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