class TestToCrs(TestCase):
def setUp(self) -> None:
# test inputs
predictors = [
nc.band1, nc.band2, nc.band3, nc.band4, nc.band5, nc.band7
]
self.stack = Raster(predictors)
# test results
self.stack_prj = None
def tearDown(self) -> None:
self.stack.close()
self.stack_prj.close()
def test_to_crs_defaults(self):
self.stack_prj = self.stack.to_crs({"init": "EPSG:4326"})
# check raster object
self.assertIsInstance(self.stack_prj, Raster)
self.assertEqual(self.stack_prj.count, self.stack.count)
self.assertEqual(self.stack_prj.read(masked=True).count(), 1012061)
# test nodata value is recognized
self.assertEqual(
self.stack_prj.read(masked=True).min(),
self.stack.read(masked=True).min())
self.assertEqual(
self.stack_prj.read(masked=True).max(),
self.stack.read(masked=True).max())
def test_to_crs_custom_nodata(self):
self.stack_prj = self.stack.to_crs({"init": "EPSG:4326"}, nodata=-999)
# check raster object
self.assertIsInstance(self.stack_prj, Raster)
self.assertEqual(self.stack_prj.count, self.stack.count)
self.assertEqual(self.stack_prj.read(masked=True).count(), 1012061)
# test nodata value is recognized
self.assertEqual(
self.stack_prj.read(masked=True).min(),
self.stack.read(masked=True).min())
self.assertEqual(
self.stack_prj.read(masked=True).max(),
self.stack.read(masked=True).max())
def test_to_crs_in_memory(self):
self.stack_prj = self.stack.to_crs({"init": "EPSG:4326"},
in_memory=True)
# check raster object
self.assertIsInstance(self.stack_prj, Raster)
os.chdir(os.path.join('.', 'examples'))
band1 = os.path.join(basedir, 'pyspatialml', 'tests', 'lsat7_2000_10.tif')
band2 = os.path.join(basedir, 'pyspatialml', 'tests', 'lsat7_2000_20.tif')
band3 = os.path.join(basedir, 'pyspatialml', 'tests', 'lsat7_2000_30.tif')
band4 = os.path.join(basedir, 'pyspatialml', 'tests', 'lsat7_2000_40.tif')
band5 = os.path.join(basedir, 'pyspatialml', 'tests', 'lsat7_2000_50.tif')
band7 = os.path.join(basedir, 'pyspatialml', 'tests', 'lsat7_2000_70.tif')
multiband = os.path.join(basedir, 'pyspatialml', 'tests',
'landsat_multiband.tif')
predictors = [band1, band2, band3, band4, band5, band7]
# Create a RasterStack instance
stack = Raster([band1, band2, band3, band4, band5, band7])
stack.count
stack_rs = stack.to_crs(crs={'init': 'EPSG:4326'}, progress=False)
stack_rs.plot()
# Aggregate a raster to a coarser cell size
stack_new = stack.aggregate(out_shape=(100, 100))
stack_new.iloc[0].plot()
# Plot a RasterLayer
stack.lsat7_2000_10.plot()
# Plot a Raster
stack.lsat7_2000_10.cmap = 'plasma'
stack.plot(label_fontsize=8, title_fontsize=8)
# Iterate through RasterLayers
for name, layer in stack:
# masking
training_py = geopandas.read_file(nc.polygons)
mask_py = training_py.iloc[0:1, :]
mask_py.plot()
masked_object = stack.mask(mask_py, invert=False, pad=True)
masked_object.plot()
# cropping
training_py = geopandas.read_file(nc.polygons)
crop_bounds = training_py.loc[0, 'geometry'].bounds
stack_cropped = stack.crop(crop_bounds)
stack_cropped.plot()
# reprojection
stack_prj = stack.to_crs({'init': 'EPSG:4326'})
stack_prj.plot()
# Perform band math
ndvi = (stack.iloc[3] - stack.iloc[2]) / (stack.iloc[3] + stack.iloc[2])
ndvi = Raster(ndvi)
ndvi.plot()
tmp = ndvi.aggregate(out_shape=(50, 50))
tmp.plot()
# Perform OR operation (union two layers)
(stack.iloc[5] | stack.iloc[0]).plot()
plt.show()
# Perform AND operation (intersect two layers)
