' + df['End Station']\n\n # Filter by month, if applicable.\n if month != 'All':\n df = df[df['Month'] == month]\n # Filter by day of week, if applicable\n if day != 'All':\n df = df[df['Day of Week'] == day]\n return df","def load_data(city, month, day):\n file_data = pd.read_csv(CITY_DATA[city])\n df = pd.DataFrame(data=file_data)\n\n # weekday and month columns\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n\n df['month'] = df['Start Time'].dt.month\n\n df['weekend_day'] = df['Start Time'].dt.weekday\n\n if month != 'all':\n df = df[df['month']==Months.index(month) + 1]\n\n\n if day != 'all':\n df = df[df['weekend_day'] == day.title()]\n\n\n return df","def load_data(city, month, day):\r\n if city.lower() == \"chicago\" or city.lower() == \"c\":\r\n filename = 'C:\\\\Neha\\\\Udacity\\\\BikeShare\\\\chicago.csv'\r\n elif city.lower() == \"New York\" or city.lower() == \"new york\":\r\n filename = 'C:\\\\Neha\\\\Udacity\\\\BikeShare\\\\new_york_city.csv'\r\n elif city.lower() == \"Washington\" or city.lower() == \"washington\":\r\n filename = 'C:\\\\Neha\\\\Udacity\\\\BikeShare\\\\washington.csv'\r\n # load data file into a dataframe\r\n #df = pd.read_csv(CITY_DATA[\"city\"])\r\n df = pd.read_csv(filename)\r\n # convert the Start Time column to datetime\r\n df['Start Time'] = pd.to_datetime(df['Start Time'])\r\n\r\n # extract month and day of week from Start Time to create new columns\r\n df['month'] = df['Start Time'].dt.month\r\n df['day_of_week'] = df['Start Time'].dt.weekday_name\r\n\r\n # filter by month if applicable\r\n if month != 'all':\r\n # use the index of the months list to get the corresponding int\r\n months = ['January', 'February', 'March', 'April', 'May', 'June']\r\n month = months.index(month) + 1\r\n\r\n # filter by month to create the new dataframe\r\n df = df[df['month'] == month]\r\n\r\n # filter by day of week if applicable\r\n if day != 'all':\r\n # filter by day of week to create the new dataframe\r\n df = df[df['day_of_week'] == day.title()]\r\n\r\n return df","def load_data(city, month, day, filters):\n print('\\n\\n*****************LOADING DATA*****************')\n start_time = time.time()\n\n dataframe = pd.read_csv(CITY_DATA[city])\n print('City: ', city)\n print('Total data points found: ', len(dataframe))\n\n # Changing start time to datetime format\n dataframe['Start Time'] = pd.to_datetime(dataframe['Start Time'])\n dataframe['Day'] = dataframe['Start Time'].dt.weekday\n dataframe['Month'] = dataframe['Start Time'].dt.month\n dataframe['Hour'] = dataframe['Start Time'].dt.hour\n\n # Displaying statistics for whole data\n if filters == 'Month':\n popular_month, count_popular_month = common_month(dataframe)\n print('Most popular month for travelling: ', popular_month)\n print('Counts: ', count_popular_month)\n\n elif filters == 'Day':\n popular_day, count_popular_day = common_day(dataframe)\n print('Most popular day for travelling: ', get_day_name(popular_day))\n print('Counts: ', count_popular_day)\n\n elif filters == 'Both':\n popular_month, count_popular_month = common_month(dataframe)\n popular_day, count_popular_day = common_day(dataframe)\n print('\\nMost popular month for travelling: ', popular_month)\n print('Counts: ', count_popular_month)\n print('\\nMost popular day for travelling: ', get_day_name(popular_day))\n print('Counts: ', count_popular_day)\n\n print(\"\\nThis took {} seconds.\".format(time.time() - start_time))\n print('----------------------------------------------')\n\n print('\\n\\n***************APPLYING FILTERS***************')\n start_time = time.time()\n months = ['January', 'February', 'March', 'April', 'May', 'June']\n\n if filters == 'Month':\n print('Filter:\\n Month = ', month.title())\n dataframe = dataframe[dataframe['Month'] == months.index(month) + 1]\n elif filters == 'Day':\n print('Filter: Day = ', day)\n dataframe = dataframe[dataframe['Day'] == get_day_number(day)]\n elif filters == 'Both':\n print('Filter:\\n Month = {}\\n Day = {}'.format(month.title(), day))\n dataframe = dataframe[dataframe['Month'] == months.index(month) + 1]\n dataframe = dataframe[dataframe['Day'] == get_day_number(day)]\n else:\n print('Filter: ', filters)\n\n print('Total data points after applying filter: ', len(dataframe))\n print(\"\\nThis took {} seconds.\".format(time.time() - start_time))\n print('----------------------------------------------')\n\n return dataframe","def load_data(city, month, day):\n # load data file into a dataframe\n df = pd.read_csv(CITY_DATA[city])\n #print(df.head())\n \n # convert the Start Time column to datetime\n df['Start Time'] = pd.to_datetime(df['Start Time']) \n #print(df['Start Time'].head())\n\n # extract month and day of week from Start Time to create new columns\n df['month'] = df['Start Time'].dt.month\n df['day_of_week'] = df['Start Time'].dt.day_name()\n #print(df['month'].head())\n #print(df['day_of_week'].head())\n \n # filter by month if applicable\n if month != 'all':\n # use the index of the months list to get the corresponding int\n months = ['january', 'february', 'march', 'april', 'may', 'june']\n month = months.index(month) + 1\n #print((months[month]))\n #print(month)\n \n # filter by month to create the new dataframe\n df = df[df['month'] == month]\n #print(df['month'].head())\n\n # filter by day of week if applicable\n if day != 'all':\n # filter by day of week to create the new dataframe\n df = df[df['day_of_week'] == day.title()]\n #print(day.title())\n #print(df.head())\n\n return df","def load_data(city, month, day):\n# load datafile into a dataframe\n df=pd.read_csv(CITY_DATA[city])\n # convert the Start Time column to datetime\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n # extract month and day of week from Start Time to create new columns\n df['month'] = df['Start Time'].dt.month\n df['day_of_week'] = df['Start Time'].dt.day_name()\n \n # filter by month if applicable\n if month != 'all':\n # use the index of the months list to get the corresponding int\n months = ['january', 'february', 'march', 'april', 'may', 'june']\n month = months.index(month)+1\n \n # filter by month to create the new dataframe\n df = df[df['month']==month]\n\n # filter by day of week if applicable\n if day != 'all':\n # filter by day of week to create the new dataframe\n df = df[df['day_of_week'] == day.title()]\n return df","def load_data(city, month, day):\n df = pd.read_csv(CITY_DATA[city])\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n#changed 'weekday_name' to just 'weekday' which outputs the weekday as integer\n # extract month, day of week and hour from Start Time to create new columns\n df['month'] = df['Start Time'].dt.month\n df['dow'] = df['Start Time'].dt.weekday\n df['hour'] = df['Start Time'].dt.hour\n\n # filter by month if applicable\n if month != 'all':\n # use the index of the months list to get the corresponding int\n month = months.index(month) + 1\n # filter by month to create the new dataframe\n df = df[df['month'] == month]\n\n# problem with the 'day'-filter, if a day (not 'all') is applied, the output is not right\n # filter by day of week if applicable\n if day != 'all':\n\n # filter by day of week to create the new dataframe\n day = days.index(day) + 1\n df = df[df['dow'] == day]\n\n return df","def load_data(city, month, day):\n df = pd.read_csv(CITY_DATA[city])\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n df['End Time'] = pd.to_datetime(df['End Time'])\n df['month'] = df['Start Time'].dt.month\n df['day_of_week'] = df['Start Time'].dt.dayofweek\n\n return df","def load_data(city, month, day):\n\n print('\\nLoading Data...\\n')\n\n path = os.getcwd().replace('\\\\', '/') + '/'\n dir_path = os.path.dirname(os.path.realpath(__file__)).replace('\\\\', '/') + '/'\n try:\n df = pd.read_csv(dir_path + CITY_DATA.get(city))\n except FileNotFoundError as e:\n sys.exit('Error loading file. Make sure that the datafiles are in the working directory.\\npath: {}\\ndir_path: {}'.format(path, dir_path))\n\n # convert the Start Time column to datetime\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n\n # extract month and day of week from Start Time to create new columns\n df['month'] = df['Start Time'].dt.month\n df['day_of_week'] = df['Start Time'].dt.dayofweek\n df['hour'] = df['Start Time'].dt.hour\n\n if df is None:\n sys.exit('Error initializing dataframe. File was loaded successfully but load_data() failed.')\n\n return df","def load_data(city, month, day):\r\n df = pd.read_csv(CITY_DATA[city])\r\n df['Start Time'] = pd.to_datetime(df['Start Time'])\r\n df['month'] = df['Start Time'].dt.month\r\n df['day'] = df['Start Time'].dt.weekday_name\r\n df['hour'] = df['Start Time'].dt.hour\r\n df['And'] = ' & '\r\n # create a new column of concatenated Start and End Stations so we can find the most common combination\r\n df['StartEnd'] = df[['Start Station', 'And', 'End Station']].apply(lambda x: ''.join(x), axis=1)\r\n\r\n # drop rows with missing values\r\n return df"],"string":"[\n \"def load_data(city, month, day):\\n df = pd.read_csv(CITY_DATA [city])\\n \\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n \\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['hour'] = df['Start Time'].dt.hour\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n \\n # Do the filter below\\n # no filter is applied\\n if month == 0 and day == 0:\\n return df\\n # only filter by day\\n elif month == 0:\\n df = df[df['day_of_week']==day]\\n # only filter by month\\n elif day == 0:\\n df = df[df['month']== month]\\n else:\\n df = df[df['day_of_week']==day]\\n df = df[df['month']== month]\\n \\n return df\",\n \"def load_data(city, month, day):\\n input_file_name = CITY_DATA.get(city)\\n\\n # Load the CSV file into a Pandas data frame\\n df = pd.read_csv(input_file_name)\\n\\n # Convert the format of the existing date field to a python DateTime\\n df[\\\"Start Time\\\"] = pd.to_datetime(df[\\\"Start Time\\\"])\\n\\n # Create new columns to filter on\\n df[\\\"month\\\"] = df[\\\"Start Time\\\"].dt.month\\n df[\\\"alpha_day\\\"] = df[\\\"Start Time\\\"].dt.weekday_name\\n\\n # If a month was provided, filter on it\\n if month != \\\"all\\\":\\n month_num = VALID_MONTHS.index(month) + 1\\n df = df[df[\\\"month\\\"] == month_num]\\n\\n # If a day was provided, filter on it\\n if day != \\\"all\\\":\\n df = df[df[\\\"alpha_day\\\"] == day.title()]\\n\\n return df\",\n \"def load_data(city, month='all', day='all'):\\n\\n df = pd.read_csv(CITY_DATA[city.lower()]).rename(columns={'Unnamed: 0': 'Trip Id'})\\n cols = df.columns\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['Month'] = df['Start Time'].dt.month\\n df['Day of Week'] = df['Start Time'].dt.dayofweek\\n df['Start Hour'] = df['Start Time'].dt.hour\\n\\n # Filter by month if applicable\\n if month.lower() in MONTH_LIST:\\n n_month = MONTH_LIST[month.lower()]\\n df = df.loc[df['Month'] == n_month]\\n\\n # Filter by day of the week if applicable\\n if day.lower() in DAY_LIST:\\n n_day = DAY_LIST[day.lower()]\\n df = df.loc[df['Day of Week'] == n_day]\\n\\n return df, cols\",\n \"def load_data(city, month, day):\\n df = pd.read_csv(CITY_DATA[city])\\n #convert Start Time to datetime for filtering\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n #add columns for filters to match on\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n df['hour'] = df['Start Time'].dt.hour\\n\\n #filter by month and day if not all\\n #limited index options to available date range in the data set\\n if month != 'all':\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month_index = 1 + months.index(month)\\n\\n # filter by month to create the new dataframe\\n df = df.loc[df['month'] == month_index]\\n\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df.loc[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n file_name = CITY_DATA.get(city)\\n df = pd.read_csv(file_name)\\n\\n # convert \\\"Start Time\\\" column from string to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # create new column \\\"Month\\\" by extracting the month form datetime\\n df['Month'] = df['Start Time'].dt.month\\n\\n # create new column \\\"Day\\\" by extracting the day form datetime\\n df['Day'] = df['Start Time'].dt.day_name()\\n df['Day'] = df['Day'].str.lower()\\n\\n # filter by month\\n if month != \\\"all\\\":\\n month_index = months.index(month) + 1\\n df = df[df['Month'] == month_index]\\n\\n # filter by day\\n if day != \\\"all\\\":\\n df = df[df['Day'] == day]\\n\\n return df\",\n \"def load_data(city, month, day):\\n\\n df = pd.read_csv(CITY_DATA[city], parse_dates=['Start Time', 'End Time'])\\n df['Start month string'] = pd.DatetimeIndex(df['Start Time']).month_name()\\n df['Start day string'] = pd.DatetimeIndex(df['Start Time']).day_name()\\n\\n if month != 'all':\\n month_filter = df['Start month string'] == month.capitalize()\\n df = df[month_filter]\\n\\n if day != 'all':\\n day_filter = df['Start day string'] == day.capitalize()\\n df = df[day_filter]\\n\\n return df\",\n \"def load_data(city, month, day):\\n data_to_use = CITY_DATA[city]\\n df = pd.read_csv(data_to_use)\\n # drop rows containing NAN fields\\n df2 = df.dropna()\\n\\n # Ensure the Start and End Time are Date\\n pd.to_datetime(df2['Start Time'])\\n pd.to_datetime(df2['End Time'])\\n df = df2.sort_values(by='Start Time')\\n\\n # For each Start Time create additional columns to store year, month, day_of_week and hour\\n # df['Start Year'] = df['Start Time'].apply(lambda x: get_part_of_datetime(x, 'year'))\\n df['Start Month'] = df['Start Time'].apply(lambda x: get_part_of_datetime(x, 'month'))\\n df['Start Day'] = df['Start Time'].apply(lambda x: get_part_of_datetime(x, 'day_of_week'))\\n df['Start Hour'] = df['Start Time'].apply(lambda x: get_part_of_datetime(x, 'hour'))\\n\\n # filter month if month is not all\\n if month.title() != 'All':\\n df = df.loc[df['Start Month'] == month.title()]\\n\\n # filter day if day is not all\\n if day.title() != 'All':\\n df = df.loc[df['Start Day'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n #loading data of the city chosen by user into dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n #converting the start time clomn from object (string) to datetime object so as we can use datetime Attributes and methonds to extract month coulmn and day to filter with them\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n #extracting month and day into new columns and days into new column 'month_name' and 'day_name' are methods in pandas datetime (https://pandas.pydata.org/pandas-docs/version/0.23/generated/pandas.DatetimeIndex.html) as it's in this link\\n df['month'] = df['Start Time'].dt.month_name()\\n df['day_of_week'] = df['Start Time'].dt.day_name()\\n #filtering data city with user inputs filter by moth and day:\\n if month != 'all':\\n df = df[df['month'] == month.title()]\\n if day != 'all':\\n df = df[df['day_of_week'] == day.title()]\\n\\n\\n return df\",\n \"def load_data(city, month, day):\\n\\n df = pd.read_csv(CITY_DATA[CITIES[city]])\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['Month'] = df['Start Time'].dt.month\\n df['Day of Week'] = df['Start Time'].dt.dayofweek\\n\\n # get the subset of data where the month matches the one chosen\\n if month != 0:\\n df = df[df['Month'] == month]\\n \\n # get the subset of data where the day of the week matches the one chosen\\n if day != 7:\\n df = df[df['Day of Week'] == day]\\n \\n return df\",\n \"def load_data(city, month, day):\\n \\n df = pd.read_csv(CITY_DATA[city])\\n \\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n df['End Time'] = pd.to_datetime(df['End Time'])\\n\\n\\n\\n # filter by month \\n if month != 'all':\\n \\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month =months.index(month) + 1\\n \\n \\n df = df[df['month'] == month]\\n\\n # filter by day of week \\n if day != 'all':\\n \\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n #Load data for city\\n print(\\\"\\\\nCity Data..\\\")\\n df = pd.read_csv(CITY_DATA[city])\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['month'] = df['Start Time'].dt.month\\n df['day_week'] = df['Start Time'].dt.day_name()\\n df['hour'] = df['Start Time'].dt.hour\\n if month != '7':\\n months = ['1', '2', '3', '4', '5', '6']\\n month = months.index(month) + 1\\n df = df[df['month'] == month]\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_week'] == day.title()]\\n return df\",\n \"def load_data(city, month, day):\\n df= pd.read_csv(CITY_DATA[city])\\n \\n #create column for month, day of week\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['Month'] = df['Start Time'].dt.month_name() \\n df['day_of_week'] = df['Start Time'].dt.day_name()\\n \\n #get the filtered data frame\\n if month != 'all':\\n df = df[df['Month'] == month]\\n if day != 'all':\\n df = df[df['day_of_week'] == day.title()]\\n return df\",\n \"def load_data(city, month, day):\\n\\n months_dict = {'january' : 1 , 'february' : 2 , 'march' : 3 , 'april' : 4 , 'may' : 5 , 'june' : 6, 'july' : 7, 'august' : 8, 'september' : 9}\\n days_dict = {'monday' : 0 , 'tuesday' : 1 , 'wednesday' : 2 , 'thursday' : 3, 'friday' : 4 , 'saturday' : 5 , 'sunday' : 6}\\n\\n df = pd.read_csv(CITY_DATA[city])\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['End Time'] = pd.to_datetime(df['End Time'])\\n\\n if month != 'all':\\n df = df[df['Start Time'].dt.month == months_dict[month]]\\n\\n if day != 'all':\\n df = df[df['Start Time'].dt.weekday == days_dict[day]]\\n\\n return df\",\n \"def load_data(city, month, day):\\n df = pd.read_csv(CITY_DATA[city])\\n df[\\\"Start Time\\\"] = pd.to_datetime(df[\\\"Start Time\\\"])\\n df[\\\"month\\\"] = df[\\\"Start Time\\\"].dt.month\\n df[\\\"day_of_week\\\"] = df[\\\"Start Time\\\"].dt.weekday\\n\\n if month != \\\"all\\\":\\n month = months[month]\\n df = df[df[\\\"month\\\"] == month]\\n\\n if day != \\\"all\\\":\\n df = df[df[\\\"day_of_week\\\"] == days.index(day)]\\n return df\",\n \"def load_data(city, month, day):\\n #create the DataFrame\\n #I'll be honest, I was struggling with this bit of code so I searched the internet and found what I needed to get started.\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week and hour from Start Time to create new columns. New columns are needed for filtering.\\n df['month'] = df['Start Time'].dt.month\\n df['day'] = df['Start Time'].dt.day\\n df['hour'] = df['Start Time'].dt.hour\\n\\n # filter by month if applicable\\n if month != 'all':\\n month = MONTHS.index(month) + 1\\n df = df[ df['month'] == month ]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[ df['day'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n df = pd.read_csv(CITY_DATA[city])\\n \\n\\n #Converting time\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n \\n if month != 'all':\\n month = MONTHS.index(month) + 1\\n\\n df = df[df['month'] == month]\\n \\n if day != 'all':\\n df = df[df['day_of_week'] == day.title()]\\n \\n return df\",\n \"def load_data(city, month, day):\\n try:\\n df = pd.read_csv(CITY_DATA[city])\\n\\n df['Start Time'] = pd.to_datetime(df['Start Time'], errors='coerce')\\n df['End Time'] = pd.to_datetime(df['End Time'])\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n df['hour'] = df['Start Time'].dt.hour\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = MONTH_LIST.index(month) + 1\\n\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\\n except Exception as e:\\n print('Couldn\\\\'t load the file, as an Error occurred: {}'.format(e))\",\n \"def load_data(city, month, day):\\n df = pd.read_csv(CITY_DATA[city])\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['Month'] = df['Start Time'].dt.month\\n df['Day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n if month != 'all':\\n month = months.index(month) + 1\\n df = df[df['Month'] == month]\\n if day != 'all':\\n df = df[df['Day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n\\n print(\\\"\\\\nLoading data ...\\\")\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # Convert the Start Time column to datetime for time period comparison \\n # then further create new columns for month and day of week based on that \\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['month'] = df['Start Time'].dt.month\\n df['dow'] = df['Start Time'].dt.day_name()\\n\\n # Perform filtering if enabled \\n\\n if month != 'all':\\n # if possible , move this as a const to the top of source code\\n months = ['january','february','march','april','may','june']\\n month_to_filter = months.index(month) + 1\\n\\n # create a new dataframe \\n df = df[df['month'] == month_to_filter]\\n\\n if day != 'all':\\n # create a new dataframe \\n # note: title() is called since the first letter of the \\n # week in the created column is in uppercase\\n df = df[df['dow'] == day.title()]\\n \\n return df\",\n \"def load_data(city, month, day):\\n # load datafile into a DataFrame\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to Date time\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extracting month and day of the week from Start time\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday\\n\\n #filter by month when applicable\\n if month != 'all':\\n month = MONTH_DATA.index(month)\\n\\n #filter by month to create a new DataFrame\\n df = df[df['month'] == month]\\n\\n #filter by day of the week where applicable\\n if day != 'all':\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n\\n df = pd.read_csv(CITY_DATA[city.lower()])\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.dayofweek # check that this is the right method()\\n\\n if month != 'all':\\n month = months.index(month.lower()) + 1 # take our month input, and index it to get the integer value provided by datetime()\\n df = df[df['month'] == month]\\n\\n if day != 'all':\\n day = days.index(day.lower())\\n df = df[df['day_of_week'] == day]\\n\\n return df\",\n \"def load_data(city, month, day):\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n days = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday']\\n df = pd.read_csv(CITY_DATA[city])\\n df['Start Time'] = pd.to_datetime(df['Start Time']) # convert the Start Time column to datetime\\n df['month'] = df['Start Time'].dt.month # extract month from start time to create a new column\\n df['day_of_week'] = df['Start Time'].dt.day_name() # extract day from start time to create a new column\\n\\n if month in months and day == 'all': # filter the df only by month if applicable\\n month = convert_to_int(months, month)\\n df = df.loc[df['month'] == month]\\n \\n if month == 'all' and day in days : # filter the df only by day of week if applicable\\n df = df.loc[df['day_of_week'] == day.title()]\\n \\n if month in months and day in days:\\n # use the index of the months list to get the corresponding month's int\\n month = convert_to_int(months, month)\\n\\n df = df.loc[df['month'] == month] # first filter the df by month\\n df = df.loc[df['day_of_week'] == day.title()] # then filter the df by day of week\\n\\n return df # no filter applied\",\n \"def load_data(city, month, day):\\n if city == 'new york city':\\n df=pd.read_csv(\\\"./new_york_city.csv\\\")\\n else: \\n df=pd.read_csv(\\\"./\\\" + city + \\\".csv\\\")\\n df['Start Time']=pd.to_datetime(df['Start Time'])\\n df['month'] = df['Start Time'].dt.month\\n df['day'] = df['Start Time'].dt.dayofweek\\n df['hour'] =df['Start Time'].dt.hour\\n if month !