | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n80454701.0 | \nNone | \n
| 1 | \n1 | \n2017-09-25 | \n2017-09-25 10:13:54.356722 | \n1 | \nlxb | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 2 | \n2 | \n2017-09-25 | \n2017-09-25 10:14:11.489530 | \n1 | \n习胞子 | \n0 | \n0 | \n0 | \n0 | \nno_results | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 3 | \n3 | \n2017-09-25 | \n2017-09-25 10:14:29.667395 | \n1 | \n刘晓波 | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 4 | \n4 | \n2017-09-25 | \n2017-09-25 10:14:49.107078 | \n1 | \npokemon | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n5705260.0 | \npop culture | \n
| 5 | \n5 | \n2017-09-25 | \n2017-09-25 10:15:09.762484 | \n1 | \njay chou | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n881.0 | \npop culture | \n
| 6 | \n6 | \n2017-09-25 | \n2017-09-25 10:15:28.100418 | \n1 | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n63401495.0 | \nsocial media | \n|
| 7 | \n7 | \n2017-09-25 | \n2017-09-25 10:15:46.214464 | \n2 | \nhello | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe | \nNone | \n80454634.0 | \nNone | \n
| 8 | \n8 | \n2017-09-25 | \n2017-09-25 10:16:03.274804 | \n2 | \nlxb | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 9 | \n9 | \n2017-09-25 | \n2017-09-25 10:16:19.035805 | \n2 | \n习胞子 | \n0 | \n0 | \n0 | \n0 | \nno_results | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 10 | \n10 | \n2017-09-25 | \n2017-09-25 10:16:36.021837 | \n2 | \n刘晓波 | \n0 | \n0 | \n0 | \n0 | \ncensored | \nmy dataframe | \nNone | \nNaN | \nNone | \n
| 11 | \n11 | \n2017-09-25 | \n2017-09-25 10:16:53.766351 | \n1 | \nzhongnanhai | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe2 | \nNone | \n109.0 | \nlocation | \n
| 12 | \n12 | \n2017-09-25 | \n2017-09-25 10:17:14.124440 | \n1 | \ncats | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nmy dataframe2 | \nNone | \n648313.0 | \npop culture | \n
| 13 | \n13 | \n2017-09-25 | \n2017-09-25 10:17:36.205255 | \n1 | \ncats | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nlist | \nNone | \n648313.0 | \nNone | \n
| 14 | \n14 | \n2017-09-25 | \n2017-09-25 10:17:54.330039 | \n1 | \nyes | \n0 | \n0 | \n0 | \n0 | \nhas_results | \nlist | \nNone | \n28413048.0 | \nNone | \n
| 15 | \n15 | \n2017-09-25 | \n2017-09-25 10:19:47.007930 | \n1 | \n自由亚洲电台 | \n0 | \n0 | \n0 | \n0 | \nreset | \nlist | \nNone | \nNaN | \nNone | \n
| 16 | \n16 | \n2017-09-25 | \n2017-09-25 10:20:03.491231 | \n1 | \n刘晓波 | \n0 | \n0 | \n0 | \n0 | \ncensored | \nlist | \nNone | \nNaN | \nNone | \n
| 17 | \n17 | \n2017-09-25 | \n2017-09-25 10:20:18.747414 | \n1 | \ndhfjkdashfjkasdsf87 | \n0 | \n0 | \n0 | \n0 | \nno_results | \nlist | \nNone | \nNaN | \nNone | \n
| 18 | \n18 | \n2017-11-15 | \n2017-11-15 12:38:32.931313 | \n1 | \n江蛤 | \n0 | \n0 | \n0 | \n1 | \ncensored | \nlist | \n江蛤 | \nNaN | \nNone | \n
| 19 | \n19 | \n2017-11-15 | \n2017-11-15 12:38:32.963135 | \n1 | \n江蛤 | \n0 | \n0 | \n0 | \n0 | \ncensored | \nlist | \nNone | \nNaN | \nNone | \n
| 20 | \n20 | \n2017-11-15 | \n2017-11-15 12:40:21.294841 | \n1 | \n江蛤 | \n0 | \n0 | \n0 | \n1 | \ncensored | \nlist | \n江泽民江蛤蟆 | \nNaN | \nNone | \n
| 21 | \n21 | \n2017-11-15 | \n2017-11-15 12:40:21.326378 | \n1 | \n江泽民江蛤蟆 | \n0 | \n0 | \n0 | \n0 | \ncensored | \nlist | \nNone | \nNaN | \nNone | \n
\\n\\n# IMPORTANT: Upgrading from v0.1 with an existing database? \\nThe database table has been modified to accomodate tracking of minimum keyword strings triggering \\ncensorship. If you used blockedonweibo v0.1 and you used a database to store results, you will \\nneed to update your database file. \\n\\n**To migrate your older database file**\\n1. Move `update_db.py` to the same file directory as your database file (\\\"results.sqlite\\\" if you followed\\nthis setup guide)\\n2. In terminal, run `python update_db.py` and confirm database file. \\n\\n### Version 0.2 changes \\n* now includes a feature to find canonical censored keywords (minimum set of keywords required to trigger explicit censorship message)\\n * to run, pass `get_canonical=True` with rest of variables into `run()`\\n * see section 4.8\\n\\n\\n# Table of Contents\\n \\n\\n# Install the blockedonweibo package\\n\\nThe github repo for this Weibo keyword testing script is located at https://github.com/jasonqng/blocked-on-weibo.\\n\\nTo begin using this python package, inside your terminal, run\\n\\n```\\npip install blockedonweibo\\n```\\n\\nAlternatively, you can clone this repo, `cd` into the repo directory and manually install the requirements and package:\\n\\n```\\npip install -r requirements.txt\\npython setup.py install\\n```\\n\\nTo confirm the installation works, in a python shell (you can start by running `python` from terminal), try importing the package:\\n\\n```\\nimport blockedonweibo\\n```\\n\\nIf you don't get any errors, things have installed successfully. If not, you may need to fiddle with your python paths and settings to ensure it's being installed to the correct location.\\n\\n# Adjust your settings\\n\\nYour python script only requires the following. All other imports are handled by the package.\\n\\n\\n```python\\nfrom blockedonweibo import weibo\\nimport pandas as pd\\n```\\n\\nYou have the option of saving your test results to a file. You'll need to pass a path to to a file which will store the results in sqlite format. It can be helpful to set this at the top of your script and pass the variable each time you run the test.\\n\\n\\n```python\\nsqlite_file = 'results.sqlite' # name of sqlite file to read from/write to\\n```\\n\\nIf you want to erase any existing data you have in the sqlite file defined above, just pass overwrite=True to the `create_database` function. Otherwise any new results will be appended to the end of the database.\\n\\n\\n```python\\nweibo.create_database(sqlite_file, overwrite=True)\\n```\\n\\nThis testing script is enhanced if you allow it to log into Weibo, which increases your rate limit threshold as well as returns the number of results a search says it has. This script will work without your supplying credentials, but it is highly recommended. To do so, edit the `weibo_credentials.py` with your email address and password. The file is ignored and will not be uploaded by default when you push commits to github. You can inspect the code to verify that the credentials don't go anywhere except to weibo.\\n\\nUsing those credentials, the script logs you in and fetches a cookie for the user session you create. This cookie can be saved to a file by passing the `write_cookie` parameter in the `user_login` function.\\n\\n\\n```python\\nsession = weibo.user_login(write_cookie=True)\\n```\\n\\nThere is a helper function to verify that the cookie actually works\\n\\n\\n```python\\ncookie = session.cookies.get_dict()\\nprint(weibo.verify_cookies_work(cookie))\\n```\\n\\n True\\n\\n\\nIf you have the cookie already written to disk, you don't need to perform another `user_login` and instead, you can just use the `load_cookies` function to fetch the cookie from the file. Again, you can verify that it works. Just store the cookie's contents (a dictionary) to a variable and pass that to the `run` function below if you want to test as if you were logged in. Otherwise, it will emulate a search by a logged out user.\\n\\n\\n```python\\ncookie = weibo.load_cookies()\\nprint(weibo.verify_cookies_work(cookie))\\n```\\n\\n True\\n\\n\\n# Let's start testing!\\n\\n## Pass a dictionary of keywords to start testing\\n\\n\\n```python\\nsample_keywords_df = pd.DataFrame(\\n [{'keyword':'hello','source':'my dataframe'},\\n {'keyword':'lxb','source':'my dataframe'},\\n {'keyword':u'习胞子','source':'my dataframe'}\\n ])\\n```\\n\\n\\n```python\\nsample_keywords_df\\n```\\n\\n\\n\\n\\n\\n\\n \\n
\\n
\\n\\n\\n\\n\\n```python\\nweibo.run(sample_keywords_df,insert=False,return_df=True)\\n```\\n\\n (0, u'hello', 'has_results')\\n (1, u'lxb', 'censored')\\n (2, u'\\\\u4e60\\\\u80de\\\\u5b50', 'no_results')\\n\\n\\n\\n\\n\\n| \\n | keyword | \\nsource | \\n
|---|---|---|
| 0 | \\nhello | \\nmy dataframe | \\n
| 1 | \\nlxb | \\nmy dataframe | \\n
| 2 | \\n习胞子 | \\nmy dataframe | \\n
\\n\\n \\n
\\n
\\n\\n\\n\\n## Pass in cookies so you can also get the number of results. Pass in sqlite_file to save the results to disk so you can load it later\\n\\n\\n```python\\nweibo.run(sample_keywords_df,sqlite_file=sqlite_file,cookies=cookie)\\n```\\n\\n (0, u'hello', 'has_results')\\n (1, u'lxb', 'censored')\\n (2, u'\\\\u4e60\\\\u80de\\\\u5b50', 'no_results')\\n\\n\\n\\n```python\\nweibo.sqlite_to_df(sqlite_file)\\n```\\n\\n\\n\\n\\n| \\n | date | \\ndatetime | \\nis_canonical | \\nkeyword | \\nnum_results | \\norig_keyword | \\nresult | \\nsource | \\ntest_number | \\n
|---|---|---|---|---|---|---|---|---|---|
| 0 | \\n2017-09-25 | \\n2017-09-25 10:12:45.280812 | \\nFalse | \\nhello | \\n[] | \\nNone | \\nhas_results | \\nmy dataframe | \\n1 | \\n
| 0 | \\n2017-09-25 | \\n2017-09-25 10:13:00.191900 | \\nFalse | \\nlxb | \\nNone | \\nNone | \\ncensored | \\nmy dataframe | \\n1 | \\n
| 0 | \\n2017-09-25 | \\n2017-09-25 10:13:16.356805 | \\nFalse | \\n习胞子 | \\nNone | \\nNone | \\nno_results | \\nmy dataframe | \\n1 | \\n
\\n\\n \\n
\\n
\\n\\n\\n\\n## If your test gets interrupted or you add more keywords, you can pick up where you left off\\n\\nLet's pretend I wanted to test four total keywords, but I was only able to complete the first three above. I'll go ahead and add one more keyword to the test list to replicate an unfinished keyword.\\n\\n\\n```python\\nsample_keywords_df.loc[len(sample_keywords_df.index)] = ['刘晓波','my dataframe']\\n```\\n\\n\\n```python\\nsample_keywords_df\\n```\\n\\n\\n\\n\\n| \\n | id | \\ndate | \\ndatetime_logged | \\ntest_number | \\nkeyword | \\ncensored | \\nno_results | \\nreset | \\nis_canonical | \\nresult | \\nsource | \\norig_keyword | \\nnum_results | \\nnotes | \\n
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \\n0 | \\n2017-09-25 | \\n2017-09-25 10:13:37.816720 | \\n1 | \\nhello | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n80454701.0 | \\nNone | \\n
| 1 | \\n1 | \\n2017-09-25 | \\n2017-09-25 10:13:54.356722 | \\n1 | \\nlxb | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 2 | \\n2 | \\n2017-09-25 | \\n2017-09-25 10:14:11.489530 | \\n1 | \\n习胞子 | \\n0 | \\n0 | \\n0 | \\n0 | \\nno_results | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
\\n\\n \\n
\\n
\\n\\n\\n\\n\\n```python\\nweibo.run(sample_keywords_df,sqlite_file=sqlite_file,cookies=cookie)\\n```\\n\\n (3, u'\\\\u5218\\\\u6653\\\\u6ce2', 'censored')\\n\\n\\nNeat-o, it was smart enough to start right at that new keyword and not start all over again!\\n\\n\\n```python\\nweibo.sqlite_to_df(sqlite_file)\\n```\\n\\n\\n\\n\\n| \\n | keyword | \\nsource | \\n
|---|---|---|
| 0 | \\nhello | \\nmy dataframe | \\n
| 1 | \\nlxb | \\nmy dataframe | \\n
| 2 | \\n习胞子 | \\nmy dataframe | \\n
| 3 | \\n刘晓波 | \\nmy dataframe | \\n
\\n\\n \\n
\\n
\\n\\n\\n\\n## You can attach notes or categorizations to your keywords for easy querying and analysis later\\n\\n\\n```python\\nnew_keywords_df = pd.DataFrame(\\n [{'keyword':'pokemon','source':'my dataframe',\\\"notes\\\":\\\"pop culture\\\"},\\n {'keyword':'jay chou','source':'my dataframe',\\\"notes\\\":\\\"pop culture\\\"},\\n {'keyword':u'weibo','source':'my dataframe',\\\"notes\\\":\\\"social media\\\"}\\n ])\\nmerged_keywords_df = pd.concat([sample_keywords_df,new_keywords_df]).reset_index(drop=True)\\nmerged_keywords_df\\n```\\n\\n\\n\\n\\n| \\n | id | \\ndate | \\ndatetime_logged | \\ntest_number | \\nkeyword | \\ncensored | \\nno_results | \\nreset | \\nis_canonical | \\nresult | \\nsource | \\norig_keyword | \\nnum_results | \\nnotes | \\n
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \\n0 | \\n2017-09-25 | \\n2017-09-25 10:13:37.816720 | \\n1 | \\nhello | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n80454701.0 | \\nNone | \\n
| 1 | \\n1 | \\n2017-09-25 | \\n2017-09-25 10:13:54.356722 | \\n1 | \\nlxb | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 2 | \\n2 | \\n2017-09-25 | \\n2017-09-25 10:14:11.489530 | \\n1 | \\n习胞子 | \\n0 | \\n0 | \\n0 | \\n0 | \\nno_results | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 3 | \\n3 | \\n2017-09-25 | \\n2017-09-25 10:14:29.667395 | \\n1 | \\n刘晓波 | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
\\n\\n \\n
\\n
\\n\\n\\n\\n\\n```python\\nweibo.run(merged_keywords_df,sqlite_file=sqlite_file,cookies=cookie)\\n```\\n\\n (4, u'pokemon', 'has_results')\\n (5, u'jay chou', 'has_results')\\n (6, u'weibo', 'has_results')\\n\\n\\n\\n```python\\nweibo.sqlite_to_df(sqlite_file)\\n```\\n\\n\\n\\n\\n| \\n | keyword | \\nnotes | \\nsource | \\n
|---|---|---|---|
| 0 | \\nhello | \\nNaN | \\nmy dataframe | \\n
| 1 | \\nlxb | \\nNaN | \\nmy dataframe | \\n
| 2 | \\n习胞子 | \\nNaN | \\nmy dataframe | \\n
| 3 | \\n刘晓波 | \\nNaN | \\nmy dataframe | \\n
| 4 | \\npokemon | \\npop culture | \\nmy dataframe | \\n
| 5 | \\njay chou | \\npop culture | \\nmy dataframe | \\n
| 6 | \\nsocial media | \\nmy dataframe | \\n
\\n\\n \\n
\\n
\\n\\n\\n\\n\\n```python\\nresults = weibo.sqlite_to_df(sqlite_file)\\nresults.query(\\\"notes=='pop culture'\\\")\\n```\\n\\n\\n\\n\\n| \\n | id | \\ndate | \\ndatetime_logged | \\ntest_number | \\nkeyword | \\ncensored | \\nno_results | \\nreset | \\nis_canonical | \\nresult | \\nsource | \\norig_keyword | \\nnum_results | \\nnotes | \\n
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \\n0 | \\n2017-09-25 | \\n2017-09-25 10:13:37.816720 | \\n1 | \\nhello | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n80454701.0 | \\nNone | \\n
| 1 | \\n1 | \\n2017-09-25 | \\n2017-09-25 10:13:54.356722 | \\n1 | \\nlxb | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 2 | \\n2 | \\n2017-09-25 | \\n2017-09-25 10:14:11.489530 | \\n1 | \\n习胞子 | \\n0 | \\n0 | \\n0 | \\n0 | \\nno_results | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 3 | \\n3 | \\n2017-09-25 | \\n2017-09-25 10:14:29.667395 | \\n1 | \\n刘晓波 | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 4 | \\n4 | \\n2017-09-25 | \\n2017-09-25 10:14:49.107078 | \\n1 | \\npokemon | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n5705260.0 | \\npop culture | \\n
| 5 | \\n5 | \\n2017-09-25 | \\n2017-09-25 10:15:09.762484 | \\n1 | \\njay chou | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n881.0 | \\npop culture | \\n
| 6 | \\n6 | \\n2017-09-25 | \\n2017-09-25 10:15:28.100418 | \\n1 | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n63401495.0 | \\nsocial media | \\n
\\n\\n \\n
\\n
\\n\\n\\n\\n\\n```python\\nresults.query(\\\"notes=='pop culture'\\\").num_results.mean()\\n```\\n\\n\\n\\n\\n 2853070.5\\n\\n\\n\\n## If you want to test multiple times a day, just pass in the `test_number` param\\n\\nYou can off `verbose` output in case you don't need to troubleshoot anything...\\n\\n\\n```python\\nweibo.run(sample_keywords_df,sqlite_file=sqlite_file,cookies=cookie,verbose='none',test_number=2)\\n```\\n\\n\\n```python\\nweibo.sqlite_to_df(sqlite_file)\\n```\\n\\n\\n\\n\\n| \\n | id | \\ndate | \\ndatetime_logged | \\ntest_number | \\nkeyword | \\ncensored | \\nno_results | \\nreset | \\nis_canonical | \\nresult | \\nsource | \\norig_keyword | \\nnum_results | \\nnotes | \\n
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | \\n4 | \\n2017-09-25 | \\n2017-09-25 10:14:49.107078 | \\n1 | \\npokemon | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n5705260.0 | \\npop culture | \\n
| 5 | \\n5 | \\n2017-09-25 | \\n2017-09-25 10:15:09.762484 | \\n1 | \\njay chou | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n881.0 | \\npop culture | \\n
\\n\\n \\n
\\n
\\n\\n\\n\\n## It can skip redundant keywords\\n\\n\\n```python\\nmore_keywords_df = pd.DataFrame(\\n [{'keyword':'zhongnanhai','source':'my dataframe2',\\\"notes\\\":\\\"location\\\"},\\n {'keyword':'cats','source':'my dataframe2',\\\"notes\\\":\\\"pop culture\\\"},\\n {'keyword':'zhongnanhai','source':'my dataframe2',\\\"notes\\\":\\\"location\\\"}\\n ])\\n```\\n\\n\\n```python\\nmore_keywords_df\\n```\\n\\n\\n\\n\\n| \\n | id | \\ndate | \\ndatetime_logged | \\ntest_number | \\nkeyword | \\ncensored | \\nno_results | \\nreset | \\nis_canonical | \\nresult | \\nsource | \\norig_keyword | \\nnum_results | \\nnotes | \\n
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \\n0 | \\n2017-09-25 | \\n2017-09-25 10:13:37.816720 | \\n1 | \\nhello | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n80454701.0 | \\nNone | \\n
| 1 | \\n1 | \\n2017-09-25 | \\n2017-09-25 10:13:54.356722 | \\n1 | \\nlxb | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 2 | \\n2 | \\n2017-09-25 | \\n2017-09-25 10:14:11.489530 | \\n1 | \\n习胞子 | \\n0 | \\n0 | \\n0 | \\n0 | \\nno_results | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 3 | \\n3 | \\n2017-09-25 | \\n2017-09-25 10:14:29.667395 | \\n1 | \\n刘晓波 | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 4 | \\n4 | \\n2017-09-25 | \\n2017-09-25 10:14:49.107078 | \\n1 | \\npokemon | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n5705260.0 | \\npop culture | \\n
| 5 | \\n5 | \\n2017-09-25 | \\n2017-09-25 10:15:09.762484 | \\n1 | \\njay chou | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n881.0 | \\npop culture | \\n
| 6 | \\n6 | \\n2017-09-25 | \\n2017-09-25 10:15:28.100418 | \\n1 | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n63401495.0 | \\nsocial media | \\n|
| 7 | \\n7 | \\n2017-09-25 | \\n2017-09-25 10:15:46.214464 | \\n2 | \\nhello | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n80454634.0 | \\nNone | \\n
| 8 | \\n8 | \\n2017-09-25 | \\n2017-09-25 10:16:03.274804 | \\n2 | \\nlxb | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 9 | \\n9 | \\n2017-09-25 | \\n2017-09-25 10:16:19.035805 | \\n2 | \\n习胞子 | \\n0 | \\n0 | \\n0 | \\n0 | \\nno_results | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 10 | \\n10 | \\n2017-09-25 | \\n2017-09-25 10:16:36.021837 | \\n2 | \\n刘晓波 | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
\\n\\n \\n
\\n
\\n\\n\\n\\n\\n```python\\nweibo.run(more_keywords_df,sqlite_file=sqlite_file,cookies=cookie)\\n```\\n\\n (0, u'zhongnanhai', 'has_results')\\n (1, u'cats', 'has_results')\\n\\n\\n\\n```python\\nweibo.sqlite_to_df(sqlite_file)\\n```\\n\\n\\n\\n\\n| \\n | keyword | \\nnotes | \\nsource | \\n
|---|---|---|---|
| 0 | \\nzhongnanhai | \\nlocation | \\nmy dataframe2 | \\n
| 1 | \\ncats | \\npop culture | \\nmy dataframe2 | \\n
| 2 | \\nzhongnanhai | \\nlocation | \\nmy dataframe2 | \\n
\\n\\n \\n
\\n
\\n\\n\\n## You can also pass in lists if you prefer (though you can't include the source or notes)\\n\\n\\n```python\\nsample_keywords_list = [\\\"cats\\\",'yes','自由亚洲电台','刘晓波','dhfjkdashfjkasdhf']\\n```\\n\\n**See below how it handles connection reset errors (it waits a little extra to make sure your connection clears before continuing testing)**\\n\\n\\n```python\\nweibo.run(sample_keywords_list,sqlite_file=sqlite_file,cookies=cookie)\\n```\\n\\n (0, u'cats', 'has_results')\\n (1, u'yes', 'has_results')\\n 自由亚洲电台 caused connection reset, waiting 95\\n (2, u'\\\\u81ea\\\\u7531\\\\u4e9a\\\\u6d32\\\\u7535\\\\u53f0', 'reset')\\n (3, u'\\\\u5218\\\\u6653\\\\u6ce2', 'censored')\\n (4, u'dhfjkdashfjkasdsf87', 'no_results')\\n\\n\\n\\n```python\\nweibo.sqlite_to_df(sqlite_file)\\n```\\n\\n\\n\\n\\n| \\n | id | \\ndate | \\ndatetime_logged | \\ntest_number | \\nkeyword | \\ncensored | \\nno_results | \\nreset | \\nis_canonical | \\nresult | \\nsource | \\norig_keyword | \\nnum_results | \\nnotes | \\n
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \\n0 | \\n2017-09-25 | \\n2017-09-25 10:13:37.816720 | \\n1 | \\nhello | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n80454701.0 | \\nNone | \\n
| 1 | \\n1 | \\n2017-09-25 | \\n2017-09-25 10:13:54.356722 | \\n1 | \\nlxb | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 2 | \\n2 | \\n2017-09-25 | \\n2017-09-25 10:14:11.489530 | \\n1 | \\n习胞子 | \\n0 | \\n0 | \\n0 | \\n0 | \\nno_results | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 3 | \\n3 | \\n2017-09-25 | \\n2017-09-25 10:14:29.667395 | \\n1 | \\n刘晓波 | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 4 | \\n4 | \\n2017-09-25 | \\n2017-09-25 10:14:49.107078 | \\n1 | \\npokemon | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n5705260.0 | \\npop culture | \\n
| 5 | \\n5 | \\n2017-09-25 | \\n2017-09-25 10:15:09.762484 | \\n1 | \\njay chou | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n881.0 | \\npop culture | \\n
| 6 | \\n6 | \\n2017-09-25 | \\n2017-09-25 10:15:28.100418 | \\n1 | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n63401495.0 | \\nsocial media | \\n|
| 7 | \\n7 | \\n2017-09-25 | \\n2017-09-25 10:15:46.214464 | \\n2 | \\nhello | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n80454634.0 | \\nNone | \\n
| 8 | \\n8 | \\n2017-09-25 | \\n2017-09-25 10:16:03.274804 | \\n2 | \\nlxb | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 9 | \\n9 | \\n2017-09-25 | \\n2017-09-25 10:16:19.035805 | \\n2 | \\n习胞子 | \\n0 | \\n0 | \\n0 | \\n0 | \\nno_results | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 10 | \\n10 | \\n2017-09-25 | \\n2017-09-25 10:16:36.021837 | \\n2 | \\n刘晓波 | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 11 | \\n11 | \\n2017-09-25 | \\n2017-09-25 10:16:53.766351 | \\n1 | \\nzhongnanhai | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe2 | \\nNone | \\n109.0 | \\nlocation | \\n
| 12 | \\n12 | \\n2017-09-25 | \\n2017-09-25 10:17:14.124440 | \\n1 | \\ncats | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe2 | \\nNone | \\n648313.0 | \\npop culture | \\n
\\n\\n \\n
\\n
\\n\\n\\n## It can detect the canonical (minimum) set of characters in the search query triggering censorship\\n\\nSet `get_canonical=True` when running to find which part of a censored search query is actually triggering the censorship. Note: this will only work on explicitly censored search queries.\\n\\nFinding canonical censored keywords can take a large number of search cycles, especially with larger original queries. \\n\\n```python\\nweibo.run(['江蛤','江泽民江蛤蟆'],sqlite_file=sqlite_file,cookies=cookie,continue_interruptions=False,get_canonical=True)\\n```\\n\\nIf we find a minimum keyword component, we'll record it as a keyword, set column `is_canonical` to True, and record our full search query in `orig_keyword`. For completeness, we'll also include the original keyword as its own entry with `is_canonical=False`\\n\\n```python\\nweibo.sqlite_to_df(sqlite_file)\\n```\\n\\n\\n| \\n | id | \\ndate | \\ndatetime_logged | \\ntest_number | \\nkeyword | \\ncensored | \\nno_results | \\nreset | \\nis_canonical | \\nresult | \\nsource | \\norig_keyword | \\nnum_results | \\nnotes | \\n
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \\n0 | \\n2017-09-25 | \\n2017-09-25 10:13:37.816720 | \\n1 | \\nhello | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n80454701.0 | \\nNone | \\n
| 1 | \\n1 | \\n2017-09-25 | \\n2017-09-25 10:13:54.356722 | \\n1 | \\nlxb | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 2 | \\n2 | \\n2017-09-25 | \\n2017-09-25 10:14:11.489530 | \\n1 | \\n习胞子 | \\n0 | \\n0 | \\n0 | \\n0 | \\nno_results | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 3 | \\n3 | \\n2017-09-25 | \\n2017-09-25 10:14:29.667395 | \\n1 | \\n刘晓波 | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 4 | \\n4 | \\n2017-09-25 | \\n2017-09-25 10:14:49.107078 | \\n1 | \\npokemon | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n5705260.0 | \\npop culture | \\n
| 5 | \\n5 | \\n2017-09-25 | \\n2017-09-25 10:15:09.762484 | \\n1 | \\njay chou | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n881.0 | \\npop culture | \\n
| 6 | \\n6 | \\n2017-09-25 | \\n2017-09-25 10:15:28.100418 | \\n1 | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n63401495.0 | \\nsocial media | \\n|
| 7 | \\n7 | \\n2017-09-25 | \\n2017-09-25 10:15:46.214464 | \\n2 | \\nhello | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n80454634.0 | \\nNone | \\n
| 8 | \\n8 | \\n2017-09-25 | \\n2017-09-25 10:16:03.274804 | \\n2 | \\nlxb | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 9 | \\n9 | \\n2017-09-25 | \\n2017-09-25 10:16:19.035805 | \\n2 | \\n习胞子 | \\n0 | \\n0 | \\n0 | \\n0 | \\nno_results | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 10 | \\n10 | \\n2017-09-25 | \\n2017-09-25 10:16:36.021837 | \\n2 | \\n刘晓波 | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 11 | \\n11 | \\n2017-09-25 | \\n2017-09-25 10:16:53.766351 | \\n1 | \\nzhongnanhai | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe2 | \\nNone | \\n109.0 | \\nlocation | \\n
| 12 | \\n12 | \\n2017-09-25 | \\n2017-09-25 10:17:14.124440 | \\n1 | \\ncats | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe2 | \\nNone | \\n648313.0 | \\npop culture | \\n
| 13 | \\n13 | \\n2017-09-25 | \\n2017-09-25 10:17:36.205255 | \\n1 | \\ncats | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nlist | \\nNone | \\n648313.0 | \\nNone | \\n
| 14 | \\n14 | \\n2017-09-25 | \\n2017-09-25 10:17:54.330039 | \\n1 | \\nyes | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nlist | \\nNone | \\n28413048.0 | \\nNone | \\n
| 15 | \\n15 | \\n2017-09-25 | \\n2017-09-25 10:19:47.007930 | \\n1 | \\n自由亚洲电台 | \\n0 | \\n0 | \\n0 | \\n0 | \\nreset | \\nlist | \\nNone | \\nNaN | \\nNone | \\n
| 16 | \\n16 | \\n2017-09-25 | \\n2017-09-25 10:20:03.491231 | \\n1 | \\n刘晓波 | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nlist | \\nNone | \\nNaN | \\nNone | \\n
| 17 | \\n17 | \\n2017-09-25 | \\n2017-09-25 10:20:18.747414 | \\n1 | \\ndhfjkdashfjkasdsf87 | \\n0 | \\n0 | \\n0 | \\n0 | \\nno_results | \\nlist | \\nNone | \\nNaN | \\nNone | \\n
\\n\\n \\n
\\n
\\n\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.6037599444389343,\n \"alphanum_fraction\": 0.6088213920593262,\n \"avg_line_length\": 39.10144805908203,\n \"blob_id\": \"2418231d5e29f78ec21bffba7bdc461b21f652fd\",\n \"content_id\": \"ecfe6322ff0def9a7bcecc48feff51a2fbae643f\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2766,\n \"license_type\": \"permissive\",\n \"max_line_length\": 122,\n \"num_lines\": 69,\n \"path\": \"/update_db.py\",\n \"repo_name\": \"jasonqng/blocked-on-weibo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sqlite3\\nimport os, sys\\n\\n\\ndef find_db():\\n \\\"\\\"\\\"\\n Search for database files in the current directory of this script file. Gets user input for confirmation of db file\\n if only 1 match; user input for selection if >1 db file present.\\n \\\"\\\"\\\"\\n\\n print(\\\"Finding database files...\\\")\\n files = list(os.walk('.'))[0][2]\\n db_files = [file for file in files if \\\".db\\\" in file or \\\".sqlite\\\" in file]\\n if len(db_files)>0:\\n if len(db_files) == 1:\\n user_input = raw_input(\\\"Is database file %s/%s? (y/N) \\\" %(os.getcwd(), db_files[0]))\\n if user_input.lower() == 'y':\\n migrate_db(db_files[0])\\n else:\\n break_script()\\n else:\\n print(\\\"%d database files found in dir\\\" %(len(db_files)))\\n for i in range(len(db_files)):\\n print(\\\"%d. %s\\\" %(i+1, db_files[i]))\\n print(\\\"**************\\\")\\n user_input = raw_input(\\\"Which db file to upgrade? (Enter NUMBER) \\\")\\n if user_input.isdigit() and 0 < int(user_input) < len(db_files):\\n migrate_db(db_files[int(user_input) - 1])\\n else:\\n print(\\\"Invalid input.\\\")\\n break_script()\\n else:\\n break_script()\\n\\n\\ndef migrate_db(db_location):\\n \\\"\\\"\\\"\\n sqlite3 code that conducts the actual database modifications. Renames the previous table, creates a new table\\n with new columns, moves contents of previous table over, then deletes the previous table.\\n :param db_location: filename of database file.\\n \\\"\\\"\\\"\\n conn = sqlite3.connect(db_location)\\n c = conn.cursor()\\n c.execute('ALTER TABLE results RENAME TO resultsOld')\\n c.execute('''CREATE TABLE results (id INT, date DATE, datetime_logged DATETIME, test_number INT, keyword string, \\n censored bool, no_results bool, reset bool, is_canonical bool, result string, source string, orig_keyword string, \\n num_results INT, notes string, PRIMARY KEY(date,source,test_number,keyword,orig_keyword))''')\\n c.execute('''INSERT INTO results (id, date, datetime_logged, test_number, keyword, censored, no_results, reset,\\n result, source, num_results, notes)\\n SELECT id, date, datetime_logged, test_number, keyword, censored, no_results, reset, result, source,\\n num_results, notes FROM resultsOld\\n ''')\\n c.execute('DROP TABLE resultsOld')\\n conn.commit()\\n conn.close()\\n print(\\\"Updates completed successfully\\\")\\n sys.exit()\\n\\n\\ndef break_script():\\n \\\"\\\"\\\"\\n Print an error message to console before triggering sys.exit().\\n \\\"\\\"\\\"\\n print(\\\"Please place this .py script in the same directory as your database file, then re-run. No changes were made\\\")\\n sys.exit()\\n\\n\\nif __name__ == \\\"__main__\\\":\\n find_db()\"\n },\n {\n \"alpha_fraction\": 0.6517150402069092,\n \"alphanum_fraction\": 0.6569920778274536,\n \"avg_line_length\": 33.45454406738281,\n \"blob_id\": \"191344d189d160fd1356d8beeb8c3e9d276561e6\",\n \"content_id\": \"11acae76c58eb9f4ce733b18035ef783d00460df\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 379,\n \"license_type\": \"permissive\",\n \"max_line_length\": 74,\n \"num_lines\": 11,\n \"path\": \"/setup.py\",\n \"repo_name\": \"jasonqng/blocked-on-weibo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from setuptools import setup, find_packages\\n\\nsetup(name='blockedonweibo',\\n version='0.2',\\n description='Testing if search keywords are censored on Sina Weibo',\\n url='http://github.com/jasonqng/blocked-on-weibo',\\n author='Jason Q. Ng',\\n author_email='jason.q.ng@gmail.com',\\n license='BSD License',\\n packages=find_packages(exclude=['docs']),\\n zip_safe=False)\\n\"\n },\n {\n \"alpha_fraction\": 0.5565217137336731,\n \"alphanum_fraction\": 0.5565217137336731,\n \"avg_line_length\": 28,\n \"blob_id\": \"b71817df477d628a1f890e95e4de6baaa2a155e0\",\n \"content_id\": \"17db01e6a064484fe84f2e5822bc2b6a6f6227da\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 115,\n \"license_type\": \"permissive\",\n \"max_line_length\": 36,\n \"num_lines\": 4,\n \"path\": \"/blockedonweibo/weibo_credentials.py\",\n \"repo_name\": \"jasonqng/blocked-on-weibo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Creds(object):\\n def __init__(self):\\n self.username = 'your_email_address_you_use_to_log_into_weibo@domain.com'\\n self.password = 'weibo_password'\"\n }\n]"},"num_files":{"kind":"number","value":6,"string":"6"}}},{"rowIdx":1170,"cells":{"repo_name":{"kind":"string","value":"SPARR0W1/SaLo"},"repo_url":{"kind":"string","value":"https://github.com/SPARR0W1/SaLo"},"snapshot_id":{"kind":"string","value":"cdf77e3e8c5e14bdbade411aaebc60c678919654"},"revision_id":{"kind":"string","value":"a5fb938c6224e8af816a4d1f0a1fc597ecfa80aa"},"directory_id":{"kind":"string","value":"eaebd0704f117c07b8f5ac2b1c10c36508a723a9"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"string","value":"2023-03-28T02:59:18.617809"},"revision_date":{"kind":"string","value":"2021-04-03T09:00:22"},"committer_date":{"kind":"string","value":"2021-04-03T09:00:22"},"github_id":{"kind":"number","value":354243074,"string":"354,243,074"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6714285612106323,"alphanum_fraction":0.6714285612106323,"avg_line_length":20.77777862548828,"blob_id":"e63866c2b78a82e49df44744bd72e23a61f694a2","content_id":"f43616d482ef1b4e1f5adb7475d1d63ab6edcaaf","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":210,"license_type":"no_license","max_line_length":43,"num_lines":9,"path":"/SaLo.py","repo_name":"SPARR0W1/SaLo","src_encoding":"UTF-8","text":"#Import to Module\r\nimport pickle\r\n\r\n#Function\r\ndef save(object, namefile=\"dump.p\"):\r\n\tpickle.dump(object, open(namefile, \"wb\"))\r\n\r\ndef load(object, namefile):\r\n\tobject = pickle.load(open(namefile, \"rb\"))\r\n\r\n\r\n\t"},{"alpha_fraction":0.763012170791626,"alphanum_fraction":0.763012170791626,"avg_line_length":27.15625,"blob_id":"7e9fd55a45218ad512639b8dc31615a4dca25c9f","content_id":"9bfb1a1b28b6ce31f2e02549800f680ea1e9a0b3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":903,"license_type":"no_license","max_line_length":102,"num_lines":32,"path":"/README.md","repo_name":"SPARR0W1/SaLo","src_encoding":"UTF-8","text":"# SaLo\nI will try to simplify your life as a programmer.\nAnd I'll show you this library.\n\nIt is used to store variables, lists, tuples, dictionaries and other typical data in Python.\n\nThe library is made on the basis of the Pickle module.\n\nAnd be in beta mode.\n\n\nHow to use:\n\nFirst, transfer the downloaded SaLo.py file to the project folder. It is very important.\nThen import the functions with the given code:\nfrom SaLo import *\n\nThe end\n\nFunction:\n\nFunction - Save allows you to save the object\nIt looks like save (object, namefile). \nWhere object is the object itself, and namefile is the name of the document in which it will be saved.\n\nImportant: the name of the file must be with the extension .p\n\nFunction - Load allows you to load an object\nfrom file\n\nIt looks like load (object, namefile). \nWhere object is the object itself, and namefile is the name of the document in which it is located.\n\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6714285612106323,\n \"alphanum_fraction\": 0.6714285612106323,\n \"avg_line_length\": 20.77777862548828,\n \"blob_id\": \"e63866c2b78a82e49df44744bd72e23a61f694a2\",\n \"content_id\": \"f43616d482ef1b4e1f5adb7475d1d63ab6edcaaf\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 210,\n \"license_type\": \"no_license\",\n \"max_line_length\": 43,\n \"num_lines\": 9,\n \"path\": \"/SaLo.py\",\n \"repo_name\": \"SPARR0W1/SaLo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#Import to Module\\r\\nimport pickle\\r\\n\\r\\n#Function\\r\\ndef save(object, namefile=\\\"dump.p\\\"):\\r\\n\\tpickle.dump(object, open(namefile, \\\"wb\\\"))\\r\\n\\r\\ndef load(object, namefile):\\r\\n\\tobject = pickle.load(open(namefile, \\\"rb\\\"))\\r\\n\\r\\n\\r\\n\\t\"\n },\n {\n \"alpha_fraction\": 0.763012170791626,\n \"alphanum_fraction\": 0.763012170791626,\n \"avg_line_length\": 27.15625,\n \"blob_id\": \"7e9fd55a45218ad512639b8dc31615a4dca25c9f\",\n \"content_id\": \"9bfb1a1b28b6ce31f2e02549800f680ea1e9a0b3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 903,\n \"license_type\": \"no_license\",\n \"max_line_length\": 102,\n \"num_lines\": 32,\n \"path\": \"/README.md\",\n \"repo_name\": \"SPARR0W1/SaLo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# SaLo\\nI will try to simplify your life as a programmer.\\nAnd I'll show you this library.\\n\\nIt is used to store variables, lists, tuples, dictionaries and other typical data in Python.\\n\\nThe library is made on the basis of the Pickle module.\\n\\nAnd be in beta mode.\\n\\n\\nHow to use:\\n\\nFirst, transfer the downloaded SaLo.py file to the project folder. It is very important.\\nThen import the functions with the given code:\\nfrom SaLo import *\\n\\nThe end\\n\\nFunction:\\n\\nFunction - Save allows you to save the object\\nIt looks like save (object, namefile). \\nWhere object is the object itself, and namefile is the name of the document in which it will be saved.\\n\\nImportant: the name of the file must be with the extension .p\\n\\nFunction - Load allows you to load an object\\nfrom file\\n\\nIt looks like load (object, namefile). \\nWhere object is the object itself, and namefile is the name of the document in which it is located.\\n\\n\\n\"\n }\n]"},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":1171,"cells":{"repo_name":{"kind":"string","value":"smithCoderLeo/MachineLearning"},"repo_url":{"kind":"string","value":"https://github.com/smithCoderLeo/MachineLearning"},"snapshot_id":{"kind":"string","value":"92af2ee518823ec2be80329040be32eb06672651"},"revision_id":{"kind":"string","value":"9dc5eaae19b84c3db5f1b359f4f56a6004c99ee3"},"directory_id":{"kind":"string","value":"222b56e008e8bf7ffb8c6d34f495e6837f3ae833"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"string","value":"2020-05-07T21:31:40.885159"},"revision_date":{"kind":"string","value":"2019-11-13T01:59:00"},"committer_date":{"kind":"string","value":"2019-11-13T01:59:00"},"github_id":{"kind":"number","value":180908102,"string":"180,908,102"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.8709677457809448,"alphanum_fraction":0.8709677457809448,"avg_line_length":14.5,"blob_id":"d33256a8643adb5df13d9e02f5fc30dd99a64af1","content_id":"6fde82cc8796e7a92aba7009d61d4176cb94a0b0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":55,"license_type":"no_license","max_line_length":17,"num_lines":2,"path":"/README.md","repo_name":"smithCoderLeo/MachineLearning","src_encoding":"UTF-8","text":"# MachineLearning\n机器学习中的常用函数代码\n"},{"alpha_fraction":0.6227678656578064,"alphanum_fraction":0.6294642686843872,"avg_line_length":36.33333206176758,"blob_id":"0693e891b0d1d9d168265a2b4df970826df6438d","content_id":"94982e24f1fa711c323a540bf9edbc52b2f6ccea","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":484,"license_type":"no_license","max_line_length":54,"num_lines":12,"path":"/UtilFunction.py","repo_name":"smithCoderLeo/MachineLearning","src_encoding":"UTF-8","text":"#data原始数据转为csv\ndef data2Csv(filename):\n handled_file = 'kdd99_handled.csv'\n data_to_file = open(handled_file, 'w', newline='')\n with open(filename, 'r') as fr: #读入的file为原始data文件\n csv_reader = csv.reader(fr)\n csv_writer = csv.writer(data_to_file)\n for each_line in csv_reader:\n temp_line = np.array(each_line) #转换类型\n csv_writer.writerow(temp_line)\n data_to_file.close()\n return handled_file\n"},{"alpha_fraction":0.649452269077301,"alphanum_fraction":0.6674491167068481,"avg_line_length":78.875,"blob_id":"24425468e7f665d9ae6c73e63fc14ab5b4a1bfd3","content_id":"bbe42794730d913756f0f3b0d2aaf48451bd36d4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1304,"license_type":"no_license","max_line_length":628,"num_lines":16,"path":"/OneHotEncoder.py","repo_name":"smithCoderLeo/MachineLearning","src_encoding":"UTF-8","text":"def oneHotEncoder_process():\n service_list=['aol','auth','bgp','courier','csnet_ns','ctf','daytime','discard','domain','domain_u','echo','eco_i','ecr_i','efs','exec','finger','ftp','ftp_data','gopher','harvest','hostnames','http','http_2784','http_443','http_8001','imap4','IRC','iso_tsap','klogin','kshell','ldap','link','login','mtp','name','netbios_dgm','netbios_ns','netbios_ssn','netstat','nnsp','nntp','ntp_u','other','pm_dump','pop_2','pop_3','printer','private','red_i','remote_job','rje','shell','smtp','sql_net','ssh','sunrpc','supdup','systat','telnet','tftp_u','tim_i','time','urh_i','urp_i','uucp','uucp_path','vmnet','whois','X11','Z39_50']\n values = np.array(service_list)\n label_encoder = LabelEncoder()\n integer_encoded = label_encoder.fit_transform(values)\n #source_file = 'corrected_handled2.csv'\n #handled_file_onehot = 'corrected_handled_onehot.csv'\n # binary encode\n onehot_encoder = OneHotEncoder(sparse=False)\n integer_encoded = integer_encoded.reshape(len(integer_encoded), 1) #调整为单列\n onehot_encoded = onehot_encoder.fit_transform(integer_encoded)\n print(onehot_encoded)\n return \n #invert first example 反编译第一个样例\n inverted = label_encoder.inverse_transform([np.argmax(onehot_encoded[0, :])])\n print(inverted)\n"}],"string":"[\n {\n \"alpha_fraction\": 0.8709677457809448,\n \"alphanum_fraction\": 0.8709677457809448,\n \"avg_line_length\": 14.5,\n \"blob_id\": \"d33256a8643adb5df13d9e02f5fc30dd99a64af1\",\n \"content_id\": \"6fde82cc8796e7a92aba7009d61d4176cb94a0b0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 55,\n \"license_type\": \"no_license\",\n \"max_line_length\": 17,\n \"num_lines\": 2,\n \"path\": \"/README.md\",\n \"repo_name\": \"smithCoderLeo/MachineLearning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# MachineLearning\\n机器学习中的常用函数代码\\n\"\n },\n {\n \"alpha_fraction\": 0.6227678656578064,\n \"alphanum_fraction\": 0.6294642686843872,\n \"avg_line_length\": 36.33333206176758,\n \"blob_id\": \"0693e891b0d1d9d168265a2b4df970826df6438d\",\n \"content_id\": \"94982e24f1fa711c323a540bf9edbc52b2f6ccea\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 484,\n \"license_type\": \"no_license\",\n \"max_line_length\": 54,\n \"num_lines\": 12,\n \"path\": \"/UtilFunction.py\",\n \"repo_name\": \"smithCoderLeo/MachineLearning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#data原始数据转为csv\\ndef data2Csv(filename):\\n handled_file = 'kdd99_handled.csv'\\n data_to_file = open(handled_file, 'w', newline='')\\n with open(filename, 'r') as fr: #读入的file为原始data文件\\n csv_reader = csv.reader(fr)\\n csv_writer = csv.writer(data_to_file)\\n for each_line in csv_reader:\\n temp_line = np.array(each_line) #转换类型\\n csv_writer.writerow(temp_line)\\n data_to_file.close()\\n return handled_file\\n\"\n },\n {\n \"alpha_fraction\": 0.649452269077301,\n \"alphanum_fraction\": 0.6674491167068481,\n \"avg_line_length\": 78.875,\n \"blob_id\": \"24425468e7f665d9ae6c73e63fc14ab5b4a1bfd3\",\n \"content_id\": \"bbe42794730d913756f0f3b0d2aaf48451bd36d4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1304,\n \"license_type\": \"no_license\",\n \"max_line_length\": 628,\n \"num_lines\": 16,\n \"path\": \"/OneHotEncoder.py\",\n \"repo_name\": \"smithCoderLeo/MachineLearning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def oneHotEncoder_process():\\n service_list=['aol','auth','bgp','courier','csnet_ns','ctf','daytime','discard','domain','domain_u','echo','eco_i','ecr_i','efs','exec','finger','ftp','ftp_data','gopher','harvest','hostnames','http','http_2784','http_443','http_8001','imap4','IRC','iso_tsap','klogin','kshell','ldap','link','login','mtp','name','netbios_dgm','netbios_ns','netbios_ssn','netstat','nnsp','nntp','ntp_u','other','pm_dump','pop_2','pop_3','printer','private','red_i','remote_job','rje','shell','smtp','sql_net','ssh','sunrpc','supdup','systat','telnet','tftp_u','tim_i','time','urh_i','urp_i','uucp','uucp_path','vmnet','whois','X11','Z39_50']\\n values = np.array(service_list)\\n label_encoder = LabelEncoder()\\n integer_encoded = label_encoder.fit_transform(values)\\n #source_file = 'corrected_handled2.csv'\\n #handled_file_onehot = 'corrected_handled_onehot.csv'\\n # binary encode\\n onehot_encoder = OneHotEncoder(sparse=False)\\n integer_encoded = integer_encoded.reshape(len(integer_encoded), 1) #调整为单列\\n onehot_encoded = onehot_encoder.fit_transform(integer_encoded)\\n print(onehot_encoded)\\n return \\n #invert first example 反编译第一个样例\\n inverted = label_encoder.inverse_transform([np.argmax(onehot_encoded[0, :])])\\n print(inverted)\\n\"\n }\n]"},"num_files":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":1172,"cells":{"repo_name":{"kind":"string","value":"grimmweeper/50.012-networks-OT"},"repo_url":{"kind":"string","value":"https://github.com/grimmweeper/50.012-networks-OT"},"snapshot_id":{"kind":"string","value":"bddc116e36bfccc596c20e90dfef6b8f3d7160c8"},"revision_id":{"kind":"string","value":"d9ff44cfd83e2b67fd8935f59ff1d0051e18c08d"},"directory_id":{"kind":"string","value":"5a4ee64aefbd917a46b5083635254fb5793e8ba8"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"string","value":"2023-01-29T11:45:29.546852"},"revision_date":{"kind":"string","value":"2020-11-29T19:13:35"},"committer_date":{"kind":"string","value":"2020-11-29T19:13:35"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5560632944107056,"alphanum_fraction":0.5775043964385986,"avg_line_length":28.03061294555664,"blob_id":"beaf3d6bed0718696aa94de9b02feeb2f0fdfd83","content_id":"7f9dc303f7a342da095a3d7152e351272202c91f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2845,"license_type":"no_license","max_line_length":85,"num_lines":98,"path":"/convergence-test/multiclient.py","repo_name":"grimmweeper/50.012-networks-OT","src_encoding":"UTF-8","text":"# import the socket module\nimport socket\n\n# Create a socket instance\nsocketObject = socket.socket()\nhost = '127.0.0.1'\nport = 2004\n\nclientNumber = int(input(\"Input the number of clients to spawn: \"))\n\nfor i in range(clientNumber):\n\n socketObject = socket.socket()\n host = '127.0.0.1'\n port = 2004\n print(\"Waiting for connection response\")\n\n try:\n socketObject.connect((host, port))\n print(\"Client {} connected to server.