def search(
username, password, dataset, location, bbox, clouds, start, end, output, limit
):
"""Search for scenes."""
api = API(username, password)
where = {"dataset": dataset}
if location:
latitude, longitude = location
where.update(latitude=latitude, longitude=longitude)
if bbox:
where.update(bbox=bbox)
if clouds:
where.update(max_cloud_cover=clouds)
if start:
where.update(start_date=start)
if end:
where.update(end_date=end)
if limit:
where.update(max_results=limit)
results = api.search(**where)
api.logout()
if not results:
return
if output == "entity_id":
for scene in results:
click.echo(scene["entity_id"])
if output == "display_id":
for scene in results:
click.echo(scene["display_id"])
if output == "json":
dump = json.dumps(results, indent=True)
click.echo(dump)
if output == "csv":
with StringIO("tmp.csv") as f:
w = csv.DictWriter(f, results[0].keys())
w.writeheader()
w.writerows(results)
click.echo(f.getvalue())
def downloader(sat_choice=None,
block=2027,
dates=None,
indice_requested=[False, False, False, True, False],
test=False,
root=str):
api = API(username, password)
if test == True:
sat_choice = satellite_selector()
dates = get_dates(sat_choice=sat_choice, test=False)
indice_requested = indices_requested()
request = str(random.randint(5, 101))
try:
try:
os.rmdir(f'./Images/Request.{request}')
except:
os.mkdir(f'./Images/Request.{request}')
os.mkdir(f"./Images/Request.{request}/Start_date")
os.mkdir(f"./Images/Request.{request}/End_date")
except:
pass
co_ord = csv_crawler(block=block, clipper=True, root=root)
# print(co_ord)
for (id, date) in enumerate(dates):
scenes = scene_finder(api, sat_choice, date, coordinates=co_ord)
print(scenes)
# scene_list, scene_data = download_selector(scenes, test=True, sat_choice=sat_choice)
scene_list, scene_data = download_selector(scenes,
sat_choice=sat_choice)
scene_downloader(scene_list,
id=id,
request=request,
sat_choice=sat_choice)
api.logout()
return sat_choice, indice_requested
class QCConnectLandsat:
def __init__(self, username, password, archive, backup_archive):
"""Connect API.
Raise ProcessorFailedError on failure
:param str username: username
:param str password: password
:param str archive: not used by Landsat implementation
:param str backup_archive: not used by Landsat implementation
"""
from landsatxplore.api import API as LandsatAPI
from landsatxplore.exceptions import EarthExplorerError
try:
self.api = LandsatAPI(
username, password
)
except EarthExplorerError as e:
raise ProcessorFailedError(
self,
"Unable to connect API: {}".format(e),
set_status=False
)
except json.JSONDecodeError as e:
raise ProcessorFailedError(
self,
"Landsat server is down. It raised a JSON exception: "
"{}".format(e),
set_status=False
)
def __del__(self):
if not hasattr(self, "api"):
return
from landsatxplore.exceptions import EarthExplorerError
try:
self.api.logout()
except EarthExplorerError as e:
Logger.error("Landsat server is down. {}".format(e))
def query(self, footprint, kwargs):
"""Query API.
:return: result
"""
from landsatxplore.exceptions import EarthExplorerError
kwargs['bbox'] = wkt2bbox(footprint, switch_axis=True)
kwargs['max_results'] = 500
del kwargs['producttype'] # used only for testing
try:
items = self.api.search(**kwargs)
except EarthExplorerError as e:
raise ProcessorFailedError(
self,
"Landsat server is down. "
"{}".format(e),
set_status=False
)
dict_items = {}
for item in items:
selected = False
if self.filter_by_tiles:
for tile in self.filter_by_tiles:
if str(tile) in item['entityId']:
selected = True
break
else:
selected = True
if selected:
dict_items[item['entityId']] = item
# used tests only
dict_items[item['entityId']]['producttype'] = item['displayId'].split('_')[1]
dict_items[item['entityId']]['beginposition'] = \
datetime.strptime(item['startTime'], '%Y-%m-%d')
else:
Logger.info("IP {} skipped by tile filter".format(item['entityId']))
return dict_items
b5 = landsat_array[4][y][x].astype('int32')
nvdi_array[i][y][x] = (b5 - b4) / (b5 + b4) * 10000.
# print(min(max(nvdi_array[i][y][x],-10000),10000))
except:
# pass
print("x = " + str(x) + "; y = " + str(y))
print("b4 = " + str(b4) + "; b5 = " + str(b5))
print(nvdi_array[i])
# Convert NVDI matrix to tif file
print("Converting NVDI matrix to GeoTiff file...")
wkt = dataset.GetProjection()
driver = gdal.GetDriverByName("GTiff")
band = dataset.GetRasterBand(1)
gt = dataset.GetGeoTransform()
output_file = os.path.join(landsat_dir, landsat_product_id + "_NVDI.TIF")
dst_ds = driver.Create(output_file, XSize, YSize, 1, gdal.GDT_Int16)
dst_ds.GetRasterBand(1).WriteArray(nvdi_array[i]) # write output raster
dst_ds.GetRasterBand(1).SetNoDataValue(0) # set nodata value
dst_ds.SetGeoTransform(gt) # set geotransform from dataset
srs = osr.SpatialReference() # set spatial reference of output raster
srs.ImportFromWkt(wkt)
dst_ds.SetProjection(srs.ExportToWkt())
ds = None # close output raster dataset
dst_ds = None # close output raster dataset
print("GeoTiff file created at" + output_file)
# Log out of api
print("Logging out of API...")
api.logout()