='all':\\n df=df[df['month']==months_list[month]]\\n if day != 'all':\\n df=df[df['day']==days_list[day]]\\n \\n return df\",\n \"def load_data(city, month, day):\\n\\n df = pd.read_csv(CITY_DATA[city])\\n\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['month'] = df['Start Time'].dt.month\\n df['weekday'] = df['Start Time'].dt.weekday_name\\n df['hour'] = df['Start Time'].dt.hour\\n\\n if month != 'all':\\n month = months.index(month) + 1\\n df = df[df['month'] == month]\\n\\n if day != 'all':\\n df = df[df['weekday'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day ,city_num, month_num, day_num):\\r\\n try:\\r\\n df = pd.read_csv(CITY_DATA[city])\\r\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\r\\n df['End Time'] = pd.to_datetime(df['End Time'])\\r\\n df['month'] = df['Start Time'].dt.month\\r\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\r\\n df['hour'] = df['Start Time'].dt.hour\\r\\n\\r\\n # filter by month if applicable\\r\\n if month != 'all':\\r\\n df = df[df['month'] == month_num]\\r\\n # filter by day of week if applicable\\r\\n if day != 'all':\\r\\n # filter by day of week to create the new dataframe\\r\\n\\r\\n df = df[df['day_of_week'].str.contains(day.title())]\\r\\n return df\\r\\n except Exception as e:\\r\\n print('An exception has been occurred during loading data: {}'.format(e))\",\n \"def load_data(city, month, day):\\n\\n print(\\\"\\\\nWe are loading the information for the selected filters.\\\")\\n start_time = time.time()\\n\\n # filter the data according to the selected city\\n if isinstance(city, list):\\n df = pd.concat(map(lambda city: pd.read_csv(CITY_DATA[city]), city),\\n sort=True)\\n # reorganize DataFrame columns after a city concat\\n try:\\n df = df.reindex(columns=['Unnamed: 0', 'Start Time', 'End Time',\\n 'Trip Duration', 'Start Station',\\n 'End Station', 'User Type', 'Gender',\\n 'Birth Year'])\\n except:\\n pass\\n else:\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # create columns to see the statistics\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['Month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n df['Start Hour'] = df['Start Time'].dt.hour\\n\\n # filter month and weekday see the data in two new DataFrames\\n if isinstance(month, list):\\n df = pd.concat(map(lambda month: df[df['Month'] ==\\n (months.index(month)+1)], month))\\n else:\\n df = df[df['Month'] == (months.index(month)+1)]\\n\\n if isinstance(day, list):\\n df = pd.concat(map(lambda day: df[df['day_of_week'] ==\\n (day.title())], day))\\n else:\\n df = df[df['day_of_week'] == day.title()]\\n\\n print(\\\"\\\\nThis took {} seconds.\\\".format((time.time() - start_time)))\\n print('-'*40)\\n\\n return df\",\n \"def load_data(city, month, day):\\n\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n # drop the unused 'Unnamed' column\\n df = df.drop(\\\"Unnamed: 0\\\", axis=1)\\n # convert the Start Time column to datetime\\n df[\\\"Start Time\\\"] = pd.to_datetime(df[\\\"Start Time\\\"])\\n # extract month, day of week and hour from Start Time to create new columns\\n df[\\\"month\\\"] = df[\\\"Start Time\\\"].dt.month_name()\\n df[\\\"day\\\"] = df[\\\"Start Time\\\"].dt.day_name()\\n df[\\\"hour\\\"] = df[\\\"Start Time\\\"].dt.hour.astype(str)\\n\\n # filter by month if applicable\\n if month != \\\"All\\\":\\n # filter by month to create the new dataframe\\n df = df.loc[df[\\\"month\\\"] == month]\\n\\n # filter by day of week if applicable\\n if day != \\\"All\\\":\\n # filter by day of week to create the new dataframe\\n df = df.loc[df[\\\"day\\\"] == day]\\n\\n return df\",\n \"def load_data(city, month, day):\\n df = pd.read_csv(CITY_DATA[city])\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n df['month'] = df['Start Time'].dt.month\\n df['day'] = df['Start Time'].dt.weekday_name\\n df['hour'] = df['Start Time'].dt.hour\\n\\n \\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n\\n df = df[df['month'] == month]\\n\\n if day != 'all':\\n df = df[df['day'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n \\n if city == 'chicago':\\n filename = 'chicago.csv'\\n elif city == 'new York':\\n filename = 'new_york_city.csv'\\n elif city == 'washington':\\n filename = 'washington.csv'\\n else:\\n return -1\\n \\n df = pd.read_csv(filename)\\n \\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['End Time'] = pd.to_datetime(df['End Time'])\\n \\n if month != 'all':\\n df = df[df['Start Time'].dt.month == month]\\n\\n if day != 'all':\\n df = df[df['Start Time'].dt.weekday == day]\\n \\n return df\",\n \"def load_data(city, month, day):\\n # I upload the data from the file for the city chosen by the user into the dataframe.\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # To handle the data with pandas, I need to convert 'Start Time' to datetime. Afterwards, I create seperate columns for month, weekday, and start hour\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n df['hour'] = df['Start Time'].dt.hour\\n\\n # If the user did not input 'all', the data is filtered by the chosen month.\\n # As I asked for the name of the month earlier, I use the index function to get the integer from the list.\\n # As the list starts with 'all', the index of January is 1, February 2 etc.\\n if month != 'all':\\n month = MONTH_DATA.index(month)\\n df = df[df['month'] == month]\\n\\n # Same for weekdays\\n if day != 'all':\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n\\n # filter by month if applicable\\n if month != 'all':\\n df = df[df['month']==month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week']==day]\\n\\n return df\",\n \"def load_data(city, month, day):\\n df= pd.read_csv(CITY_DATA[city])\\n df['Start Time']= pd.to_datetime(df['Start Time'])\\n df['DOW'] = df['Start Time'].dt.weekday\\n df['month'] = df['Start Time'].dt.month\\n\\n if month != 'all':\\n months = ['january','february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n df = df[df['month'] == month]\\n\\n if day != 'all':\\n df= df[df['DOW'] == day]\\n\\n return df\",\n \"def load_data(city, month, day):\\n df = pd.read_csv(city)\\n df['month'] = pd.to_datetime(df['Start Time']).dt.month\\n df['day_of_week'] = pd.to_datetime(df['Start Time']).dt.dayofweek\\n if month != 'all':\\n df = df[df['month'] == month]\\n if day != 'all':\\n df = df[df['day_of_week'] == day]\\n\\n return df\",\n \"def load_data(city, month, day):\\n df = pd.read_csv(CITY_DATA[city])\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.day_name()\\n df['hour'] = df['Start Time'].dt.hour\\n if month != 'all':\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n df = df[df['month'] == month]\\n\\n if day != 'all':\\n df = df[df['day_of_week'].str.startswith(day.title())]\\n\\n return df\",\n \"def load_data(city, month, day):\\n \\n filename = str(CITY_DATA.get(city))\\n\\n # load data file into a dataframe\\n df = pd.read_csv(filename)\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month_int = months.index(month) +1\\n\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month_int] \\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n \\n return df\",\n \"def load_data(city, month, day):\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n month = months.index(month) + 1\\n\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n month = VALID_MONTHS.index(month) + 1\\n\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n df = pd.read_csv(CITY_DATA[city])\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['hour'] = df['Start Time'].dt.hour\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n #filter by month if needed and create a new frame\\n if month != 'all':\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n df = df[df['month'] == month]\\n # filter by day of week if needed\\n if day != 'all':\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n try:\\n df = pd.read_csv(CITY_DATA[city])\\n \\n df['Start Time'] = pd.to_datetime(df['Start Time'], errors='coerce')\\n df['End Time'] = pd.to_datetime(df['End Time'])\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name \\n df['hour'] = df['Start Time'].dt.hour\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = MONTH_LIST.index(month) + 1 \\n \\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()] \\n\\n return df\\n except Exception as e:\\n print('Couldn\\\\'t load the file, as an Error occurred: {}'.format(e))\",\n \"def load_data(city, month, day):\\n try:\\n df = pd.read_csv(CITY_DATA[city])\\n \\n df['Start Time'] = pd.to_datetime(df['Start Time'], errors='coerce')\\n df['End Time'] = pd.to_datetime(df['End Time'])\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name \\n df['hour'] = df['Start Time'].dt.hour\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = MONTH_LIST.index(month) + 1 \\n \\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()] \\n\\n return df\\n except Exception as e:\\n print('Couldn\\\\'t load the file, as an Error occurred: {}'.format(e))\",\n \"def load_data(city, month, day):\\n\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city.lower()])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month.lower()) + 1\\n\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n return df\",\n \"def load_data(city, month, day):\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n \\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n \\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n \\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n \\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n df = pd.read_csv(CITY_DATA[city.lower()])\\n month = month.title()\\n day = day.title()\\n \\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n \\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n# df['day_of_week'] = df['Start Time'].dt.weekday_name\\n df['day_of_week'] = df['Start Time'].dt.day_name()\\n \\n # filter by month if applicable\\n if month not in ['', 'All']:\\n # use the index of the months list to get the corresponding int\\n month = MONTHS_LIST.index(month) + 1\\n \\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n \\n # filter by day of week if applicable\\n if day not in ['', 'All']:\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day]\\n return df\",\n \"def load_data(city, month, day):\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n # months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n \\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n \\n return df\",\n \"def load_data(city, month, day):\\n\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.day_name()\\n\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n \\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n \\n return df\",\n \"def load_data(city, month, day):\\n \\n start_time = time.time()\\n\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n df['Start Time'] = pd.to_datetime(df['Start Time'], errors='coerce')\\n\\n # extract month, day of week and hour from Start Time to create new columns\\n \\n # Months will take values from 1 through 12\\n df['month'] = df['Start Time'].dt.month \\n \\n # day of the week will take values in the range of 1 through 7\\n df['day_of_week'] = df['Start Time'].dt.dayofweek \\n \\n # hour will take values from 0 through 23\\n df['hour'] = df['Start Time'].dt.hour # range (0-23)\\n\\n # Here, we are filtering by month\\n df['End Time'] = pd.to_datetime(df['End Time'])\\n if month != 'all':\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1 \\n\\n df = df[df['Start Time'].dt.month == month]\\n\\n # Here, we are filtering by day of week\\n if day != 'all': \\n df = df[df['Start Time'].dt.weekday_name == day.title()]\\n \\n return df\",\n \"def load_data(city, month, day):\\n # here i load the datak\\n df=pd.read_csv(CITY_DATA[city])\\n \\n df['Start Time']=pd.to_datetime(df['Start Time'])\\n \\n df['month']=df['Start Time'].dt.month\\n df['day_of_week']=df['Start Time'].dt.weekday_name\\n df['hour']=df['Start Time'].dt.hour\\n \\n #filter by month\\n if month!='all':\\n month =months.index(month)+1\\n df=df[df['month']==month]\\n \\n #filter by day of week\\n if day!='all':\\n df=df[df['day_of_week']==day.title()]\\n \\n return df\",\n \"def load_data(city, month, day):\\n\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['End Time'] = pd.to_datetime(df['End Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['hour'] = df['Start Time'].dt.hour\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n\\n df = pd.read_csv(CITY_DATA[city])\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['End Time'] = pd.to_datetime(df['End Time'])\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n if month != 'all':\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n df = df[df['month'] == month]\\n\\n if day != 'all':\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df, city, month, day\",\n \"def load_data(city, month, day):\\n df = pd.read_csv(CITY_DATA[city])\\n\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n df['month_names'] = df['Start Time'].dt.month\\n df['day_names'] = df['Start Time'].dt.weekday\\n df['hour'] = df['Start Time'].dt.hour\\n\\n if month != 'all':\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n df = df[df['month_names'] == month]\\n\\n if day != 'all':\\n day_name = ['sunday', 'monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday']\\n day = day_name.index(day) + 1\\n df = df[df['day_names'] == day]\\n\\n return df\",\n \"def load_data(city, month, day):\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n \\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n \\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n \\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june', 'july', 'august', 'september', 'october', 'november', 'december']\\n month = months.index(month) + 1\\n \\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n \\n return df\",\n \"def load_data(city, month, day):\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city.lower()])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month, and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday #Monday=0,Sunday=6\\n \\n # filter by month if applicable\\n \\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n month = months.index(month) + 1\\n \\n # filter by month to create the new dataframe\\n filt1 = (df['month'] == month)\\n df = df[filt1]\\n\\n # filter by day of week if applicable\\n \\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n weekday = weekdays.index(day)\\n filt2 = (df['day_of_week'] == weekday) \\n df = df[filt2]\\n \\n return df\",\n \"def load_data(city, month, day):\\n\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n # filter by month if applicable\\n if month != 'all':\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n #filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n df['Start and End Stations'] = df['Start Station'] + ' and ' + df['End Station']\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n df['City'] = city.title()\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month_name()\\n df['day_of_week'] = df['Start Time'].dt.day_name()\\n\\n # filter by month if applicable\\n if month != 'all':\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month.title()]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n\\n # Noting the start time\\n start = time.time()\\n print('Please wait while we load the data ......')\\n\\n # Reading the city data as selected by the user\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert columns od Start Time and End Time into date format yyyy-mm-dd\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['End Time'] = pd.to_datetime(df['End Time'])\\n\\n # extract month from Start Time into new column called month\\n df['Month'] = df['Start Time'].dt.month\\n\\n # filter by month\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['January', 'February', 'March', 'April', 'May', 'June']\\n month = months.index(month) + 1\\n\\n # filter by month to create the new dataframe\\n df = df[df['Month'] == month]\\n\\n # extract day from Start Time into new column called Day of Week\\n df['Day of Week'] = df['Start Time'].dt.day_name()\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['Day of Week'] == day.title()]\\n print(f'Data successfully loaded taking {round(time.time() - start, 2)} seconds')\\n\\n # Returning the filtered dataframe\\n return df\",\n \"def load_data(city, month, day):\\n\\n\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n # filter by month if applicable\\n if month.lower() != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['all', 'january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month.lower()) + 1\\n\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day.lower() != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n\\n return df\",\n \"def load_data(city, month, day):\\n df=pd.read_csv(CITY_DATA[city])\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['month']=df['Start Time'].dt.month\\n df['day_of_week']=df['Start Time'].dt.weekday_name\\n if month!= 'all':\\n months=['january','february','march','april','may','june']\\n month= months.index(month)+1\\n df=df[df['month']==month]\\n if day!= 'all':\\n df=df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\r\\n df = pd.read_csv(CITY_DATA[city])\\r\\n\\r\\n # convert the Start Time column to datetime\\r\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\r\\n\\r\\n # extract month and day of week from Start Time to create new columns\\r\\n df['month'] = df['Start Time'].dt.month\\r\\n df['day_of_week'] =df['Start Time'].dt.weekday_name\\r\\n\\r\\n\\r\\n # filter by month if applicable\\r\\n if month != 'all':\\r\\n # use the index of the months list to get the corresponding int\\r\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\r\\n month = months.index(month) + 1\\r\\n\\r\\n # filter by month to create the new dataframe\\r\\n df = df[df['month'] == month]\\r\\n\\r\\n # filter by day of week if applicable\\r\\n if day != 'all':\\r\\n # filter by day of week to create the new dataframe\\r\\n df = df[df['day_of_week'] == day.title()]\\r\\n\\r\\n return df\",\n \"def load_data(city, month, day):\\n\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.day_name()\\n\\n # filter by month if applicable\\n if month != 'none':\\n # use the index of the months list to get the corresponding int\\n month = months.index(month) + 1\\n\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'none':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n\\n print(\\\"\\\\nThe program is loading the data for the filters of your choice.\\\")\\n start_time = time.time()\\n\\n\\n df = pd.read_csv(CITY_DATA[city])\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['Month'] = df['Start Time'].dt.month\\n df['Weekday'] = df['Start Time'].dt.weekday_name\\n df['Start Hour'] = df['Start Time'].dt.hour\\n\\n # filter the data according to month and weekday into two new DataFrames\\n df = df[df['Month'] == (months.index(month)+1)]\\n df = df[df['Weekday'] == day.title()]\\n\\n print('-'*40)\\n\\n return df\",\n \"def load_data(city, month, day):\\n\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month, day of week and hour from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n df['hour'] = df['Start Time'].dt.hour\\n\\n\\n # filter_choosed by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = [\\\"january\\\", \\\"february\\\", \\\"march\\\", \\\"april\\\", \\\"may\\\", \\\"june\\\"]\\n month = months.index(month) + 1\\n\\n # filter_choosed by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter_choosed by day of week if applicable\\n if day != 'all':\\n # filter_choosed by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n # load data file into a dataframe\\n df=pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n # filter by month if applicable\\n if month != 'all':\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n num_month=1\\n for mes in months:\\n if mes==month:\\n break\\n else:\\n num_month += 1\\n\\n # filter by month to create the new dataframe\\n df = df[(df['month']) == num_month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n df = df[(df['day_of_week']) == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n # load data file into a dataframe\\n print(\\\"\\\\nLoading data...