\".format(i+1))\n\n except socket.error as e:\n print(str(e))\n\n res = socketObject.recv(1024)\n # Writing something\n if(i == 0):\n message = \"Test message from Client {} \\n\".format(i+1)\n f = open(\"sharedfile.txt\", \"w\")\n f.write(message)\n f.close()\n messageBytes = str.encode(message)\n socketObject.sendall(messageBytes)\n data = socketObject.recv(1024)\n print(data)\n #Adding on to client 1\n elif(i == 1):\n message = \"Test message from other client {} \\n\".format(i+1)\n f = open(\"sharedfile.txt\", \"a\")\n f.write(message)\n f.close()\n messageBytes = str.encode(message)\n socketObject.sendall(messageBytes)\n data = socketObject.recv(1024)\n print(data)\n #Delete\n elif(i == 2):\n message = \"Client 3: I've deleted edits made by client 2 \\n\"\n open_file = open(\"sharedfile.txt\", \"r\")\n lines = open_file.readlines()\n open_file.close()\n del lines[1]\n f = open(\"sharedfile.txt\", \"w+\")\n for line in lines:\n f.write(line)\n f.write(message)\n f.close()\n messageBytes = str.encode(message)\n socketObject.sendall(messageBytes)\n data = socketObject.recv(1024)\n print(data)\n #Insert \n elif(i == 3):\n open_file = open(\"sharedfile.txt\", \"r\")\n lines = open_file.readlines()\n open_file.close()\n message = \"Client 4 has inserted this message between the first two lines \\n\"\n lines.insert(1,message)\n f = open(\"sharedfile.txt\",\"w+\")\n for line in lines:\n f.write(line)\n f.close()\n messageBytes = str.encode(message)\n socketObject.sendall(messageBytes)\n data = socketObject.recv(1024)\n print(data)\n #Move\n else: \n open_file = open(\"sharedfile.txt\",\"r\")\n lines = open_file.readlines()\n open_file.close()\n first_line = lines[0]\n lines.pop(0)\n lines.append(first_line)\n f = open(\"sharedfile.txt\",\"w+\")\n for line in lines:\n f.write(line)\n message = \"Client 5 has moved the edit made by client 1 \\n\"\n f.write(message)\n f.close()\n messageBytes = str.encode(message)\n socketObject.sendall(messageBytes)\n data = socketObject.recv(1024)\n print(data)\n\n\n\nsocketObject.close()\n"},{"alpha_fraction":0.7173523902893066,"alphanum_fraction":0.7450805306434631,"avg_line_length":20.480770111083984,"blob_id":"ebc9fb8f7b8b754092c2ecf77a980416e52966df","content_id":"21a50f79323728ece5480fb1a3cf5d24473a562b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1118,"license_type":"no_license","max_line_length":81,"num_lines":52,"path":"/README.md","repo_name":"grimmweeper/50.012-networks-OT","src_encoding":"UTF-8","text":"# 50.012 Networks - Group 2 Operational Transformation\n\n### Experiments involving client-server implementations with Quill-Delta Object\n\n### To start:\n```git clone https://github.com/GrimmWeeper/50.012-networks-OT.git```\n\n### Time analysis experiment\n\n```\n1) cd time-experiment\n2) Open 2 terminals\n3) python server.py\n4) python multiclient.py\n```\n\n### Concurrency experiment\n\n```\n1) cd concurrency-test\n2) Open 2 terminals\n3) python server.py\n4) python client.py\n5) Type in inputs from both terminals\n```\n\n### Convergence experiment\n\n```\n1) cd convergence-test\n2) Open >=2 terminals\n3) python server.py\n4) python client.py\n5) Type in inputs from terminals\n6) Abruptly close the server terminal\n7) Type in input from client terminal\n8) Reconnects back the server terminal\n```\n\n### Authors\n[GrimmWeeper](https://github.com/GrimmWeeper)\n\n[alicezhangjy](https://github.com/alicezhangjy)\n\n[xmliszt](https://github.com/xmliszt)\n\n[jesterlyn](https://github.com/jesterlyn)\n\n[cinnamon-rolling](https://github.com/cinnamon-rolling)\n\n### Credits\n[forgeworks/quill-delta-python](https://github.com/forgeworks/quill-delta-python)\n\n"},{"alpha_fraction":0.6343545913696289,"alphanum_fraction":0.643897533416748,"avg_line_length":23,"blob_id":"99a48dd135c29d1dd321980b8d182c6cf21eb94a","content_id":"e2653794a52c1da08f7e0e6e015cdb5edfb9f8a8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1991,"license_type":"no_license","max_line_length":88,"num_lines":83,"path":"/concurrency-test/client.py","repo_name":"grimmweeper/50.012-networks-OT","src_encoding":"UTF-8","text":"#import the socket module\nimport socket\n\nimport sys\nsys.path.append('../')\nfrom delta import Delta\n\nimport utils\n\n#Create a socket instance\nsocketObject = socket.socket()\nhost = '127.0.0.1'\nport = 2005\n\nprint(\"Waiting for connection response\")\n#Using the socket connect to a server...in this case localhost\n\ntry:\n socketObject.connect((host, port))\n print(\"Connected to localhost\")\n\nexcept socket.error as e:\n print(str(e))\n\nres = socketObject.recv(1024)\n\ndelta_object = Delta().insert('ABCD')\n\n# Receive the data\nwhile(True):\n\n operation = ''\n retain = ''\n\n retain = input(\"Input position value to retain: \")\n operation = input(\"Input Operation (insert/delete): \")\n value = input(\"Input value for operation (insert: text | delete: position value): \")\n\n\n if retain == '':\n if operation == 'insert':\n client_delta = Delta().insert(value)\n \n elif operation == 'delete':\n client_delta = Delta().delete(int(value))\n \n else:\n if operation == 'insert':\n client_delta = Delta().retain(int(retain)).insert(value)\n \n elif operation == 'delete':\n client_delta = Delta().retain(int(retain)).delete(int(value)) \n\n \n\n print(\"\\n\")\n print(\"[Initial State]:\", delta_object)\n print(\"[Client Operation]:\", client_delta)\n\n client_local = delta_object.compose(client_delta)\n\n print(\"[State after Client Operation]:\", client_local)\n print(\"\\n\")\n \n messageBytes = str.encode(str(client_delta))\n socketObject.sendall(messageBytes)\n\n data = socketObject.recv(1024).decode('utf-8')\n\n server_delta = utils.string_to_delta(data)\n\n print(\"[Server Operation]:\", server_delta)\n\n server_transform = client_delta.transform(server_delta)\n print(\"[Transformed Server Operation]:\", server_transform)\n print(\"\\n\")\n\n client_result = client_local.compose(server_transform)\n print(\"[Eventual State in Client]:\", client_result)\n\n\n\nsocketObject.close()"},{"alpha_fraction":0.5996860265731812,"alphanum_fraction":0.6169544458389282,"avg_line_length":25.58333396911621,"blob_id":"9a8d3be4b5433652c88f508afcc3a9368e7886c2","content_id":"22a70827984d1dd57b1654e8eaeda8c9141ab7aa","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":637,"license_type":"no_license","max_line_length":67,"num_lines":24,"path":"/time-experiment/counter.py","repo_name":"grimmweeper/50.012-networks-OT","src_encoding":"UTF-8","text":"import random\n\ndef gen_operation():\n value = str(random.randint(1,10))\n operation = random.choice([\"plus\",\"minus\",\"multiply\",\"divide\"])\n\n return operation + ',' + value\n\ndef compute(current, operation):\n operation_list = operation.split(',')\n\n if operation_list[0] == 'plus':\n result = current + int(operation_list[1])\n \n elif operation_list[0] == 'minus':\n result = current - int(operation_list[1])\n\n elif operation_list[0] == 'multiply':\n result = current * int(operation_list[1])\n\n elif operation_list[0] == 'divide':\n result = current / int(operation_list[1])\n\n return result"},{"alpha_fraction":0.6083385944366455,"alphanum_fraction":0.6279216408729553,"avg_line_length":21.614286422729492,"blob_id":"6047a5bf7c831c80ab5dce0d81dc6eb26796cd83","content_id":"85127d520291e5793e7727943c5d3709b626b562","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1583,"license_type":"no_license","max_line_length":77,"num_lines":70,"path":"/time-experiment/multiclient.py","repo_name":"grimmweeper/50.012-networks-OT","src_encoding":"UTF-8","text":"# import the socket module\nimport socket\n\nimport counter\nimport time\nimport matplotlib.pyplot as plt\nimport csv\n\ntime_list = []\nidx_list = []\n\n\n\nfor i in range(30):\n start = time.time()\n time.sleep(0.2)\n \n for j in range(i+1):\n\n socketObject = socket.socket()\n host = '127.0.0.1'\n port = 2004\n print(\"Waiting for connection response\")\n\n try:\n socketObject.connect((host, port))\n print(\"Client {} connected to server.\".format(i+1))\n\n except socket.error as e:\n print(str(e))\n\n res = socketObject.recv(1024)\n\n message = counter.gen_operation()\n messageBytes = str.encode(message)\n socketObject.sendall(messageBytes)\n data = socketObject.recv(1024)\n print(data)\n\n end = time.time() - start\n time_list.append(end)\n idx_list.append(i+1)\n\nprint(time_list)\n\n\n# Plot graph\nfig = plt.figure()\nax = fig.add_subplot(1, 1, 1)\nax.plot(idx_list, time_list, 'go-', alpha=0.5)\nax.set_title(\n \"Time Taken to Reach Eventual Consistency (ms) against Number of Clients\"\n)\nax.set_xlabel(\"Number of clients\")\nax.set_ylabel(\"Time Taken to reach eventual consistency (ms)\")\nplt.savefig('client_server.png', format='png')\n\n\n# Save values as csv file\nwith open('client_server.csv', 'w', encoding='utf-8', newline='') as fh:\n writer = csv.writer(fh)\n writer.writerow([\n \"Number of Clients\", \"Time Taken to Reach Eventual Consistency (ms)\"\n ])\n for _each in list(zip(idx_list, time_list)):\n writer.writerow(list(_each))\n\n\n\nsocketObject.close()\n"},{"alpha_fraction":0.5652173757553101,"alphanum_fraction":0.6304348111152649,"avg_line_length":14.666666984558105,"blob_id":"1e5f312df52ac6b3b4d0d962f7cf850bc9acc024","content_id":"c347aecb363a477938743ce487a68abe14c8bcfd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":46,"license_type":"no_license","max_line_length":23,"num_lines":3,"path":"/delta/__init__.py","repo_name":"grimmweeper/50.012-networks-OT","src_encoding":"UTF-8","text":"from .base import Delta\n\n__version__ = '1.0.1'"},{"alpha_fraction":0.5320637226104736,"alphanum_fraction":0.557326078414917,"avg_line_length":26.32978630065918,"blob_id":"037864af0d65bb2e1c27de1a1563e2deca66b77e","content_id":"f4e50a3605660714b8a59553a7fb73e28f42d7bd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2573,"license_type":"no_license","max_line_length":110,"num_lines":94,"path":"/single/single.py","repo_name":"grimmweeper/50.012-networks-OT","src_encoding":"UTF-8","text":"import sys\nsys.path.append('../')\nfrom delta import Delta\n\ndef string_to_delta(text):\n temp_text = text[7:]\n delta_text = temp_text[:-2]\n delta_list = delta_text.split(',')\n\n if len(delta_list) == 1:\n ops = delta_list[0][2:8]\n \n if ops == 'insert':\n value = delta_list[0][12:-2]\n print(value)\n return Delta().insert(value)\n \n elif ops == 'delete':\n value = int(delta_list[0][11:-1])\n return Delta().delete(value)\n\n elif len(delta_list) == 2:\n for i in range(2):\n if i == 0:\n ops_1 = delta_list[0][2:8]\n value_1 = int(delta_list[0][11:12])\n\n elif i == 1:\n ops_2 = delta_list[1][3:9]\n\n if ops_2 == 'insert':\n value_2 = delta_list[1][13:-2]\n return Delta().retain(value_1).insert(value_2)\n \n elif ops_2 == 'delete':\n value_2 = int(delta_list[1][12:-1])\n return Delta().retain(value_1).delete(value_2)\n\n return Delta()\n\n\n# Test on string_to_delta\ndef string_to_delta_test():\n a = Delta().insert('ABCDE')\n b = Delta().retain(3).delete(4)\n c = Delta().delete(4)\n\n print(a)\n print(string_to_delta(str(a)))\n print(b)\n print(string_to_delta(str(b)))\n print(c)\n print(string_to_delta(str(c)))\n\n# Operation test: Sequential insert/delete\ndef seq_OT():\n a = Delta().insert('ABCDE')\n print('a')\n print(a)\n b = Delta().retain(3).insert('Z')\n c = Delta().delete(4)\n\n result_1 = a.compose(b)\n result_2 = result_1.compose(c)\n print(\"result 1:\")\n print(result_1)\n print(\"result 2:\")\n print(result_2)\n\n\n# Transformation test: Concurrency\ndef concurrency_OT():\n\n initial = Delta().insert('ABCD')\n client_operation = Delta().retain(3).insert(\"X\") # ABCXD -> ACXD\n server_operation = Delta().retain(1).delete(1) # ACD -> ACDX, ACXD\n\n # Client\n client_local = initial.compose(client_operation)\n client_transform = client_operation.transform(server_operation)\n client_result = client_local.compose(client_transform)\n\n # Server\n server_local = initial.compose(server_operation)\n server_transform = server_operation.transform(client_operation) # Change from retaining 3rd pos to 2nd pos\n server_result = server_local.compose(server_transform)\n\n print(client_result) #ACXD\n print(server_result) #ACXD\n\nif __name__ == \"__main__\":\n string_to_delta_test()\n # seq_OT()\n # concurrency_OT()\n "},{"alpha_fraction":0.595162570476532,"alphanum_fraction":0.6030927896499634,"avg_line_length":25.840425491333008,"blob_id":"0ea5b64130075db812ca574636adf2c542640c1b","content_id":"66899f0d45cc572b754bc49aa81ae5c1804b72f3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2522,"license_type":"no_license","max_line_length":92,"num_lines":94,"path":"/concurrency-test/server.py","repo_name":"grimmweeper/50.012-networks-OT","src_encoding":"UTF-8","text":"import socket\nimport os\nfrom _thread import *\n\nimport sys\nsys.path.append('../')\nfrom delta import Delta\n\nimport utils\n\nServerSideSocket = socket.socket()\nhost = '127.0.0.1'\nport = 2005\nThreadCount = 0\nclients= set()\n\ndelta_object = Delta().insert('ABCD')\n\ntry:\n ServerSideSocket.bind((host, port))\nexcept socket.error as e:\n print(str(e))\n\nprint('Socket is listening..')\nServerSideSocket.listen(5)\n\n\ndef multi_threaded_client(connection):\n global delta_object\n connection.send(str.encode('Server is working:'))\n while True:\n operation = ''\n retain = ''\n\n retain = input(\"Input position value to retain: \")\n operation = input(\"Input Operation (insert/delete): \")\n value = input(\"Input value for operation (insert: text | delete: position value): \")\n\n\n if retain == '':\n if operation == 'insert':\n server_delta = Delta().insert(value)\n \n elif operation == 'delete':\n server_delta = Delta().delete(int(value))\n \n else:\n if operation == 'insert':\n server_delta = Delta().retain(int(retain)).insert(value)\n \n elif operation == 'delete':\n server_delta = Delta().retain(int(retain)).delete(int(value)) \n\n\n print(\"\\n\")\n print(\"[Initial State]:\", delta_object)\n print(\"[Server Operation]:\", server_delta)\n\n server_local = delta_object.compose(server_delta)\n\n print(\"[State after Server Operation]:\", server_local)\n print(\"\\n\")\n \n data = connection.recv(2048)\n client_message = data.decode('utf-8')\n print(client_message)\n\n client_delta = utils.string_to_delta(client_message)\n\n print(\"[Client Operation]:\", client_delta)\n\n client_transform = server_delta.transform(client_delta)\n print(\"[Transformed Client Operation]:\", client_transform)\n\n server_result = server_local.compose(client_transform)\n print(\"\\n\")\n print(\"[Eventual State in Server]:\", server_result)\n\n response = str(server_delta)\n if not data:\n break\n connection.sendall(str.encode(response))\n connection.close()\n\nwhile True:\n\n Client, address = ServerSideSocket.accept()\n clients.add(Client)\n print('Connected to: ' + address[0] + ':' + str(address[1]))\n start_new_thread(multi_threaded_client, (Client, ))\n ThreadCount += 1\n print('Thread Number: ' + str(ThreadCount))\n\nServerSideSocket.close()"},{"alpha_fraction":0.40913161635398865,"alphanum_fraction":0.452103853225708,"avg_line_length":27.66666603088379,"blob_id":"052de38d8ccc721b4c31a44146f226a4946e4a60","content_id":"b55b587c065b5caf14ac2687a856d3b1c4bfbc1c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1117,"license_type":"no_license","max_line_length":66,"num_lines":39,"path":"/convergence-test/utils.py","repo_name":"grimmweeper/50.012-networks-OT","src_encoding":"UTF-8","text":"import sys\nsys.path.append('../')\nfrom delta import Delta\n\ndef string_to_delta(text):\n temp_text = text[7:]\n delta_text = temp_text[:-2]\n delta_list = delta_text.split(',')\n\n if len(delta_list) == 1:\n ops = delta_list[0][2:8]\n \n if ops == 'insert':\n value = delta_list[0][12:-2]\n print(value)\n return Delta().insert(value)\n \n elif ops == 'delete':\n value = int(delta_list[0][11:-1])\n return Delta().delete(value)\n\n elif len(delta_list) == 2:\n for i in range(2):\n if i == 0:\n ops_1 = delta_list[0][2:8]\n value_1 = int(delta_list[0][11:12])\n\n elif i == 1:\n ops_2 = delta_list[1][3:9]\n\n if ops_2 == 'insert':\n value_2 = delta_list[1][13:-2]\n return Delta().retain(value_1).insert(value_2)\n \n elif ops_2 == 'delete':\n value_2 = int(delta_list[1][12:-1])\n return Delta().retain(value_1).delete(value_2)\n\n return Delta()"},{"alpha_fraction":0.5998507142066956,"alphanum_fraction":0.609929084777832,"avg_line_length":25.01941680908203,"blob_id":"4760c6b0c44c466269d9423d69dd5f5f1609d4cd","content_id":"5b64fe8021bfbce7ea0237509fbcd2fc85533a19","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2679,"license_type":"no_license","max_line_length":88,"num_lines":103,"path":"/convergence-test/client.py","repo_name":"grimmweeper/50.012-networks-OT","src_encoding":"UTF-8","text":"#import the socket module\nimport socket\nimport random\n\nimport sys\nsys.path.append('../')\nfrom delta import Delta\n\n#Create a socket instance\nsocketObject = socket.socket()\nhost = '127.0.0.1'\nport = 2006\n\nprint(\"Waiting for connection response\")\n#Using the socket connect to a server...in this case localhost\n\ntry:\n socketObject.connect((host, port))\n print(\"Connected to localhost\")\n\nexcept socket.error as e:\n print(str(e))\n\nres = socketObject.recv(1024)\nwaitingForServerToComeback = True\nunsentBuffer = []\nsecondSocket = socket.socket()\n\n\n# Receive the data\nwhile(True):\n if len(unsentBuffer) > 0: \n print(\"Re-sending lost messages to server\")\n for i in range(len(unsentBuffer)):\n messageBytes = str.encode(unsentBuffer[i])\n secondSocket.sendall(messageBytes)\n res = secondSocket.recv(1024)\n print(res)\n unsentBuffer = []\n\n # message = input(\"Input message to send to server: \")\n\n operation = ''\n retain = ''\n\n retain = input(\"Input position value to retain: \")\n operation = input(\"Input Operation (insert/delete): \")\n value = input(\"Input value for operation (insert: text | delete: position value): \")\n\n\n if retain == '':\n if operation == 'insert':\n client_delta = Delta().insert(value)\n \n elif operation == 'delete':\n client_delta = Delta().delete(int(value))\n \n else:\n if operation == 'insert':\n client_delta = Delta().retain(int(retain)).insert(value)\n \n elif operation == 'delete':\n client_delta = Delta().retain(int(retain)).delete(int(value)) \n\n\n print(\"[Client Operation]:\", client_delta)\n\n message = str(client_delta)\n\n try:\n if waitingForServerToComeback == True:\n messageBytes = str.encode(message)\n socketObject.sendall(messageBytes)\n else:\n messageBytes = str.encode(message)\n secondSocket.sendall(messageBytes)\n\n\n except ConnectionResetError as e: #server connection shut down\n unsentBuffer.append(message) #place the unsent message in the buffer\n \n while(waitingForServerToComeback):\n \n try:\n secondSocket.connect((host,port))\n print(\"Server is back\")\n waitingForServerToComeback = False\n \n\n except ConnectionRefusedError:\n print(str(e))\n\n \n if waitingForServerToComeback == True:\n data = socketObject.recv(1024)\n print(data)\n else:\n data = secondSocket.recv(1024)\n print(data)\n\n\nsocketObject.close()\nsecondSocket.close()"},{"alpha_fraction":0.5302325487136841,"alphanum_fraction":0.55898517370224,"avg_line_length":25,"blob_id":"c6e1596a1661eff06c62830e8830d28b55e99c58","content_id":"ee265e8c749ca9abcc2c765ae37849d324664c22","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2365,"license_type":"no_license","max_line_length":66,"num_lines":91,"path":"/cs-delta/server.py","repo_name":"grimmweeper/50.012-networks-OT","src_encoding":"UTF-8","text":"import socket\nimport os\nfrom _thread import *\n\nimport sys\nsys.path.append('../')\nfrom delta import Delta\n\nServerSideSocket = socket.socket()\nhost = '127.0.0.1'\nport = 2004\nThreadCount = 0\nclients= set()\n\ndelta_object = Delta().insert('ABCDE')\n\ntry:\n ServerSideSocket.bind((host, port))\nexcept socket.error as e:\n print(str(e))\n\nprint('Socket is listening..')\nServerSideSocket.listen(5)\n\ndef string_to_delta(text):\n temp_text = text[7:]\n delta_text = temp_text[:-2]\n delta_list = delta_text.split(',')\n\n if len(delta_list) == 1:\n ops = delta_list[0][2:8]\n \n if ops == 'insert':\n value = delta_list[0][12:-2]\n print(value)\n return Delta().insert(value)\n \n elif ops == 'delete':\n value = int(delta_list[0][11:-1])\n return Delta().delete(value)\n\n elif len(delta_list) == 2:\n for i in range(2):\n if i == 0:\n ops_1 = delta_list[0][2:8]\n value_1 = int(delta_list[0][11:12])\n\n elif i == 1:\n ops_2 = delta_list[1][3:9]\n\n if ops_2 == 'insert':\n value_2 = delta_list[1][13:-2]\n return Delta().retain(value_1).insert(value_2)\n \n elif ops_2 == 'delete':\n value_2 = int(delta_list[1][12:-1])\n return Delta().retain(value_1).delete(value_2)\n\n return Delta()\n\n\ndef multi_threaded_client(connection):\n global delta_object\n connection.send(str.encode('Server is working:'))\n while True:\n data = connection.recv(2048)\n client_message = data.decode('utf-8')\n print(client_message)\n\n client_delta = string_to_delta(client_message)\n delta_object = delta_object.compose(client_delta)\n\n print(client_delta)\n print(delta_object)\n\n response = 'Server message: ' + str(delta_object)\n if not data:\n break\n print(response)\n connection.sendall(str.encode(response))\n connection.close()\n\nwhile True:\n Client, address = ServerSideSocket.accept()\n clients.add(Client)\n print('Connected to: ' + address[0] + ':' + str(address[1]))\n start_new_thread(multi_threaded_client, (Client, ))\n ThreadCount += 1\n print('Thread Number: ' + str(ThreadCount))\n\nServerSideSocket.close()"},{"alpha_fraction":0.6200448870658875,"alphanum_fraction":0.6335078477859497,"avg_line_length":22.068965911865234,"blob_id":"ce88790b8d36205396d9821f9525051ae71b4f76","content_id":"b6aa588edec7316de73e05a1adb25dd6159f190d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1337,"license_type":"no_license","max_line_length":88,"num_lines":58,"path":"/cs-delta/client.py","repo_name":"grimmweeper/50.012-networks-OT","src_encoding":"UTF-8","text":"#import the socket module\nimport socket\n\nimport sys\nsys.path.append('../')\nfrom delta import Delta\n\n#Create a socket instance\nsocketObject = socket.socket()\nhost = '127.0.0.1'\nport = 2004\n\nprint(\"Waiting for connection response\")\n#Using the socket connect to a server...in this case localhost\n\ntry:\n socketObject.connect((host, port))\n print(\"Connected to localhost\")\n\nexcept socket.error as e:\n print(str(e))\n\nres = socketObject.recv(1024)\n\n# Receive the data\nwhile(True):\n\n operation = ''\n retain = ''\n\n retain = input(\"Input position value to retain: \")\n operation = input(\"Input Operation (insert/delete): \")\n value = input(\"Input value for operation (insert: text | delete: position value): \")\n\n\n if retain == '':\n if operation == 'insert':\n client_delta = Delta().insert(value)\n \n elif operation == 'delete':\n client_delta = Delta().delete(int(value))\n \n else:\n if operation == 'insert':\n client_delta = Delta().retain(int(retain)).insert(value)\n \n elif operation == 'delete':\n client_delta = Delta().retain(int(retain)).delete(int(value)) \n\n\n messageBytes = str.encode(str(client_delta))\n socketObject.sendall(messageBytes)\n\n data = socketObject.recv(1024)\n print(data)\n\n\nsocketObject.close()"},{"alpha_fraction":0.6352313160896301,"alphanum_fraction":0.6539145708084106,"avg_line_length":22.93617057800293,"blob_id":"351e38aa2621b468256dc172490920584985f745","content_id":"84b110f1739c636b6a14cc16d43c8ce1e5b8d20a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1124,"license_type":"no_license","max_line_length":64,"num_lines":47,"path":"/time-experiment/server.py","repo_name":"grimmweeper/50.012-networks-OT","src_encoding":"UTF-8","text":"import socket\nimport os\nfrom _thread import *\n\nimport counter\n\nServerSideSocket = socket.socket()\nhost = '127.0.0.1'\nport = 2004\nThreadCount = 0\nclients= set()\n\nvalue = 1\n\ntry:\n ServerSideSocket.bind((host, port))\nexcept socket.error as e:\n print(str(e))\n\nprint('Socket is listening..')\nServerSideSocket.listen(5)\n\ndef multi_threaded_client(connection):\n global value\n connection.send(str.encode('Server is working:'))\n while True:\n data = connection.recv(2048)\n client_message = data.decode('utf-8')\n print(client_message)\n value = counter.compute(value, client_message)\n print(value)\n response = 'Server message: ' + str(value)\n if not data:\n break\n print(response)\n connection.sendall(str.encode(response))\n # connection.close()\n\nwhile True:\n Client, address = ServerSideSocket.accept()\n clients.add(Client)\n print('Connected to: ' + address[0] + ':' + str(address[1]))\n start_new_thread(multi_threaded_client, (Client, ))\n ThreadCount += 1\n print('Thread Number: ' + str(ThreadCount))\n\nServerSideSocket.close()"}],"string":"[\n {\n \"alpha_fraction\": 0.5560632944107056,\n \"alphanum_fraction\": 0.5775043964385986,\n \"avg_line_length\": 28.03061294555664,\n \"blob_id\": \"beaf3d6bed0718696aa94de9b02feeb2f0fdfd83\",\n \"content_id\": \"7f9dc303f7a342da095a3d7152e351272202c91f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2845,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 98,\n \"path\": \"/convergence-test/multiclient.py\",\n \"repo_name\": \"grimmweeper/50.012-networks-OT\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# import the socket module\\nimport socket\\n\\n# Create a socket instance\\nsocketObject = socket.socket()\\nhost = '127.0.0.1'\\nport = 2004\\n\\nclientNumber = int(input(\\\"Input the number of clients to spawn: \\\"))\\n\\nfor i in range(clientNumber):\\n\\n socketObject = socket.socket()\\n host = '127.0.0.1'\\n port = 2004\\n print(\\\"Waiting for connection response\\\")\\n\\n try:\\n socketObject.connect((host, port))\\n print(\\\"Client {} connected to server.\\\".format(i+1))\\n\\n except socket.error as e:\\n print(str(e))\\n\\n res = socketObject.recv(1024)\\n # Writing something\\n if(i == 0):\\n message = \\\"Test message from Client {} \\\\n\\\".format(i+1)\\n f = open(\\\"sharedfile.txt\\\", \\\"w\\\")\\n f.write(message)\\n f.close()\\n messageBytes = str.encode(message)\\n socketObject.sendall(messageBytes)\\n data = socketObject.recv(1024)\\n print(data)\\n #Adding on to client 1\\n elif(i == 1):\\n message = \\\"Test message from other client {} \\\\n\\\".format(i+1)\\n f = open(\\\"sharedfile.txt\\\", \\\"a\\\")\\n f.write(message)\\n f.close()\\n messageBytes = str.encode(message)\\n socketObject.sendall(messageBytes)\\n data = socketObject.recv(1024)\\n print(data)\\n #Delete\\n elif(i == 2):\\n message = \\\"Client 3: I've deleted edits made by client 2 \\\\n\\\"\\n open_file = open(\\\"sharedfile.txt\\\", \\\"r\\\")\\n lines = open_file.readlines()\\n open_file.close()\\n del lines[1]\\n f = open(\\\"sharedfile.txt\\\", \\\"w+\\\")\\n for line in lines:\\n f.write(line)\\n f.write(message)\\n f.close()\\n messageBytes = str.encode(message)\\n socketObject.sendall(messageBytes)\\n data = socketObject.recv(1024)\\n print(data)\\n #Insert \\n elif(i == 3):\\n open_file = open(\\\"sharedfile.txt\\\", \\\"r\\\")\\n lines = open_file.readlines()\\n open_file.close()\\n message = \\\"Client 4 has inserted this message between the first two lines \\\\n\\\"\\n lines.insert(1,message)\\n f = open(\\\"sharedfile.txt\\\",\\\"w+\\\")\\n for line in lines:\\n f.write(line)\\n f.close()\\n messageBytes = str.encode(message)\\n socketObject.sendall(messageBytes)\\n data = socketObject.recv(1024)\\n print(data)\\n #Move\\n else: \\n open_file = open(\\\"sharedfile.txt\\\",\\\"r\\\")\\n lines = open_file.readlines()\\n open_file.close()\\n first_line = lines[0]\\n lines.pop(0)\\n lines.append(first_line)\\n f = open(\\\"sharedfile.txt\\\",\\\"w+\\\")\\n for line in lines:\\n f.write(line)\\n message = \\\"Client 5 has moved the edit made by client 1 \\\\n\\\"\\n f.write(message)\\n f.close()\\n messageBytes = str.encode(message)\\n socketObject.sendall(messageBytes)\\n data = socketObject.recv(1024)\\n print(data)\\n\\n\\n\\nsocketObject.close()\\n\"\n },\n {\n \"alpha_fraction\": 0.7173523902893066,\n \"alphanum_fraction\": 0.7450805306434631,\n \"avg_line_length\": 20.480770111083984,\n \"blob_id\": \"ebc9fb8f7b8b754092c2ecf77a980416e52966df\",\n \"content_id\": \"21a50f79323728ece5480fb1a3cf5d24473a562b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1118,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 52,\n \"path\": \"/README.md\",\n \"repo_name\": \"grimmweeper/50.012-networks-OT\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# 50.012 Networks - Group 2 Operational Transformation\\n\\n### Experiments involving client-server implementations with Quill-Delta Object\\n\\n### To start:\\n```git clone https://github.com/GrimmWeeper/50.012-networks-OT.git```\\n\\n### Time analysis experiment\\n\\n```\\n1) cd time-experiment\\n2) Open 2 terminals\\n3) python server.py\\n4) python multiclient.py\\n```\\n\\n### Concurrency experiment\\n\\n```\\n1) cd concurrency-test\\n2) Open 2 terminals\\n3) python server.py\\n4) python client.py\\n5) Type in inputs from both terminals\\n```\\n\\n### Convergence experiment\\n\\n```\\n1) cd convergence-test\\n2) Open >=2 terminals\\n3) python server.py\\n4) python client.py\\n5) Type in inputs from terminals\\n6) Abruptly close the server terminal\\n7) Type in input from client terminal\\n8) Reconnects back the server terminal\\n```\\n\\n### Authors\\n[GrimmWeeper](https://github.com/GrimmWeeper)\\n\\n[alicezhangjy](https://github.com/alicezhangjy)\\n\\n[xmliszt](https://github.com/xmliszt)\\n\\n[jesterlyn](https://github.com/jesterlyn)\\n\\n[cinnamon-rolling](https://github.com/cinnamon-rolling)\\n\\n### Credits\\n[forgeworks/quill-delta-python](https://github.com/forgeworks/quill-delta-python)\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6343545913696289,\n \"alphanum_fraction\": 0.643897533416748,\n \"avg_line_length\": 23,\n \"blob_id\": \"99a48dd135c29d1dd321980b8d182c6cf21eb94a\",\n \"content_id\": \"e2653794a52c1da08f7e0e6e015cdb5edfb9f8a8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1991,\n \"license_type\": \"no_license\",\n \"max_line_length\": 88,\n \"num_lines\": 83,\n \"path\": \"/concurrency-test/client.py\",\n \"repo_name\": \"grimmweeper/50.012-networks-OT\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#import the socket module\\nimport socket\\n\\nimport sys\\nsys.path.append('../')\\nfrom delta import Delta\\n\\nimport utils\\n\\n#Create a socket instance\\nsocketObject = socket.socket()\\nhost = '127.0.0.1'\\nport = 2005\\n\\nprint(\\\"Waiting for connection response\\\")\\n#Using the socket connect to a server...in this case localhost\\n\\ntry:\\n socketObject.connect((host, port))\\n print(\\\"Connected to localhost\\\")\\n\\nexcept socket.error as e:\\n print(str(e))\\n\\nres = socketObject.recv(1024)\\n\\ndelta_object = Delta().insert('ABCD')\\n\\n# Receive the data\\nwhile(True):\\n\\n operation = ''\\n retain = ''\\n\\n retain = input(\\\"Input position value to retain: \\\")\\n operation = input(\\\"Input Operation (insert/delete): \\\")\\n value = input(\\\"Input value for operation (insert: text | delete: position value): \\\")\\n\\n\\n if retain == '':\\n if operation == 'insert':\\n client_delta = Delta().insert(value)\\n \\n elif operation == 'delete':\\n client_delta = Delta().delete(int(value))\\n \\n else:\\n if operation == 'insert':\\n client_delta = Delta().retain(int(retain)).insert(value)\\n \\n elif operation == 'delete':\\n client_delta = Delta().retain(int(retain)).delete(int(value)) \\n\\n \\n\\n print(\\\"\\\\n\\\")\\n print(\\\"[Initial State]:\\\", delta_object)\\n print(\\\"[Client Operation]:\\\", client_delta)\\n\\n client_local = delta_object.compose(client_delta)\\n\\n print(\\\"[State after Client Operation]:\\\", client_local)\\n print(\\\"\\\\n\\\")\\n \\n messageBytes = str.encode(str(client_delta))\\n socketObject.sendall(messageBytes)\\n\\n data = socketObject.recv(1024).decode('utf-8')\\n\\n server_delta = utils.string_to_delta(data)\\n\\n print(\\\"[Server Operation]:\\\", server_delta)\\n\\n server_transform = client_delta.transform(server_delta)\\n print(\\\"[Transformed Server Operation]:\\\", server_transform)\\n print(\\\"\\\\n\\\")\\n\\n client_result = client_local.compose(server_transform)\\n print(\\\"[Eventual State in Client]:\\\", client_result)\\n\\n\\n\\nsocketObject.close()\"\n },\n {\n \"alpha_fraction\": 0.5996860265731812,\n \"alphanum_fraction\": 0.6169544458389282,\n \"avg_line_length\": 25.58333396911621,\n \"blob_id\": \"9a8d3be4b5433652c88f508afcc3a9368e7886c2\",\n \"content_id\": \"22a70827984d1dd57b1654e8eaeda8c9141ab7aa\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 637,\n \"license_type\": \"no_license\",\n \"max_line_length\": 67,\n \"num_lines\": 24,\n \"path\": \"/time-experiment/counter.py\",\n \"repo_name\": \"grimmweeper/50.012-networks-OT\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import random\\n\\ndef gen_operation():\\n value = str(random.randint(1,10))\\n operation = random.choice([\\\"plus\\\",\\\"minus\\\",\\\"multiply\\\",\\\"divide\\\"])\\n\\n return operation + ',' + value\\n\\ndef compute(current, operation):\\n operation_list = operation.split(',')\\n\\n if operation_list[0] == 'plus':\\n result = current + int(operation_list[1])\\n \\n elif operation_list[0] == 'minus':\\n result = current - int(operation_list[1])\\n\\n elif operation_list[0] == 'multiply':\\n result = current * int(operation_list[1])\\n\\n elif operation_list[0] == 'divide':\\n result = current / int(operation_list[1])\\n\\n return result\"\n },\n {\n \"alpha_fraction\": 0.6083385944366455,\n \"alphanum_fraction\": 0.6279216408729553,\n \"avg_line_length\": 21.614286422729492,\n \"blob_id\": \"6047a5bf7c831c80ab5dce0d81dc6eb26796cd83\",\n \"content_id\": \"85127d520291e5793e7727943c5d3709b626b562\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1583,\n \"license_type\": \"no_license\",\n \"max_line_length\": 77,\n \"num_lines\": 70,\n \"path\": \"/time-experiment/multiclient.py\",\n \"repo_name\": \"grimmweeper/50.012-networks-OT\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# import the socket module\\nimport socket\\n\\nimport counter\\nimport time\\nimport matplotlib.pyplot as plt\\nimport csv\\n\\ntime_list = []\\nidx_list = []\\n\\n\\n\\nfor i in range(30):\\n start = time.time()\\n time.sleep(0.2)\\n \\n for j in range(i+1):\\n\\n socketObject = socket.socket()\\n host = '127.0.0.1'\\n port = 2004\\n print(\\\"Waiting for connection response\\\")\\n\\n try:\\n socketObject.connect((host, port))\\n print(\\\"Client {} connected to server.\\\".format(i+1))\\n\\n except socket.error as e:\\n print(str(e))\\n\\n res = socketObject.recv(1024)\\n\\n message = counter.gen_operation()\\n messageBytes = str.encode(message)\\n socketObject.sendall(messageBytes)\\n data = socketObject.recv(1024)\\n print(data)\\n\\n end = time.time() - start\\n time_list.append(end)\\n idx_list.append(i+1)\\n\\nprint(time_list)\\n\\n\\n# Plot graph\\nfig = plt.figure()\\nax = fig.add_subplot(1, 1, 1)\\nax.plot(idx_list, time_list, 'go-', alpha=0.5)\\nax.set_title(\\n \\\"Time Taken to Reach Eventual Consistency (ms) against Number of Clients\\\"\\n)\\nax.set_xlabel(\\\"Number of clients\\\")\\nax.set_ylabel(\\\"Time Taken to reach eventual consistency (ms)\\\")\\nplt.savefig('client_server.png', format='png')\\n\\n\\n# Save values as csv file\\nwith open('client_server.csv', 'w', encoding='utf-8', newline='') as fh:\\n writer = csv.writer(fh)\\n writer.writerow([\\n \\\"Number of Clients\\\", \\\"Time Taken to Reach Eventual Consistency (ms)\\\"\\n ])\\n for _each in list(zip(idx_list, time_list)):\\n writer.writerow(list(_each))\\n\\n\\n\\nsocketObject.close()\\n\"\n },\n {\n \"alpha_fraction\": 0.5652173757553101,\n \"alphanum_fraction\": 0.6304348111152649,\n \"avg_line_length\": 14.666666984558105,\n \"blob_id\": \"1e5f312df52ac6b3b4d0d962f7cf850bc9acc024\",\n \"content_id\": \"c347aecb363a477938743ce487a68abe14c8bcfd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 46,\n \"license_type\": \"no_license\",\n \"max_line_length\": 23,\n \"num_lines\": 3,\n \"path\": \"/delta/__init__.py\",\n \"repo_name\": \"grimmweeper/50.012-networks-OT\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from .base import Delta\\n\\n__version__ = '1.0.1'\"\n },\n {\n \"alpha_fraction\": 0.5320637226104736,\n \"alphanum_fraction\": 0.557326078414917,\n \"avg_line_length\": 26.32978630065918,\n \"blob_id\": \"037864af0d65bb2e1c27de1a1563e2deca66b77e\",\n \"content_id\": \"f4e50a3605660714b8a59553a7fb73e28f42d7bd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2573,\n \"license_type\": \"no_license\",\n \"max_line_length\": 110,\n \"num_lines\": 94,\n \"path\": \"/single/single.py\",\n \"repo_name\": \"grimmweeper/50.012-networks-OT\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.path.append('../')\\nfrom delta import Delta\\n\\ndef string_to_delta(text):\\n temp_text = text[7:]\\n delta_text = temp_text[:-2]\\n delta_list = delta_text.split(',')\\n\\n if len(delta_list) == 1:\\n ops = delta_list[0][2:8]\\n \\n if ops == 'insert':\\n value = delta_list[0][12:-2]\\n print(value)\\n return Delta().insert(value)\\n \\n elif ops == 'delete':\\n value = int(delta_list[0][11:-1])\\n return Delta().delete(value)\\n\\n elif len(delta_list) == 2:\\n for i in range(2):\\n if i == 0:\\n ops_1 = delta_list[0][2:8]\\n value_1 = int(delta_list[0][11:12])\\n\\n elif i == 1:\\n ops_2 = delta_list[1][3:9]\\n\\n if ops_2 == 'insert':\\n value_2 = delta_list[1][13:-2]\\n return Delta().retain(value_1).insert(value_2)\\n \\n elif ops_2 == 'delete':\\n value_2 = int(delta_list[1][12:-1])\\n return Delta().retain(value_1).delete(value_2)\\n\\n return Delta()\\n\\n\\n# Test on string_to_delta\\ndef string_to_delta_test():\\n a = Delta().insert('ABCDE')\\n b = Delta().retain(3).delete(4)\\n c = Delta().delete(4)\\n\\n print(a)\\n print(string_to_delta(str(a)))\\n print(b)\\n print(string_to_delta(str(b)))\\n print(c)\\n print(string_to_delta(str(c)))\\n\\n# Operation test: Sequential insert/delete\\ndef seq_OT():\\n a = Delta().insert('ABCDE')\\n print('a')\\n print(a)\\n b = Delta().retain(3).insert('Z')\\n c = Delta().delete(4)\\n\\n result_1 = a.compose(b)\\n result_2 = result_1.compose(c)\\n print(\\\"result 1:\\\")\\n print(result_1)\\n print(\\\"result 2:\\\")\\n print(result_2)\\n\\n\\n# Transformation test: Concurrency\\ndef concurrency_OT():\\n\\n initial = Delta().insert('ABCD')\\n client_operation = Delta().retain(3).insert(\\\"X\\\") # ABCXD -> ACXD\\n server_operation = Delta().retain(1).delete(1) # ACD -> ACDX, ACXD\\n\\n # Client\\n client_local = initial.compose(client_operation)\\n client_transform = client_operation.transform(server_operation)\\n client_result = client_local.compose(client_transform)\\n\\n # Server\\n server_local = initial.compose(server_operation)\\n server_transform = server_operation.transform(client_operation) # Change from retaining 3rd pos to 2nd pos\\n server_result = server_local.compose(server_transform)\\n\\n print(client_result) #ACXD\\n print(server_result) #ACXD\\n\\nif __name__ == \\\"__main__\\\":\\n string_to_delta_test()\\n # seq_OT()\\n # concurrency_OT()\\n \"\n },\n {\n \"alpha_fraction\": 0.595162570476532,\n \"alphanum_fraction\": 0.6030927896499634,\n \"avg_line_length\": 25.840425491333008,\n \"blob_id\": \"0ea5b64130075db812ca574636adf2c542640c1b\",\n \"content_id\": \"66899f0d45cc572b754bc49aa81ae5c1804b72f3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2522,\n \"license_type\": \"no_license\",\n \"max_line_length\": 92,\n \"num_lines\": 94,\n \"path\": \"/concurrency-test/server.py\",\n \"repo_name\": \"grimmweeper/50.012-networks-OT\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import socket\\nimport os\\nfrom _thread import *\\n\\nimport sys\\nsys.path.append('../')\\nfrom delta import Delta\\n\\nimport utils\\n\\nServerSideSocket = socket.socket()\\nhost = '127.0.0.1'\\nport = 2005\\nThreadCount = 0\\nclients= set()\\n\\ndelta_object = Delta().insert('ABCD')\\n\\ntry:\\n ServerSideSocket.bind((host, port))\\nexcept socket.error as e:\\n print(str(e))\\n\\nprint('Socket is listening..')