Skip to main content

Python module implementing the Fast Marching Method.

Project description

# pyfmm
Python module implementing the Fast Marching Method, written in pure Python. Only dependency is numpy 1.8+.

The implementation uses mostly boolean arrays for accessing and updating values. Instead of accepting only the smallest value at each iteration (step 3,, one may accept an arbitrary number of values at each step. This can speed up the computations considerably, but may in some cases be inaccurate (especially if the speed varies alot).

## Installation

## Interface

There are two ways to compute the distances:

a) Using a boolean array that specifies the exact points that defines the boundary,

b) Using an array of known distances to the boundary, in addition to a boolean array marking which values are certain.

Method a)
import pyfmm, numpy

my_boundary = numpy.array(..., dtype=numpy.bool) # All boundary points marked as "True"
solution = pyfmm.march(my_boundary)

Method b)
import pyfmm, numpy

known_distances = np.array(...) # Unknown values could for instance be set to np.inf
solution = pyfmm.march(numpy.argwhere(known_distances != numpy.inf), known_distances))

The number of values that are accepted at each iteration can be set using `batch_size`, and an array of speeds can be given using `speed`.

## Examples

The examples illustrate the following:

* ``: Distance from a ring boundary in the upper left part of the image. `batch_size` is varied to see how it affects computation time and result. In this case, the difference between the results from `batch_size=1` and `batch_size=100` seems negligible.
* ``: Straight boundary on left and right hand side, and two different `speed` fields. The examples illustrates what might happen if care is not taken when choosing a `batch_size`.
* ``: Simply a less regular boundary shape than the two above.

The example boundary defined by examples/irregular_boundary.png:


Project details

Release history Release notifications | RSS feed

This version


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions (5.5 kB view hashes)

Uploaded source

pyfmm-0.3.tar.gz (4.2 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page