def transform(
self, srs, driver=None, name=None, resampling="NearestNeighbour", max_error=0.0
):
"""
Return a copy of this raster reprojected into the given spatial
reference system.
"""
# Convert the resampling algorithm name into an algorithm id
algorithm = GDAL_RESAMPLE_ALGORITHMS[resampling]
if isinstance(srs, SpatialReference):
target_srs = srs
elif isinstance(srs, (int, str)):
target_srs = SpatialReference(srs)
else:
raise TypeError(
"Transform only accepts SpatialReference, string, and integer "
"objects."
)
if target_srs.srid == self.srid and (not driver or driver == self.driver.name):
return self.clone(name)
# Create warped virtual dataset in the target reference system
target = capi.auto_create_warped_vrt(
self._ptr,
self.srs.wkt.encode(),
target_srs.wkt.encode(),
algorithm,
max_error,
c_void_p(),
)
target = GDALRaster(target)
# Construct the target warp dictionary from the virtual raster
data = {
"srid": target_srs.srid,
"width": target.width,
"height": target.height,
"origin": [target.origin.x, target.origin.y],
"scale": [target.scale.x, target.scale.y],
"skew": [target.skew.x, target.skew.y],
}
# Set the driver and filepath if provided
if driver:
data["driver"] = driver
if name:
data["name"] = name
# Warp the raster into new srid
return self.warp(data, resampling=resampling, max_error=max_error)
def transform(self,
srid,
driver=None,
name=None,
resampling="NearestNeighbour",
max_error=0.0):
"""
Return a copy of this raster reprojected into the given SRID.
"""
# Convert the resampling algorithm name into an algorithm id
algorithm = GDAL_RESAMPLE_ALGORITHMS[resampling]
# Instantiate target spatial reference system
target_srs = SpatialReference(srid)
# Create warped virtual dataset in the target reference system
target = capi.auto_create_warped_vrt(
self._ptr,
self.srs.wkt.encode(),
target_srs.wkt.encode(),
algorithm,
max_error,
c_void_p(),
)
target = GDALRaster(target)
# Construct the target warp dictionary from the virtual raster
data = {
"srid": srid,
"width": target.width,
"height": target.height,
"origin": [target.origin.x, target.origin.y],
"scale": [target.scale.x, target.scale.y],
"skew": [target.skew.x, target.skew.y],
}
# Set the driver and filepath if provided
if driver:
data["driver"] = driver
if name:
data["name"] = name
# Warp the raster into new srid
return self.warp(data, resampling=resampling, max_error=max_error)
def transform(self, srs, driver=None, name=None, resampling='NearestNeighbour',
max_error=0.0):
"""
Return a copy of this raster reprojected into the given spatial
reference system.
"""
# Convert the resampling algorithm name into an algorithm id
algorithm = GDAL_RESAMPLE_ALGORITHMS[resampling]
if isinstance(srs, SpatialReference):
target_srs = srs
elif isinstance(srs, (int, str)):
target_srs = SpatialReference(srs)
else:
raise TypeError(
'Transform only accepts SpatialReference, string, and integer '
'objects.'
)
# Create warped virtual dataset in the target reference system
target = capi.auto_create_warped_vrt(
self._ptr, self.srs.wkt.encode(), target_srs.wkt.encode(),
algorithm, max_error, c_void_p()
)
target = GDALRaster(target)
# Construct the target warp dictionary from the virtual raster
data = {
'srid': target_srs.srid,
'width': target.width,
'height': target.height,
'origin': [target.origin.x, target.origin.y],
'scale': [target.scale.x, target.scale.y],
'skew': [target.skew.x, target.skew.y],
}
# Set the driver and filepath if provided
if driver:
data['driver'] = driver
if name:
data['name'] = name
# Warp the raster into new srid
return self.warp(data, resampling=resampling, max_error=max_error)
def transform(self, srid, driver=None, name=None, resampling='NearestNeighbour',
max_error=0.0):
"""
<<<<<<< HEAD
Returns a copy of this raster reprojected into the given SRID.
=======
Return a copy of this raster reprojected into the given SRID.
>>>>>>> 37c99181c9a6b95433d60f8c8ef9af5731096435
"""
# Convert the resampling algorithm name into an algorithm id
algorithm = GDAL_RESAMPLE_ALGORITHMS[resampling]
# Instantiate target spatial reference system
target_srs = SpatialReference(srid)
# Create warped virtual dataset in the target reference system
target = capi.auto_create_warped_vrt(
self._ptr, self.srs.wkt.encode(), target_srs.wkt.encode(),
algorithm, max_error, c_void_p()
)
target = GDALRaster(target)
# Construct the target warp dictionary from the virtual raster
data = {
'srid': srid,
'width': target.width,
'height': target.height,
'origin': [target.origin.x, target.origin.y],
'scale': [target.scale.x, target.scale.y],
'skew': [target.skew.x, target.skew.y],
}
# Set the driver and filepath if provided
if driver:
data['driver'] = driver
if name:
data['name'] = name
# Warp the raster into new srid
return self.warp(data, resampling=resampling, max_error=max_error)
def transform(self, srid, driver=None, name=None, resampling='NearestNeighbour',
max_error=0.0):
"""
Return a copy of this raster reprojected into the given SRID.
"""
# Convert the resampling algorithm name into an algorithm id
algorithm = GDAL_RESAMPLE_ALGORITHMS[resampling]
# Instantiate target spatial reference system
target_srs = SpatialReference(srid)
# Create warped virtual dataset in the target reference system
target = capi.auto_create_warped_vrt(
self._ptr, self.srs.wkt.encode(), target_srs.wkt.encode(),
algorithm, max_error, c_void_p()
)
target = GDALRaster(target)
# Construct the target warp dictionary from the virtual raster
data = {
'srid': srid,
'width': target.width,
'height': target.height,
'origin': [target.origin.x, target.origin.y],
'scale': [target.scale.x, target.scale.y],
'skew': [target.skew.x, target.skew.y],
}
# Set the driver and filepath if provided
if driver:
data['driver'] = driver
if name:
data['name'] = name
# Warp the raster into new srid
return self.warp(data, resampling=resampling, max_error=max_error)