\\nServerSideSocket.listen(5)\\n\\n\\ndef multi_threaded_client(connection):\\n global delta_object\\n connection.send(str.encode('Server is working:'))\\n while True:\\n operation = ''\\n retain = ''\\n\\n retain = input(\\\"Input position value to retain: \\\")\\n operation = input(\\\"Input Operation (insert/delete): \\\")\\n value = input(\\\"Input value for operation (insert: text | delete: position value): \\\")\\n\\n\\n if retain == '':\\n if operation == 'insert':\\n server_delta = Delta().insert(value)\\n \\n elif operation == 'delete':\\n server_delta = Delta().delete(int(value))\\n \\n else:\\n if operation == 'insert':\\n server_delta = Delta().retain(int(retain)).insert(value)\\n \\n elif operation == 'delete':\\n server_delta = Delta().retain(int(retain)).delete(int(value)) \\n\\n\\n print(\\\"\\\\n\\\")\\n print(\\\"[Initial State]:\\\", delta_object)\\n print(\\\"[Server Operation]:\\\", server_delta)\\n\\n server_local = delta_object.compose(server_delta)\\n\\n print(\\\"[State after Server Operation]:\\\", server_local)\\n print(\\\"\\\\n\\\")\\n \\n data = connection.recv(2048)\\n client_message = data.decode('utf-8')\\n print(client_message)\\n\\n client_delta = utils.string_to_delta(client_message)\\n\\n print(\\\"[Client Operation]:\\\", client_delta)\\n\\n client_transform = server_delta.transform(client_delta)\\n print(\\\"[Transformed Client Operation]:\\\", client_transform)\\n\\n server_result = server_local.compose(client_transform)\\n print(\\\"\\\\n\\\")\\n print(\\\"[Eventual State in Server]:\\\", server_result)\\n\\n response = str(server_delta)\\n if not data:\\n break\\n connection.sendall(str.encode(response))\\n connection.close()\\n\\nwhile True:\\n\\n Client, address = ServerSideSocket.accept()\\n clients.add(Client)\\n print('Connected to: ' + address[0] + ':' + str(address[1]))\\n start_new_thread(multi_threaded_client, (Client, ))\\n ThreadCount += 1\\n print('Thread Number: ' + str(ThreadCount))\\n\\nServerSideSocket.close()\"\n },\n {\n \"alpha_fraction\": 0.40913161635398865,\n \"alphanum_fraction\": 0.452103853225708,\n \"avg_line_length\": 27.66666603088379,\n \"blob_id\": \"052de38d8ccc721b4c31a44146f226a4946e4a60\",\n \"content_id\": \"b55b587c065b5caf14ac2687a856d3b1c4bfbc1c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1117,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 39,\n \"path\": \"/convergence-test/utils.py\",\n \"repo_name\": \"grimmweeper/50.012-networks-OT\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.path.append('../')\\nfrom delta import Delta\\n\\ndef string_to_delta(text):\\n temp_text = text[7:]\\n delta_text = temp_text[:-2]\\n delta_list = delta_text.split(',')\\n\\n if len(delta_list) == 1:\\n ops = delta_list[0][2:8]\\n \\n if ops == 'insert':\\n value = delta_list[0][12:-2]\\n print(value)\\n return Delta().insert(value)\\n \\n elif ops == 'delete':\\n value = int(delta_list[0][11:-1])\\n return Delta().delete(value)\\n\\n elif len(delta_list) == 2:\\n for i in range(2):\\n if i == 0:\\n ops_1 = delta_list[0][2:8]\\n value_1 = int(delta_list[0][11:12])\\n\\n elif i == 1:\\n ops_2 = delta_list[1][3:9]\\n\\n if ops_2 == 'insert':\\n value_2 = delta_list[1][13:-2]\\n return Delta().retain(value_1).insert(value_2)\\n \\n elif ops_2 == 'delete':\\n value_2 = int(delta_list[1][12:-1])\\n return Delta().retain(value_1).delete(value_2)\\n\\n return Delta()\"\n },\n {\n \"alpha_fraction\": 0.5998507142066956,\n \"alphanum_fraction\": 0.609929084777832,\n \"avg_line_length\": 25.01941680908203,\n \"blob_id\": \"4760c6b0c44c466269d9423d69dd5f5f1609d4cd\",\n \"content_id\": \"5b64fe8021bfbce7ea0237509fbcd2fc85533a19\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2679,\n \"license_type\": \"no_license\",\n \"max_line_length\": 88,\n \"num_lines\": 103,\n \"path\": \"/convergence-test/client.py\",\n \"repo_name\": \"grimmweeper/50.012-networks-OT\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#import the socket module\\nimport socket\\nimport random\\n\\nimport sys\\nsys.path.append('../')\\nfrom delta import Delta\\n\\n#Create a socket instance\\nsocketObject = socket.socket()\\nhost = '127.0.0.1'\\nport = 2006\\n\\nprint(\\\"Waiting for connection response\\\")\\n#Using the socket connect to a server...in this case localhost\\n\\ntry:\\n socketObject.connect((host, port))\\n print(\\\"Connected to localhost\\\")\\n\\nexcept socket.error as e:\\n print(str(e))\\n\\nres = socketObject.recv(1024)\\nwaitingForServerToComeback = True\\nunsentBuffer = []\\nsecondSocket = socket.socket()\\n\\n\\n# Receive the data\\nwhile(True):\\n if len(unsentBuffer) > 0: \\n print(\\\"Re-sending lost messages to server\\\")\\n for i in range(len(unsentBuffer)):\\n messageBytes = str.encode(unsentBuffer[i])\\n secondSocket.sendall(messageBytes)\\n res = secondSocket.recv(1024)\\n print(res)\\n unsentBuffer = []\\n\\n # message = input(\\\"Input message to send to server: \\\")\\n\\n operation = ''\\n retain = ''\\n\\n retain = input(\\\"Input position value to retain: \\\")\\n operation = input(\\\"Input Operation (insert/delete): \\\")\\n value = input(\\\"Input value for operation (insert: text | delete: position value): \\\")\\n\\n\\n if retain == '':\\n if operation == 'insert':\\n client_delta = Delta().insert(value)\\n \\n elif operation == 'delete':\\n client_delta = Delta().delete(int(value))\\n \\n else:\\n if operation == 'insert':\\n client_delta = Delta().retain(int(retain)).insert(value)\\n \\n elif operation == 'delete':\\n client_delta = Delta().retain(int(retain)).delete(int(value)) \\n\\n\\n print(\\\"[Client Operation]:\\\", client_delta)\\n\\n message = str(client_delta)\\n\\n try:\\n if waitingForServerToComeback == True:\\n messageBytes = str.encode(message)\\n socketObject.sendall(messageBytes)\\n else:\\n messageBytes = str.encode(message)\\n secondSocket.sendall(messageBytes)\\n\\n\\n except ConnectionResetError as e: #server connection shut down\\n unsentBuffer.append(message) #place the unsent message in the buffer\\n \\n while(waitingForServerToComeback):\\n \\n try:\\n secondSocket.connect((host,port))\\n print(\\\"Server is back\\\")\\n waitingForServerToComeback = False\\n \\n\\n except ConnectionRefusedError:\\n print(str(e))\\n\\n \\n if waitingForServerToComeback == True:\\n data = socketObject.recv(1024)\\n print(data)\\n else:\\n data = secondSocket.recv(1024)\\n print(data)\\n\\n\\nsocketObject.close()\\nsecondSocket.close()\"\n },\n {\n \"alpha_fraction\": 0.5302325487136841,\n \"alphanum_fraction\": 0.55898517370224,\n \"avg_line_length\": 25,\n \"blob_id\": \"c6e1596a1661eff06c62830e8830d28b55e99c58\",\n \"content_id\": \"ee265e8c749ca9abcc2c765ae37849d324664c22\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2365,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 91,\n \"path\": \"/cs-delta/server.py\",\n \"repo_name\": \"grimmweeper/50.012-networks-OT\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import socket\\nimport os\\nfrom _thread import *\\n\\nimport sys\\nsys.path.append('../')\\nfrom delta import Delta\\n\\nServerSideSocket = socket.socket()\\nhost = '127.0.0.1'\\nport = 2004\\nThreadCount = 0\\nclients= set()\\n\\ndelta_object = Delta().insert('ABCDE')\\n\\ntry:\\n ServerSideSocket.bind((host, port))\\nexcept socket.error as e:\\n print(str(e))\\n\\nprint('Socket is listening..')\\nServerSideSocket.listen(5)\\n\\ndef string_to_delta(text):\\n temp_text = text[7:]\\n delta_text = temp_text[:-2]\\n delta_list = delta_text.split(',')\\n\\n if len(delta_list) == 1:\\n ops = delta_list[0][2:8]\\n \\n if ops == 'insert':\\n value = delta_list[0][12:-2]\\n print(value)\\n return Delta().insert(value)\\n \\n elif ops == 'delete':\\n value = int(delta_list[0][11:-1])\\n return Delta().delete(value)\\n\\n elif len(delta_list) == 2:\\n for i in range(2):\\n if i == 0:\\n ops_1 = delta_list[0][2:8]\\n value_1 = int(delta_list[0][11:12])\\n\\n elif i == 1:\\n ops_2 = delta_list[1][3:9]\\n\\n if ops_2 == 'insert':\\n value_2 = delta_list[1][13:-2]\\n return Delta().retain(value_1).insert(value_2)\\n \\n elif ops_2 == 'delete':\\n value_2 = int(delta_list[1][12:-1])\\n return Delta().retain(value_1).delete(value_2)\\n\\n return Delta()\\n\\n\\ndef multi_threaded_client(connection):\\n global delta_object\\n connection.send(str.encode('Server is working:'))\\n while True:\\n data = connection.recv(2048)\\n client_message = data.decode('utf-8')\\n print(client_message)\\n\\n client_delta = string_to_delta(client_message)\\n delta_object = delta_object.compose(client_delta)\\n\\n print(client_delta)\\n print(delta_object)\\n\\n response = 'Server message: ' + str(delta_object)\\n if not data:\\n break\\n print(response)\\n connection.sendall(str.encode(response))\\n connection.close()\\n\\nwhile True:\\n Client, address = ServerSideSocket.accept()\\n clients.add(Client)\\n print('Connected to: ' + address[0] + ':' + str(address[1]))\\n start_new_thread(multi_threaded_client, (Client, ))\\n ThreadCount += 1\\n print('Thread Number: ' + str(ThreadCount))\\n\\nServerSideSocket.close()\"\n },\n {\n \"alpha_fraction\": 0.6200448870658875,\n \"alphanum_fraction\": 0.6335078477859497,\n \"avg_line_length\": 22.068965911865234,\n \"blob_id\": \"ce88790b8d36205396d9821f9525051ae71b4f76\",\n \"content_id\": \"b6aa588edec7316de73e05a1adb25dd6159f190d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1337,\n \"license_type\": \"no_license\",\n \"max_line_length\": 88,\n \"num_lines\": 58,\n \"path\": \"/cs-delta/client.py\",\n \"repo_name\": \"grimmweeper/50.012-networks-OT\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#import the socket module\\nimport socket\\n\\nimport sys\\nsys.path.append('../')\\nfrom delta import Delta\\n\\n#Create a socket instance\\nsocketObject = socket.socket()\\nhost = '127.0.0.1'\\nport = 2004\\n\\nprint(\\\"Waiting for connection response\\\")\\n#Using the socket connect to a server...in this case localhost\\n\\ntry:\\n socketObject.connect((host, port))\\n print(\\\"Connected to localhost\\\")\\n\\nexcept socket.error as e:\\n print(str(e))\\n\\nres = socketObject.recv(1024)\\n\\n# Receive the data\\nwhile(True):\\n\\n operation = ''\\n retain = ''\\n\\n retain = input(\\\"Input position value to retain: \\\")\\n operation = input(\\\"Input Operation (insert/delete): \\\")\\n value = input(\\\"Input value for operation (insert: text | delete: position value): \\\")\\n\\n\\n if retain == '':\\n if operation == 'insert':\\n client_delta = Delta().insert(value)\\n \\n elif operation == 'delete':\\n client_delta = Delta().delete(int(value))\\n \\n else:\\n if operation == 'insert':\\n client_delta = Delta().retain(int(retain)).insert(value)\\n \\n elif operation == 'delete':\\n client_delta = Delta().retain(int(retain)).delete(int(value)) \\n\\n\\n messageBytes = str.encode(str(client_delta))\\n socketObject.sendall(messageBytes)\\n\\n data = socketObject.recv(1024)\\n print(data)\\n\\n\\nsocketObject.close()\"\n },\n {\n \"alpha_fraction\": 0.6352313160896301,\n \"alphanum_fraction\": 0.6539145708084106,\n \"avg_line_length\": 22.93617057800293,\n \"blob_id\": \"351e38aa2621b468256dc172490920584985f745\",\n \"content_id\": \"84b110f1739c636b6a14cc16d43c8ce1e5b8d20a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1124,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 47,\n \"path\": \"/time-experiment/server.py\",\n \"repo_name\": \"grimmweeper/50.012-networks-OT\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import socket\\nimport os\\nfrom _thread import *\\n\\nimport counter\\n\\nServerSideSocket = socket.socket()\\nhost = '127.0.0.1'\\nport = 2004\\nThreadCount = 0\\nclients= set()\\n\\nvalue = 1\\n\\ntry:\\n ServerSideSocket.bind((host, port))\\nexcept socket.error as e:\\n print(str(e))\\n\\nprint('Socket is listening..')\\nServerSideSocket.listen(5)\\n\\ndef multi_threaded_client(connection):\\n global value\\n connection.send(str.encode('Server is working:'))\\n while True:\\n data = connection.recv(2048)\\n client_message = data.decode('utf-8')\\n print(client_message)\\n value = counter.compute(value, client_message)\\n print(value)\\n response = 'Server message: ' + str(value)\\n if not data:\\n break\\n print(response)\\n connection.sendall(str.encode(response))\\n # connection.close()\\n\\nwhile True:\\n Client, address = ServerSideSocket.accept()\\n clients.add(Client)\\n print('Connected to: ' + address[0] + ':' + str(address[1]))\\n start_new_thread(multi_threaded_client, (Client, ))\\n ThreadCount += 1\\n print('Thread Number: ' + str(ThreadCount))\\n\\nServerSideSocket.close()\"\n }\n]"},"num_files":{"kind":"number","value":13,"string":"13"}}},{"rowIdx":1173,"cells":{"repo_name":{"kind":"string","value":"andrely/twitter-sentiment"},"repo_url":{"kind":"string","value":"https://github.com/andrely/twitter-sentiment"},"snapshot_id":{"kind":"string","value":"55f3ab7d0f4a28b5c0b6e5d277565f78d3dd580f"},"revision_id":{"kind":"string","value":"dc322fa9e0d0122ced2593093041cf953c9ef528"},"directory_id":{"kind":"string","value":"19aa225a4740a1550a73189d9ecf18fe2df17ca3"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"string","value":"2021-01-18T12:34:49.912355"},"revision_date":{"kind":"string","value":"2015-01-22T22:11:21"},"committer_date":{"kind":"string","value":"2015-01-22T22:11:21"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.49056604504585266,"alphanum_fraction":0.6037735939025879,"avg_line_length":9.399999618530273,"blob_id":"62342cbf29a874cc330cf91c8f4b412029bb8ecd","content_id":"dea9531f8bae775a72727010b9f26668e5855afc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":53,"license_type":"no_license","max_line_length":24,"num_lines":5,"path":"/models/__init__.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"'''\nCreated on 28. sep. 2014\n\n@author: JohnArne\n'''\n\n"},{"alpha_fraction":0.4879491925239563,"alphanum_fraction":0.5752250552177429,"avg_line_length":45.900718688964844,"blob_id":"69bf16514460bf17014ba21563ef768ba20c8440","content_id":"6b771c5670b92b9ae39c8bb76761fe0591f72fcc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":39209,"license_type":"no_license","max_line_length":186,"num_lines":836,"path":"/plotting.py","repo_name":"andrely/twitter-sentiment","src_encoding":"UTF-8","text":"'''\nHandles plotting of different visualizations of data.\n@author: JohnArne\n'''\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches\nimport random\nimport numpy as np\nimport utils\nimport random\nimport pickle\nfrom Tkconstants import OFF\n\ndef plot_temporal_sentiment(data, filename=\"temporal\"):\n \"\"\"\n Plots the temporal sentiment using the given data.\n \"\"\"\n tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120), \n (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150), \n (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148), \n (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199), \n (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)] \n \n # Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts. \n for i in range(len(tableau20)):\n r, g, b = tableau20[i]\n tableau20[i] = (r / 255., g / 255., b / 255.)\n \n # You typically want your plot to be ~1.33x wider than tall. This plot is a rare \n # exception because of the number of lines being plotted on it. \n # Common sizes: (10, 7.5) and (12, 9) \n f = plt.figure(figsize=(8, 6)) \n \n # Remove the plot frame lines. They are unnecessary chartjunk. \n ax = plt.subplot(111) \n ax.spines[\"top\"].set_visible(False) \n ax.spines[\"bottom\"].set_visible(False) \n ax.spines[\"right\"].set_visible(False) \n ax.spines[\"left\"].set_visible(False) \n \n # Ensure that the axis ticks only show up on the bottom and left of the plot. \n # Ticks on the right and top of the plot are generally unnecessary chartjunk. \n ax.get_xaxis().tick_bottom() \n ax.get_yaxis().tick_left() \n \n # Limit the range of the plot to only where the data is. \n # Avoid unnecessary whitespace. \n plt.ylim(0, 1) \n plt.xlim(0, 101) \n \n # Make sure your axis ticks are large enough to be easily read. \n # You don't want your viewers squinting to read your plot.\n# y_ticks = [] \n# plt.yticks(range(0, 1, 10), [str(x) for x in range(0, 91, 10)], fontsize=14) \n plt.xticks(fontsize=10) \n plt.yticks(fontsize=10)\n # Provide tick lines across the plot to help your viewers trace along \n # the axis ticks. Make sure that the lines are light and small so they \n # don't obscure the primary data lines. \n for y in [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]: \n plt.plot(range(1,105), [y] * len(range(1,105)), \"--\", lw=0.5, color=\"black\", alpha=0.3) \n \n # Remove the tick marks; they are unnecessary with the tick lines we just plotted. \n plt.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\", \n labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\") \n \n # Now that the plot is prepared, it's time to actually plot the data! \n # Note that I plotted the labels in order of the highest % in the final year. \n labels = data.keys() \n y_poses = [] \n offsets = [0.02, 0.04,0.06,0.08,0.1,0.12, 0.14,0.16,0.18,0.2]\n for rank, column in enumerate(labels): \n # Plot each line separately with its own color, using the Tableau 20 \n # color set in order. \n plt.plot(data[column][0], data[column][1], lw=1.0, color=tableau20[rank]) \n \n # Add a text label to the right end of every line. Most of the code below \n # is adding specific offsets y position because some labels overlapped. \n y_pos = data[column][1][-1]\n# new_pos = None\n# offset_counter = 0\n for poses in y_poses:\n if y_pos < poses+0.01 and y_pos>poses:\n y_pos = y_pos+0.05\n# offset_counter += 1\n break\n if y_pos > poses-0.01 and y_pos| \\n | id | \\ndate | \\ndatetime_logged | \\ntest_number | \\nkeyword | \\ncensored | \\nno_results | \\nreset | \\nis_canonical | \\nresult | \\nsource | \\norig_keyword | \\nnum_results | \\nnotes | \\n
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \\n0 | \\n2017-09-25 | \\n2017-09-25 10:13:37.816720 | \\n1 | \\nhello | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n80454701.0 | \\nNone | \\n
| 1 | \\n1 | \\n2017-09-25 | \\n2017-09-25 10:13:54.356722 | \\n1 | \\nlxb | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 2 | \\n2 | \\n2017-09-25 | \\n2017-09-25 10:14:11.489530 | \\n1 | \\n习胞子 | \\n0 | \\n0 | \\n0 | \\n0 | \\nno_results | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 3 | \\n3 | \\n2017-09-25 | \\n2017-09-25 10:14:29.667395 | \\n1 | \\n刘晓波 | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 4 | \\n4 | \\n2017-09-25 | \\n2017-09-25 10:14:49.107078 | \\n1 | \\npokemon | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n5705260.0 | \\npop culture | \\n
| 5 | \\n5 | \\n2017-09-25 | \\n2017-09-25 10:15:09.762484 | \\n1 | \\njay chou | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n881.0 | \\npop culture | \\n
| 6 | \\n6 | \\n2017-09-25 | \\n2017-09-25 10:15:28.100418 | \\n1 | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n63401495.0 | \\nsocial media | \\n|
| 7 | \\n7 | \\n2017-09-25 | \\n2017-09-25 10:15:46.214464 | \\n2 | \\nhello | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe | \\nNone | \\n80454634.0 | \\nNone | \\n
| 8 | \\n8 | \\n2017-09-25 | \\n2017-09-25 10:16:03.274804 | \\n2 | \\nlxb | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 9 | \\n9 | \\n2017-09-25 | \\n2017-09-25 10:16:19.035805 | \\n2 | \\n习胞子 | \\n0 | \\n0 | \\n0 | \\n0 | \\nno_results | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 10 | \\n10 | \\n2017-09-25 | \\n2017-09-25 10:16:36.021837 | \\n2 | \\n刘晓波 | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nmy dataframe | \\nNone | \\nNaN | \\nNone | \\n
| 11 | \\n11 | \\n2017-09-25 | \\n2017-09-25 10:16:53.766351 | \\n1 | \\nzhongnanhai | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe2 | \\nNone | \\n109.0 | \\nlocation | \\n
| 12 | \\n12 | \\n2017-09-25 | \\n2017-09-25 10:17:14.124440 | \\n1 | \\ncats | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nmy dataframe2 | \\nNone | \\n648313.0 | \\npop culture | \\n
| 13 | \\n13 | \\n2017-09-25 | \\n2017-09-25 10:17:36.205255 | \\n1 | \\ncats | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nlist | \\nNone | \\n648313.0 | \\nNone | \\n
| 14 | \\n14 | \\n2017-09-25 | \\n2017-09-25 10:17:54.330039 | \\n1 | \\nyes | \\n0 | \\n0 | \\n0 | \\n0 | \\nhas_results | \\nlist | \\nNone | \\n28413048.0 | \\nNone | \\n
| 15 | \\n15 | \\n2017-09-25 | \\n2017-09-25 10:19:47.007930 | \\n1 | \\n自由亚洲电台 | \\n0 | \\n0 | \\n0 | \\n0 | \\nreset | \\nlist | \\nNone | \\nNaN | \\nNone | \\n
| 16 | \\n16 | \\n2017-09-25 | \\n2017-09-25 10:20:03.491231 | \\n1 | \\n刘晓波 | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nlist | \\nNone | \\nNaN | \\nNone | \\n
| 17 | \\n17 | \\n2017-09-25 | \\n2017-09-25 10:20:18.747414 | \\n1 | \\ndhfjkdashfjkasdsf87 | \\n0 | \\n0 | \\n0 | \\n0 | \\nno_results | \\nlist | \\nNone | \\nNaN | \\nNone | \\n
| 18 | \\n18 | \\n2017-11-15 | \\n2017-11-15 12:38:32.931313 | \\n1 | \\n江蛤 | \\n0 | \\n0 | \\n0 | \\n1 | \\ncensored | \\nlist | \\n江蛤 | \\nNaN | \\nNone | \\n
| 19 | \\n19 | \\n2017-11-15 | \\n2017-11-15 12:38:32.963135 | \\n1 | \\n江蛤 | \\n0 | \\n0 | \\n0 | \\n0 | \\ncensored | \\nlist | \\nNone | \\nNaN | \\nNone | \\n
| 20 | \\n20 | \\n2017-11-15 | \\n2017-11-15 12:40:21.294841 | \\n1 | \\n江蛤 | \\n0 | \\n0 | \\n0 | \\n1 | \\ncensored | \\nlist | \\n江泽民江蛤蟆 | \\nNaN | \\nNone | \\n
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YouTopia
\n\nGiving kids the ideal YouTube experience\n\nhttp://youtopia.site/\n"},{"alpha_fraction":0.6403326392173767,"alphanum_fraction":0.6455301642417908,"avg_line_length":28.121212005615234,"blob_id":"ec955a7ffaab5dcbf539f167f49740239a4dc6d6","content_id":"158c82cdd603398b2f377c2fd480c0ca6faa7c71","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":962,"license_type":"no_license","max_line_length":74,"num_lines":33,"path":"/ytapi_extractor.py","repo_name":"nityamd/Insight","src_encoding":"UTF-8","text":"\nimport pandas as pd\nimport numpy as np\nimport requests\nimport os\nfrom apiclient.discovery import build\nfrom apiclient.errors import HttpError\n\nf = open('/Users/nitya/youtopian/youtube', 'r')\nDEVELOPER_KEY = f.read().strip()\n\nYOUTUBE_API_SERVICE_NAME = \"youtube\"\nYOUTUBE_API_VERSION = \"v3\"\nyoutube = build(YOUTUBE_API_SERVICE_NAME, YOUTUBE_API_VERSION,\n developerKey=DEVELOPER_KEY)\n\n\ndef video_query(earl):\n video_id = earl.split('=')[-1]\n return video_id\n\ndef videoId_data(video_id):\n '''\n Use the API to get data\n for a single video\n by videoId\n '''\n video_response = youtube.videos().list(id=video_id,\n part='snippet').execute()\n title = video_response.get('items')[0]['snippet']['title']\n description = video_response.get('items')[0]['snippet']['description']\n ydict = {'title':title, 'description':description}\n df = pd.DataFrame(ydict, index = [0])\n return df\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6189896464347839,\n \"alphanum_fraction\": 0.6354230046272278,\n \"avg_line_length\": 40.07500076293945,\n \"blob_id\": \"9d060bc9b5436648c166160e752fca08acdc90d7\",\n \"content_id\": \"713765a6ba43ef4233524c21c8a355832f1cef15\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1643,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 40,\n \"path\": \"/scripts/classifier.py\",\n \"repo_name\": \"nityamd/Insight\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nimport pandas as pd\\nimport itertools\\nimport gensim\\nimport keras\\nimport nltk\\nimport re\\nimport codecs\\nimport os\\n\\nfrom nltk.stem.snowball import SnowballStemmer\\nfrom nltk.tokenize import RegexpTokenizer\\nfrom nltk.corpus import stopwords\\nfrom nltk.tokenize import word_tokenize\\n\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\\nfrom sklearn.decomposition import PCA, TruncatedSVD\\nfrom sklearn.metrics import accuracy_score, f1_score, precision_score\\nfrom sklearn.metrics import confusion_matrix, recall_score, classification_report\\nfrom sklearn.linear_model import LogisticRegression\\n\\ndef ttsplit(thing):\\n list_corpus = df[str(thing)].tolist()\\n list_labels = df['class_label'].tolist()\\n indices = np.arange(len(list_labels))\\n x_train, x_test, y_train, y_test, i1, i2 = train_test_split(list_corpus,\\n list_labels,\\n indices,\\n test_size=0.2,\\n random_state=40)\\n return x_train, x_test, y_train, y_test, i1, i2\\n\\nx1_ti, x2_ti, y1_ti, y2_ti, i1, i2 = ttsplit('title')\\nx1_des, x2_des, y1_des, y2_des, i1, i2 = ttsplit('description')\\nclf_tfidf = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg',\\n multi_class='multinomial', n_jobs=-1, random_state=40)\\n\\nclf_tfidf.fit(X_train_tfidf, y_train)\\ny_predicted_tfidf = clf_tfidf.predict(X_test_tfidf)\\n\"\n },\n {\n \"alpha_fraction\": 0.5963909029960632,\n \"alphanum_fraction\": 0.6021082997322083,\n \"avg_line_length\": 32.9151496887207,\n \"blob_id\": \"e7d8de6cf5bf751d182aa80dddb7455bf5d6611e\",\n \"content_id\": \"7126c1fb68708f1804a49d7829b5f96183757933\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5597,\n \"license_type\": \"no_license\",\n \"max_line_length\": 89,\n \"num_lines\": 165,\n \"path\": \"/scripts/peppa.py\",\n \"repo_name\": \"nityamd/Insight\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nimport pandas as pd\\nimport numpy as np\\nimport requests\\nimport os\\n\\n\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\\nfrom apiclient.discovery import build\\nfrom apiclient.errors import HttpError\\n#from google.auth.tools import argparser\\nfrom youtube_transcript_api import YouTubeTranscriptApi\\n\\nf = open('/Users/nitya/youtopian/youtube', 'r')\\nDEVELOPER_KEY = f.read().strip()\\nYOUTUBE_API_SERVICE_NAME = \\\"youtube\\\"\\nYOUTUBE_API_VERSION = \\\"v3\\\"\\nyoutube = build(YOUTUBE_API_SERVICE_NAME, YOUTUBE_API_VERSION,developerKey=DEVELOPER_KEY)\\n\\n\\ndef get_video_id(q, max_results,token, order=\\\"relevance\\\",\\n location=None, location_radius=None):\\n\\n search_response = youtube.search().list(\\n q=q, type=\\\"video\\\", pageToken=token,part=\\\"id,snippet\\\",\\n maxResults=max_results, location=location,\\n locationRadius=location_radius, safeSearch = 'none').execute()\\n videoId = []\\n title = []\\n description = []\\n tok = search_response['nextPageToken']\\n\\n for search_result in search_response.get(\\\"items\\\", []):\\n if search_result[\\\"id\\\"][\\\"kind\\\"] == \\\"youtube#video\\\":\\n title.append(search_result['snippet']['title'])\\n videoId.append(search_result['id']['videoId'])\\n response = youtube.videos().list(part='statistics, snippet',\\n id=search_result['id']['videoId']\\n ).execute()\\n description.append(response['items'][0]['snippet']['description'])\\n\\n ydict = {'title':title,'videoId':videoId,\\n 'description':description}\\n return ydict, tok\\n\\ndef keyword_search(keyword, results_enter):\\n\\n tests, token0 = get_video_id(keyword, 50,order = \\\"relevance\\\",\\n token= None)\\n newdf = pd.DataFrame(tests)\\n #Total number of results I need..:\\n results = results_enter-50\\n\\n while results>50:\\n tests, token0 = get_video_id(keyword, 50,order = \\\"relevance\\\",\\n token= str(token0))\\n df = pd.DataFrame(tests)\\n newdf = pd.concat([newdf,df]).reset_index(drop=True)\\n results = results-50\\n\\n filename = keyword.replace(\\\" \\\", \\\"_\\\") + '_' + str(results_enter)\\n newdf.to_csv(filename)\\n\\n return\\n\\n\\n# -- Getting Comments -----\\n\\ndef get_comment_threads(video_id, results):\\n results = youtube.commentThreads().list(\\n part=\\\"snippet\\\",\\n videoId=video_id,\\n textFormat=\\\"plainText\\\",\\n maxResults = results\\n ).execute()\\n for item in results[\\\"items\\\"]:\\n comment = item[\\\"snippet\\\"][\\\"topLevelComment\\\"]\\n author = comment[\\\"snippet\\\"][\\\"authorDisplayName\\\"]\\n text = comment[\\\"snippet\\\"][\\\"textDisplay\\\"]\\n #print \\\"Comment by %s: %s\\\" % (author, text)\\n ydict = {'videoId':videoId, 'Comment':comment}\\n return ydict\\n\\n\\n# Call the API's comments.list method to list the existing comment replies.\\ndef get_comments(youtube, parent_id):\\n results = youtube.comments().list(\\n part=\\\"snippet\\\",\\n parentId=parent_id,\\n textFormat=\\\"plainText\\\",\\n maxResults=100\\n ).execute()\\n for item in results[\\\"items\\\"]:\\n author = item[\\\"snippet\\\"][\\\"authorDisplayName\\\"]\\n text = item[\\\"snippet\\\"][\\\"textDisplay\\\"]\\n #print \\\"Comment by %s: %s\\\" % (author, text)\\n\\n return results[\\\"items\\\"]\\n\\n#Search for videos by channel ID.....\\n\\ndef channel_snatch(q, max_results, token, channelId,\\n order=\\\"relevance\\\"):\\n\\n search_response = youtube.search().list(\\n q=q, type=\\\"video\\\", pageToken=token,part=\\\"id,snippet\\\",\\n maxResults=max_results, channelId = channelId,\\n location=None, locationRadius=None,\\n safeSearch = 'strict').execute()\\n videoId = []\\n title = []\\n description = []\\n tok = search_response['nextPageToken']\\n\\n for search_result in search_response.get(\\\"items\\\", []):\\n if search_result[\\\"id\\\"][\\\"kind\\\"] == \\\"youtube#video\\\":\\n title.append(search_result['snippet']['title'])\\n videoId.append(search_result['id']['videoId'])\\n response = youtube.videos().list(part='statistics, snippet',\\n id=search_result['id']['videoId']\\n ).execute()\\n description.append(response['items'][0]['snippet']['description'])\\n\\n ydict = {'title':title,'videoId':videoId,\\n 'description':description}\\n return ydict, tok\\n\\n\\ndef search_by_channel(keyword, results_enter, channelId):\\n\\n tests, token0 = channel_snatch(keyword, 50,token= None,\\n channelId = channelId)\\n newdf = pd.DataFrame(tests)\\n #Total number of results I need..:\\n results = results_enter-50\\n\\n while results>50:\\n tests, token0 = channel_snatch(keyword, 50,\\n token= str(token0),\\n channelId = channelId)\\n df = pd.DataFrame(tests)\\n newdf = pd.concat([newdf,df]).reset_index(drop=True)\\n results = results-50\\n\\n filename = keyword.replace(\\\" \\\", \\\"_\\\") + '_' + 'channel' + str(results_enter)\\n newdf.to_csv(filename)\\n return\\n\\n\\n\\n# Call the API's captions.list method to list the existing caption tracks.\\ndef list_captions(youtube, video_id):\\n results = youtube.captions().list(\\n part=\\\"snippet\\\",\\n videoId=video_id\\n ).execute()\\n for item in results[\\\"items\\\"]:\\n id = item[\\\"id\\\"]\\n name = item[\\\"snippet\\\"][\\\"name\\\"]\\n language = item[\\\"snippet\\\"][\\\"language\\\"]\\n #print \\\"Caption track '%s(%s)' in '%s' language.\\\" % (name, id, language)\\n\\n return results[\\\"items\\\"]\\n\\n #transcripts using requests requests..?\\n\"\n },\n {\n \"alpha_fraction\": 0.6258603930473328,\n \"alphanum_fraction\": 0.6317600607872009,\n \"avg_line_length\": 27.23611068725586,\n \"blob_id\": \"45baf9d1195981ee3d8885d3031f80238b1a4765\",\n \"content_id\": \"e9533c2dd6fe15e8878d2af52a2263b479b68b6d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2034,\n \"license_type\": \"no_license\",\n \"max_line_length\": 76,\n \"num_lines\": 72,\n \"path\": \"/my_flask_app/views.py\",\n \"repo_name\": \"nityamd/Insight\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nimport json\\nimport pickle\\nimport logging\\n\\nimport pandas as pd\\nfrom flask import json, jsonify, request, render_template\\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\\n\\nfrom my_flask_app import app\\nfrom ytapi_extractor import videoId_data, video_query\\n\\n\\n\\nafile = open(r'tfidf_model_description.pkl', 'rb')\\ndes_model = pickle.load(afile, encoding = 'latin1')\\nafile.close()\\n\\nafile = open(r'tfidf_vectorizer_description.pkl', 'rb')\\ndes_vectorizer = pickle.load(afile, encoding = 'latin1')\\nafile.close()\\n\\nafile = open(r'tfidf_model_title.pkl', 'rb')\\nti_model = pickle.load(afile, encoding = 'latin1')\\nafile.close()\\n\\nafile = open(r'tfidf_vectorizer_title.pkl', 'rb')\\nti_vectorizer = pickle.load(afile, encoding='latin1')\\nafile.close()\\n\\n\\n\\n@app.route('/')\\n@app.route('/index')\\ndef index():\\n return render_template(\\\"index.html\\\")\\n\\n@app.route('/result', methods=['POST', 'GET'])\\ndef result():\\n if request.method == 'POST':\\n result = request.form\\n earl = result['URL']\\n df = videoId_data(video_query(earl))\\n #more comments here describing what's going on\\n des_vector = des_vectorizer.transform(df['description'])\\n des_rating = des_model.predict(des_vector)[0]\\n ti_vector = ti_vectorizer.transform(df['title'])\\n ti_rating = ti_model.predict(ti_vector)[0]\\n rating = des_rating + ti_rating\\n\\n if rating == 2.0:\\n output = 'Shady!'\\n elif rating == 1.0:\\n output = 'Shady? Clean?'\\n else:\\n output = 'Clean!'\\n\\n logging.warning(\\\"I'm so successful!\\\")\\n logging.warning(rating)\\n\\n return render_template(\\\"result.html\\\",\\n result = output,\\n title = df['title'][0],\\n description = df['description'][0])\\n\\n@app.route('/insight',methods = ['POST', 'GET'])\\ndef insight():\\n if request.method == 'POST':\\n insight = request.form\\n return render_template(\\\"insight.html\\\",result = result)\\n\\nif __name__ == '__main__':\\n app.run(debug = True)\\n\"\n },\n {\n \"alpha_fraction\": 0.635708212852478,\n \"alphanum_fraction\": 0.6547921895980835,\n \"avg_line_length\": 34.19403076171875,\n \"blob_id\": \"13e575ebb3491386c16f40c66efb3b299b572851\",\n \"content_id\": \"25af60b12e04259cc0f947247803217f8f148345\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2358,\n \"license_type\": \"no_license\",\n \"max_line_length\": 87,\n \"num_lines\": 67,\n \"path\": \"/scripts/tfidf.py\",\n \"repo_name\": \"nityamd/Insight\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nimport pandas as pd\\nimport itertools\\nimport gensim\\nimport keras\\nimport nltk\\nimport re\\nimport codecs\\nimport os\\n\\nfrom nltk.stem.snowball import SnowballStemmer\\nfrom nltk.tokenize import RegexpTokenizer\\nfrom nltk.corpus import stopwords\\nfrom nltk.tokenize import word_tokenize\\n\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\\nfrom sklearn.decomposition import PCA, TruncatedSVD\\nfrom sklearn.metrics import accuracy_score, f1_score, precision_score\\nfrom sklearn.metrics import confusion_matrix, recall_score, classification_report\\nfrom sklearn.linear_model import LogisticRegression\\nfrom nltk.tokenize import RegexpTokenizer\\n\\nos.chdir(\\\"/Users/nitya/childsplay/data\\\")\\ndf = pd.read_csv('peppa_dataframe_with_labels')\\ndf = df.drop_duplicates('videoId')\\n\\ndef ttsplit(thing):\\n ''''\\n Train-test splitting with indices\\n ''''\\n list_corpus = df[str(thing)].tolist()\\n list_labels = df['class_label'].tolist()\\n indices = np.arange(len(list_labels))\\n x_train, x_test, y_train, y_test, i1, i2 = train_test_split(list_corpus,\\n list_labels,\\n indices,\\n test_size=0.2,\\n random_state=40)\\n return x_train,x_test, y_train, y_test, i1, i2\\n\\nx1_ti, x2_ti, y1_ti, y2_ti, i1, i2 = ttsplit('title')\\nx1_des, x2_des, y1_des, y2_des, i1, i2 = ttsplit('description')\\n\\n\\n\\ndef tfidf(data):\\n tfidf_vectorizer = TfidfVectorizer()\\n\\n train = tfidf_vectorizer.fit_transform(data)\\n\\n return train, tfidf_vectorizer\\n\\nx1ti_tfidf, tfidf_vectorizer = tfidf(x1_ti)\\nx2ti_tfidf = tfidf_vectorizer.transform(x2_ti)\\n\\nclf_tfidf_ti = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg',\\n multi_class='multinomial', n_jobs=-1, random_state=40)\\nclf_tfidf_ti.fit(x1ti_tfidf, y1_ti)\\n\\n\\nx1des_tfidf, tfidf_vectorizer = tfidf(x1_des)\\nx2des_tfidf = tfidf_vectorizer.transform(x2_des)\\n\\nclf_tfidf_des = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg',\\n multi_class='multinomial', n_jobs=-1, random_state=40)\\nclf_tfidf_des.fit(x1des_tfidf, y1_des)\\n\"\n },\n {\n \"alpha_fraction\": 0.6214178204536438,\n \"alphanum_fraction\": 0.6244344115257263,\n \"avg_line_length\": 30.5238094329834,\n \"blob_id\": \"e192cd8722229c08356252a74d842d4f731d97d4\",\n \"content_id\": \"6bfcee5b1bfb4825771a31a7d2abae063d6cddd5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 663,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 21,\n \"path\": \"/scripts/preprocessing.py\",\n \"repo_name\": \"nityamd/Insight\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nimport pandas as pd\\nimport numpy as np\\nimport requests\\nimport os\\n\\ndef sanitize_characters(raw, clean):\\n for line in input_file:\\n out = line\\n output_file.write(line)\\n\\n\\nsanitize_characters(input_file, output_file)\\n\\ndef standardize_text(df, text_field):\\n df[text_field] = df[text_field].str.replace(r\\\"http\\\\S+\\\", \\\"\\\")\\n df[text_field] = df[text_field].str.replace(r\\\"http\\\", \\\"\\\")\\n df[text_field] = df[text_field].str.replace(r\\\"@\\\\S+\\\", \\\"\\\")\\n df[text_field] = df[text_field].str.replace(r\\\"[^A-Za-z0-9(),!?@\\\\'\\\\`\\\\\\\"\\\\_\\\\n]\\\", \\\" \\\")\\n df[text_field] = df[text_field].str.replace(r\\\"@\\\", \\\"at\\\")\\n df[text_field] = df[text_field].str.lower()\\n return df\\n\"\n },\n {\n \"alpha_fraction\": 0.6282822489738464,\n \"alphanum_fraction\": 0.6422319412231445,\n \"avg_line_length\": 35.198020935058594,\n \"blob_id\": \"0648c28762c9698a0f6b1efb9e1c187d57c0a42b\",\n \"content_id\": \"52d1b4ec9e8f3fe90a94ba896ee09beb6718074c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3656,\n \"license_type\": \"no_license\",\n \"max_line_length\": 87,\n \"num_lines\": 101,\n \"path\": \"/scripts/w2v.py\",\n \"repo_name\": \"nityamd/Insight\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nimport pandas as pd\\nimport itertools\\nimport gensim\\nimport keras\\nimport nltk\\nimport re\\nimport codecs\\nimport os\\n\\nfrom nltk.stem.snowball import SnowballStemmer\\nfrom nltk.tokenize import RegexpTokenizer\\nfrom nltk.corpus import stopwords\\nfrom nltk.tokenize import word_tokenize\\n\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\\nfrom sklearn.decomposition import PCA, TruncatedSVD\\nfrom sklearn.metrics import accuracy_score, f1_score, precision_score\\nfrom sklearn.metrics import confusion_matrix, recall_score, classification_report\\nfrom sklearn.linear_model import LogisticRegression\\nfrom nltk.tokenize import RegexpTokenizer\\n\\nos.chdir(\\\"/Users/nitya/childsplay/data\\\")\\ndf = pd.read_csv('peppa_dataframe_with_labels')\\ndf = df.drop_duplicates('videoId')\\n\\n\\ntokenizer = RegexpTokenizer(r'\\\\w+')\\ndf[\\\"des_tokens\\\"] =df[\\\"description\\\"].apply(tokenizer.tokenize)\\ndf[\\\"ti_tokens\\\"] = df[\\\"title\\\"].apply(tokenizer.tokenize)\\n\\n\\ndef get_average_word2vec(tokens_list, vector, generate_missing=False, k=300):\\n if len(tokens_list)<1:\\n return np.zeros(k)\\n if generate_missing:\\n vectorized = [vector[word] if word in vector else np.random.rand(k) for\\n word in tokens_list]\\n else:\\n vectorized = [vector[word] if word in vector else np.zeros(k) for word\\n in tokens_list]\\n length = len(vectorized)\\n summed = np.sum(vectorized, axis=0)\\n averaged = np.divide(summed, length)\\n return averaged\\n\\ndef get_word2vec_embeddings(vectors, data, generate_missing=False):\\n embeddings = data['tokens'].apply(lambda x: get_average_word2vec\\n (x, vectors, generate_missing=\\n generate_missing))\\n return list(embeddings)\\n\\n\\nembeddings = get_word2vec_embeddings(word2vec, df)\\n\\ndef ttsplit(thing):\\n '''\\n Train-test splitting with indices\\n '''\\n list_corpus = df[str(thing)].tolist()\\n list_labels = df['class_label'].tolist()\\n indices = np.arange(len(list_labels))\\n x_train, x_test, y_train, y_test, i1, i2 = train_test_split(embeddings,\\n list_labels,\\n indices,\\n test_size=0.2,\\n random_state=40)\\n return x_train,x_test, y_train, y_test, i1, i2\\n\\nx1_ti, x2_ti, y1_ti, y2_ti, i1, i2 = ttsplit('title')\\nx1_des, x2_des, y1_des, y2_des, i1, i2 = ttsplit('description')\\n\\n\\n\\nclf_tfidf_ti = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg',\\n multi_class='multinomial', n_jobs=-1, random_state=40)\\nclf_tfidf_ti.fit(x1ti_tfidf, y1_ti)\\n\\n\\nx1des_tfidf, tfidf_vectorizer = tfidf(x1_des)\\nx2des_tfidf = tfidf_vectorizer.transform(x2_des)\\n\\nclf_tfidf_des = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg',\\n multi_class='multinomial', n_jobs=-1, random_state=40)\\nclf_tfidf_des.fit(x1des_tfidf, y1_des)\\n\\n# os.chdir(\\\"/Users/nitya/Insight\\\")\\n# import pickle\\n# afile = open(r'tfidf_vectorizer_title.pkl', 'wb')\\n# pickle.dump(tfidf_vectorizer, afile)\\n# afile.close()\\n# afile = open(r'tfidf_model_title.pkl', 'wb')\\n# pickle.dump(clf_tfidf_ti, afile)\\n# afile.close()\\n# afile = open(r'tfidf_vectorizer_description.pkl', 'wb')\\n# pickle.dump(tfidf_vectorizer, afile)\\n# afile.close()\\n# afile = open(r'tfidf_model_description.pkl', 'wb')\\n# pickle.dump(clf_tfidf_des, afile)\\n# afile.close()\\n\"\n },\n {\n \"alpha_fraction\": 0.6484375,\n \"alphanum_fraction\": 0.6484375,\n \"avg_line_length\": 17.285715103149414,\n \"blob_id\": \"36def481d6dc63a1cba02512a45272830ee76ea3\",\n \"content_id\": \"443189e27112baf47f7fd320f31fd4ab974a3f89\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 128,\n \"license_type\": \"no_license\",\n \"max_line_length\": 48,\n \"num_lines\": 7,\n \"path\": \"/hobbes/popup.js\",\n \"repo_name\": \"nityamd/Insight\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// var app = chrome.runtime.getBackgroundPage();\\n\\nfunction hello() {\\n chrome.tabs.executeScript({\\n file: 'alert.js'\\n });\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7260273694992065,\n \"alphanum_fraction\": 0.7260273694992065,\n \"avg_line_length\": 23.33333396911621,\n \"blob_id\": \"3c83d3576215de3a90da0452790442f1e966b01b\",\n \"content_id\": \"aa50bcce038567bc06496a49f475f4c45c27248a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 73,\n \"license_type\": \"no_license\",\n \"max_line_length\": 28,\n \"num_lines\": 3,\n \"path\": \"/run.py\",\n \"repo_name\": \"nityamd/Insight\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\nfrom my_flask_app import app\\napp.run(debug = True)\\n\"\n },\n {\n \"alpha_fraction\": 0.7349397540092468,\n \"alphanum_fraction\": 0.759036123752594,\n \"avg_line_length\": 15.600000381469727,\n \"blob_id\": \"f1a87d64679ccc33a8435994942db8e7761e0f0c\",\n \"content_id\": \"91f6eec9a2b24666a81d24b9a13ddce87d7db84c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 83,\n \"license_type\": \"no_license\",\n \"max_line_length\": 40,\n \"num_lines\": 5,\n \"path\": \"/README.md\",\n \"repo_name\": \"nityamd/Insight\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"YouTopia
