back-end/markets/hackerone.py
def high_charts_timestamp(datetime_obj):
    """ This is a post processing function. It receives a pandas datetime element and takes care of producing
        a timestamp suitable for high charts library.
        @parameters
            datetime_obj                (datetime)
        @returns
            __high_charts_timestamp__   (timestamp)     readable by high charts library
    """

    # due to pandas processing the datetime_oj have always the structure xx:xx:xx 00:00:00 format
    # so retrieving year, month, day is all we need to begin converting

    datetime_obj_tuple = datetime_obj.timetuple()
    year = datetime_obj_tuple.tm_year
    month = datetime_obj_tuple.tm_mon
    day = datetime_obj_tuple.tm_mday
    # string
    high_charts_datetime_string = datetime(year, month, day, 14, 0, 0, 0).strftime('%Y-%m-%d %H:%M:%S.%f')
    # datetime
    high_charts_datetime_obj = datetime.strptime(high_charts_datetime_string, '%Y-%m-%d %H:%M:%S.%f')
    high_charts_timestamp = high_charts_datetime_obj.timestamp() * 1000

    return high_charts_timestamp

    # ---------------------------------------------------------------------------------------------------------------- #
 This is a post processing function. It receives a pandas datetime element and takes care of producing
        a timestamp suitable for high charts library.
        @parameters
            datetime_obj                (datetime)
        @returns
            __high_charts_timestamp__   (timestamp)     readable by high charts library
    
This is a post processing function. It receives a pandas datetime element and takes care of producing
a timestamp suitable for high charts library.
@parameters
datetime_obj                (datetime)
@returns
high_charts_timestamp__   (timestamp)     readable by high charts library
This is a post processing function . It receives a pandas datetime element and takes care of producing a timestamp suitable for high charts library . @parameters datetime_obj ( datetime ) @returns high_charts_timestamp__ ( timestamp ) readable by high charts library
def high_charts_timestamp(datetime_obj):
    datetime_obj_tuple = datetime_obj.timetuple()
    year = datetime_obj_tuple.tm_year
    month = datetime_obj_tuple.tm_mon
    day = datetime_obj_tuple.tm_mday
    high_charts_datetime_string = datetime(year, month, day, 14, 0, 0, 0).strftime('%Y-%m-%d %H:%M:%S.%f')
    high_charts_datetime_obj = datetime.strptime(high_charts_datetime_string, '%Y-%m-%d %H:%M:%S.%f')
    high_charts_timestamp = high_charts_datetime_obj.timestamp() * 1000
    return high_charts_timestamp
def high_charts_timestamp ( datetime_obj ) : datetime_obj_tuple = datetime_obj . timetuple ( ) year = datetime_obj_tuple . tm_year month = datetime_obj_tuple . tm_mon day = datetime_obj_tuple . tm_mday high_charts_datetime_string = datetime ( year , month , day , 14 , 0 , 0 , 0 ) . strftime ( '%Y-%m-%d %H:%M:%S.%f' ) high_charts_datetime_obj = datetime . strptime ( high_charts_datetime_string , '%Y-%m-%d %H:%M:%S.%f' ) high_charts_timestamp = high_charts_datetime_obj . timestamp ( ) * 1000 return high_charts_timestamp
This is a post processing function.
This is a post processing function .
""" This is a post processing function. It receives a pandas datetime element and takes care of producing
        a timestamp suitable for high charts library.
        @parameters
            datetime_obj                (datetime)
        @returns
            __high_charts_timestamp__   (timestamp)     readable by high charts library
    """ # due to pandas processing the datetime_oj have always the structure xx:xx:xx 00:00:00 format # so retrieving year, month, day is all we need to begin converting # string # datetime # ---------------------------------------------------------------------------------------------------------------- #
datetime_obj