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To use precompiled wheels: 1) go to releases (Files) and download tarball needed; 2) install it with command: python3 -m pip install /path/to/wheel.whl Or simply use URL in pip: python3 -m pip install https://sourceforge.net/projects/gdal-wheels-for-linux/files/GDAL-3.1.4-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl/download URL may be found under "View details" button (i) To use GDAL in C/C++ project you need to link gdal lib AND all libs located at dir GDAL.libs (usually this folder resides inside python site-packages) To compile your own wheels see information given at forefather project: https://github.com/youngpm/gdalmanylinux Usually this is done via command `make wheels` GDAL wheels for Windows are provided by Christoph Gohlke at https://www.lfd.uci.edu/~gohlke/pythonlibs/#gdal Built with PROJ (proj.db is included), GEOS, EXPAT. ...

.... ** = The two counters both run in their own thread. UPDATE-2015-2: * Added timeouts to sockets to prevent crashing of the script. * Updated some comments. UPDATE-2015-1: Since there have been some passed time I decided to pickup the keyboard to write for PyRadmon again, hence the new updates. Most significant changes: * Added a logger for catching and storing messages to a log file. * Complete cosmetic overhaul, fixing ugliness throughout the code, in the hope others might find it easier to read/more standardized.

streamline the DSM process in ArcGIS/Numpy/GDAL/Python using Sampling, Statistical elaboration, Prediction to allow the application of extended statistical models generated by the CUBIST (TM) or JMP (TM) software to a set of auxiliary input parameters, thereby bypassing the need to i) manually calculate the equations using Raster calculator; ii) use a grid-to-point conversion process prior to applying the prediction model in the statistical software itself and then a point-to-grid conversion process.

...The model is not very complete, but the main intention is to develop a library to be coupled with other models and use the full-mixed model as a testing version. A lot of things can be improved. I will read all the suggestions but I cannot promise that I will include all of them. Every new part of the code will contain the name of the author. If someone contributes improving the capabilities of this model in a substantial way, I will include that person as co-author of the model in the next version release. I hope this will promote people to contribute. ...