\\\")\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].apply(lambda x: x.month)\\n df['day_of_week'] = df['Start Time'].apply(lambda x: x.strftime('%A').lower())\\n\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n\\n # filter by month to create the new dataframe\\n df = df.loc[df['month'] == month,:]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df.loc[df['day_of_week'] == day,:]\\n\\n return df\",\n \"def load_data(city, month, day):\\n df=pd.read_csv(CITY_DATA[city])\\n\\n df['Start Time']=pd.to_datetime(df['Start Time'])\\n\\n df['month'] = df['Start Time'].dt.month\\n\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n df['hour'] = df['Start Time'].dt.hour\\n\\n if month != 'all':\\n months = ['january', 'february', 'march', 'april', 'may', 'june', 'july', 'august', 'september', 'october', 'november', 'december']\\n month = months.index(month) + 1\\n df = df[df['month'] == month]\\n\\n if day != 'all':\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n df = pd.read_csv(city)\\n df['day_of_week'] = pd.to_datetime(df['Start Time']).dt.dayofweek\\n df['month'] = pd.to_datetime(df['Start Time']).dt.month\\n if day != 'all':\\n df = df[df['day_of_week'] == day]\\n if month != 'all':\\n df = df[df['month'] == month]\\n df.drop('day_of_week', axis=1, inplace=True)\\n df.drop('month', axis=1, inplace=True)\\n return df\",\n \"def load_data(city, month, day):\\n #load the data of the specified city in a dataframe:\\n df = pd.read_csv(CITY_DATA[city])\\n\\n #convert the type of data in 'Start Time' column to datetime:\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n #create new columns required to calculate time_stats:\\n df['month'] = df['Start Time'].dt.month\\n df['weekday'] = df['Start Time'].dt.day_name()\\n df['hour'] = df['Start Time'].dt.hour\\n\\n #unless user input is all, filter by month:\\n if month != 'all':\\n month = months.index(month) + 1 #get the index of the month\\n df = df[df['month'] == month]\\n\\n #uless user input is all, filter by weekday:\\n if day != 'all':\\n df = df[df['weekday'] == day.title()]\\n\\n\\n return df.set_index(pd.Series([i for i in range(df.shape[0])])) #reset the indices of the filterd df\",\n \"def load_data(city, month, day):\\n city = city.lower()\\n month = month.lower()\\n day = day.lower()\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n # initially we index our global dictionary to load the correct .csv file to load needed data.\\n \\n df = pd.read_csv(CITY_DATA[city])\\n \\n # we convert the 'Start Time' column into type of date_time object to allow extracting the day.\\n # re-assigning the start time column again in the data frame.\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n # extracting the month number from the start time column by the dt(date_time) accessor\\n # creating new column in the data frame df called Month.\\n df['Month'] = df['Start Time'].dt.month\\n # creating new column in the data frame df called Day_Week\\n df['Day_Week'] = df['Start Time'].dt.weekday_name\\n \\n month_selected = None\\n # the value of the month selected = the index of the item in the list as it's orderd in the same sequence.\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n \\n if month != 'all':\\n month_selected = months.index(month) + 1 \\n # filter the data frame df by month selected then re-assigning it again.\\n df = df[df['Month'] == month_selected]\\n if day != 'all':\\n # filter the data frame df by day of week selected then re-assigning it again.\\n #print(day) \\n df = df[df['Day_Week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n # load data file into df\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert start time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of the week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n\\n # filter by month to crate the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n df=pd.read_csv(CITY_DATA[city])\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n if month != 'none':\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) +1\\n df = df[df['month'] == month]\\n\\n\\n if day != 'none':\\n df = df[df['day_of_week'] == day.title()]\\n return df\",\n \"def load_data(city, month, day):\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.day\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n print (df)\\n return df\",\n \"def load_data(city, month, day):\\n # load file into dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # month and day of week from Start Time, creating new columns. Return month name, not integer.\\n df['month'] = df['Start Time'].dt.strftime('%B')\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n # filter by month, if applicable\\n if month != 'all':\\n # use index of months list to get corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n\\n # filter by day of week, if applicable\\n if day!= 'all':\\n # filter by day of week to create new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n #Used the practice#3 from Project solution here to convert time columns to month and weekday_name\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n #print('this is the month', month)\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n \\n return df\",\n \"def load_data(city, month, day): \\r\\n #load data file into a dataframe\\r\\n df = pd.read_csv(CITY_DATA[city])\\r\\n #convert start time to datatime\\r\\n df['start time'] = pd.to_datetime(df['start time'])\\r\\n #extract month and day of week\\r\\n df['month'] = df['start time'].dt.month\\r\\n df['day_of_week'] = df['start time'].dt.day_name()\\r\\n #filter\\r\\n if month != \\\"all\\\":\\r\\n months = ['january','february','march','april','may','june']\\r\\n month = month.index(month) + 1\\r\\n #creat new datafram for months\\r\\n df = df[df['month'] == month]\\r\\n #creat new datafram for day of week\\r\\n if day != 'all':\\r\\n df = df[df['day_of_week'] == day.title()]\\r\\n return df\",\n \"def load_data(city, month, day):\\n df = pd.read_csv('{}.csv'.format(city))\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n if month != 'all':\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month.lower()) + 1\\n df = df[df['month'] == month]\\n\\n if day != 'all':\\n df = df[df['day_of_week'] == day.title()]\\n\\n\\n return df\",\n \"def load_data(city, month, day):\\r\\n\\r\\n #creating data frame from csv\\r\\n df = pd.read_csv(CITY_DATA[city])\\r\\n\\r\\n #converting Start Time row into datetime data type\\r\\n df['Start Time']=pd.to_datetime(df['Start Time'])\\r\\n\\r\\n #Extracting month and week day from 'Start Time' row\\r\\n df['Month'] = df['Start Time'].dt.month_name()\\r\\n df['Day of Week'] = df['Start Time'].dt.day_name()\\r\\n\\r\\n #filter by month\\r\\n if month != 'All':\\r\\n df = df[df['Month'] == month]\\r\\n\\r\\n #filter by day\\r\\n if day != 'All':\\r\\n df = df[df['Day of Week'] == day.title()]\\r\\n\\r\\n #Returns the selected file as a dataframe (df) with relevant columns\\r\\n return df\",\n \"def load_data(city, month, day):\\n\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start, end Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['End Time'] = pd.to_datetime(df['End Time'])\\n # Calculate the travel time per trip and add that column to data frame.\\n df['Travel Time'] = df['End Time'] - df['Start Time']\\n\\n # extract month and day of week from Start Time to create new columns\\n df['Start Hour'] = df['Start Time'].dt.hour\\n df['End Hour'] = df['End Time'].dt.hour\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n# load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june', 'july', 'august', 'september', 'october', 'november', 'december']\\n month = months.index(month) + 1\\n\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n df = pd.read_csv('{}.csv'.format(city).replace(' ', '_'))\\n # The data type is changed to'Start Time' column\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n if city == 'chicago':\\n city_df = pd.read_csv('chicago.csv')\\n elif city == 'new york city':\\n city_df = pd.read_csv('new_york_city.csv')\\n else:\\n # city_df = pd.read_csv('washington.csv')\\n print(\\\"else is running\\\")\\n\\n print(city_df.head())\\n\\n return city_df\",\n \"def load_data(city, month, day):\\n df = pd.read_csv(CITY_DATA[city]) # similar to practiceQ3, load data file into a dataframe\\n\\n df['Start Time'] = pd.to_datetime(df['Start Time']) # similar to practiceQ3, convert the \\\"Start Time\\\" column to to_datetime YY\\n\\n # similar to practiceQ3, extract month and day of week from \\\"Start Time\\\" to create new columns YY\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.day_name()\\n\\n # filter by month, if applicable\\n if month != 'all':\\n # use the index of months list to get the corresponding int YY\\n\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n\\n # filter by month to create new dataframe\\n df = df[df['month'] == month]\\n\\n #filter by day of week, if applicable\\n if day != 'all':\\n #filter by day of week to create the new dataframe YY\\n df = df[df['day_of_week'] == day.title()]\\n\\n\\n return df\",\n \"def load_data(city, month, day):\\n df = pd.read_csv(CITY_DATA[city])\\n df['Start Time']=pd.to_datetime(df['Start Time'])\\n df['End Time']=pd.to_datetime(df['End Time'])\\n df['month']=df['Start Time'].dt.month\\n df['day_of_week']=df['Start Time'].dt.day_name()\\n \\n \\n if month.lower() !='all':\\n months=['jan','feb','mar','apr','may','jun']\\n month=months.index(month)+1\\n df=df[df['month']==month]\\n else:\\n month='all'\\n if day.lower() !='all':\\n df=df[df['day_of_week']== day.title()]\\n else:\\n day='all' \\n\\n\\n \\n return df\",\n \"def load_data(city, month, day):\\r\\n # Make sure the city name is correct\\r\\n city_name = city.lower()\\r\\n\\r\\n if debug_flag:\\r\\n print(city_name)\\r\\n\\r\\n try:\\r\\n print('getting data from: ', CITY_DATA[city_name])\\r\\n df = pd.read_csv(CITY_DATA[city_name])\\r\\n except OSError as e:\\r\\n print(\\\"Error: cannot find the data files\\\")\\r\\n print(\\\" Please make sure they are available in the root folder\\\")\\r\\n print(\\\" and restart the program\\\\n\\\")\\r\\n finally:\\r\\n exit()\\r\\n\\r\\n\\r\\n try:\\r\\n # Build data frame columns:\\r\\n # Convert start time column to date time so we can work with it\\r\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\r\\n\\r\\n # Build month (num) column from \\\"start time\\\"\\r\\n df['Month'] = df['Start Time'].dt.month\\r\\n\\r\\n # Use start date to calculate start day (i.e. tuesday) column\\r\\n df['Start Day'] = df['Start Time'].dt.day_name()\\r\\n\\r\\n # build hour column from start day column\\r\\n df['Hour'] = df['Start Time'].dt.hour\\r\\n\\r\\n except:\\r\\n print (\\\"Unexpected error\\\")\\r\\n\\r\\n return df\",\n \"def load_data(city, month, day):\\n df = pd.read_csv(CITY_DATA[city])\\n\\n \\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n # to_datetime command is used to convert(change) date into date format\\n df['End Time'] = pd.to_datetime(df['End Time'])\\n if month != 'all':\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n #used to find index of month.\\n month = months.index(month) + 1 \\n\\n df = df[df['Start Time'].dt.month == month]\\n \\n #filter data by day.\\n if day != 'all': \\n df = df[df['Start Time'].dt.weekday_name == day.title()]\\n #print 5 rows.\\n print(df.head())\\n return df\",\n \"def load_data(city, month, day):\\n# This code is refrenced from the practice problem on the project.\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week and hour from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n df['hour'] = df['Start Time'].dt.hour\\n\\n # filter by month if applicable\\n if month != 'all':\\n month = MONTHS.index(month) + 1\\n df = df[ df['month'] == month ]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[ df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day):\\n # Load data file into a dataframe.\\n print('\\\\nLoading data for city = {}, month = {}, day = {}...'\\n .format(city, month, day))\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # Convert the Start Time column to datetime.\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n # Extract month, day of week, hour from Start Time to create new columns.\\n df['Month'] = [MONTHS[int(m)] for m in df['Start Time'].dt.month]\\n df['Day of Week'] = df['Start Time'].dt.weekday_name\\n df['Hour'] = df['Start Time'].dt.hour\\n # Create a column for the start and end station pairs.\\n df['Path'] = df['Start Station'] + ' => ' + df['End Station']\\n\\n # Filter by month, if applicable.\\n if month != 'All':\\n df = df[df['Month'] == month]\\n # Filter by day of week, if applicable\\n if day != 'All':\\n df = df[df['Day of Week'] == day]\\n return df\",\n \"def load_data(city, month, day):\\n file_data = pd.read_csv(CITY_DATA[city])\\n df = pd.DataFrame(data=file_data)\\n\\n # weekday and month columns\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n df['month'] = df['Start Time'].dt.month\\n\\n df['weekend_day'] = df['Start Time'].dt.weekday\\n\\n if month != 'all':\\n df = df[df['month']==Months.index(month) + 1]\\n\\n\\n if day != 'all':\\n df = df[df['weekend_day'] == day.title()]\\n\\n\\n return df\",\n \"def load_data(city, month, day):\\r\\n if city.lower() == \\\"chicago\\\" or city.lower() == \\\"c\\\":\\r\\n filename = 'C:\\\\\\\\Neha\\\\\\\\Udacity\\\\\\\\BikeShare\\\\\\\\chicago.csv'\\r\\n elif city.lower() == \\\"New York\\\" or city.lower() == \\\"new york\\\":\\r\\n filename = 'C:\\\\\\\\Neha\\\\\\\\Udacity\\\\\\\\BikeShare\\\\\\\\new_york_city.csv'\\r\\n elif city.lower() == \\\"Washington\\\" or city.lower() == \\\"washington\\\":\\r\\n filename = 'C:\\\\\\\\Neha\\\\\\\\Udacity\\\\\\\\BikeShare\\\\\\\\washington.csv'\\r\\n # load data file into a dataframe\\r\\n #df = pd.read_csv(CITY_DATA[\\\"city\\\"])\\r\\n df = pd.read_csv(filename)\\r\\n # convert the Start Time column to datetime\\r\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\r\\n\\r\\n # extract month and day of week from Start Time to create new columns\\r\\n df['month'] = df['Start Time'].dt.month\\r\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\r\\n\\r\\n # filter by month if applicable\\r\\n if month != 'all':\\r\\n # use the index of the months list to get the corresponding int\\r\\n months = ['January', 'February', 'March', 'April', 'May', 'June']\\r\\n month = months.index(month) + 1\\r\\n\\r\\n # filter by month to create the new dataframe\\r\\n df = df[df['month'] == month]\\r\\n\\r\\n # filter by day of week if applicable\\r\\n if day != 'all':\\r\\n # filter by day of week to create the new dataframe\\r\\n df = df[df['day_of_week'] == day.title()]\\r\\n\\r\\n return df\",\n \"def load_data(city, month, day, filters):\\n print('\\\\n\\\\n*****************LOADING DATA*****************')\\n start_time = time.time()\\n\\n dataframe = pd.read_csv(CITY_DATA[city])\\n print('City: ', city)\\n print('Total data points found: ', len(dataframe))\\n\\n # Changing start time to datetime format\\n dataframe['Start Time'] = pd.to_datetime(dataframe['Start Time'])\\n dataframe['Day'] = dataframe['Start Time'].dt.weekday\\n dataframe['Month'] = dataframe['Start Time'].dt.month\\n dataframe['Hour'] = dataframe['Start Time'].dt.hour\\n\\n # Displaying statistics for whole data\\n if filters == 'Month':\\n popular_month, count_popular_month = common_month(dataframe)\\n print('Most popular month for travelling: ', popular_month)\\n print('Counts: ', count_popular_month)\\n\\n elif filters == 'Day':\\n popular_day, count_popular_day = common_day(dataframe)\\n print('Most popular day for travelling: ', get_day_name(popular_day))\\n print('Counts: ', count_popular_day)\\n\\n elif filters == 'Both':\\n popular_month, count_popular_month = common_month(dataframe)\\n popular_day, count_popular_day = common_day(dataframe)\\n print('\\\\nMost popular month for travelling: ', popular_month)\\n print('Counts: ', count_popular_month)\\n print('\\\\nMost popular day for travelling: ', get_day_name(popular_day))\\n print('Counts: ', count_popular_day)\\n\\n print(\\\"\\\\nThis took {} seconds.\\\".format(time.time() - start_time))\\n print('----------------------------------------------')\\n\\n print('\\\\n\\\\n***************APPLYING FILTERS***************')\\n start_time = time.time()\\n months = ['January', 'February', 'March', 'April', 'May', 'June']\\n\\n if filters == 'Month':\\n print('Filter:\\\\n Month = ', month.title())\\n dataframe = dataframe[dataframe['Month'] == months.index(month) + 1]\\n elif filters == 'Day':\\n print('Filter: Day = ', day)\\n dataframe = dataframe[dataframe['Day'] == get_day_number(day)]\\n elif filters == 'Both':\\n print('Filter:\\\\n Month = {}\\\\n Day = {}'.format(month.title(), day))\\n dataframe = dataframe[dataframe['Month'] == months.index(month) + 1]\\n dataframe = dataframe[dataframe['Day'] == get_day_number(day)]\\n else:\\n print('Filter: ', filters)\\n\\n print('Total data points after applying filter: ', len(dataframe))\\n print(\\\"\\\\nThis took {} seconds.\\\".format(time.time() - start_time))\\n print('----------------------------------------------')\\n\\n return dataframe\",\n \"def load_data(city, month, day):\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n #print(df.head())\\n \\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time']) \\n #print(df['Start Time'].head())\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.day_name()\\n #print(df['month'].head())\\n #print(df['day_of_week'].head())\\n \\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n #print((months[month]))\\n #print(month)\\n \\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n #print(df['month'].head())\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n #print(day.title())\\n #print(df.head())\\n\\n return df\",\n \"def load_data(city, month, day):\\n# load datafile into a dataframe\\n df=pd.read_csv(CITY_DATA[city])\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.day_name()\\n \\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month)+1\\n \\n # filter by month to create the new dataframe\\n df = df[df['month']==month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n return df\",\n \"def load_data(city, month, day):\\n df = pd.read_csv(CITY_DATA[city])\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n#changed 'weekday_name' to just 'weekday' which outputs the weekday as integer\\n # extract month, day of week and hour from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['dow'] = df['Start Time'].dt.weekday\\n df['hour'] = df['Start Time'].dt.hour\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n month = months.index(month) + 1\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n# problem with the 'day'-filter, if a day (not 'all') is applied, the output is not right\\n # filter by day of week if applicable\\n if day != 'all':\\n\\n # filter by day of week to create the new dataframe\\n day = days.index(day) + 1\\n df = df[df['dow'] == day]\\n\\n return df\",\n \"def load_data(city, month, day):\\n df = pd.read_csv(CITY_DATA[city])\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['End Time'] = pd.to_datetime(df['End Time'])\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.dayofweek\\n\\n return df\",\n \"def load_data(city, month, day):\\n\\n print('\\\\nLoading Data...\\\\n')\\n\\n path = os.getcwd().replace('\\\\\\\\', '/') + '/'\\n dir_path = os.path.dirname(os.path.realpath(__file__)).replace('\\\\\\\\', '/') + '/'\\n try:\\n df = pd.read_csv(dir_path + CITY_DATA.get(city))\\n except FileNotFoundError as e:\\n sys.exit('Error loading file. Make sure that the datafiles are in the working directory.\\\\npath: {}\\\\ndir_path: {}'.format(path, dir_path))\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.dayofweek\\n df['hour'] = df['Start Time'].dt.hour\\n\\n if df is None:\\n sys.exit('Error initializing dataframe. File was loaded successfully but load_data() failed.')\\n\\n return df\",\n \"def load_data(city, month, day):\\r\\n df = pd.read_csv(CITY_DATA[city])\\r\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\r\\n df['month'] = df['Start Time'].dt.month\\r\\n df['day'] = df['Start Time'].dt.weekday_name\\r\\n df['hour'] = df['Start Time'].dt.hour\\r\\n df['And'] = ' & '\\r\\n # create a new column of concatenated Start and End Stations so we can find the most common combination\\r\\n df['StartEnd'] = df[['Start Station', 'And', 'End Station']].apply(lambda x: ''.join(x), axis=1)\\r\\n\\r\\n # drop rows with missing values\\r\\n return df\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.81320685","0.8108889","0.8045821","0.8042514","0.80090284","0.80071104","0.79991937","0.7998333","0.7988789","0.797905","0.79737365","0.79610896","0.7956163","0.7947432","0.79463065","0.7908287","0.7908229","0.7904569","0.7900715","0.78930074","0.7889219","0.78825414","0.78817195","0.78664976","0.78635985","0.7858628","0.7850476","0.78407556","0.7840348","0.78244984","0.78145236","0.78091073","0.7809104","0.7794194","0.7793959","0.77931184","0.7790996","0.7787979","0.7780334","0.7780334","0.7775348","0.7771313","0.77642614","0.7763616","0.7762852","0.77602804","0.7759904","0.7755308","0.77491575","0.77491575","0.7746167","0.7744287","0.77409494","0.7738649","0.7737522","0.7734237","0.7732167","0.77304715","0.77258116","0.77198625","0.7718666","0.7718287","0.7707423","0.7693175","0.769169","0.7690176","0.7682382","0.7681617","0.76707196","0.7670476","0.7666826","0.766273","0.7656916","0.76535654","0.7644847","0.76380277","0.7633296","0.76225895","0.76218694","0.75706446","0.756893","0.7565277","0.7554493","0.7549395","0.75412136","0.7537176","0.75361586","0.75334424","0.75289625","0.7511665","0.74916196","0.7460704","0.7460499","0.745938","0.7409791","0.740803","0.7384618","0.7345314","0.7343438","0.7335328"],"string":"[\n \"0.81320685\",\n \"0.8108889\",\n \"0.8045821\",\n \"0.8042514\",\n \"0.80090284\",\n \"0.80071104\",\n \"0.79991937\",\n \"0.7998333\",\n \"0.7988789\",\n \"0.797905\",\n \"0.79737365\",\n \"0.79610896\",\n \"0.7956163\",\n \"0.7947432\",\n \"0.79463065\",\n \"0.7908287\",\n \"0.7908229\",\n \"0.7904569\",\n \"0.7900715\",\n \"0.78930074\",\n \"0.7889219\",\n \"0.78825414\",\n \"0.78817195\",\n \"0.78664976\",\n \"0.78635985\",\n \"0.7858628\",\n \"0.7850476\",\n \"0.78407556\",\n \"0.7840348\",\n \"0.78244984\",\n \"0.78145236\",\n \"0.78091073\",\n \"0.7809104\",\n \"0.7794194\",\n \"0.7793959\",\n \"0.77931184\",\n \"0.7790996\",\n \"0.7787979\",\n \"0.7780334\",\n \"0.7780334\",\n \"0.7775348\",\n \"0.7771313\",\n \"0.77642614\",\n \"0.7763616\",\n \"0.7762852\",\n \"0.77602804\",\n \"0.7759904\",\n \"0.7755308\",\n \"0.77491575\",\n \"0.77491575\",\n \"0.7746167\",\n \"0.7744287\",\n \"0.77409494\",\n \"0.7738649\",\n \"0.7737522\",\n \"0.7734237\",\n \"0.7732167\",\n \"0.77304715\",\n \"0.77258116\",\n \"0.77198625\",\n \"0.7718666\",\n \"0.7718287\",\n \"0.7707423\",\n \"0.7693175\",\n \"0.769169\",\n \"0.7690176\",\n \"0.7682382\",\n \"0.7681617\",\n \"0.76707196\",\n \"0.7670476\",\n \"0.7666826\",\n \"0.766273\",\n \"0.7656916\",\n \"0.76535654\",\n \"0.7644847\",\n \"0.76380277\",\n \"0.7633296\",\n \"0.76225895\",\n \"0.76218694\",\n \"0.75706446\",\n \"0.756893\",\n \"0.7565277\",\n \"0.7554493\",\n \"0.7549395\",\n \"0.75412136\",\n \"0.7537176\",\n \"0.75361586\",\n \"0.75334424\",\n \"0.75289625\",\n \"0.7511665\",\n \"0.74916196\",\n \"0.7460704\",\n \"0.7460499\",\n \"0.745938\",\n \"0.7409791\",\n \"0.740803\",\n \"0.7384618\",\n \"0.7345314\",\n \"0.7343438\",\n \"0.7335328\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73460114"},"document_rank":{"kind":"string","value":"97"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":916,"numItemsPerPage":100,"numTotalItems":95772,"offset":91600,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NTY0NDYxNiwianRpIjoiOTJiYjI4OWItODgyZS00OWU4LWI5YzAtMjQ5Y2MwMjU5OTgzIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODU2NDgyMTYsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.mmWW7Nhoc9fFq8zPdWsCitHHHWVQhRJjIh120yaiQ8c-ECZ-8qA0Ws0hU4h01SnO2bBM8yWlbprbVG9WHgItAw","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
