def create_grid(geobox, coord_fn, depth=7):
"""
Interpolates a `NumPy` array based on the input coordinate function
`coord_fn`.
:param geobox:
An instance of an `GriddedGeoBox` object.
:param coord_fn:
A function that maps coordinates.
:return:
A `NumPy` array.
"""
# Define the transform funtions
func = partial(coord_fn, geobox=geobox, centre=True)
# Get some basic info about the image
shape = geobox.get_shape_yx()
# Initialise the array to contain the result
arr = numpy.zeros(shape, dtype="float64")
interpolate_grid(arr, func, depth=depth, origin=(0, 0), shape=shape)
return arr
def test_small_grid(self):
"""
Test grid too small to calculate bilinear interpolation
"""
with self.assertRaises(ValueError):
in_arr = np.zeros(1).reshape(1,1)
eval_func = lambda y, x: y * 1 + x
depth = 7
interpolate_grid(in_arr, eval_func, depth)
def test_interpolate_default_max_depth(self):
"""
Test that the wrapper defaults to max depth
"""
result = np.arange(64).reshape(8, 8)
in_arr = result.copy() # Interpolation is performed in place
eval_func = lambda y, x: 8 * y + x
depth = 10
interpolate_grid(result, eval_func, depth)
self.assertTrue(np.allclose(result, in_arr))
def test_interpolate_grid(self):
"""
Simple test case for interpolate grid
"""
result = np.arange(64).reshape(8, 8)
in_arr = result.copy() # Interpolation is performed in place
eval_func = lambda y, x: 8 * y + x
depth = 3
interpolate_grid(result, eval_func, depth)
self.assertTrue(np.allclose(result, in_arr))
def create_lon_lat_grids(
acquisition,
out_group=None,
compression=H5CompressionFilter.LZF,
filter_opts=None,
depth=7,
):
"""
Creates 2 by 2D NumPy arrays containing longitude and latitude
co-ordinates for each array element.
:param acquisition:
An instance of an `Acquisition` object.
:param out_group:
If set to None (default) then the results will be returned
as an in-memory hdf5 file, i.e. the `core` driver. Otherwise,
a writeable HDF5 `Group` object.
The dataset names will be given by:
* contants.DatasetName.LON.value
* contants.DatasetName.LAT.value
:param compression:
The compression filter to use.
Default is H5CompressionFilter.LZF
:filter_opts:
A dict of key value pairs available to the given configuration
instance of H5CompressionFilter. For example
H5CompressionFilter.LZF has the keywords *chunks* and *shuffle*
available.
Default is None, which will use the default settings for the
chosen H5CompressionFilter instance.
:return:
An opened `h5py.File` object, that is either in-memory using the
`core` driver, or on disk.
"""
geobox = acquisition.gridded_geo_box()
# Define the lon and lat transform funtions
lon_func = partial(get_lon_coordinate, geobox=geobox, centre=True)
lat_func = partial(get_lat_coordinate, geobox=geobox, centre=True)
# Get some basic info about the image
shape = geobox.get_shape_yx()
# Initialise the array to contain the result
result = numpy.zeros(shape, dtype="float64")
interpolate_grid(result, lon_func, depth=depth, origin=(0, 0), shape=shape)
# Initialise the output files
if out_group is None:
fid = h5py.File("longitude-latitude.h5",
"w",
driver="core",
backing_store=False)
else:
fid = out_group
if GroupName.LON_LAT_GROUP.value not in fid:
fid.create_group(GroupName.LON_LAT_GROUP.value)
grp = fid[GroupName.LON_LAT_GROUP.value]
# define some base attributes for the image datasets
attrs = {
"crs_wkt": geobox.crs.ExportToWkt(),
"geotransform": geobox.transform.to_gdal(),
"description": LON_DESC,
}
if filter_opts is None:
filter_opts = {}
filter_opts["chunks"] = acquisition.tile_size
kwargs = compression.config(**filter_opts).dataset_compression_kwargs()
lon_dset = grp.create_dataset(DatasetName.LON.value, data=result, **kwargs)
attach_image_attributes(lon_dset, attrs)
result = numpy.zeros(shape, dtype="float64")
interpolate_grid(result, lat_func, depth=depth, origin=(0, 0), shape=shape)
attrs["description"] = LAT_DESC
lat_dset = grp.create_dataset(DatasetName.LAT.value, data=result, **kwargs)
attach_image_attributes(lat_dset, attrs)
return fid
