Working on multiple tiles : Library

Working on multiple tiles : Library#

The _Library_ class offers the possibility to get information and perform some actions on all or a group of tiles.

Initialization:

>>> from sen2chain import Library
>>> l = Library()

## Database information and management

### Information

  • This function returns tiles in the L1C library folder

>> l.tiles_l1c

  • This function returns tiles in the L2A library folder

>>> l.tiles_l2a

  • This function returns tiles in the indices library folder

>>> l.indices

### Cleaning

The _clean_ function performs the _clean_lib_ function (See Tile Section/link) for each tile present in L1C database or in the provided list. Use the _remove = True_ parameter to effectively remove products (default value False)

>>> l.clean()    # All L1C database, nothing removed
>>> l.clean(clean_list = ["40KCB", "38KND"])    # 2 tiles analysed, nothing removed
>>> l.clean(clean_list = ["38KQE"], remove = True) # 1 tile analysed, error SAFE folders removed

## Computing products

### Computing L2A

You can compute L2A products for multiple provided tiles using multiprocessing

>>> # Compute all missing L2A products for the 2 provided tiles,
>>> # after the specified date,
>>> # and using 6 CPU cores
>>> l.compute_l2a(tile_list = ["40KCB", "38KND"],
          date_min = "2020-01-01",
          nb_proc = 6)

### Computing cloudmasks

You can compute cloudmasks products for multiple provided tiles using multiprocessing, with specific parameters (see Tile for description).

>>> l.compute_cloudmasks(tile_list = ["40KCB", "38KND"],
                 cm_version = "cm001",
                 probability = 1,
                 iterations = 5,
                 reprocess = False,
                 date_min = None,
                 date_max = None,
                 nb_proc = 4)

### Computing indices

You can compute index products for multiple provided tiles using multiprocessing, with specific parameters (see Tile for description).

>>> l.compute_indices(tile_list = ["40KCB"],
              indice_list = ["NDVI", "NDWIGAO"],
              reprocess = False,
              nodata_clouds = True,
              cm_version = "cm001",
              probability = 1,
              iterations = 5,
              date_min = None,
              date_max = "2021-12-31",
              nb_proc = 4)

### Computing L1C and L2A quicklooks

You can compute quicklooks for multiple provided tiles for L1C and/or L2A products. If no tile is provided, whole L1C + L2A product database is used. You can set specific output QL resolution (default 750m/px) and specific output format (JPEG by default or TIFF).

>>> l.compute_ql(tile_list = ["40KCB"],
         product_list = ["l1c", "l2a"],
         resolution = 750,
         jpg = True)