def run():
parser = argparse.ArgumentParser(description='Compute monthly NDVI')
parser.add_argument('--index_file', default='2015index.txt.gz', help='default=2015index.txt.gz Use gs://gcp-public-data-landsat/index.csv.gz to process full dataset')
parser.add_argument('--output_file', default='output.txt', help='default=output.txt Supply a location on GCS when running on cloud')
parser.add_argument('--output_dir', required=True, help='Where should the ndvi images be stored? Supply a GCS location when running on cloud')
known_args, pipeline_args = parser.parse_known_args()
p = beam.Pipeline(argv=pipeline_args)
index_file = known_args.index_file
output_file = known_args.output_file
output_dir = known_args.output_dir
#lat = 4.37; lon = -7.71 # Cape Palmas
lat =-21.1; lon = 55.50 # Reunion Island
# Read the index file and find the best look
scenes = (p
| 'read_index' >> beam.Read(beam.io.TextFileSource(index_file))
| 'filter_scenes' >> beam.FlatMap(lambda line: filterScenes(line, lat, lon) )
| 'least_cloudy' >> beam.CombinePerKey(clearest)
)
# write out info about scene
scenes | beam.Map(lambda (yrmon, scene): scene.__dict__) | 'scene_info' >> beam.io.textio.WriteToText(output_file)
# compute ndvi on scene
scenes | 'compute_ndvi' >> beam.Map(lambda (yrmon, scene): ndvi.computeNdvi(scene.BASE_URL, output_dir))
p.run()
def run():
import os
parser = argparse.ArgumentParser(description='Compute monthly NDVI')
parser.add_argument(
'--index_file',
default='2015index.txt.gz',
help=
'default=2015.txt.gz ... gs://cloud-training-demos/landsat/2015index.txt.gz Use gs://gcp-public-data-landsat/index.csv.gz to process full dataset'
)
parser.add_argument(
'--output_file',
default='output.txt',
help='default=output.txt Supply a location on GCS when running on cloud'
)
parser.add_argument(
'--output_dir',
required=True,
help=
'Where should the ndvi images be stored? Supply a GCS location when running on cloud'
)
known_args, pipeline_args = parser.parse_known_args()
p = beam.Pipeline(argv=pipeline_args)
index_file = known_args.index_file
output_file = known_args.output_file
output_dir = known_args.output_dir
lat = -21.1
lon = 55.50 # center of Reunion Island
dlat = 0.4
dlon = 0.4
# Read the index file and find all scenes that cover this area
allscenes = (p
|
'read_index' >> beam.Read(beam.io.TextFileSource(index_file))
| 'to_scene' >> beam.Map(lambda line: SceneInfo(line))
| 'by_area' >> beam.FlatMap(lambda scene: filterByArea(
scene, lat + dlat, lon - dlon, lat - dlat, lon + dlon)))
# for each month and spacecraft-coverage-pattern (given by the path and row), find clearest scene
scenes = (
allscenes
|
'cov_month' >> beam.Map(lambda scene: (scene.month_path_row(), scene))
| 'least_cloudy' >> beam.CombinePerKey(clearest)
|
'yrmon-scene' >> beam.Map(lambda (key, scene): (scene.yrmon(), scene)))
# write out info about scene
scenes | beam.Map(
lambda (yrmon, scene): '{}: {}'.format(yrmon, scene.SCENE_ID)
) | 'scene_info' >> beam.io.textio.WriteToText(output_file)
# compute ndvi on scene
scenes | 'compute_ndvi' >> beam.Map(lambda (yrmon, scene): ndvi.computeNdvi(
scene.BASE_URL, os.path.join(output_dir, yrmon), scene.SPACECRAFT_ID))
p.run()
def run():
import os
parser = argparse.ArgumentParser(description='Compute monthly NDVI')
parser.add_argument('--index_file', default='2015index.txt.gz', help='default=2015.txt.gz ... gs://cloud-training-demos/landsat/2015index.txt.gz Use gs://gcp-public-data-landsat/index.csv.gz to process full dataset')
parser.add_argument('--output_file', default='output.txt', help='default=output.txt Supply a location on GCS when running on cloud')
parser.add_argument('--output_dir', required=True, help='Where should the ndvi images be stored? Supply a GCS location when running on cloud')
known_args, pipeline_args = parser.parse_known_args()
p = beam.Pipeline(argv=pipeline_args)
index_file = known_args.index_file
output_file = known_args.output_file
output_dir = known_args.output_dir
lat =-21.1; lon = 55.50 # center of Reunion Island
dlat = 0.4; dlon = 0.4
# Read the index file and find all scenes that cover this area
allscenes = (p
| 'read_index' >> beam.io.ReadFromText(index_file)
| 'to_scene' >> beam.Map(lambda line: SceneInfo(line))
| 'by_area' >> beam.FlatMap(lambda scene: filterByArea(scene,lat+dlat,lon-dlon,lat-dlat,lon+dlon) )
)
# for each month and spacecraft-coverage-pattern (given by the path and row), find clearest scene
scenes = (allscenes
| 'cov_month' >> beam.Map(lambda scene: (scene.month_path_row(), scene))
| 'least_cloudy' >> beam.CombinePerKey(clearest)
| 'yrmon-scene' >> beam.Map(lambda (key,scene): (scene.yrmon(), scene))
)
# write out info about scene
scenes | beam.Map(lambda (yrmon, scene): '{}: {}'.format(yrmon,scene.SCENE_ID)) | 'scene_info' >> beam.io.WriteToText(output_file)
# compute ndvi on scene
scenes | 'compute_ndvi' >> beam.Map(lambda (yrmon, scene): ndvi.computeNdvi(scene.BASE_URL, os.path.join(output_dir,yrmon), scene.SPACECRAFT_ID))
p.run()