\\n\\nGiving kids the ideal YouTube experience\\n\\nhttp://youtopia.site/\\n\"\n },\n {\n \"alpha_fraction\": 0.6403326392173767,\n \"alphanum_fraction\": 0.6455301642417908,\n \"avg_line_length\": 28.121212005615234,\n \"blob_id\": \"ec955a7ffaab5dcbf539f167f49740239a4dc6d6\",\n \"content_id\": \"158c82cdd603398b2f377c2fd480c0ca6faa7c71\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 962,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 33,\n \"path\": \"/ytapi_extractor.py\",\n \"repo_name\": \"nityamd/Insight\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nimport pandas as pd\\nimport numpy as np\\nimport requests\\nimport os\\nfrom apiclient.discovery import build\\nfrom apiclient.errors import HttpError\\n\\nf = open('/Users/nitya/youtopian/youtube', 'r')\\nDEVELOPER_KEY = f.read().strip()\\n\\nYOUTUBE_API_SERVICE_NAME = \\\"youtube\\\"\\nYOUTUBE_API_VERSION = \\\"v3\\\"\\nyoutube = build(YOUTUBE_API_SERVICE_NAME, YOUTUBE_API_VERSION,\\n developerKey=DEVELOPER_KEY)\\n\\n\\ndef video_query(earl):\\n video_id = earl.split('=')[-1]\\n return video_id\\n\\ndef videoId_data(video_id):\\n '''\\n Use the API to get data\\n for a single video\\n by videoId\\n '''\\n video_response = youtube.videos().list(id=video_id,\\n part='snippet').execute()\\n title = video_response.get('items')[0]['snippet']['title']\\n description = video_response.get('items')[0]['snippet']['description']\\n ydict = {'title':title, 'description':description}\\n df = pd.DataFrame(ydict, index = [0])\\n return df\\n\"\n }\n]"},"num_files":{"kind":"number","value":10,"string":"10"}}},{"rowIdx":1176,"cells":{"repo_name":{"kind":"string","value":"GroundP/OOP"},"repo_url":{"kind":"string","value":"https://github.com/GroundP/OOP"},"snapshot_id":{"kind":"string","value":"a319c3255a8cba6388054c933981dffb98ebb5a8"},"revision_id":{"kind":"string","value":"ea9436b8cd105b349a833fc6734c3d08893b1b03"},"directory_id":{"kind":"string","value":"538075bfe5559b7b859d99bae61d6905e55a7e38"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"string","value":"2020-09-07T12:47:06.814684"},"revision_date":{"kind":"string","value":"2019-12-16T14:18:54"},"committer_date":{"kind":"string","value":"2019-12-16T14:18:54"},"github_id":{"kind":"number","value":220785930,"string":"220,785,930"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5159817337989807,"alphanum_fraction":0.5633561611175537,"avg_line_length":26.390625,"blob_id":"a563b9705ed5095f30dce4a4d20358eb6ea9871b","content_id":"6c807cd76335443d33c5f80151f6a37afebe6bb7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2096,"license_type":"no_license","max_line_length":86,"num_lines":64,"path":"/trapping_rain.py","repo_name":"GroundP/OOP","src_encoding":"UTF-8","text":"''' Algorithm\n 문제설명 : n x m 크기의 바닥에 1x1x1 크기의 벽돌이 놓여 있습니다. 놓여 있는 벽돌 위로 비가 내리면 아래 그림과 물이 차오르게 됩니다.\n 이때 우리는 최대로 모을 수 있는 빗물의 양을 구하려고 합니다. (1x1x1 공간에 모인 빗물의 양은 1입니다.)'''\n\ndef get_rain(buildings):\n max_val = max(buildings)\n\n rain_arr = []\n for i in range(1, len(buildings) - 1):\n # 현재 인덱스를 기준으로 양쪽에 가장 높은 건물의 위치를 구한다\n leftMax = max(buildings[:i])\n center = buildings[i]\n rightMax = max(buildings[i + 1:])\n\n # center 기준으로 좌,우측 max층 중 작은 값에서 center를 뺀 값이 비에 잠긴 양\n if leftMax > center and rightMax > center:\n rain_arr.append((min(leftMax, rightMax) - center))\n else:\n rain_arr.append(0)\n\n return rain_arr\n\ndef solutaion(bricks):\n longitudinal = len(bricks[0])\n vertical = len(bricks)\n\n verArray = [] # 수직방향 Array\n for long in range(longitudinal):\n subarray = []\n for ver in range(vertical):\n subarray.append(bricks[ver][long])\n verArray.append(subarray)\n\n longRain = [] # 길이방향 차오르는 물\n for i in range(1, vertical - 1):\n longRain.append(get_rain(bricks[i]))\n\n verRain = [] # 수직방향 차오르는 물\n for i in range(1, longitudinal -1):\n verRain.append(get_rain(verArray[i]))\n\n longSum = 0 # 길이방향 빗물 양\n verSum = 0 # 수직 방향 빗물 양\n for i in longRain:\n for j in i:\n longSum += j\n\n for i in verRain:\n for j in i:\n verSum += j\n\n print(longRain)\n print(verRain)\n print(longSum)\n print(verSum)\n print(\"\")\n\nbricks1 = [[2,4,3,2,3,2], [3,1,1,4,1,3], [2,2,1,3,3,1] ]\nbricks4 = [[5,5,5,5], [4,1,1,5], [3,1,1,5], [4,4,4,5]]\nbricks2 = [[2,2,2], [1,2,2], [1,2,1]]\nbricks5 = [[4,1,1,3,1], [4,1,2,0,2], [4,1,1,2,2]]\nsolutaion(bricks1)\nsolutaion(bricks4)\nsolutaion(bricks5)"},{"alpha_fraction":0.5466970205307007,"alphanum_fraction":0.5671981573104858,"avg_line_length":25.636363983154297,"blob_id":"65dec458128a53cba70f11a75b3631b793bfa042","content_id":"cef6c725dd43cbf26c3a81cbdc062fe1cd73a040","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1244,"license_type":"no_license","max_line_length":77,"num_lines":33,"path":"/class_method.py","repo_name":"GroundP/OOP","src_encoding":"UTF-8","text":"'''\n @classmethod 로 표현하는 클래스 메소드는 첫번째 파라미터로 클래스가 자동 전달된다.\n 클래스 메소드는 인스턴스 변수를 아예 사용하지 않는 경우에 사용한다.(인스턴스 변수를 알 수 없다)\n 인스턴스 변수 사용 -> 인스턴스 메소드\n 클래스 변수 사용 -> 클래스 메소드\n 둘다 사용 -> 인스턴스 메소드(인스턴스 변수, 클래스 변수 모두 사용 가능)\n'''\n\nclass User:\n count = 0 # 클래스 변수\n\n def __init__(self, name, email, pw):\n self.name = name\n self.email = email\n self.pw = pw\n User.count += 1 # 인스턴스가 생성될 때마다 count가 증가됨\n\n def say_hello(self):\n print(\"안녕하세요! 저는 {}입니다!\".format(self.name))\n\n def __str__(self):\n return \"사용자: {}, 이메일: {}, 비밀번호: ******\".format(self.name, self.email)\n\n @classmethod\n def number_of_users(cls):\n print(\"총 유저 수는 {} 입니다.\".format(cls.count))\n\nuser1 = User(\"강영훈\", \"younghoon@codeit.kr\", \"123456\")\nuser2 = User(\"이윤수\", \"yoonsoo@codeit.kr\", \"abcdefg\")\nuser3 = User(\"서혜린\", \"lisa@codeit.kr\", \"123456\")\n\nUser.number_of_users()\nuser1.number_of_users()"},{"alpha_fraction":0.553398072719574,"alphanum_fraction":0.5663430690765381,"avg_line_length":24.244897842407227,"blob_id":"af88d48736cc078ccaa8685dea9a06139b287397","content_id":"d6b7be69b39790de75d8334a4dd41d76a9d2a901","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1656,"license_type":"no_license","max_line_length":70,"num_lines":49,"path":"/OOP/캡슐화.py","repo_name":"GroundP/OOP","src_encoding":"UTF-8","text":"\"\"\"\n데코레이터를 통한 캡슐화\n@property\n@xxx.setter\n위의 모습으로 getter, setter를 데코레이터 형태로 정의한다.\n\"\"\"\n\nclass Citizen:\n \"\"\"주민 클래스\"\"\"\n drinking_age = 19 # 음주 가능 나이\n\n def __init__(self, name, age, resident_id):\n self.name = name\n self._age = age\n self._resident_id = resident_id\n\n def authenticate(self, id_field):\n \"\"\"본인이 맞는지 확인하는 메소드\"\"\"\n return self._resident_id == id_field\n\n def can_drink(self):\n \"\"\"음주 가능 나이인지 확인하는 메소드\"\"\"\n return self._age >= Citizen.drinking_age\n\n def __str__(self):\n \"\"\"주민 정보를 문자열로 리턴하는 메소드\"\"\"\n return self.name + \"씨는\" + str(self._age) + \"살입니다.\"\n\n @property\n def age(self):\n \"\"\"Property 데코레이터로 getter함수 정의\"\"\"\n print(\"나이를 리턴합니다\")\n return self._age\n\n @age.setter\n def age(self, value):\n \"\"\"Property 데코레이터로 setter함수 정의\"\"\"\n print(\"나이를 {}으로 설정합니다.\".format(value))\n if value < 0:\n print(\"나이를 음수로 설정할 수 없습니다. 0으로 초기화 하겠습니다.\")\n self._age = 0\n else:\n self._age = value\n\n\nyoung = Citizen(\"박지상\", 30, \"1222712\")\nprint(young.age) #인스턴스 변수는 _age이지만 데코레이터 getter로 정의된 age메소드를 통해 _age반환\nyoung.age = 21 #인스턴스 변수는 _age이지만 데코레이터 setter로 정의된 age메소드를 통해 _age 설정\nprint(young.age)"},{"alpha_fraction":0.5887005925178528,"alphanum_fraction":0.594350278377533,"avg_line_length":25.058822631835938,"blob_id":"1635ff6be2388d70d5fc2276cfcb5887a976e91d","content_id":"f098542a5dfb4e0bd1fcd76916851c79648da912","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1183,"license_type":"no_license","max_line_length":77,"num_lines":34,"path":"/OOP/static_method.py","repo_name":"GroundP/OOP","src_encoding":"UTF-8","text":"'''\n 파이썬 메소드의 종류는 3가지 - 인스턴스 메소드, 클래스 메소드, 정적 메소드\n 정적 메소드는 인스턴스 변수나 클래스 변수 중 아무것도 사용하지 않을 메소드라면 정적 메소드로 만든다.\n'''\nclass User:\n count = 0\n\n def __init__(self, name, email, pw):\n self.name = name\n self.email = email\n self.pw = pw\n\n User.count += 1\n\n def say_hello(self):\n print(\"안녕하세요! 저는 {}입니다!\".format(self.name))\n\n def __str__(self):\n return \"사용자: {}, 이메일: {}, 비밀번호: ******\".format(self.name, self.email)\n\n @classmethod\n def number_of_users(cls):\n print(\"총 유저 수는: {}입니다\".format(cls.count))\n\n @staticmethod\n def is_valid_email(email_address): # 정적메소드는 자동으로 전달되는 파라미터가 없다.\n return \"@\" in email_address\n\n# 정적 메소드는 인스턴스,클래스 두 가지 모두를 통해 사용 가능하다.\nprint(User.is_valid_email(\"taehosung\"))\nprint(User.is_valid_email(\"taehosung@codeit.kr\"))\n\nprint(user1.is_valid_email(\"taehosung\"))\nprint(user1.is_valid_email(\"taehosung@codeit.kr\"))"},{"alpha_fraction":0.5774545669555664,"alphanum_fraction":0.5807272791862488,"avg_line_length":22.109243392944336,"blob_id":"4006251d3793305b5fc7bd8d27886f7eef798a82","content_id":"d966a6fd83ac110ad3462509c4dd24fc43c6a812","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3538,"license_type":"no_license","max_line_length":71,"num_lines":119,"path":"/OOP/다형성.py","repo_name":"GroundP/OOP","src_encoding":"UTF-8","text":"from math import pi\nfrom abc import ABC, abstractmethod\n\n\"\"\"\n다형성은 하나의 변수를 여러 인스턴스로 나타낼 수 있는 형태, 특징\n그리고 추상 클래스란 '여러 클래스들의 공통점을 추상화해서 모아놓은 클래스'이며, \n다형성은 추상클래스를 통해 구현할 수 있다.\n\nABC는 Abstract Base Class로 추상클래스를 나타낸다\nabstractclassmethod는 추상 메소드를 나타낸다\n\"\"\"\n\nclass Figure(ABC):\n \"\"\"도형 클래스(추상 클래스)\"\"\"\n\n @abstractmethod\n def area(self) -> float: # 추상 메소드는 type hinting을 해주는 것이 좋다\n \"\"\"도형의 넓이를 리턴한다: 자식 클래스가 오버라이딩할 것\"\"\"\n pass\n\n @abstractmethod\n def perimeter(self) -> float:\n \"\"\"도형의 둘레를 리턴한다: 자식 클래스가 오버라이딩할 것\"\"\"\n pass\n\n\nclass Rectangle(Figure):\n \"\"\"직사각형 클래스\"\"\"\n\n def __init__(self, width, height):\n self.width = width\n self.height = height\n\n def area(self):\n \"\"\"직사각형의 넓이 리턴\"\"\"\n return self.width * self.height\n\n def perimeter(self):\n \"\"\"직사각형의 둘레 리턴\"\"\"\n return self.width * 2 + self.height * 2\n\n def __str__(self):\n \"\"\"직사각형의 정보를 문자열로 리턴한다\"\"\"\n return \"밑변 {}, 높이 {}인 직사각형\".format(self.width, self.height)\n\n\nclass Circle(Figure):\n \"\"\"원 클래스\"\"\"\n\n def __init__(self, radius):\n self.radius = radius\n\n def area(self):\n \"\"\"원의 넓이 리턴\"\"\"\n return pi * self.radius * self.radius\n\n def perimeter(self):\n \"\"\"원의 둘레 리턴\"\"\"\n return pi * 2 * self.radius\n\n def __str__(self):\n \"\"\"원의 정보를 문자열로 리턴한다\"\"\"\n return \"반지름 {}인 원\".format(self.radius)\n\n\nclass Cylinder(Figure):\n \"\"\"원통 클래스\"\"\"\n\n def __init__(self, radius, height):\n self.radius = radius\n self.height = height\n\n def area(self):\n \"\"\"원통의 넓이를 리턴\"\"\"\n return pi * self.radius * self.radius * self.height\n\n def perimeter(self):\n \"\"\"원통의 둘레를 리턴\"\"\"\n return pi * 2 * self.radius * self.height\n\n def __str__(self):\n \"\"\"원통의 정보를 문자여롤 리턴하는 메소드\"\"\"\n return \"밑면 반지름 {}, 높이 {}인 원기둥\".format(self.radius, self.height)\n\n\nclass Paint:\n \"\"\"그림판 프로그램 클래스\"\"\"\n\n def __init__(self):\n self.shapes = []\n\n def add_shape(self, shape):\n \"\"\"그림판에 도형을 추가한다\"\"\"\n if isinstance(shape, Figure):\n self.shapes.append(shape)\n else:\n print(\"넓이, 둘레를 구하는 메소드가 없는 도형은 추가할 수 없습니다!\")\n\n def total_area_of_shapes(self):\n \"\"\"그림판에 있는 모든 도형의 넓이의 합을 구한다\"\"\"\n return sum([shape.area() for shape in self.shapes])\n\n def total_perimeter_of_shapes(self):\n \"\"\"그림판에 있는 모든 도형의 둘레의 합을 구한다\"\"\"\n return sum([shape.perimeter() for shape in self.shapes])\n\n\ncylinder = Cylinder(7, 4)\nrectangle = Rectangle(3, 7)\ncircle = Circle(4)\n\npaint_program = Paint()\n\npaint_program.add_shape(cylinder)\npaint_program.add_shape(circle)\npaint_program.add_shape(rectangle)\n\nprint(paint_program.total_perimeter_of_shapes()) # 에러가 난다!\nprint(paint_program.total_area_of_shapes())\n"},{"alpha_fraction":0.6182648539543152,"alphanum_fraction":0.6292237639427185,"avg_line_length":28.62162208557129,"blob_id":"80a64d7750b0573c45afb956e8deb0beeb24c15b","content_id":"7366ca0a80684ca579699ca148d6a892e0349eb1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1467,"license_type":"no_license","max_line_length":97,"num_lines":37,"path":"/OOP/class_method_apply.py","repo_name":"GroundP/OOP","src_encoding":"UTF-8","text":"'''\n 인스턴스를 생성할 때 필요한 정보들이 항상 우리가 원하는 형태로 존재할까요? 우리는 다양한 형태의 정보에서 필요한 부분을 뽑아내서 인스턴스를 생성할 수 있어야 합니다.\n 예를 들어 유저 인스턴스 생성에 필요한 정보가 문자열일 수도 있고 리스트일 수도 있습니다.\n'''\n\nclass User:\n def __init__(self, name, email, password):\n self.name = name\n self.email = email\n self.password = password\n\n @classmethod\n def from_string(cls, string_param): # 문자열로 인스턴스 만들기\n parameter_list = string_param.split(\",\") # split 메소드를 사용해서 쉼표(,)를 기준으로 문자열을 리스트로 분리한다\n\n # 각 변수에 분리된 문자열 각각 저장\n name = parameter_list[0]\n email = parameter_list[1]\n password = parameter_list[2]\n\n return cls(name, email, password)\n\n @classmethod\n def from_list(cls, list_param): # 리스트로 인스턴스 만들기\n name = list_param[0]\n email = list_param[1]\n password = list_param[2]\n\n return cls(name, email, password)\n\n\n# 유저 생성 및 초기값 설정\nyounghoon = User.from_string(\"강영훈,younghoon@codeit.kr,123456\")\nyoonsoo = User.from_list([\"이윤수\", \"yoonsoo@codeit.kr\", \"abcdef\"])\n\nprint(younghoon.name, younghoon.email, younghoon.password)\nprint(yoonsoo.name, yoonsoo.email, yoonsoo.password)"},{"alpha_fraction":0.6229946613311768,"alphanum_fraction":0.6229946613311768,"avg_line_length":24,"blob_id":"93b5c6fd14ff9f7ca2bec0948ac933cf2db100e4","content_id":"f56f22e97bdcabaf8d34a4cd49adff8461e33385","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":600,"license_type":"no_license","max_line_length":71,"num_lines":15,"path":"/OOP/Decorator.py","repo_name":"GroundP/OOP","src_encoding":"UTF-8","text":"def add_print_to(original): # 함수를 넘겨줌, add_print_to함수는 데코레이션 함수\n def wrapper():\n print(\"함수시작\") # 데코레이션 (넘겨받은 함수의 앞뒤 등등을 꾸며준다)\n original()\n print(\"함수 끝\")\n\n return wrapper # 정의된 함수를 반환\n\n@add_print_to # 골뱅이 요부분을 붙이면, 앞으로 print_hello()만 호출해도 데코레이터 함수가 같이 불린다\ndef print_hello():\n print(\"안녕하세요!\")\n\n# 함수위에 골뱅이가 보이면 아! 데코레이터구나! 라고 생각하자\n\nprint_hello()"},{"alpha_fraction":0.5475475192070007,"alphanum_fraction":0.5895895957946777,"avg_line_length":23.317073822021484,"blob_id":"757927141ee229f2023567a1b785f43da05e290a","content_id":"ff7e660f8f741cd4f7a504d4a36df6aed383444d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1249,"license_type":"no_license","max_line_length":64,"num_lines":41,"path":"/OOP/instance_method.py","repo_name":"GroundP/OOP","src_encoding":"UTF-8","text":"class User:\n count = 0 # 클래스 변수\n\n def __init__(self, name, email, pw):\n self.name = name\n self.email = email\n self.pw = pw\n User.count += 1 # 인스턴스가 생성될 때마다 count가 증가됨\n\n def say_hello(self):\n print(\"안녕하세요! 저는 {}입니다!\".format(self.name))\n\n def login(self, my_email, my_password):\n if ( self.email == my_email and self.pw == my_password):\n print(\"로그인 성공, 환영합니다.\")\n else:\n print(\"로그인 실패, 없는 아이디이거나 잘못된 비밀번호 입니다.\")\n\n\nuser1 = User(\"김대위\", \"captain@codeit.kr\", \"12345\")\n\n# user1.login(user1, \"captain@codeit.kr\", \"12345\") ### 에러!\nuser1.login(\"captain@codeit.kr\", \"12345\")\n\n\nuser2 = User(\"강영훈\", \"younghoon@codeit.kr\", \"123456\")\nuser3 = User(\"이윤수\", \"yoonsoo@codeit.kr\", \"abcdefg\")\nuser4 = User(\"서혜린\", \"lisa@codeit.kr\", \"123456\")\n\nuser1.count = 10 # 이 방식은 인스턴스 변수를 설정하는 작업\n\nprint(User.count) #생성된 인스턴스의 개수 출력\nprint(user1.count)\nprint(user2.count)\nprint(user3.count)\n\nUser.count = 20\n\nprint(User.count)\nprint(user1.count) # 이미 user1의 count라는 인스턴스 변수는 생성이 되부렀으~\nprint(user2.count)\n\n\n"},{"alpha_fraction":0.642201840877533,"alphanum_fraction":0.6574923396110535,"avg_line_length":23.058822631835938,"blob_id":"d8c445aecf204be6cdfa9dafc0759960060a9199","content_id":"9017d1a1ba83a278873bd9aaa847b3bf6b7614f9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2257,"license_type":"no_license","max_line_length":124,"num_lines":68,"path":"/Quiz/캡슐화-신용카드.py","repo_name":"GroundP/OOP","src_encoding":"UTF-8","text":"\"\"\"\n신용 카드를 나타내는 CreditCard 클래스를 만들고 싶습니다. CreditCard 클래스에는\n\n소유자 이름\n비밀 번호\n카드 한도 정보\n가 저장됩니다. 다음 조건들에 맞게 CreditCard 클래스를 완성하세요.\n\nCreditCard 클래스의 비밀번호, 카드 한도 인스턴스 변수 이름 앞에 밑줄 한 개를 써서 다른 개발자들에게 해당 변수를 직접 사용하는 것을 자제하라는 표시를 남깁시다. (_password, _payment_limit)\n\n@property를 이용해서 앞에 밑줄 하나를 쓴 변수들을 캡슐화하세요.\n변수 _password\ngetter 메소드: 문자열 \"비밀 번호는 볼 수 없습니다\"를 리턴한다.\nsetter 메소드: 파라미터로 받은 값을 변수에 설정한다.\n변수 _payment_limit\ngetter 메소드: _payment_limit 변수를 리턴한다.\nsetter 메소드: 파라미터로 받은 값을 변수에 설정한다.\n단, setter 메소드에서 파라미터로 받은 값이 0과 MAX_PAYMENT_LIMIT 사이에 있는 값이 아니면 \"카드 한도는 0원 ~ 3천만 원 사이로 설정해주세요!\" 라는 메시지를 출력한다.\n\"\"\"\n\nclass CreditCard:\n MAX_PAYMENT_LIMIT = 30000000\n\n def __init__(self, name, password, payment_limit):\n self._name = name\n self._password = password\n self._payment_limit = payment_limit\n\n @property\n def name(self):\n return self._name\n\n @name.setter\n def name(self, name):\n self._name = name\n\n @property\n def password(self):\n return \"비밀 번호는 볼 수 없습니다\"\n\n @password.setter\n def password(self, password):\n self._password = password\n\n @property\n def payment_limit(self):\n return self._payment_limit\n\n @payment_limit.setter\n def payment_limit(self, limit):\n if 0 < limit < CreditCard.MAX_PAYMENT_LIMIT:\n self._payment_limit = limit\n else:\n print(\"카드 한도는 0원 ~ 3천만 원 사이로 설정해주세요!\")\n\ncard = CreditCard(\"강영훈\", \"123\", 100000)\n\nprint(card.name)\nprint(card.password)\nprint(card.payment_limit)\n\ncard.name = \"성태호\"\ncard.password = \"1234\"\ncard.payment_limit = -10\n\nprint(card.name)\nprint(card.password)\nprint(card.payment_limit)"}],"string":"[\n {\n \"alpha_fraction\": 0.5159817337989807,\n \"alphanum_fraction\": 0.5633561611175537,\n \"avg_line_length\": 26.390625,\n \"blob_id\": \"a563b9705ed5095f30dce4a4d20358eb6ea9871b\",\n \"content_id\": \"6c807cd76335443d33c5f80151f6a37afebe6bb7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2096,\n \"license_type\": \"no_license\",\n \"max_line_length\": 86,\n \"num_lines\": 64,\n \"path\": \"/trapping_rain.py\",\n \"repo_name\": \"GroundP/OOP\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"''' Algorithm\\n 문제설명 : n x m 크기의 바닥에 1x1x1 크기의 벽돌이 놓여 있습니다. 놓여 있는 벽돌 위로 비가 내리면 아래 그림과 물이 차오르게 됩니다.\\n 이때 우리는 최대로 모을 수 있는 빗물의 양을 구하려고 합니다. (1x1x1 공간에 모인 빗물의 양은 1입니다.)'''\\n\\ndef get_rain(buildings):\\n max_val = max(buildings)\\n\\n rain_arr = []\\n for i in range(1, len(buildings) - 1):\\n # 현재 인덱스를 기준으로 양쪽에 가장 높은 건물의 위치를 구한다\\n leftMax = max(buildings[:i])\\n center = buildings[i]\\n rightMax = max(buildings[i + 1:])\\n\\n # center 기준으로 좌,우측 max층 중 작은 값에서 center를 뺀 값이 비에 잠긴 양\\n if leftMax > center and rightMax > center:\\n rain_arr.append((min(leftMax, rightMax) - center))\\n else:\\n rain_arr.append(0)\\n\\n return rain_arr\\n\\ndef solutaion(bricks):\\n longitudinal = len(bricks[0])\\n vertical = len(bricks)\\n\\n verArray = [] # 수직방향 Array\\n for long in range(longitudinal):\\n subarray = []\\n for ver in range(vertical):\\n subarray.append(bricks[ver][long])\\n verArray.append(subarray)\\n\\n longRain = [] # 길이방향 차오르는 물\\n for i in range(1, vertical - 1):\\n longRain.append(get_rain(bricks[i]))\\n\\n verRain = [] # 수직방향 차오르는 물\\n for i in range(1, longitudinal -1):\\n verRain.append(get_rain(verArray[i]))\\n\\n longSum = 0 # 길이방향 빗물 양\\n verSum = 0 # 수직 방향 빗물 양\\n for i in longRain:\\n for j in i:\\n longSum += j\\n\\n for i in verRain:\\n for j in i:\\n verSum += j\\n\\n print(longRain)\\n print(verRain)\\n print(longSum)\\n print(verSum)\\n print(\\\"\\\")\\n\\nbricks1 = [[2,4,3,2,3,2], [3,1,1,4,1,3], [2,2,1,3,3,1] ]\\nbricks4 = [[5,5,5,5], [4,1,1,5], [3,1,1,5], [4,4,4,5]]\\nbricks2 = [[2,2,2], [1,2,2], [1,2,1]]\\nbricks5 = [[4,1,1,3,1], [4,1,2,0,2], [4,1,1,2,2]]\\nsolutaion(bricks1)\\nsolutaion(bricks4)\\nsolutaion(bricks5)\"\n },\n {\n \"alpha_fraction\": 0.5466970205307007,\n \"alphanum_fraction\": 0.5671981573104858,\n \"avg_line_length\": 25.636363983154297,\n \"blob_id\": \"65dec458128a53cba70f11a75b3631b793bfa042\",\n \"content_id\": \"cef6c725dd43cbf26c3a81cbdc062fe1cd73a040\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1244,\n \"license_type\": \"no_license\",\n \"max_line_length\": 77,\n \"num_lines\": 33,\n \"path\": \"/class_method.py\",\n \"repo_name\": \"GroundP/OOP\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\n @classmethod 로 표현하는 클래스 메소드는 첫번째 파라미터로 클래스가 자동 전달된다.\\n 클래스 메소드는 인스턴스 변수를 아예 사용하지 않는 경우에 사용한다.(인스턴스 변수를 알 수 없다)\\n 인스턴스 변수 사용 -> 인스턴스 메소드\\n 클래스 변수 사용 -> 클래스 메소드\\n 둘다 사용 -> 인스턴스 메소드(인스턴스 변수, 클래스 변수 모두 사용 가능)\\n'''\\n\\nclass User:\\n count = 0 # 클래스 변수\\n\\n def __init__(self, name, email, pw):\\n self.name = name\\n self.email = email\\n self.pw = pw\\n User.count += 1 # 인스턴스가 생성될 때마다 count가 증가됨\\n\\n def say_hello(self):\\n print(\\\"안녕하세요! 저는 {}입니다!\\\".format(self.name))\\n\\n def __str__(self):\\n return \\\"사용자: {}, 이메일: {}, 비밀번호: ******\\\".format(self.name, self.email)\\n\\n @classmethod\\n def number_of_users(cls):\\n print(\\\"총 유저 수는 {} 입니다.\\\".format(cls.count))\\n\\nuser1 = User(\\\"강영훈\\\", \\\"younghoon@codeit.kr\\\", \\\"123456\\\")\\nuser2 = User(\\\"이윤수\\\", \\\"yoonsoo@codeit.kr\\\", \\\"abcdefg\\\")\\nuser3 = User(\\\"서혜린\\\", \\\"lisa@codeit.kr\\\", \\\"123456\\\")\\n\\nUser.number_of_users()\\nuser1.number_of_users()\"\n },\n {\n \"alpha_fraction\": 0.553398072719574,\n \"alphanum_fraction\": 0.5663430690765381,\n \"avg_line_length\": 24.244897842407227,\n \"blob_id\": \"af88d48736cc078ccaa8685dea9a06139b287397\",\n \"content_id\": \"d6b7be69b39790de75d8334a4dd41d76a9d2a901\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1656,\n \"license_type\": \"no_license\",\n \"max_line_length\": 70,\n \"num_lines\": 49,\n \"path\": \"/OOP/캡슐화.py\",\n \"repo_name\": \"GroundP/OOP\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\n데코레이터를 통한 캡슐화\\n@property\\n@xxx.setter\\n위의 모습으로 getter, setter를 데코레이터 형태로 정의한다.\\n\\\"\\\"\\\"\\n\\nclass Citizen:\\n \\\"\\\"\\\"주민 클래스\\\"\\\"\\\"\\n drinking_age = 19 # 음주 가능 나이\\n\\n def __init__(self, name, age, resident_id):\\n self.name = name\\n self._age = age\\n self._resident_id = resident_id\\n\\n def authenticate(self, id_field):\\n \\\"\\\"\\\"본인이 맞는지 확인하는 메소드\\\"\\\"\\\"\\n return self._resident_id == id_field\\n\\n def can_drink(self):\\n \\\"\\\"\\\"음주 가능 나이인지 확인하는 메소드\\\"\\\"\\\"\\n return self._age >= Citizen.drinking_age\\n\\n def __str__(self):\\n \\\"\\\"\\\"주민 정보를 문자열로 리턴하는 메소드\\\"\\\"\\\"\\n return self.name + \\\"씨는\\\" + str(self._age) + \\\"살입니다.\\\"\\n\\n @property\\n def age(self):\\n \\\"\\\"\\\"Property 데코레이터로 getter함수 정의\\\"\\\"\\\"\\n print(\\\"나이를 리턴합니다\\\")\\n return self._age\\n\\n @age.setter\\n def age(self, value):\\n \\\"\\\"\\\"Property 데코레이터로 setter함수 정의\\\"\\\"\\\"\\n print(\\\"나이를 {}으로 설정합니다.\\\".format(value))\\n if value < 0:\\n print(\\\"나이를 음수로 설정할 수 없습니다. 0으로 초기화 하겠습니다.\\\")\\n self._age = 0\\n else:\\n self._age = value\\n\\n\\nyoung = Citizen(\\\"박지상\\\", 30, \\\"1222712\\\")\\nprint(young.age) #인스턴스 변수는 _age이지만 데코레이터 getter로 정의된 age메소드를 통해 _age반환\\nyoung.age = 21 #인스턴스 변수는 _age이지만 데코레이터 setter로 정의된 age메소드를 통해 _age 설정\\nprint(young.age)\"\n },\n {\n \"alpha_fraction\": 0.5887005925178528,\n \"alphanum_fraction\": 0.594350278377533,\n \"avg_line_length\": 25.058822631835938,\n \"blob_id\": \"1635ff6be2388d70d5fc2276cfcb5887a976e91d\",\n \"content_id\": \"f098542a5dfb4e0bd1fcd76916851c79648da912\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1183,\n \"license_type\": \"no_license\",\n \"max_line_length\": 77,\n \"num_lines\": 34,\n \"path\": \"/OOP/static_method.py\",\n \"repo_name\": \"GroundP/OOP\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\n 파이썬 메소드의 종류는 3가지 - 인스턴스 메소드, 클래스 메소드, 정적 메소드\\n 정적 메소드는 인스턴스 변수나 클래스 변수 중 아무것도 사용하지 않을 메소드라면 정적 메소드로 만든다.\\n'''\\nclass User:\\n count = 0\\n\\n def __init__(self, name, email, pw):\\n self.name = name\\n self.email = email\\n self.pw = pw\\n\\n User.count += 1\\n\\n def say_hello(self):\\n print(\\\"안녕하세요! 저는 {}입니다!\\\".format(self.name))\\n\\n def __str__(self):\\n return \\\"사용자: {}, 이메일: {}, 비밀번호: ******\\\".format(self.name, self.email)\\n\\n @classmethod\\n def number_of_users(cls):\\n print(\\\"총 유저 수는: {}입니다\\\".format(cls.count))\\n\\n @staticmethod\\n def is_valid_email(email_address): # 정적메소드는 자동으로 전달되는 파라미터가 없다.\\n return \\\"@\\\" in email_address\\n\\n# 정적 메소드는 인스턴스,클래스 두 가지 모두를 통해 사용 가능하다.\\nprint(User.is_valid_email(\\\"taehosung\\\"))\\nprint(User.is_valid_email(\\\"taehosung@codeit.kr\\\"))\\n\\nprint(user1.is_valid_email(\\\"taehosung\\\"))\\nprint(user1.is_valid_email(\\\"taehosung@codeit.kr\\\"))\"\n },\n {\n \"alpha_fraction\": 0.5774545669555664,\n \"alphanum_fraction\": 0.5807272791862488,\n \"avg_line_length\": 22.109243392944336,\n \"blob_id\": \"4006251d3793305b5fc7bd8d27886f7eef798a82\",\n \"content_id\": \"d966a6fd83ac110ad3462509c4dd24fc43c6a812\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3538,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 119,\n \"path\": \"/OOP/다형성.py\",\n \"repo_name\": \"GroundP/OOP\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from math import pi\\nfrom abc import ABC, abstractmethod\\n\\n\\\"\\\"\\\"\\n다형성은 하나의 변수를 여러 인스턴스로 나타낼 수 있는 형태, 특징\\n그리고 추상 클래스란 '여러 클래스들의 공통점을 추상화해서 모아놓은 클래스'이며, \\n다형성은 추상클래스를 통해 구현할 수 있다.\\n\\nABC는 Abstract Base Class로 추상클래스를 나타낸다\\nabstractclassmethod는 추상 메소드를 나타낸다\\n\\\"\\\"\\\"\\n\\nclass Figure(ABC):\\n \\\"\\\"\\\"도형 클래스(추상 클래스)\\\"\\\"\\\"\\n\\n @abstractmethod\\n def area(self) -> float: # 추상 메소드는 type hinting을 해주는 것이 좋다\\n \\\"\\\"\\\"도형의 넓이를 리턴한다: 자식 클래스가 오버라이딩할 것\\\"\\\"\\\"\\n pass\\n\\n @abstractmethod\\n def perimeter(self) -> float:\\n \\\"\\\"\\\"도형의 둘레를 리턴한다: 자식 클래스가 오버라이딩할 것\\\"\\\"\\\"\\n pass\\n\\n\\nclass Rectangle(Figure):\\n \\\"\\\"\\\"직사각형 클래스\\\"\\\"\\\"\\n\\n def __init__(self, width, height):\\n self.width = width\\n self.height = height\\n\\n def area(self):\\n \\\"\\\"\\\"직사각형의 넓이 리턴\\\"\\\"\\\"\\n return self.width * self.height\\n\\n def perimeter(self):\\n \\\"\\\"\\\"직사각형의 둘레 리턴\\\"\\\"\\\"\\n return self.width * 2 + self.height * 2\\n\\n def __str__(self):\\n \\\"\\\"\\\"직사각형의 정보를 문자열로 리턴한다\\\"\\\"\\\"\\n return \\\"밑변 {}, 높이 {}인 직사각형\\\".format(self.width, self.height)\\n\\n\\nclass Circle(Figure):\\n \\\"\\\"\\\"원 클래스\\\"\\\"\\\"\\n\\n def __init__(self, radius):\\n self.radius = radius\\n\\n def area(self):\\n \\\"\\\"\\\"원의 넓이 리턴\\\"\\\"\\\"\\n return pi * self.radius * self.radius\\n\\n def perimeter(self):\\n \\\"\\\"\\\"원의 둘레 리턴\\\"\\\"\\\"\\n return pi * 2 * self.radius\\n\\n def __str__(self):\\n \\\"\\\"\\\"원의 정보를 문자열로 리턴한다\\\"\\\"\\\"\\n return \\\"반지름 {}인 원\\\".format(self.radius)\\n\\n\\nclass Cylinder(Figure):\\n \\\"\\\"\\\"원통 클래스\\\"\\\"\\\"\\n\\n def __init__(self, radius, height):\\n self.radius = radius\\n self.height = height\\n\\n def area(self):\\n \\\"\\\"\\\"원통의 넓이를 리턴\\\"\\\"\\\"\\n return pi * self.radius * self.radius * self.height\\n\\n def perimeter(self):\\n \\\"\\\"\\\"원통의 둘레를 리턴\\\"\\\"\\\"\\n return pi * 2 * self.radius * self.height\\n\\n def __str__(self):\\n \\\"\\\"\\\"원통의 정보를 문자여롤 리턴하는 메소드\\\"\\\"\\\"\\n return \\\"밑면 반지름 {}, 높이 {}인 원기둥\\\".format(self.radius, self.height)\\n\\n\\nclass Paint:\\n \\\"\\\"\\\"그림판 프로그램 클래스\\\"\\\"\\\"\\n\\n def __init__(self):\\n self.shapes = []\\n\\n def add_shape(self, shape):\\n \\\"\\\"\\\"그림판에 도형을 추가한다\\\"\\\"\\\"\\n if isinstance(shape, Figure):\\n self.shapes.append(shape)\\n else:\\n print(\\\"넓이, 둘레를 구하는 메소드가 없는 도형은 추가할 수 없습니다!\\\")\\n\\n def total_area_of_shapes(self):\\n \\\"\\\"\\\"그림판에 있는 모든 도형의 넓이의 합을 구한다\\\"\\\"\\\"\\n return sum([shape.area() for shape in self.shapes])\\n\\n def total_perimeter_of_shapes(self):\\n \\\"\\\"\\\"그림판에 있는 모든 도형의 둘레의 합을 구한다\\\"\\\"\\\"\\n return sum([shape.perimeter() for shape in self.shapes])\\n\\n\\ncylinder = Cylinder(7, 4)\\nrectangle = Rectangle(3, 7)\\ncircle = Circle(4)\\n\\npaint_program = Paint()\\n\\npaint_program.add_shape(cylinder)\\npaint_program.add_shape(circle)\\npaint_program.add_shape(rectangle)\\n\\nprint(paint_program.total_perimeter_of_shapes()) # 에러가 난다!\\nprint(paint_program.total_area_of_shapes())\\n\"\n },\n {\n \"alpha_fraction\": 0.6182648539543152,\n \"alphanum_fraction\": 0.6292237639427185,\n \"avg_line_length\": 28.62162208557129,\n \"blob_id\": \"80a64d7750b0573c45afb956e8deb0beeb24c15b\",\n \"content_id\": \"7366ca0a80684ca579699ca148d6a892e0349eb1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1467,\n \"license_type\": \"no_license\",\n \"max_line_length\": 97,\n \"num_lines\": 37,\n \"path\": \"/OOP/class_method_apply.py\",\n \"repo_name\": \"GroundP/OOP\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\n 인스턴스를 생성할 때 필요한 정보들이 항상 우리가 원하는 형태로 존재할까요? 우리는 다양한 형태의 정보에서 필요한 부분을 뽑아내서 인스턴스를 생성할 수 있어야 합니다.\\n 예를 들어 유저 인스턴스 생성에 필요한 정보가 문자열일 수도 있고 리스트일 수도 있습니다.\\n'''\\n\\nclass User:\\n def __init__(self, name, email, password):\\n self.name = name\\n self.email = email\\n self.password = password\\n\\n @classmethod\\n def from_string(cls, string_param): # 문자열로 인스턴스 만들기\\n parameter_list = string_param.split(\\\",\\\") # split 메소드를 사용해서 쉼표(,)를 기준으로 문자열을 리스트로 분리한다\\n\\n # 각 변수에 분리된 문자열 각각 저장\\n name = parameter_list[0]\\n email = parameter_list[1]\\n password = parameter_list[2]\\n\\n return cls(name, email, password)\\n\\n @classmethod\\n def from_list(cls, list_param): # 리스트로 인스턴스 만들기\\n name = list_param[0]\\n email = list_param[1]\\n password = list_param[2]\\n\\n return cls(name, email, password)\\n\\n\\n# 유저 생성 및 초기값 설정\\nyounghoon = User.from_string(\\\"강영훈,younghoon@codeit.kr,123456\\\")\\nyoonsoo = User.from_list([\\\"이윤수\\\", \\\"yoonsoo@codeit.kr\\\", \\\"abcdef\\\"])\\n\\nprint(younghoon.name, younghoon.email, younghoon.password)\\nprint(yoonsoo.name, yoonsoo.email, yoonsoo.password)\"\n },\n {\n \"alpha_fraction\": 0.6229946613311768,\n \"alphanum_fraction\": 0.6229946613311768,\n \"avg_line_length\": 24,\n \"blob_id\": \"93b5c6fd14ff9f7ca2bec0948ac933cf2db100e4\",\n \"content_id\": \"f56f22e97bdcabaf8d34a4cd49adff8461e33385\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 600,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 15,\n \"path\": \"/OOP/Decorator.py\",\n \"repo_name\": \"GroundP/OOP\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def add_print_to(original): # 함수를 넘겨줌, add_print_to함수는 데코레이션 함수\\n def wrapper():\\n print(\\\"함수시작\\\") # 데코레이션 (넘겨받은 함수의 앞뒤 등등을 꾸며준다)\\n original()\\n print(\\\"함수 끝\\\")\\n\\n return wrapper # 정의된 함수를 반환\\n\\n@add_print_to # 골뱅이 요부분을 붙이면, 앞으로 print_hello()만 호출해도 데코레이터 함수가 같이 불린다\\ndef print_hello():\\n print(\\\"안녕하세요!\\\")\\n\\n# 함수위에 골뱅이가 보이면 아! 데코레이터구나! 라고 생각하자\\n\\nprint_hello()\"\n },\n {\n \"alpha_fraction\": 0.5475475192070007,\n \"alphanum_fraction\": 0.5895895957946777,\n \"avg_line_length\": 23.317073822021484,\n \"blob_id\": \"757927141ee229f2023567a1b785f43da05e290a\",\n \"content_id\": \"ff7e660f8f741cd4f7a504d4a36df6aed383444d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1249,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 41,\n \"path\": \"/OOP/instance_method.py\",\n \"repo_name\": \"GroundP/OOP\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class User:\\n count = 0 # 클래스 변수\\n\\n def __init__(self, name, email, pw):\\n self.name = name\\n self.email = email\\n self.pw = pw\\n User.count += 1 # 인스턴스가 생성될 때마다 count가 증가됨\\n\\n def say_hello(self):\\n print(\\\"안녕하세요! 저는 {}입니다!\\\".format(self.name))\\n\\n def login(self, my_email, my_password):\\n if ( self.email == my_email and self.pw == my_password):\\n print(\\\"로그인 성공, 환영합니다.\\\")\\n else:\\n print(\\\"로그인 실패, 없는 아이디이거나 잘못된 비밀번호 입니다.\\\")\\n\\n\\nuser1 = User(\\\"김대위\\\", \\\"captain@codeit.kr\\\", \\\"12345\\\")\\n\\n# user1.login(user1, \\\"captain@codeit.kr\\\", \\\"12345\\\") ### 에러!\\nuser1.login(\\\"captain@codeit.kr\\\", \\\"12345\\\")\\n\\n\\nuser2 = User(\\\"강영훈\\\", \\\"younghoon@codeit.kr\\\", \\\"123456\\\")\\nuser3 = User(\\\"이윤수\\\", \\\"yoonsoo@codeit.kr\\\", \\\"abcdefg\\\")\\nuser4 = User(\\\"서혜린\\\", \\\"lisa@codeit.kr\\\", \\\"123456\\\")\\n\\nuser1.count = 10 # 이 방식은 인스턴스 변수를 설정하는 작업\\n\\nprint(User.count) #생성된 인스턴스의 개수 출력\\nprint(user1.count)\\nprint(user2.count)\\nprint(user3.count)\\n\\nUser.count = 20\\n\\nprint(User.count)\\nprint(user1.count) # 이미 user1의 count라는 인스턴스 변수는 생성이 되부렀으~\\nprint(user2.count)\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.642201840877533,\n \"alphanum_fraction\": 0.6574923396110535,\n \"avg_line_length\": 23.058822631835938,\n \"blob_id\": \"d8c445aecf204be6cdfa9dafc0759960060a9199\",\n \"content_id\": \"9017d1a1ba83a278873bd9aaa847b3bf6b7614f9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2257,\n \"license_type\": \"no_license\",\n \"max_line_length\": 124,\n \"num_lines\": 68,\n \"path\": \"/Quiz/캡슐화-신용카드.py\",\n \"repo_name\": \"GroundP/OOP\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\n신용 카드를 나타내는 CreditCard 클래스를 만들고 싶습니다. CreditCard 클래스에는\\n\\n소유자 이름\\n비밀 번호\\n카드 한도 정보\\n가 저장됩니다. 다음 조건들에 맞게 CreditCard 클래스를 완성하세요.\\n\\nCreditCard 클래스의 비밀번호, 카드 한도 인스턴스 변수 이름 앞에 밑줄 한 개를 써서 다른 개발자들에게 해당 변수를 직접 사용하는 것을 자제하라는 표시를 남깁시다. (_password, _payment_limit)\\n\\n@property를 이용해서 앞에 밑줄 하나를 쓴 변수들을 캡슐화하세요.\\n변수 _password\\ngetter 메소드: 문자열 \\\"비밀 번호는 볼 수 없습니다\\\"를 리턴한다.\\nsetter 메소드: 파라미터로 받은 값을 변수에 설정한다.\\n변수 _payment_limit\\ngetter 메소드: _payment_limit 변수를 리턴한다.\\nsetter 메소드: 파라미터로 받은 값을 변수에 설정한다.\\n단, setter 메소드에서 파라미터로 받은 값이 0과 MAX_PAYMENT_LIMIT 사이에 있는 값이 아니면 \\\"카드 한도는 0원 ~ 3천만 원 사이로 설정해주세요!\\\" 라는 메시지를 출력한다.\\n\\\"\\\"\\\"\\n\\nclass CreditCard:\\n MAX_PAYMENT_LIMIT = 30000000\\n\\n def __init__(self, name, password, payment_limit):\\n self._name = name\\n self._password = password\\n self._payment_limit = payment_limit\\n\\n @property\\n def name(self):\\n return self._name\\n\\n @name.setter\\n def name(self, name):\\n self._name = name\\n\\n @property\\n def password(self):\\n return \\\"비밀 번호는 볼 수 없습니다\\\"\\n\\n @password.setter\\n def password(self, password):\\n self._password = password\\n\\n @property\\n def payment_limit(self):\\n return self._payment_limit\\n\\n @payment_limit.setter\\n def payment_limit(self, limit):\\n if 0 < limit < CreditCard.MAX_PAYMENT_LIMIT:\\n self._payment_limit = limit\\n else:\\n print(\\\"카드 한도는 0원 ~ 3천만 원 사이로 설정해주세요!\\\")\\n\\ncard = CreditCard(\\\"강영훈\\\", \\\"123\\\", 100000)\\n\\nprint(card.name)\\nprint(card.password)\\nprint(card.payment_limit)\\n\\ncard.name = \\\"성태호\\\"\\ncard.password = \\\"1234\\\"\\ncard.payment_limit = -10\\n\\nprint(card.name)\\nprint(card.password)\\nprint(card.payment_limit)\"\n }\n]"},"num_files":{"kind":"number","value":9,"string":"9"}}},{"rowIdx":1177,"cells":{"repo_name":{"kind":"string","value":"Mela2014/lc_punch"},"repo_url":{"kind":"string","value":"https://github.com/Mela2014/lc_punch"},"snapshot_id":{"kind":"string","value":"a230af2c9d40b1af4932c800e72698de5b77d61a"},"revision_id":{"kind":"string","value":"498308e6a065af444a1d5570341231e4c51dfa3f"},"directory_id":{"kind":"string","value":"89e4c3dd91ceb3a4a5e74cfaedbb795152ebd1f9"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"string","value":"2023-07-13T03:44:56.963033"},"revision_date":{"kind":"string","value":"2021-08-25T05:44:40"},"committer_date":{"kind":"string","value":"2021-08-25T05:44:40"},"github_id":{"kind":"number","value":313742939,"string":"313,742,939"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5480349063873291,"alphanum_fraction":0.5545851588249207,"avg_line_length":34.230770111083984,"blob_id":"d347fe015c11150c938d8eb731dd835a73f71e3d","content_id":"afa85aebd028822adb5938b6b3cc6cb3330ae670","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":458,"license_type":"no_license","max_line_length":76,"num_lines":13,"path":"/lc327_sortedlist.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"from sortedcontainers import SortedList\nclass Solution:\n def countRangeSum(self, nums: List[int], lower: int, upper: int) -> int:\n maps = SortedList([0])\n rslt, cumsum = 0, 0\n for num in nums:\n cumsum += num\n left = maps.bisect_left(cumsum-upper)\n right = maps.bisect_right(cumsum-lower)\n if right > left:\n rslt += right-left\n maps.add(cumsum)\n return rslt\n"},{"alpha_fraction":0.3951434791088104,"alphanum_fraction":0.40176600217819214,"avg_line_length":29.200000762939453,"blob_id":"3a201150172241c9b4b4c303dc9346716847dbd8","content_id":"5417dcd7963241d015d6a0af5802b36404c00d6d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":453,"license_type":"no_license","max_line_length":51,"num_lines":15,"path":"/lc1510_minmax.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def winnerSquareGame(self, n: int) -> bool:\n memo = {}\n def dfs(n):\n if n in memo:\n return memo[n]\n if n == 0:\n return False\n for i in range(1, int(math.sqrt(n))+1):\n if i*i <= n and not dfs(n-i*i):\n memo[n] = True\n return True\n memo[n] = False\n return False\n return dfs(n)\n"},{"alpha_fraction":0.2914072275161743,"alphanum_fraction":0.30510586500167847,"avg_line_length":31.1200008392334,"blob_id":"d2cf0ea4453746c31bea86dd3150df8e3b295532","content_id":"47bb21048afdb4d0c9c946ed5f3a72c219302a97","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":803,"license_type":"no_license","max_line_length":54,"num_lines":25,"path":"/lc1028_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def recoverFromPreorder(self, S: str) -> TreeNode:\n currD, num = 0, \"\"\n root = None\n stk = []\n for c in S+'-':\n if c == \"-\":\n if not num:\n currD += 1\n else:\n curr = TreeNode(int(num))\n if not root:\n root = curr\n elif stk[-1][1] < currD:\n stk[-1][0].left = curr\n else:\n while stk[-1][1] >= currD:\n stk.pop()\n stk[-1][0].right = curr\n stk.append((curr, currD))\n num = \"\"\n currD = 1\n else:\n num += c\n return root\n"},{"alpha_fraction":0.4420772194862366,"alphanum_fraction":0.4607190489768982,"avg_line_length":45.9375,"blob_id":"329184f99068c8c6c2a68302731543954c9699c3","content_id":"1ed8a1be893ed796df20d53b043e8d75a10e21db","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":751,"license_type":"no_license","max_line_length":136,"num_lines":16,"path":"/lc1654_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minimumJumps(self, forbidden: List[int], a: int, b: int, x: int) -> int:\n dque, seen1, seen2, rslt, limit = collections.deque([(0, 1)]), set(forbidden), set(forbidden), 0, max(x, max(forbidden)) + a + b\n while dque:\n for _ in range(len(dque)):\n cur, last_forward = dque.popleft()\n if cur == x:\n return rslt\n if cur + a <= limit and cur + a not in seen1:\n seen1.add(cur + a)\n dque.append((cur + a, 1))\n if cur - b > 0 and cur - b not in seen2 and last_forward:\n seen2.add(cur - b)\n dque.append((cur - b, 0))\n rslt += 1\n return -1\n"},{"alpha_fraction":0.4156534969806671,"alphanum_fraction":0.43009117245674133,"avg_line_length":41.45161437988281,"blob_id":"fc1e99a275940910963950d2e2e1bed98048fcc4","content_id":"bc32ac165a32b9a0f09448c7a06173f017c1a81f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1316,"license_type":"no_license","max_line_length":109,"num_lines":31,"path":"/lc675_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def cutOffTree(self, forest: List[List[int]]) -> int:\n nrow, ncol, rslt = len(forest), len(forest[0]), 0\n heap = []\n for i in range(nrow):\n for j in range(ncol):\n if forest[i][j] > 0:\n heapq.heappush(heap, (forest[i][j], i, j))\n def bfs(nextval, nextx, nexty, currx, curry):\n dque, seen, step = collections.deque([(currx, curry)]), {(currx, curry)}, 0\n while dque:\n size = len(dque)\n for _ in range(size):\n x,y = dque.popleft()\n if x == nextx and y == nexty:\n return step\n for nx, ny in [(x+1, y), (x-1, y), (x, y+1), (x, y-1)]:\n if 0 <= nx < nrow and 0 <= ny < ncol and forest[nx][ny] > 0 and (nx, ny) not in seen:\n seen.add((nx, ny))\n dque.append((nx, ny))\n step += 1\n return -1\n\n currx, curry, visited = 0, 0, {(0, 0)}\n while heap:\n nextval, nextx, nexty = heapq.heappop(heap)\n step = bfs(nextval, nextx, nexty, currx, curry)\n if step == -1: return -1\n rslt += step\n currx, curry = nextx, nexty\n return rslt\n"},{"alpha_fraction":0.5039028525352478,"alphanum_fraction":0.5203816294670105,"avg_line_length":31.02777862548828,"blob_id":"f195a63cc4ab0f7832ec1b00b02f8ce795acb76b","content_id":"3d558678b9077ff2d8bea588e82980fa816f6763","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1153,"license_type":"no_license","max_line_length":77,"num_lines":36,"path":"/lc395_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"#divide and conquer\nclass Solution:\n def longestSubstring(self, s: str, k: int) -> int:\n counts = collections.Counter(s)\n for t in counts:\n if counts[t] < k:\n return max(self.longestSubstring(ss, k) for ss in s.split(t))\n return len(s)\n\n#sliding window\nclass Solution:\n def longestSubstring(self, s, k):\n count = 0\n for i in range(1, 27):\n count = max(count, self.helper(s, k, i))\n return count\n\n def helper(self, s, k, numUniqueTarget):\n start = end = numUnique = numNoLessThanK = count = 0\n chMap = [0]*128\n\n while end < len(s):\n if chMap[ord(s[end])] == 0: numUnique += 1\n chMap[ord(s[end])] += 1\n if chMap[ord(s[end])] == k: numNoLessThanK += 1\n end += 1\n\n while numUnique > numUniqueTarget:\n if chMap[ord(s[start])] == k: numNoLessThanK -= 1\n chMap[ord(s[start])] -= 1\n if chMap[ord(s[start])] == 0: numUnique -= 1\n start += 1\n\n if numUnique == numNoLessThanK: count = max(count, end-start)\n\n return count\n"},{"alpha_fraction":0.470016211271286,"alphanum_fraction":0.4797406792640686,"avg_line_length":35.29411697387695,"blob_id":"2f36fa1f14f9627fe23063fc90f8b124a898e7d4","content_id":"59b7854d460439836ad616650c4003d440dc499f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":617,"license_type":"no_license","max_line_length":66,"num_lines":17,"path":"/lc103_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def zigzagLevelOrder(self, root: TreeNode) -> List[List[int]]:\n if not root:\n return []\n queue, left, rslt = collections.deque([root]), 1, []\n while queue:\n lq = len(queue)\n temp = [0]*lq\n for i in range(lq):\n curr = queue.popleft()\n if left == 1: temp[i] = curr.val\n else:temp[lq-i-1] = curr.val\n if curr.left: queue.append(curr.left)\n if curr.right: queue.append(curr.right)\n left = (left+1)%2\n rslt.append(temp)\n return rslt\n"},{"alpha_fraction":0.4793578088283539,"alphanum_fraction":0.4931192696094513,"avg_line_length":38.6363639831543,"blob_id":"e68c093c341c44fb558fe7fb82d17744ee8e7980","content_id":"8da7356d5e183a7ed54283fdfd9edc2cedae404f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":436,"license_type":"no_license","max_line_length":75,"num_lines":11,"path":"/lc494_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findTargetSumWays(self, nums: List[int], S: int) -> int:\n memo = collections.defaultdict(int)\n def dfs(i, curr):\n if (i, curr) in memo:\n return memo[(i, curr)]\n if i == len(nums):\n return 1 if curr == S else 0\n memo[(i, curr)] = dfs(i+1, curr+nums[i])+dfs(i+1, curr-nums[i])\n return memo[(i, curr)]\n return dfs(0, 0)\n"},{"alpha_fraction":0.43008315563201904,"alphanum_fraction":0.448223739862442,"avg_line_length":44.620689392089844,"blob_id":"1e767327d4f1bc7f4db042487ade73f4e9cc03fb","content_id":"cbd6ecdca0ecd6f215235f4164a408a86564e31e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1323,"license_type":"no_license","max_line_length":82,"num_lines":29,"path":"/lc1810_dijkstra.