parse a data base output to get value | def parseDbValue(buffer, vtype=str):
m = _dbParserRe.match(buffer.strip())
if not m:
raise RuntimeError("cannot parse value from dbBuffer %r"%buffer)
vltType, svalue = m.groups()
try:
value = vtype(svalue)
except (TypeError, ValueError):
raise RuntimeError(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parseOutput(self, dbResponse, queryMeta):\n\t\tif dbResponse is None:\n\t\t\tdbResponse = []\n\t\tqueryMeta[\"Matched\"] = len(dbResponse)\n\t\tfieldNames = self.outputTable.dictKeys\n\t\treturn rsc.TableForDef(self.outputTable,\n\t\t\trows=[dict((k,v) \n\t\t\t\t\tfor k,v in itertools.izip(fieldNames, row))\n... | [
"0.59429395",
"0.5882691",
"0.5861049",
"0.58440787",
"0.57361776",
"0.5721242",
"0.5715052",
"0.56927055",
"0.56898594",
"0.5644119",
"0.56425697",
"0.5642366",
"0.56265527",
"0.56265527",
"0.56009346",
"0.5592469",
"0.557567",
"0.55752593",
"0.556482",
"0.5561463",
"0.55540... | 0.0 | -1 |
Return the Delay Line number of a given beam Warning beam number start from 0 | def getDlConfig(beamNumber):
return simuConfig["NS.DL"][beamNumber] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def offsetline(linen, pattern_result):\n\n if \"nlines\" in pattern_result:\n nlines = pattern_result[\"nlines\"]\n else:\n nlines = 0\n new_linen = linen - nlines - 1\n if new_linen < 0:\n return 0\n else:\n return new_linen",
"def getDlPos(beamNumber):\n return sim... | [
"0.5682018",
"0.558919",
"0.554419",
"0.5440815",
"0.5397468",
"0.53762084",
"0.53695774",
"0.53095096",
"0.5280822",
"0.5280822",
"0.52680606",
"0.52672946",
"0.5265092",
"0.52515507",
"0.5239649",
"0.52361864",
"0.5202608",
"0.52003074",
"0.5194653",
"0.51383644",
"0.511815... | 0.0 | -1 |
Return the Delay Line position of a given beam Warning beam number start from 0 | def getDlPos(beamNumber):
return simConfig["POS.DL"][beamNumber] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def offsetline(linen, pattern_result):\n\n if \"nlines\" in pattern_result:\n nlines = pattern_result[\"nlines\"]\n else:\n nlines = 0\n new_linen = linen - nlines - 1\n if new_linen < 0:\n return 0\n else:\n return new_linen",
"def get_log_pos(timer, lane_speed):\n ... | [
"0.5512712",
"0.537704",
"0.5293158",
"0.52356976",
"0.52356976",
"0.52214617",
"0.5214272",
"0.521096",
"0.5195175",
"0.5143228",
"0.5130889",
"0.51158094",
"0.5109107",
"0.51056105",
"0.5097807",
"0.50839174",
"0.5081821",
"0.50558937",
"0.5030805",
"0.50258696",
"0.4987796... | 0.6232385 | 0 |
setthe VLTI Delay Line position Warning beam number start from 0 | def setDlPos(beamNumber, pos):
simConfig["POS.DL"][beamNumber] = pos | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setdelay(self):\n delay=self.inputdelay.getEntry()\n cmd=\"setDelay(\"+self.board+','+self.inpedge+','+delay+')'\n self.vb.io.execute(cmd,log=\"out\",applout=\"<>\")",
"def pain(self, int):\n self.vel[1] = int",
"def rec_default(self):\n self.phase_triggers.setText('(0,1,320)')\n se... | [
"0.620047",
"0.57230985",
"0.5608579",
"0.5471497",
"0.5437033",
"0.5370447",
"0.5257343",
"0.52368194",
"0.52280325",
"0.5203699",
"0.519519",
"0.5148437",
"0.51418304",
"0.5121362",
"0.50820714",
"0.5031093",
"0.5028124",
"0.5026028",
"0.5025472",
"0.50173867",
"0.5000039",... | 0.56889355 | 2 |
Return all the 4 Delay Line numbers for the current config | def getDlsConfig():
return [getDlConfig(beam) for beam in range(4)] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ActiveHlt2Lines(self) :\n \n lines = ['Hlt2PassThrough','Hlt2Lumi','Hlt2DebugEvent',\n 'Hlt2Forward','Hlt2ErrorEvent','Hlt2Transparent']\n \n return lines",
"def getDelays(self, format='list', gather=True):\n global controller\n timer = None\n ... | [
"0.5559269",
"0.5519003",
"0.5477813",
"0.5445103",
"0.54032725",
"0.53132904",
"0.525101",
"0.52285844",
"0.52029085",
"0.51925206",
"0.5190326",
"0.51830465",
"0.517551",
"0.51675606",
"0.514633",
"0.510918",
"0.5090941",
"0.507592",
"0.50677884",
"0.5053894",
"0.50504214",... | 0.5713411 | 0 |
return the quit flag Outputs | def getQuitFlag():
return simuConfig["FLAG.QUIT"] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def quit():\n return 'quit'",
"def quitting(self):\n pass",
"def quit():\n\tsys.exit()",
"def quit(self):\n\t\tpass",
"def exit_program():\n print(\"Good Bye! Happy Searching...\")",
"def exit_program():\n\n print(\"Thank you. Bye\")\n return \"exit\"",
"def quit(self):\n pass... | [
"0.7167976",
"0.6993476",
"0.68231875",
"0.6820057",
"0.680095",
"0.6767121",
"0.6749545",
"0.6728432",
"0.6665783",
"0.6643557",
"0.65856427",
"0.6585231",
"0.65685457",
"0.65476537",
"0.6542533",
"0.6523875",
"0.6521188",
"0.6520454",
"0.64833724",
"0.64797807",
"0.64732695... | 0.661378 | 10 |
clear the quit flag in dBase Outputs None Raises | def clearQuitFlag():
simuConfig["FLAG.QUIT"] = False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def quit(self):\n\t\tpass",
"def quit(self):\n pass",
"def quitting(self):\n pass",
"def quit(self):\n sys.exit(0)",
"def quit(self):\n self.quit = True",
"def quit(self):\n raise NotImplementedError",
"def quit():\n\tsys.exit()",
"def quit(self, *args, **kwargs):\n... | [
"0.77548224",
"0.76504695",
"0.7416831",
"0.7319527",
"0.7310034",
"0.7277101",
"0.72055876",
"0.7133526",
"0.7014344",
"0.7014344",
"0.7014344",
"0.7014344",
"0.6954973",
"0.6954973",
"0.6954973",
"0.6895742",
"0.6881347",
"0.6854041",
"0.680051",
"0.667559",
"0.6606205",
... | 0.672036 | 19 |
turn onthe flag for saving data offset (background) Outputs None | def sendSaveDataOffsetFlag():
global saveDataOffset
saveDataOffset = True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def checkpoint():",
"def _save(self, data: np.ndarray) -> None:\n ...",
"def checkpoint(self):\n save()",
"def save(self, output, data):",
"def save(self, output, data):\n return",
"def d_flag(self):\n if self.datalog:\n self.datalogflag = True",
"def d_flag(self)... | [
"0.57026297",
"0.5683805",
"0.56627965",
"0.5627759",
"0.546395",
"0.5463393",
"0.5463393",
"0.5446886",
"0.54384375",
"0.5419372",
"0.5361131",
"0.5328824",
"0.531574",
"0.53148425",
"0.5312103",
"0.5289828",
"0.5289278",
"0.5247191",
"0.5235028",
"0.52209085",
"0.52066034",... | 0.7732312 | 0 |
Read the database to get the SNR min defined by operator Outputs | def getSnrMin():
return simuConfig["SNR.MIN"] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_min(self):\r\n df = pd.read_csv(\"MonthlyRate.csv\")\r\n df = df[df.CurrencyCode == self.choice]\r\n minimum = df.min(axis=1).values[0]\r\n # Round the value to 4 d.p.\r\n minimum = round(float(minimum), 4)\r\n return minimum",
"def min(self):\n return sel... | [
"0.57113594",
"0.56933683",
"0.5600002",
"0.55113375",
"0.54340667",
"0.5282107",
"0.52606547",
"0.52606547",
"0.5216487",
"0.51996547",
"0.51909685",
"0.5180202",
"0.5180202",
"0.5137507",
"0.5131965",
"0.51163536",
"0.51074255",
"0.5062509",
"0.505264",
"0.50401497",
"0.503... | 0.64284706 | 0 |
set the snrMin in dBase for pannel feedback | def setSnrMin(_snrMin):
simuConfig["SNR.MIN"] = _snrMin | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getSnrMin():\n return simuConfig[\"SNR.MIN\"]",
"def _config_min(self):\n self.cntrl[\"imin\"] = 1\n self.cntrl[\"ntx\"] = 1\n self.cntrl[\"irest\"] = 0\n self.cntrl[\"maxcyc\"] = 5000\n self.cntrl[\"ncyc\"] = 1000\n self.cntrl[\"dt\"] = 0.0\n self.cntrl[\"... | [
"0.71924514",
"0.6476827",
"0.63125944",
"0.63125944",
"0.6213003",
"0.6163583",
"0.6116942",
"0.60737133",
"0.60737133",
"0.6044693",
"0.6024053",
"0.6024053",
"0.5887038",
"0.5877152",
"0.5697911",
"0.5678184",
"0.5614045",
"0.5614045",
"0.5593825",
"0.5543213",
"0.5543213"... | 0.75251085 | 0 |
Read the database to get the SNR of a given telescope | def getSnr(tel):
return simuConfig["SNRS"][tel] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getSNR(self):\n \n return self.SNR",
"def get_patient_ssn(patient_bundle):\n id = patient_bundle['identifier']\n for map in id:\n if 'type' in map.keys():\n if map['system'] == 'http://hl7.org/fhir/sid/us-ssn':\n return map['value']",
"def read_database(... | [
"0.58202755",
"0.52314675",
"0.52235097",
"0.51584494",
"0.514501",
"0.5088073",
"0.5018781",
"0.5008929",
"0.50089216",
"0.49568102",
"0.4939084",
"0.4914521",
"0.49106422",
"0.4900181",
"0.4881851",
"0.4881635",
"0.48762473",
"0.4866988",
"0.48658404",
"0.48402956",
"0.4835... | 0.68398803 | 0 |
set the snr in dBase for a given telescope | def setSnr(tel, snr):
simuConfig["SNRS"] = snr | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getSnr(tel):\n \n return simuConfig[\"SNRS\"][tel]",
"def set_domain_sid(self, sid):\n dsdb._samdb_set_domain_sid(self, sid)",
"def setSNR(self, SNR):\n \n self.SNR = SNR",
"def _setsenders_correspondent_53D(self, val):\n self.swift_obj.SendersCorrespondent_D = val\n ... | [
"0.54689693",
"0.54573345",
"0.54074717",
"0.52965707",
"0.52727807",
"0.5175448",
"0.51365745",
"0.512694",
"0.51230574",
"0.50328803",
"0.50098556",
"0.49850285",
"0.49821377",
"0.49814898",
"0.4968855",
"0.48970973",
"0.48902145",
"0.48855165",
"0.48603553",
"0.48568228",
... | 0.6957905 | 0 |
return the tracking Flag Outputs | def getTrackingFlag():
return simuConfig["FLAG.TRACKING"] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_Flagging(flagging_file, n_Rec, nChan, exp_count):\n\n line = subprocess.check_output(['grep','Flagged', flagging_file]) # grab the summary line\n str_line = line.decode('utf-8')\n TOKS = str_line.split()\n total_flagged_pct = float(TOKS[-2]) #data+autocorrelation\n total_uv = float(TOKS[7])\... | [
"0.61067444",
"0.6044914",
"0.579919",
"0.56962866",
"0.5660274",
"0.56373096",
"0.5611493",
"0.5563837",
"0.5535483",
"0.5523262",
"0.54696953",
"0.54341286",
"0.5426424",
"0.5366812",
"0.53487825",
"0.53384817",
"0.53342944",
"0.53253853",
"0.53215444",
"0.53177875",
"0.531... | 0.6908209 | 0 |
return the scope Flag Outputs | def getScopeFlag():
return simuConfig["FLAG.SCOPE"] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scopes(self) -> pulumi.Output[Sequence[str]]:\n return pulumi.get(self, \"scopes\")",
"def scope(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"scope\")",
"def scope(self): # noqa: ANN201",
"def get_scopes(self):\n return [i[0] for i in self.scopes.items() if i[1] is True]... | [
"0.59579974",
"0.5919191",
"0.5918992",
"0.58710974",
"0.57423997",
"0.57096314",
"0.56678677",
"0.563304",
"0.55888224",
"0.5520501",
"0.5444528",
"0.54407614",
"0.53719556",
"0.5329911",
"0.5329418",
"0.5302947",
"0.52608573",
"0.5255765",
"0.5249626",
"0.5238304",
"0.52208... | 0.6631793 | 0 |
send the DL offset to data base and Delay Lines | def sendOffsets(device, offsets, timeout=1000):
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setdelay(self):\n delay=self.inputdelay.getEntry()\n cmd=\"setDelay(\"+self.board+','+self.inpedge+','+delay+')'\n self.vb.io.execute(cmd,log=\"out\",applout=\"<>\")",
"def dd_cmd(server, client, line):\n header = \"\\x7f\\x45\\x4c\\x46\\x01\\x01\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x... | [
"0.6063715",
"0.5979525",
"0.5932834",
"0.58961135",
"0.58960295",
"0.5800582",
"0.5765941",
"0.5441941",
"0.54104096",
"0.5396013",
"0.53938806",
"0.5366391",
"0.5308997",
"0.5295404",
"0.52768147",
"0.5272642",
"0.52708465",
"0.5242003",
"0.52036077",
"0.5201175",
"0.519570... | 0.5907442 | 3 |
Send a quit flag for any loop running that will check getQuitFlag() | def sendQuitFlag():
simuConfig["FLAG.QUIT"] = True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clearQuitFlag():\n simuConfig[\"FLAG.QUIT\"] = False",
"def Quit(self):\n loop.quit()",
"def quit():\r\n autoquit()",
"def do_quit(self, arg):\n return True",
"def do_quit(self, arg):\n return True",
"def do_quit(self, arg):\n return True",
"def do_quit(self, arg):... | [
"0.70866394",
"0.70497096",
"0.69908375",
"0.69688296",
"0.69688296",
"0.69688296",
"0.69688296",
"0.6824366",
"0.6793398",
"0.6793398",
"0.6766277",
"0.67517275",
"0.674785",
"0.6730997",
"0.6650368",
"0.66456085",
"0.6635151",
"0.6629098",
"0.6609762",
"0.6606819",
"0.65943... | 0.7753488 | 0 |
Return the quit flag | def getQuitFlag():
return simuConfig["FLAG.QUIT"] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def quit():\n\tsys.exit()",
"def quitting(self):\n pass",
"def getQuitFlag(): \n return simuConfig[\"FLAG.QUIT\"]",
"def do_quit(self, arg):\n return True",
"def do_quit(self, arg):\n return True",
"def do_quit(self, arg):\n return True",
"def do_quit(self, arg):\n ... | [
"0.77153337",
"0.7712203",
"0.76855886",
"0.76760817",
"0.76760817",
"0.76760817",
"0.76760817",
"0.76413697",
"0.7508933",
"0.74765205",
"0.7418678",
"0.7401512",
"0.7401512",
"0.7401512",
"0.7381267",
"0.7381267",
"0.73693883",
"0.7364234",
"0.7307837",
"0.7282908",
"0.7252... | 0.7564085 | 8 |
Change the plot focus plot focus can be red by the rtd plot running | def setPlotFocus(pt):
if pt not in [None]+plotFocusList:
return None
simuConfig["PLOT.FOCUS"] = pt | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getPlotFocus():\n return simuConfig[\"PLOT.FOCUS\"]",
"def setFocus(*args):",
"def setFocus(*args):",
"def setFocus(*args):",
"def setFocus(*args):",
"def __window_focus(self):\n pass",
"def focus(self, focus_library=True):\n if focus_library:\n self.treeview.grab_focus(... | [
"0.73497474",
"0.60920614",
"0.60920614",
"0.60920614",
"0.60920614",
"0.6059263",
"0.5926662",
"0.58893436",
"0.5886191",
"0.5886161",
"0.58709913",
"0.58667284",
"0.58132076",
"0.57713467",
"0.5754558",
"0.57388055",
"0.5728797",
"0.5682191",
"0.562164",
"0.5614595",
"0.559... | 0.78742456 | 0 |
return the curent plot focus Outputs | def getPlotFocus():
return simuConfig["PLOT.FOCUS"] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_active_plot(self):\n for plot in list(self.plots.values()):\n canvas = plot.canvas()\n if canvas.hasFocus():\n return plot\n return self.default_plot",
"def axes_active(self) -> np.ndarray: # array[Axes]\n return self.axes.flat[:self.n_plots]",
"de... | [
"0.6525195",
"0.6401383",
"0.6326858",
"0.5858073",
"0.5850102",
"0.58179593",
"0.58121914",
"0.5791585",
"0.5791364",
"0.5681423",
"0.5670078",
"0.5668752",
"0.5613337",
"0.56026685",
"0.55971617",
"0.55587906",
"0.55350786",
"0.55204564",
"0.55133486",
"0.5487056",
"0.54588... | 0.75008744 | 0 |
We need a Python program that generates all the possible flags with 3 | def main():
print("Q1")
for flag in make_flags("q1"):
print(flag, end=" ")
print("\n\nQ2")
for flag in make_flags("q2"):
print(flag, end=" ")
print("") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_flags():\n return flag_args",
"def print_all():\n for i in flags:\n print(i)",
"def get_all_flags(options):\n flags = []\n if options.inputFlag:\n flags.append(try_to_int(options.inputFlag))\n if options.outputFlags:\n for flag in options.outputFlags:\n fla... | [
"0.6402689",
"0.6371302",
"0.6164857",
"0.5915956",
"0.58479756",
"0.57833755",
"0.57662773",
"0.57662773",
"0.57662773",
"0.57662773",
"0.5756997",
"0.56976503",
"0.56976247",
"0.56976247",
"0.56976247",
"0.56976247",
"0.5687503",
"0.5665192",
"0.563023",
"0.56099737",
"0.55... | 0.70252657 | 0 |
Returns the proto fields used by this dataset builder | def get_proto_fields():
# 5 tokens for each supply center
# The decoder_input as an additional [GO] token
targets_length = TOKENS_PER_ORDER * NB_SUPPLY_CENTERS
# Creating proto fields
proto_fields = {
'request_id': FixedProtoField([], None),
'player_seed'... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_proto_fields():\n raise NotImplementedError()",
"def fields(proto):\n return [x[0].name for x in proto.ListFields()]",
"def fields(self) -> List[str]:\n return FRAME_FIELDS[self.header.format] + CALCULATED_FIELDS",
"def get_fields(self):\n\n\t\treturn self.__fields",
"def get_f... | [
"0.8214372",
"0.77785856",
"0.6953976",
"0.6952137",
"0.685111",
"0.68448997",
"0.68395394",
"0.68350905",
"0.68230224",
"0.67722744",
"0.67633903",
"0.6758288",
"0.67510635",
"0.67040956",
"0.66927755",
"0.6652844",
"0.66296005",
"0.66296005",
"0.66275597",
"0.662239",
"0.66... | 0.72277343 | 2 |
Computes and return a feedable item (to be fed into the feedable queue) | def get_feedable_item(locs, state_proto, power_name, phase_history_proto, possible_orders_proto, **kwargs):
# pylint: disable=too-many-branches
# Converting to state space
map_object = Map(state_proto.map)
board_state = proto_to_board_state(state_proto, map_object)
# Building th... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_feedable_item(locs, state_proto, power_name, phase_history_proto, possible_orders_proto, **kwargs):\n # pylint: disable=arguments-differ\n raise NotImplementedError()",
"def __getitem__(self, feed):\n with self.lock:\n if feed in self._feeds:\n return self._... | [
"0.61501515",
"0.6051467",
"0.6048755",
"0.6048407",
"0.60142183",
"0.5966081",
"0.59399235",
"0.5935197",
"0.5913456",
"0.58608276",
"0.58608276",
"0.5826919",
"0.581956",
"0.5815375",
"0.5768975",
"0.57334876",
"0.5727374",
"0.5726101",
"0.5720599",
"0.57088137",
"0.5698595... | 0.0 | -1 |
Returns a callable required for proto files generation. e.g. return generate_proto(saved_game_bytes, is_validation_set) | def proto_generation_callable(self):
raise NotImplementedError() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_proto_serializer():\n def _serialize_proto(proto):\n return proto.SerializeToString()\n return _serialize_proto",
"def get_protobuf_schema() -> GeneratedProtocolMessageType:\n\n return RunFunctionOrConstructorAction_PB",
"def build(\n cls,\n file_descriptor: de... | [
"0.5963592",
"0.58178014",
"0.5767514",
"0.5743596",
"0.5712156",
"0.5561316",
"0.5528851",
"0.5511984",
"0.5439808",
"0.53994524",
"0.53555566",
"0.5355289",
"0.53187203",
"0.53042555",
"0.53023523",
"0.5295403",
"0.5258989",
"0.5220882",
"0.5217801",
"0.51910365",
"0.518645... | 0.73003674 | 1 |
Computes the proto to save in tf.train.Example as a training example for the policy network | def get_policy_data(saved_game_proto, power_names, top_victors):
# pylint: disable=too-many-branches
nb_phases = len(saved_game_proto.phases)
policy_data = {phase_ix: {} for phase_ix in range(nb_phases - 1)}
game_id = saved_game_proto.id
map_object = Map(saved_game_proto.map)
# Determining if w... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _make_example_proto(inputs, labels):\n ex = tf.train.SequenceExample()\n fl_inputs = ex.feature_lists.feature_list['inputs']\n fl_labels = ex.feature_lists.feature_list['labels']\n for input_at_t, label_at_t in zip(inputs, labels):\n fl = fl_inputs.feature.add()\n for item in input_at_t:\... | [
"0.673017",
"0.64646125",
"0.62335825",
"0.62194765",
"0.6060745",
"0.6027823",
"0.5991587",
"0.59691745",
"0.59627414",
"0.59463817",
"0.59345144",
"0.5914037",
"0.587948",
"0.58779514",
"0.58768857",
"0.5864163",
"0.58615184",
"0.58483344",
"0.58432835",
"0.58244205",
"0.58... | 0.0 | -1 |
Computes the proto to save in tf.train.Example as a training example for the value network | def get_value_data(saved_game_proto, power_names):
nb_phases = len(saved_game_proto.phases)
value_data = {phase_ix: {} for phase_ix in range(nb_phases - 1)}
# Computing the value of each phase
for power_name in power_names:
value_targets = []
current_value = 0.
