ee.FeatureCollection.iterate

Menerapkan fungsi yang disediakan pengguna ke setiap elemen koleksi. Fungsi yang disediakan pengguna diberi dua argumen: elemen saat ini, dan nilai yang ditampilkan oleh panggilan sebelumnya ke iterate() atau argumen pertama, untuk iterasi pertama. Hasilnya adalah nilai yang ditampilkan oleh panggilan terakhir ke fungsi yang disediakan pengguna.

Menampilkan hasil panggilan Collection.iterate().

Penggunaan	Hasil
FeatureCollection.iterate(algorithm, first)	ComputedObject

Argumen	Jenis	Detail
ini: collection	Koleksi	Instance Koleksi.
algorithm	Fungsi	Fungsi yang akan diterapkan ke setiap elemen. Harus mengambil dua argumen: elemen koleksi dan nilai dari iterasi sebelumnya.
first	Objek, opsional	Status awal.

Contoh

Code Editor (JavaScript)

/**
 * CAUTION: ee.FeatureCollection.iterate can be less efficient than alternative
 * solutions implemented using ee.FeatureCollection.map or by converting feature
 * properties to an ee.Array object and using ee.Array.slice and
 * ee.Array.arrayAccum methods. Avoid ee.FeatureCollection.iterate if possible.
 */

// Monthly precipitation accumulation for 2020.
var climate = ee.ImageCollection('IDAHO_EPSCOR/TERRACLIMATE')
                  .filterDate('2020-01-01', '2021-01-01')
                  .select('pr');

// Region of interest: north central New Mexico, USA.
var roi = ee.Geometry.BBox(-107.19, 35.27, -104.56, 36.83);

// A FeatureCollection of mean monthly precipitation accumulation for the
// region of interest.
var meanPrecipTs = climate.map(function(image) {
  var meanPrecip = image.reduceRegion(
      {reducer: ee.Reducer.mean(), geometry: roi, scale: 5000});
  return ee.Feature(roi, meanPrecip)
      .set('system:time_start', image.get('system:time_start'));
});

// A cumulative sum function to apply to each feature in the
// precipitation FeatureCollection. The first input is the current feature and
// the second is a list of features that accumulates at each step of the
// iteration. The function fetches the last feature in the feature list, gets
// the cumulative precipitation sum value from it, and adds it to the current
// feature's precipitation value. The new cumulative precipitation sum is set
// as a property of the current feature, which is appended to the feature list
// that is passed onto the next step of the iteration.
var cumsum = function(currentFeature, featureList) {
  featureList = ee.List(featureList);
  var previousSum = ee.Feature(featureList.get(-1)).getNumber('pr_cumsum');
  var currentVal = ee.Feature(currentFeature).getNumber('pr');
  var currentSum = previousSum.add(currentVal);
  return featureList.add(currentFeature.set('pr_cumsum', currentSum));
};

// Use "iterate" to cumulatively sum monthly precipitation over the year with
// the above defined "cumsum" function. Note that the feature list used in the
// "cumsum" function is initialized as the "first" variable. It includes a
// temporary feature with the "pr_cumsum" property set to 0; this feature is
// filtered out of the final FeatureCollection.
var first = ee.List([ee.Feature(null, {pr_cumsum: 0, first: true})]);
var precipCumSum =
    ee.FeatureCollection(ee.List(meanPrecipTs.iterate(cumsum, first)))
        .filter(ee.Filter.notNull(['pr']));

// Inspect the outputs.
print('Note cumulative precipitation ("pr_cumsum") property',
      precipCumSum);
print(ui.Chart.feature.byFeature(
      precipCumSum, 'system:time_start', ['pr', 'pr_cumsum']));

Penyiapan Python

Lihat halaman Lingkungan Python untuk mengetahui informasi tentang Python API dan penggunaan geemap untuk pengembangan interaktif.

import ee
import geemap.core as geemap

Colab (Python)

import altair as alt

# CAUTION: ee.FeatureCollection.iterate can be less efficient than alternative
# solutions implemented using ee.FeatureCollection.map or by converting feature
# properties to an ee.Array object and using ee.Array.slice and
# ee.Array.arrayAccum methods. Avoid ee.FeatureCollection.iterate if possible.

# Monthly precipitation accumulation for 2020.
climate = (
    ee.ImageCollection('IDAHO_EPSCOR/TERRACLIMATE')
    .filterDate('2020-01-01', '2021-01-01')
    .select('pr')
)

# Region of interest: north central New Mexico, USA.
roi = ee.Geometry.BBox(-107.19, 35.27, -104.56, 36.83)


# A FeatureCollection of mean monthly precipitation accumulation for the
# region of interest.
def mean_precip_ts_fun(image):
  mean_precip = image.reduceRegion(
      reducer=ee.Reducer.mean(), geometry=roi, scale=5000
  )
  return ee.Feature(roi, mean_precip).set(
      'system:time_start', image.get('system:time_start')
  )


mean_precip_ts = climate.map(mean_precip_ts_fun)


# A cumulative sum function to apply to each feature in the
# precipitation FeatureCollection. The first input is the current feature and
# the second is a list of features that accumulates at each step of the
# iteration. The function fetches the last feature in the feature list, gets
# the cumulative precipitation sum value from it, and adds it to the current
# feature's precipitation value. The new cumulative precipitation sum is set
# as a property of the current feature, which is appended to the feature list
# that is passed onto the next step of the iteration.
def cumsum(current_feature, feature_list):
  feature_list = ee.List(feature_list)
  previous_sum = ee.Feature(feature_list.get(-1)).getNumber('pr_cumsum')
  current_val = ee.Feature(current_feature).getNumber('pr')
  current_sum = previous_sum.add(current_val)
  return feature_list.add(current_feature.set('pr_cumsum', current_sum))


# Use "iterate" to cumulatively sum monthly precipitation over the year with
# the above defined "cumsum" function. Note that the feature list used in the
# "cumsum" function is initialized as the "first" variable. It includes a
# temporary feature with the "pr_cumsum" property set to 0 this feature is
# filtered out of the final FeatureCollection.
first = ee.List([ee.Feature(None, {'pr_cumsum': 0, 'first': True})])
precip_cum_sum = ee.FeatureCollection(
    ee.List(mean_precip_ts.iterate(cumsum, first))
).filter(ee.Filter.notNull(['pr']))

precip_cum_sum = precip_cum_sum.map(
    lambda feature: feature.set(
        'date',
        ee.Date(feature.getNumber('system:time_start')).format('YYYY-MM-dd'),
    )
)

# Inspect the outputs.
display('Note cumulative precipitation ("pr_cumsum") property', precip_cum_sum)

df = geemap.ee_to_df(precip_cum_sum, ['date', 'pr', 'pr_cumsum'])
display(df)

chart = (
    alt.Chart(df)
    .mark_line()
    .encode(x='date:T', y='pr:Q', color=alt.value('blue'))
)
chart += (
    alt.Chart(df)
    .mark_line()
    .encode(x='date:T', y='pr_cumsum:Q', color=alt.value('red'))
)
chart