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution(object):\n def findShortestPath(self, master: 'GridMaster') -> int:\n directions = {'U':'D', 'D':'U', 'L':'R', 'R':'L'}\n steps = {'U':(-1, 0), 'D':(1, 0), 'L':(0, -1), 'R':(0, 1)}\n self.target = None\n graph = {(0, 0): 0}\n # backtracking build graph\n def backtracking(x, y):\n if master.isTarget(): self.target = (x, y)\n for d in directions:\n nx, ny = x + steps[d][0], y+steps[d][1]\n if (nx, ny) in graph: continue\n if master.canMove(d):\n graph[(nx, ny)] = master.move(d)\n backtracking(nx, ny)\n master.move(directions[d])\n backtracking(0, 0)\n if self.target is None: return -1\n # bfs find rslt\n heap = [[0, 0, 0]] # cost, x, y\n seen = collections.defaultdict(lambda: float(\"inf\"))\n while heap:\n cost, x, y = heapq.heappop(heap)\n if (x, y) == self.target: return cost\n for nx, ny in [(x-1, y), (x+1, y), (x, y-1), (x, y+1)]:\n if (nx, ny) in graph and cost + graph[(nx, ny)] < seen[(nx, ny)] :\n seen[(nx, ny)] = cost + graph[(nx, ny)]\n heapq.heappush(heap, (cost + graph[(nx, ny)], nx, ny))\n return -1\n"},{"alpha_fraction":0.4340527653694153,"alphanum_fraction":0.4508393406867981,"avg_line_length":33.75,"blob_id":"13ae9507ba2df4bad736fe9333e05e9e39c32936","content_id":"975d7e989e249480027add086503c25c5a04ace9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":834,"license_type":"no_license","max_line_length":73,"num_lines":24,"path":"/lc403_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def canCross(self, stones: List[int]) -> bool:\n n = len(stones)\n dp = [False]*n\n dp[0] = True\n cand_steps =collections.defaultdict(set)\n cand_steps[0] = {1}\n for i in range(1, n):\n for j in range(i):\n need_step = stones[i] - stones[j]\n if dp[j] and need_step in cand_steps[j]:\n dp[i] = True\n cand_steps[i]|= {need_step, need_step+1, need_step-1}\n return dp[-1]\n\n def canCross(self, stones):\n maps = {x: set() for x in stones}\n maps[0].add(0)\n for x in stones[:-1]:\n for j in maps[x]:\n for k in range(max(1, j-1), j+2):\n if x + k in maps:\n maps[x+k].add(k)\n return bool(maps[stones[-1]])\n"},{"alpha_fraction":0.3972148597240448,"alphanum_fraction":0.4118037223815918,"avg_line_length":38.68421173095703,"blob_id":"38a12fed015208ae28761dc0a18ef23bd77f0d34","content_id":"6feeca8b38518ed917366d856855bc4dff12dbd7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1508,"license_type":"no_license","max_line_length":81,"num_lines":38,"path":"/lc1728_minmax.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def canMouseWin(self, grid: List[str], catJump: int, mouseJump: int) -> bool:\n nrow, ncol, thred = len(grid), len(grid[0]), 0\n for i in range(nrow):\n for j in range(ncol):\n if grid[i][j] == \"C\": catloc = (i, j)\n if grid[i][j] == \"M\": mouseloc = (i, j)\n if grid[i][j] == \"F\": foodloc = (i, j)\n if grid[i][j] != \"#\": thred += 1\n @lru_cache(None)\n def dfs(cat, mouse, step):\n if cat == mouse or cat == foodloc or step > 2*thred:\n return False\n if mouse == foodloc:\n return True\n if step%2 == 0:\n for nmouse in nextstep(mouse, mouseJump):\n if dfs(cat, nmouse, step + 1):\n return True\n return False\n else:\n for ncat in nextstep(cat, catJump):\n if not dfs(ncat, mouse, step+1):\n return False\n return True\n\n def nextstep(curr, step):\n rslt = []\n for dx, dy in [(1,0), (-1, 0), (0, 1), (0, -1)]:\n for jump in range(step+1):\n nx, ny = curr[0]+dx*jump, curr[1]+dy*jump\n if 0 <= nx < nrow and 0 <= ny < ncol and grid[nx][ny] != \"#\":\n rslt.append((nx, ny))\n else:\n break\n return rslt\n\n return dfs(catloc, mouseloc, 0)\n"},{"alpha_fraction":0.43044188618659973,"alphanum_fraction":0.4369885325431824,"avg_line_length":32.94444274902344,"blob_id":"97a1bfefcddace7bc2147cea4281f8e7dcaf7c3d","content_id":"ff1921bbfb0182276ed2c547e1f18252263ba36f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":611,"license_type":"no_license","max_line_length":84,"num_lines":18,"path":"/lc1300_bs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findBestValue(self, arr: List[int], target: int) -> int:\n left, right = target//len(arr), max(arr)\n def check(mid):\n rslt = 0\n for num in arr:\n if num < mid:\n rslt += num\n else:\n rslt += mid\n return rslt\n while left <= right:\n mid = (left + right)//2\n if check(mid) <= target:\n left = mid + 1\n else:\n right = mid - 1\n return left if abs(check(left)-target) < abs(check(right)-target) else right\n"},{"alpha_fraction":0.42088091373443604,"alphanum_fraction":0.4274061918258667,"avg_line_length":35.05882263183594,"blob_id":"972a581610e1d5edff78ff1ab0f7db0a39daa630","content_id":"d8851bfea52fc36f729981c40738b679981419c8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":613,"license_type":"no_license","max_line_length":78,"num_lines":17,"path":"/lc140_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def wordBreak(self, s: str, wordDict: List[str]) -> List[str]:\n if not wordDict: return []\n n, wordDict, ml = len(s), set(wordDict), len(max(wordDict, key = len))\n @lru_cache(None)\n def dfs(i):\n if i == n:\n return [[]]\n rslt = []\n for j in range(i, n):\n if j-i < ml and s[i:j+1] in wordDict:\n temp = dfs(j+1)\n for t in temp:\n rslt.append([s[i:j+1]]+t)\n return rslt\n temp = dfs(0)\n return [\" \".join(t) for t in temp]\n"},{"alpha_fraction":0.38760632276535034,"alphanum_fraction":0.42891860008239746,"avg_line_length":30.653846740722656,"blob_id":"bc8d555b2398bb91009c89b28e5ab7e57c8d7738","content_id":"2534016c1dabf6bfeb5bb533a9b8e19e20b854eb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":823,"license_type":"no_license","max_line_length":64,"num_lines":26,"path":"/lc850_array.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def rectangleArea(self, rectangles: List[List[int]]) -> int:\n events = []\n for x1, y1, x2, y2 in rectangles:\n events.append((y1, 1, x1, x2))\n events.append((y2, -1, x1, x2))\n events.sort()\n\n def cal_x_len():\n l_x, lastx = 0, 0\n for x1, x2 in active:\n lastx = max(lastx, x1)\n l_x += max(0, x2 -lastx)\n lastx = max(lastx, x2)\n return l_x\n active = []\n lasty = events[0][0]\n rslt = 0\n for y, status, x1, x2 in events:\n rslt += cal_x_len()*(y-lasty)\n if status == 1:\n bisect.insort(active, (x1, x2))\n else:\n active.remove((x1, x2))\n lasty = y\n return rslt % (10**9 + 7)\n"},{"alpha_fraction":0.25806450843811035,"alphanum_fraction":0.28535979986190796,"avg_line_length":25.866666793823242,"blob_id":"43917e1401edc0824b338f45741a612d022cc38a","content_id":"23082c089e53c7f58624f8caeda8e0c2b2fa88ec","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":403,"license_type":"no_license","max_line_length":43,"num_lines":15,"path":"/lc1541_str.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minInsertions(self, s: str) -> int:\n bal, rslt = 0, 0\n for c in s:\n if c == \"(\":\n if bal % 2 == 1:\n rslt += 1\n bal -= 1\n bal += 2\n else:\n bal -= 1\n if bal < 0:\n rslt += 1\n bal += 2\n return rslt + bal\n"},{"alpha_fraction":0.44805195927619934,"alphanum_fraction":0.4588744640350342,"avg_line_length":36.75,"blob_id":"a59ba58760e99e11d9437f2bd2d8583ea291951c","content_id":"eeeef588a565ced060783710049419b35fff958e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":462,"license_type":"no_license","max_line_length":80,"num_lines":12,"path":"/lc1340_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxJumps(self, arr: List[int], d: int) -> int:\n n = len(arr)\n @lru_cache(None)\n def dfs(i):\n rslt = 1\n for direction in [-1, 1]:\n for j in range(i+direction, i+d*direction+direction, direction):\n if j < 0 or j >= n or arr[j] >= arr[i]: break\n rslt = max(rslt, dfs(j)+1)\n return rslt\n return max(map(dfs, range(n)))\n \n"},{"alpha_fraction":0.489130437374115,"alphanum_fraction":0.49275362491607666,"avg_line_length":38.42856979370117,"blob_id":"b179d8616d77e0c28113b1a90a763eca56a6346a","content_id":"5ca46de261c412bc61b63106e8d632a51c1b7aaa","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":552,"license_type":"no_license","max_line_length":78,"num_lines":14,"path":"/lc743_graph.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def networkDelayTime(self, times: List[List[int]], N: int, K: int) -> int:\n graph = collections.defaultdict(list)\n for s, t, w in times:\n graph[s].append((w, t))\n hp, dist = [(0, K)], {}\n while hp:\n w, t = heapq.heappop(hp)\n if t in dist: continue\n dist[t] = w\n for wnext, tnext in graph[t]:\n if tnext not in dist:\n heapq.heappush(hp, (wnext + w, tnext))\n return max(dist.values()) if len(dist) == N else -1\n"},{"alpha_fraction":0.42007797956466675,"alphanum_fraction":0.4376218318939209,"avg_line_length":26.7297306060791,"blob_id":"698f59747869bc87136df7822ada29e91ff6ce0e","content_id":"5d619d1516af897e5cc44afe041dbd71dde9124c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1026,"license_type":"no_license","max_line_length":77,"num_lines":37,"path":"/lc315_segtree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Segtree:\n def __init__(self, n):\n self.n = n\n self.seg_tree = [0]*(2*n)\n\n def update(self, idx):\n idx = self.n+idx\n self.seg_tree[idx] += 1\n while idx > 1:\n self.seg_tree[idx>>1] = self.seg_tree[idx] + self.seg_tree[idx^1]\n idx >>= 1\n\n def sumRange(self, idx):\n l = self.n\n r = self.n + idx\n rslt = -self.seg_tree[r]\n while l <= r:\n if l&1:\n rslt += self.seg_tree[l]\n l += 1\n if r&1 == 0:\n rslt += self.seg_tree[r]\n r -= 1\n l >>= 1\n r >>= 1\n return rslt\n\nclass Solution:\n def countSmaller(self, nums: List[int]) -> List[int]:\n maps = {val:i for i, val in enumerate(sorted(nums))}\n n = len(nums)\n segtree = Segtree(n)\n rslt = [0]*n\n for i in range(n-1, -1, -1):\n segtree.update(maps[nums[i]])\n rslt[i] = segtree.sumRange(maps[nums[i]])\n return rslt\n"},{"alpha_fraction":0.4312500059604645,"alphanum_fraction":0.45781248807907104,"avg_line_length":39,"blob_id":"6f880938edd06562b84816f77a155dcb6db8fb3b","content_id":"f2ecb321a80541fd4830a06b494863763c7613ea","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":640,"license_type":"no_license","max_line_length":124,"num_lines":16,"path":"/lc329_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def longestIncreasingPath(self, matrix: List[List[int]]) -> int:\n if not matrix or not matrix[0]:\n return 0\n @lru_cache(None)\n def dfs(i, j):\n temp = 1\n for x, y in [(-1, 0), (1, 0), (0, -1), (0, 1)]:\n if i+x >= 0 and i+x < len(matrix) and j+y >= 0 and j+y < len(matrix[0]) and matrix[i+x][j+y] > matrix[i][j]:\n temp = max(temp, dfs(i+x, j+y)+1)\n return temp\n rslt = 0\n for i in range(len(matrix)):\n for j in range(len(matrix[0])):\n rslt = max(rslt, dfs(i, j))\n return rslt\n"},{"alpha_fraction":0.3567010164260864,"alphanum_fraction":0.38762885332107544,"avg_line_length":31.33333396911621,"blob_id":"ba6a58358cfd4e536253b6cd98b62787c71b4153","content_id":"7a99fc827e571d9d0696ee6be3c2ff688b69beb1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":485,"license_type":"no_license","max_line_length":73,"num_lines":15,"path":"/lc526_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def countArrangement(self, n: int) -> int:\n ini = (1 << n)-1\n @lru_cache(None)\n def dfs(i, bitmask):\n if i == n+1:\n return 1\n elif bitmask == 0:\n return 0\n rslt = 0\n for j in range(n):\n if (1 << j) & bitmask and ((j+1)%i == 0 or i%(j+1) == 0):\n rslt += dfs(i+1, bitmask & (~(1 << j)))\n return rslt\n return dfs(1, ini)\n"},{"alpha_fraction":0.516581654548645,"alphanum_fraction":0.5255101919174194,"avg_line_length":34.6363639831543,"blob_id":"fbe54e14935e7199004d57824d7619163926b04b","content_id":"b2a841a71a85820ce0609a0189e88234a479450d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":784,"license_type":"no_license","max_line_length":57,"num_lines":22,"path":"/lc687_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def longestUnivaluePath(self, root: TreeNode) -> int:\n self.rslt = 0\n def helper(root):\n if not root: return 0\n left = helper(root.left)\n right = helper(root.right)\n left_l, right_l = 0, 0\n if root.left and root.left.val == root.val:\n left_l = left + 1\n if root.right and root.right.val == root.val:\n right_l = right + 1\n self.rslt = max(self.rslt, left_l+right_l)\n return max(left_l, right_l)\n helper(root)\n return self.rslt\n"},{"alpha_fraction":0.416570782661438,"alphanum_fraction":0.4303797483444214,"avg_line_length":32.42307662963867,"blob_id":"a8915df713aea16aa6fa0f6128bfbbb7f7bc42e3","content_id":"bbb6a30fcd3dc32347e4621ee61c56e1c109c70e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":869,"license_type":"no_license","max_line_length":53,"num_lines":26,"path":"/lc1140_minmax.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def stoneGameII(self, piles: List[int]) -> int:\n memo = {}\n def dfs(i, m):\n if (i, m) in memo:\n return memo[(i, m)]\n if i+2*m >= len(piles):\n return sum(piles[i:])\n rslt = float(\"inf\")\n for j in range(i+1, i+2*m+1):\n rslt = min(rslt, dfs(j, max(j-i, m)))\n memo[(i, m)] = sum(piles[i:]) - rslt\n return memo[(i, m)]\n return dfs(0, 1)\n\nclass Solution:\n def stoneGameII(self, piles: List[int]) -> int:\n @lru_cache(None)\n def dfs(i, m):\n if i+2*m >= len(piles):\n return sum(piles[i:])\n rslt = float(\"inf\")\n for j in range(i+1, i+2*m+1):\n rslt = min(rslt, dfs(j, max(j-i, m)))\n return sum(piles[i:]) - rslt\n return dfs(0, 1)\n"},{"alpha_fraction":0.45179063081741333,"alphanum_fraction":0.4655647277832031,"avg_line_length":29.25,"blob_id":"9d3f41eea078167e1c4f19cb6eb03bd719525fb0","content_id":"b4d54361dd1121578bbb67bc6f3591f54a6a9d5a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":363,"license_type":"no_license","max_line_length":81,"num_lines":12,"path":"/lc1564_greedy.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxBoxesInWarehouse(self, boxes: List[int], warehouse: List[int]) -> int:\n boxes.sort()\n count = 0\n idx = 0\n for box in boxes[::-1]:\n if warehouse[idx] >= box:\n count += 1\n idx += 1\n if idx >= len(warehouse):\n return count\n return count\n"},{"alpha_fraction":0.46716004610061646,"alphanum_fraction":0.47641071677207947,"avg_line_length":29.885713577270508,"blob_id":"5abd3fce38f3297ec09d4380dd2cba2acb5db51a","content_id":"ff5f2e3b412b0d1ab3778e0f01aad36373555fa4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1081,"license_type":"no_license","max_line_length":83,"num_lines":35,"path":"/lc685_unionfind.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class DSU:\n def __init__(self, n):\n self.parents = list(range(n))\n self.size = [1]*n\n def find(self, p):\n while self.parents[p] != p:\n self.parents[p] = self.parents[self.parents[p]]\n p = self.parents[p]\n return p\n def union(self, p, q):\n pp, pq = self.find(p), self.find(q)\n if pp == pq: return True\n if self.size[pp] < self.size[pq]:\n pp, pq = pq, pp\n self.parents[pq] = pp\n self.size[pp] += self.size[pq]\n return False\n\nclass Solution:\n def findRedundantDirectedConnection(self, edges: List[List[int]]) -> List[int]:\n cand1, cand2, parents = None, None, {}\n for u, v in edges:\n if v in parents:\n cand1 = [parents[v], v]\n cand2 = [u, v]\n break\n parents[v] = u\n\n dsu = DSU(len(edges)+1)\n for u, v in edges:\n if cand2 == [u, v]: continue\n if dsu.union(u, v):\n if cand1: return cand1\n return [u, v]\n return cand2\n"},{"alpha_fraction":0.431314617395401,"alphanum_fraction":0.4549483060836792,"avg_line_length":26.079999923706055,"blob_id":"9566c4e7f1fe2479ca2819b2dddc589c023532af","content_id":"f622502f973a736e7efbe935ca5ccfe6a50195b5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":677,"license_type":"no_license","max_line_length":59,"num_lines":25,"path":"/lc1442_bit.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def countTriplets(self, arr: List[int]) -> int:\n rslt, n = 0, len(arr)\n prefix = [0]*(n+1)\n for i in range(n): prefix[i+1] = prefix[i] ^ arr[i]\n for left in range(n-1):\n for right in range(left+1, n):\n if prefix[right+1]^prefix[left] == 0:\n rslt += right-left\n return rslt\n\n\n\n\n\nclass Solution:\n def countTriplets(self, A):\n res = cur = 0\n count = {0: [1, 0]}\n for i, a in enumerate(A):\n cur ^= a\n n, total = count.get(cur, [0, 0])\n res += i * n - total\n count[cur] = [n + 1, total + i + 1]\n return res\n"},{"alpha_fraction":0.3865671753883362,"alphanum_fraction":0.4029850661754608,"avg_line_length":30.904762268066406,"blob_id":"1e121e386117baded936bb2ded0370efb1ee18dc","content_id":"09621e42fbded19807ecf0900a110f5a923875b8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":670,"license_type":"no_license","max_line_length":82,"num_lines":21,"path":"/lc996_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def numSquarefulPerms(self, nums: List[int]) -> int:\n count = collections.Counter(nums)\n maps = {x: {y for y in count if int((x+y)**0.5)**2 == x+y} for x in count}\n l = len(nums)\n def bk(curr, curr_c):\n if curr_c != 0:\n rslt = 0\n count[curr] -= 1\n for nxt in maps[curr]:\n if count[nxt]:\n rslt += bk(nxt, curr_c-1)\n count[curr] += 1\n else:\n rslt = 1\n return rslt\n rslt = 0\n for c in count:\n t = bk(c, l-1)\n rslt += t\n return rslt\n"},{"alpha_fraction":0.49809885025024414,"alphanum_fraction":0.5107731223106384,"avg_line_length":31.875,"blob_id":"c6d872a56efde6ff7d5eddcc309ea71b824bdee8","content_id":"5f1b1e0adff20cb076a2b5b3e7a7be9f4e9c75b4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":789,"license_type":"no_license","max_line_length":80,"num_lines":24,"path":"/lc1214_bt.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def twoSumBSTs(self, root1: TreeNode, root2: TreeNode, target: int) -> bool:\n stk, pool = [], set()\n while stk or root1:\n while root1:\n stk.append(root1)\n root1 = root1.left\n temp = stk.pop()\n pool.add(temp.val)\n root1 = temp.right\n stk = [root2]\n while stk:\n temp = stk.pop()\n if target-temp.val in pool:\n return True\n if temp.right: stk.append(temp.right)\n if temp.left: stk.append(temp.left)\n return False\n"},{"alpha_fraction":0.4743589758872986,"alphanum_fraction":0.4839743673801422,"avg_line_length":24.25,"blob_id":"93a14ae806c0064a739cdaa0d9b773fceff78d77","content_id":"9b036386de200c68be0d2ae7a737d070de3c5c98","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":312,"license_type":"no_license","max_line_length":62,"num_lines":12,"path":"/lc1402_greedy.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxSatisfaction(self, satisfaction: List[int]) -> int:\n satisfaction.sort(reverse=True)\n curr = 0\n rslt = 0\n for val in satisfaction:\n curr += val\n if curr < 0:\n break\n rslt += curr\n\n return rslt\n \n"},{"alpha_fraction":0.3292181193828583,"alphanum_fraction":0.35185185074806213,"avg_line_length":31.399999618530273,"blob_id":"4927d0a7826bc84d24f5881ccd7bb63dfa412625","content_id":"09f51eeedeeb3dc17a1224e374818556ff4d84f7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":486,"license_type":"no_license","max_line_length":48,"num_lines":15,"path":"/lc443_string.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def compress(self, chars: List[str]) -> int:\n rslt, cnt, left = 0, 0, 0\n for i, ch in enumerate(chars+[\")\"]):\n if i > 0 and ch != chars[i-1]:\n chars[left] = chars[i-1]\n left += 1\n if cnt > 1:\n for t in str(cnt):\n chars[left] = t\n left += 1\n cnt = 1\n else:\n cnt += 1\n return left\n"},{"alpha_fraction":0.44989773631095886,"alphanum_fraction":0.4621676802635193,"avg_line_length":33.92856979370117,"blob_id":"47fb4b02413f86ee816d32e394c03ad0b0666c12","content_id":"7a822dadca3981d751762f63fe914a3d5323f2ed","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":489,"license_type":"no_license","max_line_length":83,"num_lines":14,"path":"/lc1052_sw.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxSatisfied(self, customers: List[int], grumpy: List[int], X: int) -> int:\n if X > len(customers):\n return sum(customers)\n i, curr, wind, cumsum= 0, 0, 0, 0\n for c, g in zip(customers, grumpy):\n cumsum += (1-g)*c\n if i < X:\n curr += g*c\n else:\n curr += g*c - grumpy[i-X]*customers[i-X]\n wind = max(wind, curr)\n i += 1\n return cumsum + wind\n"},{"alpha_fraction":0.352769672870636,"alphanum_fraction":0.38678327202796936,"avg_line_length":37.11111068725586,"blob_id":"6ff9e12c00a82362dd2b60962848d75c83837b17","content_id":"655e9f50624ee83d390a076037db008235a1f85d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1029,"license_type":"no_license","max_line_length":113,"num_lines":27,"path":"/lc289_array.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def gameOfLife(self, board: List[List[int]]) -> None:\n \"\"\"\n Do not return anything, modify board in-place instead.\n \"\"\"\n nrow, ncol = len(board), len(board[0])\n def helper(i, j):\n # -2: change from 1 to 0\n # -1: change from 0 to 1\n cl = 0\n for x, y in [(i-1, j-1), (i-1, j), (i-1, j+1), (i, j -1), (i, j+1),(i+1, j-1), (i+1, j), (i+1, j+1)]:\n if x < 0 or x >= nrow or y < 0 or y >= ncol:\n continue\n cl += 1 if board[x][y] in (1, -2) else 0\n return cl\n\n for i in range(nrow):\n for j in range(ncol):\n cl = helper(i, j)\n if board[i][j] == 1 and (cl < 2 or cl > 3):\n board[i][j] = -2\n if board[i][j] == 0 and cl == 3:\n board[i][j] = -1\n for i in range(nrow):\n for j in range(ncol):\n if board[i][j] < 0:\n board[i][j] += 2\n"},{"alpha_fraction":0.5032397508621216,"alphanum_fraction":0.5032397508621216,"avg_line_length":32.07143020629883,"blob_id":"baa3542686b62558bd71670a51ed9b640f8635ee","content_id":"ffcbadaffc7e9ad6e1f6c77955ed415b3dbbb10f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":463,"license_type":"no_license","max_line_length":57,"num_lines":14,"path":"/lc199_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def rightSideView(self, root: TreeNode) -> List[int]:\n if not root:\n return []\n queue = collections.deque([root])\n rslt = []\n while queue:\n lth = len(queue)\n for _ in range(lth):\n temp = queue.popleft()\n if temp.left: queue.append(temp.left)\n if temp.right: queue.append(temp.right)\n rslt.append(temp.val)\n return rslt\n"},{"alpha_fraction":0.45728641748428345,"alphanum_fraction":0.4673366844654083,"avg_line_length":33.6363639831543,"blob_id":"bb189ecf57b1f1434dd356240b1352e4a3d21905","content_id":"ece9fa6555c80365a375aaa8ff5c55dc0dc7d956","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":398,"license_type":"no_license","max_line_length":65,"num_lines":11,"path":"/lc90_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def subsetsWithDup(self, nums: List[int]) -> List[List[int]]:\n nums.sort()\n rslt = []\n def backtracking(temp, i):\n rslt.append(temp)\n for j, num in enumerate(nums[i:]):\n if j == 0 or num != nums[j+i-1]:\n backtracking(temp+[num], i+j+1)\n backtracking([], 0)\n return rslt\n \n"},{"alpha_fraction":0.403474897146225,"alphanum_fraction":0.42374518513679504,"avg_line_length":44.043479919433594,"blob_id":"384f660eb7a9141a61ee6c11efe5aa2b9d095074","content_id":"495786a7f23c3244c0fb5cdc07a8c9e92097ae37","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1036,"license_type":"no_license","max_line_length":94,"num_lines":23,"path":"/lc499_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findShortestWay(self, maze: List[List[int]], ball: List[int], hole: List[int]) -> str:\n directions = {\"d\":(1, 0), \"l\":(0, -1), \"r\":(0, 1),\"u\":(-1, 0)}\n heap, visited = [(0,\"\", ball[0], ball[1])], set()\n nrow, ncol = len(maze), len(maze[0])\n while heap:\n step, path, currx, curry = heapq.heappop(heap)\n if currx == hole[0] and curry == hole[1]:\n return path\n if (currx, curry) in visited:\n continue\n visited.add((currx, curry))\n for dr in [\"d\", \"l\", \"r\", \"u\"]:\n dx, dy = directions[dr]\n tx, ty, l = currx, curry, 0\n while 0 <= tx+dx < nrow and 0 <= ty+dy < ncol and maze[tx+dx][ty+dy] == 0:\n tx += dx\n ty += dy\n l += 1\n if tx == hole[0] and ty == hole[1]:\n break\n heapq.heappush(heap, (step+l, path+dr, tx, ty))\n return \"impossible\"\n"},{"alpha_fraction":0.3906705677509308,"alphanum_fraction":0.40816327929496765,"avg_line_length":29.272727966308594,"blob_id":"9adb5381687a16c7af2000073740cf46de44ec1e","content_id":"2f240bd3ed54b5d9d0126d0f65b346c9836b394a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":343,"license_type":"no_license","max_line_length":43,"num_lines":11,"path":"/lc54_greedy.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def jump(self, nums: List[int]) -> int:\n rslt, last, curr = 0, 0, 0\n for i, num in enumerate(nums[:-1]):\n curr = max(curr, i+num)\n if i == last:\n rslt += 1\n last = curr\n if last >= len(nums)-1:\n break\n return rslt\n\n \n"},{"alpha_fraction":0.40556368231773376,"alphanum_fraction":0.4231332242488861,"avg_line_length":28.69565200805664,"blob_id":"af7dc6631061067e9e1caf2d70f0306c295b0cab","content_id":"8cdf1524aab9ea43358ccce3a55c563b7f581590","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":683,"license_type":"no_license","max_line_length":67,"num_lines":23,"path":"/lc377_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def combinationSum4(self, nums: List[int], target: int) -> int:\n dp = [0]*(target+1)\n dp[0] = 1\n for i in range(target+1):\n for num in nums:\n if i-num >= 0:\n dp[i] = dp[i] + dp[i-num]\n return dp[-1]\n \nclass Solution:\n def combinationSum4(self, nums: List[int], target: int) -> int:\n memo = {0:1}\n def dfs(t):\n if t in memo:\n return memo[t]\n rslt = 0\n for num in nums:\n if num <= t:\n rslt += dfs(t-num)\n memo[t] = rslt\n return rslt\n return dfs(target)\n"},{"alpha_fraction":0.5171840190887451,"alphanum_fraction":0.5254988670349121,"avg_line_length":29.576271057128906,"blob_id":"f9279054ae7bd1cae7c75b6c6c6bea95a4e07a19","content_id":"d43ff81f25ac98e85795426b2d4c16591492736a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1804,"license_type":"no_license","max_line_length":55,"num_lines":59,"path":"/lc460_ood.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Node:\n def __init__(self, val = 0, key = 0, freq = 1):\n self.val = val\n self.key = key\n self.left = self.right = None\n self.freq = 1\n\nclass Dll:\n def __init__(self):\n self.snode = Node()\n self.snode.left =self.snode.right = self.snode\n self.size = 0\n def append(self, node):\n node.left = self.snode.left\n node.right = self.snode\n self.snode.left = node\n node.left.right = node\n self.size += 1\n def remove(self, node):\n node.left.right = node.right\n node.right.left = node.left\n node.left, node.right = None, None\n self.size -= 1\n def pop(self):\n temp = self.snode.right\n self.remove(temp)\n return temp\n\nclass LFUCache:\n\n def __init__(self, capacity: int):\n self.keynode = {}\n self.freq = collections.defaultdict(Dll)\n self.capacity = capacity\n self.min_freq = 1\n def get(self, key: int) -> int:\n if key not in self.keynode: return -1\n curr = self.keynode[key]\n dll = self.freq[curr.freq]\n dll.remove(curr)\n curr.freq += 1\n self.freq[curr.freq].append(curr)\n if self.freq[self.min_freq].size == 0:\n self.min_freq += 1\n return curr.val\n def put(self, key: int, value: int) -> None:\n if self.capacity <= 0:\n return\n if key in self.keynode:\n self.keynode[key].val = value\n self.get(key)\n else:\n if self.capacity == len(self.keynode):\n temp = self.freq[self.min_freq].pop()\n self.keynode.pop(temp.key)\n curr = Node(val = value, key = key)\n self.keynode[key] = curr\n self.freq[1].append(curr)\n self.min_freq = 1\n"},{"alpha_fraction":0.4553571343421936,"alphanum_fraction":0.460317462682724,"avg_line_length":36.25925827026367,"blob_id":"875dd47140461742199f08e513e9f2f8f8960d50","content_id":"a35ec8d83cfb701a6d35f76f714d7faf2116f233","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1008,"license_type":"no_license","max_line_length":74,"num_lines":27,"path":"/lc721_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def accountsMerge(self, accounts: List[List[str]]) -> List[List[str]]:\n email_g, email_m = collections.defaultdict(set), {}\n for account in accounts:\n for email in account[1:]:\n if email != account[1]:\n email_g[account[1]].add(email)\n email_g[email].add(account[1])\n email_m[email] = account[0]\n rslt, visited = [], set()\n def dfs(email):\n if email not in email_g:\n visited.add(email)\n return [email]\n else:\n rslt = [email]\n for em in email_g[email]:\n if em not in visited:\n visited.add(em)\n rslt += dfs(em)\n return rslt\n for email, holder in email_m.items():\n if email not in visited:\n visited.add(email)\n rslt.append([holder]+sorted(dfs(email)))\n\n return rslt \n"},{"alpha_fraction":0.4060773551464081,"alphanum_fraction":0.42817679047584534,"avg_line_length":28.351350784301758,"blob_id":"f3ed4a2c7ecd94b2bf79cd39bb19fc55c9b4903a","content_id":"ce95626b39a66b70b92b1b5ac33ce649ad773dd5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1086,"license_type":"no_license","max_line_length":61,"num_lines":37,"path":"/lc1387_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# dfs + memoization\nclass Solution:\n def getKth(self, lo: int, hi: int, k: int) -> int:\n memo = {1:1}\n def dfs(i):\n if i in memo:\n return memo[i]\n elif i%2 == 0:\n memo[i] = dfs(i//2) + 1\n else:\n memo[i] = dfs(3*i + 1) + 1\n return memo[i]\n rslt = []\n for i in range(lo, hi+1):\n rslt.append((i, dfs(i)))\n rslt.sort(key=lambda x: x[1])\n return rslt[k-1][0]\n\n\n# slow bfs\nclass Solution:\n def getKth(self, lo: int, hi: int, k: int) -> int:\n queue = collections.deque()\n for i in range(lo, hi+1):\n queue.append((i, 0, i))\n rslt = []\n while queue:\n ori, curr_power, curr = queue.popleft()\n if curr == 1:\n rslt.append(ori)\n elif curr %2 == 0:\n queue.append((ori, curr_power+1, curr//2))\n else:\n queue.append((ori, curr_power+1, curr*3 + 1))\n if len(rslt) == k:\n break\n return rslt[-1]\n"},{"alpha_fraction":0.4226190447807312,"alphanum_fraction":0.494047611951828,"avg_line_length":44.818180084228516,"blob_id":"d832a86dcef45960c5c63ba153287f924f738336","content_id":"15a793533335efe6251f748f510c6815062cc56b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":504,"license_type":"no_license","max_line_length":105,"num_lines":11,"path":"/lc1458_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxDotProduct(self, nums1: List[int], nums2: List[int]) -> int:\n l_1, l_2 = len(nums1), len(nums2)\n dp = [nums1[0]*nums2[0]]\n for i in range(1, l_1): dp.append(max(dp[-1], nums1[i]*nums2[0]))\n for j in range(1, l_2):\n dp2 = [max(dp[0], nums1[0]*nums2[j])]\n for i in range(1, l_1):\n dp2.append(max(dp[i-1], dp2[i-1], dp[i], dp[i-1] + nums1[i]*nums2[j], nums1[i]*nums2[j]))\n dp = dp2\n return dp[-1]\n"},{"alpha_fraction":0.41450777649879456,"alphanum_fraction":0.4257340133190155,"avg_line_length":32.08571243286133,"blob_id":"8d6c22e72faff098bc692f54c9e35666e19e18f0","content_id":"64c80e35f26a670e3a3604334a83a795b78e7afe","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1158,"license_type":"no_license","max_line_length":63,"num_lines":35,"path":"/lc1552_bs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxDistance(self, position: List[int], m: int) -> int:\n position.sort()\n # def helper(mid):\n # rslt, left = 1, 0\n # for right in range(1, len(position)):\n # if position[right]-position[left] >= mid:\n # rslt += 1\n # left = right\n # return rslt\n # while left <= right:\n # mid = (left + right)//2\n # if helper(mid) >= m:\n # left = mid + 1\n # else:\n # right = mid - 1\n\nclass Solution:\n def maxDistance(self, position: List[int], m: int) -> int:a\n def helper(mid):\n pre = position[0]\n for _ in range(m-1):\n pos = bisect.bisect_left(position, pre + mid)\n if pos == len(position):\n return False\n pre = position[pos]\n return True\n left, right = 0, max(position)\n while left <= right:\n mid = (left + right)//2\n if helper(mid):\n left = mid + 1\n else:\n right = mid - 1\n return right\n"},{"alpha_fraction":0.5052381157875061,"alphanum_fraction":0.5057142972946167,"avg_line_length":30.34328269958496,"blob_id":"c4ce2f4f9598a57d0aaad954e99ccbc53dbd44ae","content_id":"57b301f888bf5ef4e55f01b3afff75f1b9cf2c9a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2100,"license_type":"no_license","max_line_length":66,"num_lines":67,"path":"/lc545_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def boundaryOfBinaryTree(self, root: TreeNode) -> List[int]:\n rslt = []\n # root\n rslt.append(root.val)\n if not root.left and not root.right:\n return rslt\n # left boundary\n if root.left:\n left = root.left\n while left.left or left.right:\n rslt.append(left.val)\n left = left.left if left.left else left.right\n # leaves\n stack = [root]\n while stack:\n temp = stack.pop()\n if not temp.left and not temp.right:\n rslt.append(temp.val)\n if temp.right:\n stack.append(temp.right)\n if temp.left:\n stack.append(temp.left)\n # right boundary\n temp = []\n if root.right:\n right = root.right\n while right.right or right.left:\n temp.append(right.val)\n right = right.right if right.right else right.left\n return rslt + temp[::-1]\n\nclass Solution(object):\n def boundaryOfBinaryTree(self, root):\n def dfs_leftmost(node):\n if not node or not node.left and not node.right:\n return\n boundary.append(node.val)\n if node.left:\n dfs_leftmost(node.left)\n else:\n dfs_leftmost(node.right)\n\n def dfs_leaves(node):\n if not node:\n return\n if node != root and not node.left and not node.right:\n boundary.append(node.val)\n dfs_leaves(node.left)\n dfs_leaves(node.right)\n\n def dfs_rightmost(node):\n if not node or not node.left and not node.right:\n return\n if node.right:\n dfs_rightmost(node.right)\n else:\n dfs_rightmost(node.left)\n boundary.append(node.val)\n\n if not root:\n return []\n boundary = [root.val]\n dfs_leftmost(root.left)\n dfs_leaves(root)\n dfs_rightmost(root.right)\n return boundary\n"},{"alpha_fraction":0.4729166626930237,"alphanum_fraction":0.4791666567325592,"avg_line_length":33.28571319580078,"blob_id":"3e2c80aa1feaf8846e1dbcfd11a8aade227dfbfd","content_id":"adbcb29d4720763d3d854c35e89a5f4bea26a044","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":480,"license_type":"no_license","max_line_length":64,"num_lines":14,"path":"/lc47_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def permuteUnique(self, nums: List[int]) -> List[List[int]]:\n if not nums: return []\n rslt = []\n nums.sort()\n def backtracking(temp, cand):\n if not cand:\n rslt.append(temp)\n for i, num in enumerate(cand):\n if i != 0 and num == cand[i-1]:\n continue\n backtracking(temp+[num], cand[:i]+cand[i+1:])\n backtracking([], nums)\n return rslt\n"},{"alpha_fraction":0.3826998770236969,"alphanum_fraction":0.39842724800109863,"avg_line_length":41.38888931274414,"blob_id":"6f694b7f6e3ee007f10d486a7688893739416ca8","content_id":"bd28fae0e00a6452d22d08d9fd19dfbd7d9e7a04","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":763,"license_type":"no_license","max_line_length":101,"num_lines":18,"path":"/lc51_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def solveNQueens(self, n: int) -> List[List[str]]:\n rslt, visited= [], set()\n def backtracking(i, curr):\n if len(curr) == n:\n rslt.append(curr)\n else:\n for j in range(n):\n if (2, j) not in visited and (3, j-i) not in visited and (4, j+i) not in visited:\n visited.add((2, j))\n visited.add((3, j-i))\n visited.add((4, j+i))\n backtracking(i+1, curr + [\".\"*j+\"Q\"+\".\"*(n-j-1)] )\n visited.remove((2, j))\n visited.remove((3, j-i))\n visited.remove((4, j+i))\n backtracking(0, [])\n return rslt\n"},{"alpha_fraction":0.36244019865989685,"alphanum_fraction":0.4019138813018799,"avg_line_length":38.80952453613281,"blob_id":"e2ef0f852b2d8ec0bf99432ba64fbdca93618c57","content_id":"32c6f5769f3247048e4340edc2349c52a60a98d2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":836,"license_type":"no_license","max_line_length":76,"num_lines":21,"path":"/lc764_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def orderOfLargestPlusSign(self, N: int, mines: List[List[int]]) -> int:\n mines = {tuple(x) for x in mines}\n dp = [[[0,0] for _ in range(N+2)] for _ in range(N+2)]\n # left, above, right, below\n # left, above\n for i in range(N):\n for j in range(N):\n if (i, j) not in mines:\n dp[i+1][j+1][0] = dp[i+1][j][0] + 1\n dp[i+1][j+1][1] = dp[i][j+1][1] + 1\n # right, below\n rslt = 0\n for i in range(N, 0, -1):\n for j in range(N, 0, -1):\n if (i-1, j-1) not in mines:\n dp[i][j][0] = min(dp[i][j][0], dp[i][j+1][0] + 1)\n dp[i][j][1] = min(dp[i][j][1], dp[i+1][j][1] + 1)\n rslt = max(rslt, min(dp[i][j]))\n\n return rslt\n"},{"alpha_fraction":0.45351043343544006,"alphanum_fraction":0.47438329458236694,"avg_line_length":36.64285659790039,"blob_id":"b0ddfe3297c3c7e05ea763bb1bc14dccf9055bdf","content_id":"dced0f05a6bd49f6d482ca031e83535c2d4f42b6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":527,"license_type":"no_license","max_line_length":76,"num_lines":14,"path":"/lc340_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def lengthOfLongestSubstringKDistinct(self, s: str, k: int) -> int:\n container, count, left, rslt = collections.defaultdict(int), 0, 0, 0\n for right, c in enumerate(s):\n if container[c] == 0:\n count += 1\n container[c] += 1\n while count > k:\n container[s[left]] -= 1\n if container[s[left]] == 0:\n count -= 1\n left += 1\n rslt = max(rslt, right-left+1)\n return rslt\n"},{"alpha_fraction":0.4835423231124878,"alphanum_fraction":0.4858934283256531,"avg_line_length":33.486488342285156,"blob_id":"4d1fdfbf48457605f06cf93dc3ec75876052764b","content_id":"636a584229de66963e2f2ca9407c5d3e8845ed1e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1276,"license_type":"no_license","max_line_length":97,"num_lines":37,"path":"/lc1644_lca.