ee.FeatureCollection.iterate

將使用者提供的函式套用至集合的每個元素。使用者提供的函式會收到兩個引數：目前的元素，以及前一次呼叫 iterate() 傳回的值，或第一次疊代的引數。結果是最後一次呼叫使用者提供的函式時傳回的值。

傳回 Collection.iterate() 呼叫的結果。

用量傳回
FeatureCollection.iterate(algorithm, first)ComputedObject
引數類型詳細資料
這個：collection集合Collection 執行個體。
algorithm函式要套用至每個元素的函式。必須採用兩個引數：集合的元素和前一次疊代的值。
first物件 (選用)初始狀態。

範例

程式碼編輯器 (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']));

Python 設定

請參閱 Python 環境頁面，瞭解 Python API 和如何使用 geemap 進行互動式開發。

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