In impact-initiatives-geospatial/easyrgee: easy rgee - wrappers for `rgee` functions
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library(easyrgee)
library(rgee)
library(ggplot2)
ee_Initialize()
dat<- easyrgee::adm1_ne_nga
chirps <- ee$ImageCollection("UCSB-CHG/CHIRPS/DAILY")$
filterDate("2016-01-01","2016-12-31")
dat_internal <- readRDS("dat_internal.rds")
median_ndvi <- dat_internal$median_ndvi
median_cumulative_rainfall <- dat_internal$median_cumulative_rainfall
Zonal Stats
ee_extract_long is a tool that can be used to extract values or zonal statistics over both Images and Image collections. It is a wrapper around rgee::ee_extract which does the same, but returns the output in wide format. ee_extract_long also contains a several server-side processes and client-side regex manipulations to help standardize the output. The result is a data.frame ready for processing/plotting.
Here is a short example
modis_ndvi <- ee$ImageCollection("MODIS/006/MOD13Q1")$select("NDVI" )$filterDate("2016-01-01","2016-12-31")
median_ndvi<- ee_extract_long(ic = modis_ndvi,
sf = dat,
sf_col = "ADM1_EN",
scale = 250,
reducer = "median")
Visualize NDVI
median_ndvi |>
ggplot(aes(x=date, y= value, color=ADM1_EN))+
geom_line(lwd=1)+
scale_color_manual(name="State (NGA)" ,values =c("#ee5859","#DBD6C7", "#818183") )+
theme_minimal()
Identify Start of Rainy Season
Start time of rainy season can have implications on crop harvest which can then impact well-being/livelihoods. Therefore, we have made a function to help identify a date which can be considered start of rainy season. The function works by
- Creating a cumulative precipitation
ee$ImageCollection with each all pixels in each consecutive image containing the sum of all previous values
- It then finds the first day the user-defined threshold was met for each pixel. The result is a raster ranging from 0-365 (days of year)
precip_threshold_doy<- ee_accum_reduce_to_doy(ic = chirps,thresh = 20,band = "precipitation")
#visualize
Map$setCenter(zoom = 3)
Map$addLayer(precip_threshold_doy,
visParams = list(min=0, max=365, palette='#9ecae1,#ffffff,#ffeda0,#feb24c,#f03b20'))
The function also relies on the internal function: ee_accumulate_band_ic which is handy on it's own. Using this function is handy to explore the data and set a reasonable threshold for your context. Here we create an imageCollection of cumulative precipitation values.
precip_cumulative<- ee_accumulate_band_ic(ic = chirps, band = "precipitation")
Next we again use ee_extract_long to extract the daily median values of 3 different geographic zones
median_cumulative_rainfall<- ee_extract_long(ic = precip_cumulative,sf = dat,sf_col = "ADM1_EN",scale = 5000,reducer = "median")
Plot Rainfall Accumulation
median_cumulative_rainfall |>
ggplot(aes(x=date, y= value, color=ADM1_EN))+
geom_line(lwd=1)+
scale_color_manual(name="State (NGA)" ,values =c("#ee5859","#DBD6C7", "#818183") )+
theme_minimal()
# the code that got me there
#####################3
tst_ic <- chirps_one_yr$select("precipitation")
tst_img <- tst_ic$first()
first2 <- ee$List(list(ee$Image(tst_ic$first())))
previous <- ee$Image(ee$List(first2)$get(-1))
added <- tst_img$add(previous)$
set('system:time_start', tst_img$get('system:time_start'))
ret_list <- ee$List(list(first2))$add(added)
ret_list$getInfo()
# ret_list_list <- ret_list$toList(ret_list$size())
ret_list |> ee_print()
ee$ImageCollection$fromImages(ret_list$flatten()) |> ee_print()