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def lowestCommonAncestor(self, root: 'TreeNode', p: 'TreeNode', q: 'TreeNode') -> 'TreeNode':\n self.mark = 0\n def dfs(root, p, q):\n if not root: return None\n left = dfs(root.left, p, q)\n right = dfs(root.right, p, q)\n if root == p or root == q:\n self.mark += 1\n return root\n if left and right:\n return root\n return left or right\n\n rslt = dfs(root, p, q)\n return rslt if self.mark == 2 else None\n def lowestCommonAncestor(self, root: 'TreeNode', p: 'TreeNode', q: 'TreeNode') -> 'TreeNode':\n parents = {root:None}\n stack = [root]\n while stack:\n parent = stack.pop()\n if parent.right:\n parents[parent.right] = parent\n stack.append(parent.right)\n if parent.left:\n parents[parent.left] = parent\n stack.append(parent.left)\n cands = set()\n if p not in parents or q not in parents:\n return None\n while p in parents:\n cands.add(p)\n p = parents[p]\n while q in parents:\n if q in cands:\n return q\n q = parents[q]\n"},{"alpha_fraction":0.5107376575469971,"alphanum_fraction":0.518207311630249,"avg_line_length":34.70000076293945,"blob_id":"9929b145b3d6f4f0547a8f7251546178501b7a20","content_id":"4b62afa6b32498e3551a3fa9d946079340880865","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1071,"license_type":"no_license","max_line_length":77,"num_lines":30,"path":"/lc105_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def buildTree(self, preorder: List[int], inorder: List[int]) -> TreeNode:\n\n if not preorder or not inorder: return None\n root = TreeNode(preorder[0])\n idx = inorder.index(root.val)\n root.left = self.buildTree(preorder[1:idx+1], inorder[:idx])\n root.right = self.buildTree(preorder[idx+1:], inorder[idx+1:])\n return root\n\n\nclass Solution:\n def buildTree(self, preorder: List[int], inorder: List[int]) -> TreeNode:\n nodemap = {val:idx for idx, val in enumerate(inorder)}\n stk, head = [], None\n\n for val in preorder:\n if not head:\n head = TreeNode(val)\n stk.append(head)\n else:\n temp = TreeNode(val)\n if nodemap[val] < nodemap[stk[-1].val]:\n stk[-1].left = temp\n else:\n while stk and nodemap[stk[-1].val] < nodemap[val]:\n last = stk.pop()\n last.right = temp\n stk.append(temp)\n return head\n"},{"alpha_fraction":0.3719165027141571,"alphanum_fraction":0.4079696536064148,"avg_line_length":39.53845977783203,"blob_id":"21e5c317d0a80623cb5b1cc8a4f9a6bbb5cd01a5","content_id":"597dcf00fc3c12e24bb19422ac5ae3447cd32463","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":527,"license_type":"no_license","max_line_length":71,"num_lines":13,"path":"/lc576_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findPaths(self, m: int, n: int, N: int, i: int, j: int) -> int:\n memo = {}\n def dfs(x, y, step):\n if (x, y, step) in memo: return memo[(x, y, step)]\n if (x < 0 or x >= m or y < 0 or y >= n): return 1\n if step == 0: return 0\n rslt = 0\n for dx, dy in [(-1, 0), (1, 0), (0, -1), (0, 1)]:\n rslt += dfs(x+dx, y+dy, step-1)\n memo[(x, y, step)] = rslt\n return rslt\n return dfs(i, j, N)%(10**9+7)\n"},{"alpha_fraction":0.5033259391784668,"alphanum_fraction":0.5133037567138672,"avg_line_length":36.58333206176758,"blob_id":"b5167451c7fa3798d9ee1f9b7c3b0e83a74799b3","content_id":"5956b942f7d05f1ef5614a99de700bc0f9971139","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":902,"license_type":"no_license","max_line_length":72,"num_lines":24,"path":"/lc1425_deque.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def constrainedSubsetSum(self, nums: List[int], k: int) -> int:\n dque, dp= collections.deque(), [0]*len(nums)\n for idx, num in enumerate(nums):\n while dque and idx - dque[0] > k:\n dque.popleft()\n dp[idx] = max(num, dp[dque[0]]+num) if idx != 0 else num\n while dque and dp[dque[-1]] < dp[idx]:\n dque.pop()\n dque.append(idx)\n return max(dp)\n\n\nclass Solution:\n def constrainedSubsetSum(self, nums: List[int], k: int) -> int:\n dque = collections.deque()\n for idx, num in enumerate(nums):\n if dque and idx - dque[0] > k:\n dque.popleft()\n nums[idx] = max(num, nums[dque[0]]+num) if idx != 0 else num\n while dque and nums[dque[-1]] <= nums[idx]:\n dque.pop()\n dque.append(idx)\n return max(nums)\n"},{"alpha_fraction":0.4988713264465332,"alphanum_fraction":0.5101580023765564,"avg_line_length":39.272727966308594,"blob_id":"ab02af74017c3e4bf2b0db3ae88e2ceae6cb81cf","content_id":"b2c17440c0317f95ee95e850a30a48a2e891198b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":443,"license_type":"no_license","max_line_length":102,"num_lines":11,"path":"/lc887_minmax.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def stoneGame(self, piles: List[int]) -> bool:\n memo = {}\n def dfs(left, right):\n if (left, right) in memo:\n return memo[(left, right)]\n if left == right:\n return piles[left]\n memo[(left, right)] = max(piles[left]-dfs(left+1, right), piles[right]-dfs(left, right-1))\n return memo[(left, right)]\n return dfs(0, len(piles)-1) > 0\n"},{"alpha_fraction":0.409411758184433,"alphanum_fraction":0.4176470637321472,"avg_line_length":37.6363639831543,"blob_id":"fcff6264570d94f2212d80a19326d64b5935f053","content_id":"130aa41003d73c79da6870dcda50388a6fee9446","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":850,"license_type":"no_license","max_line_length":73,"num_lines":22,"path":"/lc1597_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def expTree(self, s):\n op_pr = {\"+\":2, \"-\": 2, \"*\":1, \"/\":1, \"(\":3, \")\":2}\n node_stack, op_stack = [], []\n for c in s:\n if c.isdigit():\n node_stack.append(Node(c))\n elif c == '(':\n op_stack.append(c)\n else:\n while op_stack and op_pr[c] >= op_pr[op_stack[-1]]:\n temp = Node(op_stack.pop(), right = node_stack.pop())\n temp.left = node_stack.pop()\n node_stack.append(temp)\n if c == ')': op_stack.pop()\n else:\n op_stack.append(c)\n while op_stack:\n temp = Node(op_stack.pop(), right = node_stack.pop())\n temp.left = node_stack.pop()\n node_stack.append(temp)\n return node_stack.pop()\n"},{"alpha_fraction":0.38461539149284363,"alphanum_fraction":0.4282744228839874,"avg_line_length":39.08333206176758,"blob_id":"8f9572047b55fc30618fb58f36c76e3d93ffcece","content_id":"e69181adfdf8e4780bc0ea106a190f6d24e9bd08","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":481,"license_type":"no_license","max_line_length":72,"num_lines":12,"path":"/lc1312_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minInsertions(self, s: str) -> int:\n n = len(s)\n dp = [1 if s[0] == s[-1] else 0]\n for i in range(1, n): dp.append(1 if s[i] == s[-1] else dp[i-1])\n for j in range(1, n):\n tdp = [max(dp[0], 1 if s[0] == s[n-j-1] else 0)]\n for i in range(1, n-j):\n temp = 1 if s[i] == s[n-j-1] else 0\n tdp.append(max(dp[i-1]+ temp, dp[i], tdp[i-1]))\n dp = tdp\n return n-dp[-1]\n"},{"alpha_fraction":0.43044620752334595,"alphanum_fraction":0.4356955289840698,"avg_line_length":30.75,"blob_id":"c3e435606c51e6f76788163182b3461dc86166b1","content_id":"e3d6fe2d4b6dad08078b8ae666d10a04e45c25c8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":381,"license_type":"no_license","max_line_length":57,"num_lines":12,"path":"/lc784_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def letterCasePermutation(self, S: str) -> List[str]:\n rslt, n = [], len(S)\n def backtracking(i, curr):\n if i == n:\n rslt.append(curr)\n else:\n x = S[i]\n for y in {x.upper(), x.lower()}:\n backtracking(i+1, curr+y)\n backtracking(0, \"\")\n return rslt\n"},{"alpha_fraction":0.40512821078300476,"alphanum_fraction":0.44102564454078674,"avg_line_length":33.411766052246094,"blob_id":"8b89834cd6e0aaa7091cc80eaebc9e198451bb71","content_id":"954540332dbd9f102ae08c6693864b3488e14ea1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":585,"license_type":"no_license","max_line_length":66,"num_lines":17,"path":"/lc1870_bs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minSpeedOnTime(self, dist: List[int], hour: float) -> int:\n if hour < len(dist) - 1: return -1\n left, right = 1, int(100000/0.01)\n def check(mid):\n rslt = 0\n for d in dist[:-1]:\n rslt += (d-1)//mid + 1\n return rslt + dist[-1]/mid\n while left <= right:\n mid = (left + right)//2\n temp = check(mid)\n if temp <= hour:\n right = mid - 1\n else:\n left = mid + 1\n return int(left) if check(left) <= hour else -1\n"},{"alpha_fraction":0.37295082211494446,"alphanum_fraction":0.3862704932689667,"avg_line_length":41.39130401611328,"blob_id":"c4d6f75fd0060aeae61f69a9b7e447837cb6ece2","content_id":"4e415d00257980aa7e6106e183996ce0777f1f9e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":976,"license_type":"no_license","max_line_length":87,"num_lines":23,"path":"/lc1284_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minFlips(self, mat: List[List[int]]) -> int:\n start, nrow, ncol = 0, len(mat), len(mat[0])\n for i in range(nrow):\n for j in range(ncol):\n start |= mat[i][j]<< (i*ncol+j)\n dque, visited, step = collections.deque([start]), {start}, 0\n while dque:\n size = len(dque)\n for _ in range(size):\n curr = dque.popleft()\n if curr == 0: return step\n for i in range(nrow):\n for j in range(ncol):\n temp = curr\n for di, dj in [(i, j), (i+1, j), (i-1, j), (i, j+1), (i, j-1)]:\n if 0 <= di < nrow and 0 <= dj < ncol:\n temp ^= 1 <<(di*ncol + dj)\n if temp not in visited:\n visited.add(temp)\n dque.append(temp)\n step += 1\n return -1 \n"},{"alpha_fraction":0.5529412031173706,"alphanum_fraction":0.5630252361297607,"avg_line_length":38.66666793823242,"blob_id":"3ad51e06bb61cfbf427a97b2a68fd3198ddf1b1f","content_id":"49b7a63a923a16ac0eecc8986d1ebbb00f6e1c63","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":595,"license_type":"no_license","max_line_length":86,"num_lines":15,"path":"/lc464_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def canIWin(self, maxChoosableInteger: int, desiredTotal: int) -> bool:\n @lru_cache(None)\n def dfs(cands, target):\n if not cands:\n return False\n if cands[-1] >= target:\n return True\n\n for i, cand in enumerate(cands):\n if not dfs(cands[:i]+cands[i+1:], target-cand):\n return True\n return False\n if maxChoosableInteger*(maxChoosableInteger+1)//2 < desiredTotal: return False\n return dfs(tuple(range(1, maxChoosableInteger+1)), desiredTotal)\n"},{"alpha_fraction":0.4523809552192688,"alphanum_fraction":0.46536797285079956,"avg_line_length":34.53845977783203,"blob_id":"7b62f30cc03b50229f9d2a659cb2e502f93496bd","content_id":"6e7323f9fa98fd7af1c552ca3f63f91d4f0b73e5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":462,"license_type":"no_license","max_line_length":65,"num_lines":13,"path":"/lc547_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findCircleNum(self, isConnected: List[List[int]]) -> int:\n def dfs(i):\n for j in range(len(isConnected)):\n if isConnected[i][j] == 1 and visited[j] == 0:\n visited[j] = 1\n dfs(j)\n visited, rslt= [0]*len(isConnected), 0\n for i in range(len(isConnected)):\n if not visited[i]:\n dfs(i)\n rslt += 1\n return rslt\n"},{"alpha_fraction":0.49275362491607666,"alphanum_fraction":0.5113871693611145,"avg_line_length":39.25,"blob_id":"d275170c709442b2206f70ff58a1a4181185ce23","content_id":"60975968dcadc8f418d29193cae59d50a0513eb3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":966,"license_type":"no_license","max_line_length":95,"num_lines":24,"path":"/lc57_array.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def insert(self, intervals: List[List[int]], newInterval: List[int]) -> List[List[int]]:\n intervals.append(newInterval)\n intervals.sort()\n rslt = [intervals[0]]\n for x, y in intervals[1:]:\n if x > rslt[-1][1]:\n rslt.append([x, y])\n else:\n rslt[-1][0], rslt[-1][1] = min(rslt[-1][0], x), max(rslt[-1][1], y)\n return rslt\n\n def insert(self, intervals: List[List[int]], newInterval: List[int]) -> List[List[int]]:\n # intervals are initially sorted\n rslt, mark = [], True\n for i, (x, y) in enumerate(intervals):\n if y < newInterval[0]:\n rslt.append([x, y])\n elif x > newInterval[1]:\n rslt.append(newInterval)\n return rslt + intervals[i:]\n else:\n newInterval[0], newInterval[1] = min(x, newInterval[0]), max(y, newInterval[1])\n return rslt\n"},{"alpha_fraction":0.462981253862381,"alphanum_fraction":0.4856860935688019,"avg_line_length":47.238094329833984,"blob_id":"5b6f198cc7776d6a98685263be72a00de35f333a","content_id":"c4d8c990a6b1812068f190cb29d01a986c25dcd3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1013,"license_type":"no_license","max_line_length":83,"num_lines":21,"path":"/lc407_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def trapRainWater(self, heightMap: List[List[int]]) -> int:\n heap, visited = [], set()\n nrow, ncol = len(heightMap), len(heightMap[0])\n for i in range(nrow):\n heapq.heappush(heap, (heightMap[i][0], i, 0))\n heapq.heappush(heap, (heightMap[i][ncol-1], i, ncol-1))\n visited |= {(i, 0), (i, ncol-1)}\n for j in range(ncol):\n heapq.heappush(heap, (heightMap[0][j], 0, j))\n heapq.heappush(heap, (heightMap[nrow-1][j], nrow-1, j))\n visited |= {(0, j), (nrow-1, j)}\n rslt = 0\n while heap:\n border, x, y = heapq.heappop(heap)\n for nx, ny in [(x+1, y), (x-1, y), (x, y+1), (x, y-1)]:\n if 0 < nx < nrow-1 and 0 < ny < ncol-1 and (nx, ny) not in visited:\n rslt += max(0, border-heightMap[nx][ny])\n visited.add((nx, ny))\n heapq.heappush(heap, (max(border, heightMap[nx][ny]), nx, ny))\n return rslt\n"},{"alpha_fraction":0.39461883902549744,"alphanum_fraction":0.4125560522079468,"avg_line_length":32.45000076293945,"blob_id":"5f5dbc686376d18b2b3d95dddd6fa2369e574c54","content_id":"c3ede426470358e99521d0b26702e5bf2ee8ee5f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":669,"license_type":"no_license","max_line_length":72,"num_lines":20,"path":"/lc1353_heap.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxEvents(self, events: List[List[int]]) -> int:\n events.sort()\n rslt, last = 0, 0\n heap = []\n i = 0\n while i < len(events) or heap:\n if not heap: last = events[i][0]\n if i < len(events) and events[i][0] <= last <= events[i][1]:\n heapq.heappush(heap, events[i][1])\n i += 1\n elif i < len(events) and events[i][1] < last:\n i += 1\n continue\n elif heap:\n temp = heapq.heappop(heap)\n if temp >= last:\n rslt += 1\n last += 1\n return rslt\n"},{"alpha_fraction":0.4757462739944458,"alphanum_fraction":0.4981343150138855,"avg_line_length":39.53845977783203,"blob_id":"de06dfa1b18c75e62eb48a2969564b80790a3412","content_id":"57a3bbe731f2af65c2c407a9194850851a8b86d7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":536,"license_type":"no_license","max_line_length":76,"num_lines":13,"path":"/lc1648_bs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxProfit(self, inventory: List[int], orders: int) -> int:\n # find k\n left, right = 0, max(inventory)\n helper = lambda k: sum(max(0, i-k) for i in inventory)\n while left <= right:\n mid = (left+right)//2\n if helper(mid) < orders:\n right = mid -1\n else:\n left = mid + 1\n rslt = sum((i+right+1)*(i-right)//2 for i in inventory if i > right)\n return (rslt - (helper(right)-orders)*(right+1))%(10**9+7)\n \n"},{"alpha_fraction":0.5472440719604492,"alphanum_fraction":0.5629921555519104,"avg_line_length":35.28571319580078,"blob_id":"33d25c3e9815a67e45f3fa00d485b8f4c891eafc","content_id":"03a259827e870e36569f4d180c70750d36e9fc0d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":254,"license_type":"no_license","max_line_length":68,"num_lines":7,"path":"/lc252_array.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def canAttendMeetings(self, intervals: List[List[int]]) -> bool:\n intervals.sort()\n for i, it in enumerate(intervals):\n if i > 0 and it[0] < intervals[i-1][1]:\n return False\n return True\n"},{"alpha_fraction":0.39065107703208923,"alphanum_fraction":0.404006689786911,"avg_line_length":28.950000762939453,"blob_id":"1d76f3f41c205ed02714e52669aa5b4a921a2c76","content_id":"4ad55871520552491494ed5998f132da3785dc1f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":599,"license_type":"no_license","max_line_length":44,"num_lines":20,"path":"/lc1234_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def balancedString(self, s: str) -> int:\n count = collections.Counter(s)\n extra = {}\n n = len(s)\n a = n//4\n for key in count:\n if count[key] > a:\n extra[key] = count[key]-a\n if not extra: return 0\n l, rslt = 0, len(s)\n for r in range(n):\n if s[r] in extra:\n extra[s[r]]-=1\n while max(extra.values()) <= 0:\n rslt = min(rslt, r-l+1)\n if s[l] in extra:\n extra[s[l]] += 1\n l += 1\n return rslt\n"},{"alpha_fraction":0.505084753036499,"alphanum_fraction":0.508474588394165,"avg_line_length":35.875,"blob_id":"5213c4d2d2568da614c526c6eab45022c79aa45c","content_id":"d169b6cd3b4fd57071c8b6f6134ad9f73abd57c9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":295,"license_type":"no_license","max_line_length":68,"num_lines":8,"path":"/lc1673_stk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def mostCompetitive(self, nums: List[int], k: int) -> List[int]:\n stk, tl= [], len(nums)\n for i, num in enumerate(nums):\n while stk and stk[-1] > num and tl-i+len(stk) > k:\n stk.pop()\n stk.append(num)\n return stk[:k]\n"},{"alpha_fraction":0.49066388607025146,"alphanum_fraction":0.5072613954544067,"avg_line_length":34.703704833984375,"blob_id":"c969cda9758950c7e5e845c76aba64500068e6ef","content_id":"22262dbca46d2c321d86ece8195fccb6bcbed8a2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":964,"license_type":"no_license","max_line_length":86,"num_lines":27,"path":"/lc322_dp_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# BK\nclass Solution:\n def coinChange(self, coins: List[int], amount: int) -> int:\n coins.sort(reverse = True)\n self.rslt = float(\"inf\")\n def backtracking(amount, i, val):\n if amount == 0:\n self.rslt = min(self.rslt, val)\n return\n if i>= len(coins) or amount < 0 or val + amount //coins[i] +1 > self.rslt:\n return\n temp = amount //coins[i]\n for j in range(temp, -1, -1):\n backtracking(amount-coins[i]*j, i+1, val+j)\n\n backtracking(amount, 0, 0)\n return self.rslt if self.rslt < float(\"inf\") else -1\n\n# DP\nclass Solution:\n def coinChange(self, coins: List[int], amount: int) -> int:\n dp = [float(\"inf\")]*(amount+1)\n dp[0] = 0\n for coin in coins:\n for x in range(coin, amount+1):\n dp[x] = min(dp[x], dp[x-coin] + 1)\n return dp[-1] if dp[amount] < float(\"inf\") else -1\n"},{"alpha_fraction":0.4126984179019928,"alphanum_fraction":0.4285714328289032,"avg_line_length":30.5,"blob_id":"57d6ea1d17bf8f8f313ae398fe60fa1a6688b77a","content_id":"3e9511c02518ac039eabadf5368c717067b54cc0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":630,"license_type":"no_license","max_line_length":69,"num_lines":20,"path":"/lc1231_bs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maximizeSweetness(self, sweetness: List[int], K: int) -> int:\n def check(mid):\n cuts, curr = 0, 0\n for s in sweetness:\n curr += s\n if curr >= mid:\n cuts += 1\n curr = 0\n return cuts\n\n if len(sweetness) == K+1: return min(sweetness)\n left, right = min(sweetness), sum(sweetness)//(K+1)+1\n while left <= right:\n mid = (left + right)//2\n if check(mid) > K:\n left = mid + 1\n else:\n right = mid - 1\n return right\n"},{"alpha_fraction":0.41683366894721985,"alphanum_fraction":0.4308617115020752,"avg_line_length":26.72222137451172,"blob_id":"d69a5d24d77677438a4c8d679a71e92d0cd358d6","content_id":"fcfa448a333831c6f655893a3ab0bbb4e1ac7578","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":499,"license_type":"no_license","max_line_length":66,"num_lines":18,"path":"/lc32_str.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def longestValidParentheses(self, s: str) -> int:\n rslt = max(self.helper(s, '('), self.helper(s[::-1], ')'))\n return rslt\n\n def helper(self, s, check):\n left = right = rslt = 0\n for c in s:\n if c == check:\n left += 1\n else:\n right += 1\n\n if right > left:\n left, right = 0, 0\n elif right == left:\n rslt = max(rslt, 2*right)\n return rslt\n"},{"alpha_fraction":0.445333331823349,"alphanum_fraction":0.4560000002384186,"avg_line_length":30.25,"blob_id":"0352e68dd8ae6f88cd14c9f81fa7017be1e16b28","content_id":"1e0822bca3f1e78e89970976efedc890aa0756ae","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":375,"license_type":"no_license","max_line_length":51,"num_lines":12,"path":"/lc763_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def partitionLabels(self, S: str) -> List[int]:\n temp = {}\n for i, c in enumerate(S):\n temp[c] = i\n left, right, rslt = 0, 0, []\n for i, c in enumerate(S):\n right = max(right, temp[c])\n if i == right:\n rslt.append(right-left+1)\n left = right+1\n return rslt\n"},{"alpha_fraction":0.4900990128517151,"alphanum_fraction":0.5,"avg_line_length":35.727272033691406,"blob_id":"5ee213038cdcec74cfa71a7ccf301ee9ea714e11","content_id":"1ca34a8bc95199318806a475c9f537dbc907a5fb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":404,"license_type":"no_license","max_line_length":58,"num_lines":11,"path":"/lc1590_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minSubarray(self, nums: List[int], p: int) -> int:\n maps = {0:-1}\n target = sum(nums)%p\n curr, rslt = 0, len(nums)\n for i, num in enumerate(nums):\n curr = (curr+num)%p\n maps[curr] = i\n if (curr-target)%p in maps:\n rslt = min(rslt, i-maps[(curr-target)%p])\n return rslt if rslt < len(nums) else -1\n"},{"alpha_fraction":0.606914222240448,"alphanum_fraction":0.617157518863678,"avg_line_length":44.94117736816406,"blob_id":"efdf0d89163409a5eaabc6388e9e983552785ce5","content_id":"2d718a1a94fa1aa2405ccc05731b73ebd221dd44","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":781,"license_type":"no_license","max_line_length":226,"num_lines":17,"path":"/lc609_hash.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def copyRandomList(self, head: 'Node') -> 'Node':\n if not head: return None\n nodemap = {}\n def dfs(curr):\n if not curr: return None\n if curr in nodemap: return nodemap[curr]\n copy = Node(curr.val)\n nodemap[curr] = copy\n copy.next = dfs(curr.next)\n copy.random = dfs(curr.random)\n return copy\n return dfs(head)\n\n# Follow up:\n# - DFS: as we must arrive at the file, like a leaf node in the tree, and we don't care the directory unless there's files in there, DFS will be a better choice, as DFS can resuse the shared path before leaving that directory.\n# - 1. Use File size. 2. Compare random sampled rows. 3. Check MD5 or SHA256 hash, 4 check byte by byte\n"},{"alpha_fraction":0.47546011209487915,"alphanum_fraction":0.493865042924881,"avg_line_length":31.600000381469727,"blob_id":"db5d7f4f24cc97101dfca1159e8582623cc8b7ed","content_id":"cca04d70b6a11ff74789f74b5737973f11c33ecd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":326,"license_type":"no_license","max_line_length":67,"num_lines":10,"path":"/lc881_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def numRescueBoats(self, people: List[int], limit: int) -> int:\n people.sort()\n left, right, rslt = 0, len(people)-1, 0\n while left <= right:\n if people[left]+people[right] <= limit:\n left += 1\n rslt += 1\n right -= 1\n return rslt\n"},{"alpha_fraction":0.542397677898407,"alphanum_fraction":0.5482456088066101,"avg_line_length":39.235294342041016,"blob_id":"1059e1b30def08e83836d82836ef5fb0cc11c2d0","content_id":"e02269b918661d8fd9003a97b4b59904fa3a93d0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":684,"license_type":"no_license","max_line_length":88,"num_lines":17,"path":"/lc210_graph.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findOrder(self, numCourses: int, prerequisites: List[List[int]]) -> List[int]:\n graph, degree = collections.defaultdict(list), collections.defaultdict(int)\n for curr, pre in prerequisites:\n graph[pre].append(curr)\n degree[curr] += 1\n\n dque = collections.deque([crs for crs in range(numCourses) if degree[crs] == 0])\n rslt = []\n while dque:\n pre = dque.popleft()\n rslt.append(pre)\n for post in graph[pre]:\n degree[post] -= 1\n if degree[post] == 0:\n dque.append(post)\n return rslt if len(rslt) == numCourses else []\n"},{"alpha_fraction":0.4795081913471222,"alphanum_fraction":0.49707260727882385,"avg_line_length":35.34042739868164,"blob_id":"360f44db67d4c816cf734e53add8efcc889efcf1","content_id":"351a58faf1629c3278c9aff388a0cd894c4a3bb6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1708,"license_type":"no_license","max_line_length":85,"num_lines":47,"path":"/lc1562_deque.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findLatestStep(self, arr: List[int], m: int) -> int:\n if m == len(arr): return m\n border, rslt =[0]*(len(arr)+2), -1\n for i, idx in enumerate(arr):\n left = right = idx\n if border[right + 1] > 0: right = border[right + 1]\n if border[left - 1] > 0: left = border[left - 1]\n border[left], border[right] = right, left\n if (right-idx==m) or (idx-left ==m): rslt = i\n return rslt\n\n# deque + hashtable\n\nclass Solution:\n def findLatestStep(self, arr: List[int], m: int) -> int:\n if len(arr) == m: return m\n dp = [0]*(len(arr)+1)\n for i, idx in enumerate(arr): dp[idx] = i+1\n dque, rslt = collections.deque(), -1\n for i in range(1, len(dp)):\n while dque and dp[dque[-1]] < dp[i]:\n dque.pop()\n while dque and i- dque[0] >= m:\n dque.popleft()\n dque.append(i)\n if i < m: continue\n left, right, maxDay = float(\"inf\"), float(\"inf\"), dp[dque[0]]\n if i-m >= 1: left = dp[i-m]\n if i+1 <= len(arr): right = dp[i+1]\n if maxDay < left and maxDay < right:\n rslt = max(rslt, min(left,right)-1)\n return rslt\n\n\n\n# too slow, binary search\nclass Solution:\n def findLatestStep(self, arr: List[int], m: int) -> int:\n if m == len(arr): return m\n cands = [0, len(arr)+1]\n for i, idx in enumerate(arr[::-1]):\n temp = bisect.bisect(cands, idx)\n cands.insert(temp, idx)\n if cands[temp]-cands[temp-1]-1 == m or cands[temp+1]- cands[temp]-1 == m:\n return len(arr)-i-1\n return -1\n"},{"alpha_fraction":0.44771721959114075,"alphanum_fraction":0.44771721959114075,"avg_line_length":32.95000076293945,"blob_id":"2287affe9fc737bd9b48966644875cfc510156d8","content_id":"357308f7d0e438c833fd8b8414167eff1b9ba7d9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":679,"license_type":"no_license","max_line_length":82,"num_lines":20,"path":"/lc851_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def loudAndRich(self, richer: List[List[int]], quiet: List[int]) -> List[int]:\n richer_map = collections.defaultdict(set)\n for x, y in richer:\n richer_map[y].add(x)\n memo = {}\n def dfs(x):\n if x in memo:\n return memo[x]\n if not richer_map[x]:\n return x\n curr, rslt = quiet[x], x\n for t in richer_map[x]:\n temp = dfs(t)\n if quiet[temp] < curr:\n curr = quiet[temp]\n rslt = temp\n memo[x] = rslt\n return rslt\n return [dfs(x) for x in range(len(quiet))]\n"},{"alpha_fraction":0.4402298927307129,"alphanum_fraction":0.44712644815444946,"avg_line_length":33.79999923706055,"blob_id":"f643f13ae3d8728eb96adb461704f8c181b4d36a","content_id":"559f14c28df7f1d82f9873ed76dc3eb780534e93","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":870,"license_type":"no_license","max_line_length":91,"num_lines":25,"path":"/lc1627_unionfind.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def areConnected(self, n: int, threshold: int, queries: List[List[int]]) -> List[bool]:\n parent = list(range(n+1))\n size = [1]*(n+1)\n def find(p):\n while p != parent[p]:\n parent[p] = parent[parent[p]]\n p = parent[p]\n return p\n def union(p, q):\n rootp, rootq = find(p), find(q)\n if rootp == rootq: return\n if size[rootp] < size[rootq]:\n rootp, rootq = rootq, rootp\n parent[rootq] = rootp\n size[rootp] += size[rootq]\n return\n for i in range(n+1):\n if i > threshold and parent[i] == i:\n for j in range(2*i, n+1, i):\n union(i, j)\n rslt = []\n for a, b in queries:\n rslt.append(find(a) == find(b))\n return rslt\n"},{"alpha_fraction":0.36692014336586,"alphanum_fraction":0.427756667137146,"avg_line_length":39.53845977783203,"blob_id":"c687efa51c25b2dfd329f0aee64524b916fdb450","content_id":"131999370e224d35dcbd7ad7eb944fda3b3f9a1d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":526,"license_type":"no_license","max_line_length":75,"num_lines":13,"path":"/lc72_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minDistance(self, word1: str, word2: str) -> int:\n l1, l2 = len(word1), len(word2)\n dp = [[0]*(l1 +1) for _ in range(l2+1)]\n for j in range(l2+1): dp[j][0] = j\n for i in range(l1+1): dp[0][i] = i\n for i, c1 in enumerate(word1):\n for j, c2 in enumerate(word2):\n if c1 == c2:\n dp[j+1][i+1] = dp[j][i]\n else:\n dp[j+1][i+1] = min(dp[j][i+1], dp[j+1][i], dp[j][i]) +1\n return dp[-1][-1]"},{"alpha_fraction":0.4745260775089264,"alphanum_fraction":0.4768957197666168,"avg_line_length":26.672130584716797,"blob_id":"c62f2080d93cd26daa57152e73b1b4434bd28f23","content_id":"44ac8482cf3a7052301df39df724e7d661fc02cf","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1688,"license_type":"no_license","max_line_length":69,"num_lines":61,"path":"/lc323_unionfind.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def countComponents(self, n: int, edges: List[List[int]]) -> int:\n uf = UF(n)\n for x, y in edges:\n uf.union(x, y)\n return uf.getsize()\n\n\nclass UF:\n def __init__(self, n):\n self.parent = list(range(n))\n self.size = [1]*n\n self.count = n\n def union(self, p, q):\n rootp = self.find(p)\n rootq = self.find(q)\n if rootp == rootq:return\n if self.size[rootp] < self.size[rootq]:\n rootp, rootq = rootq, rootp\n self.parent[rootq] = rootp\n self.size[rootp] += self.size[rootq]\n self.count -= 1\n return\n def find(self, p):\n while self.parent[p] != p:\n self.parent[p] = self.parent[self.parent[p]]\n p = self.parent[p]\n return p\n def getsize(self):\n return self.count\n\n \nclass Solution:\n def countComponents(self, n: int, edges: List[List[int]]) -> int:\n graph = {}\n for i in range(n):\n graph[i] = []\n for u, v in edges:\n graph[u].append(v)\n graph[v].append(u)\n rslt = 0\n for i in range(n):\n if i in graph:\n self.bfs(i, graph)\n rslt += 1\n return rslt\n \n def dfs(self, i, graph):\n kids = graph[i]\n graph.pop(i)\n for kid in kids:\n if kid in graph:\n self.dfs(kid, graph)\n def bfs(self, i, graph):\n queue = collections.deque([i])\n while queue:\n temp = queue.popleft()\n if temp in graph:\n for kid in graph[temp]:\n queue.append(kid)\n graph.pop(temp)\n"},{"alpha_fraction":0.4309816062450409,"alphanum_fraction":0.45858895778656006,"avg_line_length":35.22222137451172,"blob_id":"f9d208dc245772b21468d3ca212a38d40dea24a8","content_id":"c4710d4fb8b6e059eeed0edb229cadddbe3fac8e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":652,"license_type":"no_license","max_line_length":55,"num_lines":18,"path":"/lc969_sort.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def pancakeSort(self, arr: List[int]) -> List[int]:\n s_arr, rslt, n = sorted(arr), [], len(arr)\n for i in range(n-1, 0, -1):\n idx = arr.index(s_arr[i])\n arr[:idx+1] = arr[:idx+1][::-1]\n arr[:i+1] = arr[:i+1][::-1]\n rslt.extend([idx+1, i+1])\n return rslt\n\n def pancakeSort(self, arr: List[int]) -> List[int]:\n n, rslt = len(arr), []\n for nmax in range(n, 1, -1):\n idx = arr.index(nmax)\n #arr = arr[nmax-1:idx:-1] + arr[:idx+1]\n arr = arr[:idx:-1] + arr[:idx]\n rslt.extend([idx+1, nmax])\n return rslt\n"},{"alpha_fraction":0.3892405033111572,"alphanum_fraction":0.4177215099334717,"avg_line_length":27.727272033691406,"blob_id":"3f321883a7b9da3e19dd289598a3d2abe68c238d","content_id":"00080a768fa68fb671ac89fc35a8de4148113104","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":316,"license_type":"no_license","max_line_length":49,"num_lines":11,"path":"/lc119_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def getRow(self, rowIndex: int) -> List[int]:\n if rowIndex == 0:\n return [1]\n dp = [1]\n for j in range(1, rowIndex+1):\n temp = [1]*(j+1)\n for i in range(1, j):\n temp[i] = dp[i-1] + dp[i]\n dp = temp\n return dp\n"},{"alpha_fraction":0.5128834247589111,"alphanum_fraction":0.5490797758102417,"avg_line_length":36.022727966308594,"blob_id":"521e06ab1e9fc44e945c33b5e006273413555274","content_id":"522c582990e8c32cd64916c236e48a4ea09cd20e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1630,"license_type":"no_license","max_line_length":118,"num_lines":44,"path":"/lc468_string.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def validIPAddress(self, IP: str) -> str:\n # check IPv4 or IPv6\n if IP.count(\".\") == 3:\n # IPv4 address validation\n return \"IPv4\" if self.checkIPv4(IP) else \"Neither\"\n elif IP.count(\":\") == 7:\n # IPv6 address validation\n return \"IPv6\" if self.checkIPv6(IP) else \"Neither\"\n else:\n return \"Neither\"\n\n def checkIPv4(self, IP):\n tokens = IP.split(\".\")\n for token in tokens:\n if not token.isdigit() or len(token) != len(str(int(token))) or int(token) > 255:\n return False\n return True\n\n def checkIPv6(self, IP):\n tokens = IP.split(\":\")\n for token in tokens:\n if len(token) <1 or len(token) > 4 or any(x in token for x in \"ghijklmnopqrstuvwxyzGHIJKLMNOPQRSTUVWXYZ\"):\n return False\n return True\n\nfrom ipaddress import ip_address, IPv6Address\nclass Solution:\n def validIPAddress(self, IP: str) -> str:\n try:\n return \"IPv6\" if type(ip_address(IP)) is IPv6Address else \"IPv4\"\n except ValueError:\n return \"Neither\"\n\nimport re\nclass Solution:\n def validIPAddress(self, IP: str) -> str:\n chunk_IPv4 = r'([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])'\n patten_IPv4 = re.compile(r'^(' + chunk_IPv4 + r'\\.){3}' + chunk_IPv4 + r'$')\n chunk_IPv6 = r'([0-9a-fA-F]{1,4})'\n patten_IPv6 = re.compile(r'^(' + chunk_IPv6 + r'\\:){7}' + chunk_IPv6 + r'$')\n if patten_IPv4.match(IP):\n return \"IPv4\"\n return \"IPv6\" if patten_IPv6.match(IP) else \"Neither\" \n"},{"alpha_fraction":0.41897234320640564,"alphanum_fraction":0.44071146845817566,"avg_line_length":35.14285659790039,"blob_id":"26d9b4f04981fe24298f11ad628f150f5ee61701","content_id":"6f26d71efbf64a072d2439ca4104be1d517d511d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":506,"license_type":"no_license","max_line_length":77,"num_lines":14,"path":"/lc1358_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def numberOfSubstrings(self, s: str) -> int:\n left, rslt, count , count_dict= 0, 0, 0, collections.defaultdict(int)\n for right, c in enumerate(s):\n if count_dict[c] == 0:\n count += 1\n count_dict[c] += 1\n while count == 3:\n count_dict[s[left]] -= 1\n if count_dict[s[left]] == 0:\n count -= 1\n left += 1\n rslt += len(s) - right\n return rslt\n"},{"alpha_fraction":0.5182482004165649,"alphanum_fraction":0.5456204414367676,"avg_line_length":48.818180084228516,"blob_id":"f59368fb2826cc3338a3d9db76107245b34722bd","content_id":"58c17a6f9579c22ff27474a157143e5be1cd73a9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":548,"license_type":"no_license","max_line_length":94,"num_lines":11,"path":"/lc1499_deque.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findMaxValueOfEquation(self, points: List[List[int]], k: int) -> int:\n dque, rslt = collections.deque(), -float(\"inf\")\n for idx, point in enumerate(points):\n while dque and point[0] - points[dque[0]][0] > k:\n dque.popleft()\n if dque: rslt = max(rslt, points[dque[0]][1]-points[dque[0]][0]+point[1]+point[0])\n while dque and points[dque[-1]][1]-points[dque[-1]][0] <= point[1]-point[0]:\n dque.pop()\n dque.append(idx)\n return rslt\n"},{"alpha_fraction":0.3621908128261566,"alphanum_fraction":0.3621908128261566,"avg_line_length":30.44444465637207,"blob_id":"f76057e9acedf8a61e879e7b7d8278262059fd6a","content_id":"fd2da11de6026c462fe08016b20a856fabd2dfcb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":566,"license_type":"no_license","max_line_length":47,"num_lines":18,"path":"/lc1087_string.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def expand(self, S: str) -> List[str]:\n rslt, curr, append = [\"\"], [], False\n for c in S:\n if c == \",\": continue\n if c == \"{\": append = True\n elif c == \"}\":\n append = False\n temp = []\n while curr:\n t = curr.pop()\n temp += [x+t for x in rslt]\n rslt = temp\n elif append:\n curr.append(c)\n else:\n rslt = [x+c for x in rslt]\n return sorted(rslt)\n"},{"alpha_fraction":0.38630378246307373,"alphanum_fraction":0.39859527349472046,"avg_line_length":34.59375,"blob_id":"d106af9da7c7eee262241ce73113f49a1c674183","content_id":"a941d07ac06538166616d1847f780a864ff070f4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1139,"license_type":"no_license","max_line_length":99,"num_lines":32,"path":"/lc726_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def countOfAtoms(self, expression: str) -> List[str]:\n stk, curr, atom, num = [], [], \"\", \"\"\n for c in expression+\")\":\n if c in \"1234567890\":\n num += c\n elif c in \"abcdefghijklmnopqrstuvwxyz\":\n atom += c\n elif atom:\n curr.append((atom, int(num) if num else 1))\n atom, num = \"\", \"\"\n elif num:\n t = int(num) if num else 1\n num = \"\"\n if stk:\n temp = stk.pop()\n curr = [(x, y*t) for (x, y) in temp]\n curr = curr+stk.pop() if stk else curr\n if c in \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\":\n atom = c\n if c == \")\" and curr:\n stk.append(curr)\n curr = []\n elif c == \"(\":\n stk.append(curr)\n curr = []\n rslt = {}\n for t in stk:\n for x,y in t:\n rslt[x] = rslt.get(x, 0)+y\n\n return \"\".join(key+str(rslt[key]) if rslt[key] > 1 else key for key in sorted(rslt.keys()))\n"},{"alpha_fraction":0.41883766651153564,"alphanum_fraction":0.4328657388687134,"avg_line_length":32.266666412353516,"blob_id":"a894527ff29ac22f40a59dc5b9023c1df3760d06","content_id":"79efa2d2f19f0f6c56898467a65d67ee75fbbeaa","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":499,"license_type":"no_license","max_line_length":67,"num_lines":15,"path":"/lc259_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def threeSumSmaller(self, nums: List[int], target: int) -> int:\n nums.sort()\n rslt = 0\n for left, num in enumerate(nums[:-2]):\n if num*3 >= target:\n return rslt\n mid, right = left+1, len(nums)-1\n while mid < right:\n if nums[mid]+nums[right]+num < target:\n rslt += right-mid\n mid += 1\n else:\n right -= 1\n return rslt\n"},{"alpha_fraction":0.4730134904384613,"alphanum_fraction":0.48875561356544495,"avg_line_length":35.054054260253906,"blob_id":"a67c8ff5bfb73d44ead654fe42a17e0c275204b8","content_id":"0a33b797ce6c99d357058ae5d2596868688a4f30","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1334,"license_type":"no_license","max_line_length":62,"num_lines":37,"path":"/lc1696_deque.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxResult(self, nums: List[int], k: int) -> int:\n dque, rslt, dp = collections.deque(), 0, [0]*len(nums)\n for idx, num in enumerate(nums):\n while dque and idx-dque[0] > k:\n dque.popleft()\n dp[idx] = num if idx == 0 else dp[dque[0]]+num\n while dque and dp[dque[-1]] <= dp[idx]:\n dque.pop()\n dque.append(idx)\n return dp[-1]\n\nclass Solution:\n def maxResult(self, nums: List[int], k: int) -> int:\n dque = collections.deque()\n for idx, num in enumerate(nums):\n if idx == 0: dque.append(num)\n else:\n dque.append(dque[0]+num)\n while len(dque) > k:\n dque.popleft()\n while dque and dque[0] < dque[-1]:\n dque.popleft()\n return dque[-1]\n\nclass Solution:\n def maxResult(self, nums: List[int], k: int) -> int:\n rslt, dque = 0, collections.deque()\n for idx, num in enumerate(nums):\n if idx == 0: dque.append((idx, num))\n while dque and idx - dque[0][0] > k:\n dque.popleft()\n rslt = dque[0][1]+num if idx != 0 else num\n while dque and dque[-1][1] <= rslt:\n dque.pop()\n dque.append((idx, rslt))\n return rslt\n"},{"alpha_fraction":0.43699732422828674,"alphanum_fraction":0.445040225982666,"avg_line_length":30.08333396911621,"blob_id":"cc73ffb88dae352885ef0319cb7f2133dc1f53fe","content_id":"bd19da8eb6073cf67d9a53348064c2b19e225be4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":373,"license_type":"no_license","max_line_length":57,"num_lines":12,"path":"/lc77_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def combine(self, n: int, k: int) -> List[List[int]]:\n rslt = []\n def backtracking(temp, i):\n if len(temp) == k:\n rslt.append(temp)\n return\n if i == n: return\n for t in range(i, n):\n backtracking(temp+[t+1], t+1)\n backtracking([], 0)\n return rslt\n"},{"alpha_fraction":0.44994375109672546,"alphanum_fraction":0.46681663393974304,"avg_line_length":36.04166793823242,"blob_id":"95eb18ebf4c6b4f13cf895e6486c2fc64146d838","content_id":"4a60281aea880aecfff44fff5b820c1925b56219","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":889,"license_type":"no_license","max_line_length":75,"num_lines":24,"path":"/lc1345_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minJumps(self, arr: List[int]) -> int:\n valmap, l = collections.defaultdict(list), len(arr)\n if l == 1: return 0\n if arr[0] == arr[l-1]: return 1\n\n for i, num in enumerate(arr):\n valmap[num].append(i)\n dque, visited, visitednum = collections.deque([(0, 0)]), {0}, set()\n while dque:\n jump, idx = dque.popleft()\n if idx == l-1:\n return jump\n for i in [idx+1, idx-1]:\n if i not in visited and 0<= i < l:\n dque.append((jump+1, i))\n visited.add(i)\n if arr[idx] in visitednum: continue\n visitednum.add(arr[idx])\n for i in valmap[arr[idx]]:\n if i not in visited:\n dque.append((jump+1, i))\n visited.add(i)\n return len(arr)-1\n"},{"alpha_fraction":0.475452184677124,"alphanum_fraction":0.48578810691833496,"avg_line_length":47.375,"blob_id":"fa3a1a68d767e52f0934e560bc33577a44a7b20a","content_id":"bafcb68fe427b8fcf97524897f7dcb096706d3f0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":774,"license_type":"no_license","max_line_length":83,"num_lines":16,"path":"/lc1439_dijkstra.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def kthSmallest(self, mat: List[List[int]], k: int) -> int:\n # Dijkstra: node is labeled by the row index sequences\n nrow, ncol = len(mat), len(mat[0])\n visited = {(0,)*nrow}\n heap = [(sum(row[0] for row in mat), (0,)*nrow)]\n for i in range(k):\n cur_val, cur_node = heapq.heappop(heap)\n for j in range(nrow):\n if cur_node[j] + 1 < ncol:\n nxt_val = cur_val - mat[j][cur_node[j]] + mat[j][cur_node[j]+1]\n nxt_node = cur_node[:j]+(cur_node[j]+1, )+ cur_node[j+1:]\n if nxt_node not in visited:\n visited.add(nxt_node)\n heapq.heappush(heap, (nxt_val, nxt_node))\n return cur_val\n"},{"alpha_fraction":0.4615384638309479,"alphanum_fraction":0.4764957129955292,"avg_line_length":35,"blob_id":"cf51afd26177015c18c70d05fbca08f764fe2272","content_id":"8764c5ea196ca6b796c01edf305a346fa94071b7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":468,"license_type":"no_license","max_line_length":60,"num_lines":13,"path":"/lc1658_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minOperations(self, nums: List[int], x: int) -> int:\n total, left, rslt = sum(nums), 0, len(nums)+1\n if total < x:\n return -1\n for right, num in enumerate(nums):\n total -= num\n while total < x:\n total += nums[left]\n left += 1\n if total == x:\n rslt = min(rslt, len(nums)-right-1+left)\n return rslt if rslt < len(nums)+1 else -1\n"},{"alpha_fraction":0.42603549361228943,"alphanum_fraction":0.43589743971824646,"avg_line_length":27.16666603088379,"blob_id":"7ef605cc4a184fba236094c908e1fb219ae91009","content_id":"434874b5f9ccf763c2df20f83d90b2535d65ea88","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":507,"license_type":"no_license","max_line_length":45,"num_lines":18,"path":"/lc1025_minmax.