def test_pad_data_vertically_bad_shape(self):
"""Test the error handling for the vertical hrv padding."""
data = xr.DataArray(data=np.zeros((10, 1)), dims=('y', 'x'))
south_bound = 5
north_bound = 10
final_size = (20, 1)
with self.assertRaises(IndexError):
pad_data_vertically(data, final_size, south_bound, north_bound)
def test_pad_data_vertically():
"""Test the vertical hrv padding."""
data = xr.DataArray(data=np.zeros((10, 1)), dims=('y', 'x'))
south_bound = 4
north_bound = 13
final_size = (20, 1)
res = pad_data_vertically(data, final_size, south_bound, north_bound)
expected = np.zeros(final_size)
expected[:] = np.nan
expected[south_bound-1:north_bound] = 0.
np.testing.assert_equal(res, expected)
def pad_data(self, dataset):
"""Pad data to full disk with empty pixels."""
logger.debug('Padding data to full disk')
data_list = []
for south_bound, north_bound, east_bound, west_bound in zip(
*self._img_bounds.values()):
nlines = north_bound - south_bound + 1
data = self._extract_data_to_pad(dataset, south_bound, north_bound)
padded_data = pad_data_horizontally(data,
(nlines, self._final_shape[1]),
east_bound, west_bound)
data_list.append(padded_data)
padded_data = da.vstack(data_list)
# If we're dealing with RSS or ROI data, we also need to pad vertically in order to form a full disk array
if not self._is_full_disk:
padded_data = pad_data_vertically(padded_data, self._final_shape,
south_bound, north_bound)
return xr.DataArray(padded_data,
dims=('y', 'x'),
attrs=dataset.attrs.copy())