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import ee
import logging
import multiprocessing
from retry import retry
This tool downloads data from Earth Engine using parallel requests.
It extracts the timeseries of 8-day max LST from MODIS MOD11A2 per GAUL level-2 region
for all regions in South America, with each time-series written to its own file.
# All the results will be stored here so we can output a single file at the end.
def getRequests():
"""Generates a list of work items to be downloaded.
Extract the ADM2_CODEs from the GAUL level 2 dataset as work units.
southAmerica = ee.Geometry.BBox(-84.0, -56.5, -32.0, 12.5)
gaul2 = (ee.FeatureCollection('FAO/GAUL_SIMPLIFIED_500m/2015/level2')
return gaul2.aggregate_array('ADM2_CODE').getInfo()
@retry(tries=10, delay=1, backoff=2)
def getResult(index, regionID):
"""Handle the HTTP requests to download one result."""
region = (ee.FeatureCollection('FAO/GAUL_SIMPLIFIED_500m/2015/level2')
.filter(ee.Filter.eq('ADM2_CODE', regionID))
def maxLST(image):
# Mappable function to aggregate the max LST for one image.
# It builds an output tuple of (max_LST, date, regionID)
# This function uses -999 to indicate no data.
date ='YYYY-MM-dd')
image = image.multiply(0.02).subtract(273.15)
maxValue = (image.reduceRegion(ee.Reducer.max(), region.geometry())
# set a default in case there's no data in the region
.combine({'LST_Day_1km': -999}, False)
# format to 2 decimal places.
return image.set('output', [maxValue, date, regionID])
# Get the max LST for this region, in each image.
timeSeries = (ee.ImageCollection('MODIS/006/MOD11A2')
result = timeSeries.aggregate_array('output').getInfo()
# Write the results to a file.
filename = 'results_%d.csv' % regionID
with open(filename, 'w') as out_file:
for items in result:
line = ','.join([str(item) for item in items])
print(line, file=out_file)
print("Done: ", index)
if __name__ == '__main__':
items = getRequests()
pool = multiprocessing.Pool(25)
pool.starmap(getResult, enumerate(items))
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