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def divisorGame(self, N: int) -> bool:\n memo = {}\n def dfs(n):\n if n in memo: return memo[n]\n if n == 1: return False\n rslt = False\n for i in range(1, n):\n if n%i == 0 and not dfs(n-i):\n memo[n] = True\n return True\n memo[n] = False\n return False\n return dfs(N)\n\nclass Solution:\n def divisorGame(self, N: int) -> bool:\n return N % 2 == 0\n"},{"alpha_fraction":0.4937993288040161,"alphanum_fraction":0.5039458870887756,"avg_line_length":45.68421173095703,"blob_id":"1fc0ddf09e93c7e15122ac6234ae3b57e1b053a5","content_id":"bbdf675dcc1d9e8550565a79097142fd28b68530","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":887,"license_type":"no_license","max_line_length":101,"num_lines":19,"path":"/lc787_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findCheapestPrice(self, n: int, flights: List[List[int]], src: int, dst: int, K: int) -> int:\n graph = collections.defaultdict(list)\n for start, end, w in flights:\n graph[start].append((end, w))\n heap, visitedD, visitedS = [(0, 0, src)], [float(\"inf\")]*n, [float(\"inf\")]*n\n visitedD[src], visitedS[src] = 0, 0\n while heap:\n cost, step, pre = heapq.heappop(heap)\n if pre == dst: return cost\n if step == K+1: continue\n for curr, w in graph[pre]:\n if visitedD[curr] > cost + w:\n visitedD[curr] = cost + w\n visitedS[curr] = step+1\n heapq.heappush(heap, (cost+w, step+1, curr))\n elif step < visitedS[curr]:\n heapq.heappush(heap, (cost+w, step+1, curr))\n return -1\n"},{"alpha_fraction":0.45742905139923096,"alphanum_fraction":0.46243739128112793,"avg_line_length":34.235294342041016,"blob_id":"f000ad746b38b0527fde66813df6c9f00dc72df1","content_id":"4b07ca2887cb5e68b5026aab414d1e3b57195ac0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":599,"license_type":"no_license","max_line_length":67,"num_lines":17,"path":"/lc491_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findSubsequences(self, nums: List[int]) -> List[List[int]]:\n rslt = []\n def backtracking(stk, candidate):\n if len(stk) >= 2:\n rslt.append(stk)\n if not candidate:\n return\n seen = set()\n for i, cand in enumerate(candidate):\n if cand in seen:\n continue\n if not stk or stk[-1] <= candidate[i]:\n seen.add(cand)\n backtracking(stk+[cand], candidate[i+1:])\n backtracking([], nums)\n return rslt\n"},{"alpha_fraction":0.46986469626426697,"alphanum_fraction":0.48216482996940613,"avg_line_length":39.650001525878906,"blob_id":"7933c18bf0e9e0703aa34bdede01e7fda3121e10","content_id":"e707314160aa5c770c1e3e21929d989ab253ac93","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":813,"license_type":"no_license","max_line_length":84,"num_lines":20,"path":"/lc444_graph.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def sequenceReconstruction(self, org: List[int], seqs: List[List[int]]) -> bool:\n graph, degree = collections.defaultdict(list), collections.defaultdict(int)\n for seq in seqs:\n for i, x in enumerate(seq):\n if i != len(seq)-1:\n graph[x].append(seq[i+1])\n degree[seq[i+1]]+= 1\n degree[x] += 0\n dque = collections.deque([x for x in degree if degree[x] == 0])\n temp = []\n while dque:\n if len(dque) != 1: return False\n t = dque.popleft()\n temp.append(t)\n for end in graph[t]:\n degree[end] -= 1\n if degree[end] == 0:\n dque.append(end)\n return temp == org and sum(degree.values()) == 0\n"},{"alpha_fraction":0.37249666452407837,"alphanum_fraction":0.3791722357273102,"avg_line_length":33.04545593261719,"blob_id":"f70d4c22fcafc3973e85de8a414a816466996364","content_id":"d636fff43b41a28703482d1e4b9099fc27847a42","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":749,"license_type":"no_license","max_line_length":58,"num_lines":22,"path":"/lc1087_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def expand(self, S: str) -> List[str]:\n rslt = []\n def backtracking(prefix, remain):\n if not remain:\n rslt.append(prefix)\n return\n if remain[0] == '{':\n temp, cans = 0, []\n for i, c in enumerate(remain):\n if c == '}':\n temp = i\n break\n if c in ('{', ','):\n continue\n cans.append(c)\n for can in cans:\n backtracking(prefix+can, remain[i+1:])\n else:\n backtracking(prefix+remain[0], remain[1:])\n backtracking(\"\", S)\n return sorted(rslt)\n"},{"alpha_fraction":0.4444444477558136,"alphanum_fraction":0.4627285599708557,"avg_line_length":31.31818199157715,"blob_id":"d5bdb83b176ec5415e24c37db0e58b8291a52b66","content_id":"34fe1a12dfb1acdd1706152d51e63b3d79b581d0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":711,"license_type":"no_license","max_line_length":71,"num_lines":22,"path":"/lc477_bit.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def totalHammingDistance(self, nums: List[int]) -> int:\n if not nums: return 0\n bit_count, n, rslt = collections.defaultdict(int), len(nums), 0\n for num in nums:\n i = 0\n while num:\n if num & 1:\n bit_count[i] += 1\n num >>= 1\n i += 1\n for _ , val in bit_count.items():\n rslt += val*(n-val)\n return rslt\n\n def totalHammingDistance(self, nums: List[int]) -> int:\n if not nums: return 0\n n, rslt = len(nums), 0\n for bits in zip(*map(\"{:032b}\".format, nums)):\n c = bits.count(\"1\")\n rslt += c*(n-c)\n return rslt\n"},{"alpha_fraction":0.4378698170185089,"alphanum_fraction":0.44378697872161865,"avg_line_length":27.16666603088379,"blob_id":"ef051c0de96e3dbd5b10dc5376b58256b0cea9fa","content_id":"7baa586da95a189e26f5d68fec8f39d0b8a2df52","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":338,"license_type":"no_license","max_line_length":53,"num_lines":12,"path":"/lc1063_stk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def validSubarrays(self, nums: List[int]) -> int:\n n = len(nums)\n stk = []\n rslt = 0\n for i in range(n):\n while stk and nums[i] < nums[stk[-1]]:\n rslt += i- stk.pop()\n stk.append(i)\n while stk:\n rslt += n-stk.pop()\n return rslt\n"},{"alpha_fraction":0.5455567836761475,"alphanum_fraction":0.5500562191009521,"avg_line_length":42.36585235595703,"blob_id":"c2c5d3f0a355c517fda788d02b3aad9c6e378b9b","content_id":"91810e80d895ebf0d866ccc0717fb23633f35f4a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1778,"license_type":"no_license","max_line_length":90,"num_lines":41,"path":"/lc1373_bt.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def maxSumBST(self, root: TreeNode) -> int:\n self.rslt = 0\n def helper(root):\n if not root: return 0, 0, True, 0\n if not root.right and not root.left: return root.val, root.val, True, root.val\n maxRR, minLR, isBSTR, ssumR = helper(root.right)\n maxRL, minLL, isBSTL, ssumL = helper(root.left)\n temp, ss = False, max(ssumR, ssumL)\n if not root.left and isBSTR and minLR > root.val:\n temp, ss, minLL = True, ssumR + root.val, root.val\n if not root.right and isBSTL and maxRL < root.val:\n temp, ss, maxRR = True, ssumL+root.val, root.val\n if isBSTR and isBSTL and maxRL < root.val < minLR:\n temp, ss= True, ssumR+ssumL+root.val\n self.rslt = max(self.rslt, ss)\n return maxRR, minLL, temp, ss\n _, _, _, rslt = helper(root)\n return self.rslt\n\nclass Solution:\n def maxSumBST(self, root: TreeNode) -> int:\n self.rslt = 0\n def helper(root):\n if not root: return -float(\"inf\"), float(\"inf\"), True, 0\n maxR, minR, isBSTR, sumR = helper(root.right)\n maxL, minL, isBSTL, sumL = helper(root.left)\n isBST, total= False, 0\n if isBSTR and isBSTL and minR > root.val > maxL:\n isBST = True\n total = sumR+sumL+root.val\n self.rslt = max(self.rslt, sumR+sumL+root.val)\n return max(maxR, root.val), min(minL, root.val), isBST, total\n helper(root)\n return self.rslt\n"},{"alpha_fraction":0.39137381315231323,"alphanum_fraction":0.420127809047699,"avg_line_length":31.473684310913086,"blob_id":"7cd42b2afef62ddee273c7cd6f1aa7d61fe39a8b","content_id":"45c57ca03000076a7987eaf1e95b9766716ac845","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":626,"license_type":"no_license","max_line_length":50,"num_lines":19,"path":"/lc678_str.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def checkValidString(self, s: str) -> bool:\n countl = 0\n for c in s:\n countl += 1 if c in (\"(\", \"*\") else -1\n if countl < 0: return False\n countr = 0\n for c in s[::-1]:\n countr += 1 if c in (\")\", \"*\") else -1\n if countr < 0: return False\n return True\n def checkValidString(self, s:str) -> bool:\n low, hi= 0, 0\n for c in s:\n low += 1 if c == '(' else -1\n hi += 1 if c in ('(', '*') else -1\n low = max(0, low)\n if hi < 0: return False\n return low == 0\n \n"},{"alpha_fraction":0.3991416394710541,"alphanum_fraction":0.4177396297454834,"avg_line_length":30.772727966308594,"blob_id":"9ebe263cadbbbacd4b3e15731d6bcd7d42872166","content_id":"8c3da2a6fa91b38d8d8240445d06aab80ce815cc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":699,"license_type":"no_license","max_line_length":64,"num_lines":22,"path":"/lc975_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def oddEvenJumps(self, arr: List[int]) -> int:\n n = len(arr)\n nxt_h, nxt_l = [0]*n, [0]*n\n stack = []\n for i in sorted(range(n), key = lambda x: (arr[x], x)):\n while stack and stack[-1] < i:\n nxt_h[stack.pop()] = i\n stack.append(i)\n\n stack = []\n for i in sorted(range(n), key = lambda x: (-arr[x], x)):\n while stack and stack[-1] < i:\n nxt_l[stack.pop()] = i\n stack.append(i)\n\n nh, nl = [0]*n, [0]*n\n nh[-1], nl[-1] = 1, 1\n for j in range(n-2, -1, -1):\n nh[j] = nl[nxt_h[j]]\n nl[j] = nh[nxt_l[j]]\n return sum(nh)\n"},{"alpha_fraction":0.5145413875579834,"alphanum_fraction":0.5190156698226929,"avg_line_length":36.25,"blob_id":"80f93d2630130578c0ed3053e9fbedc5f0d25779","content_id":"53392511f302024681ab3fe850cc1402622a0964","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":447,"license_type":"no_license","max_line_length":98,"num_lines":12,"path":"/lc1376_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def numOfMinutes(self, n: int, headID: int, manager: List[int], informTime: List[int]) -> int:\n mmap = collections.defaultdict(list)\n for i, m in enumerate(manager):\n if m != -1:\n mmap[m].append(i)\n def dfs(mid):\n rslt = 0\n for sub in mmap[mid]:\n rslt = max(rslt, informTime[mid] + dfs(sub))\n return rslt\n return dfs(headID)\n"},{"alpha_fraction":0.44329896569252014,"alphanum_fraction":0.4639175236225128,"avg_line_length":31.33333396911621,"blob_id":"9c10534f1eecf2817e7926f8738a82ed59b325b5","content_id":"210c64df56a8d41f4dfbb34fd5f3d743eee5a422","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":291,"license_type":"no_license","max_line_length":56,"num_lines":9,"path":"/lc118_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def generate(self, numRows: int) -> List[List[int]]:\n rslt = []\n for i in range(numRows):\n temp = [1]*(i+1)\n for j in range(1, i):\n temp[j] = rslt[i-1][j-1]+rslt[i-1][j]\n rslt.append(temp)\n return rslt\n"},{"alpha_fraction":0.4553191363811493,"alphanum_fraction":0.48085105419158936,"avg_line_length":34.153846740722656,"blob_id":"7a6489120bca3f6406ea5ac4cc35123da9c289ab","content_id":"4d153c189aea88106e380a8cbfa28a89880abd84","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":470,"license_type":"no_license","max_line_length":82,"num_lines":13,"path":"/lc496_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def nextGreaterElement(self, nums1: List[int], nums2: List[int]) -> List[int]:\n if not nums2: return [-1]*len(nums1)\n keeper, stk = {}, [nums2[0]]\n for num in nums2[1:]:\n while stk and num > stk[-1]:\n temp = stk.pop()\n keeper[temp] = num\n stk.append(num)\n rslt = []\n for num in nums1:\n rslt.append(keeper.get(num, -1))\n return rslt\n \n"},{"alpha_fraction":0.4484732747077942,"alphanum_fraction":0.46310433745384216,"avg_line_length":34.325843811035156,"blob_id":"a660e6a7bf3086da821b352cde0564d2ff230b1b","content_id":"0c2f53540ab07c220972f4f93d42c87f7f673a32","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3144,"license_type":"no_license","max_line_length":81,"num_lines":89,"path":"/lc411_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minAbbreviation(self, target: str, dictionary: List[str]) -> str:\n # get all the abbr of target stored by length\n self.target_rslt = collections.defaultdict(list)\n self.backtracking(0, [], target)\n for i in range(1, len(target)):\n for path in self.target_rslt[i]:\n for word in dictionary:\n if self.check_abbr(path, word):\n break\n else:\n return \"\".join(path)\n return target\n\n def backtracking(self, i, path, target):\n '''\n generate abbreviations for target word\n '''\n if i == len(target):\n self.target_rslt[len(path)].append(path)\n else:\n if path and path[-1].isdigit():\n self.backtracking(i+1, path[:-1]+ [str(int(path[-1])+1)], target)\n else:\n self.backtracking(i+1, path+[\"1\"], target)\n self.backtracking(i+1, path+[target[i]], target)\n\n def check_abbr(self, path, word):\n '''\n check if path is word's abbrevation\n '''\n cw = 0\n for ch in path:\n if cw >= len(word):\n return False\n if not ch.isdigit():\n if ch != word[cw]: return False\n cw += 1\n else:\n cw += int(ch)\n return cw == len(word)\n\nclass Solution:\n def minAbbreviation(self, target: str, dictionary: List[str]) -> str:\n # get all the abbr of target stored by length\n self.target_rslt, n = collections.defaultdict(list), len(target)\n self.backtracking(0, 1, target, 0)\n for i in range(1, n):\n for abbr in self.target_rslt[i]:\n for word in dictionary:\n if self.check_abbr(abbr, target, word): break\n else:\n cnt, rslt = 0, \"\"\n for j in range(n-1, -1, -1):\n if abbr & 1:\n cnts = \"\" if cnt == 0 else str(cnt)\n rslt, cnt = target[j]+cnts+rslt, 0\n else: cnt += 1\n abbr >>= 1\n if cnt: rslt = str(cnt)+rslt\n return rslt\n return target\n\n def backtracking(self, i, abbr, target, l):\n '''\n generate abbreviations for target word use bitmask\n '''\n if i == len(target):\n self.target_rslt[l].append(abbr)\n else:\n if abbr&1 == 0:\n self.backtracking(i+1, abbr << 1, target, l)\n else:\n self.backtracking(i+1, abbr << 1, target, l+1)\n self.backtracking(i+1, (abbr << 1)+1, target, l+1)\n\n def check_abbr(self, abbr, target, word):\n '''\n check if path is word's abbrevation\n '''\n cw, n = len(word)-1, len(target)\n for i in range(n-1, -1, -1):\n if cw < 0:\n return False\n if abbr & 1 == 1 and word[cw] != target[i]:\n return False\n cw -= 1\n abbr >>= 1\n return cw == -1\n"},{"alpha_fraction":0.400778204202652,"alphanum_fraction":0.43968871235847473,"avg_line_length":31.125,"blob_id":"b8741756ca3a4ff25d655c75b3c5208e20c1006a","content_id":"db01945aeda47f0efda923c4789d8945d26a20b8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":514,"license_type":"no_license","max_line_length":69,"num_lines":16,"path":"/lc1215_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def countSteppingNumbers(self, low: int, high: int) -> List[int]:\n rslt = []\n def backtracking(i):\n if i >= low and i <= high:\n rslt.append(i)\n if i > high:\n return\n if i != 0 and i%10 < 9:\n backtracking(i*10 + i%10+1)\n if i != 0 and i%10 > 0:\n backtracking(i*10 + i%10-1)\n return\n for j in range(10):\n backtracking(j)\n return sorted(rslt)\n"},{"alpha_fraction":0.47252747416496277,"alphanum_fraction":0.4813186824321747,"avg_line_length":34,"blob_id":"471d005aa8357da785577fb71ef1191b4c4386a1","content_id":"c09417c3ba27546adfd06cc13d5c85e96be53d6b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":455,"license_type":"no_license","max_line_length":83,"num_lines":13,"path":"/lc946_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def validateStackSequences(self, pushed: List[int], popped: List[int]) -> bool:\n popidx, stk = 0, []\n for num in pushed:\n mark = True\n if num == popped[popidx]:\n mark = False\n popidx +=1\n while stk and popped[popidx] == stk[-1]:\n stk.pop()\n popidx += 1\n if mark: stk.append(num)\n return False if stk else True\n"},{"alpha_fraction":0.5314685106277466,"alphanum_fraction":0.5314685106277466,"avg_line_length":34.75,"blob_id":"db3cd830ac64275eb67ce45905eb7d33eb417964","content_id":"efe690d7fc54e62448564847cb7a7dbae387961d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":429,"license_type":"no_license","max_line_length":83,"num_lines":12,"path":"/lc582_stack.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def killProcess(self, pid: List[int], ppid: List[int], kill: int) -> List[int]:\n mmap = collections.defaultdict(list)\n for child, parent in zip(pid, ppid):\n mmap[parent].append(child)\n rslt, stk = [], [kill]\n while stk:\n temp = stk.pop()\n rslt.append(temp)\n for child in mmap[temp]:\n stk.append(child)\n return rslt\n"},{"alpha_fraction":0.5215845704078674,"alphanum_fraction":0.5256475210189819,"avg_line_length":32.94827651977539,"blob_id":"8c715df7947919b6248fe9d8bc77eb51b257960a","content_id":"726653e9fb00c94a1d9b543b631da93030ff881f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1969,"license_type":"no_license","max_line_length":85,"num_lines":58,"path":"/lc307_segtree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Node:\n def __init__(self, start, end):\n self.total = 0\n self.start, self.end = start, end\n self.left, self.right = None, None\n\nclass SegTree:\n def __init__(self, nums):\n self.nums = nums\n self.root = self.create_tree(0, len(self.nums)-1)\n\n def create_tree(self, start, end):\n if start == end:\n rslt = Node(start, end)\n rslt.total = self.nums[start]\n return rslt\n mid = (start + end)//2\n rslt = Node(start, end)\n rslt.left = self.create_tree(start, mid)\n rslt.right = self.create_tree(mid+1, end)\n rslt.total = rslt.left.total + rslt.right.total\n return rslt\n\n def update(self, i, val):\n def helper(root, i, val):\n if root.left == root.right:\n root.total = val\n return val\n mid = (root.start + root.end)//2\n if i <= mid:\n helper(root.left, i, val)\n else:\n helper(root.right, i, val)\n root.total = root.left.total + root.right.total\n return root.total\n helper(self.root, i, val)\n def sum_range(self, start, end):\n def helper(root, start, end):\n if root.start == start and root.end == end:\n return root.total\n mid = (root.start + root.end)//2\n if end <= mid:\n return helper(root.left, start, end)\n elif start > mid:\n return helper(root.right, start, end)\n else:\n return helper(root.left, start, mid) + helper(root.right, mid+1, end)\n return helper(self.root, start, end)\n\nclass NumArray:\n def __init__(self, nums: List[int]):\n self.segtree = SegTree(nums)\n\n def update(self, index: int, val: int) -> None:\n self.segtree.update(index, val)\n\n def sumRange(self, left: int, right: int) -> int:\n return self.segtree.sum_range(left, right)\n"},{"alpha_fraction":0.4947267472743988,"alphanum_fraction":0.5100671052932739,"avg_line_length":44.34782791137695,"blob_id":"4161260fe2a181fc35790239b61dace0e1d5c325","content_id":"ef8a7e12f99d01a1d6bf1003ab106ae7a32dd09d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1043,"license_type":"no_license","max_line_length":100,"num_lines":23,"path":"/lc1272_array.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def removeInterval(self, intervals: List[List[int]], toBeRemoved: List[int]) -> List[List[int]]:\n rslt = []\n for x, y in intervals:\n if x < toBeRemoved[0] and y <= toBeRemoved[1]:\n rslt.append([x, min(toBeRemoved[0], y)])\n elif x >= toBeRemoved[0] and y > toBeRemoved[1]:\n rslt.append([max(x, toBeRemoved[1]), y])\n elif x < toBeRemoved[0] and y > toBeRemoved[1]:\n rslt.append([x, toBeRemoved[0]])\n rslt.append([toBeRemoved[1], y])\n return rslt\n def removeInterval(self, intervals: List[List[int]], toBeRemoved: List[int]) -> List[List[int]]:\n rslt = []\n for x, y in intervals:\n if y < toBeRemoved[0] or x > toBeRemoved[1]:\n rslt.append([x, y])\n else:\n if x < toBeRemoved[0]:\n rslt.append([x, toBeRemoved[0]])\n if y > toBeRemoved[1]:\n rslt.append([toBeRemoved[1], y])\n return rslt\n"},{"alpha_fraction":0.4474761188030243,"alphanum_fraction":0.46384719014167786,"avg_line_length":37.578948974609375,"blob_id":"f8d3c893f0900f2e720e7e5a1128374ffe01478e","content_id":"2981f62fe2eadaf24e41b047eaf8b5eecaba26a4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":733,"license_type":"no_license","max_line_length":70,"num_lines":19,"path":"/lc164_bucket.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maximumGap(self, nums: List[int]) -> int:\n n = len(nums)\n left, right = min(nums), max(nums)\n if n < 2 or left == right: return 0\n s_bkts = (right - left) //(n-1) or 1\n n_bkts = (right-left)//s_bkts + 1\n bkts = [[float(\"inf\"), -float(\"inf\")] for _ in range(n_bkts)]\n for num in nums:\n idx = (num-left)//s_bkts\n bkts[idx] = min(num, bkts[idx][0]), max(num, bkts[idx][1])\n last, rslt = None, 0\n for i in range(n_bkts):\n if not last:\n last = bkts[i][1]\n elif bkts[i][0] < float(\"inf\"):\n rslt = max(rslt, bkts[i][0]-last)\n last = bkts[i][1]\n return rslt\n"},{"alpha_fraction":0.46292585134506226,"alphanum_fraction":0.4749498963356018,"avg_line_length":32.266666412353516,"blob_id":"c697d3be74ff2548ecbe9f82da28f28036f13804","content_id":"1bf65d49a1ae92bceef317f92f8bfcf27b47a55d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":499,"license_type":"no_license","max_line_length":57,"num_lines":15,"path":"/lc1079_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def numTilePossibilities(self, tiles: str) -> int:\n self.rslt = 0\n temp = ''.join(sorted(list(tiles)))\n def backtracking(pref, cand):\n self.rslt +=1\n if not cand:\n return\n for i, c in enumerate(cand):\n if i !=0 and c == cand[i-1]:\n continue\n backtracking(pref+c, cand[:i]+cand[i+1:])\n return\n backtracking('', temp)\n return self.rslt-1\n"},{"alpha_fraction":0.5156826376914978,"alphanum_fraction":0.5221402049064636,"avg_line_length":39.14814758300781,"blob_id":"5a2ca47802f699acafd41d876f37d91e9b7d8edb","content_id":"52a651a0f6e7c2a3197ae60f83a7a18804e45439","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1084,"license_type":"no_license","max_line_length":78,"num_lines":27,"path":"/lc106_tree.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def buildTree(self, inorder: List[int], postorder: List[int]) -> TreeNode:\n nodemap = {val:idx for idx, val in enumerate(inorder)}\n stk, head = [], None\n for node in postorder[::-1]:\n if not head:\n head = TreeNode(node)\n stk.append(head)\n else:\n temp = TreeNode(node)\n if nodemap[node] > nodemap[stk[-1].val]:\n stk[-1].right = temp\n else:\n while stk and nodemap[stk[-1].val] > nodemap[node]:\n last = stk.pop()\n last.left = temp\n stk.append(temp)\n return head\n\nclass Solution:\n def buildTree(self, inorder: List[int], postorder: List[int]) -> TreeNode:\n if not inorder or not postorder: return None\n root = TreeNode(postorder[-1])\n idx = inorder.index(root.val)\n root.right = self.buildTree(inorder[idx+1:], postorder[idx:-1])\n root.left = self.buildTree(inorder[:idx], postorder[:idx])\n return root\n"},{"alpha_fraction":0.4265822768211365,"alphanum_fraction":0.4430379867553711,"avg_line_length":34.90909194946289,"blob_id":"9a6fa01d453ee8898451c27746d240c1763af7e5","content_id":"745d7ab5acbdec60366445fd783cfc190bad6998","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1580,"license_type":"no_license","max_line_length":74,"num_lines":44,"path":"/lc130_unionfind.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def solve(self, board: List[List[str]]) -> None:\n \"\"\"\n Do not return anything, modify board in-place instead.\n \"\"\"\n if not board or not board[0]: return\n r, c= len(board), len(board[0])\n uf, edge = UF(r*c+1), r*c\n for i in range(r):\n if board[i][0] == \"O\": uf.union(i*c, edge)\n if board[i][c-1] == \"O\": uf.union(i*c + c-1, edge)\n for j in range(c):\n if board[0][j] == \"O\": uf.union(j, edge)\n if board[r-1][j] == \"O\": uf.union(c*(r-1)+j, edge)\n for i in range(1, r-1):\n for j in range(1, c-1):\n if board[i][j] == 'O':\n for x, y in [(1, 0), (-1, 0), (0, 1), (0, -1)]:\n if board[i+x][j+y] == \"O\":\n uf.union(i*c+j, (i+x)*c+j+y)\n for i in range(1, r-1):\n for j in range(1, c-1):\n if board[i][j] == \"O\" and uf.find(i*c+j) != uf.find(edge):\n board[i][j] = 'X'\n\n\nclass UF:\n def __init__(self, n):\n self.parent = list(range(n))\n self.size = [1]*n\n def union(self, p, q):\n rootp = self.find(p)\n rootq = self.find(q)\n if rootp == rootq: return\n if self.size[rootp] > self.size[rootq]:\n rootp, rootq = rootq, rootp\n self.parent[rootp] = rootq\n self.size[rootq] += self.size[rootp]\n\n def find(self, x):\n while self.parent[x] != x:\n self.parent[x] = self.parent[self.parent[x]]\n x = self.parent[x]\n return x\n"},{"alpha_fraction":0.4560000002384186,"alphanum_fraction":0.46666666865348816,"avg_line_length":30.25,"blob_id":"c2c9bb7ac09ba36ffd5a4a03f9c2001dda00b436","content_id":"8c70ecbf273f30c06da3a8810632bd3a1d25b593","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":375,"license_type":"no_license","max_line_length":61,"num_lines":12,"path":"/lc325_twopoiner.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def maxSubArrayLen(self, nums: List[int], k: int) -> int:\n holder = {0:-1}\n curr = 0\n rslt = 0\n for i, num in enumerate(nums):\n curr += num\n if curr - k in holder:\n rslt = max(rslt, i - holder[curr-k])\n if curr not in holder:\n holder[curr] = i\n return rslt\n"},{"alpha_fraction":0.4441041350364685,"alphanum_fraction":0.4486983120441437,"avg_line_length":37.411766052246094,"blob_id":"57068679ab9a051f356476fdcb13f345249ea858","content_id":"06da429ee8eaeab49e6fe1512992088f22192e22","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":653,"license_type":"no_license","max_line_length":65,"num_lines":17,"path":"/lc785_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def isBipartite(self, graph: List[List[int]]) -> bool:\n dque = collections.deque()\n visited, mark = {}, 0\n for i in range(len(graph)):\n if i not in visited:\n dque.append((i, mark))\n while dque:\n cur, mark = dque.popleft()\n mark = (mark+1)%2\n for post in graph[cur]:\n if post in visited and visited[post] != mark:\n return False\n elif post not in visited:\n visited[post] = mark\n dque.append((post, mark))\n return True\n"},{"alpha_fraction":0.4290465712547302,"alphanum_fraction":0.4678492248058319,"avg_line_length":25.52941131591797,"blob_id":"a9a8b865b53710cc4ac016ce0e26d268f6d1c885","content_id":"18dc02cf126b853191d531906e52e0d0251a8a0e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":902,"license_type":"no_license","max_line_length":68,"num_lines":34,"path":"/lc445_linkedlist.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def addTwoNumbers(self, l1: ListNode, l2: ListNode) -> ListNode:\n temp1, temp2 = l1, l2\n while temp1 and temp2:\n temp1 = temp1.next\n temp2 = temp2.next\n if temp2:\n l1, l2 = l2, l1\n temp = temp1 if temp1 else temp2\n rslt = None\n\n while temp:\n rslt = ListNode(l1.val, rslt)\n l1 = l1.next\n temp = temp.next\n while l1:\n rslt = ListNode(l1.val + l2.val, rslt)\n l1 = l1.next\n l2 = l2.next\n\n carry, prev = 0, None\n\n while rslt:\n tempv = rslt.val + carry\n carry, tempv = tempv//10, tempv%10\n rslt.val = tempv\n temp = rslt.next\n rslt.next = prev\n prev = rslt\n rslt = temp\n\n if carry:\n prev = ListNode(carry, prev)\n return prev\n"},{"alpha_fraction":0.5,"alphanum_fraction":0.5088757276535034,"avg_line_length":32.79999923706055,"blob_id":"210021f031fa08a8eaaf7b749108a11600224e89","content_id":"b1acba2f5c5b79ecc155b59323648ed8d5313ceb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":338,"license_type":"no_license","max_line_length":54,"num_lines":10,"path":"/lc3_twopointer.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def lengthOfLongestSubstring(self, s: str) -> int:\n if not s: return 0\n left, holder, rslt = -1, {}, 0\n for right, c in enumerate(s):\n if c in holder:\n left = max(holder[c], left)\n rslt = max(rslt, right-left)\n holder[c] = right\n return rslt\n"},{"alpha_fraction":0.5716981291770935,"alphanum_fraction":0.5716981291770935,"avg_line_length":36.85714340209961,"blob_id":"bc531b507b0e326b3a6225df021f8f74458359bb","content_id":"00202e548d6af1cfb257b4287fbc4691019c1c5c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":530,"license_type":"no_license","max_line_length":63,"num_lines":14,"path":"/lc1485_dfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def copyRandomBinaryTree(self, root: 'Node') -> 'NodeCopy':\n if not root: return None\n constructed = {}\n def dfs(root):\n if not root: return None\n if root in constructed: return constructed[root]\n copyroot = NodeCopy(root.val)\n constructed[root] = copyroot\n copyroot.left = dfs(root.left)\n copyroot.right = dfs(root.right)\n copyroot.random = dfs(root.random)\n return copyroot\n return dfs(root)\n"},{"alpha_fraction":0.4059869050979614,"alphanum_fraction":0.41253507137298584,"avg_line_length":36.29069900512695,"blob_id":"5ff12983e63c320b9857f4a4bc6e344afcb017ec","content_id":"b1a53ec2ea4cdb2d8a66e8accf64d8ea2135e83c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3207,"license_type":"no_license","max_line_length":101,"num_lines":86,"path":"/lc212_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def findWords(self, board: List[List[str]], words: List[str]) -> List[str]:\n # build trie\n trie = {}\n for word in words:\n curr = trie\n for ch in word:\n curr[ch] = curr.get(ch, {})\n curr = curr[ch]\n curr[\"end\"] = word\n nrow, ncol, rslt, visited = len(board), len(board[0]), set(), set()\n def backtracking(i, j, curr):\n if \"end\" in curr:\n rslt.add(curr[\"end\"])\n for x, y in [(i+1, j), (i, j+1), (i-1, j), (i, j-1)]:\n if 0 <= x < nrow and 0 <= y < ncol and board[x][y] in curr and (x, y) not in visited:\n visited.add((x, y))\n backtracking(x, y, curr[board[x][y]])\n visited.remove((x, y))\n for i in range(nrow):\n for j in range(ncol):\n if board[i][j] in trie:\n visited.add((i, j))\n backtracking(i, j, trie[board[i][j]])\n visited.remove((i, j))\n return rslt\n\n\nclass Solution:\n def findWords(self, board: List[List[str]], words: List[str]) -> List[str]:\n # build trie\n trie = {}\n for word in words:\n curr = trie\n for ch in word:\n curr[ch] = curr.get(ch, {})\n curr = curr[ch]\n curr[\"end\"] = word\n nrow, ncol, rslt = len(board), len(board[0]), set()\n def backtracking(i, j, curr):\n if \"end\" in curr:\n rslt.add(curr[\"end\"])\n for x, y in [(i+1, j), (i, j+1), (i-1, j), (i, j-1)]:\n if 0 <= x < nrow and 0 <= y < ncol and board[x][y] in curr:\n ch = board[x][y]\n board[x][y] = \"\"\n backtracking(x, y, curr[ch])\n board[x][y] = ch\n for i in range(nrow):\n for j in range(ncol):\n if board[i][j] in trie:\n ch = board[i][j]\n board[i][j] = \"\"\n backtracking(i, j, trie[ch])\n board[i][j] = ch\n return rslt\n\nclass Solution:\n def findWords(self, board: List[List[str]], words: List[str]) -> List[str]:\n # build trie\n trie = {}\n for word in words:\n curr = trie\n for ch in word:\n curr[ch] = curr.get(ch, {})\n curr = curr[ch]\n curr[\"end\"] = word\n nrow, ncol, rslt = len(board), len(board[0]), set()\n def backtracking(i, j, parent):\n pch = board[i][j]\n curr = parent[pch]\n board[i][j] = \"\"\n if \"end\" in curr:\n rslt.add(curr[\"end\"])\n curr.pop(\"end\")\n for x, y in [(i+1, j), (i, j+1), (i-1, j), (i, j-1)]:\n if 0 <= x < nrow and 0 <= y < ncol and board[x][y] in curr:\n backtracking(x, y, curr)\n board[i][j] = pch\n if not curr:\n parent.pop(pch)\n for i in range(nrow):\n for j in range(ncol):\n if board[i][j] in trie:\n backtracking(i, j, trie)\n return rslt\n"},{"alpha_fraction":0.47415730357170105,"alphanum_fraction":0.483146071434021,"avg_line_length":43.5,"blob_id":"18ca799e219c4e712fa3cae596e86103ac4f21a0","content_id":"fd3aa9782fd7953172f90d313ed73a5ef4e9bbf5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":890,"license_type":"no_license","max_line_length":85,"num_lines":20,"path":"/lc127_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def ladderLength(self, beginWord: str, endWord: str, wordList: List[str]) -> int:\n if not wordList or endWord not in wordList:\n return 0\n lword, word_map, visited = len(beginWord), defaultdict(list), {beginWord}\n queue = deque([(beginWord, 1)])\n for wrd in wordList:\n for i in range(lword):\n word_map[wrd[:i]+\"*\"+wrd[i+1:]].append(wrd)\n while queue:\n wrd, step = queue.popleft()\n for i in range(lword):\n for wrd_cand in word_map[wrd[:i]+\"*\"+wrd[i+1:]]:\n if wrd_cand == endWord:\n return step +1\n if wrd_cand not in visited:\n visited.add(wrd_cand)\n queue.append((wrd_cand, step+1))\n word_map[wrd[:i]+\"*\"+wrd[i+1:]] = []\n return 0\n"},{"alpha_fraction":0.4480234384536743,"alphanum_fraction":0.4846266508102417,"avg_line_length":47.78571319580078,"blob_id":"02bbf072c4fb7466c2729f5613fe100c8a9e5721","content_id":"86299aa3d0ab5cce7659f5543b9b8b09172fad57","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":683,"license_type":"no_license","max_line_length":92,"num_lines":14,"path":"/lc1314_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def matrixBlockSum(self, mat: List[List[int]], k: int) -> List[List[int]]:\n nrow, ncol = len(mat), len(mat[0])\n presum = [[0]*(ncol+1) for _ in range(nrow+1)]\n for i in range(nrow):\n for j in range(ncol):\n presum[i+1][j+1] = presum[i][j+1]+ presum[i+1][j] + mat[i][j] - presum[i][j]\n rslt = [[0]*ncol for _ in range(nrow)]\n for i in range(nrow):\n for j in range(ncol):\n r1, r2 = max(0, i-k), min(nrow, i+k+1)\n c1, c2 = max(0, j-k), min(ncol, j+k+1)\n rslt[i][j] = presum[r2][c2] - presum[r2][c1]-presum[r1][c2]+ presum[r1][c1]\n return rslt\n"},{"alpha_fraction":0.45151033997535706,"alphanum_fraction":0.45627981424331665,"avg_line_length":33.94444274902344,"blob_id":"fe01177fb70fdf9f8e0b21639444e36b0493cceb","content_id":"191c29f6d56a8c3067059988cd9799f4adacaa16","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":629,"license_type":"no_license","max_line_length":68,"num_lines":18,"path":"/lc698_bk.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def canPartitionKSubsets(self, nums: List[int], k: int) -> bool:\n nums.sort()\n target, rm = divmod(sum(nums), k)\n if rm or target < nums[-1]: return False\n def dfs():\n if not nums: return True\n right = nums.pop()\n for i, ttt in enumerate(tt_list):\n if ttt + right <= target:\n tt_list[i] += right\n if dfs(): return True\n tt_list[i] -= right\n if ttt == 0: break\n nums.append(right)\n return False\n tt_list = [0]*k\n return dfs()\n"},{"alpha_fraction":0.4285714328289032,"alphanum_fraction":0.4559270441532135,"avg_line_length":31.899999618530273,"blob_id":"9871198a9ddfac45279190dcc4c2cda6211264c7","content_id":"1bd309aae5b90664ac6299cb0d25abe69000885a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":329,"license_type":"no_license","max_line_length":65,"num_lines":10,"path":"/lc256_dp.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minCost(self, costs: List[List[int]]) -> int:\n if not costs: return 0\n dp = costs[0]\n for i in range(1, len(costs)):\n temp = [0]*3\n for j in range(3):\n temp[j] = costs[i][j] + min(dp[(j+1)%3], dp[j-1])\n dp = temp\n return min(dp)\n"},{"alpha_fraction":0.4718792736530304,"alphanum_fraction":0.4801097512245178,"avg_line_length":35.45000076293945,"blob_id":"6a19767680525b61a22d57270940a58d881f154f","content_id":"a68802e33e4b302a8582c9c02ebd33bb59fc2d34","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":729,"license_type":"no_license","max_line_length":67,"num_lines":20,"path":"/lc30_swind.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# BK\nclass Solution:\n def findSubstring(self, s: str, words: List[str]) -> List[int]:\n wordmap = collections.Counter(words)\n n, m, l = len(words), len(words[0]), len(s)\n lsub = n*m\n if lsub == l and \"\".join(words) == s: return [0]\n rslt = []\n def backtracking(right, checked):\n if checked == lsub:\n rslt.append(right-lsub)\n elif right < l:\n temp = s[right:right+m]\n if temp in wordmap and wordmap[temp] > 0:\n wordmap[temp] -= 1\n backtracking(right+m, checked+m)\n wordmap[temp] += 1\n for i in range(l):\n backtracking(i, 0)\n return rslt\n"},{"alpha_fraction":0.4473067820072174,"alphanum_fraction":0.4496487081050873,"avg_line_length":37.818180084228516,"blob_id":"9ab606030f1a1b900eebf5efae344835e8f967c4","content_id":"fc5a1f02e4facc376d1d1014c32a274335e1e750","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":427,"license_type":"no_license","max_line_length":91,"num_lines":11,"path":"/lc1249_str.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def minRemoveToMakeValid(self, s: str) -> str:\n stack = []\n for i, c in enumerate(s+'*'):\n if c == \"(\":\n stack.append(i)\n elif c == \")\":\n if stack and s[stack[-1]] == '(': stack.pop()\n else: stack.append(i)\n stack = set(stack)\n return \"\".join(c for i, c in enumerate(s) if c not in ['(', ')'] or i not in stack)\n"},{"alpha_fraction":0.44514501094818115,"alphanum_fraction":0.4464060664176941,"avg_line_length":23,"blob_id":"10b7c20e97ca5de963ebc4c3f5ca34f67f617b7e","content_id":"bfd73b37c659111408ca676918d0d6ec91913354","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":793,"license_type":"no_license","max_line_length":61,"num_lines":33,"path":"/lc143_linkedlist.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, val=0, next=None):\n# self.val = val\n# self.next = next\nclass Solution:\n def reorderList(self, head: ListNode) -> None:\n \"\"\"\n Do not return anything, modify head in-place instead.\n \"\"\"\n s, f = head, head\n while f and f.next:\n s = s.next\n f = f.next.next\n\n prev, curr = None, s.next\n s.next = None\n while curr:\n temp = curr.next\n curr.next = prev\n prev = curr\n curr = temp\n\n a, b = head, prev\n while b:\n temp = a.next\n a.next = b\n a = temp\n\n temp = b.next\n b.next = a\n b = temp\n return \n"},{"alpha_fraction":0.34657835960388184,"alphanum_fraction":0.3653421700000763,"avg_line_length":44.25,"blob_id":"1b429e468134f49bf8c808f8dc5a5eec45e434bc","content_id":"dfa4bbf6c8020f945fb6e1347d83d74f5f3b1649","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":906,"license_type":"no_license","max_line_length":115,"num_lines":20,"path":"/lc200_bfs.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Solution:\n def numIslands(self, grid: List[List[str]]) -> int:\n if not grid or not grid[0]: return 0\n n_row, n_col = len(grid), len(grid[0])\n queue, rslt = collections.deque(), 0\n for i in range(n_row):\n for j in range(n_col):\n if grid[i][j] == \"1\":\n grid[i][j] = None\n queue.append((i, j))\n while queue:\n curr_i, curr_j = queue.popleft()\n for x, y in [(-1, 0), (1, 0), (0, 1), (0, -1)]:\n t_x, t_y = curr_i+x, curr_j + y\n if t_x >= 0 and t_x < n_row and t_y >= 0 and t_y < n_col and grid[t_x][t_y] == \"1\":\n grid[t_x][t_y] = None\n queue.append((t_x, t_y))\n\n rslt += 1\n return rslt \n"},{"alpha_fraction":0.5074441432952881,"alphanum_fraction":0.5124069452285767,"avg_line_length":31.675676345825195,"blob_id":"f7644841b6b3379a8769cc7fc2b286298441475f","content_id":"dd911e47f951163235bef0dcb4dc489258af3325","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2418,"license_type":"no_license","max_line_length":96,"num_lines":74,"path":"/lc432_ood.py","repo_name":"Mela2014/lc_punch","src_encoding":"UTF-8","text":"class Block:\n def __init__(self, val = 0, left = None, right = None):\n self.val = val\n self.left = left\n self.right = right\n self.kids = set()\nclass AllOne:\n def __init__(self):\n \"\"\"\n Initialize your data structure here.\n \"\"\"\n self.sleft = Block()\n self.sright = Block(left = self.sleft)\n self.sleft.right = self.sright\n self.keyblock = {}\n def inc(self, key: str) -> None:\n \"\"\"\n Inserts a new key\n* I use git bash to execute my script.\n\n# usage\n* look and click on a tweet you want to get the quoted replies of.
\n* on the address bar of your browser, copy the tweet URL.
\n* on your command line interface or terminal type ``python quotedreplies.py pastetheurloftweethere ``
\n* the script would then generate a URL string containing the full query of your quoted replies.
\n* copy this string and paste it on your browser\n"},{"alpha_fraction":0.703832745552063,"alphanum_fraction":0.7177700400352478,"avg_line_length":46.83333206176758,"blob_id":"2de12ca8d11498f6dba0fb19e0b96e9c5a01ad5a","content_id":"bd6f046b2403d46c7905e12bc90aa5e0d6ac4650","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":287,"license_type":"no_license","max_line_length":118,"num_lines":6,"path":"/quotedreplies.py","repo_name":"ogspeace/quotedreps","src_encoding":"UTF-8","text":"import sys\n\ntweet_id = sys.argv[1].split('/')[-1]\nurl_string = f\"https://twitter.com/search?q=-from:quotedreplies%20url:{tweet_id}&src=typed_query&f=live\"\nif tweet_id:\n print(f\"copy the following url to your browser to view all quoted replies of your chosen tweet: \\n\\n{url_string}\")\n"}],"string":"[\n {\n \"alpha_fraction\": 0.7558139562606812,\n \"alphanum_fraction\": 0.7591361999511719,\n \"avg_line_length\": 45.30769348144531,\n \"blob_id\": \"3da401259d15195b409a3f3056b396eeb5354ab8\",\n \"content_id\": \"bd37033ea60551d19effc5af3a11827eed083fd8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 602,\n \"license_type\": \"no_license\",\n \"max_line_length\": 110,\n \"num_lines\": 13,\n \"path\": \"/README.md\",\n \"repo_name\": \"ogspeace/quotedreps\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# quotedreps\\nsimple command line python script that generates a URL string containing the quoted replies of a user's tweet.\\n\\n# requirements\\n* Use python3.6+
\\n* I use git bash to execute my script.\\n\\n# usage\\n* look and click on a tweet you want to get the quoted replies of.
\\n* on the address bar of your browser, copy the tweet URL.
\\n* on your command line interface or terminal type ``python quotedreplies.py pastetheurloftweethere ``
\\n* the script would then generate a URL string containing the full query of your quoted replies.
