ee.FeatureCollection.flatten

Làm phẳng các bộ sưu tập của bộ sưu tập.

Cách sử dụng	Giá trị trả về
FeatureCollection.flatten()	FeatureCollection

Đối số	Loại	Thông tin chi tiết
this: collection	FeatureCollection	Bộ sưu tập đầu vào của các bộ sưu tập.

Ví dụ

Trình soạn thảo mã (JavaScript)

// Counties in New Mexico, USA.
var counties = ee.FeatureCollection('TIGER/2018/Counties')
                   .filter('STATEFP == "35"');

// Monthly climate and climatic water balance surfaces for January 2020.
var climate = ee.ImageCollection('IDAHO_EPSCOR/TERRACLIMATE')
                  .filterDate('2020-01', '2020-02');

// Calculate mean climate variables for each county per climate surface
// time step. The result is a FeatureCollection of FeatureCollections.
var countiesClimate = climate.map(function(image) {
  return image.reduceRegions({
    collection: counties,
    reducer: ee.Reducer.mean(),
    scale: 5000,
    crs: 'EPSG:4326'
  });
});

// Note that a printed FeatureCollection of FeatureCollections is not
// recursively expanded, you cannot view metadata of the features within the
// nested collections until you isolate a single collection or flatten the
// collections.
print('FeatureCollection of FeatureCollections', countiesClimate);

print('Flattened FeatureCollection of FeatureCollections',
      countiesClimate.flatten());

Thiết lập Python

Hãy xem trang Môi trường Python để biết thông tin về API Python và cách sử dụng geemap cho quá trình phát triển tương tác.

import ee
import geemap.core as geemap

Colab (Python)

# Counties in New Mexico, USA.
counties = ee.FeatureCollection('TIGER/2018/Counties').filter('STATEFP == "35"')

# Monthly climate and climatic water balance surfaces for January 2020.
climate = ee.ImageCollection('IDAHO_EPSCOR/TERRACLIMATE').filterDate(
    '2020-01', '2020-02')

# Calculate mean climate variables for each county per climate surface
# time step. The result is a FeatureCollection of FeatureCollections.
def reduce_mean(image):
  return image.reduceRegions(**{
      'collection': counties,
      'reducer': ee.Reducer.mean(),
      'scale': 5000,
      'crs': 'EPSG:4326'
      })
counties_climate = climate.map(reduce_mean)

# Note that a printed FeatureCollection of FeatureCollections is not
# recursively expanded, you cannot view metadata of the features within the
# nested collections until you isolate a single collection or flatten the
# collections.
display('FeatureCollection of FeatureCollections:', counties_climate)

display('Flattened FeatureCollection of FeatureCollections:',
        counties_climate.flatten())