Merge pull request #101 from nansencenter/apg_based

Apg based

.gitignore

@@ -66,3 +66,4 @@ target/
 .DS_Store
 
 fit_on_one
+*png

README.md

@@ -22,19 +22,13 @@ is to use [Anaconda](https://docs.conda.io/en/latest/miniconda.html).
 
 ```shell
 # create conda environment with key requirements
-conda create -y -n s1denoise gdal cartopy pip
+conda env create -f environment.yml
 
 # activate environment
 conda activate s1denoise
 
-# install other reqs using pip
-pip install pythesint netcdf4 nansat
-
-# update metadata vocabularies
-python -c 'import pythesint as pti; pti.update_all_vocabularies()'
-
 # install s1denoise
-pip install https://github.com/nansencenter/sentinel1denoised/archive/v1.3.3.tar.gz
+pip install https://github.com/nansencenter/sentinel1denoised/archive/v1.4.0.tar.gz
 
 ```
 
@@ -55,30 +49,42 @@ Do processing inside Python environment:
 ```python
 from s1denoise import Sentinel1Image
 # open access to file with S1 data
-s1 = Sentinel1Image('/path/to/data/S1B_EW_GRDM_1SDH_INPUTFILE.zip')
+input_file = '/path/to/data/S1B_EW_GRDM_1SDH_INPUTFILE.zip'
+s1 = Sentinel1Image(input_file)
 
 # run thermal noise correction in HV polarisation with the default ESA algorithm
 s0hve = s1.remove_thermal_noise('HV', algorithm='ESA')
 
-# run thermal noise correction in HV polarisation with the NEW algorithm
+# run thermal noise correction in HV polarisation with the default NERSC algorithm
 s0_hv = s1.remove_thermal_noise('HV')
 
+# run thermal noise correction in HV polarisation with the  NERSC_TG algorithm applicable for old Sentinel-1 data
+s0_hv = s1.remove_thermal_noise('HV', algorithm='NERSC_TG')
+
 # run thermal and texture noise correction in HV polarisation
 s0_hv = s1.remove_texture_noise('HV')
 
+# High level function for processing both polarisations
+from s1denoise.tools import run_correction
+d = run_correction(input_file)
+
 ```
 
-Process a single file with thermal, textural and angular correction and export in dB:
+Process a single file with thermal, textural and angular correction and export in [dB] in a numpy file:
 
-`s1_correction.py INPUTFILE.zip OUTPUTFILE.tif`
+`s1_correction.py INPUTFILE.zip OUTPUTFILE.npz`
 
-Process a single file using Docker (replace `input_dir` and `output_dir` with actual directories):
+Process a single file and export as GeoTIFF (requires Nansat):
 
-`docker run --rm -v /input_dir:/input_dir -v /output_dir:/output_dir s1denoise s1_correction.py /input_dir/INPUTFILE.zip /output_dir/OUPUTFILE.tif`
+`s1_correction.py INPUTFILE.zip OUTPUTFILE.npz -g`
 
 With option `-m` the script will also save landmask in Numpy format in a separate file with name `OUTPUTFILE.tif_mask.npz`:
 
-`s1_correction.py -m INPUTFILE.zip OUTPUTFILE.tif`
+`s1_correction.py INPUTFILE.zip OUTPUTFILE.tif -m`
+
+Process a single file using Docker (replace `input_dir` and `output_dir` with actual directories):
+
+`docker run --rm -v /data_dir:/data_dir s1denoise s1_correction.py /data_dir/INPUTFILE.zip /data_dir/OUPUTFILE.tif`
 
 Note that for enabling the landmask functionality, you need to download and install the watermask:
 
@@ -95,8 +101,7 @@ rm $MOD44WPATH/MOD44W.tgz
 
 ## Experimental scripts
 
-Sub-directories in `s1denoise/experimentalData` contain scripts for training the noise scaling and power balancing coefficients and extra scaling.
-See README files in these sub-dirs for details.
+Sub-directories in `s1denoise/training` and `s1denoise/validation` contain scripts for training and validation of the noise scaling and power balancing coefficients and extra scaling. See README files in these sub-dirs for details.
 
