ee.FeatureCollection.flatten

Aplatit les collections de collections.

Utilisation	Renvoie
FeatureCollection.flatten()	FeatureCollection

Argument	Type	Détails
ceci : collection	FeatureCollection	Collection d'entrée de collections.

Exemples

Éditeur de code (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());

Configuration de Python

Consultez la page Environnement Python pour en savoir plus sur l'API Python et sur l'utilisation de geemap pour le développement interactif.

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())