pandas_x.algorithms module

pandas_x.algorithms.ds_from_s(distance)[source]

Calculate point-to-point displacements from cumulative distances.

The chosen scheme: displacement at [i] represents the distance from [i-1] to [i].

Parameters

distance (pandas.Series) – cumulative distances along the route in meters. Must be numeric dtype.

Returns

point-to-point displacements along the route in meters.

Return type

pandas.Series

pandas_x.algorithms.ds_from_xy(lat, lon)[source]

Calculate point-to-point displacements from GPS coordinates.

The chosen scheme: displacement at [i] represents the distance from [i-1] to [i].

Parameters

  • lat (pandas.Series) – latitude coordinates along the route in degrees N (-90, 90). Must be numeric dtype.

  • lon (pandas.Series) – longitude coordinates along the route in degrees E (-180, 180). Must be numeric dtype.

Returns

point-to-point displacements along the route in meters.

Return type

pandas.Series

Assumptions:

  • Earth is a perfect sphere, with radius = pandas_x.scalar.EARTH_RADIUS_METERS.

  • The point-to-point distances are sufficiently short (so that latitude distortion and curvature have limited effects).

pandas_x.algorithms.reduced_point_index(lat, lon, min_dist=15.0)[source]

Eliminates gps points that are too close together.

No matter how far apart the points, always returns the start and end points.

Originally developed in my mapmatching package; an old version still exists there.

Parameters

  • lat (pandas.Series) – latitude values along the path.

  • lon (pandas.Series) – longitude values along the path.

  • min_dist (float) – The minimum distance (meters) between the resulting downsampled GPS coordinates. Default 15.

Returns

A boolean array that can be used to subsample a pandas.Series corresponding to this GPS trace, based on this minimum distance scheme.

Return type

list

pandas_x.algorithms.s_from_ds(displacement)[source]

Calculate cumulative distances from point-to-point displacements.

The chosen scheme: displacement at [i] represents the distance from [i-1] to [i]. This scheme means converting displacements to cumulative distances does not require any extrapolation.

Parameters

displacement (pandas.Series) – point-to-point displacements along the route in meters. Must be numeric dtype.

Returns

cumulative distances along the route in meters.

Return type

pandas.Series

pandas_x.algorithms.s_from_v(speed, time=None)[source]

Calculate point-to-point displacements from speed.

The chosen scheme: speed at [i] represents the distance from [i] to [i+1]. This means distance.diff() and time.diff() are shifted by one index from speed. I have chosen to extrapolate the position at the first index by assuming we start at a cumulative distance of 0.

Parameters

  • speed (pandas.Series) – speed along the route in meters per second. Must be numeric dtype.

  • time (pandas.Series) – cumulative time from start along the route in seconds. Must be numeric dtype. Default None.

Returns

point-to-point displacements along the route in meters.

Return type

pandas.Series

pandas_x.algorithms.v_from_ds(displacement, time=None)[source]

Calculate speed from point-to-point displacements.

The chosen scheme: displacement at [i] represents the distance from [i-1] to [i], while speed at [i] represents the distance from [i] to [i+1]. This means displacements and time.diff() are shifted by one index from speed. I have chosen to extrapolate the speed at the final position by ffill.

Parameters

  • displacement (pandas.Series) – point-to-point displacements along the route in meters. Must be numeric dtype.

  • time (pandas.Series) – cumulative time from start along the route in seconds. Must be numeric dtype. Default None.

Returns

speed along the route in meters per second.

Return type

pandas.Series

pandas_x.algorithms.v_from_s(distance, time=None)[source]

Calculate speed from cumulative distances.

The chosen scheme: speed at [i] represents the distance from [i] to [i+1]. This means distance.diff() and time.diff() are shifted by one index from speed. I have chosen to extrapolate the speed at the final position by ffill.

Parameters

  • distance (pandas.Series) – cumulative distances along the route in meters. Must be numeric dtype.

  • time (pandas.Series) – cumulative time from start along the route in seconds. Must be numeric dtype. Default None.

Returns

speed along the route in meters per second.

Return type

pandas.Series