def warp(self, ds_input, resampling="NearestNeighbour", max_error=0.0):
"""
Return a warped GDALRaster with the given input characteristics.
The input is expected to be a dictionary containing the parameters
of the target raster. Allowed values are width, height, SRID, origin,
scale, skew, datatype, driver, and name (filename).
By default, the warp functions keeps all parameters equal to the values
of the original source raster. For the name of the target raster, the
name of the source raster will be used and appended with
_copy. + source_driver_name.
In addition, the resampling algorithm can be specified with the "resampling"
input parameter. The default is NearestNeighbor. For a list of all options
consult the GDAL_RESAMPLE_ALGORITHMS constant.
"""
# Get the parameters defining the geotransform, srid, and size of the raster
ds_input.setdefault("width", self.width)
ds_input.setdefault("height", self.height)
ds_input.setdefault("srid", self.srs.srid)
ds_input.setdefault("origin", self.origin)
ds_input.setdefault("scale", self.scale)
ds_input.setdefault("skew", self.skew)
# Get the driver, name, and datatype of the target raster
ds_input.setdefault("driver", self.driver.name)
if "name" not in ds_input:
ds_input["name"] = self.name + "_copy." + self.driver.name
if "datatype" not in ds_input:
ds_input["datatype"] = self.bands[0].datatype()
# Instantiate raster bands filled with nodata values.
ds_input["bands"] = [{"nodata_value": bnd.nodata_value} for bnd in self.bands]
# Create target raster
target = GDALRaster(ds_input, write=True)
# Select resampling algorithm
algorithm = GDAL_RESAMPLE_ALGORITHMS[resampling]
# Reproject image
capi.reproject_image(
self._ptr,
self.srs.wkt.encode(),
target._ptr,
target.srs.wkt.encode(),
algorithm,
0.0,
max_error,
c_void_p(),
c_void_p(),
c_void_p(),
)
# Make sure all data is written to file
target._flush()
return target
def warp(self, ds_input, resampling='NearestNeighbour', max_error=0.0):
"""
Return a warped GDALRaster with the given input characteristics.
The input is expected to be a dictionary containing the parameters
of the target raster. Allowed values are width, height, SRID, origin,
scale, skew, datatype, driver, and name (filename).
By default, the warp functions keeps all parameters equal to the values
of the original source raster. For the name of the target raster, the
name of the source raster will be used and appended with
_copy. + source_driver_name.
In addition, the resampling algorithm can be specified with the "resampling"
input parameter. The default is NearestNeighbor. For a list of all options
consult the GDAL_RESAMPLE_ALGORITHMS constant.
"""
# Get the parameters defining the geotransform, srid, and size of the raster
ds_input.setdefault('width', self.width)
ds_input.setdefault('height', self.height)
ds_input.setdefault('srid', self.srs.srid)
ds_input.setdefault('origin', self.origin)
ds_input.setdefault('scale', self.scale)
ds_input.setdefault('skew', self.skew)
# Get the driver, name, and datatype of the target raster
ds_input.setdefault('driver', self.driver.name)
if 'name' not in ds_input:
ds_input['name'] = self.name + '_copy.' + self.driver.name
if 'datatype' not in ds_input:
ds_input['datatype'] = self.bands[0].datatype()
# Instantiate raster bands filled with nodata values.
ds_input['bands'] = [{'nodata_value': bnd.nodata_value} for bnd in self.bands]
# Create target raster
target = GDALRaster(ds_input, write=True)
# Select resampling algorithm
algorithm = GDAL_RESAMPLE_ALGORITHMS[resampling]
# Reproject image
capi.reproject_image(
self._ptr, self.srs.wkt.encode(),
target._ptr, target.srs.wkt.encode(),
algorithm, 0.0, max_error,
c_void_p(), c_void_p(), c_void_p()
)
# Make sure all data is written to file
target._flush()
return target
def warp(self, ds_input, resampling='NearestNeighbour', max_error=0.0):
"""
Returns a warped GDALRaster with the given input characteristics.
The input is expected to be a dictionary containing the parameters
of the target raster. Allowed values are width, height, SRID, origin,
scale, skew, datatype, driver, and name (filename).
By default, the warp functions keeps all parameters equal to the values
of the original source raster. For the name of the target raster, the
name of the source raster will be used and appended with
_copy. + source_driver_name.
In addition, the resampling algorithm can be specified with the "resampling"
input parameter. The default is NearestNeighbor. For a list of all options
consult the GDAL_RESAMPLE_ALGORITHMS constant.
"""
# Get the parameters defining the geotransform, srid, and size of the raster
if 'width' not in ds_input:
ds_input['width'] = self.width
if 'height' not in ds_input:
ds_input['height'] = self.height
if 'srid' not in ds_input:
ds_input['srid'] = self.srs.srid
if 'origin' not in ds_input:
ds_input['origin'] = self.origin
if 'scale' not in ds_input:
ds_input['scale'] = self.scale
if 'skew' not in ds_input:
ds_input['skew'] = self.skew
# Get the driver, name, and datatype of the target raster
if 'driver' not in ds_input:
ds_input['driver'] = self.driver.name
if 'name' not in ds_input:
ds_input['name'] = self.name + '_copy.' + self.driver.name
if 'datatype' not in ds_input:
ds_input['datatype'] = self.bands[0].datatype()
# Set the number of bands
ds_input['nr_of_bands'] = len(self.bands)
# Create target raster
target = GDALRaster(ds_input, write=True)
# Copy nodata values to warped raster
for index, band in enumerate(self.bands):
target.bands[index].nodata_value = band.nodata_value
# Select resampling algorithm
algorithm = GDAL_RESAMPLE_ALGORITHMS[resampling]
# Reproject image
capi.reproject_image(self._ptr, self.srs.wkt.encode(), target._ptr,
target.srs.wkt.encode(), algorithm, 0.0,
max_error, c_void_p(), c_void_p(), c_void_p())
# Make sure all data is written to file
target._flush()
return target
