blogs/landsat/dfndvi.py (63 lines of code) (raw):

#!/usr/bin/env python """ Copyright Google Inc. 2016 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import apache_beam as beam import argparse import datetime import ndvi class SceneInfo: def __init__ (self, line): try: self.SCENE_ID, self.SPACECRAFT_ID, self.SENSOR_ID, self.DATE_ACQUIRED, self.COLLECTION_NUMBER, self.COLLECTION_CATEGORY,self.DATA_TYPE, self.WRS_PATH, self.WRS_ROW, self.CLOUD_COVER, self.NORTH_LAT, self.SOUTH_LAT, self.WEST_LON, self.EAST_LON, self.TOTAL_SIZE, self.BASE_URL = line.split(',') self.DATE_ACQUIRED = datetime.datetime.strptime(self.DATE_ACQUIRED, '%Y-%m-%d') self.NORTH_LAT = float(self.NORTH_LAT) self.SOUTH_LAT = float(self.SOUTH_LAT) self.WEST_LON = float(self.WEST_LON) self.EAST_LON = float(self.EAST_LON) self.CLOUD_COVER = float(self.CLOUD_COVER) except: print "WARNING! format error on {", line, "}" def contains(self, lat, lon): return (lat > self.SOUTH_LAT) and (lat < self.NORTH_LAT) and (lon > self.WEST_LON) and (lon < self.EAST_LON) def intersects(self, slat, wlon, nlat, elon): return (nlat > self.SOUTH_LAT) and (slat < self.NORTH_LAT) and (elon > self.WEST_LON) and (wlon < self.EAST_LON) def month_path_row(self): return '{}-{}-{}'.format(self.yrmon(), self.WRS_PATH, self.WRS_ROW) def yrmon(self): return '{}-{:02d}'.format(self.DATE_ACQUIRED.year, self.DATE_ACQUIRED.month) def filterByLocation(scene, lat, lon): if scene.contains(lat, lon): yield scene def filterByArea(scene, slat, wlon, nlat, elon): if scene.intersects(slat, wlon, nlat, elon): yield scene def clearest(scenes): if scenes: return min(scenes, key=lambda s: s.CLOUD_COVER) else: return None 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() if __name__ == '__main__': run()