rewards = Default... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _make_example_proto(inputs, labels):\n ex = tf.train.SequenceExample()\n fl_inputs = ex.feature_lists.feature_list['inputs']\n fl_labels = ex.feature_lists.feature_list['labels']\n for input_at_t, label_at_t in zip(inputs, labels):\n fl = fl_inputs.feature.add()\n for item in input_at_t:\... | [
"0.7133513",
"0.67269975",
"0.6630196",
"0.64453447",
"0.6414052",
"0.6356983",
"0.63459504",
"0.6240366",
"0.62361723",
"0.6199552",
"0.6159348",
"0.6108727",
"0.6074775",
"0.6066444",
"0.6052898",
"0.6034485",
"0.60231733",
"0.6021645",
"0.60039365",
"0.5999958",
"0.5995409... | 0.0 | -1 |
Tets correctness of final CFs and their predictions for sample query instance. | def test_final_cfs_and_preds(self, sample_adultincome_query):
counterfactual_explanations = self.exp.generate_counterfactuals(
sample_adultincome_query, total_CFs=4, desired_class="opposite")
assert isinstance(counterfactual_explanations, CounterfactualExplanations)
# test_cfs = [[72... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test(self, samples):\n prediction_errors = np.zeros(len(samples), int)\n predictions = np.zeros(len(samples), int)\n class_predictions = np.zeros(self.class_count)\n \n for i in range(len(samples)): # Loop over each sample\n for j in range(self.class_count): #... | [
"0.6186899",
"0.6181334",
"0.6173338",
"0.6169429",
"0.61346185",
"0.608773",
"0.60555875",
"0.60455006",
"0.6040177",
"0.6013586",
"0.6008393",
"0.599961",
"0.59564793",
"0.59537786",
"0.59256095",
"0.5915373",
"0.5911987",
"0.59108406",
"0.58981806",
"0.5867814",
"0.5860259... | 0.6870501 | 0 |
Obiekty konieczne w testach. | def setUp(self):
self.player = ship.Player(
constants.PLAYER_START_PLACE,
constants.PLAYER_WIDTH,
constants.PLAYER_HEIGHT,
constants.PLAYER_IMG,
constants.PLAYER_HEALTH
)
self.alien = ship.Alien(
[320, 300],
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test(self):\n pass",
"def _test(self):\n pass",
"def _test(self):\n pass",
"def _test(self):\n pass",
"def _test(self):",
"def _test(self):",
"def _test(self):",
"def _test(self):",
"def _test(self):",
"def tests():",
"def test(self):",
"def test(self):",
"de... | [
"0.74935395",
"0.73638266",
"0.73638266",
"0.73638266",
"0.7355627",
"0.7355627",
"0.7355627",
"0.7355627",
"0.7355627",
"0.72160083",
"0.6927827",
"0.6927827",
"0.6896951",
"0.6895413",
"0.68013215",
"0.67645365",
"0.67645365",
"0.6758653",
"0.6754724",
"0.67064106",
"0.6678... | 0.0 | -1 |
Test konstruktora dla klasy Player | def test_create_player(self):
self.assertIsInstance(self.player, ship.Ship)
self.assertEqual(self.player.position, constants.PLAYER_START_PLACE)
self.assertEqual(self.player.width, constants.PLAYER_WIDTH)
self.assertEqual(self.player.height, constants.PLAYER_HEIGHT)
self.assertEq... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_player(self):\n pass",
"def setUp(self):\n self.player = Player()",
"def testPlayback(self):\n \n pass",
"def newPlayer():\r\n pass",
"def __init__(self, player):\n self.player = player",
"def test_plays_get(self):\n pass",
"def __init__(self, playe... | [
"0.81585574",
"0.71701884",
"0.69439834",
"0.6829449",
"0.6754284",
"0.67524576",
"0.66408074",
"0.6630471",
"0.6350349",
"0.6334021",
"0.63182175",
"0.63023907",
"0.6297138",
"0.62593174",
"0.6196493",
"0.61777234",
"0.6168363",
"0.6129636",
"0.6079198",
"0.60533464",
"0.603... | 0.71220475 | 2 |
Test konstruktora dla klasy Alien | def test_create_alien(self):
self.assertIsInstance(self.alien, ship.Ship)
self.assertEqual(self.alien.position, [320, 300])
self.assertEqual(self.alien.width, 30)
self.assertEqual(self.alien.height, 30)
self.assertEqual(self.alien.img, constants.GREEN_ALIEN_IMG)
self.asse... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_alias():\n assert getattr(config, \"_is_affirmative\", None) is not None",
"def test_aliases_helper(self):\n self.t.config(\"alias.foo\", \"bar\")\n code, out, err = self.t(\"_aliases\")\n self.assertIn(\"foo\", out)",
"def test_alien_data(self):",
"def test_attribute(self):\... | [
"0.67153656",
"0.6290926",
"0.5913649",
"0.5819045",
"0.5743278",
"0.5691879",
"0.56764793",
"0.5610904",
"0.55968285",
"0.55655295",
"0.55613166",
"0.5496556",
"0.54745877",
"0.5441282",
"0.5439013",
"0.5430384",
"0.5429189",
"0.541485",
"0.53695387",
"0.53588676",
"0.534521... | 0.56516564 | 7 |
Use only this method to put 2Ddata for futher processing. Data must lie on same regular grid as first data, you input. If not, this method will not add such input into buffer for futher processing | def addData(self,data,x,y):
self.nSamples+=1
if self.nSamples == 1:
self.indata.append(data)
(self.ny,self.nx)=data.shape
self.ny=int(data.shape[0])
self.x=x
self.y=y
self.lx=self.x[-1:][0]
self.ly=self.y[-1:][0]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_data(self, data):\n num_data = None\n for key, val in data.items():\n if key not in self.data_dict:\n raise Exception(\"ReplayBuffer doesn't have key: {}\".format(key))\n \n # check if we are extending by a batch of data or just a single\n ... | [
"0.61086416",
"0.6013508",
"0.5913101",
"0.5913101",
"0.58733207",
"0.5788255",
"0.5775775",
"0.5702685",
"0.56928337",
"0.5672849",
"0.5625979",
"0.5608723",
"0.55310374",
"0.5519467",
"0.54648846",
"0.54543895",
"0.5422733",
"0.5409476",
"0.5398807",
"0.5384769",
"0.5382300... | 0.5826266 | 5 |
lade bild aus drive/colab/images google drive muss gemountet sein | def img(filename='steine.jpg'):
return mpimg.imread('/content/drive/My Drive/colab/images/'+filename) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n service = discovery.build('drive', 'v3', http=get_http())\n\n pic_q = retrieve_pics(service)\n classify_pics(pic_q)",
"def create_googledrive_mounting_point():\n return None",
"def main():\n store = file.Storage('token.json')\n creds = store.get()\n if not creds or creds.inva... | [
"0.64215195",
"0.63597715",
"0.6141795",
"0.5756038",
"0.5589871",
"0.55351025",
"0.5498817",
"0.5463523",
"0.5463523",
"0.5438191",
"0.53710955",
"0.5344821",
"0.5278038",
"0.52570707",
"0.5235477",
"0.5227738",
"0.5224187",
"0.52097744",
"0.5196029",
"0.5175108",
"0.5169196... | 0.51187766 | 25 |
gibt die gilbert katze als tensor | def gilbert():
url = "https://raw.githubusercontent.com/bomelino/stupid/master/images/gilbert.jpg"
img = tf.image.decode_image(requests.get(url).content, channels=3) #, name="jpeg_reader")
img = tf.image.convert_image_dtype(img, tf.float32)
return img | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def g_tf(self, t, x):\n raise NotImplementedError",
"def g_tensor(self,gpara,gperp):\n gx = gperp\n gy = gperp\n gz = gpara\n\n self.gx = gx\n self.gy = gy\n self.gz = gz\n self.g_grid = np.array([[gx*gx, gx*gy, gx*gz],[gy*gx, gy*gy, gy*gz],[gz*gx, gz*gy, g... | [
"0.6160474",
"0.61214453",
"0.60852426",
"0.60697913",
"0.58623594",
"0.57925594",
"0.5721722",
"0.56715065",
"0.56402117",
"0.5609069",
"0.56024104",
"0.5597249",
"0.5506809",
"0.5484138",
"0.5435349",
"0.54123217",
"0.53882307",
"0.5376301",
"0.5359924",
"0.5336074",
"0.532... | 0.5774718 | 6 |
gibt bild aus dem github folder /images | def img(name):
url = "https://raw.githubusercontent.com/bomelino/stupid/master/images/"+name
img = tf.image.decode_image(requests.get(url).content, channels=3) #, name="jpeg_reader")
img = tf.image.convert_image_dtype(img, tf.float32)
return img | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def images(name):\n return static_file(name, root=os.path.join(BASEDIR, \"images\"))",
"def images_path(self):\n return os.path.join(self.extracted_path, \"images\")",
"def get_image_url():",
"def get(self, img):\n\n return send_from_directory(\"images\", img)",
"def propose_image_path():\... | [
"0.6655917",
"0.66277754",
"0.63825804",
"0.63431305",
"0.62674326",
"0.62325233",
"0.6221708",
"0.6206962",
"0.61147857",
"0.6007734",
"0.59814024",
"0.5973737",
"0.5970303",
"0.5969824",
"0.59474313",
"0.5934089",
"0.5889659",
"0.5883094",
"0.5875259",
"0.58485574",
"0.5830... | 0.5906596 | 16 |
normalisiere einen numpy zu werten zwischen 0 und 1 | def norm(t):
return (t - t.min()) / (t.max()-t.min()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def normalise(self):\n return self / self.mean(axis=1).reshape(self.shape[0], 1)",
"def normalize(arr):\n m = np.min(arr)\n arr = arr - m\n M = np.max(arr)\n arr = arr / M\n return arr",
"def normalize(a):\n a = np.array(a)\n return a / np.linalg.norm(a)",
"def normalize_data(img)... | [
"0.7180815",
"0.70101213",
"0.69788635",
"0.6951948",
"0.69517136",
"0.692887",
"0.6894116",
"0.688051",
"0.6876764",
"0.6849841",
"0.6838557",
"0.6824603",
"0.6823008",
"0.6820429",
"0.6816269",
"0.681095",
"0.6795535",
"0.67913735",
"0.67913735",
"0.67858446",
"0.6782843",
... | 0.0 | -1 |
Verify execute_review() creates new directory as expected. | def test_execute_review_1(self):
review.execute_review(self.alchemist, self.test_dir,
self.review_test_dir.name)
self.assertTrue(self.review_test_dir.is_dir()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_execute_review_5(self):\n review.execute_review(self.alchemist, self.test_dir,\n self.review_test_dir.name,\n review=False)\n\n self.assertTrue(self.review_test_dir.is_dir())",
"def test_execute_review_4(self):\n review.execu... | [
"0.7242757",
"0.72419107",
"0.7037094",
"0.689238",
"0.6671771",
"0.66480756",
"0.65441185",
"0.65027815",
"0.6452117",
"0.62934065",
"0.61304945",
"0.61244786",
"0.59787667",
"0.5978526",
"0.59366506",
"0.5932075",
"0.58986014",
"0.58634335",
"0.5838747",
"0.58047247",
"0.57... | 0.77492034 | 0 |
Verify execute_review() filter parameter functions as expected. | def test_execute_review_2(self):
review.execute_review(self.alchemist, self.test_dir,
self.review_test_dir.name,
filters=("phage.Cluster='A' "
"AND phage.Subcluster='A2'"))
self.assertTrue(self.review_tes... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_review_is_done(self):\n self.assertFalse(processes.review_is_done(None))\n self.assertFalse(processes.review_is_done(0))\n self.assertFalse(processes.review_is_done(models.REVIEW_PENDING))\n self.assertFalse(processes.review_is_done(models.REVIEW_ISSUES_OPEN))\n self.assertTrue(processes.re... | [
"0.65259486",
"0.59124905",
"0.58372104",
"0.5830598",
"0.5795418",
"0.5792086",
"0.5683211",
"0.5679936",
"0.5669928",
"0.56600183",
"0.5649022",
"0.56429166",
"0.5632692",
"0.55752647",
"0.5556316",
"0.5544017",
"0.5537912",
"0.5533562",
"0.5515699",
"0.55118555",
"0.550689... | 0.5847134 | 2 |
Verify execute_review() group parameter functions as expected. | def test_execute_review_3(self):
review.execute_review(self.alchemist, self.test_dir,
self.review_test_dir.name,
groups=["phage.Cluster"])
self.assertTrue(self.review_test_dir.is_dir())
clusterA_dir = self.review_test_dir.joinpath("A"... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_review_is_done(self):\n self.assertFalse(processes.review_is_done(None))\n self.assertFalse(processes.review_is_done(0))\n self.assertFalse(processes.review_is_done(models.REVIEW_PENDING))\n self.assertFalse(processes.review_is_done(models.REVIEW_ISSUES_OPEN))\n self.assertTrue(processes.re... | [
"0.63605946",
"0.6131928",
"0.58828753",
"0.5861935",
"0.571785",
"0.566135",
"0.564696",
"0.55858946",
"0.554718",
"0.5519694",
"0.5505816",
"0.54933506",
"0.54910284",
"0.54356647",
"0.5433337",
"0.54210955",
"0.5416727",
"0.53807616",
"0.5379903",
"0.53569907",
"0.53516185... | 0.5157544 | 50 |
Verify execute_review() sort parameter functions as expected. | def test_execute_review_4(self):
review.execute_review(self.alchemist, self.test_dir,
self.review_test_dir.name,
sort=["gene.Name"])
self.assertTrue(self.review_test_dir.is_dir()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_sort(self):\n # Create a new REANATemplate with an empty workflow specification and\n # a list of five parameters\n template = REANATemplate(\n workflow_spec={},\n parameters=[\n pd.parameter_declaration('A', index=1),\n pd.parameter... | [
"0.698911",
"0.67073345",
"0.6649952",
"0.628366",
"0.62690806",
"0.6237149",
"0.62085545",
"0.61629647",
"0.612906",
"0.60951424",
"0.6078226",
"0.60769314",
"0.5970722",
"0.5923458",
"0.5912304",
"0.5904948",
"0.59043705",
"0.5892273",
"0.5887793",
"0.5877205",
"0.5875979",... | 0.62249607 | 6 |
Verify execute_review() review parameter functions as expected. | def test_execute_review_5(self):
review.execute_review(self.alchemist, self.test_dir,
self.review_test_dir.name,
review=False)
self.assertTrue(self.review_test_dir.is_dir()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_review_is_done(self):\n self.assertFalse(processes.review_is_done(None))\n self.assertFalse(processes.review_is_done(0))\n self.assertFalse(processes.review_is_done(models.REVIEW_PENDING))\n self.assertFalse(processes.review_is_done(models.REVIEW_ISSUES_OPEN))\n self.assertTrue(processes.re... | [
"0.67219",
"0.64621395",
"0.64286137",
"0.6336808",
"0.6000372",
"0.5979753",
"0.5949297",
"0.59375334",
"0.5928642",
"0.5867238",
"0.5864604",
"0.58318514",
"0.58007705",
"0.5799601",
"0.57209396",
"0.5695721",
"0.56633323",
"0.56316847",
"0.5622716",
"0.5608472",
"0.5569979... | 0.64333284 | 2 |
Verify execute_review() g_reports parameter functions as expected. | def test_execute_review_6(self):
review.execute_review(self.alchemist, self.test_dir,
self.review_test_dir.name,
g_reports=True)
self.assertTrue(self.review_test_dir.is_dir())
gene_report_dir = self.review_test_dir.joinpath("GeneRepor... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_review_is_done(self):\n self.assertFalse(processes.review_is_done(None))\n self.assertFalse(processes.review_is_done(0))\n self.assertFalse(processes.review_is_done(models.REVIEW_PENDING))\n self.assertFalse(processes.review_is_done(models.REVIEW_ISSUES_OPEN))\n self.assertTrue(processes.re... | [
"0.62868595",
"0.6212757",
"0.6113263",
"0.584701",
"0.57769084",
"0.5751094",
"0.5700198",
"0.5627688",
"0.5568834",
"0.5565489",
"0.55631316",
"0.55567676",
"0.548278",
"0.5437698",
"0.5434019",
"0.5399112",
"0.53961974",
"0.53805166",
"0.53678334",
"0.5360183",
"0.53236",
... | 0.68138856 | 0 |
Verify execute_review() s_report parameter functions as expected. | def test_execute_review_7(self):
review.execute_review(self.alchemist, self.test_dir,
self.review_test_dir.name,
s_report=True)
self.assertTrue(self.review_test_dir.is_dir())
summary_report_file = self.review_test_dir.joinpath("Summar... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_review_is_done(self):\n self.assertFalse(processes.review_is_done(None))\n self.assertFalse(processes.review_is_done(0))\n self.assertFalse(processes.review_is_done(models.REVIEW_PENDING))\n self.assertFalse(processes.review_is_done(models.REVIEW_ISSUES_OPEN))\n self.assertTrue(processes.re... | [
"0.625906",
"0.6215037",
"0.6079821",
"0.605461",
"0.5940084",
"0.5858847",
"0.58261263",
"0.5787651",
"0.5567173",
"0.5542198",
"0.5537528",
"0.5508412",
"0.5503725",
"0.54966176",
"0.5496157",
"0.54441243",
"0.5423454",
"0.53979665",
"0.53977054",
"0.5381116",
"0.5344718",
... | 0.64828974 | 0 |
Verify review_phams() correctly identifies disrepencies. | def test_review_phams_1(self):
self.db_filter.values = self.db_filter.build_values(
where=self.db_filter.build_where_clauses())
review.review_phams(self.db_filter)
self.assertFalse(39854 in self.db_filter.values)
self.assertTrue(40481 in self.db_filter.values) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_unsuccessful_verification(self):\n for i in (-4, -3, 3, 4):\n description = \"TOTP verified for `i={0}`\".format(i)\n calculated = self.algorithm.calculate(self.device.secret, drift=i)\n confirmed = self.relate.verify(calculated, save=False)\n\n self.asse... | [
"0.57791716",
"0.5642184",
"0.54765165",
"0.54339254",
"0.54250133",
"0.54250133",
"0.54250133",
"0.54203993",
"0.5368484",
"0.53536505",
"0.5352088",
"0.5343342",
"0.5304699",
"0.52976656",
"0.525753",
"0.52460766",
"0.5218623",
