API

Top-level API

read_csv(f, **kwargs)	Construct a CF-compliant trajectory dataset from a CSV file.
from_dataframe(df[, lon, lat, time, name, ...])	Construct a CF-compliant trajectory dataset from a pd.DataFrame of positions.
trajectory_dict_to_dataset(trajectory_dict)	Create a CF-compatible trajectory file from dictionary of drifter positions

Dataset

Attributes

Dataset.traj.plot	See trajan.plot.Plot.
Dataset.traj.animate	See trajan.animation.Animation.
Dataset.traj.tx	Trajectory x coordinates (usually longitude).
Dataset.traj.ty	Trajectory y coordinates (usually latitude).
Dataset.traj.tlon	Retrieve the trajectories in geographic coordinates (longitudes).
Dataset.traj.tlat	Retrieve the trajectories in geographic coordinates (latitudes).
Dataset.traj.crs	Retrieve the pyproj.crs.CRS object from the CF-defined grid-mapping in the dataset.

Methods

Dataset.traj.transform(to_crs)	Transform this datasets to to_crs coordinate system.
Dataset.traj.transformer(from_crs)	Create a transformer useful for transforming other coordinates to the CRS of this dataset.
Dataset.traj.set_crs(crs)	Returns a new dataset with the CF-supported grid-mapping / projection set to crs.
Dataset.traj.assign_cf_attrs([creator_name, ...])	Return a new dataset with CF-standard and common attributes set.
Dataset.traj.index_of_last()	Find index of last valid position along each trajectory.
Dataset.traj.speed()	Returns the speed [m/s] along trajectories.
Dataset.traj.time_to_next()	Returns the timedelta between time steps.
Dataset.traj.distance_to(other)	Distance between trajectories or a single point.
Dataset.traj.distance_to_next()	Returns distance in meters from one position to the next along trajectories.
Dataset.traj.azimuth_to_next()	Returns azimution travel direction in degrees from one position to the next.
Dataset.traj.length()	Returns distance in meters of each trajectory.
Dataset.traj.skill(other[, method])	Compare the skill score between this trajectory and other.
Dataset.traj.velocity_components()	Returns velocity components [m/s] from one position to the next.
Dataset.traj.velocity_spectrum()
Dataset.traj.convex_hull()	Return the scipy convex hull for all particles, in geographical coordinates.
Dataset.traj.convex_hull_contains_point(lon, lat)	Return True if given point is within the scipy convex hull for all particles.
Dataset.traj.get_area_convex_hull()	Return the area [m2] of the convex hull spanned by all positions.
Dataset.traj.gridtime(times[, time_varname])	Interpolate dataset to a regular time interval or a different grid.
Dataset.traj.sel(*args, **kwargs)	Select on each trajectory.
Dataset.traj.seltime([t0, t1])	Select observations in time window between t0 and t1 (inclusive).
Dataset.traj.iseltime(i)	Select observations by index (of non-nan, time, observation) across trajectories.
Dataset.traj.crop([lonmin, lonmax, latmin, ...])	Remove parts of trajectories outside of given geographical bounds.
Dataset.traj.contained_in([lonmin, lonmax, ...])	Return only trajectories fully within given geographical bounds.
Dataset.traj.is_1d()	Returns True if dataset is 1D, i.e. time is a 1D coordinate variable.
Dataset.traj.is_2d()	Returns True if dataset is 2D, i.e. time is a 2D variable and not a coordinate variable.
Dataset.traj.to_1d()	Convert dataset into a 1D dataset from.
Dataset.traj.to_2d([obs_dim])	Convert dataset into a 2D dataset from.
Dataset.traj.condense_obs()	Move all observations to the first index, so that the observation dimension is reduced to a minimum.

Plotting

plot.Plot(ds)
animation.Animation(ds)