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Coastal Python Packages

Python packages with the GitHub topic coastal. Sorted by relevance, with stars and monthly downloads.
opengeos
hypercoast

A Python Package for Visualizing and Analyzing Hyperspectral Data in Coastal Environments

9K 277 56
GeoscienceAustralia
eo-tides

Tide modelling tools for large-scale satellite Earth observation analysis

6K 105 18
chrisleaman
py-wave-runup

Empirical wave runup models implemented in Python

2K 45 19
GeoscienceAustralia
dea-intertidal

Remote sensing tools for high-resolution mapping of the intertidal zone

1K 105 18
openearth
xbtools

Collection of Python modules for pre- and postprocessing XBeach models and output files

674 25 12
robjameswall
dhitools

Python tools for working with DHI MIKE21

456 30 18
mansurjisan
coops-mcp

MCP server providing access to NOAA CO-OPS tides, water levels, currents, and meteorological data

426 5 1
mansurjisan
erddap-mcp

MCP server for accessing ocean data from ERDDAP servers worldwide

409 5 1
mansurjisan
ofs-mcp

MCP server for NOAA Operational Forecast System (OFS) regional ocean model access

403 5 1
mansurjisan
nhc-mcp

MCP server for NHC storm tracks, advisories, and hurricane best track data

401 5 1
mansurjisan
stofs-mcp

MCP server for NOAA STOFS storm surge forecast access and validation against CO-OPS observations

390 5 1
UNCG-DAISY
psi-collect

Collect Post-Storm Imagery from NOAA

266 5 3
mansurjisan
recon-mcp

MCP server for NOAA hurricane reconnaissance data (HDOB, VDM, ATCF fixes)

249 5 1
mansurjisan
ww3-mcp

MCP server for NOAA GFS-Wave (WAVEWATCH III) forecasts and NDBC buoy observations

236 5 1
mansurjisan
usgs-mcp

MCP server providing access to USGS Water Services for streamflow, flood stages, and peak events

161 5 1
mansurjisan
rtofs-mcp

MCP server for NOAA RTOFS global ocean forecast data via HYCOM THREDDS

130 5 1
mansurjisan
ndbc-mcp

MCP servers for coastal oceanography

128 5 1
mansurjisan
winds-mcp

MCP server providing access to NWS surface wind observations from ASOS, AWOS, and other stations

117 5 1
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