chirps = ee$ImageCollection("UCSB-CHG/CHIRPS/DAILY")
chirps_one_yr = chirps$filterDate("2016-01-01","2016-12-31")
time0 = chirps_one_yr$first()$get('system:time_start')
# ee$Image(0)$set('system:time_start', time0) |> ee_print()
# first = ee$List([
#
# ee$Image(0)$set('system:time_start', time0)$select([0], ['precipitation'])
# ee$Image(0)$set('system:time_start', time0)$select(0)$rename('precipitation') |> ee_print()
# ])
# ee$Image(0)$set('system:time_start', time0)$select(0) |> ee_print()
#
first = ee$List(
ee$Image(0)$set('system:time_start', time0)$select(0)$rename("precipitation")
)
# first$add(chirps$first())
first_img_test <- ee$Image(0)$set('system:time_start', time0)$select(0)$rename("precipitation")
# ee$List(list(first_img_test))$getInfo() |> ee_print()
system:time_start
ee$Image(0)$set('system:time_start', time0) |> ee_print()
first2 <- ee$List(
list(ee$Image(0)$set('system:time_start', time0)$select(0)$rename("precipitation"))
)
prev <- ee$Image(ee$List(list(first))$get(-1))
prev2<- ee$Image(ee$List(list(first2))$get(-1))
prev |> ee_print()
prev2 |> ee_print()
added_ck <- ee$Image(chirps_one_yr$first())$
add(prev)
added_ck |> ee_print()
accum_output_raw<- ee$List(list(first))$add(ee$Image(added_ck))
accum_output_raw<- ee$List(list(first))$add(added_ck)
# accum_output_raw<- ee$List(list(first))$add(added_ck)
ee$ImageCollection(ee$List(accum_output_raw)) |> ee_print()
ilist_ck$add(added_ck) |> ee_print()
ee$List(list(first))$add(added_ck) |> ee_print()
ee$List(list(ilist_ck))$add(added_ck) |> ee_print()
ee$Image(ee$List(list)$get(-1))
debugonce(ee$List$get)
ee$Image(ee$List(first)$get(-1)) |> ee_print()
first$getInfo()
first$getInfo()
chirps_one_yr$first() |> ee_print()
# This is a function to pass to Iterate().
bs = chirps_one_yr$iterate(
ee_utils_pyfunc(function(img,lst){
previous= ee$Image(ee$List(list(lst)))$get(-1) # if i could only not have this in
added= img$add(ee$Image(previous))$
set('system:time_start', img$get('system:time_start'))
return(ee$List(ee$Image(lst))$add(added))
}), first
)
ee$ImageCollection(bs) |> ee_print()
bs$getInfo()
bs |> ee_print()
ee$ImageCollection(ee$List(list(bs))) |> ee_print()
whatsthis <- chirps_one_yr$iterate(
ee_utils_pyfunc(
function(image,iterate_list){
previous = ee$Image(ee$List(list(iterate_list))$get(-1))#$
date_ymd = ee$Date(image$get("system:time_start"))
added = image$add(ee$Image(previous))$
# Propagate metadata to the new image.
set('system:time_start', image$get('system:time_start'))$
set('date_ymd',date_ymd)
return(ee$List(ee$Image(iterate_list)$add(added)))
},first
)
)
whatsthis <- chirps_one_yr$iterate(
ee_utils_pyfunc(accumulate()),first
)
ee$List(list(whatsthis)) |> length()
ee$ImageCollection(ee$List(list(whatsthis))) |> ee_print()
ee$List(ee$Image(iterate_list)$add(added))
accu2 <- function(ic,lst){
ic$iterate(
ee_utils_pyfunc(
function(img,lst){
prev= ee$Image(ee$List(list(lst))$get(-1))
added= img$add(ee$Image(prev))$
set('system:time_start', image$get('system:time_start'))
return(ee$List(lst$add(added)))
}
),lst
)
}
accu2 |> debugonce(accu2)
accu2(ic = chirps_one_yr,lst = first)
rgee::ee_list(first)
rgee::ee$ee_list(first)
class(first)
chirps$get(1)
chirps_filt<- chirps$
filterDate("2022-01-01","2022-02-01")
chirps_filt |> ee_get_date_ic()
chirps_filt_list <- chirps_filt$toList(chirps$size())
ee$Image(chirps_filt_list$get(-1)) |> ee_print()
ee$Image(first$add(chirps$first())) |> ee_print()
