The utils Module

gtfs_toolkit.utils.combine_time_series(time_series_dict, kind, split_directions=True)

Given a dictionary of time series data frames, combine the time series into one time series data frame with multi-index (hierarchical columns) and return the result. The top level columns are the keys of the dictionary and the second and third level columns are ‘route_id’ and ‘direction_id’, if kind == 'route', or ‘stop_id’ and ‘direction_id’, if kind == 'stop'. If split_directions == False, then there is no third column level, no ‘direction_id’ column.

gtfs_toolkit.utils.date_to_str(date, format_str='%Y%m%d', inverse=False)

Given a datetime.date object, convert it to a string in the given format and return the result. If inverse == True, then assume the given date is in the given string format and return its corresponding date object.

gtfs_toolkit.utils.downsample(time_series, freq)

Downsample the given route or stop time series, which is the output of Feed.get_routes_time_series() or Feed.get_stops_time_series(), to the given Pandas-style frequency. Can’t downsample to frequencies less one minute (‘1Min’), because the time series are generated with one-minute frequency.

gtfs_toolkit.utils.get_segment_length(linestring, p, q=None)

Given a Shapely linestring and two Shapely points or coordinate pairs, project the points onto the linestring, and return the distance along the linestring between the two points. If q is None, then return the distance from the start of the linestring to the projection of p. The distance is measured in the native coordinates of the linestring.

gtfs_toolkit.utils.plot_headways(stats, max_headway_limit=60)

Given a stops or routes stats data frame, return bar charts of the max and mean headways as a MatplotLib figure. Only include the stops/routes with max headways at most max_headway_limit minutes. If max_headway_limit is None, then include them all in a giant plot. If there are no stops/routes within the max headway limit, then return None.

NOTES:

Take the resulting figure f and do f.tight_layout() for a nice-looking plot.

gtfs_toolkit.utils.plot_routes_time_series(time_series)

Given a stops or routes time series data frame, sum each time series statistic over all stops/routes, plot each series statistic using MatplotLib, and return the resulting figure of subplots.

NOTES:

Take the resulting figure f and do f.tight_layout() for a nice-looking plot.

gtfs_toolkit.utils.seconds_to_timestr(seconds, inverse=False)

Return the given number of integer seconds as the time string ‘%H:%M:%S’. If inverse == True, then do the inverse operation. In keeping with GTFS standards, the hours entry may be greater than 23.

gtfs_toolkit.utils.time_it(f)
gtfs_toolkit.utils.timestr_mod_24(timestr)

Given a GTFS time string in the format %H:%M:%S, return a timestring in the same format but with the hours taken modulo 24.

gtfs_toolkit.utils.weekday_to_str(weekday, inverse=False)

Given a weekday, that is, an integer in range(7), return it’s corresponding weekday name as a lowercase string. Here 0 -> ‘monday’, 1 -> ‘tuesday’, and so on. If inverse == True, then perform the inverse operation.

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