def _impute_lat_lon(cov_file, subchunk, config):
cov = geoio.RasterioImageSource(cov_file)
cov_data = features.extract_subchunks(cov, subchunk,
config.n_subchunks,
config.patchsize)
nn_imputer = transforms.NearestNeighboursImputer()
cov_data = nn_imputer(cov_data.reshape(cov_data.shape[0], 1))
return cov_data
def f(image_source):
r = features.extract_subchunks(image_source, subchunk_index=0,
n_subchunks=1,
patchsize=config.patchsize)
if frac < 1.0:
np.random.seed(1)
r = r[np.random.rand(r.shape[0]) < frac]
return r
def chunk_nancount(image_source, n_subchunks, subchunk_index):
r = features.extract_subchunks(image_source,
subchunk_index,
n_subchunks,
patchsize=0)
nan_count = np.isnan(r).sum()
# if the r is entirely masked (due to nodata) maskedconstant is returned
if isinstance(nan_count, np.ma.core.MaskedConstant):
return 0
else:
return nan_count
def f(image_source):
r_t = features.extract_features(image_source, targets, n_subchunks=1,
patchsize=config.patchsize)
r_a = features.extract_subchunks(image_source, subchunk_index=0,
n_subchunks=1,
patchsize=config.patchsize)
if frac < 1.0:
np.random.seed(1)
r_a = r_a[np.random.rand(r_a.shape[0]) < frac]
r_data = np.concatenate([r_t.data, r_a.data], axis=0)
r_mask = np.concatenate([r_t.mask, r_a.mask], axis=0)
r = np.ma.masked_array(data=r_data, mask=r_mask)
return r
def _mask_rows(x, subchunk, config):
mask = config.mask
if mask:
mask_source = geoio.RasterioImageSource(mask)
mask_data = features.extract_subchunks(mask_source, subchunk,
config.n_subchunks,
config.patchsize)
mask_data = mask_data.reshape(mask_data.shape[0], 1)
mask_x = mask_data.data[:, 0] != config.retain
log.info('Areas with mask={} will be predicted'.format(config.retain))
assert x.shape[0] == mask_x.shape[0], 'shape mismatch of ' \
'mask and inputs'
x.mask = np.tile(mask_x, (x.shape[1], 1)).T
return x
def f(image_source):
r = features.extract_subchunks(image_source, subchunk_index,
config.n_subchunks, config.patchsize)
return r
def mask_subchunks(subchunk, config):
image_source = geoio.RasterioImageSource(config.mask)
result = features.extract_subchunks(image_source, subchunk,
config.n_subchunks, config.patchsize)
return result