\\n* copy this string and paste it on your browser\\n\"\n },\n {\n \"alpha_fraction\": 0.703832745552063,\n \"alphanum_fraction\": 0.7177700400352478,\n \"avg_line_length\": 46.83333206176758,\n \"blob_id\": \"2de12ca8d11498f6dba0fb19e0b96e9c5a01ad5a\",\n \"content_id\": \"bd6f046b2403d46c7905e12bc90aa5e0d6ac4650\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 287,\n \"license_type\": \"no_license\",\n \"max_line_length\": 118,\n \"num_lines\": 6,\n \"path\": \"/quotedreplies.py\",\n \"repo_name\": \"ogspeace/quotedreps\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\n\\ntweet_id = sys.argv[1].split('/')[-1]\\nurl_string = f\\\"https://twitter.com/search?q=-from:quotedreplies%20url:{tweet_id}&src=typed_query&f=live\\\"\\nif tweet_id:\\n print(f\\\"copy the following url to your browser to view all quoted replies of your chosen tweet: \\\\n\\\\n{url_string}\\\")\\n\"\n }\n]"},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":1179,"cells":{"repo_name":{"kind":"string","value":"GhostingRecon/Oblig1"},"repo_url":{"kind":"string","value":"https://github.com/GhostingRecon/Oblig1"},"snapshot_id":{"kind":"string","value":"93a3bc95ae892783582a800e19d6685ae6ed99d8"},"revision_id":{"kind":"string","value":"0277e07f4035608ec38c78d666fb8bf09a459b06"},"directory_id":{"kind":"string","value":"b5fb6683979705b9ff8c63c2446979e4b0f049e7"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"string","value":"2020-07-31T10:56:50.681797"},"revision_date":{"kind":"string","value":"2019-10-22T12:13:25"},"committer_date":{"kind":"string","value":"2019-10-22T12:13:25"},"github_id":{"kind":"number","value":210580691,"string":"210,580,691"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5264720320701599,"alphanum_fraction":0.5685304403305054,"avg_line_length":17.44748878479004,"blob_id":"bed8c5370bfb4face09192d37c3a6bd8c94f7022","content_id":"f425bf8612d581b3adc3458bc6f85a46ebc2f2f6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4053,"license_type":"no_license","max_line_length":84,"num_lines":219,"path":"/sem_1.py","repo_name":"GhostingRecon/Oblig1","src_encoding":"UTF-8","text":"#Oblig_1\n\n\n\n#1\n\ntall_1 = int(input(\"Tall nr 1: \"))\ntall_2 = int(input(\"Tall nr 2: \"))\ntall_3 = int(input(\"Tall nr 3: \"))\nprint()\n\nprint(\"Tall nr 1: \" + str(tall_1))\nprint(\"Tall nr 2: \" + str(tall_2))\nprint(\"Tall nr 2: \" + str(tall_3))\n\n\nif tall_1 < tall_2 < tall_3:\n print(\"Strengt stigende\")\nelif tall_1 > tall_2 > tall_3:\n print(\"Strengt synkende\")\nelse:\n print(\"Verken strengt stigende eller synkende\")\n\n#2\n\nprint()\n#2.a\n\n\n\nkort = input(\"Skriv inn \\\"kort tegn\\\" for konvertering (D, H, S, C): \").upper()\n\nif kort == \"D\":\n print(\"Ruter\")\nelif kort == \"H\":\n print(\"Hjerter\")\nelif kort == \"S\":\n print(\"Spar\")\nelif kort == \"C\":\n print(\"Kløver\")\nelse:\n print(\"ikke et tegn i kortstokken\")\n\nprint()\n\n#2.b\n\"\"\"lager en array med alle tall og tegn\"\"\"\n\ntegnDict = {\n \"A\": \"Ess\",\n \"2\": \"To\",\n \"3\": \"Tre\",\n \"4\": \"Fire\",\n \"5\": \"Fem\",\n \"6\": \"Seks\",\n \"7\": \"Syv\",\n \"8\": \"Åtte\",\n \"9\": \"Ni\",\n \"10\": \"Ti\",\n \"J\": \"Knekt\",\n \"Q\": \"Dame\",\n \"K\": \"Konge\"\n\n}\n\n\"\"\"Legger til upper her siden alle ord er med stor bokstav\"\"\"\n\nbruker = input(\"tast inn tegn eller tall for konvertering: \").upper()\n\n\"\"\"Printer så verdien\"\"\"\n\nprint(tegnDict[bruker])\n\n\nprint()\n\n#2.a og 2.b\n\nkort_konvertering = input(\"Skriv inn nummer og korttegn for konvertering: \").upper()\n\nliste = list(kort_konvertering)\n\n\n\ndef kort(type):\n\n\n kortNummer = {\n \"A\": \"Ess\",\n \"2\": \"To\",\n \"3\": \"Tre\",\n \"4\": \"Fire\",\n \"5\": \"Fem\",\n \"6\": \"Seks\",\n \"7\": \"Syv\",\n \"8\": \"Åtte\",\n \"9\": \"Ni\",\n \"10\": \"Ti\",\n \"J\": \"Knekt\",\n \"Q\": \"Dame\",\n \"K\": \"Konge\"\n}\n\n kortTegn = {\n \"D\": \"Ruter\",\n \"H\": \"Hjerter\",\n \"S\": \"Spar\",\n \"C\": \"Kløver\"\n}\n return kortNummer[type[0]] + \" \" + kortTegn[type[1]]\n\n\nprint(kort(liste))\n\n#3\n\nprint()\n\n#3.a\n\n\n\nvaluta = {\n \"EUR\": 9.68551,\n \"USD\": 8.50373,\n \"GBP\": 11.0134,\n \"SEK\": 0.92950,\n \"AUD\": 6.06501,\n \"NOK\": 1.00000\n}\nprint(\"valuttakalkulator til NOK\")\nverdiInput = float(input(\"Verdi for konvertering: \"))\nvalutaInput = input(\"Fra hvilke valutta: \").upper()\n\nprint(verdiInput * valuta[valutaInput], \"NOK\")\n\nprint()\n#3.b\n\"\"\"skal bare gå andre veien her så da kan jeg bruke samme dictionary som i a\"\"\"\n\nprint(\"valutakalkulator fra NOK\")\nverdiInputFra = float(input(\"Verdi for konvertering: \"))\nvalutaInputFra = input(\"Til hvilke valutta: \").upper()\n\nprint(verdiInputFra / valuta[valutaInputFra], valutaInputFra)\n\nprint()\n#4\n\"\"\"Printe ut alle heltall fra 0-9 i potens 3\"\"\"\n\nfor x in range(0, 10+1):\n print(str(x) + \"**\" + str(3), \"=\", str(x**3))\n\n#5.a\nprint()\n\ninputStart = int(input(\"Start: \"))\ninputStop = int(input(\"Stop: \"))\ninputN = int(input(\"n: \"))\n\nprint(\"Verdier mellom\", str(inputStart), \"og\", str(inputStop),\n \"som er delelige på\", str(inputN) + \":\")\n\nfor n in range(inputStart, inputStop+1):\n\n if n % inputN == 0:\n print(n)\n\nprint()\n#6\n\n#6.a\n\ndef tilFahrenheit(celsius):\n return int(celsius * 1.800)+32\nprint(\"Celsius Fahrenheit\")\nfor n in range(0, 100+1, 10):\n print(\"%7d %12d\" % (n, tilFahrenheit(n)))\n\n\nprint()\n#6.b\n\n\"\"\"def tilFahrenheit(celsius):\n return int(celsius * 1.800)+32\nprint(\"Celsius Fahrenheit Status\")\nfor n in range(0, 100+1, 10):\n\n if n >= 60:\n print(\"%7d %12d %s\" % (n, tilFahrenheit(n), str(\"Jeg svetter ihjel! \")))\n else:\n print(\"7d %12d %s\" % (n, tilFahrenheit(n) ,str(\"Jeg har det bra\")))\"\"\"\n\n\n\ndef tilFahrenheit(celsius):\n return int(celsius * 1.800) + 32\nprint(\"Celsius Fahrenheit Status\")\nfor n in range(0, 100+1, 10):\n if n >= 60:\n print(\"%7d %12d %23s\" % (n, tilFahrenheit(n), str(\"jeg svetter ihjel! \")))\n else:\n print(\"%7d %12d %21s\" % (n, tilFahrenheit(n), str(\"jeg har det bra. \")))\n\n#7\n\nprint()\n\nverdi = float(input(\"Hvor mye har du?: \"))\nfra = float(input(\"Fra år: \"))\ntil = float(input(\"Til år: \"))\n\ndef renteOkning(verdi, fra, til):\n\n tid = til - fra\n endring = verdiInputFra * (1.02 ** tid)\n return endring\nprint(round(renteOkning(verdi, fra, til), 2),\n \"Kroner med 2% inflasjonsøkning\")\n\n\n"},{"alpha_fraction":0.5002036094665527,"alphanum_fraction":0.5622963905334473,"avg_line_length":24.57291603088379,"blob_id":"77013bac7bc624bfe922f43fbe538e3478afffa7","content_id":"38c30e91bc8becb4f0649c02f1be1bc5c5f75fe2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4926,"license_type":"no_license","max_line_length":96,"num_lines":192,"path":"/semester_oppgave_2.py","repo_name":"GhostingRecon/Oblig1","src_encoding":"UTF-8","text":"\n\nimport matplotlib.pyplot as plt\nimport random\n\n\ndef oppgave1():\n \"\"\" Printer ut store tall i kort og lang form,\n ofte kalt amerikansk og britisk form.\n \"\"\"\n store_tall = [\n [\"Million\", \"10^6 \", \"10^6 \"],\n [\"Milliard\", \" \", \"10^9 \"],\n [\"Billion\", \"10^9 \", \"10^12\"],\n [\"Billiard\", \" \", \"10^15\"],\n [\"Trillion\", \"10^12\", \"10^18\"],\n [\"Quadrillion\",\"10^15\", \"10^24\"],\n [\"Quintillion\",\"10^18\", \"10^30\"],\n [\"Sextillion\", \"10^21\", \"10^36\"]\n ] # Hentet fra https://en.wikipedia.org/wiki/Names_of_large_numbers\n\n print(\"%-15s %9s %9s\" % (\"Navn\", \"lang\", \"Kort\"))\n for i in store_tall:\n print(\"%-15s %9s %9s\" % tuple(i))\n\n\n\n# I billion USD (kort form)\n\nmicrosoft_inntekt_dollar = [\n [2002, 28.37],\n [2003, 32.19],\n [2004, 36.84],\n [2005, 39.79],\n [2006, 44.28],\n [2007, 51.12],\n [2008, 60.42],\n [2009, 58.44],\n [2010, 62.48],\n [2011, 69.94],\n [2012, 73.12],\n [2013, 77.85],\n [2014, 86.83],\n [2015, 93.58],\n [2016, 85.32],\n [2017, 89.95],\n [2018, 110.36],\n [2019, 125.84]\n] # Hentet fra https://www.statista.com/statistics/267805/microsofts-global-revenue-since-2002/\n\n#legger inn kursen\nvaluta = float(8.6862)\n\n\ndef oppgave2a():\n \"\"\" Henter en rekke tall fra brukeren og ut hva som\n var medianen i listen\n \"\"\"\n\n tall_liste = []\n while True:\n bruker_input = input(\"Skriv inn tall (avslutt med ferdig): \")\n if bruker_input == \"ferdig\":\n break\n tall_liste.append(int(bruker_input))\n print(\"Dine tall:\", tall_liste)\n\n tall_liste.sort()\n if len(tall_liste) % 2 == 0:\n ny_1 = int(len(tall_liste) /2)\n ny_2 = ny_1 - 1\n median = (tall_liste[ny_1] + tall_liste[ny_2]) /2\n print(\"Medianen til tallene: \", median)\n else:\n print(\"Medianen til tallene: \", tall_liste[int(len(tall_liste)/2)])\n\n pass\n#kopierer listen\nmicrosoft_inntekt_kroner_ny = microsoft_inntekt_dollar.copy()\ndef oppgave3a():\n \"\"\" Konverterer inntektene fra dollar til kroner\n uten å gjøre endringer på originallisten.\n \"\"\"\n #multipliserer index i ny liste med valuta\n microsoft_inntekt_kroner = [valuta * i[-1] for i in microsoft_inntekt_kroner_ny]\n print(\"I NOK:\", [float(round(n, 2)) for n in microsoft_inntekt_kroner])\n print(\"Orginal:\", microsoft_inntekt_dollar)\n\n \"\"\" berge sin kommentar: for n in microsoft_inntekt_dollar:\n print(\"%-15s %-15s %10s\" %(n[0], n[1], n[1]*usdtilNOK))\"\"\"\n\n\n\ndef oppgave3b():\n \"\"\" Tegner et histogram over inntektene til Microsoft\n \"\"\"\n year = []\n inntekt = []\n for i in range(0, len(microsoft_inntekt_dollar)):\n year.append(microsoft_inntekt_dollar[i][0])\n inntekt.append(microsoft_inntekt_dollar[i][1])\n plt.bar(year, inntekt)\n plt.show()\n\n\n\ndef oppgave3c():\n \"\"\" Summerer opp inntektene til Microsoft\n \"\"\"\n inntekt = []\n for i in range(0, len(microsoft_inntekt_dollar)):\n inntekt.append(microsoft_inntekt_dollar[i][1])\n total_milliard = sum(inntekt)\n total_billion = int(total_milliard * 100)\n print(\"Microsoft tjente\", total_billion, \"billioner dollar i perioden 2002-2019\")\n\n\ndef print_kart(spiller, monster):\n x = 0\n while x <= 10:\n y = 0\n kart_liste = []\n while y <= 10:\n if x == spiller[1] and y == spiller[0]:\n kart_liste.append(\"S\")\n elif x == monster[1] and y == monster[0]:\n kart_liste.append(\"M\")\n else: kart_liste.append(\"* \")\n\n y = y + 1\n\n samlet = \" \".join(kart_liste)\n\n print(samlet)\n\n x = x + 1\n\nspiller = [2, 6]\nmonster = [4, 5]\n\ndef spiller_bevegelse():\n flytt = input()\n\n if flytt == \"W\":\n spiller[1] = spiller[1] - 1\n if flytt == \"a\":\n spiller[0] = spiller[0] - 1\n if flytt == \"s\":\n spiller[1] = spiller[1] + 1\n if flytt == \"d\":\n spiller[0] = spiller[0] + 1\n\n monster[0] = monster[0] + random.randint(-1, 1)\n monster[1] = monster[1] + random.randint(-1, 1)\n\ndef spill():\n\n print(\"wasd + enter for å bevege seg\")\n print_kart(spiller, monster)\n\n try:\n while True:\n spiller_bevegelse()\n print_kart(spiller, monster)\n except KeyboardInterrupt:\n exit()\n\n\n\n\n\ndef main():\n \"\"\" Dette er filens main-funksjon, det er denne funksjonen som kjører\n når hele filen blir kjørt. \n Hvis du vil kjøre en av oppgave-funksjonene nedenfor fjerner du #-tegnet \n foran oppgave-funksjonen slik at den blir \"skrudd på\".\n Før du leverer kan det være lurt å sjekke alle funksjonene. Dette gjør \n du ved å fjerne alle #-tegnene nedenfor.\n \"\"\"\n oppgave1()\n print()\n oppgave2a()\n print()\n print()\n oppgave3a()\n print()\n oppgave3b()\n print()\n oppgave3c()\n print()\n spill()\n print()\n\nmain()\n"},{"alpha_fraction":0.7777777910232544,"alphanum_fraction":0.8333333134651184,"avg_line_length":17,"blob_id":"cd1fd084005d7394e80f7aea08d4022221555946","content_id":"b52b4b1a1690bb9af5006113cedb6f665684296f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":36,"license_type":"no_license","max_line_length":26,"num_lines":2,"path":"/README.md","repo_name":"GhostingRecon/Oblig1","src_encoding":"UTF-8","text":"# Oblig1\nobligatorisk innlevering 1\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5264720320701599,\n \"alphanum_fraction\": 0.5685304403305054,\n \"avg_line_length\": 17.44748878479004,\n \"blob_id\": \"bed8c5370bfb4face09192d37c3a6bd8c94f7022\",\n \"content_id\": \"f425bf8612d581b3adc3458bc6f85a46ebc2f2f6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4053,\n \"license_type\": \"no_license\",\n \"max_line_length\": 84,\n \"num_lines\": 219,\n \"path\": \"/sem_1.py\",\n \"repo_name\": \"GhostingRecon/Oblig1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#Oblig_1\\n\\n\\n\\n#1\\n\\ntall_1 = int(input(\\\"Tall nr 1: \\\"))\\ntall_2 = int(input(\\\"Tall nr 2: \\\"))\\ntall_3 = int(input(\\\"Tall nr 3: \\\"))\\nprint()\\n\\nprint(\\\"Tall nr 1: \\\" + str(tall_1))\\nprint(\\\"Tall nr 2: \\\" + str(tall_2))\\nprint(\\\"Tall nr 2: \\\" + str(tall_3))\\n\\n\\nif tall_1 < tall_2 < tall_3:\\n print(\\\"Strengt stigende\\\")\\nelif tall_1 > tall_2 > tall_3:\\n print(\\\"Strengt synkende\\\")\\nelse:\\n print(\\\"Verken strengt stigende eller synkende\\\")\\n\\n#2\\n\\nprint()\\n#2.a\\n\\n\\n\\nkort = input(\\\"Skriv inn \\\\\\\"kort tegn\\\\\\\" for konvertering (D, H, S, C): \\\").upper()\\n\\nif kort == \\\"D\\\":\\n print(\\\"Ruter\\\")\\nelif kort == \\\"H\\\":\\n print(\\\"Hjerter\\\")\\nelif kort == \\\"S\\\":\\n print(\\\"Spar\\\")\\nelif kort == \\\"C\\\":\\n print(\\\"Kløver\\\")\\nelse:\\n print(\\\"ikke et tegn i kortstokken\\\")\\n\\nprint()\\n\\n#2.b\\n\\\"\\\"\\\"lager en array med alle tall og tegn\\\"\\\"\\\"\\n\\ntegnDict = {\\n \\\"A\\\": \\\"Ess\\\",\\n \\\"2\\\": \\\"To\\\",\\n \\\"3\\\": \\\"Tre\\\",\\n \\\"4\\\": \\\"Fire\\\",\\n \\\"5\\\": \\\"Fem\\\",\\n \\\"6\\\": \\\"Seks\\\",\\n \\\"7\\\": \\\"Syv\\\",\\n \\\"8\\\": \\\"Åtte\\\",\\n \\\"9\\\": \\\"Ni\\\",\\n \\\"10\\\": \\\"Ti\\\",\\n \\\"J\\\": \\\"Knekt\\\",\\n \\\"Q\\\": \\\"Dame\\\",\\n \\\"K\\\": \\\"Konge\\\"\\n\\n}\\n\\n\\\"\\\"\\\"Legger til upper her siden alle ord er med stor bokstav\\\"\\\"\\\"\\n\\nbruker = input(\\\"tast inn tegn eller tall for konvertering: \\\").upper()\\n\\n\\\"\\\"\\\"Printer så verdien\\\"\\\"\\\"\\n\\nprint(tegnDict[bruker])\\n\\n\\nprint()\\n\\n#2.a og 2.b\\n\\nkort_konvertering = input(\\\"Skriv inn nummer og korttegn for konvertering: \\\").upper()\\n\\nliste = list(kort_konvertering)\\n\\n\\n\\ndef kort(type):\\n\\n\\n kortNummer = {\\n \\\"A\\\": \\\"Ess\\\",\\n \\\"2\\\": \\\"To\\\",\\n \\\"3\\\": \\\"Tre\\\",\\n \\\"4\\\": \\\"Fire\\\",\\n \\\"5\\\": \\\"Fem\\\",\\n \\\"6\\\": \\\"Seks\\\",\\n \\\"7\\\": \\\"Syv\\\",\\n \\\"8\\\": \\\"Åtte\\\",\\n \\\"9\\\": \\\"Ni\\\",\\n \\\"10\\\": \\\"Ti\\\",\\n \\\"J\\\": \\\"Knekt\\\",\\n \\\"Q\\\": \\\"Dame\\\",\\n \\\"K\\\": \\\"Konge\\\"\\n}\\n\\n kortTegn = {\\n \\\"D\\\": \\\"Ruter\\\",\\n \\\"H\\\": \\\"Hjerter\\\",\\n \\\"S\\\": \\\"Spar\\\",\\n \\\"C\\\": \\\"Kløver\\\"\\n}\\n return kortNummer[type[0]] + \\\" \\\" + kortTegn[type[1]]\\n\\n\\nprint(kort(liste))\\n\\n#3\\n\\nprint()\\n\\n#3.a\\n\\n\\n\\nvaluta = {\\n \\\"EUR\\\": 9.68551,\\n \\\"USD\\\": 8.50373,\\n \\\"GBP\\\": 11.0134,\\n \\\"SEK\\\": 0.92950,\\n \\\"AUD\\\": 6.06501,\\n \\\"NOK\\\": 1.00000\\n}\\nprint(\\\"valuttakalkulator til NOK\\\")\\nverdiInput = float(input(\\\"Verdi for konvertering: \\\"))\\nvalutaInput = input(\\\"Fra hvilke valutta: \\\").upper()\\n\\nprint(verdiInput * valuta[valutaInput], \\\"NOK\\\")\\n\\nprint()\\n#3.b\\n\\\"\\\"\\\"skal bare gå andre veien her så da kan jeg bruke samme dictionary som i a\\\"\\\"\\\"\\n\\nprint(\\\"valutakalkulator fra NOK\\\")\\nverdiInputFra = float(input(\\\"Verdi for konvertering: \\\"))\\nvalutaInputFra = input(\\\"Til hvilke valutta: \\\").upper()\\n\\nprint(verdiInputFra / valuta[valutaInputFra], valutaInputFra)\\n\\nprint()\\n#4\\n\\\"\\\"\\\"Printe ut alle heltall fra 0-9 i potens 3\\\"\\\"\\\"\\n\\nfor x in range(0, 10+1):\\n print(str(x) + \\\"**\\\" + str(3), \\\"=\\\", str(x**3))\\n\\n#5.a\\nprint()\\n\\ninputStart = int(input(\\\"Start: \\\"))\\ninputStop = int(input(\\\"Stop: \\\"))\\ninputN = int(input(\\\"n: \\\"))\\n\\nprint(\\\"Verdier mellom\\\", str(inputStart), \\\"og\\\", str(inputStop),\\n \\\"som er delelige på\\\", str(inputN) + \\\":\\\")\\n\\nfor n in range(inputStart, inputStop+1):\\n\\n if n % inputN == 0:\\n print(n)\\n\\nprint()\\n#6\\n\\n#6.a\\n\\ndef tilFahrenheit(celsius):\\n return int(celsius * 1.800)+32\\nprint(\\\"Celsius Fahrenheit\\\")\\nfor n in range(0, 100+1, 10):\\n print(\\\"%7d %12d\\\" % (n, tilFahrenheit(n)))\\n\\n\\nprint()\\n#6.b\\n\\n\\\"\\\"\\\"def tilFahrenheit(celsius):\\n return int(celsius * 1.800)+32\\nprint(\\\"Celsius Fahrenheit Status\\\")\\nfor n in range(0, 100+1, 10):\\n\\n if n >= 60:\\n print(\\\"%7d %12d %s\\\" % (n, tilFahrenheit(n), str(\\\"Jeg svetter ihjel! \\\")))\\n else:\\n print(\\\"7d %12d %s\\\" % (n, tilFahrenheit(n) ,str(\\\"Jeg har det bra\\\")))\\\"\\\"\\\"\\n\\n\\n\\ndef tilFahrenheit(celsius):\\n return int(celsius * 1.800) + 32\\nprint(\\\"Celsius Fahrenheit Status\\\")\\nfor n in range(0, 100+1, 10):\\n if n >= 60:\\n print(\\\"%7d %12d %23s\\\" % (n, tilFahrenheit(n), str(\\\"jeg svetter ihjel! \\\")))\\n else:\\n print(\\\"%7d %12d %21s\\\" % (n, tilFahrenheit(n), str(\\\"jeg har det bra. \\\")))\\n\\n#7\\n\\nprint()\\n\\nverdi = float(input(\\\"Hvor mye har du?: \\\"))\\nfra = float(input(\\\"Fra år: \\\"))\\ntil = float(input(\\\"Til år: \\\"))\\n\\ndef renteOkning(verdi, fra, til):\\n\\n tid = til - fra\\n endring = verdiInputFra * (1.02 ** tid)\\n return endring\\nprint(round(renteOkning(verdi, fra, til), 2),\\n \\\"Kroner med 2% inflasjonsøkning\\\")\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5002036094665527,\n \"alphanum_fraction\": 0.5622963905334473,\n \"avg_line_length\": 24.57291603088379,\n \"blob_id\": \"77013bac7bc624bfe922f43fbe538e3478afffa7\",\n \"content_id\": \"38c30e91bc8becb4f0649c02f1be1bc5c5f75fe2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4926,\n \"license_type\": \"no_license\",\n \"max_line_length\": 96,\n \"num_lines\": 192,\n \"path\": \"/semester_oppgave_2.py\",\n \"repo_name\": \"GhostingRecon/Oblig1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\nimport matplotlib.pyplot as plt\\nimport random\\n\\n\\ndef oppgave1():\\n \\\"\\\"\\\" Printer ut store tall i kort og lang form,\\n ofte kalt amerikansk og britisk form.\\n \\\"\\\"\\\"\\n store_tall = [\\n [\\\"Million\\\", \\\"10^6 \\\", \\\"10^6 \\\"],\\n [\\\"Milliard\\\", \\\" \\\", \\\"10^9 \\\"],\\n [\\\"Billion\\\", \\\"10^9 \\\", \\\"10^12\\\"],\\n [\\\"Billiard\\\", \\\" \\\", \\\"10^15\\\"],\\n [\\\"Trillion\\\", \\\"10^12\\\", \\\"10^18\\\"],\\n [\\\"Quadrillion\\\",\\\"10^15\\\", \\\"10^24\\\"],\\n [\\\"Quintillion\\\",\\\"10^18\\\", \\\"10^30\\\"],\\n [\\\"Sextillion\\\", \\\"10^21\\\", \\\"10^36\\\"]\\n ] # Hentet fra https://en.wikipedia.org/wiki/Names_of_large_numbers\\n\\n print(\\\"%-15s %9s %9s\\\" % (\\\"Navn\\\", \\\"lang\\\", \\\"Kort\\\"))\\n for i in store_tall:\\n print(\\\"%-15s %9s %9s\\\" % tuple(i))\\n\\n\\n\\n# I billion USD (kort form)\\n\\nmicrosoft_inntekt_dollar = [\\n [2002, 28.37],\\n [2003, 32.19],\\n [2004, 36.84],\\n [2005, 39.79],\\n [2006, 44.28],\\n [2007, 51.12],\\n [2008, 60.42],\\n [2009, 58.44],\\n [2010, 62.48],\\n [2011, 69.94],\\n [2012, 73.12],\\n [2013, 77.85],\\n [2014, 86.83],\\n [2015, 93.58],\\n [2016, 85.32],\\n [2017, 89.95],\\n [2018, 110.36],\\n [2019, 125.84]\\n] # Hentet fra https://www.statista.com/statistics/267805/microsofts-global-revenue-since-2002/\\n\\n#legger inn kursen\\nvaluta = float(8.6862)\\n\\n\\ndef oppgave2a():\\n \\\"\\\"\\\" Henter en rekke tall fra brukeren og ut hva som\\n var medianen i listen\\n \\\"\\\"\\\"\\n\\n tall_liste = []\\n while True:\\n bruker_input = input(\\\"Skriv inn tall (avslutt med ferdig): \\\")\\n if bruker_input == \\\"ferdig\\\":\\n break\\n tall_liste.append(int(bruker_input))\\n print(\\\"Dine tall:\\\", tall_liste)\\n\\n tall_liste.sort()\\n if len(tall_liste) % 2 == 0:\\n ny_1 = int(len(tall_liste) /2)\\n ny_2 = ny_1 - 1\\n median = (tall_liste[ny_1] + tall_liste[ny_2]) /2\\n print(\\\"Medianen til tallene: \\\", median)\\n else:\\n print(\\\"Medianen til tallene: \\\", tall_liste[int(len(tall_liste)/2)])\\n\\n pass\\n#kopierer listen\\nmicrosoft_inntekt_kroner_ny = microsoft_inntekt_dollar.copy()\\ndef oppgave3a():\\n \\\"\\\"\\\" Konverterer inntektene fra dollar til kroner\\n uten å gjøre endringer på originallisten.\\n \\\"\\\"\\\"\\n #multipliserer index i ny liste med valuta\\n microsoft_inntekt_kroner = [valuta * i[-1] for i in microsoft_inntekt_kroner_ny]\\n print(\\\"I NOK:\\\", [float(round(n, 2)) for n in microsoft_inntekt_kroner])\\n print(\\\"Orginal:\\\", microsoft_inntekt_dollar)\\n\\n \\\"\\\"\\\" berge sin kommentar: for n in microsoft_inntekt_dollar:\\n print(\\\"%-15s %-15s %10s\\\" %(n[0], n[1], n[1]*usdtilNOK))\\\"\\\"\\\"\\n\\n\\n\\ndef oppgave3b():\\n \\\"\\\"\\\" Tegner et histogram over inntektene til Microsoft\\n \\\"\\\"\\\"\\n year = []\\n inntekt = []\\n for i in range(0, len(microsoft_inntekt_dollar)):\\n year.append(microsoft_inntekt_dollar[i][0])\\n inntekt.append(microsoft_inntekt_dollar[i][1])\\n plt.bar(year, inntekt)\\n plt.show()\\n\\n\\n\\ndef oppgave3c():\\n \\\"\\\"\\\" Summerer opp inntektene til Microsoft\\n \\\"\\\"\\\"\\n inntekt = []\\n for i in range(0, len(microsoft_inntekt_dollar)):\\n inntekt.append(microsoft_inntekt_dollar[i][1])\\n total_milliard = sum(inntekt)\\n total_billion = int(total_milliard * 100)\\n print(\\\"Microsoft tjente\\\", total_billion, \\\"billioner dollar i perioden 2002-2019\\\")\\n\\n\\ndef print_kart(spiller, monster):\\n x = 0\\n while x <= 10:\\n y = 0\\n kart_liste = []\\n while y <= 10:\\n if x == spiller[1] and y == spiller[0]:\\n kart_liste.append(\\\"S\\\")\\n elif x == monster[1] and y == monster[0]:\\n kart_liste.append(\\\"M\\\")\\n else: kart_liste.append(\\\"* \\\")\\n\\n y = y + 1\\n\\n samlet = \\\" \\\".join(kart_liste)\\n\\n print(samlet)\\n\\n x = x + 1\\n\\nspiller = [2, 6]\\nmonster = [4, 5]\\n\\ndef spiller_bevegelse():\\n flytt = input()\\n\\n if flytt == \\\"W\\\":\\n spiller[1] = spiller[1] - 1\\n if flytt == \\\"a\\\":\\n spiller[0] = spiller[0] - 1\\n if flytt == \\\"s\\\":\\n spiller[1] = spiller[1] + 1\\n if flytt == \\\"d\\\":\\n spiller[0] = spiller[0] + 1\\n\\n monster[0] = monster[0] + random.randint(-1, 1)\\n monster[1] = monster[1] + random.randint(-1, 1)\\n\\ndef spill():\\n\\n print(\\\"wasd + enter for å bevege seg\\\")\\n print_kart(spiller, monster)\\n\\n try:\\n while True:\\n spiller_bevegelse()\\n print_kart(spiller, monster)\\n except KeyboardInterrupt:\\n exit()\\n\\n\\n\\n\\n\\ndef main():\\n \\\"\\\"\\\" Dette er filens main-funksjon, det er denne funksjonen som kjører\\n når hele filen blir kjørt. \\n Hvis du vil kjøre en av oppgave-funksjonene nedenfor fjerner du #-tegnet \\n foran oppgave-funksjonen slik at den blir \\\"skrudd på\\\".\\n Før du leverer kan det være lurt å sjekke alle funksjonene. Dette gjør \\n du ved å fjerne alle #-tegnene nedenfor.\\n \\\"\\\"\\\"\\n oppgave1()\\n print()\\n oppgave2a()\\n print()\\n print()\\n oppgave3a()\\n print()\\n oppgave3b()\\n print()\\n oppgave3c()\\n print()\\n spill()\\n print()\\n\\nmain()\\n\"\n },\n {\n \"alpha_fraction\": 0.7777777910232544,\n \"alphanum_fraction\": 0.8333333134651184,\n \"avg_line_length\": 17,\n \"blob_id\": \"cd1fd084005d7394e80f7aea08d4022221555946\",\n \"content_id\": \"b52b4b1a1690bb9af5006113cedb6f665684296f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 36,\n \"license_type\": \"no_license\",\n \"max_line_length\": 26,\n \"num_lines\": 2,\n \"path\": \"/README.md\",\n \"repo_name\": \"GhostingRecon/Oblig1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Oblig1\\nobligatorisk innlevering 1\\n\"\n }\n]"},"num_files":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":1180,"cells":{"repo_name":{"kind":"string","value":"moritzhertler/Artemis"},"repo_url":{"kind":"string","value":"https://github.com/moritzhertler/Artemis"},"snapshot_id":{"kind":"string","value":"d6b063ffe7e4b664f073929fbd125858dcb59cf9"},"revision_id":{"kind":"string","value":"ae7f91c050c6db996cab78da9c2a1b675cab4e21"},"directory_id":{"kind":"string","value":"11bbc8931c047946a7ef58a9af7bb2bbafa7d993"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"string","value":"2023-04-06T06:32:25.071429"},"revision_date":{"kind":"string","value":"2021-04-09T22:12:08"},"committer_date":{"kind":"string","value":"2021-04-09T22:12:08"},"github_id":{"kind":"number","value":357192785,"string":"357,192,785"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"string","value":"MIT"},"gha_created_at":{"kind":"string","value":"2021-04-12T12:53:56"},"gha_updated_at":{"kind":"string","value":"2021-04-12T11:03:55"},"gha_pushed_at":{"kind":"string","value":"2021-04-12T12:21:15"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6124572157859802,"alphanum_fraction":0.6164271235466003,"avg_line_length":43.272727966308594,"blob_id":"dd536202cf5bae9f3d69472c3b2f38f3200efd6c","content_id":"e7cde517c75456c9b31a84e90dd70e5809f8fc59","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":7305,"license_type":"permissive","max_line_length":180,"num_lines":165,"path":"/src/test/javascript/spec/component/lecture-unit/video-unit/edit-video-unit.component.spec.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import * as chai from 'chai';\nimport * as sinonChai from 'sinon-chai';\nimport * as sinon from 'sinon';\nimport * as moment from 'moment';\n\nimport { VideoUnitFormData } from 'app/lecture/lecture-unit/lecture-unit-management/video-unit-form/video-unit-form.component';\nimport { VideoUnitService } from 'app/lecture/lecture-unit/lecture-unit-management/videoUnit.service';\nimport { MockRouter } from '../../../helpers/mocks/mock-router';\nimport { Component, EventEmitter, Input, Output } from '@angular/core';\nimport { ComponentFixture, TestBed } from '@angular/core/testing';\nimport { EditVideoUnitComponent } from 'app/lecture/lecture-unit/lecture-unit-management/edit-video-unit/edit-video-unit.component';\nimport { MockProvider } from 'ng-mocks';\nimport { JhiAlertService } from 'ng-jhipster';\nimport { ActivatedRoute, Router } from '@angular/router';\nimport { Observable, of } from 'rxjs';\nimport { VideoUnit } from 'app/entities/lecture-unit/videoUnit.model';\nimport { HttpResponse } from '@angular/common/http';\nimport { By } from '@angular/platform-browser';\n\nchai.use(sinonChai);\nconst expect = chai.expect;\n\n@Component({ selector: 'jhi-video-unit-form', template: '' })\nclass VideoUnitFormStubComponent {\n @Input() isEditMode = false;\n @Input() formData: VideoUnitFormData;\n @Output() formSubmitted: EventEmitter
${text}
`]]);\n expect(component.shortAnswerQuestion).to.deep.equal(alternativeQuestion);\n expect(component.renderedQuestion.text['changingThisBreaksApplicationSecurity']).to.equal(`${text}
`);\n expect(component.renderedQuestion.hint['changingThisBreaksApplicationSecurity']).to.equal(`${hint}
`);\n expect(component.renderedQuestion.explanation['changingThisBreaksApplicationSecurity']).to.equal(`${explanation}
`);\n });\n\n it('should set submitted texts', () => {\n const alternativeQuestion = new ShortAnswerQuestion();\n alternativeQuestion.id = 10;\n const text = 'Please explain this question about [-spot 1]';\n alternativeQuestion.text = text;\n alternativeQuestion.hint = 'new Hint!';\n alternativeQuestion.explanation = 'This is a very good explanation!';\n const spot = new ShortAnswerSpot();\n spot.spotNr = 1;\n alternativeQuestion.spots = [spot];\n component.fnOnSubmittedTextUpdate = function () {\n return true;\n };\n const returnValue = ({ value: text } as unknown) as HTMLElement;\n const getNavigationStub = stub(document, 'getElementById').returns(returnValue);\n\n component.question = alternativeQuestion;\n component.setSubmittedText();\n\n expect(getNavigationStub).to.have.been.called;\n expect(component.submittedTexts.length).to.equal(1);\n expect(component.submittedTexts[0].text).to.equal(text);\n expect(component.submittedTexts[0].spot).to.deep.equal(spot);\n });\n\n it('should show sample solution', () => {\n const alternativeQuestion = new ShortAnswerQuestion();\n alternativeQuestion.id = 10;\n alternativeQuestion.text = 'Please explain this question about stuff';\n alternativeQuestion.hint = 'new Hint!';\n alternativeQuestion.explanation = 'This is a very good explanation!';\n const spot = new ShortAnswerSpot();\n spot.spotNr = 1;\n alternativeQuestion.spots = [spot];\n const solution = new ShortAnswerSolution();\n const mapping = new ShortAnswerMapping(spot, solution);\n alternativeQuestion.correctMappings = [mapping];\n\n component.shortAnswerQuestion = alternativeQuestion;\n component.showSampleSolution();\n\n expect(component.sampleSolutions.length).to.equal(1);\n expect(component.sampleSolutions[0]).to.deep.equal(solution);\n expect(component.showingSampleSolution).to.be.true;\n });\n\n it('should toggle show sample solution', () => {\n const alternativeQuestion = new ShortAnswerQuestion();\n alternativeQuestion.spots = [];\n component.shortAnswerQuestion = alternativeQuestion;\n component.showResult = true;\n component.showingSampleSolution = true;\n component.forceSampleSolution = true;\n expect(component.showingSampleSolution).to.be.true;\n component.forceSampleSolution = false;\n component.hideSampleSolution();\n\n expect(component.showingSampleSolution).to.be.false;\n });\n\n it('should get submitted text size for spot', () => {\n const submitted = new ShortAnswerSubmittedText();\n submitted.text = 'expectedReturnText';\n const spot = new ShortAnswerSpot();\n spot.spotNr = 1;\n submitted.spot = spot;\n const tag = '[-spot 1]';\n component.submittedTexts = [submitted];\n\n expect(component.getSubmittedTextSizeForSpot(tag)).to.deep.equal(submitted.text.length + 2);\n });\n\n it('should get sample solution size for spot', () => {\n const alternativeQuestion = new ShortAnswerQuestion();\n alternativeQuestion.id = 10;\n const spot = new ShortAnswerSpot();\n spot.spotNr = 1;\n alternativeQuestion.spots = [new ShortAnswerSpot(), spot];\n component.shortAnswerQuestion = alternativeQuestion;\n\n const solution = new ShortAnswerSolution();\n solution.text = 'expectedReturnText';\n component.sampleSolutions = [new ShortAnswerSolution(), solution];\n const tag = '[-spot 1]';\n\n expect(component.getSampleSolutionSizeForSpot(tag)).to.deep.equal(solution.text.length + 2);\n });\n\n it('should get background color for input field', () => {\n const text = 'This is the solution text for the ultimate solution';\n const submittedText = new ShortAnswerSubmittedText();\n const spot = new ShortAnswerSpot();\n spot.spotNr = 1;\n submittedText.spot = spot;\n submittedText.isCorrect = true;\n submittedText.text = text;\n component.submittedTexts = [submittedText];\n const tag = '[-spot 1]';\n\n const alternativeQuestion = new ShortAnswerQuestion();\n alternativeQuestion.id = 10;\n alternativeQuestion.spots = [spot];\n const solution = new ShortAnswerSolution();\n solution.id = 1;\n solution.text = text;\n const mapping = new ShortAnswerMapping(spot, solution);\n alternativeQuestion.correctMappings = [mapping];\n\n component.shortAnswerQuestion = alternativeQuestion;\n expect(component.getBackgroundColourForInputField(tag)).to.equal('lightgreen');\n component.shortAnswerQuestion.correctMappings = [];\n expect(component.getBackgroundColourForInputField(tag)).to.equal('yellow');\n component.submittedTexts = [];\n expect(component.getBackgroundColourForInputField(tag)).to.equal('red');\n });\n});\n"},{"alpha_fraction":0.7206922173500061,"alphanum_fraction":0.7217473983764648,"avg_line_length":54.098838806152344,"blob_id":"9adb5a3183680b0e1b9e5f89c0a361018a595288","content_id":"3358653a4aca1a2c47955bc2e2b048c1818b9658","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":18954,"license_type":"permissive","max_line_length":180,"num_lines":344,"path":"/src/main/java/de/tum/in/www1/artemis/service/ScoreService.java","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"package de.tum.in.www1.artemis.service;\n\nimport static de.tum.in.www1.artemis.service.util.RoundingUtil.round;\n\nimport java.util.List;\nimport java.util.Optional;\nimport java.util.stream.Collectors;\n\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\nimport org.springframework.security.core.Authentication;\nimport org.springframework.security.core.context.SecurityContextHolder;\nimport org.springframework.stereotype.Service;\nimport org.springframework.transaction.annotation.Propagation;\nimport org.springframework.transaction.annotation.Transactional;\n\nimport de.tum.in.www1.artemis.domain.*;\nimport de.tum.in.www1.artemis.domain.participation.Participation;\nimport de.tum.in.www1.artemis.domain.participation.StudentParticipation;\nimport de.tum.in.www1.artemis.domain.scores.ParticipantScore;\nimport de.tum.in.www1.artemis.domain.scores.StudentScore;\nimport de.tum.in.www1.artemis.domain.scores.TeamScore;\nimport de.tum.in.www1.artemis.repository.*;\nimport de.tum.in.www1.artemis.security.SecurityUtils;\n\n@Service\npublic class ScoreService {\n\n private final StudentScoreRepository studentScoreRepository;\n\n private final ParticipantScoreRepository participantScoreRepository;\n\n private final TeamScoreRepository teamScoreRepository;\n\n private final ParticipationRepository participationRepository;\n\n private final ResultRepository resultRepository;\n\n private final Logger logger = LoggerFactory.getLogger(ScoreService.class);\n\n public ScoreService(StudentScoreRepository studentScoreRepository, TeamScoreRepository teamScoreRepository, ParticipationRepository participationRepository,\n ResultRepository resultRepository, ParticipantScoreRepository participantScoreRepository) {\n this.studentScoreRepository = studentScoreRepository;\n this.participationRepository = participationRepository;\n this.participantScoreRepository = participantScoreRepository;\n this.teamScoreRepository = teamScoreRepository;\n this.resultRepository = resultRepository;\n }\n\n /**\n * Either updates or removes an existing participant score when a result is removed\n * The annotation \"@Transactional\" is ok because it means that this method does not support run in an outer transactional context, instead the outer transaction is paused\n *\n * @param resultToBeDeleted result that will be removes\n */\n @Transactional(propagation = Propagation.NOT_SUPPORTED) // ok (see JavaDoc)\n public void removeOrUpdateAssociatedParticipantScore(Result resultToBeDeleted) {\n // In this method we use custom @Query methods that will fail if no authentication is available, therefore\n // we check this here and set a dummy authentication if none is available (this is the case in a scheduled service or\n // websocket)\n Authentication authentication = SecurityContextHolder.getContext().getAuthentication();\n if (authentication == null) {\n SecurityUtils.setAuthorizationObject();\n }\n\n OptionalThe version of Orion you are currently using is outdated!
\n\n {{ 'artemisApp.orion.version.usedVersion' | translate }}{{ versionString }}!\n
\n \n {{ 'artemisApp.orion.version.allowedVersion' | translate }}{{ allowedMinimumVersion }}\n
\n `,\n})\nexport class OrionOutdatedComponent implements OnInit {\n versionString: string;\n allowedMinimumVersion: string;\n\n constructor(private activatedRoute: ActivatedRoute, private profileService: ProfileService) {}\n\n /**\n * On initialization, sets the values of the used version and the minimum allowed version of orion.\n */\n ngOnInit(): void {\n this.activatedRoute.queryParams.subscribe((params) => {\n this.versionString = params['versionString'];\n this.profileService\n .getProfileInfo()\n .pipe(\n filter(Boolean),\n tap((info: ProfileInfo) => {\n this.allowedMinimumVersion = info.allowedMinimumOrionVersion;\n }),\n )\n .subscribe();\n });\n }\n}\n"},{"alpha_fraction":0.6175332069396973,"alphanum_fraction":0.6175332069396973,"avg_line_length":32.174312591552734,"blob_id":"b85ea7179bbb31fa15269064910ab585897da972","content_id":"095fee3bb472e2146edbc16f0787ae578d2a8077","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":3616,"license_type":"permissive","max_line_length":125,"num_lines":109,"path":"/src/main/webapp/app/exercises/shared/exercise-hint/manage/exercise-hint.component.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { Component, OnDestroy, OnInit } from '@angular/core';\nimport { ActivatedRoute } from '@angular/router';\nimport { HttpErrorResponse, HttpResponse } from '@angular/common/http';\nimport { Subject, Subscription } from 'rxjs';\nimport { filter, map } from 'rxjs/operators';\nimport { JhiEventManager } from 'ng-jhipster';\n\nimport { ExerciseHint } from 'app/entities/exercise-hint.model';\nimport { ExerciseHintService } from './exercise-hint.service';\nimport { onError } from 'app/shared/util/global.utils';\nimport { JhiAlertService } from 'ng-jhipster';\n\n@Component({\n selector: 'jhi-exercise-hint',\n templateUrl: './exercise-hint.component.html',\n})\nexport class ExerciseHintComponent implements OnInit, OnDestroy {\n exerciseId: number;\n exerciseHints: ExerciseHint[];\n eventSubscriber: Subscription;\n\n private dialogErrorSource = new Subject>:)
',\n creationDate: undefined,\n author: user2,\n answers: [],\n } as StudentQuestion;\n\n beforeEach(async () => {\n return TestBed.configureTestingModule({\n imports: [TranslateModule.forRoot(), ArtemisTestModule],\n declarations: [\n StudentQuestionComponent,\n MockDirective(MarkdownEditorComponent),\n MockDirective(ConfirmIconComponent),\n MockDirective(StudentVotesComponent),\n MockDirective(NgbTooltip),\n MockPipe(ArtemisDatePipe),\n MockPipe(ArtemisTranslatePipe),\n // Don't mock this since we want to test this pipe, too\n HtmlForMarkdownPipe,\n ],\n providers: [\n {\n provide: ActivatedRoute,\n useValue: {\n snapshot: {\n paramMap: {\n get: () => {\n return { courseId: 1 };\n },\n },\n },\n },\n },\n ],\n })\n .compileComponents()\n .then(() => {\n componentFixture = TestBed.createComponent(StudentQuestionComponent);\n component = componentFixture.componentInstance;\n });\n });\n\n it('should toggle edit mode and reset editor Text', () => {\n component.studentQuestion = studentQuestion;\n component.isEditMode = true;\n component.editText = 'test';\n component.toggleEditMode();\n expect(component.editText).to.deep.equal('question');\n expect(component.isEditMode).to.be.false;\n component.toggleEditMode();\n expect(component.isEditMode).to.be.true;\n });\n\n it('should update questionText', () => {\n component.studentQuestion = studentQuestion;\n component.isEditMode = true;\n component.editText = 'test';\n component.saveQuestion();\n expect(component.studentQuestion.questionText).to.deep.equal('test');\n });\n\n it('should not display malicious html in question texts', () => {\n component.studentQuestion = maliciousStudentQuestion;\n componentFixture.detectChanges();\n\n const text = componentFixture.debugElement.nativeElement.querySelector('#questionText');\n expect(text.innerHTML).to.not.equal(maliciousStudentQuestion.questionText);\n expect(text.innerHTML).to.equal('>:)');\n });\n});\n"},{"alpha_fraction":0.6826608777046204,"alphanum_fraction":0.6844329237937927,"avg_line_length":53.34074020385742,"blob_id":"3862d5fdcb14de1d45f736ee1026152415becf85","content_id":"94c148682717f433ff8eed9fcc446679c3c70ea7","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":7336,"license_type":"permissive","max_line_length":140,"num_lines":135,"path":"/src/test/javascript/spec/component/shared/notification/notification-popup.component.spec.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { ComponentFixture, TestBed, fakeAsync, tick } from '@angular/core/testing';\nimport { By } from '@angular/platform-browser';\nimport { Router } from '@angular/router';\nimport { TranslateService } from '@ngx-translate/core';\nimport { LocalStorageService, SessionStorageService } from 'ngx-webstorage';\nimport { BehaviorSubject } from 'rxjs';\nimport * as chai from 'chai';\nimport * as sinonChai from 'sinon-chai';\nimport * as sinon from 'sinon';\nimport { NotificationPopupComponent } from 'app/shared/notification/notification-popup/notification-popup.component';\nimport { NotificationService } from 'app/shared/notification/notification.service';\nimport { AccountService } from 'app/core/auth/account.service';\nimport { ArtemisTestModule } from '../../../test.module';\nimport { MockSyncStorage } from '../../../helpers/mocks/service/mock-sync-storage.service';\nimport { MockNotificationService } from '../../../helpers/mocks/service/mock-notification.service';\nimport { MockTranslateService } from '../../../helpers/mocks/service/mock-translate.service';\nimport { MockAccountService } from '../../../helpers/mocks/service/mock-account.service';\nimport { Notification } from 'app/entities/notification.model';\nimport { RouterTestingModule } from '@angular/router/testing';\n\nchai.use(sinonChai);\nconst expect = chai.expect;\n\ndescribe('Notification Popup Component', () => {\n let notificationPopupComponent: NotificationPopupComponent;\n let notificationPopupComponentFixture: ComponentFixture\n * Note: In the case of two exercise units referencing the same exercise, only the first exercise unit will be used.\n *
\n * Note: Please note that we take always the last submission into account here. Even submissions after the due date. This means for example that a student can improve his/her\n * progress by re-trying a quiz as often as you like. It is therefore normal, that the points here might differ from the points officially achieved in an exercise.\n *\n * @param exerciseUnits exercise units to check\n * @param user user to check for\n * @param useParticipantScoreTable use the participant score table instead of going through participation -> submission -> result\n * @return progress of the user in the exercise units\n */\n public Set \n * Note: In the case of two exercise units referencing the same exercise, only the first exercise unit will be used.\n * \n * Note: Please note that we take always the last submission into account here. Even submissions after the due date. This means for example that a student can improve his/her\n * progress by re-trying a quiz as often as you like. It is therefore normal, that the points here might differ from the points officially achieved in an exercise.\n *\n * @param exerciseUnits exercise units to check\n * @param useParticipantScoreTable use the participant score table instead of going through participation -> submission -> result\n * @return progress of the course in the exercise units\n */\n private Set ${value}\n \n \n\n\n\n \n"},{"alpha_fraction":0.5625653266906738,"alphanum_fraction":0.5660508871078491,"avg_line_length":42.14285659790039,"blob_id":"0a1355f2b0a3075e94a7981284cac099c89c0041","content_id":"068134a230f94cf9a70955917f4322e23aed3a74","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":5738,"license_type":"permissive","max_line_length":169,"num_lines":133,"path":"/src/test/javascript/spec/component/lecture-unit/video-unit/create-video-unit.component.spec.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { Component, EventEmitter, Input, Output } from '@angular/core';\nimport * as chai from 'chai';\nimport * as sinonChai from 'sinon-chai';\nimport { VideoUnitFormData } from 'app/lecture/lecture-unit/lecture-unit-management/video-unit-form/video-unit-form.component';\nimport * as sinon from 'sinon';\nimport { ComponentFixture, fakeAsync, TestBed, tick } from '@angular/core/testing';\nimport { CreateVideoUnitComponent } from 'app/lecture/lecture-unit/lecture-unit-management/create-video-unit/create-video-unit.component';\nimport { VideoUnitService } from 'app/lecture/lecture-unit/lecture-unit-management/videoUnit.service';\nimport { MockProvider } from 'ng-mocks';\nimport { JhiAlertService } from 'ng-jhipster';\nimport { ActivatedRoute, Router } from '@angular/router';\nimport { MockRouter } from '../../../helpers/mocks/mock-router';\nimport { Observable, of } from 'rxjs';\nimport { VideoUnit } from 'app/entities/lecture-unit/videoUnit.model';\nimport * as moment from 'moment';\nimport { HttpResponse } from '@angular/common/http';\nimport { By } from '@angular/platform-browser';\n\nchai.use(sinonChai);\nconst expect = chai.expect;\n\n@Component({ selector: 'jhi-video-unit-form', template: '' })\nclass VideoUnitFormStubComponent {\n @Input() isEditMode = false;\n @Output() formSubmitted: EventEmitter\n \n \n `,\n})\nexport class ChartComponent {\n @ViewChild(BaseChartDirective) chart: BaseChartDirective;\n\n @Input() set preset(preset: ChartPreset) {\n preset.applyTo(this);\n }\n\n @Input() set type(type: ChartType) {\n this.chartType = type;\n }\n @Input() set datasets(datasets: ChartDataSets[]) {\n this.chartDatasets = datasets;\n }\n @Input() set labels(labels: Label[]) {\n this.chartLabels = labels;\n }\n @Input() set options(options: ChartOptions) {\n this.chartOptions = options;\n }\n\n chartDatasets: ChartDataSets[] = [];\n chartType: ChartType = 'bar';\n chartLabels: Label[] = [];\n chartOptions: ChartOptions = {\n responsive: true,\n maintainAspectRatio: false,\n layout: {\n padding: {\n left: 0,\n right: 0,\n top: 0,\n bottom: 0,\n },\n },\n legend: {\n display: false,\n },\n title: {\n display: false,\n },\n tooltips: {\n enabled: false,\n },\n scales: {\n yAxes: [],\n xAxes: [],\n },\n };\n\n setType(type: ChartType) {\n this.chartType = type;\n }\n\n private applyOptions(fn: (options: ChartOptions) => void, shouldUpdate = true) {\n if (this.chart) {\n fn(this.chart.options);\n if (shouldUpdate) {\n this.chart.update();\n }\n } else {\n fn(this.chartOptions);\n }\n }\n\n setPadding(padding: ChartLayoutPaddingObject, shouldUpdate = true) {\n this.applyOptions((options) => {\n Object.assign(options.layout!.padding, padding);\n }, shouldUpdate);\n }\n\n setLegend(legend: ChartLegendOptions | boolean, shouldUpdate = true) {\n this.applyOptions((options) => {\n if (typeof legend === 'boolean') {\n Object.assign(options.legend, { display: legend });\n } else {\n Object.assign(options.legend, { display: true }, legend);\n }\n }, shouldUpdate);\n }\n\n setTooltip(tooltip: ChartTooltipOptions | boolean, shouldUpdate = true) {\n this.applyOptions((options) => {\n if (typeof tooltip === 'boolean') {\n Object.assign(options.tooltips, { enabled: tooltip });\n } else {\n Object.assign(options.tooltips, { enabled: true }, tooltip);\n }\n }, shouldUpdate);\n }\n\n setHover(hover: ChartHoverOptions, shouldUpdate = true) {\n this.applyOptions((options) => {\n if (!options.hover) {\n options.hover = hover;\n } else {\n Object.assign(options.hover, hover);\n }\n }, shouldUpdate);\n }\n\n setYAxe(index: number, axe: ChartYAxe, shouldUpdate = true) {\n this.applyOptions((options) => {\n if (!options.scales!.yAxes![index]) {\n options.scales!.yAxes![index] = axe;\n } else {\n Object.assign(options.scales!.yAxes![index], axe);\n }\n }, shouldUpdate);\n }\n\n setXAxe(index: number, axe: ChartXAxe, shouldUpdate = true) {\n this.applyOptions((options) => {\n if (!options.scales!.xAxes![index]) {\n options.scales!.xAxes![index] = axe;\n } else {\n Object.assign(options.scales!.xAxes![index], axe);\n }\n }, shouldUpdate);\n }\n\n updateDataset(index: number, dataset: ChartDataSets) {\n if (!this.chartDatasets[index]) {\n this.chartDatasets[index] = dataset;\n } else {\n Object.assign(this.chartDatasets[index], dataset);\n }\n }\n\n setLabels(labels: Label[]) {\n this.chartLabels = labels;\n }\n}\n"},{"alpha_fraction":0.7422183156013489,"alphanum_fraction":0.7444103360176086,"avg_line_length":43.72549057006836,"blob_id":"f0779bc1a3407e9264b2935170f2b8e87c48c729","content_id":"c68cdf89310885de0480b434b9b744fbd0dfc2ec","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":2281,"license_type":"permissive","max_line_length":175,"num_lines":51,"path":"/src/main/webapp/app/course/learning-goals/learning-goal-detail-modal/learning-goal-detail-modal.component.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { Component, Input, OnInit } from '@angular/core';\nimport { NgbActiveModal } from '@ng-bootstrap/ng-bootstrap';\nimport { LearningGoal } from 'app/entities/learningGoal.model';\nimport { LectureUnitService } from 'app/lecture/lecture-unit/lecture-unit-management/lectureUnit.service';\nimport { SortService } from 'app/shared/service/sort.service';\nimport { IndividualLearningGoalProgress } from 'app/course/learning-goals/learning-goal-individual-progress-dtos.model';\nimport { round } from 'app/shared/util/utils';\n\n@Component({\n selector: 'jhi-learning-goal-detail-modal',\n templateUrl: './learning-goal-detail-modal.component.html',\n})\nexport class LearningGoalDetailModalComponent implements OnInit {\n readonly round = round;\n @Input()\n learningGoal: LearningGoal;\n @Input()\n learningGoalProgress: IndividualLearningGoalProgress;\n\n public lectureUnitIdToLectureUnitProgress = new Map();\n\n public isProgressAvailable = false;\n public progressInPercent = 0;\n public connectedLectureUnitsPredicate = 'id';\n public connectedLectureUnitsReverse = false;\n\n constructor(public activeModal: NgbActiveModal, public lectureUnitService: LectureUnitService, public sortService: SortService) {}\n\n ngOnInit(): void {\n if (this.learningGoalProgress && this.learningGoalProgress.totalPointsAchievableByStudentsInLearningGoal > 0) {\n this.isProgressAvailable = true;\n\n if (this.learningGoalProgress.progressInLectureUnits) {\n this.lectureUnitIdToLectureUnitProgress = new Map(this.learningGoalProgress.progressInLectureUnits.map((i) => [i.lectureUnitId, i]));\n }\n\n const progress = (this.learningGoalProgress.pointsAchievedByStudentInLearningGoal / this.learningGoalProgress.totalPointsAchievableByStudentsInLearningGoal) * 100;\n this.progressInPercent = round(progress);\n }\n }\n\n getLectureUnitProgress(lectureUnitId: number) {\n return this.lectureUnitIdToLectureUnitProgress.get(lectureUnitId);\n }\n\n sortConnectedLectureUnits() {\n if (this.learningGoal.lectureUnits) {\n this.sortService.sortByProperty(this.learningGoal.lectureUnits, this.connectedLectureUnitsPredicate, this.connectedLectureUnitsReverse);\n }\n }\n}\n"},{"alpha_fraction":0.6036431789398193,"alphanum_fraction":0.6071361303329468,"avg_line_length":44.12711715698242,"blob_id":"3ad04cc08e52f1b6d8e3be562db998459e4e53f0","content_id":"2a24a162ab6b92c2186ac335acc0288087aa1c7a","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":26625,"license_type":"permissive","max_line_length":169,"num_lines":590,"path":"/src/test/javascript/spec/component/exercises/quiz/quiz-participation.component.spec.ts","repo_name":"moritzhertler/Artemis","src_encoding":"UTF-8","text":"import { HttpResponse } from '@angular/common/http';\nimport { HttpClientTestingModule, HttpTestingController } from '@angular/common/http/testing';\nimport { ComponentFixture, discardPeriodicTasks, fakeAsync, TestBed, tick } from '@angular/core/testing';\nimport { ActivatedRoute } from '@angular/router';\nimport { NgbTooltip } from '@ng-bootstrap/ng-bootstrap';\nimport { TranslateService } from '@ngx-translate/core';\nimport { JhiWebsocketService } from 'app/core/websocket/websocket.service';\nimport { StudentParticipation } from 'app/entities/participation/student-participation.model';\nimport { QuizExercise } from 'app/entities/quiz/quiz-exercise.model';\nimport { QuizQuestion, QuizQuestionType } from 'app/entities/quiz/quiz-question.model';\nimport { QuizSubmission } from 'app/entities/quiz/quiz-submission.model';\nimport { SubmittedAnswer } from 'app/entities/quiz/submitted-answer.model';\nimport { Result } from 'app/entities/result.model';\nimport { QuizExerciseService } from 'app/exercises/quiz/manage/quiz-exercise.service';\nimport { QuizParticipationComponent } from 'app/exercises/quiz/participate/quiz-participation.component';\nimport { DragAndDropQuestionComponent } from 'app/exercises/quiz/shared/questions/drag-and-drop-question/drag-and-drop-question.component';\nimport { MultipleChoiceQuestionComponent } from 'app/exercises/quiz/shared/questions/multiple-choice-question/multiple-choice-question.component';\nimport { ShortAnswerQuestionComponent } from 'app/exercises/quiz/shared/questions/short-answer-question/short-answer-question.component';\nimport { ParticipationService } from 'app/exercises/shared/participation/participation.service';\nimport { AlertComponent } from 'app/shared/alert/alert.component';\nimport { ButtonComponent } from 'app/shared/components/button.component';\nimport { JhiConnectionStatusComponent } from 'app/shared/connection-status/connection-status.component';\nimport { ArtemisDatePipe } from 'app/shared/pipes/artemis-date.pipe';\nimport { ArtemisTranslatePipe } from 'app/shared/pipes/artemis-translate.pipe.ts';\nimport { ArtemisQuizService } from 'app/shared/quiz/quiz.service';\nimport * as chai from 'chai';\nimport * as moment from 'moment';\nimport { JhiAlertService, JhiTranslateDirective } from 'ng-jhipster';\nimport { MockComponent, MockDirective, MockPipe } from 'ng-mocks';\nimport { LocalStorageService, SessionStorageService } from 'ngx-webstorage';\nimport { of } from 'rxjs';\nimport * as sinon from 'sinon';\nimport * as sinonChai from 'sinon-chai';\nimport { MockLocalStorageService } from '../../../helpers/mocks/service/mock-local-storage.service';\nimport { MockSyncStorage } from '../../../helpers/mocks/service/mock-sync-storage.service';\nimport { MockTranslateService } from '../../../helpers/mocks/service/mock-translate.service';\nimport { ArtemisTestModule } from '../../../test.module';\nimport { AnswerOption } from 'app/entities/quiz/answer-option.model';\nimport { DragAndDropMapping } from 'app/entities/quiz/drag-and-drop-mapping.model';\nimport { ShortAnswerSubmittedText } from 'app/entities/quiz/short-answer-submitted-text.model';\n\nchai.use(sinonChai);\nconst expect = chai.expect;\n\n// Store a copy of now to avoid timing issues\nconst now = moment();\nconst question1 = { id: 1, type: QuizQuestionType.DRAG_AND_DROP, points: 1 } as QuizQuestion;\nconst question2 = { id: 2, type: QuizQuestionType.MULTIPLE_CHOICE, points: 2, answerOptions: [] } as QuizQuestion;\nconst question3 = { id: 3, type: QuizQuestionType.SHORT_ANSWER, points: 3 } as QuizQuestion;\n\nconst quizExercise = (