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r-spatial
TypeError: 'ComputedObject' object is not callable
- rgee version:1.1.4 , 1.1.5
- R version:4.2.1, 4.2.2
- Operating System:win10
At submit an issue, please attached the following information of your rgee session:
- [yes ] You have the Python API installed (from terminal):
earthengine -h
- [ yes] You can find the credentials file on your system:
library(rgee)
ee_path <- path.expand("~/.config/earthengine/credentials")
file.exists(ee_path)
- [yes ] You can run a simple EE command from R:
library(rgee)
# Initialize the Earth Engine module.
ee_Initialize()
# Print metadata for a DEM dataset.
print(ee$Image('USGS/SRTMGL1_003')$getInfo())
Attach your Python (reticulate) configuration:
library(reticulate)
py_config()
Description
I am trying to convert JS version of GEE codes to rgee codes： JS code: https://code.earthengine.google.com/aa97a56d2df0dfc6796175859da470fe
JS code 160-170 convert rgee codes report error：TypeError: 'ComputedObject' object is not callable
What I Did
library(rgee)
ee_Initialize(drive = T)
#掩膜掉过火和农田区域
#过火区域
firedata = ee$ImageCollection("ESA/CCI/FireCCI/5_1")
years = ee$List$sequence(2001,2008)
byYears = ee$ImageCollection$fromImages(
years$map(
ee_utils_pyfunc(function(y){
return(
firedata$filter(ee$Filter$calendarRange(y, y, 'year'))$
select('ConfidenceLevel')$max()$ #选择过火数据的置信度波段，最高置信度
set('year', y) #set函数用于覆盖元数据属性
)
})
)
)
byYearsc = ee$ImageCollection$fromImages(
years$map(
ee_utils_pyfunc(function(y){
return(
firedata$filter(ee$Filter$calendarRange(y, y, 'year'))$
select('BurnDate')$max()$
set('year', y) #set函数用于覆盖元数据属性
)
})
)
)
filtereq = ee$Filter$equals(leftField = 'system:index', rightField = 'system:index')
join = ee$Join$saveFirst(matchKey = 'match')
datam = ee$ImageCollection(join$apply(byYears,byYearsc,filtereq))$
map(function(image){
return(image$addBands(image$get('match')))
})
mask = function(img){
img1 = img$select('ConfidenceLevel')$gte(90) #大于或登玉
img2 = img$select('BurnDate')$updateMask(img1)
return(img2)
}
yearsM = datam$map(mask)
ee_print(yearsM)
firearea_o = yearsM$sum()
Map$addLayer(firearea_o)
firearea_0 = firearea_o$eq(0)
firearea = firearea_0$unmask(1) #获取过火区域掩膜
Map$addLayer(firearea)
#农田区域
cropdata = ee$ImageCollection("COPERNICUS/Landcover/100m/Proba-V-C3/Global")$
select('discrete_classification')
cb = function(img){
croparea = img$eq(40)$
Not() #哥白尼土地覆被农田像元为40，eq相等的返回1，not：输入非0返回0，其他1，最终结果农田设为0，其余1
build = img$eq(50)$
Not() #建筑设为0，其他1
Mask = croparea$multiply(build) #建筑或农田部分为0
return(Mask)
}
mask = cropdata$map(cb)
Mask = mask$mean()$gt(0)
Mask = firearea$multiply(Mask)
m1 = cropdata$first()
Map$addLayer(m1)
#计算逐月NDVI数据
#计算MOD13Q1数据，250米分辨率，年份：2001-2018
modis = ee$ImageCollection("MODIS/006/MOD13Q1")$
select('NDVI')
noaa = ee$ImageCollection("NOAA/CDR/AVHRR/NDVI/V5")$
select('NDVI')
#创建MODIS时间序列
startyear = 2001
endyear = 2018
startmonth = 1
endmonth= 12
startdate = ee$Date$fromYMD(startyear, startmonth, 1)
enddate = ee$Date$fromYMD(endyear+1, endmonth, 1)
years = ee$List$sequence(startyear, endyear)
months = ee$List$sequence(startmonth,endmonth)
Modis = modis$filterDate(startdate, enddate)$
sort('system:time_start', FALSE) #按时间对MODIS NDVI影像集排序
#MODIS 月度NDVI
Modism = ee$ImageCollection$fromImages(
years$map(
ee_utils_pyfunc(function(y){
return(
months$map(
ee_utils_pyfunc(function(m){
w = Modis$filter(ee$Filter$calendarRange(y, y, 'year'))$
filter(ee$Filter$calendarRange(m, m, 'month'))$
max()$
multiply(0.0001)$
updateMask(Mask)
return(
w$set('year', y)$
set('month', m)$
set('system:time_start',ee$Date$fromYMD(y,m,1)$millis())
)
})
)
)
})
)$flatten()
)
# AVHRR NDVI逐月数据
Noaa = noaa$filterDate(startdate, enddate)$
sort('system:time_start', FALSE)
#2001-2018年逐月AVHRR NDVI
Noaa2 = ee$ImageCollection$fromImages(
years$map(
ee_utils_pyfunc(function(y){
return(
months$map(
ee_utils_pyfunc(function(m){
w = Noaa$filter(ee$Filter$calendarRange(y, y, 'year'))$
filter(ee$Filter$calendarRange(m, m, 'month'))$
