def test_small_data_coherence(gamma_or_mexicoa_conf):
work_dir = Path(tempfile.mkdtemp())
params = common.manipulate_test_conf(conf_file=gamma_or_mexicoa_conf, work_dir=work_dir)
params[c.COH_MASK] = 1
ifg_multilist = copy(params[c.INTERFEROGRAM_FILES])
conv2tif.main(params)
for i in ifg_multilist:
p = Path(i.converted_path)
p.chmod(0o664) # assign write permission as conv2tif output is readonly
ifg = pyrate.core.shared.dem_or_ifg(data_path=p.as_posix())
if not isinstance(ifg, Ifg):
continue
ifg.open()
# now do coherence masking and compare
ifg = pyrate.core.shared.dem_or_ifg(data_path=p.as_posix())
ifg.open()
converted_coh_file_path = pyrate.core.prepifg_helper.coherence_paths_for(p, params, tif=True)
gdal_python.coherence_masking(ifg.dataset,
coh_file_path=converted_coh_file_path,
coh_thr=params[c.COH_THRESH]
)
nans = np.isnan(ifg.phase_data)
coherence_path = pyrate.core.prepifg_helper.coherence_paths_for(p, params, tif=True)
cifg = Ifg(coherence_path)
cifg.open()
cifg_below_thrhold = cifg.phase_data < params[c.COH_THRESH]
np.testing.assert_array_equal(nans, cifg_below_thrhold)
shutil.rmtree(work_dir)
def test_small_data_coherence(gamma_params):
gamma_params[cf.COH_MASK] = 1
ifg_multilist = copy(gamma_params[cf.INTERFEROGRAM_FILES])
conv2tif.main(gamma_params)
for i in ifg_multilist:
p = Path(i.converted_path)
p.chmod(
0o664) # assign write permission as conv2tif output is readonly
ifg = pyrate.core.shared.dem_or_ifg(data_path=p.as_posix())
if not isinstance(ifg, Ifg):
continue
ifg.open()
# now do coherence masking and compare
ifg = pyrate.core.shared.dem_or_ifg(data_path=p.as_posix())
ifg.open()
converted_coh_file_path = cf.coherence_paths_for(p,
gamma_params,
tif=True)
gdal_python.coherence_masking(
ifg.dataset,
coherence_file_path=converted_coh_file_path,
coherence_thresh=gamma_params[cf.COH_THRESH])
nans = np.isnan(ifg.phase_data)
coherence_path = cf.coherence_paths_for(p, gamma_params, tif=True)
cifg = Ifg(coherence_path)
cifg.open()
cifg_below_thrhold = cifg.phase_data < gamma_params[cf.COH_THRESH]
np.testing.assert_array_equal(nans, cifg_below_thrhold)
def test_coherence_files_not_converted(self):
# define constants
NO_DATA_VALUE = 0
driver = gdal.GetDriverByName('GTiff')
# create a sample gdal dataset
# sample gdal dataset
sample_gdal_filename = 'sample_gdal_dataset.tif'
sample_gdal_dataset = driver.Create(sample_gdal_filename, 5, 5, 1,
gdal.GDT_Float32)
srs = osr.SpatialReference()
wkt_projection = srs.ExportToWkt()
sample_gdal_dataset.SetProjection(wkt_projection)
sample_gdal_band = sample_gdal_dataset.GetRasterBand(1)
sample_gdal_band.SetNoDataValue(NO_DATA_VALUE)
sample_gdal_band.WriteArray(np.arange(25).reshape(5, 5))
# create a coherence mask dataset
coherence_mask_filename = 'coherence_mask_dataset.tif'
coherence_mask_dataset = driver.Create(coherence_mask_filename, 5, 5,
1, gdal.GDT_Float32)
srs = osr.SpatialReference()
wkt_projection = srs.ExportToWkt()
coherence_mask_dataset.SetProjection(wkt_projection)
coherence_mask_band = coherence_mask_dataset.GetRasterBand(1)
coherence_mask_band.SetNoDataValue(NO_DATA_VALUE)
coherence_mask_band.WriteArray(
np.arange(0, 75, 3).reshape(5, 5) / 100.0)
# create a artificial masked dataset
expected_result_array = np.nan_to_num(
np.array([[np.nan, np.nan, np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan],
[10., 11., 12., 13., 14.], [15., 16., 17., 18., 19.],
[20., 21., 22., 23., 24.]]))
