def get_time_series(aoi, start_date=None, end_date=None, bands=['B8'],
satellite='Landsat', sensor=None,
out_dir='', api='devseed', mirror='gcloud',
cloud_masks=False, check_empty=False,
parallel_downloads=multiprocessing.cpu_count()):
"""
Main function: crop and download a time series of Sentinel-2 images.
Args:
aoi (geojson.Polygon): area of interest
start_date (datetime.datetime, optional): start of the time range
end_date (datetime.datetime, optional): end of the time range
bands (list, optional): list of bands
satellite (str, optional): either L1...L8
sensor (str, optional): MSS, TM, ETM, OLI
see https://landsat.usgs.gov/what-are-band-designations-landsat-satellites
out_dir (str, optional): path where to store the downloaded crops
api (str, optional): either devseed (default), scihub, planet or gcloud
mirror (str, optional): either 'aws' or 'gcloud'
cloud_masks (bool, optional): if True, cloud masks are downloaded and
cloudy images are discarded
check_empty (bool, optional): if True, QA masks are downloaded and
empty images are discarded
parallel_downloads (int): number of parallel gml files downloads
"""
# check access to the selected search api and download mirror
check_args(api, mirror)
# default date range
if end_date is None:
end_date = datetime.date.today()
if start_date is None:
start_date = end_date - datetime.timedelta(91) # 3 months
# list available images
images = search(aoi, start_date, end_date, satellite, sensor, api=api)
# download crops
download(images, bands, aoi, mirror, out_dir, parallel_downloads)
# discard images that failed to download
images = [i for i in images if bands_files_are_valid(i, bands, api, out_dir)]
# embed all metadata as GeoTIFF tags in the image files
for img in images:
metadata = vars(img)
metadata['downloaded_by'] = 'TSD on {}'.format(datetime.datetime.now().isoformat())
for b in bands:
filepath = os.path.join(out_dir, '{}_band_{}.tif'.format(img.filename, b))
utils.set_geotif_metadata_items(filepath, metadata)
if cloud_masks: # discard images that are totally covered by clouds
read_cloud_masks(images, bands, parallel_downloads,
out_dir=out_dir)
if check_empty: # discard images that are totally empty
read_empty_images(images, bands, parallel_downloads,
out_dir=out_dir)
def get_time_series(aoi=None,
start_date=None,
end_date=None,
bands=["B04"],
tile_id=None,
title=None,
relative_orbit_number=None,
out_dir="",
api="stac",
mirror="aws",
product_type="L2A",
cloud_masks=False,
parallel_downloads=multiprocessing.cpu_count(),
satellite_angles=False,
no_crop=False,
timeout=60):
"""
Main function: crop and download a time series of Sentinel-2 images.
Args:
aoi (geojson.Polygon): area of interest
start_date (datetime.datetime, optional): start of the time range
end_date (datetime.datetime, optional): end of the time range
bands (list, optional): list of bands
tile_id (str): MGRS tile identifier, e.g. "31TCJ"
title (str): product title, e.g. "S2A_MSIL1C_20160105T143732_N0201_R096_T19KGT_20160105T143758"
relative_orbit_number (int): relative orbit number, from 1 to 143
out_dir (str, optional): path where to store the downloaded crops
api (str, optional): either stac (default), scihub, planet or gcloud
mirror (str, optional): either 'aws' (default) or 'gcloud'
product_type (str, optional): either 'L1C' or 'L2A' (default)
cloud_masks (bool, optional): if True, cloud masks are downloaded and
cloudy images are discarded
parallel_downloads (int): number of parallel gml files downloads
satellite_angles (bool): whether or not to download satellite zenith
and azimuth angles and include them in metadata
no_crop (bool): if True, download original JP2 files rather than crops
timeout (scalar, optional): timeout for images download, in seconds
"""
# list available images
images = search(aoi,
start_date,
end_date,
relative_orbit_number=relative_orbit_number,
tile_id=tile_id,
title=title,
product_type=product_type,
api=api)
# download crops
download(images, bands, aoi, mirror, out_dir, parallel_downloads, no_crop,
timeout)
# discard images that failed to download
images = [i for i in images if bands_files_are_valid(i, bands, out_dir)]
if satellite_angles: # retrieve satellite elevation and azimuth angles
for img in images:
img.get_satellite_angles()
# embed all metadata as GeoTIFF tags in the image files
for img in images:
img['downloaded_by'] = 'TSD on {}'.format(
datetime.datetime.now().isoformat())
for b in bands:
filepath = os.path.join(out_dir,
'{}_band_{}.tif'.format(img.filename, b))
utils.set_geotif_metadata_items(filepath, img)
if cloud_masks: # discard images that are totally covered by clouds
read_cloud_masks(aoi,
images,
bands,
mirror,
parallel_downloads,
out_dir=out_dir)
def get_time_series(aoi,
start_date=None,
end_date=None,
item_types=['PSScene3Band'],
asset_type='analytic',
out_dir='',
parallel_downloads=multiprocessing.cpu_count(),
clip_and_ship=True,
no_crop=False,
satellite_id=None,
item_id=None,
search_type='contains',
remove_duplicates=True):
"""
Main function: crop and download Planet images.
