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()