# As anomaly images are computed, add them to the list.
iterated_nested_output<- chirps_one_yr$iterate(accum3,first2)
ee$List(iterated_nested_output)$getInfo()
ee$ImageCollection$fromImages(iterated_nested_output$flatten())
hmmm <- ee$ImageCollection$fromImages(ee$List(iterated_nested_output)$flatten())
hmmm |> ee_print()
cumulative <- ee$ImageCollection(ee$List(list(ret_list))) #|> ee_print()
cumulative |> ee_print()
previous <- ee$Image($first()$get(-1))
previous |> ee_print()
first2 |> ee_print()
previous <- ee$Image(ee$List(list(ee$Image(first2)))$get(-1))#$
previous |> ee_print()
accumulate = function(image, iterate_list) {
# maybe not working with list casting -- try entire iteration here
# iterate_list <- ee$List(list(iterate_list))
previous <- ee$Image(ee$List(list(iterate_list))$get(-1))#$
# previous <- ee$Image(iterate_list$get(-1))#$
added = ee$Image(image$add(ee$Image(previous))$
set('system:time_start', image$get('system:time_start'))
)
return (
# ee$List(list(iterate_list))$add(ee$List(list(added)))
# ee$List(list(iterate_list))$add(ee$List(list(added)))
ee$List(list(iterate_list))$add(added)
)
# Get the latest cumulative anomaly image from the end of the list with
# get(-1). Since the type of the list argument to the function is unknown,
# it needs to be cast to a List. Since the return type of get() is unknown,
# cast it to Image.
# previous = ee$Image(ee$List(list(iterate_list))$get(-1))#$
# # set('system:time_start', image$get('system:time_start'))
# # Add the current anomaly to make a new cumulative anomaly image.
# date_ymd = ee$Date(image$get("system:time_start"))
#
# added = image$add(ee$Image(previous))$
# # Propagate metadata to the new image.
# set('system:time_start', image$get('system:time_start'))$
# set('date_ymd',date_ymd)
# Return the list with the cumulative anomaly inserted.
# iterate_list <- ee$List(ee$Image(iterate_list)$add(added))
# added_list <- ee$List(added)
# added_list <- added$toList()
# rgee::ee$List$add
# return (
# ee$List(list(iterate_list))$add(ee$Image(added))
# ee$List(list(iterate_list))$add(ee$Image(added))
# ee$List(list(list))$add(added)
# ee$List(list(ee$Image(iterate_list)$add(added)))
# this does seem to work, but might only be returning 1 img and not filling list
# ee$Image(iterate_list)$add(added)
# ee$List(ee$Image(iterate_list)$add(added))
# iterate_list
# ee$List(iterate_list)$add(added)
# ee$List(list(iterate_list))$add(added)
# ee$List(list(iterate_list))$add(ee$List(list((added))))
# ee$List(list(iterate_list)$add(ee$Image(added)))
# ee$List(ee$Image(iterate_list)$add(added)) # works but just one image
# ee$List(list(ee$Image(iterate_list)$add(added)))
# iterate_list$map(
# ee_utils_pyfunc(function(x){
# ee$Image(x)$add(added)
# })
# )
# ee$List(list(iterate_list))$add(ee$List(list(added)))
# ee$List(list(iterate_list))$add(ee$List(added))
# ee$List(iterate_list)$add(ee$List(added))
# ee$List(iterate_list)$add(ee$List(list(added)))
# iterate_list$add(added)
# ee$List(iterate_list)$add(added_list)
# )
}
cmon<- chirps_one_yr$iterate(accumulate,first)
# ee$ImageCollection
# 4ee$ImageCollection(cmon) |> ee_print()
# // Create first image for iteration
first2 <- ee$List(list(ee$Image(chirps_one_yr$select("precipitation")$first())))
ee$ImageCollection$fromImages(
chirps_one_yr$select("precipitation")$iterate(accumulate,first2)
) |> ee_print()
cumulative <- ee$ImageCollection(ee$List(list(chirps_one_yr$iterate(accumulate,first)))) #|> ee_print()
cumulative |> ee_print()
# acumulative |> ee_print()
cumulative$first() |> ee_print()
# ee$Image$add( )
chirps_one_yr$select("precipitation") |> ee_print()
firs
# debugonce(accumulate)
# ee$ImageCollection(ee$List(list(chirps_one_yr$iterate(accumulate,first2)))) |> ee_print()
ee$List(list(chirps_one_yr$iterate(accumulate,first)))$length()$getInfo()
cumulative$first() |> ee_print()
cumulative |> ee_get_date_ic()
