def loadAll(products, key, bands):
ds = []
for product in products:
dc = datacube.Datacube()
gw = GridWorkflow(dc.index, product=product)
# Get the list of tiles (one for each time point) for this product
tile_list = gw.list_tiles(product=product,
cell_index=key,
group_by='solar_day')
dc.close()
# Load all tiles
for tile_index, tile in tile_list.items():
dataset = gw.load(tile, measurements=bands)
if (dataset.variables):
ds.append(dataset)
return ds
def loadByTile(products, key, min_y, max_y, min_x, max_x, bands):
ds = []
for product in products:
dc = datacube.Datacube()
# Create the GridWorkflow object for this product
curr_gw = GridWorkflow(dc.index, product=product)
# Get the list of tiles (one for each time point) for this product
tile_list = curr_gw.list_tiles(product=product,
cell_index=key,
group_by='solar_day')
dc.close()
# Retrieve the specified pixel for each tile in the list
for tile_index, tile in tile_list.items():
dataset = curr_gw.load(tile[0:1, min_y:max_y, min_x:max_x],
measurements=bands)
if (dataset.variables):
ds.append(dataset)
return ds
# Create GridWorkflow object so we can work with tiles
gw = GridWorkflow(dc.index, product=sref_products[-1])
# List to store the three datasets (LS5, LS7, LS8)
sref_ds = []
# The key represents which tile we are using
key = (5, -28)
# Need to fetch the tiles for each product seperately
for product in sref_products:
gw = GridWorkflow(dc.index, product=product)
# Get the list of tiles (one for each time point) for this product
tile_list = gw.list_tiles(product=product, cell_index=key)
# Load all tiles
for tile_index, tile in tile_list.items():
dataset = gw.load(tile[0:1, 400:401, 0:1],
measurements=['red', 'nir', 'blue',
'green']) # 200ish/400ish
if (dataset.variables):
sref_ds.append(dataset)
# Close datacube connection to database
dc.close()
# Concatenate the three datasets
sref = xr.concat(sref_ds, dim='time')