"""
# list available images
items = search_planet.search(aoi,
start_date,
end_date,
item_types=item_types,
satellite_id=satellite_id,
item_id=item_id,
search_type=search_type,
remove_duplicates=remove_duplicates)
print('Found {} images'.format(len(items)))
# list the requested asset for each available (and allowed) image
print('Listing available {} assets...'.format(asset_type),
flush=True,
end=' ')
assets = parallel.run_calls(get_item_asset_info,
items,
extra_args=(asset_type, ),
pool_type='threads',
nb_workers=parallel_downloads,
timeout=600)
# remove 'None' (ie not allowed) assets and corresponding items
items = [i for (i, a) in zip(items, assets) if a]
assets = [a for a in assets if a]
print('Have permissions for {} images'.format(len(items)))
# activate the allowed assets
print('Requesting activation of {} images...'.format(len(assets)),
flush=True,
end=' ')
parallel.run_calls(request_activation,
assets,
pool_type='threads',
nb_workers=parallel_downloads,
timeout=600)
# warn user about quota usage
n = len(assets)
if clip_and_ship:
a = n * area.area(aoi)
else:
a = sum(area.area(i['geometry']) for i in items)
print('Your current quota usage is {}'.format(get_quota()), flush=True)
print('Downloading these {} images will increase it by {:.3f} km²'.format(
n, a / 1e6),
flush=True)
# build filenames
ext = 'zip' if clip_and_ship else 'tif'
out_dir = os.path.abspath(os.path.expanduser(out_dir))
fnames = [
os.path.join(out_dir, '{}.{}'.format(fname_from_metadata(i), ext))
for i in items
]
if clip_and_ship:
print('Requesting clip of {} images...'.format(len(assets)),
flush=True,
end=' ')
clips = parallel.run_calls(request_clip,
list(zip(items, assets)),
extra_args=(aoi, ),
pool_type='threads',
nb_workers=parallel_downloads,
timeout=3600)
# remove clips that were rejected
ok = [i for i, x in enumerate(clips) if x]
clips = [clips[i] for i in range(len(clips)) if i in ok]
fnames = [fnames[i] for i in range(len(fnames)) if i in ok]
print('Downloading {} clips...'.format(len(clips)),
end=' ',
flush=True)
parallel.run_calls(download_clip,
list(zip(clips, fnames)),
pool_type='threads',
nb_workers=parallel_downloads,
timeout=3600)
elif no_crop: # download full images
os.makedirs(out_dir, exist_ok=True)
print('Downloading {} full images...'.format(len(assets)), end=' ')
parallel.run_calls(download_asset,
list(zip(fnames, assets)),
pool_type='threads',
nb_workers=parallel_downloads,
timeout=1200)
else:
if asset_type in [
'udm', 'visual', 'analytic', 'analytic_dn', 'analytic_sr'
]:
aoi_type = 'utm_rectangle'
aoi = utils.utm_bbx(aoi)
else:
aoi_type = 'lonlat_polygon'
# download crops with gdal through vsicurl
os.makedirs(out_dir, exist_ok=True)
print('Downloading {} crops...'.format(len(assets)), end=' ')
parallel.run_calls(download_crop,
list(zip(fnames, assets)),
extra_args=(aoi, aoi_type),
pool_type='threads',
nb_workers=parallel_downloads,
timeout=300)
# embed some metadata in the image files
for f, img in zip(fnames,
items): # embed some metadata as gdal geotiff tags
if os.path.isfile(f):
utils.set_geotif_metadata_items(
f, metadata_from_metadata_dict(img))
def get_time_series(aoi,
start_date=None,
end_date=None,
bands=['B04'],
out_dir='',
api='devseed',
mirror='gcloud',
product_type=None,
cloud_masks=False,
parallel_downloads=multiprocessing.cpu_count(),
satellite_angles=False):
"""
Main function: crop and download a time series of Sentinel-2 images.
Args:
aoi (geojson.Polygon): area of interest
start_date (datetime.datetime, optional): start of the time range
end_date (datetime.datetime, optional): end of the time range
bands (list, optional): list of bands
out_dir (str, optional): path where to store the downloaded crops
api (str, optional): either devseed (default), scihub, planet or gcloud
mirror (str, optional): either 'aws' or 'gcloud'
product_type (str, optional): either 'L1C' or 'L2A'
cloud_masks (bool, optional): if True, cloud masks are downloaded and
cloudy images are discarded
parallel_downloads (int): number of parallel gml files downloads
satellite_angles (bool): whether or not to download satellite zenith
and azimuth angles and include them in metadata
"""
# check access to the selected search api and download mirror
check_args(api, mirror, product_type)
# default date range
if end_date is None:
end_date = datetime.datetime.now()
if start_date is None:
start_date = end_date - datetime.timedelta(91) # 3 months
# list available images
images = search(aoi,
start_date,
end_date,
product_type=product_type,
api=api)
# download crops
download(images, bands, aoi, mirror, out_dir, parallel_downloads)
# discard images that failed to download
images = [
i for i in images if bands_files_are_valid(i, bands, api, out_dir)
]
if satellite_angles: # retrieve satellite elevation and azimuth angles
for img in images:
img.get_satellite_angles()
# embed all metadata as GeoTIFF tags in the image files
for img in images:
img['downloaded_by'] = 'TSD on {}'.format(
datetime.datetime.now().isoformat())
for b in bands:
filepath = os.path.join(out_dir,
'{}_band_{}.tif'.format(img.filename, b))
utils.set_geotif_metadata_items(filepath, img)
if cloud_masks: # discard images that are totally covered by clouds
read_cloud_masks(aoi,
images,
bands,
mirror,
parallel_downloads,
out_dir=out_dir)
