Skip to main content

Implementation of the SPADS shoreline prediction algorithm from the paper 'A Multiscale Approach to Shoreline Prediction' by Montano et. al. (2020).

Project description

pySPADS

re-implementation of SPADS model for coastal forecasting

Project details


Download files

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

Source Distribution

pyspads-0.0.1.tar.gz (16.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pyspads-0.0.1-py3-none-any.whl (20.9 kB view details)

Uploaded Python 3

File details

Details for the file pyspads-0.0.1.tar.gz.

File metadata

  • Download URL: pyspads-0.0.1.tar.gz
  • Upload date:
  • Size: 16.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.4

File hashes

Hashes for pyspads-0.0.1.tar.gz
Algorithm Hash digest
SHA256 2512cb5f1fe827a4c170bca5344a2c40bc6d23d58e234eeecf83e40dbb37cf96
MD5 68814a26a352158e932e32285a22517b
BLAKE2b-256 dde5cd6cf1f53c23b42c7ee10d43c7ff9c0c9b1031c8e31b15649a2d9a51ba6e

See more details on using hashes here.

File details

Details for the file pyspads-0.0.1-py3-none-any.whl.

File metadata

  • Download URL: pyspads-0.0.1-py3-none-any.whl
  • Upload date:
  • Size: 20.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.4

File hashes

Hashes for pyspads-0.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 13f79d8a11522cc5a70d0900b19aa73ee623e4de71981ddacb6fd12f58c42285
MD5 9a1a5a1064f656b8cc4ab25e8f637713
BLAKE2b-256 a9732b5f6e9588305948954d69f0b015cdd9a362e8b567a78f9aedfc63206fe9

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page