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遥感云计算
GISer
2022-05-24
目录

Reduce

# 简介

Reducer 是PIE-Engine中用来统计计算或者执行聚合的对象。用户可按照时间、空间、波段、数组或其他数据结构进行数据聚合和统计。PIE-Engine Studio提供最大值、最小值、平均值、求和、线性回归等相关算法。

# count

得到一个返回非空个数的统计器。

函数 返回值
count() Reducer

# 示例：

list =pie.List([5.50,6.75,7.25])
resultCount = list.reduce(pie.Reducer.count())
print(resultCount.getInfo())

# covariance

得到一个返回求协方差矩阵的统计器。

函数 返回值
covariance() Reducer

# 示例：

image=pie.Image("LC08/01/T1/LC08_121031_20181019").select(['B3','B4'])
geometry = pie.Geometry.Polygon([[[119.5, 41.5], [120, 41.5], [120, 42],[119.5,42],[119.5, 41.5]]], None)
result=image.reduceRegion(pie.Reducer.covariance(),geometry,30)
print(result.getInfo())

# first

得到一个返回输入中第一个项的统计器。

函数 返回值
first() Reducer

# 示例

list1 = pie.List([1, 2, 3])
first_num = list1.reduce(pie.Reducer.first())
print(first_num.getInfo())

# fixedHistogram

返回一个Reducer。它将使用固定数量、固定宽度的容器计算输入的直方图；超出[最小值，最大值]范围的值将被忽略。

函数 返回值
fixedHistogram(max,min,steps) Reducer
参数 类型 说明
max Int 直方图横轴最大值
min Int 直方图横轴最小值
steps Int 直方图横轴间隔

# 示例

image = pie.Image("LC08/01/T1/LC08_121031_20181019").select(['B3','B4'])
geometry = pie.Geometry.Polygon([[[119.5, 41.5], [120, 41.5], [120, 42],[119.5,42],[119.5, 41.5]]], None)
result = image.reduceRegion(pie.Reducer.fixedHistogram([1000,100],[10000,2000],5),geometry,30)
print(result.getInfo())

# last

得到一个返回结束值的统计器。

函数 返回值
last() Reducer

# 示例：

list =pie.List([5.50,6.75,7.25,12.5])
resultLast = list.reduce(pie.Reducer.last())
print(resultLast.getInfo())

# linearFit

得到一个返回线性回归的统计器，返回的结果包括offset和scale两项信息。

函数 返回值
linearFit() Reducer

# 示例

# 自变量
x1 = [
    -0.05, 0.25,0.60,0, 0.25,0.20, 0.15,0.05,
    -0.15, 0.15, 0.20, 0.10,0.40,0.45,0.35,
    0.30, 0.50,0.50, 0.40,-0.05, -0.05,-0.10,
    0.20,0.10,0.50,0.60,-0.05,0, 0.05, 0.55
]

# 因变量
y = [
    7.38,8.51,9.52,7.50,9.33,8.28,8.75,7.87,
    7.10,8.00, 7.89,8.15,9.10,8.86,8.90,8.87,
    9.26,9.00,8.75,7.95, 7.65,7.27,8.00,8.50,
    8.75,9.21,8.27,7.67,7.93,9.26
]
list = pie.List([x1,y])
result = list.reduce(pie.Reducer.linearFit())
print(result.getInfo())

# linearRegression

得到一个返回多元线性回归的统计器，统计器返回的结果包括针对每一个因变量的各个自变量参数。

函数 返回值
linearRegression(numX,numY) Reducer
参数 类型 说明
numX Number 自变量的个数
numY Number 因变量的个数

# 示例

# 自变量
x0 = [
    1,1,1,1,1,1,1,1,1,1,
    1,1,1,1,1,1,1,1,1,1,
    1,1,1,1,1,1,1,1,1,1
]
x1 = [
    -0.05, 0.25,0.60,0, 0.25,0.20, 0.15,0.05,
    -0.15, 0.15, 0.20, 0.10,0.40,0.45,0.35,
    0.30, 0.50,0.50, 0.40,-0.05, -0.05,-0.10,
    0.20,0.10,0.50,0.60,-0.05,0, 0.05, 0.55
]
x2 = [
    5.50,6.75,7.25,5.50,7.00,6.50,6.75,5.25,
    5.25,6.00, 6.50,6.25,7.00,6.90,6.80,6.80,
    7.10,7.00,6.80,6.50, 6.25,6.00,6.50,7.00,
    6.80,6.80,6.50,5.75,5.80,6.80
]
# 因变量
y = [
    7.38,8.51,9.52,7.50,9.33,8.28,8.75,7.87,
    7.10,8.00, 7.89,8.15,9.10,8.86,8.90,8.87,
    9.26,9.00,8.75,7.95, 7.65,7.27,8.00,8.50,
    8.75,9.21,8.27,7.67,7.93,9.26
]
list = pie.List([x0,x1,x2,y])
result = list.reduce(pie.Reducer.linearRegression(3,1))
print(result.getInfo())

# max

返回一个输出其输入的最大值的Reducer。

函数 返回值
max() Reducer

# 示例

x1 = [
    -0.05, 0.25,0.60,0, 0.25,0.20, 0.15,0.05,
    -0.15, 0.15, 0.20, 0.10,0.40,0.45,0.35,
    0.30, 0.50,0.50, 0.40,-0.05, -0.05,-0.10,
    0.20,0.10,0.50,0.60,-0.05,0, 0.05, 0.55
]
list = pie.List(x1)
result = list.reduce(pie.Reducer.max())
print(result.getInfo())

