def get_stop_count_data(self, start_ut, end_ut):
        """
        Get stop count data.

        Parameters
        ----------
        start_ut : int
            start time in unixtime
        end_ut : int
            end time in unixtime

        Returns
        -------
        stopData : pandas.DataFrame
            each row in the stopData dataFrame is a dictionary with the following elements
                stop_I, count, lat, lon, name
            with data types
                (int, int, float, float, str)
        """
        # TODO! this function could perhaps be made a single sql query now with the new tables?
        trips_df = self.get_tripIs_active_in_range(start_ut, end_ut)
        # stop_I -> count, lat, lon, name
        stop_counts = Counter()

        # loop over all trips:
        for row in trips_df.itertuples():
            # get stop_data and store it:
            stops_seq = self.get_trip_stop_time_data(row.trip_I, row.day_start_ut)
            for stop_time_row in stops_seq.itertuples(index=False):
                if (stop_time_row.dep_time_ut >= start_ut) and (stop_time_row.dep_time_ut <= end_ut):
                    stop_counts[stop_time_row.stop_I] += 1

        all_stop_data = self.stops()
        counts = [stop_counts[stop_I] for stop_I in all_stop_data["stop_I"].values]

        all_stop_data.loc[:, "count"] = pd.Series(counts, index=all_stop_data.index)
        return all_stop_data
def get_stop_count_data(self, start_ut, end_ut):
        
        
        trips_df = self.get_tripIs_active_in_range(start_ut, end_ut)
        
        stop_counts = Counter()

        
        for row in trips_df.itertuples():
            
            stops_seq = self.get_trip_stop_time_data(row.trip_I, row.day_start_ut)
            for stop_time_row in stops_seq.itertuples(index=False):
                if (stop_time_row.dep_time_ut >= start_ut) and (stop_time_row.dep_time_ut <= end_ut):
                    stop_counts[stop_time_row.stop_I] += 1

        all_stop_data = self.stops()
        counts = [stop_counts[stop_I] for stop_I in all_stop_data["stop_I"].values]

        all_stop_data.loc[:, "count"] = pd.Series(counts, index=all_stop_data.index)
        return all_stop_data
def get_stop_count_data ( self , start_ut , end_ut ) : # TODO! this function could perhaps be made a single sql query now with the new tables? trips_df = self . get_tripIs_active_in_range ( start_ut , end_ut ) # stop_I -> count, lat, lon, name stop_counts = Counter ( ) # loop over all trips: for row in trips_df . itertuples ( ) : # get stop_data and store it: stops_seq = self . get_trip_stop_time_data ( row . trip_I , row . day_start_ut ) for stop_time_row in stops_seq . itertuples ( index = False ) : if ( stop_time_row . dep_time_ut >= start_ut ) and ( stop_time_row . dep_time_ut <= end_ut ) : stop_counts [ stop_time_row . stop_I ] += 1 all_stop_data = self . stops ( ) counts = [ stop_counts [ stop_I ] for stop_I in all_stop_data [ "stop_I" ] . values ] all_stop_data . loc [ : , "count" ] = pd . Series ( counts , index = all_stop_data . index ) return all_stop_data
Get stop count data.

        Parameters
        ----------
        start_ut : int
            start time in unixtime
        end_ut : int
            end time in unixtime

        Returns
        -------
        stopData : pandas.DataFrame
            each row in the stopData dataFrame is a dictionary with the following elements
                stop_I, count, lat, lon, name
            with data types
                (int, int, float, float, str)
Get stop count data .