--------------------- REQUIREMENT --------------------- - You'll need python and the packages numpy, pyhdf, netCDF4 to run this sofware in directory GLOBCOAST_TOOLS. - You also need the file coastmask_index_sinusoidale.hdf that you can find in the directory GLOBCOAST_LUTS/ You must define an environment variable call GLOBCOAST_LUTS_PATH where coastmask_index_sinusoidale.hdf is copy. (add a line like this in your .bashrc: export GLOBCOAST_LUTS_PATH='/path/to/my/GLOBCOAST/LUTS') --------------------- SOFTWARE --------------------- This directory include two distinct softwares : - example_class_GC.py with class_GC.py is a small example of geolocation and data extraction from GlobCoast data file. The aim of this short code is to introduce you with GlobCoast data format. - extract.py with geoutils/ is a software developped by HYGEOS with which you can extract data from GlobCoast files in ascii, netCDF or hdf. run extract.py without argument for more information. This tools have been develloped and tested on various GNU/Linux distributions. --------------------- DATA GEOLOCATION --------------------- A sinusoidal grid has been choose for reference on which GlobCoast data have been projected. This grid has 40000 pixels at equator which lead to a spatial resolution of 1km. Each pixel (Xi,Yi) has a unique index position computed this way : Index = Yi*40000 + Xi With this index, geolocation of each point can be easily retrieved. Data are recorded as a vector of size 64070397 in 32-bit floating-point; invalid or missing value are set to -32767. The position in the vector represents always the same geolocation, this choice does not save space but ease processing, data extraction and time series analysis. Indexes of pixels are not store in the product files so the link between data and geolocation is done using the coastal mask file: coastmask_index_sinusoidale.hdf which is use to compute geolocation for all products. This file is store in the directory GLOBCOAST_LUTS/ -------------------- CONTACT -------------------- david.dessailly@univ-littoral.fr http://log.cnrs.fr/