# mean

返回一个计算其输入的（加权）算术平均值的Reducer。

函数 返回值
mean() Reducer

# 示例

x1 = [
    -0.05, 0.25,0.60,0, 0.25,0.20, 0.15,0.05,
    -0.15, 0.15, 0.20, 0.10,0.40,0.45,0.35,
    0.30, 0.50,0.50, 0.40,-0.05, -0.05,-0.10,
    0.20,0.10,0.50,0.60,-0.05,0, 0.05, 0.55
]
list = pie.List(x1)
result = list.reduce(pie.Reducer.mean())
print(result.getInfo())

# median

返回一个计算其输入的中值的Reducer。

函数 返回值
median(maxBuckets=None,minBuckets=None,maxRaw=None) Reducer
参数 类型 说明
maxBuckets int 默认为None
minBuckets Int 默认为None
maxRaw Int 默认为None

# 示例

x1 = [
    -0.05, 0.25,0.60,0, 0.25,0.20, 0.15,0.05,
    -0.15, 0.15, 0.20, 0.10,0.40,0.45,0.35,
    0.30, 0.50,0.50, 0.40,-0.05, -0.05,-0.10,
    0.20,0.10,0.50,0.60,-0.05,0, 0.05, 0.55
]
list = pie.List(x1)
result = list.reduce(pie.Reducer.median())
print(result.getInfo())

# min

返回一个输出其输入的最小值的Reducer。

函数 返回值
min() Reducer

# 示例

x1 = [
    -0.05, 0.25,0.60,0, 0.25,0.20, 0.15,0.05,
    -0.15, 0.15, 0.20, 0.10,0.40,0.45,0.35,
    0.30, 0.50,0.50, 0.40,-0.05, -0.05,-0.10,
    0.20,0.10,0.50,0.60,-0.05,0, 0.05, 0.55
]
list = pie.List(x1)
result = list.reduce(pie.Reducer.min())
print(result.getInfo())

# minMax

得到一个返回最大、最小值的统计器。

函数 返回值
minMax() Reducer

# 示例：

list =pie.List([8,11,6])
result = list.reduce(pie.Reducer.minMax())
print (result.getInfo())

# pearsonsCorrelation

计算皮尔森相关系数。

函数 返回值
pearsonsCorrelation() Reducer

# 示例

x1 = [
    -0.05, 0.25,0.60,0, 0.25,0.20, 0.15,0.05,
    -0.15, 0.15, 0.20, 0.10,0.40,0.45,0.35,
    0.30, 0.50,0.50, 0.40,-0.05, -0.05,-0.10,
    0.20,0.10,0.50,0.60,-0.05,0, 0.05, 0.55
]
x2 = [
    5.50,6.75,7.25,5.50,7.00,6.50,6.75,5.25,
    5.25,6.00, 6.50,6.25,7.00,6.90,6.80,6.80,
    7.10,7.00,6.80,6.50, 6.25,6.00,6.50,7.00,
    6.80,6.80,6.50,5.75,5.80,6.80]
list = pie.List([x1, x2])
result = list.reduce(pie.Reducer.pearsonsCorrelation())
print(result.getInfo())

# percentile

得到一个返回指定百分位上数值的统计器。

函数 返回值
percentile(percentiles, outputNames) Reducer
参数 类型 说明
percentiles List 由0到100之间数值组成的数组
outputNames List 输出的名称列表，默认为null

# 示例：

list =pie.List([1,2,5,8,5,2,2,6,6,2,9,2,3,4,5,6,7,8,9,10])
result = list.reduce(pie.Reducer.percentile([0,10,60,100]))
print (result.getInfo())

# stdDev

得到一个返回求标准差的统计器。

函数 返回值
stdDev() Reducer

# 示例：

list =pie.List([8,11,6])
result = list.reduce(pie.Reducer.stdDev())
print (result.getInfo())

# sum

返回一个计算其输入的（加权）和的Reducer。

函数 返回值
sum() Reducer

# 示例

x1 = [
    -0.05, 0.25,0.60,0, 0.25,0.20, 0.15,0.05,
    -0.15, 0.15, 0.20, 0.10,0.40,0.45,0.35,
    0.30, 0.50,0.50, 0.40,-0.05, -0.05,-0.10,
    0.20,0.10,0.50,0.60,-0.05,0, 0.05, 0.55
]
list = pie.List(x1)
result = list.reduce(pie.Reducer.sum())
print(result.getInfo())

# toList

返回一个Reducer。它将其输入放入一个列表中，可以选择分组为元组。

函数 返回值
toList(tupleSize=1,numOptional=None) Reducer
参数 类型 说明
tupleSize Int 列表最大尺寸
numOptional Sting 默认为None

# 示例

list1 = pie.List([1, 2, 3])
result = list1.reduce(pie.Reducer.toList())
print(result.getInfo())

# variance

得到一个返回求方差的统计器。

函数 返回值
variance() Reducer

# 示例：

list =pie.List([8,11,6])
result = list.reduce(pie.Reducer.variance())
print (result.getInfo())
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上次更新: 2022/05/25, 07:37:39
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