# hmm |> ee_print()
# ee$ImageCollection(ee$List(chirps_one_yr$iterate(accumulate,first2))) |> ee_print()
# ee$Image$from
# Create an ImageCollection of cumulative anomaly images by iterating.
# Since the return type of iterate is unknown, it needs to be cast to a List.
# accumulate |> debugonce()
cumulative = ee$ImageCollection(ee$List(chirps_one_yr$iterate(accumulate, first)))
cumulative = ee$ImageCollection(ee.List(series.iterate(accumulate, first)));
# cumulative$getInfo()
cumulative |> ee_print()
thresh_doy = function(img){
doy = img$date()$getRelative('day', 'year');
doyBand = ee$Image$constant(doy)$uint16()$rename('doy')
const1000 = ee$Image$constant(1000)$uint16()$rename('const1000')
lt_thresh_mask = img$select("precipitation")$lt(20)
gte_thresh_mask = img$select("precipitation")$gte(20)
img_w_doy = img$addBands(doyBand$select("doy"))$addBands(const1000$select('const1000'))
lets_see = img_w_doy$select("doy")$where(lt_thresh_mask, const1000)
return(lets_see)
}
cumulative_lt= cumulative$map(thresh_doy)
cumulative_lt |> ee_print()
print(cumulative_lt,"cumulative_lt")
Map.addLayer(cumulative_lt$min(),
{min:50,max:250,palette:'#9ecae1,#ffffff,#ffeda0,#feb24c,#f03b20'},
'Max Accum');
#' accumulate_band
#' @param ic imageCollection
#' @param band band to accumulate
#' @description accumulate band values iteratively.
#' @references https://developers.google.com/earth-engine/guides/ic_iterating
accumulate_img <- function(img, band){
# img <- chirps_2016$first()
# band="precipitation"
time0 = img$get('system:time_start')
# The first image in the list is just 0, with the time0 timestamp.
iterate_list = rgee::ee$List(ee$Image(0)$set("system:time_start",time0)$select(0,"precipitation"))
previous = ee$Image(ee$List(iterate_list)$get(-1));
added = img$add(previous)$
# // Propagate metadata to the new image.
set('system:time_start', img$get('system:time_start'))
return(ee$List(iterate_list)$add(added))
}
accumulate_band_values_ic <- function(ic, band){
iterate_list = rgee::ee$List(ee$Image(0)$set("system:time_start",time0)$select(0,band))
accumulated <- ee$ImageCollection(ee$List(ic$iterate(accumulate_img, iterate_list)))
# accumulated <- ee$ImageCollection(ee$List(ic$iterate(accumulate_img)))
return(accumulated)
}
# accumulate_band_values_ic |> debugonce()
test <- accumulate_band_values_ic(ic = chirps_2016,band = "precipitation")
test |> ee_print()
chirps_2016 |> ee_print()
accumulate_band_values <- function(ic,
band#,
# time_step = 1 # will need to consider
){
time0 = ic$get('system:time_start')
# The first image in the list is just 0, with the time0 timestamp.
iterate_list = rgee::ee$List(ee$Image(0)$set("system:time_start",time0)$select(0,band))
previous = ee$Image(ee$List(iterate_list)$get(-1));
ic2<- ic$map(
function(img){
added = img$add(previous)$
# // Propagate metadata to the new image.
set('system:time_start', img$get('system:time_start'))
}
accumulated <- ee.ImageCollection(ee.List(chirps_one_yr.iterate(accumulate, first)));
)
}
library(rgee)
ee_Initialize()
chirps <- ee$ImageCollection("UCSB-CHG/CHIRPS/DAILY")
chirps_2016 = chirps$
filterDate("2016-01-01","2016-12-31")
ck<- accumulate_band_values(ic = chirps_2016,band = "precipitation")
ck |> ee_print()
impact-initiatives-geospatial/easyrgee documentation built on March 26, 2022, 10:42 p.m.
