pysteps.motion

Implementations of optical flow methods.

pysteps.motion.constant

Implementation of a constant advection field estimation by maximizing the correlation between two images.

constant(R, \*\*kwargs)

Compute a constant advection field by finding a translation vector that maximizes the correlation between two successive images.

pysteps.motion.darts

Implementation of the DARTS algorithm.

DARTS(R, \*\*kwargs)

Compute the advection field from a sequence of input images by using the DARTS method.

pysteps.motion.lucaskanade

OpenCV implementation of the Lucas-Kanade method with interpolated motion vectors for areas with no precipitation.

dense_lucaskanade(R, \*\*kwargs)

pysteps.motion.vet

Variational Echo Tracking (VET) Module

This module implements the VET algorithm presented by Laroche and Zawadzki (1995) and used in the McGill Algorithm for Prediction by Lagrangian Extrapolation (MAPLE) described in Germann and Zawadzki (2002).

The morphing and the cost functions are implemented in Cython and parallelized for performance.

vet(input_images[, sectors, smooth_gain, …])	Variational Echo Tracking Algorithm presented in Laroche and Zawadzki (1995) and used in the McGill Algorithm for Prediction by Lagrangian Extrapolation (MAPLE) described in Germann and Zawadzki (2002).
vet_cost_function(sector_displacement_1d, …)
vet_cost_function_gradient(\*args, \*\*kwargs)	Compute the vet cost function gradient.
morph(image, displacement[, gradient])	Morph image by applying a displacement field (Warping).
round_int(scalar)	Round number to nearest integer.
ceil_int(scalar)	Round number to nearest integer.
get_padding(dimension_size, sectors)	Get the padding at each side of the one dimensions of the image so the new image dimensions are divided evenly in the number of sectors specified.