pyxccd.imagetool.prepare_ard module

pyxccd.imagetool.prepare_ard.mask_value(vector, val)

Build a boolean mask around a certain value in the vector.

Parameters:

• vector – 1-d ndarray of values

• val – values to mask on

Returns:

1-d boolean ndarray

pyxccd.imagetool.prepare_ard.qabitval_array_HLS(packedint_array: ndarray)

Institute a hierarchy of qa values that may be flagged in the bitpacked value.

fill > cloud > shadow > snow > water > clear

Parameters:

packedint – int value to bit check

Returns:

offset value to use

pyxccd.imagetool.prepare_ard.qabitval_array(packedint_array: ndarray)

Institute a hierarchy of qa values that may be flagged in the bitpacked value. fill > cloud > shadow > snow > water > clear :Parameters: packedint – int value to bit check

Returns:

offset value to use

pyxccd.imagetool.prepare_ard.qabitval_array_c2(packedint_array: ndarray)

Institute a hierarchy of qa values that may be flagged in the bitpacked value for c2

fill > cloud > shadow > snow > water > clear

Parameters:

packedint – int value to bit check

Returns:

offset value to use

pyxccd.imagetool.prepare_ard.single_image_stacking_hls(source_dir: str, out_dir: str, logger: Logger, dataset_info: DatasetInfo, is_partition: bool, clear_threshold: float, low_date_bound: str, upp_date_bound: str, folder: str)

unzip single image, convert bit-pack qa to byte value, and save as numpy :param source_dir: the parent folder to save image ‘folder’ :param out_dir: the folder to save result :param logger: the handler of logger file :param data_info: data info data class :param is_partition: True, partition each image into blocks; False, save original size of image :param clear_threshold: threshold of clear pixel percentage, if lower than threshold, won’t be processed :param low_date_bound: the lower date of user interested date range :param upp_date_bound: the upper date of user interested date range :param folder: the folder name of image :return:

pyxccd.imagetool.prepare_ard.single_image_stacking(tmp_path: str, source_dir: str, out_dir: str, clear_threshold: float, path_array: ndarray, logger: Logger, dataset_info: DatasetInfo, is_partition: bool, low_date_bound: str, upp_date_bound: str, b_c2: bool, folder: str)

unzip single image, convert bit-pack qa to byte value, and save as numpy :param tmp_path: tmp folder to save unzip image :param source_dir: image folder save source zipped files :param out_dir: the folder to save result :param clear_threshold: threshold of clear pixel percentage, if lower than threshold, won’t be processed :param path_array: path array has the same dimension of inputted image, and the pixel value indicates

the path which the pixel belongs to; if path_array == none, we will use all path

Parameters:

• logger – the handler of logger file

• dataset_info – dataset information

• is_partition – True, partition each image into blocks; False, save original size of image

• low_date_bound – the lower bound of user interested year range

• upp_date_bound – the upper bound of user interested year range

• b_c2 – False

• folder – the folder name of image

Returns:

pyxccd.imagetool.prepare_ard.single_image_stacking_collection2(tmp_path: str, source_dir: str, out_dir: str, clear_threshold: float, logger: Logger, dataset_info: DatasetInfo, reference_path: str, is_partition: bool, low_date_bound: str, upp_date_bound: str, folder: str)

for collection 2 :param tmp_path: tmp folder to save unzip image :param source_dir: image folder save source zipped files :param out_dir: the folder to save result :param clear_threshold: threshold of clear pixel percentage, if lower than threshold, won’t be processed :param logger: the handler of logger file :param dataset_info:DatasetInfo :param is_partition: True, partition each image into blocks; False, save original size of image :param low_date_bound: the lower bound of user interested date range :param upp_date_bound: the upper bound of user interested date range :param bounds :param folder: the folder name of image :return:

pyxccd.imagetool.prepare_ard.checkfinished_step1(tmp_path)

Parameters:

tmp_path

Returns:

pyxccd.imagetool.prepare_ard.checkfinished_step2(out_dir, n_cores)

Parameters:

• out_dir

• n_cores

Returns:

pyxccd.imagetool.prepare_ard.checkfinished_step3_partition(out_dir)

Parameters:

out_dir

Returns:

pyxccd.imagetool.prepare_ard.checkfinished_step3_nopartition(out_dir)

Parameters:

out_dir

Returns:

pyxccd.imagetool.prepare_ard.get_extent(extent_geojson, res, buf=0)

read shapefile of a tile from an S3 bucket, and convert projection to be aligned with sample image. arg:

‘extent_geojson’: sharply geojson object res: planet resolution

Returns:

(float, float, float, float), (int, int)) tuple

pyxccd.imagetool.prepare_ard.get_feature(shp, id)

pyxccd.imagetool.prepare_ard.explode(coords)

Explode a GeoJSON geometry’s coordinates object and yield coordinate tuples. As long as the input is conforming, the type of the geometry doesn’t matter.

pyxccd.imagetool.prepare_ard.bbox(f)

pyxccd.imagetool.prepare_ard.checkfinished(signal_path) → bool

Check if the signal file exists

pyxccd.imagetool.prepare_ard.safe_stack_single_image_hls(source_dir: str, out_dir: str, data_info: DatasetInfo, logger: Logger, folder: str, low_date_bound: str, upp_date_bound: str) → bool

Process single HLS image stacking

pyxccd.imagetool.prepare_ard.process_folder(folder_list, out_path, logger, data_info, low_date_bound, upp_date_bound)

Process single folder