scikit-fmm is a Python extension module which implements the fast marching method.
The fast marching method is used to model the evolution of boundaries
and interfaces in a variety of application areas. More specifically,
the fast marching method is a numerical technique for finding
approximate solutions to boundary value problems of the Eikonal
F(x) | grad T(x) | = 1
Typically, such a problem describes the evolution of a closed curve as a function of time T with speed F(x)>0 in the normal direction at a point x on the curve. The speed function is specified, and the time at which the contour crosses a point x is obtained by solving the equation.
scikit-fmm is a simple module which provides functions to calculate the signed distance and travel time to an interface described by the zero contour of the input array phi.
import skfmm import numpy as np phi = np.ones((3, 3)) phi[1, 1] = -1 skfmm.distance(phi)
array([[ 1.20710678, 0.5 , 1.20710678], [ 0.5 , -0.35355339, 0.5 ], [ 1.20710678, 0.5 , 1.20710678]])
skfmm.travel_time(phi, speed = 3.0 * np.ones_like(phi))
array([[ 0.40236893, 0.16666667, 0.40236893], [ 0.16666667, 0.11785113, 0.16666667], [ 0.40236893, 0.16666667, 0.40236893]])
The input array can be of 1, 2, 3 or higher dimensions and can be a masked array. A function is provided to compute extension velocities.
pip install scikit-fmm
conda install libgcc.
python setup.py install
conda install scikit-fmm
masterafter each commit.
python -c "import skfmm; skfmm.test(True)"
python setup.py develop
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Copyright 2022 The scikit-fmm team.
BSD-style license. See LICENSE.txt in the source directory.