def test_median(self): mo = Mosaic(domain=self.domain) mo.median([self.test_file_gcps, self.test_file_stere], bands=['L_645', 'L_555', 'L_469']) mask = mo['mask'] L_645 = mo['L_645'] L_555 = mo['L_555'] L_469 = mo['L_469'] tmpfilename = os.path.join(ntd.tmp_data_path, 'mosaic_median_export.nc') bands = { 'mask': { 'type': '>i1' }, 'L_645': { 'type': '>i1' }, 'L_555': { 'type': '>i1' }, 'L_469': { 'type': '>i1' }, } mo.set_metadata('time_coverage_start', '2016-01-19') mo.export2thredds(tmpfilename, bands)
def test_average(self): mo = Mosaic(domain=self.domain) mo.average([self.test_file_gcps, self.test_file_stere], bands=['L_645', 'L_555', 'L_469']) mask = mo['mask'] L_645 = mo['L_645'] L_555 = mo['L_555'] L_469 = mo['L_469'] tmpfilename = os.path.join(ntd.tmp_data_path, 'mosaic_export.nc') bands = { 'L_645' : {'type': '>i1'}, 'L_555' : {'type': '>i1'}, 'L_469' : {'type': '>i1'}, } mo.export2thredds(tmpfilename, bands)
def test_median(self): mo = Mosaic(domain=self.domain) mo.median([self.test_file_gcps, self.test_file_stere], bands=['L_645', 'L_555', 'L_469']) mask = mo['mask'] L_645 = mo['L_645'] L_555 = mo['L_555'] L_469 = mo['L_469'] tmpfilename = os.path.join(ntd.tmp_data_path, 'mosaic_median_export.nc') bands = { 'mask' : {'type': '>i1'}, 'L_645' : {'type': '>i1'}, 'L_555' : {'type': '>i1'}, 'L_469' : {'type': '>i1'}, } mo.set_metadata('time_coverage_start', '2016-01-19') mo.export2thredds(tmpfilename, bands)
def test_average(self): mo = Mosaic(domain=self.domain) mo.average([self.test_file_gcps, self.test_file_stere], bands=['L_645', 'L_555', 'L_469']) mask = mo['mask'] L_645 = mo['L_645'] L_555 = mo['L_555'] L_469 = mo['L_469'] tmpfilename = os.path.join(ntd.tmp_data_path, 'mosaic_export.nc') bands = { 'L_645': { 'type': '>i1' }, 'L_555': { 'type': '>i1' }, 'L_469': { 'type': '>i1' }, } mo.export2thredds(tmpfilename, bands)
''' Mosaic class includes mosaicing methods mosaicing methods: 1. average 2. median 3. latest (add the latest image on top) ''' # Create target domain domain = Domain(4326, '-lle 27 70 31 72 -ts 1400 1300') # A. Perform averaging of several files # 1. Create destination Nansat object with desired projection nMosaic = Mosaic(domain=domain) # 2. Perfom averaging nMosaic.average(iFileNames, bands=['L_645', 'L_555', 'L_469']) # 3. Get mask of valid pixels mask = nMosaic['mask'] # 4. Output averaged data using the mask nMosaic.write_figure(fileName=oFileName + '.png', bands=['L_645', 'L_555', 'L_469'], clim='hist', mask_array=mask, mask_lut={0: [128,128,128]}) # 5. Get values of standard deviation from averaging of input files L_469_std = nMosaic['L_469_std'] # B. calculate median from the first band (very slow thus comented) #nMosaic.median(['gcps.tif', 'stere.tif'])
def test_init(self): mo = Mosaic(domain=self.domain) self.assertEqual(type(mo), Mosaic)
# 6. Get Nansat object with watermask # 7. Get array from Nansat object. 0 - land, 1 - water wm = n.watermask() # 6. wmArray = wm[1] # 7. # 8. Write the projected image with transparent land mask and image background # transparentMask: boolean, defult = False # If True, the masked pixels will be transparent when saving to png #transparency: int #transparency of the image background, set for PIL in Figure.save() #default transparent color is [0,0,0] n.write_figure(fileName=oFileName + '_proj.png', bands=[1,2,3], mask_array=wmArray, mask_lut={0: [128, 128, 128]}, clim='hist', transparency=[128, 128, 128]) # 8. # make KML file for the exported image n.write_kml_image(kmlFileName=oFileName + '.kml', kmlFigureName=oFileName + '_proj.png') # Perform batch averaging of several files # 1. Create destination Nansat object with desired projection nMosaic = Mosaic(domain=dStereo) # 2. Perfom averaging nMosaic.average(['gcps.tif', 'stere.tif'], bands=['L_645', 'L_555', 'L_469']) # 3. Get mask of non-valid pixels mask = nMosaic['mask'] # 4. Output averaged data using the mask nMosaic.write_figure(fileName=oFileName + '_mosaic.png', bands=['L_645', 'L_555', 'L_469'], clim='hist', mask_array=mask, mask_lut={0:[128,128,128]}) print 'Tutorial completed successfully. Output files are found here:' + oFileName