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Estimating wave heights from 20 orthogonal parameters derived from SAR images

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DeepCWAVE

To produce predictions

  • Call python RunModel.py specifying the input, which can be an absolute or relative path to a netcdf file (passing entire directories here is currently unstable, working on fixing that)
  • Optionally can specify the directory where the output file will be saved with the output flag, otherwise it will just save in the current working directory
  • Don't use the weights argument yet, there's currently only one model anyway
  • Call format: python RunModel.py [-h] [--outdir OUTDIR] [--weights WEIGHTS] input
  • Calling python RunModel.py -h will give you more information about the arguments
  • Sample call: python RunModel.py /path/to/data/S1A_ALT_coloc201701S.nc --outdir /path/to/destination/
    • This will read /path/to/data/S1A_ALT_coloc201701S.nc and produce an output file at /path/to/destination/S1A_ALT_coloc201701S_preds.csv

Dependencies

  • Python 3.6.8
  • 'numpy': '1.16.2',
  • 'sklearn': '0.20.3',
  • 'pandas': '0.23.4',
  • 'keras': '2.2.4',
  • 'tensorflow': '1.11.0',
  • 'netCDF4': '1.4.2'

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Estimating wave heights from 20 orthogonal parameters derived from SAR images

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