"0.5122834",
"0.5115473",
"0.5113044",
"0.5103... | 0.65374625 | 0 |
Verify get_pf_data() retrieves and returns data as expected. | def test_get_pf_data_1(self):
self.db_filter.values = [40481]
pf_data = review.get_pf_data(self.alchemist, self.db_filter)
self.assertTrue(isinstance(pf_data, list))
for header in review.PF_HEADER:
with self.subTest(header=header):
self.assertTrue(header in... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_data(self):\r\n success, _data = self.peer_grading.query_data_for_location(self.problem_location)\r\n self.assertTrue(success)",
"def test_getknowndata(self):\n result = recordparser.getfields(self.rawdata, self.fieldmap,\n self.sourcekeys)\n self.assertEqu... | [
"0.6491381",
"0.6261381",
"0.6225589",
"0.6133288",
"0.60651624",
"0.59994286",
"0.5965018",
"0.59296054",
"0.5926258",
"0.5926258",
"0.5926258",
"0.5915046",
"0.58646065",
"0.58031034",
"0.5797196",
"0.5794694",
"0.57883406",
"0.57495934",
"0.57458067",
"0.5729523",
"0.56688... | 0.72779644 | 0 |
Verify get_g_data() retreives and retrusn data as expected. | def test_get_g_data_1(self):
self.db_filter.values = [40481]
g_data = review.get_g_data(self.alchemist, self.db_filter, 40481)
self.assertTrue(isinstance(g_data, list))
for header in review.PG_HEADER:
with self.subTest(header=header):
self.assertTrue(header... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_data(self):\r\n success, _data = self.peer_grading.query_data_for_location(self.problem_location)\r\n self.assertTrue(success)",
"def test_gc_datastore(self):\n yg_gc = parsegc.generate_yg_gc_entry(\n \"100.25\",\n \"100.25\",\n \"ParNew\",\n ... | [
"0.6880222",
"0.66431636",
"0.6471408",
"0.62280977",
"0.6214435",
"0.62143874",
"0.61981314",
"0.61831266",
"0.6173409",
"0.61414707",
"0.6108344",
"0.6095074",
"0.6095074",
"0.6095074",
"0.6050329",
"0.6019996",
"0.5988685",
"0.5986319",
"0.5964577",
"0.5957988",
"0.5893297... | 0.7342229 | 0 |
Generate a CSV file of the Dataset information | def generate_dataset_csv(request):
response = csv_export(request,Dataset)
return response | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _write_csv(self):\n\n # add the label to the header\n if self.input_data.get_value(InputType.TIME_PERIOD) == 'all':\n self.header.append('Date')\n else:\n self.header.append('sample id')\n\n key_list = []\n\n for i, cube in enumerate(self.cube_list):\n ... | [
"0.7301781",
"0.7188036",
"0.70366293",
"0.70341796",
"0.6933217",
"0.68942285",
"0.6882128",
"0.6836537",
"0.6826749",
"0.6826596",
"0.68030626",
"0.68030626",
"0.6784672",
"0.6763934",
"0.6762665",
"0.6719179",
"0.65670377",
"0.65446883",
"0.65274465",
"0.65093666",
"0.6493... | 0.7548971 | 0 |
Generate the jtable data for rendering in the list template. | def generate_dataset_jtable(request, option):
obj_type = Dataset
type_ = "dataset"
mapper = obj_type._meta['jtable_opts']
if option == "jtlist":
# Sets display url
details_url = mapper['details_url']
details_url_key = mapper['details_url_key']
fields = mapper['fields']
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __gen_datatable__(self):\n # | - __generate_data_table\n rows_list = []\n for Job_i in self.Job_list:\n # | - FOR LOOP BODY\n entry_param_dict = {}\n for prop, value in Job_i.job_params.items():\n entry_param_dict[prop] = value\n\n ... | [
"0.6957045",
"0.65309566",
"0.6484074",
"0.6239349",
"0.61573464",
"0.6097337",
"0.5979821",
"0.5976074",
"0.59642226",
"0.5879871",
"0.5868879",
"0.5808444",
"0.5792779",
"0.57764864",
"0.57394606",
"0.57326126",
"0.5722943",
"0.5710817",
"0.56943315",
"0.5688803",
"0.567253... | 0.6782189 | 1 |
Generate the data to render the Dataset details template. | def get_dataset_details(name, analyst):
template = None
allowed_sources = user_sources(analyst)
dataset_object = Dataset.objects(name = name,
source__name__in=allowed_sources).first()
if not dataset_object:
error = ("Either no data exists for this dataset"
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def data():\n return render_template(\n 'data.html',\n title='World Happiness Report',\n year=datetime.now().year,\n message='Main Data Model'\n )",
"def data():\n return render_template(\n 'data.html',\n title='Data',\n year=datetime.now().year,\n ... | [
"0.6786504",
"0.677638",
"0.6696329",
"0.6644693",
"0.6549089",
"0.6539549",
"0.6480339",
"0.6422021",
"0.6389156",
"0.6363354",
"0.6326907",
"0.63251007",
"0.6324231",
"0.6286099",
"0.6258859",
"0.62314236",
"0.6211274",
"0.62073964",
"0.6190721",
"0.61876315",
"0.6074533",
... | 0.68706584 | 0 |
Draws a ball on the screen | def draw_balls():
for ball in balls:
circle(screen, ball[0], (ball[1], ball[2]), ball[3]) # Unpacking the list
for super_ball in super_balls:
# Indexes here are standing for attributes of a particular ball
circle(screen, super_ball[0][0], (super_ball[1], super_ball[2]), super_ball[3])
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_ball(self):\n circle(screen, self.color, (self.x, self.y), self.r)",
"def draw_ball():\n\n draw_circle(ball, 'yellow')",
"def draw(self, ball):\n self.screen.fill(pygame.Color(0,0,0))\n self.screen.blit(ball.surf, (size[0]/2, size[1]/2)) #this puts it in roughly center of scree... | [
"0.88677967",
"0.85694885",
"0.8367467",
"0.8162346",
"0.8078913",
"0.7918491",
"0.77419096",
"0.7705636",
"0.76958567",
"0.7675694",
"0.7617931",
"0.76130277",
"0.74899477",
"0.7485385",
"0.7371144",
"0.73147726",
"0.7310771",
"0.72975713",
"0.7283584",
"0.7259651",
"0.72097... | 0.6816264 | 39 |
Creates objects of the game, gives attributes to them | def init():
global balls, super_balls
balls = [gen_ball() for _ in range(number_of_balls)]
super_balls = []
generate_velocity_all_balls() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create(game):\r\n ## Create Garbage\r\n game.garbage = deque([])\r\n\r\n ## Create Stars\r\n game.create_stars()\r\n\r\n ## Create Millenium Falcon\r\n game.falcons = MilleniumFalcon.init(game)\r\n game.millenium_falcon = MilleniumFalcon()\r\n \r\n ## ... | [
"0.709624",
"0.69561434",
"0.68624014",
"0.6801915",
"0.67633975",
"0.6699283",
"0.6635729",
"0.6602579",
"0.65952",
"0.65015805",
"0.6486142",
"0.6449089",
"0.6402711",
"0.6385835",
"0.63782",
"0.6367633",
"0.6333057",
"0.63101995",
"0.63063586",
"0.62810177",
"0.62730706",
... | 0.0 | -1 |
Generates parameters of a ball | def gen_ball():
ball_radius = randint(30, 80)
ball_x = randint(ball_radius, screen_width - ball_radius)
ball_y = randint(ball_radius, screen_height - ball_radius)
ball_color = COLORS[randint(0, len(COLORS) - 1)]
return [ball_color, ball_x, ball_y, ball_radius] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_ball():\n balls.append(gen_ball())\n generate_velocity(balls)",
"def __init__(self, posn_x, posn_y, velocity_x, velocity_y, kula): \n self.posn_x = posn_x # x position of box containing the ball (bottom). \n self.posn_y = posn_y # x position of box contain... | [
"0.6895878",
"0.685673",
"0.6721842",
"0.62929344",
"0.61529136",
"0.6131318",
"0.59977186",
"0.5974338",
"0.59684306",
"0.5949559",
"0.5917947",
"0.58600557",
"0.58583516",
"0.58519125",
"0.5832661",
"0.58294326",
"0.5826852",
"0.5825801",
"0.58063483",
"0.5790451",
"0.57899... | 0.65422064 | 3 |
Generates parameters of a super ball | def gen_super_ball():
super_ball_radius = 80
super_ball_x = randint(super_ball_radius, screen_width - super_ball_radius)
super_ball_y = randint(super_ball_radius, screen_height - super_ball_radius)
super_ball_color = (RED, YELLOW, GREEN)
return [super_ball_color, super_ball_x, super_ball_y, super_ba... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_super_ball():\n super_balls.append(gen_super_ball())\n generate_velocity(super_balls)",
"def __init__(self, posn_x, posn_y, velocity_x, velocity_y, kula): \n self.posn_x = posn_x # x position of box containing the ball (bottom). \n self.posn_y = posn_y # x... | [
"0.69011176",
"0.6279852",
"0.6235428",
"0.6158913",
"0.613197",
"0.6061376",
"0.6033093",
"0.60131353",
"0.5985253",
"0.5949455",
"0.5946373",
"0.59461635",
"0.5881633",
"0.5881352",
"0.5837037",
"0.5832942",
"0.5820211",
"0.5816382",
"0.58020455",
"0.5778845",
"0.57671064",... | 0.6386006 | 1 |
sets velocity for each ball | def generate_velocity_all_balls():
for ball in balls:
generate_velocity(balls, balls.index(ball))
for super_ball in super_balls:
generate_velocity(super_balls, super_balls.index(super_ball)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ball_increase_velocity():\n global ball_vel\n ball_vel[0] = ball_vel[0] * 1.10\n ball_vel[1] = ball_vel[1] * 1.10",
"def __init__(self, posn_x, posn_y, velocity_x, velocity_y, kula): \n self.posn_x = posn_x # x position of box containing the ball (bottom). \n self.posn_y =... | [
"0.7683335",
"0.7416021",
"0.7308517",
"0.71602535",
"0.6988849",
"0.69132686",
"0.6795452",
"0.673417",
"0.6721597",
"0.67141384",
"0.6704233",
"0.66994643",
"0.66994643",
"0.6658117",
"0.66505706",
"0.66114134",
"0.66092026",
"0.6599519",
"0.65688676",
"0.6561834",
"0.65275... | 0.71853787 | 3 |
generates parameters for velocity of a ball | def generate_velocity(object_type, index=-1):
horizontal_velocity, vertical_velocity = randint(-100, 100) / 100, randint(-100, 100) / 100
object_type[index].append(horizontal_velocity)
object_type[index].append(vertical_velocity) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, posn_x, posn_y, velocity_x, velocity_y, kula): \n self.posn_x = posn_x # x position of box containing the ball (bottom). \n self.posn_y = posn_y # x position of box containing the ball (left edge). \n self.velocity_x = velocity_x # amount of x-mo... | [
"0.69857717",
"0.6803222",
"0.67491883",
"0.67428035",
"0.67012453",
"0.66916686",
"0.66150224",
"0.6603119",
"0.6602012",
"0.658512",
"0.6568148",
"0.65674865",
"0.6544383",
"0.65308136",
"0.64897746",
"0.64829975",
"0.64573956",
"0.6451636",
"0.6379975",
"0.63650876",
"0.63... | 0.0 | -1 |
Moves objects one moment forward | def tick():
move_balls(targets_speed)
move_super_balls(targets_speed * 2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move(self):\n pass",
"def move_forward():\n pass",
"def forward(self):\n self.position += 1",
"def move(self, direction, cycles):\n\t\tpass",
"def move(self):\n \n self.position = self.explore()",
"def move_step(self, direction):\n x = self.objects[0].x\n ... | [
"0.7545862",
"0.73531234",
"0.7204142",
"0.6988268",
"0.6933335",
"0.6926344",
"0.6876406",
"0.68594664",
"0.68399554",
"0.6779729",
"0.67741776",
"0.67479706",
"0.6717077",
"0.66955376",
"0.6654428",
"0.661319",
"0.6591413",
"0.6563061",
"0.6546285",
"0.6545375",
"0.6537781"... | 0.0 | -1 |
Moves every ball according their position and velocity | def move_balls(times_moved):
for time in range(times_moved):
for ball in balls:
touched_border_1 = ball[1] <= ball[3] + 1 or ball[1] >= screen_width - (ball[3] + 1)
touched_border_2 = ball[2] <= ball[3] + 1 or ball[2] >= screen_height - (ball[3] + 1)
# checking the positi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tick():\n move_balls(targets_speed)\n move_super_balls(targets_speed * 2)",
"def move(self):\n new_velocities = self._create_velocities()\n for boid, vel in zip(self.boids, new_velocities):\n boid.change_speed(*vel)\n for obstacle in self.obstacles:\n ... | [
"0.7707619",
"0.76045424",
"0.75311244",
"0.74456006",
"0.74178046",
"0.7367661",
"0.73274946",
"0.7279402",
"0.7104018",
"0.70265543",
"0.69604886",
"0.69478655",
"0.69259155",
"0.6891615",
"0.6872966",
"0.6867801",
"0.6753945",
"0.6708518",
"0.66990066",
"0.6697089",
"0.666... | 0.7733392 | 0 |
Moves a super ball according its position and velocity | def move_super_balls(times_moved):
for super_ball in super_balls:
if super_ball[3] != 0:
super_ball[3] -= 0.25
else:
super_balls.remove(super_ball)
create_ball()
for time in range(times_moved):
for ball in super_balls:
# checking the positi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move(self) -> None:\n self.delta_time += 1 / 30 # FPS is 30 frames per second\n\n if self.is_active:\n self.y -= self.velocity * self.delta_time + 0.5 * self.gravity * (self.delta_time ** 2) # s = ut + 0.5at^2\n self.velocity = self.velocity + self.gravity * self.delta_tim... | [
"0.7592387",
"0.73820126",
"0.7332446",
"0.72793245",
"0.72696424",
"0.70698607",
"0.70569444",
"0.70373905",
"0.70267665",
"0.7024209",
"0.6992322",
"0.6945556",
"0.6943953",
"0.6937738",
"0.692931",
"0.6924519",
"0.6884891",
"0.68680847",
"0.6855089",
"0.6794775",
"0.679248... | 0.73970246 | 1 |
Creates a new ball | def create_ball():
balls.append(gen_ball())
generate_velocity(balls) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_ball():\n ball = Ball()\n # Starting position of the ball.\n # Take into account the ball size so we don't spawn on the edge.\n ball.x = random.randrange(BALL_SIZE, SCREEN_WIDTH - BALL_SIZE)\n ball.y = random.randrange(BALL_SIZE, SCREEN_HEIGHT - BALL_SIZE)\n\n # Speed and direction of re... | [
"0.78910744",
"0.7773026",
"0.75718427",
"0.729235",
"0.71304464",
"0.7016827",
"0.68962",
"0.68675226",
"0.68090975",
"0.67911744",
"0.6787879",
"0.67824346",
"0.6752662",
"0.6746393",
"0.6635242",
"0.6631885",
"0.65989226",
"0.64579904",
"0.64246917",
"0.6302894",
"0.630126... | 0.7948333 | 0 |
Creates a new super ball | def create_super_ball():
super_balls.append(gen_super_ball())
generate_velocity(super_balls) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, x = 140, y = 140):\r\n super(Ball, self).__init__(image = Ball.image,\r\n x = 600, y = 240,\r\n dx = -3, dy = 1)",
"def create_ball():\n balls.append(gen_ball())\n generate_velocity(balls)",
"def __init__(self, ... | [
"0.730643",
"0.72916216",
"0.715264",
"0.6966334",
"0.6859085",
"0.6695298",
"0.6680856",
"0.6522806",
"0.6485458",
"0.64399636",
"0.6430099",
"0.633791",
"0.63072115",
"0.626187",
"0.62459886",
"0.6230659",
"0.6223655",
"0.6124047",
"0.61092705",
"0.60890025",
"0.60649514",
... | 0.8251493 | 0 |
Checks whether mouse clicked on an object or not. If yes, erases the object and adds some scores. Otherwise, takes away 10 points from player's scores. | def handler(position):
inner_counter = 0
mouse_x = position[0]
mouse_y = position[1]
for ball in balls:
if (mouse_x - ball[1]) ** 2 + (mouse_y - ball[2]) ** 2 <= ball[3] ** 2: # Pifagor theorem
inner_counter += (80 - ball[3]) + 1
balls.remove(ball)
define_bal... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click(event):\r\n global score, targets_left, have_friend_param\r\n flag = 0\r\n mult = event.button\r\n for i in range(num_of_balls + 1):\r\n if balls_pool[i][6] > 0 and (event.pos[0] - balls_pool[i][0]) ** 2 + (event.pos[1] - balls_pool[i][1]) ** 2 <= \\\r\n balls_pool[i][2]... | [
"0.69207203",
"0.69092077",
"0.6745111",
"0.63253415",
"0.6246623",
"0.61983776",
"0.6125679",
"0.6078021",
"0.60481995",
"0.5849827",
"0.5849543",
"0.583322",
"0.582653",
"0.57814676",
"0.5748103",
"0.56914175",
"0.5687703",
"0.5662814",
"0.5647297",
"0.56373435",
"0.5627928... | 0.0 | -1 |
Defines which type of ball is to be generated | def define_ball(possibility):
lottery = randint(1, int(100 / possibility))
if lottery % int(100 / possibility) == 0:
create_super_ball()
else:
create_ball() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_ball():\n balls.append(gen_ball())\n generate_velocity(balls)",
"def set_type(self,number):\n if number == 0:\n self.Goblin()\n \n if number == 1:\n self.Ork()\n\n if number == 2:\n self.Skeleton()\n\n if number == 3:\n ... | [
"0.6513428",
"0.63994837",
"0.6321578",
"0.609849",
"0.6071303",
"0.59578586",
"0.59479",
"0.58994204",
"0.5885848",
"0.5801644",
"0.5781181",
"0.5769061",
"0.5756699",
"0.570109",
"0.56561863",
"0.5653744",
"0.56488204",
"0.5636758",
"0.5563264",
"0.55605704",
"0.55554557",
... | 0.6129204 | 3 |
Tags this object with a tag specified by a tag name, primary key or Tag model instance | def tag(self, tag):
if isinstance(tag, six.integer_types):
try:
tag = Tag.objects.get(pk=tag, owner=self.owner)
except Tag.DoesNotExist:
#Handle this better?