max()$
multiply(0.0001)$
resample('bicubic')$
updateMask(Mask)
return(
w$set('year', y)$
set('month', m)$
set('system:time_start',ee$Date$fromYMD(y,m,1)$millis())
)
})
)
)
})
)$flatten()
)
##1982-2000年逐月AVHRR NDVI
startyear = 1982
endyear = 2000
startmonth = 1
endmonth= 12
startdate = ee$Date$fromYMD(startyear, startmonth, 1)
enddate = ee$Date$fromYMD(endyear+1, endmonth, 1)
years = ee$List$sequence(startyear, endyear)
months = ee$List$sequence(startmonth,endmonth)
Noaa = noaa$filterDate(startdate, enddate)$
sort('system:time_start', FALSE) #按时间对MODIS NDVI影像集排序
#AVHRR 月度NDVI
Noaa1 = ee$ImageCollection$fromImages(
years$map(
ee_utils_pyfunc(function(y){
return(
months$map(
ee_utils_pyfunc(function(m){
w = Noaa$filter(ee$Filter$calendarRange(y, y, 'year'))$
filter(ee$Filter$calendarRange(m, m, 'month'))$
max()$
multiply(0.0001)$
resample('bicubic')$
updateMask(Mask)
return(
w$set('year', y)$
set('month', m)$
set('system:time_start',ee$Date$fromYMD(y,m,1)$millis())
)
})
)
)
})
)$flatten()
)
#计算逐月中值
sm = 1
em = 12
month_med = ee$List$sequence(1, 12)
#The following code runs an error：Error in py_call_impl(callable, dots$args, dots$keywords) :
TypeError: 'ComputedObject' object is not callable
Modis_med = ee$ImageCollection$fromImages(month_med$map(ee_utils_pyfunc(
function(f){
month1 = Modism$filter(ee$Filter$eq('month', f))
return(month1$reduce(ee$Reducer$median())$
rename('MMD')$
set('month', f))
}
)))
#The code inside the function runs without error：
month1 = Modism$filter(ee$Filter$eq('month', 1))
med = month1$reduce(ee$Reducer$median())$
rename('MMD')$
set('month', 1)
Map$addLayer(med)
I suspect that there is a bug when rgee executes map and reduce, or is there any other reason why the rgee version code cannot be executed?
Thank you for your help!
Hi @TianyaImpression, thanks for sharing a reproducible example. It's a very weird bug that occurs on the Python side (I will report it on the weekend!). Here is a solution, so basically avoid using 'f' as an argument.
Replace:
month_med = ee$List$sequence(1, 12)
#The following code runs an error：Error in py_call_impl(callable, dots$args, dots$keywords) :
TypeError: 'ComputedObject' object is not callable
Modis_med = ee$ImageCollection$fromImages(month_med$map(ee_utils_pyfunc(
function(f){
month1 = Modism$filter(ee$Filter$eq('month', f))
return(month1$reduce(ee$Reducer$median())$
rename('MMD')$
set('month', f))
}
)))
By:
monthly_reducer <- function(x) {
month1 <- Modism$filter(ee$Filter$eq('month', x))
month1$reduce(ee$Reducer$median())$
rename('MMD')$
set('month', x)
}
Modis_med <- ee$List$sequence(1, 12) %>%
ee$List$map(
ee_utils_pyfunc(monthly_reducer)
) %>%
ee$ImageCollection$fromImages()
UPDATE: No, this bug happens on the rgee side :face_with_spiral_eyes: , basically it's a dumb mistake in the ee_utils_pyfunc, I will solve it ASAP! Thank you!
Hi @TianyaImpression, thanks for sharing a reproducible example. It's a very weird bug that occurs on the Python side (I will report it on the weekend!). Here is a solution, so basically avoid using 'f' as an argument.
Replace:
month_med = ee$List$sequence(1, 12) #The following code runs an error：Error in py_call_impl(callable, dots$args, dots$keywords) : TypeError: 'ComputedObject' object is not callable Modis_med = ee$ImageCollection$fromImages(month_med$map(ee_utils_pyfunc( function(f){ month1 = Modism$filter(ee$Filter$eq('month', f)) return(month1$reduce(ee$Reducer$median())$ rename('MMD')$ set('month', f)) } )))By:
monthly_reducer <- function(x) { month1 <- Modism$filter(ee$Filter$eq('month', x)) month1$reduce(ee$Reducer$median())$ rename('MMD')$ set('month', x) } Modis_med <- ee$List$sequence(1, 12) %>% ee$List$map( ee_utils_pyfunc(monthly_reducer) ) %>% ee$ImageCollection$fromImages()UPDATE: No, this bug happens on the rgee side 😵💫 , basically it's a dumb mistake in the
ee_utils_pyfunc, I will solve it ASAP! Thank you!
Thank you for your reply!
I replaced "f" for "x", the code is ok!