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knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(easyrgee) library(rgee) library(ggplot2) ee_Initialize() dat<- easyrgee::adm1_ne_nga chirps <- ee$ImageCollection("UCSB-CHG/CHIRPS/DAILY")$ filterDate("2016-01-01","2016-12-31")
dat_internal <- readRDS("dat_internal.rds") median_ndvi <- dat_internal$median_ndvi median_cumulative_rainfall <- dat_internal$median_cumulative_rainfall
Zonal Stats
ee_extract_long is a tool that can be used to extract values or zonal statistics over both Images and Image collections. It is a wrapper around rgee::ee_extract which does the same, but returns the output in wide format. ee_extract_long also contains a several server-side processes and client-side regex manipulations to help standardize the output. The result is a data.frame ready for processing/plotting.
Here is a short example
modis_ndvi <- ee$ImageCollection("MODIS/006/MOD13Q1")$select("NDVI" )$filterDate("2016-01-01","2016-12-31") median_ndvi<- ee_extract_long(ic = modis_ndvi, sf = dat, sf_col = "ADM1_EN", scale = 250, reducer = "median")
Visualize NDVI
median_ndvi |> ggplot(aes(x=date, y= value, color=ADM1_EN))+ geom_line(lwd=1)+ scale_color_manual(name="State (NGA)" ,values =c("#ee5859","#DBD6C7", "#818183") )+ theme_minimal()
Identify Start of Rainy Season
Start time of rainy season can have implications on crop harvest which can then impact well-being/livelihoods. Therefore, we have made a function to help identify a date which can be considered start of rainy season. The function works by
- Creating a cumulative precipitation
ee$ImageCollectionwith each all pixels in each consecutive image containing the sum of all previous values - It then finds the first day the user-defined threshold was met for each pixel. The result is a raster ranging from 0-365 (days of year)
precip_threshold_doy<- ee_accum_reduce_to_doy(ic = chirps,thresh = 20,band = "precipitation") #visualize Map$setCenter(zoom = 3) Map$addLayer(precip_threshold_doy, visParams = list(min=0, max=365, palette='#9ecae1,#ffffff,#ffeda0,#feb24c,#f03b20'))
The function also relies on the internal function: ee_accumulate_band_ic which is handy on it's own. Using this function is handy to explore the data and set a reasonable threshold for your context. Here we create an imageCollection of cumulative precipitation values.
precip_cumulative<- ee_accumulate_band_ic(ic = chirps, band = "precipitation")
Next we again use ee_extract_long to extract the daily median values of 3 different geographic zones
median_cumulative_rainfall<- ee_extract_long(ic = precip_cumulative,sf = dat,sf_col = "ADM1_EN",scale = 5000,reducer = "median")
Plot Rainfall Accumulation
median_cumulative_rainfall |> ggplot(aes(x=date, y= value, color=ADM1_EN))+ geom_line(lwd=1)+ scale_color_manual(name="State (NGA)" ,values =c("#ee5859","#DBD6C7", "#818183") )+ theme_minimal()
# the code that got me there #####################3 tst_ic <- chirps_one_yr$select("precipitation") tst_img <- tst_ic$first() first2 <- ee$List(list(ee$Image(tst_ic$first()))) previous <- ee$Image(ee$List(first2)$get(-1)) added <- tst_img$add(previous)$ set('system:time_start', tst_img$get('system:time_start')) ret_list <- ee$List(list(first2))$add(added) ret_list$getInfo() # ret_list_list <- ret_list$toList(ret_list$size()) ret_list |> ee_print() ee$ImageCollection$fromImages(ret_list$flatten()) |> ee_print()
chirps = ee$ImageCollection("UCSB-CHG/CHIRPS/DAILY") chirps_one_yr = chirps$filterDate("2016-01-01","2016-12-31") time0 = chirps_one_yr$first()$get('system:time_start') # ee$Image(0)$set('system:time_start', time0) |> ee_print() # first = ee$List([ # # ee$Image(0)$set('system:time_start', time0)$select([0], ['precipitation']) # ee$Image(0)$set('system:time_start', time0)$select(0)$rename('precipitation') |> ee_print() # ]) # ee$Image(0)$set('system:time_start', time0)$select(0) |> ee_print() # first = ee$List( ee$Image(0)$set('system:time_start', time0)$select(0)$rename("precipitation") ) # first$add(chirps$first()) first_img_test <- ee$Image(0)$set('system:time_start', time0)$select(0)$rename("precipitation") # ee$List(list(first_img_test))$getInfo() |> ee_print() system:time_start ee$Image(0)$set('system:time_start', time0) |> ee_print() first2 <- ee$List( list(ee$Image(0)$set('system:time_start', time0)$select(0)$rename("precipitation")) ) prev <- ee$Image(ee$List(list(first))$get(-1)) prev2<- ee$Image(ee$List(list(first2))$get(-1)) prev |> ee_print() prev2 |> ee_print() added_ck <- ee$Image(chirps_one_yr$first())$ add(prev) added_ck |> ee_print() accum_output_raw<- ee$List(list(first))$add(ee$Image(added_ck)) accum_output_raw<- ee$List(list(first))$add(added_ck) # accum_output_raw<- ee$List(list(first))$add(added_ck) ee$ImageCollection(ee$List(accum_output_raw)) |> ee_print() ilist_ck$add(added_ck) |> ee_print() ee$List(list(first))$add(added_ck) |> ee_print() ee$List(list(ilist_ck))$add(added_ck) |> ee_print() ee$Image(ee$List(list)$get(-1)) debugonce(ee$List$get) ee$Image(ee$List(first)$get(-1)) |> ee_print() first$getInfo() first$getInfo() chirps_one_yr$first() |> ee_print() # This is a function to pass to Iterate(). bs = chirps_one_yr$iterate( ee_utils_pyfunc(function(img,lst){ previous= ee$Image(ee$List(list(lst)))$get(-1) # if i could only not have this in added= img$add(ee$Image(previous))$ set('system:time_start', img$get('system:time_start')) return(ee$List(ee$Image(lst))$add(added)) }), first ) ee$ImageCollection(bs) |> ee_print() bs$getInfo() bs |> ee_print() ee$ImageCollection(ee$List(list(bs))) |> ee_print() whatsthis <- chirps_one_yr$iterate( ee_utils_pyfunc( function(image,iterate_list){ previous = ee$Image(ee$List(list(iterate_list))$get(-1))#$ date_ymd = ee$Date(image$get("system:time_start")) added = image$add(ee$Image(previous))$ # Propagate metadata to the new image. set('system:time_start', image$get('system:time_start'))$ set('date_ymd',date_ymd) return(ee$List(ee$Image(iterate_list)$add(added))) },first ) ) whatsthis <- chirps_one_yr$iterate( ee_utils_pyfunc(accumulate()),first ) ee$List(list(whatsthis)) |> length() ee$ImageCollection(ee$List(list(whatsthis))) |> ee_print() ee$List(ee$Image(iterate_list)$add(added)) accu2 <- function(ic,lst){ ic$iterate( ee_utils_pyfunc( function(img,lst){ prev= ee$Image(ee$List(list(lst))$get(-1)) added= img$add(ee$Image(prev))$ set('system:time_start', image$get('system:time_start')) return(ee$List(lst$add(added))) } ),lst ) } accu2 |> debugonce(accu2) accu2(ic = chirps_one_yr,lst = first) rgee::ee_list(first) rgee::ee$ee_list(first) class(first) chirps$get(1) chirps_filt<- chirps$ filterDate("2022-01-01","2022-02-01") chirps_filt |> ee_get_date_ic() chirps_filt_list <- chirps_filt$toList(chirps$size()) ee$Image(chirps_filt_list$get(-1)) |> ee_print() ee$Image(first$add(chirps$first())) |> ee_print() # As anomaly images are computed, add them to the list. iterated_nested_output<- chirps_one_yr$iterate(accum3,first2) ee$List(iterated_nested_output)$getInfo() ee$ImageCollection$fromImages(iterated_nested_output$flatten()) hmmm <- ee$ImageCollection$fromImages(ee$List(iterated_nested_output)$flatten()) hmmm |> ee_print() cumulative <- ee$ImageCollection(ee$List(list(ret_list))) #|> ee_print() cumulative |> ee_print() previous <- ee$Image($first()$get(-1)) previous |> ee_print() first2 |> ee_print() previous <- ee$Image(ee$List(list(ee$Image(first2)))$get(-1))#$ previous |> ee_print() accumulate = function(image, iterate_list) { # maybe not working with list casting -- try entire iteration here # iterate_list <- ee$List(list(iterate_list)) previous <- ee$Image(ee$List(list(iterate_list))$get(-1))#$ # previous <- ee$Image(iterate_list$get(-1))#$ added = ee$Image(image$add(ee$Image(previous))$ set('system:time_start', image$get('system:time_start')) ) return ( # ee$List(list(iterate_list))$add(ee$List(list(added))) # ee$List(list(iterate_list))$add(ee$List(list(added))) ee$List(list(iterate_list))$add(added) ) # Get the latest cumulative anomaly image from the end of the list with # get(-1). Since the type of the list argument to the function is unknown, # it needs to be cast to a List. Since the return type of get() is unknown, # cast it to Image. # previous = ee$Image(ee$List(list(iterate_list))$get(-1))#$ # # set('system:time_start', image$get('system:time_start')) # # Add the current anomaly to make a new cumulative anomaly image. # date_ymd = ee$Date(image$get("system:time_start")) # # added = image$add(ee$Image(previous))$ # # Propagate metadata to the new image. # set('system:time_start', image$get('system:time_start'))$ # set('date_ymd',date_ymd) # Return the list with the cumulative anomaly inserted. # iterate_list <- ee$List(ee$Image(iterate_list)$add(added)) # added_list <- ee$List(added) # added_list <- added$toList() # rgee::ee$List$add # return ( # ee$List(list(iterate_list))$add(ee$Image(added)) # ee$List(list(iterate_list))$add(ee$Image(added)) # ee$List(list(list))$add(added) # ee$List(list(ee$Image(iterate_list)$add(added))) # this does seem to work, but might only be returning 1 img and not filling list # ee$Image(iterate_list)$add(added) # ee$List(ee$Image(iterate_list)$add(added)) # iterate_list # ee$List(iterate_list)$add(added) # ee$List(list(iterate_list))$add(added) # ee$List(list(iterate_list))$add(ee$List(list((added)))) # ee$List(list(iterate_list)$add(ee$Image(added))) # ee$List(ee$Image(iterate_list)$add(added)) # works but just one image # ee$List(list(ee$Image(iterate_list)$add(added))) # iterate_list$map( # ee_utils_pyfunc(function(x){ # ee$Image(x)$add(added) # }) # ) # ee$List(list(iterate_list))$add(ee$List(list(added))) # ee$List(list(iterate_list))$add(ee$List(added)) # ee$List(iterate_list)$add(ee$List(added)) # ee$List(iterate_list)$add(ee$List(list(added))) # iterate_list$add(added) # ee$List(iterate_list)$add(added_list) # ) } cmon<- chirps_one_yr$iterate(accumulate,first) # ee$ImageCollection # 4ee$ImageCollection(cmon) |> ee_print() # // Create first image for iteration first2 <- ee$List(list(ee$Image(chirps_one_yr$select("precipitation")$first()))) ee$ImageCollection$fromImages( chirps_one_yr$select("precipitation")$iterate(accumulate,first2) ) |> ee_print() cumulative <- ee$ImageCollection(ee$List(list(chirps_one_yr$iterate(accumulate,first)))) #|> ee_print() cumulative |> ee_print() # acumulative |> ee_print() cumulative$first() |> ee_print() # ee$Image$add( ) chirps_one_yr$select("precipitation") |> ee_print() firs # debugonce(accumulate) # ee$ImageCollection(ee$List(list(chirps_one_yr$iterate(accumulate,first2)))) |> ee_print() ee$List(list(chirps_one_yr$iterate(accumulate,first)))$length()$getInfo() cumulative$first() |> ee_print() cumulative |> ee_get_date_ic() # hmm |> ee_print() # ee$ImageCollection(ee$List(chirps_one_yr$iterate(accumulate,first2))) |> ee_print() # ee$Image$from # Create an ImageCollection of cumulative anomaly images by iterating. # Since the return type of iterate is unknown, it needs to be cast to a List. # accumulate |> debugonce() cumulative = ee$ImageCollection(ee$List(chirps_one_yr$iterate(accumulate, first))) cumulative = ee$ImageCollection(ee.List(series.iterate(accumulate, first))); # cumulative$getInfo() cumulative |> ee_print() thresh_doy = function(img){ doy = img$date()$getRelative('day', 'year'); doyBand = ee$Image$constant(doy)$uint16()$rename('doy') const1000 = ee$Image$constant(1000)$uint16()$rename('const1000') lt_thresh_mask = img$select("precipitation")$lt(20) gte_thresh_mask = img$select("precipitation")$gte(20) img_w_doy = img$addBands(doyBand$select("doy"))$addBands(const1000$select('const1000')) lets_see = img_w_doy$select("doy")$where(lt_thresh_mask, const1000) return(lets_see) } cumulative_lt= cumulative$map(thresh_doy) cumulative_lt |> ee_print() print(cumulative_lt,"cumulative_lt") Map.addLayer(cumulative_lt$min(), {min:50,max:250,palette:'#9ecae1,#ffffff,#ffeda0,#feb24c,#f03b20'}, 'Max Accum');
#' accumulate_band #' @param ic imageCollection #' @param band band to accumulate #' @description accumulate band values iteratively. #' @references https://developers.google.com/earth-engine/guides/ic_iterating accumulate_img <- function(img, band){ # img <- chirps_2016$first() # band="precipitation" time0 = img$get('system:time_start') # The first image in the list is just 0, with the time0 timestamp. iterate_list = rgee::ee$List(ee$Image(0)$set("system:time_start",time0)$select(0,"precipitation")) previous = ee$Image(ee$List(iterate_list)$get(-1)); added = img$add(previous)$ # // Propagate metadata to the new image. set('system:time_start', img$get('system:time_start')) return(ee$List(iterate_list)$add(added)) } accumulate_band_values_ic <- function(ic, band){ iterate_list = rgee::ee$List(ee$Image(0)$set("system:time_start",time0)$select(0,band)) accumulated <- ee$ImageCollection(ee$List(ic$iterate(accumulate_img, iterate_list))) # accumulated <- ee$ImageCollection(ee$List(ic$iterate(accumulate_img))) return(accumulated) } # accumulate_band_values_ic |> debugonce() test <- accumulate_band_values_ic(ic = chirps_2016,band = "precipitation") test |> ee_print() chirps_2016 |> ee_print() accumulate_band_values <- function(ic, band#, # time_step = 1 # will need to consider ){ time0 = ic$get('system:time_start') # The first image in the list is just 0, with the time0 timestamp. iterate_list = rgee::ee$List(ee$Image(0)$set("system:time_start",time0)$select(0,band)) previous = ee$Image(ee$List(iterate_list)$get(-1)); ic2<- ic$map( function(img){ added = img$add(previous)$ # // Propagate metadata to the new image. set('system:time_start', img$get('system:time_start')) } accumulated <- ee.ImageCollection(ee.List(chirps_one_yr.iterate(accumulate, first))); ) } library(rgee) ee_Initialize() chirps <- ee$ImageCollection("UCSB-CHG/CHIRPS/DAILY") chirps_2016 = chirps$ filterDate("2016-01-01","2016-12-31") ck<- accumulate_band_values(ic = chirps_2016,band = "precipitation") ck |> ee_print()
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