def temporal_network(gtfs,
                     start_time_ut=None,
                     end_time_ut=None,
                     route_type=None):
    """
    Compute the temporal network of the data, and return it as a pandas.DataFrame

    Parameters
    ----------
    gtfs : gtfspy.GTFS
    start_time_ut: int | None
        start time of the time span (in unix time)
    end_time_ut: int | None
        end time of the time span (in unix time)
    route_type: int | None
        Specifies which mode of public transport are included, or whether all modes should be included.
        The int should be one of the standard GTFS route_types:
        (see also gtfspy.route_types.TRANSIT_ROUTE_TYPES )
        If route_type is not specified, all modes are included.

    Returns
    -------
    events_df: pandas.DataFrame
        Columns: departure_stop, arrival_stop, departure_time_ut, arrival_time_ut, route_type, route_I, trip_I
    """
    events_df = gtfs.get_transit_events(start_time_ut=start_time_ut,
                                        end_time_ut=end_time_ut,
                                        route_type=route_type)
    events_df.drop('to_seq', 1, inplace=True)
    events_df.drop('shape_id', 1, inplace=True)
    events_df.drop('duration', 1, inplace=True)
    events_df.drop('route_id', 1, inplace=True)
    events_df.rename(
        columns={
            'from_seq': "seq"
        },
        inplace=True
    )
    return events_df
def temporal_network(gtfs,
                     start_time_ut=None,
                     end_time_ut=None,
                     route_type=None):
    """
    Compute the temporal network of the data, and return it as a pandas.DataFrame

    Parameters
    ----------
    gtfs : gtfspy.GTFS
    start_time_ut: int | None
        start time of the time span (in unix time)
    end_time_ut: int | None
        end time of the time span (in unix time)
    route_type: int | None
        Specifies which mode of public transport are included, or whether all modes should be included.
        The int should be one of the standard GTFS route_types:
        (see also gtfspy.route_types.TRANSIT_ROUTE_TYPES )
        If route_type is not specified, all modes are included.

    Returns
    -------
    events_df: pandas.DataFrame
        Columns: departure_stop, arrival_stop, departure_time_ut, arrival_time_ut, route_type, route_I, trip_I
    """
    events_df = gtfs.get_transit_events(start_time_ut=start_time_ut,
                                        end_time_ut=end_time_ut,
                                        route_type=route_type)
    events_df.drop('to_seq', 1, inplace=True)
    events_df.drop('shape_id', 1, inplace=True)
    events_df.drop('duration', 1, inplace=True)
    events_df.drop('route_id', 1, inplace=True)
    events_df.rename(
        columns={
            'from_seq': "seq"
        },
        inplace=True
    )
    return events_df
def temporal_network ( gtfs , start_time_ut = None , end_time_ut = None , route_type = None ) : events_df = gtfs . get_transit_events ( start_time_ut = start_time_ut , end_time_ut = end_time_ut , route_type = route_type ) events_df . drop ( 'to_seq' , 1 , inplace = True ) events_df . drop ( 'shape_id' , 1 , inplace = True ) events_df . drop ( 'duration' , 1 , inplace = True ) events_df . drop ( 'route_id' , 1 , inplace = True ) events_df . rename ( columns = { 'from_seq' : "seq" } , inplace = True ) return events_df
Compute the temporal network of the data, and return it as a pandas.DataFrame

    Parameters
    ----------
    gtfs : gtfspy.GTFS
    start_time_ut: int | None
        start time of the time span (in unix time)
    end_time_ut: int | None
        end time of the time span (in unix time)
    route_type: int | None
        Specifies which mode of public transport are included, or whether all modes should be included.
        The int should be one of the standard GTFS route_types:
        (see also gtfspy.route_types.TRANSIT_ROUTE_TYPES )
        If route_type is not specified, all modes are included.

    Returns
    -------
    events_df: pandas.DataFrame
        Columns: departure_stop, arrival_stop, departure_time_ut, arrival_time_ut, route_type, route_I, trip_I
Compute the temporal network of the data and return it as a pandas . DataFrame