# use the gdal_python.coherence_masking to find the actual mask dataset
threshold = 0.3
gdal_python.coherence_masking(sample_gdal_dataset,
coherence_mask_dataset, threshold)
sample_gdal_array = np.nan_to_num(
sample_gdal_dataset.GetRasterBand(1).ReadAsArray())
# compare the artificial masked and actual masked datasets
self.assertTrue(
np.array_equiv(sample_gdal_array, expected_result_array))
# del the tmp datasets created
del coherence_mask_dataset
os.remove(coherence_mask_filename)
del sample_gdal_dataset
os.remove(sample_gdal_filename)
def test_coherence_files_not_converted():
# define constants
NO_DATA_VALUE = 0
driver = gdal.GetDriverByName('GTiff')
# create a sample gdal dataset
# sample gdal dataset
sample_gdal_filename = "dataset_01122000.tif"
options = ['PROFILE=GeoTIFF']
sample_gdal_dataset = driver.Create(sample_gdal_filename, 5, 5, 1, gdal.GDT_Float32, options=options)
srs = osr.SpatialReference()
wkt_projection = srs.ExportToWkt()
sample_gdal_dataset.SetProjection(wkt_projection)
sample_gdal_band = sample_gdal_dataset.GetRasterBand(1)
sample_gdal_band.SetNoDataValue(NO_DATA_VALUE)
sample_gdal_band.WriteArray(np.arange(25).reshape(5, 5))
sample_gdal_dataset.SetMetadataItem(ifc.FIRST_DATE, '2019-10-20')
sample_gdal_dataset.SetMetadataItem(ifc.SECOND_DATE, '2019-11-01')
sample_gdal_dataset.SetMetadataItem(ifc.PYRATE_WAVELENGTH_METRES, '10.05656')
sample_gdal_dataset.FlushCache()
sample_gdal_dataset = None
ifg = Ifg(sample_gdal_filename)
ifg.open()
# create a coherence mask dataset
tmpdir = tempfile.mkdtemp()
out_dir = Path(tmpdir) # we won't be creating any output coherence mask files as there are already GeoTIFFs
params = common.min_params(out_dir)
coherence_mask_filename = MultiplePaths(Path("mask_dataset_01122000-02122000.tif").as_posix(), params)
coherence_mask_dataset = driver.Create(coherence_mask_filename.converted_path, 5, 5, 1, gdal.GDT_Float32)
srs = osr.SpatialReference()
wkt_projection = srs.ExportToWkt()
coherence_mask_dataset.SetProjection(wkt_projection)
coherence_mask_band = coherence_mask_dataset.GetRasterBand(1)
coherence_mask_band.SetNoDataValue(NO_DATA_VALUE)
arr = np.arange(0, 75, 3).reshape(5, 5) / 100.0
arr[3, 4] = 0.25 # insert some random lower than threshold number
arr[4, 2] = 0.20 # insert some random lower than threshold number
coherence_mask_band.WriteArray(arr)
# del the tmp handler datasets created
del coherence_mask_dataset
# create an artificial masked dataset
expected_result_array = np.nan_to_num(
np.array(
[
[np.nan, np.nan, np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan],
[10.0, 11.0, 12.0, 13.0, 14.0],
[15.0, 16.0, 17.0, 18.0, np.nan],
[20.0, 21.0, np.nan, 23.0, 24.0],
]
)
)
# use the gdal_python.coherence_masking to find the actual mask dataset
coherence_thresh = 0.3
gdal_python.coherence_masking(ifg.dataset, coherence_mask_filename.converted_path, coherence_thresh)
sample_gdal_array = np.nan_to_num(ifg.phase_data)
# compare the artificial masked and actual masked datasets
np.testing.assert_array_equal(sample_gdal_array, expected_result_array)
# del the tmp datasets created
os.remove(coherence_mask_filename.converted_path)
ifg.close()
os.remove(sample_gdal_filename)