Source code for trackintel.model.triplegs

import pandas as pd

import trackintel as ti
from trackintel.model.util import (

_required_columns = ["user_id", "started_at", "finished_at"]

[docs] @_register_trackintel_accessor("as_triplegs") class Triplegs(TrackintelBase, TrackintelGeoDataFrame): """Trackintel class to treat a GeoDataFrame as a collections of `Tripleg`. Requires at least the following columns: ['user_id', 'started_at', 'finished_at'] Requires valid line geometries; the 'index' of the GeoDataFrame will be treated as unique identifier of the `triplegs` For several usecases, the following additional columns are required: ['mode', 'trip_id'] Notes ----- A `Tripleg` (also called `stage`) is defined as continuous movement without changing the mode of transport. 'started_at' and 'finished_at' are timezone aware pandas datetime objects. Examples -------- >>> triplegs.generate_trips() """ def __init__(self, *args, validate=True, **kwargs): super().__init__(*args, **kwargs) if validate: self.validate(self) # create circular reference directly -> avoid second call of init via accessor @property def as_triplegs(self): return self @staticmethod def validate(obj): assert obj.shape[0] > 0, f"Geodataframe is empty with shape: {obj.shape}" # check columns if any([c not in obj.columns for c in _required_columns]): raise AttributeError( "To process a DataFrame as a collection of triplegs, it must have the properties" f" {_required_columns}, but it has [{', '.join(obj.columns)}]." ) # check timestamp dtypes assert isinstance( obj["started_at"].dtype, pd.DatetimeTZDtype ), f"dtype of started_at is {obj['started_at'].dtype} but has to be datetime64 and timezone aware" assert isinstance( obj["finished_at"].dtype, pd.DatetimeTZDtype ), f"dtype of finished_at is {obj['finished_at'].dtype} but has to be datetime64 and timezone aware" # check geometry assert ( obj.geometry.is_valid.all() ), "Not all geometries are valid. Try x[~ x.geometry.is_valid] where x is you GeoDataFrame" if obj.geometry.iloc[0].geom_type != "LineString": raise TypeError("The geometry must be a LineString (only first checked).")
[docs] @doc(_shared_docs["write_csv"], first_arg="", long="triplegs", short="tpls") def to_csv(self, filename, *args, **kwargs):, filename, *args, **kwargs)
[docs] @doc(_shared_docs["write_postgis"], first_arg="", long="triplegs", short="tpls") def to_postgis( self, name, con, schema=None, if_exists="fail", index=True, index_label=None, chunksize=None, dtype=None ):, name, con, schema, if_exists, index, index_label, chunksize, dtype)
[docs] def calculate_distance_matrix(self, Y=None, dist_metric="haversine", n_jobs=0, **kwds): """ Calculate a distance matrix based on a specific distance metric. See :func:`trackintel.geogr.calculate_distance_matrix` for full documentation. """ return ti.geogr.calculate_distance_matrix(self, Y=Y, dist_metric=dist_metric, n_jobs=n_jobs, **kwds)
[docs] def spatial_filter(self, areas, method="within", re_project=False): """ Filter Triplegs on a geo extent. See :func:`trackintel.geogr.spatial_filter` for full documentation. """ return ti.geogr.spatial_filter(self, areas, method=method, re_project=re_project)
[docs] def generate_trips(self, staypoints, gap_threshold=15, add_geometry=True): """ Generate trips based on staypoints and triplegs. See :func:`trackintel.preprocessing.generate_trips` for full documentation. """ return ti.preprocessing.generate_trips(staypoints, self, gap_threshold=gap_threshold, add_geometry=add_geometry)
[docs] def predict_transport_mode(self, method="simple-coarse", **kwargs): """ Predict the transport mode of triplegs. See :func:`trackintel.analysis.predict_transport_mode` for full documentation. """ return ti.analysis.predict_transport_mode(self, method=method, **kwargs)
[docs] def calculate_modal_split(self, freq=None, metric="count", per_user=False, norm=False): """ Calculate the modal split of triplegs. See :func:`trackintel.analysis.calculate_modal_split` for full documentation. """ return ti.analysis.calculate_modal_split(self, freq=freq, metric=metric, per_user=per_user, norm=norm)
[docs] def temporal_tracking_quality(self, granularity="all"): """ Calculate per-user temporal tracking quality (temporal coverage). See :func:`trackintel.analysis.temporal_tracking_quality` for full documentation. """ return ti.analysis.temporal_tracking_quality(self, granularity=granularity)
[docs] def get_speed(self, positionfixes=None, method="tpls_speed"): """ Compute the average speed per positionfix for each tripleg (in m/s) See :func:`trackintel.geogr.get_speed_triplegs` for full documentation. """ return ti.geogr.get_speed_triplegs(self, positionfixes=positionfixes, method=method)