def get_clipped_asset(params, asset_activation):
'''
Second step is to download the item to the desired path
'''
gaia = Gaia()
filepath = params['filepath']
success = gaia.get_clipped_asset(asset_activation, filepath)
if success == 0:
get_clipped_asset.apply_async((params, asset_activation), countdown=20)
return False
elif success == 1:
generate_analytic_assets.delay(params)
return True
else:
return False
def get_clipped_asset(params, asset_activation):
'''
Second step is to download the item to the desired path
'''
gaia = Gaia()
filepath = params['filepath']
print('filepath ', str(filepath))
success = gaia.get_clipped_asset(asset_activation, filepath)
if success == 0:
get_clipped_asset.apply_async((params, asset_activation), countdown=20)
return False
elif success == 1:
if params['pixels_usability_test_pass'] == 0:
udm_filename = glob.glob(filepath + '*udm_clip.tif')[0]
athena = Athena()
is_cloudy_udm = athena.is_cloudy_udm(udm_filename)
if is_cloudy_udm > 0:
# remove that asset and end the process for this asset
shutil.rmtree(filepath)
print('Fail cloud test ')
return # if udm is > 2 % cloudy then skip processing
else:
# remove the current content <croped UDM>and download the remaining asset
# and then Start the analysis Process
print('Successfully pass the Cloudy Test')
params['pixels_usability_test_pass'] = 1
params['pixels_ratio'] = is_cloudy_udm
# Gather directory contents
contents = [os.path.join(filepath, i) for i in os.listdir(filepath)]
# Iterate and remove each item in the appropriate manner
[os.remove(i) if os.path.isfile(i) or os.path.islink(i) else shutil.rmtree(i) for i in contents]
# Update the params['asset_type'] to analytic
params['asset_type'] = 'analytic'
activate_clipped_asset.apply_async((params,), countdown=1)
elif params['pixels_usability_test_pass'] == 1:
generate_analytic_assets.delay(params)
return True
else:
return False