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ee.FeatureCollection.iterate
使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
将用户提供的函数应用于集合的每个元素。用户提供的函数会获得两个实参:当前元素,以及对 iterate() 的上一次调用返回的值(如果是第一次迭代,则为第一个实参)。结果是用户提供的函数的最终调用所返回的值。
返回 Collection.iterate() 调用的结果。
用法 | 返回 |
---|
FeatureCollection.iterate(algorithm, first) | ComputedObject |
参数 | 类型 | 详细信息 |
---|
此:collection | 集合 | Collection 实例。 |
algorithm | 函数 | 要应用于每个元素的函数。必须接受两个实参:集合的一个元素和上一次迭代的值。 |
first | 对象,可选 | 初始状态。 |
示例
代码编辑器 (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 设置
如需了解 Python API 和如何使用 geemap
进行交互式开发,请参阅
Python 环境页面。
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
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最后更新时间 (UTC):2025-07-26。
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