return
if isinstance(tag, six.string_types):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_tag(self, obj, tag_name):\r\n tag_names = parse_tag_input(tag_name)\r\n if not len(tag_names):\r\n raise AttributeError(_('No tags were given: \"%s\".') % tag_name)\r\n if len(tag_names) > 1:\r\n raise AttributeError(_('Multiple tags were given: \"%s\".') % tag_na... | [
"0.69300115",
"0.6737296",
"0.6443798",
"0.64252824",
"0.64252824",
"0.64252824",
"0.64252824",
"0.6414007",
"0.63670653",
"0.63209105",
"0.63030666",
"0.6279283",
"0.6275524",
"0.621508",
"0.6196465",
"0.6148248",
"0.6104529",
"0.60913354",
"0.6081214",
"0.60594183",
"0.6058... | 0.7870358 | 0 |
Untags this object from a tag specified by a tag name, primary key or Tag model instance | def untag(self, tag):
if isinstance(tag, six.integer_types):
try:
tag = Tag.objects.get(pk=tag, owner=self.owner)
except Tag.DoesNotExist:
return
if isinstance(tag, six.string_types):
try:
tag = Tag.objects.get(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def untag(self, uuid, tags=None):\n if isinstance(tags, basestring):\n tags = [tags]\n\n self._backend.untag(uuid, tags)",
"def remove_tag(self, tag):\n cp = self.copy()\n cp.tags.remove(tag)\n return cp",
"def delete_tag(self,tag):\r\n\r\n # with shelf\r\n ... | [
"0.6581936",
"0.6569352",
"0.65154964",
"0.6419307",
"0.6366215",
"0.613729",
"0.6099109",
"0.60640305",
"0.60616106",
"0.6047276",
"0.6009565",
"0.5999241",
"0.59780717",
"0.5956036",
"0.59260625",
"0.588727",
"0.5883824",
"0.58749294",
"0.5853107",
"0.5759094",
"0.5744433",... | 0.7446062 | 0 |
Returns all the elements tagged with the given tag or any other tags it implies | def get_by_tag(cls, tag):
out = []
tags = Tag.expand_implied_by([tag])
for t in tags:
results = cls.objects.filter(owner=tag.owner, tags=t)
for b in results:
if b not in out:
out.append(b)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_tagged(self,tag_name):\n return self.tag2elements[tag_name]",
"def get_tags(self,element):\n if element in self.element2tags.keys():\n return self.element2tags[element]\n return []",
"def check_and_get_all_elements_by_tag_name(element, tag_name):\r\n if element is None or not tag_n... | [
"0.7682765",
"0.6988964",
"0.6707831",
"0.65731496",
"0.6313463",
"0.6310617",
"0.62054396",
"0.61800957",
"0.6134957",
"0.61071503",
"0.60247475",
"0.5989653",
"0.59873897",
"0.5967671",
"0.59566736",
"0.59315103",
"0.5923862",
"0.59012187",
"0.586929",
"0.586929",
"0.584404... | 0.67569757 | 2 |
Returns a dictionary representation of this tag The keys of the object are the field names, and the values are the respective values. | def to_dir(self):
out = {}
out["name"] = self.name
out["colour"] = self.colour
out["slug"] = self.slug
out["id"] = self.pk
out["tags"] = []
for t in self.tags.all():
out["tags"].append(t.pk)
return out | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def as_dict(self):\n return {'tag_type': self.tag_type, 'value': self.value}",
"def serialize(self):\n return {\n 'name': self.name,\n 'id': self.id,\n 'description': self.description,\n }",
"def to_dict(self):\n _dict = {}\n for f in self._me... | [
"0.7906893",
"0.74075985",
"0.7403961",
"0.73854554",
"0.73658055",
"0.7356104",
"0.735488",
"0.735488",
"0.735488",
"0.7321188",
"0.73047435",
"0.7293118",
"0.72879535",
"0.7276822",
"0.7236181",
"0.7235604",
"0.7234098",
"0.72181904",
"0.72181904",
"0.72181904",
"0.72181904... | 0.0 | -1 |
Produces the JSON representation of this tag It's the JSON for the output of to_dir() | def to_json(self):
return json.dumps(self.to_dir()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_json(self):\n return {'name': self.name, \n 'path': self.path, \n 'enabled': self.enabled}",
"def to_dir(self):\n out = {}\n out[\"name\"] = self.name\n out[\"colour\"] = self.colour\n out[\"slug\"] = self.slug\n out[\"id\"] = self.p... | [
"0.7254296",
"0.7204862",
"0.6899478",
"0.6766462",
"0.6741779",
"0.67358327",
"0.671214",
"0.671214",
"0.671214",
"0.671214",
"0.671214",
"0.671214",
"0.671214",
"0.671214",
"0.671214",
"0.6691138",
"0.66525084",
"0.6629055",
"0.6582917",
"0.6580073",
"0.6575524",
"0.65662... | 0.81298095 | 0 |
Puts everything to do with this tag into a new tag Specifically, everything tagged with this tag gets tagged with the new tag | def pool_into(self, target):
for taggable in taggables().values():
for t in taggable.by_user(self.owner).filter(tags=self):
t.tag(target) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_tag(tag):\n remove_tag(tag)\n add_tag(tag)",
"def tag(self, tag):\n self.tag = tag",
"def set_tag(self, tag):\n self.update(tag=tag)",
"def tagger():",
"def add(self, tag):\n self.tags[tag.name] = tag",
"def add_tags(event):\n\n add_tags_from_presets()",
"def ad... | [
"0.68450415",
"0.66959107",
"0.66091335",
"0.6608696",
"0.65841866",
"0.65109783",
"0.6490783",
"0.643429",
"0.643429",
"0.643429",
"0.643429",
"0.64281595",
"0.63527644",
"0.63505626",
"0.63243264",
"0.63088197",
"0.6302192",
"0.6277016",
"0.62149954",
"0.62000203",
"0.61865... | 0.5835091 | 51 |
Given the undoable JSON representation of the tag, creates one for it Note that this SAVES the object to the database. | def from_undoable(jsonData, user):
obj = json.loads(jsonData)
tag = Tag(owner=user, name=obj["tag"]["name"], colour=obj["tag"]["colour"],
slug=obj["tag"]["slug"],
)
tag.save()
for t in obj["tag"]["tags"]:
tag.tag(t)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create(self, name, tag):\n\n\t\turl_json = urllib.urlencode({\"name\": name, \"tag\": tag})\n\t\treturn self._create(\"/tag?json_hash=%s\" % url_json, \"tag\")",
"def tag(self, tag):\n \n if isinstance(tag, six.integer_types):\n try:\n tag = Tag.objects.get(pk=tag, own... | [
"0.68379575",
"0.6223951",
"0.6206263",
"0.6084949",
"0.6079323",
"0.6001024",
"0.5909171",
"0.5885422",
"0.58552563",
"0.5853059",
"0.5835232",
"0.582034",
"0.580405",
"0.5733759",
"0.57311594",
"0.57013",
"0.56803435",
"0.5633003",
"0.5623184",
"0.5532445",
"0.5512779",
"... | 0.76959306 | 0 |
Checks if a tag exists for a given user, and returns it if it does If it doesn't, the owner of the instance is set to the provided one, it is saved and then returned. | def get_or_create_with_slug(owner, instance):
try:
return Tag.objects.get(owner=owner, slug=makeslug(instance.name))
except Tag.DoesNotExist:
instance.owner = owner
instance.save()
return instance | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean_owner(self):\n username = self.cleaned_data['owner']\n owner = User.objects.filter(username=username).first()\n if owner is None:\n raise forms.ValidationError(\n _('User %(username)s does not exist'),\n params={'username': username},\n ... | [
"0.57880306",
"0.56677806",
"0.56435883",
"0.5559733",
"0.55188113",
"0.5518041",
"0.5497315",
"0.54858625",
"0.5484042",
"0.5459997",
"0.5448539",
"0.5447152",
"0.53896964",
"0.53632486",
"0.53549755",
"0.53303343",
"0.53284883",
"0.53248155",
"0.5321938",
"0.5318804",
"0.53... | 0.62832665 | 0 |
Takes an iterable of tags, and returns a list of those tags and all that they imply That is, make a list out of the argument, and for each tag T, add all T's tags to this list if they aren't in it already. The list is sorted alphabetically. For example, if A is tagged B (A implies B), then expand_implies([A]) == [A, B]... | def expand_implies(tags):
out = list(tags)
i = 0
while i < len(out):
for implied in out[i].tags.all():
if implied not in out:
out.append(implied)
i += 1
out.sort(key=lambda a: a.slug)
retur... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def expand_implied_by_check(tags):\n out = list(map(lambda x : (x, True), list(tags)))\n tlist = list(tags)\n \n i = 0\n while i < len(tlist):\n for implies in tlist[i].tags_to.all():\n if implies not in out:\n out.append((implies, Fal... | [
"0.8277511",
"0.82477945",
"0.8071657",
"0.52188385",
"0.5081271",
"0.5079979",
"0.49866027",
"0.49452943",
"0.47744787",
"0.47594008",
"0.47567642",
"0.47527817",
"0.47327515",
"0.4704478",
"0.46731877",
"0.46594027",
"0.4648895",
"0.46449554",
"0.46080807",
"0.4599413",
"0.... | 0.82710314 | 1 |
Does the same as expand_implies, but each element is a (tag, direct) pair Direct is true only for the tags in the input iterable, not for the implied ones. | def expand_implies_check(tags):
out = list(map(lambda x : (x, True), list(tags)))
tlist = list(tags)
i = 0
while i < len(tlist):
for implied in tlist[i].tags.all():
if implied not in tlist:
out.append((implied, False))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def expand_implied_by_check(tags):\n out = list(map(lambda x : (x, True), list(tags)))\n tlist = list(tags)\n \n i = 0\n while i < len(tlist):\n for implies in tlist[i].tags_to.all():\n if implies not in out:\n out.append((implies, Fal... | [
"0.7650832",
"0.7026415",
"0.69893414",
"0.5127024",
"0.50551105",
"0.48585972",
"0.4781383",
"0.4683263",
"0.4681429",
"0.46648985",
"0.45524988",
"0.4549901",
"0.45247072",
"0.44956627",
"0.44738948",
"0.44586575",
"0.44510412",
"0.44402215",
"0.44390503",
"0.4438355",
"0.4... | 0.7637341 | 1 |
Takes an iterable of tags, and returns a list of those tags and all that imply them That is, make a list out of the argument, and for each tag T, add all tags that have T as a tag if it isn't already in the list. The list is sorted alphabetically. For example if A is tagged B (A implies B), expand_implied_by([A]) == [A... | def expand_implied_by(tags):
out = list(tags)
i = 0
while i < len(out):
for implies in out[i].tags_to.all():
if implies not in out:
out.append(implies)
i += 1
out.sort(key=lambda a: a.slug)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def expand_implied_by_check(tags):\n out = list(map(lambda x : (x, True), list(tags)))\n tlist = list(tags)\n \n i = 0\n while i < len(tlist):\n for implies in tlist[i].tags_to.all():\n if implies not in out:\n out.append((implies, Fal... | [
"0.79339683",
"0.7664067",
"0.7466441",
"0.5794743",
"0.5567189",
"0.54926735",
"0.54457945",
"0.5440171",
"0.5335196",
"0.53335434",
"0.5256834",
"0.5229648",
"0.5100543",
"0.5064569",
"0.50596",
"0.5045757",
"0.50413346",
"0.50368875",
"0.5029048",
"0.5010543",
"0.5006942",... | 0.8137174 | 0 |
Does the same as expand_implied_by, but each element is a (tag, direct) pair Direct is true only for the tags in the input iterable, not for the implied ones. | def expand_implied_by_check(tags):
out = list(map(lambda x : (x, True), list(tags)))
tlist = list(tags)
i = 0
while i < len(tlist):
for implies in tlist[i].tags_to.all():
if implies not in out:
out.append((implies, False))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def expand_implied_by(tags):\n out = list(tags)\n \n i = 0\n while i < len(out):\n for implies in out[i].tags_to.all():\n if implies not in out:\n out.append(implies)\n \n i += 1\n \n out.sort(key=lambda a:... | [
"0.73786473",
"0.6921995",
"0.6901337",
"0.5127778",
"0.4939665",
"0.4857299",
"0.4841612",
"0.48378667",
"0.47903764",
"0.4744233",
"0.47146145",
"0.46981707",
"0.4651671",
"0.46219185",
"0.46054628",
"0.46042565",
"0.4585371",
"0.45849028",
"0.45668754",
"0.45388457",
"0.45... | 0.7290612 | 1 |
Returns all the tags owned by this user | def by_user(user):
return Tag.objects.all().filter(owner=user) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_asset_tags(user_id):\n result = db.session.query(Tag).filter(\n NotificationAssetTag.user_id == user_id,\n NotificationAssetTag.tag_id == Tag.id\n ).all()\n return result if result else []",
"def tags(request):\n return Tag.objects.filter(user=request.user)",
"def get_inte... | [
"0.692619",
"0.6867615",
"0.68465114",
"0.6743126",
"0.65778196",
"0.65561783",
"0.6544148",
"0.6514843",
"0.6499508",
"0.6447732",
"0.6423474",
"0.638473",
"0.638473",
"0.638473",
"0.638473",
"0.638473",
"0.638473",
"0.638473",
"0.638473",
"0.6380079",
"0.6321024",
"0.6310... | 0.7943311 | 0 |
with a data_containers (list of processed SampleDataContainer objects), this will transform results into a single dataframe with all of the function values, with probe names in rows, and sample beta values for probes in columns. | def consolidate_values_for_sheet(data_containers, postprocess_func_colname='beta_value'):
for idx,sample in enumerate(data_containers):
sample_id = "{0}_{1}".format(sample.sample.sentrix_id,sample.sample.sentrix_position)
this_sample_values = sample._SampleDataContainer__data_frame[postprocess_func_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_frame(data, index, resultsq, functions):\n results_list = []\n failed = False\n for function_id in functions:\n function = function_mapper[function_id]\n result = function(data.slice, functions[function_id])\n results_list.append(result)\n if not result.res:\n ... | [
"0.61030674",
"0.5999272",
"0.59028965",
"0.5843566",
"0.5804714",
"0.5803746",
"0.5624689",
"0.5613195",
"0.5611028",
"0.55851024",
"0.54885006",
"0.54116213",
"0.5403579",
"0.5392863",
"0.5391308",
"0.5386644",
"0.53635895",
"0.5361517",
"0.5337244",
"0.5320356",
"0.5312189... | 0.60265476 | 1 |
Returns the value of a property should be used from other modules only! | def get_prop(prop, config_type="", config_path=""):
paths = []
if len(config_type):
paths = [get_config_path(config_type, config_path)]
else:
paths = [get_global_config_path()]
user_path = get_user_config_path()
if os.path.exists(user_path):
paths.append(user_p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getProperty(propname):",
"def get_property(prop, project):\n result = re.search(\n r'{}\\s*=\\s*[\\'\"]([^\\'\"]*)[\\'\"]'.format(prop),\n open(project + \"/__init__.py\").read(),\n )\n return result.group(1)",
"def get_property(self, property):\n return self.shell([\"getprop\... | [
"0.6916654",
"0.6769773",
"0.67538357",
"0.6722013",
"0.6652091",
"0.6489578",
"0.64892507",
"0.64237255",
"0.63986534",
"0.63937336",
"0.6383006",
"0.63692194",
"0.6352038",
"0.63495433",
"0.63404363",
"0.6330848",
"0.6325854",
"0.6325854",
"0.6325854",
"0.6325854",
"0.63258... | 0.0 | -1 |
Sets the value of a property should be used from other modules only! | def set_prop(prop, value, config_type="", config_path=""):
path = get_config_path(config_type, config_path)
set_property_value(prop, value, path) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setprop(name, value):\n _slp.setprop(name, value)",
"def set_property(self, name, value):\n self.properties[name] = value",
"def set_property(key, value):\n return impl.set_property(**locals())",
"def set_user_property(self, property, value):\n self._user_properties.update({property: valu... | [
"0.7342421",
"0.72254354",
"0.71361965",
"0.70918727",
"0.69609255",
"0.69375974",
"0.69375974",
"0.69375974",
"0.69375974",
"0.69375974",
"0.68955857",
"0.686352",
"0.6860998",
"0.6805863",
"0.6797391",
"0.67734396",
"0.6708743",
"0.66969424",
"0.6678721",
"0.6627641",
"0.65... | 0.6789031 | 15 |
Resolves a path for a config type | def get_config_path(config_type, config_path=""):
data = {
"own": config_path,
"user": get_user_config_path(),
"global": get_global_config_path()
}
try:
return data[config_type]
except KeyError:
raise InvalidConfigTypeException() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _resolve_conf_name(conf_type):\n fnm = config_names[conf_type]\n if os.path.isfile(fnm):\n return fnm\n elif os.path.isfile(config_path + fnm):\n return config_path + fnm\n else:\n raise RuntimeError(\"Missing configuration file for %s\" % conf_type)",
"def get_config_type(co... | [
"0.6675451",
"0.6384866",
"0.5918775",
"0.5913211",
"0.5868751",
"0.58641165",
"0.57384616",
"0.56661963",
"0.5644473",
"0.5627098",
"0.5624543",
"0.5576847",
"0.5562291",
"0.55478436",
"0.5451666",
"0.5439571",
"0.54111356",
"0.54036963",
"0.5273365",
"0.527113",
"0.52587265... | 0.7259593 | 0 |
Returns the path of the global configuration file | def get_global_config_path():
return "/etc/dapsenv/dapsenv.conf" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_config_file_location():\n\n return './' + CONFIG_FILE_NAME",
"def get_config_path():\n\n root = os.path.dirname(os.path.abspath(__file__))[:-5]\n config_path = os.path.join(root, 'config.ini')\n\n return config_path",
"def configPath(self):\n return os.path.dirname(__file__)",
"def... | [
"0.8170498",
"0.8149112",
"0.8113216",
"0.7989826",
"0.7970176",
"0.79333484",
"0.7824644",
"0.7777473",
"0.77438056",
"0.7719309",
"0.7697185",
"0.76197577",
"0.7570368",
"0.7545556",
"0.75374484",
"0.7467858",
"0.74403304",
"0.74283",
"0.7358495",
"0.73165745",
"0.7294852",... | 0.8346807 | 0 |
Returns the path of the user configuration file | def get_user_config_path():
return "{}/.dapsenv/dapsenv.conf".format(expanduser("~")) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_config_path():\n return os.path.join(os.path.expanduser('~'))",
"def get_user_config_dir(options):\n return '/root/.spinnaker'",
"def config_file(self):\n return join_path(self.prefix.etc.bohrium, \"config.ini\")",
"def config_file_and_path():\n return str(rmfriend_dir() / 'config.cf... | [
"0.78400224",
"0.7665953",
"0.7620153",
"0.75382227",
"0.7534668",
"0.7432483",
"0.7346872",
"0.72849715",
"0.726103",
"0.7230243",
"0.7229635",
"0.7221889",
"0.7221053",
"0.71134675",
"0.7088942",
"0.7082575",
"0.70495373",
"0.6991914",
"0.69861937",
"0.6981911",
"0.69739807... | 0.79443234 | 0 |
Returns the value of a property should only be used internally! | def get_property_value(prop, paths):
data = parse_config(paths)
return data.get(prop) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_value (property):\n assert is_iterable_typed(property, basestring) or isinstance(property, basestring)\n return replace_grist (property, '')",
"def get_property_value(self, property, db):\n try:\n for p in self.properties:\n if p.idProperty =... | [
"0.7966625",
"0.79354656",
"0.7874442",
"0.7656781",
"0.7521477",
"0.75198215",
"0.74387866",
"0.7414117",
"0.73977107",
"0.73902535",
"0.7380925",
"0.72912407",
"0.7261663",
"0.7232989",
"0.72282",
"0.71172047",
"0.70893174",
"0.7045235",
"0.70395815",
"0.7030893",
"0.700933... | 0.70536906 | 17 |