 ## License
 The project is licensed under the GNU general public license version 3.

environment.yml

@@ -0,0 +1,9 @@
+name: s1denoise
+dependencies:
+  - gdal
+  - lxml
+  - pip
+  - pip:
+    - beautifulsoup4
+    - requests
+

s1denoise/scripts/s1_correction.py

@@ -3,23 +3,59 @@
 import argparse
 
 import numpy as np
+try:
+    from nansat import Nansat
+except ImportError:
+    NANSAT_AVAILABLE = False
+else:
+    NANSAT_AVAILABLE = True
 
 from s1denoise.tools import run_correction
 
 def parse_args(args):
     ''' parse input arguments '''
     parser = argparse.ArgumentParser(
         description="Correct Sentinel-1 TOPSAR EW GRDM for thermal and texture noise and angular dependence")
-    parser.add_argument('ifile', type=str, help='input Sentinel-1 file in SAFE format')
-    parser.add_argument('ofile', type=str, help='output GeoTIFF file')
-    parser.add_argument('-m', '--mask', help='Export land mask as a numpy file', action='store_true')
+    parser.add_argument('ifile', type=str, help='Input Sentinel-1 file in SAFE or zip format')
+    parser.add_argument('ofile', type=str, help='Output file')
+    parser.add_argument('-a', '--algorithm', 
+                        help='Name of the algorithm to use', 
+                        type=str, 
+                        default='NERSC',
+                        choices=['ESA', 'NERSC', 'NERSC_TG'])
+    parser.add_argument('-g', '--geotiff', help='Export resulst as a GeoTIFF file', action='store_true')
+    parser.add_argument('-m', '--mask', help='Also export land mask as a numpy file', action='store_true')
     return parser.parse_args(args)
 
+def export_geotiff(ifile, ofile, d, mask):
+    """ Use Nansat to export the arrays as a geotiff file with georeference as in the input dataset """
+    remove_metadata = ['dataType', 'PixelFunctionType', 'SourceBand', 'SourceFilename', 'colormap', 'minmax', 'units', 'wkv']
+    n_inp = Nansat(ifile)
+    n_out = Nansat.from_domain(n_inp)
+    for pol in d:
+        parameters = n_inp.get_metadata(band_id='sigma0_%s' % pol)
+        for i in remove_metadata:
+            parameters.pop(i)
+        n_out.add_band(array=d[pol], parameters=parameters)
+        n_out.set_metadata(n_inp.get_metadata())    
+    n_out.export(ofile, driver='GTiff')
+
+def export_mask(ifile, ofile):
+    """ Export landmask as a numpy file """
+    n_inp = Nansat(ifile)
+    wm = n_inp.watermask()
+    np.savez_compressed(ofile + '_mask.npz', mask=wm[1])
 
 if __name__ == '__main__':
     args = parse_args(sys.argv[1:])
-    s1 = run_correction(args.ifile)
-    s1.export(args.ofile, driver='GTiff')
+    if (args.geotiff or args.mask) and not NANSAT_AVAILABLE:
+        raise ImportError(' Nansat is not installed and I can\'t export geotif or landmask. '
+                          ' Install Nansat or do not use "-g" and "-m". ')
+
+    d = run_correction(args.ifile, algorithm=args.algorithm)
+    if args.geotiff:
+        export_geotiff(args.ifile, args.ofile, d, args.mask)
+    else:
+        np.savez_compressed(args.ofile, **d)
     if args.mask:
-        wm = s1.watermask()
-        np.savez_compressed(args.ofile + '_mask.npz', mask=wm[1])
+        export_mask(args.ifile, args.ofile)