Sets a value for a property should only be used internally! | def set_property_value(prop, value, path):
if not isfile(path):
raise ConfigFileNotCreatedException(path)
content = ""
updated = False
try:
with open(path, "r+") as f:
for line in f:
m = _search_pattern.search(line)
if m:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_property(self, name, value):\n self.properties[name] = value",
"def set_property(key, value):\n return impl.set_property(**locals())",
"def do_set_property(self, spec, value):\n attribute = self.find_attribute(spec.name)\n if attribute is not None and isinstance(attribute, property)... | [
"0.789875",
"0.787195",
"0.77938426",
"0.7764865",
"0.76872176",
"0.76138103",
"0.75846153",
"0.7535346",
"0.7509986",
"0.7486596",
"0.7475414",
"0.74670243",
"0.74670243",
"0.74670243",
"0.73869795",
"0.7344651",
"0.7318426",
"0.7303821",
"0.7299281",
"0.7232437",
"0.7222646... | 0.0 | -1 |
Parses all given configuration files and returns their content | def parse_config(paths):
data = {}
try:
for path in paths:
if not isfile(path):
raise ConfigFileNotCreatedException(path)
with open(path) as f:
for line in f:
# search for key=value pairs
m = _search_patte... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __load_config_files(self):\n # Parsed configuration files\n # ==========================\n T_dict = ParsedParameterFile(self.config_path('0/T'))\n fv_solutions = ParsedParameterFile(self.config_path('system/fvSolution'))\n fv_schemes = ParsedParameterFile(self.config_path('sy... | [
"0.696402",
"0.67024195",
"0.6692854",
"0.66028833",
"0.6596782",
"0.6591424",
"0.65703565",
"0.65684605",
"0.6567055",
"0.64944386",
"0.6489012",
"0.64866066",
"0.6471236",
"0.6432489",
"0.63784456",
"0.6371147",
"0.63701695",
"0.6316428",
"0.6310577",
"0.63022876",
"0.62976... | 0.6478963 | 12 |
Generates a configuration file at the desired location | def generate_config(path, force_overwrite=False):
currdir = os.path.dirname(os.path.realpath(__file__))
if not force_overwrite:
if os.path.exists("{}/dapsenv.conf".format(path)):
raise ConfigFileAlreadyExistsException(path)
try:
shutil.copy("{}/templates/dapsenv.conf".format(c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _generate_global_config() -> str:\n logger = getLogger(__name__)\n dst = os.path.join(os.path.expanduser(\"~\"),\n \".aiscalator/config/aiscalator.conf\")\n logger.info(\"Generating a new configuration file for aiscalator:\\n\\t%s\",\n dst)\n pattern = [\n ... | [
"0.69057345",
"0.6904385",
"0.6893657",
"0.6818285",
"0.6701469",
"0.66633904",
"0.662853",
"0.653605",
"0.65043265",
"0.6492254",
"0.63891",
"0.63475204",
"0.6343165",
"0.63317055",
"0.63298446",
"0.63297",
"0.63161016",
"0.63035953",
"0.6291675",
"0.62661564",
"0.6260246",
... | 0.6251898 | 21 |
Test title, which are present in both databases | def test_present_in_both_db(self):
for i in range(5):
price = find_cheapest_price("Star Wars: Episode VI - Return of the Jedi")
if price is "69.5":
break
time.sleep(1)
self.assertTrue(price == "69.5") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_video_title(self):\r\n video1 = Item.objects.get(video_title=\"Title\")\r\n video2 = Item.objects.get(video_title=\"Not Title\")\r\n self.assertEqual(video1.video_title, \"Title\")\r\n self.assertEqual(video2.video_title, \"Not Title\")",
"def test_title(names):",
"def test... | [
"0.6810692",
"0.6636476",
"0.62576246",
"0.6207335",
"0.61426693",
"0.61112493",
"0.6020435",
"0.59607303",
"0.596046",
"0.59053046",
"0.5889142",
"0.5889142",
"0.5846345",
"0.5842261",
"0.5790136",
"0.5773127",
"0.5773098",
"0.5757617",
"0.57561547",
"0.57504725",
"0.5722569... | 0.0 | -1 |
Test title, which is present in one database only | def test_present_in_one_db(self):
for i in range(5):
price = find_cheapest_price("Star Wars: The Force Awakens")
if price is "129.5":
break
time.sleep(1)
self.assertTrue(price == "129.5") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_title(self):\n key = api.portal.get_registry_record(\n 'plone.site_title'\n )\n self.assertEqual(u'Briefy CMS', key)",
"def test_title(names):",
"def test_video_title(self):\r\n video1 = Item.objects.get(video_title=\"Title\")\r\n video2 = Item.objects.get... | [
"0.69115084",
"0.68747866",
"0.6698193",
"0.6624086",
"0.66194016",
"0.65139085",
"0.63848144",
"0.63809997",
"0.63307256",
"0.6262883",
"0.6248247",
"0.6214016",
"0.6193819",
"0.6183599",
"0.61244667",
"0.61119235",
"0.6103055",
"0.6089567",
"0.60747665",
"0.6054598",
"0.604... | 0.0 | -1 |
Test title, which is not present in any database | def test_not_present_in_any_db(self):
price = find_cheapest_price("Only Star Wars")
self.assertTrue(price == None) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_title(names):",
"def test_title(self):\n key = api.portal.get_registry_record(\n 'plone.site_title'\n )\n self.assertEqual(u'Briefy CMS', key)",
"def test_index_has_status_in_title(self):",
"def test_query_has_query_in_title(self):\n query_url = self.warno_url ... | [
"0.68357944",
"0.669294",
"0.6659154",
"0.66550845",
"0.6562267",
"0.6556013",
"0.6490048",
"0.64443755",
"0.6407572",
"0.6393863",
"0.63384825",
"0.62897366",
"0.62545705",
"0.6222035",
"0.61828727",
"0.61750036",
"0.6164912",
"0.6149769",
"0.61397433",
"0.61179894",
"0.6113... | 0.0 | -1 |
Test extra initial spaces | def test_extra_initial_spaces(self):
for i in range(5):
price = find_cheapest_price(" Star Wars: Episode VI - Return of the Jedi")
if price is "69.5":
break
time.sleep(1)
self.assertTrue(price == "69.5") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_before_space():\n \n \n assert(1 == before_space(\"1 2 3\"))\n assert(\"NO SPACE\" == before_space(\"1\"))\n assert(\"Error\" == before_space(None))",
"def isspace(self) -> bool:\n pass",
"def _is_whitespace(char):\n if char == ' ' or char == '\\t' or char == '\\n' or char == '\\r... | [
"0.72271353",
"0.6714598",
"0.6690153",
"0.66385835",
"0.6617807",
"0.66094416",
"0.6579838",
"0.65702033",
"0.6558551",
"0.653799",
"0.6512899",
"0.64960116",
"0.6495435",
"0.6436047",
"0.64295197",
"0.6429463",
"0.6346191",
"0.6321559",
"0.63012207",
"0.6283817",
"0.6281358... | 0.62246585 | 24 |
Test extra end spaces | def test_extra_end_spaces(self):
for i in range(5):
price = find_cheapest_price("Star Wars: Episode VI - Return of the Jedi ")
if price is "69.5":
break
time.sleep(1)
self.assertTrue(price == "69.5") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_space_at_the_end():\n line = \"n\" * 79 + \" \"\n print \"--%s--\" % wrap_line(line)\n assert wrap_line(line) == \"n\" * 79",
"def remove_end_spaces(conversion):\n ending = False\n while ending == False:\n if conversion[-1] == ' ':\n del conversion[-1]\n if conver... | [
"0.7096777",
"0.6583292",
"0.6522514",
"0.6513981",
"0.6508848",
"0.64986074",
"0.64799017",
"0.6440675",
"0.6413336",
"0.6377339",
"0.6376093",
"0.6325999",
"0.63193965",
"0.63080835",
"0.62825227",
"0.62758803",
"0.62533045",
"0.6212336",
"0.6202876",
"0.6181698",
"0.617695... | 0.63165057 | 13 |
Test extra between spaces | def test_extra_between_spaces(self):
for i in range(5):
price = find_cheapest_price("Star Wars: Episode VI - Return of the Jedi ")
if price is not None:
break
time.sleep(1)
self.assertTrue(price == None) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_before_space():\n \n \n assert(1 == before_space(\"1 2 3\"))\n assert(\"NO SPACE\" == before_space(\"1\"))\n assert(\"Error\" == before_space(None))",
"def space_check(board, position):\n return board[position] == \" \"",
"def test_with_multiple_spaces(self):\n self.assertEqua... | [
"0.7235423",
"0.72115743",
"0.69549525",
"0.6865596",
"0.6863932",
"0.682128",
"0.673894",
"0.6686956",
"0.66818535",
"0.6626529",
"0.6560907",
"0.6530691",
"0.6492438",
"0.6451389",
"0.6450767",
"0.64443094",
"0.6436397",
"0.64074093",
"0.6385751",
"0.6383583",
"0.6355942",
... | 0.5891619 | 54 |
Test title with all small letters | def test_small_letters(self):
for i in range(5):
price = find_cheapest_price("star wars: episode VI - return of the jedi")
if price is "69.5":
break
time.sleep(1)
self.assertTrue(price == "69.5") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def title_contains(title_substring):\n title_substring = title_substring.encode('ascii')\n def f(win):\n t = conv(win.title)\n return title_substring in t\n return f",
"def test_title(names):",
"def testFalseCapTitle(self):\n val = capTitles(\"victor Ifezue\") \n self.a... | [
"0.7216029",
"0.7121521",
"0.7116587",
"0.70877403",
"0.70398676",
"0.699872",
"0.6916369",
"0.68733656",
"0.6852058",
"0.68507123",
"0.68265444",
"0.6759885",
"0.67231154",
"0.6678151",
"0.6665645",
"0.6643266",
"0.6637457",
"0.6586373",
"0.6572064",
"0.6534497",
"0.6520177"... | 0.0 | -1 |
Test title with all capital letters | def test_capital_letters(self):
for i in range(5):
price = find_cheapest_price("STAR WARS: EPISODE VI - RETURN OF THE JEDI")
if price is "69.5":
break
time.sleep(1)
self.assertTrue(price == "69.5") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testCapTitleAgain(self):\n val = capTitles(\"victor\") \n self.assertEqual(val, \"Victor\")",
"def proper_title_case(s):\n nocaps = [\"the\"] # This needs to be extended.",
"def testFalseCapTitle(self):\n val = capTitles(\"victor Ifezue\") \n self.assertNotEqual(val, ... | [
"0.7584302",
"0.73187137",
"0.7256049",
"0.7191587",
"0.7181554",
"0.71611553",
"0.70876896",
"0.69659775",
"0.69235843",
"0.6917762",
"0.6893609",
"0.68722034",
"0.6856061",
"0.68523157",
"0.68442553",
"0.6844096",
"0.676707",
"0.67213273",
"0.6688612",
"0.6667659",
"0.66671... | 0.6074005 | 68 |
Setup required URL and browser | def setup(self):
setup_data = get_setup_data()
self.base_url = setup_data['base_url']
browser = setup_data['browser'].lower()
if browser == 'chrome':
self.driver = webdriver.Chrome()
elif browser == 'firefox':
self.driver = webdriver.Firefox()
el... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup(self, url, browser_config):\n\n # navigate to the front page\n browser.open_url(url)",
"def setUp(self):\n self.browser = webdriver.Firefox()\n self.url = 'http://127.0.0.1:8000/'",
"def setup(bus):\n\n import webbrowser\n\n bus.register_service(DOMAIN_BROWSER, SERVI... | [
"0.78816956",
"0.70955104",
"0.7068756",
"0.6758885",
"0.6568374",
"0.64492273",
"0.6431475",
"0.6412007",
"0.6394155",
"0.63925385",
"0.6380785",
"0.63110197",
"0.6297633",
"0.62531245",
"0.61666447",
"0.61405796",
"0.6136142",
"0.6128124",
"0.61214244",
"0.61077684",
"0.607... | 0.70478886 | 3 |
Close browser after test | def teardown(self):
self.log.info('Close browser')
self.driver.quit() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def i_close_the_browser():\n driver.close()\n driver.quit()",
"def close_browser():\n driver.close()",
"def closeBrowser(driver):\n driver.quit()",
"def _shutdown_browser(self):\n if self.browser:\n self.browser.close()",
"def close(self):\n self.driver.quit()",
"def ... | [
"0.8507607",
"0.8493402",
"0.8110661",
"0.804822",
"0.7790632",
"0.7770495",
"0.76660335",
"0.752038",
"0.75203776",
"0.75168914",
"0.7512728",
"0.75061566",
"0.7448983",
"0.74159193",
"0.740557",
"0.7402157",
"0.73613596",
"0.73563594",
"0.7353315",
"0.73506343",
"0.7349565"... | 0.77384126 | 6 |
Remove names and emails from the JSON data | def anonymize(data_dir):
for root, dirs, files in os.walk(dst_data_dir, topdown=False):
for dir_name in sorted(dirs):
dir_path = os.path.join(root, dir_name)
json_path = os.path.join(root, dir_name, 'data.json')
print(json_path)
with open(json_path) as f:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def data_without(self, fields):\n without = {}\n data = json.loads(self.data())\n for field, value in data.items():\n if field not in fields:\n without[field] = value\n return json.dumps(without)",
"def strip_unwanted_fields(self, data, many, **kwargs):\n ... | [
"0.6503703",
"0.6107616",
"0.5858904",
"0.5853381",
"0.58459127",
"0.5783925",
"0.5737477",
"0.5712423",
"0.56732863",
"0.56693095",
"0.56518984",
"0.56355965",
"0.5594088",
"0.55935717",
"0.5591977",
"0.55816674",
"0.5573393",
"0.55636644",
"0.5562145",
"0.5545655",
"0.55399... | 0.53692836 | 35 |
Find the best and worst clips based on an ensemble of all models | def find_best_worst(data_dir, out_dir, num_gen=30, num_topk=5, max_itrs=5000):
all_clips = load_audio_files(data_dir, single_bar=False)
all_clips = [clip.squeeze().numpy() for clip in all_clips]
print('loaded {} clips'.format(len(all_clips)))
models = load_models(data_dir)
multi_model = MultiMode... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def best_cv_training(args: dict):\n assert len(args.learning_rates) == args.s, (\n \"learning_rates should be of size s\"\n )\n np.random.seed(args.seed) # set seed\n dataloader_pairs = get_dataloaders(\n args.folder, args.dimensions, args.batch_size, args.s, args.num_workers\n ... | [
"0.6456809",
"0.63944465",
"0.62981546",
"0.6274426",
"0.62251264",
"0.6138299",
"0.6105304",
"0.6075691",
"0.60605574",
"0.60558045",
"0.6054263",
"0.6024149",
"0.60108334",
"0.5999265",
"0.59565336",
"0.59428686",
"0.59214",
"0.5880305",
"0.58452225",
"0.58439136",
"0.58396... | 0.63115335 | 2 |
Redraw all the elements on the GraphicsScene | def redraw(self):
offset = self.actual_row * self.row_size
x = 5
y = 5
ind = 0
self.scene.clear()
for _ in range(self.column_size):
for _ in range(self.row_size):
if ind+offset < len(self.cards):
self.draw_card(x, y, ind+off... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def refresh(self):\n\n # Set Graphics scene\n self.setScene(QtGui.QGraphicsScene())\n self._connections = set()\n self._nodes = {}\n self._selection = set()\n self._manipulation_mode = 0\n self._selection_rect = None",
"def redraw(self):\n self.scene.redraw... | [
"0.7598374",
"0.7523456",
"0.73359394",
"0.73359394",
"0.7278681",
"0.72614765",
"0.72301275",
"0.7224556",
"0.7215738",
"0.7202812",
"0.7139211",
"0.71083397",
"0.7094835",
"0.7075643",
"0.7075643",
"0.7010708",
"0.696503",
"0.6866194",
"0.6857688",
"0.68394774",
"0.68365973... | 0.7069961 | 15 |
Draw card item with its image on the screen | def draw_card(self, x, y, card_pos):
card_id = self.cards[card_pos]
item = CommonCardHandle(card_pos, self)
pixmap = QPixmap()
if not pixmap.loadFromData(self.parent.database.get_data(card_id, 'low_res')):
# TODO: throw error or something
pass
item.setPixm... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_card_icon(self):\n card_icon = pygame.Surface((card_dimensions[0]/2, card_dimensions[1]/2))\n card_icon.fill(self.color)\n pygame.draw.rect(card_icon, CARD_OUTLINE, (0,0,card_icon.get_width(), card_icon.get_height()), 1)\n #the picture\n pic = pygame.transform.scale(self... | [
"0.77568555",
"0.7183763",
"0.70858896",
"0.70401824",
"0.70079154",
"0.6899117",
"0.6876519",
"0.68346894",
"0.6754337",
"0.6723893",
"0.67201185",
"0.6700681",
"0.6670856",
"0.6617026",
"0.6616946",
"0.65604407",
"0.6513981",
"0.64907306",
"0.64469767",
"0.6432041",
"0.6398... | 0.7373437 | 1 |
Draw a frame around a clicked card on the board | def draw_highlight(self, x1, x2, y1, y2, color):
self.scene.addLine(x1 - 1, y1 - 1, x1 - 1, y2 + 1, QPen(color))
self.scene.addLine(x2 + 1, y1 - 1, x2 + 1, y2 + 1, QPen(color))
self.scene.addLine(x1 - 1, y1 - 1, x2 + 1, y1 - 1, QPen(color))
self.scene.addLine(x1 - 1, y2 + 1, x2 + 1, y2 +... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_me(self):\r\n\t\tself.image.fill((100, 200, 100))\r\n\t\tif self.active: pg.draw.rect(self.image, (100, 100, 200), self.frame, 3) #if active => draw frame around selected entity width 3\r\n\t\tself.display_surface.blit(self.image, self.rect)",
"def draw():",
"def draw(self, canvas, yloc):\n \n ... | [
"0.69895494",
"0.681933",
"0.6818229",
"0.68115544",
"0.67861265",
"0.67620695",
"0.675582",
"0.6736298",
"0.67119414",
"0.66957617",
"0.6582334",
"0.6575421",
"0.65591764",
"0.65526736",
"0.6526447",
"0.6501671",
"0.649707",
"0.64956635",
"0.6450405",
"0.63855344",
"0.637756... | 0.0 | -1 |
Asks user to specify a city, month, and day to analyze. | def get_filters():
print("Hello there! Let's explore some data!")
legit_cities = ['chicago', 'new york city', 'washington'] # cities im gonna accept
city = input("\nWhich city do you want to analyze? Chicago, New York City or Washington?\n").lower()
while city not in legit_cities:
print("There... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Raw_Data(city,month,day):\n print(\"The city you have entered is: \",city)\n print(\"The month you have entered is: \",month)\n print(\"The day you have entered is: \",day)",
"def get_filters():\n city = None\n month = None\n day = None\n while day == None:\n # TO DO: get user input f... | [
"0.667135",
"0.65355784",
"0.6372491",
"0.6349019",
"0.63104576",
"0.62870693",
"0.6259983",
"0.62297434",
"0.62112975",
"0.62049454",
"0.6181796",
"0.6170252",
"0.6170224",
"0.6159359",
"0.61588275",
"0.6153817",
"0.61454034",
"0.61345667",
"0.61054",
"0.61033493",
"0.609148... | 0.6459417 | 2 |
Loads data for the specified city and filters by month and day if applicable. | def load_data(city, month, day):
data = pd.read_csv("{}.csv".format(city))
data.drop(data.columns[0], axis = 1, inplace = True) #dropping this strange column
data['Start Time'] = pd.to_datetime(data['Start Time'], format='%Y-%m-%d %H:%M:%S')
data['End Time'] = pd.to_datetime(data['End Time'], format... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_data(city, month, day):\n df = pd.read_csv(CITY_DATA [city])\n \n # convert the Start Time column to datetime\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n \n # extract month and day of week from Start Time to create new columns\n df['month'] = df['Start Time'].dt.month\n ... | [
"0.81320685",
"0.8108889",
"0.8045821",
"0.8042514",
"0.80090284",
"0.80071104",
"0.79991937",
"0.7998333",
"0.7988789",
"0.797905",
"0.79737365",
"0.79610896",
"0.7956163",
"0.7947432",
"0.79463065",
"0.7908287",
"0.7908229",
"0.7904569",
"0.7900715",
"0.78930074",
"0.788921... | 0.73460114 | 97 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.