-
Notifications
You must be signed in to change notification settings - Fork 0
/
sens_slope_pixelwise.R
284 lines (212 loc) · 8.84 KB
/
sens_slope_pixelwise.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
# oct 7th 2020
# start of pixel wise sens slop analysis
# need to firgure out the best way to cut full year vertical columns out for anlaysis and stich them back
library(easypackages)
library(BiocManager)
library(rhdf5)
library(tidyverse)
library(rgeos)
library(raster)
library(rgdal)
library(parallel)
library(data.table)
library(gtools)
setwd("/Volumes/JTARRICONE/UNR_summer_20/margulis_data/swe/code_test")
edit_r_environ()
#####################################################
### first steps
# show proof concept for method wiht smaller file size
#set path and file name for hdf5 SWE file
hdf_path <- "/Volumes/jt/projects/margulis/swe/hdf/" #create path
hdf_name <- "SN_SWE_WY2016.h5" #create file name
hdf_file <- paste(hdf_path, hdf_name, sep="") #full path
h5ls(hdf_file) #list contains 3 groups. lat, long, and SWE
h5closeAll()
h5readAttributes(hdf_file, name = "SWE") #$units = mm
# full raster with just three days
#read in SWE data which is an array, or stack of matrixes
swe_2016 <- h5read(hdf_file, "/SWE", index = list(1:6601, 1:5701, 180:182)) #read in SWE group
class(swe_2016) #inspect
dim(swe_2016) #dimensions
swe_brick <-brick(swe_2016, xmn=-123.3, xmx=-117.6, ymn=35.4, ymx=42,CRS("+proj=leac +ellps=clrk66"))
swe_brick[ swe_brick[] == -32768 ] <- NA
swe_brick
max_test <-stackApply(swe_brick, max, indices = rep(1,nlayers(swe_brick)), na.rm=TRUE)
plot(max_test)
hist(max_test)
# 100x100x366 test, full year
test_block<- h5read(hdf_file, "/SWE", index = list(4243:4252, 4175:4164, 1:366)) # read in array
test_brick <-brick(test_block, xmn=-123.3, xmx=-117.6, ymn=35.4, ymx=42, CRS("+proj=leac +ellps=clrk66")) # convert to brick
maxi <-stackApply(test_brick, max, indices = rep(1,nlayers(swe_brick)), na.rm=TRUE) # create pixel wise max raster
plot(maxi)
hist(maxi)
# creat row block sequence
seq(0, 6600, by=300 )
b1test <-(3301:3600)
test_block<- h5read(hdf_file, "/SWE", index = list(b1test, NULL, NULL))
test_block[ test_block[] == -32768 ] <- NA
test_brick <-brick(test_block, xmn=-123.3, xmx=-117.6, ymn=41.7, ymx=42, CRS("+proj=leac +ellps=clrk66"))
maxi <-stackApply(test_brick, max, indices = rep(1,nlayers(swe_brick)), na.rm=TRUE) # create pixel wise max raster
plot(maxi)
#############################################
hdf_path2 <- "/Volumes/jt/projects/margulis/swe/hdf/" #create path
hdf_name2 <- "SN_SWE_WY1993.h5" #create file name
hdf_file2 <- paste(hdf_path2, hdf_name2, sep="") #full path
h5ls(hdf_file2) #list contains 3 groups. lat, long, and SWE
h5closeAll()
h5readAttributes(hdf_file2, name = "SWE") #$units = mm
b1test <-(3301:3600)
test_block2<- h5read(hdf_file2, "/SWE", index = list(b1test, NULL, NULL))
test_block2[ test_block2[] == -32768 ] <- NA
test_brick2 <-brick(test_block2, xmn=-121.3, xmx=-119.6, ymn=41.7, ymx=42, CRS("+proj=leac +ellps=clrk66"))
maxi2 <-stackApply(test_brick2, max, indices = rep(1,nlayers(test_brick2)), na.rm=TRUE) # create pixel wise max raster
plot(maxi2)
chunk1 <- h5read(hdf_file2, "/SWE", index = list(1:2000, NULL, 220))
chunk2 <- h5read(hdf_file2, "/SWE", index = list(2001:4000, NULL, 220))
chunk3 <- h5read(hdf_file2, "/SWE", index = list(4001:6000, NULL, 220))
chunk4 <- h5read(hdf_file2, "/SWE", index = list(6001:6601, NULL, 220))
chunk1 <- as.matrix(chunk1[,,1])
chunk2 <- as.matrix(chunk2[,,1])
chunk3 <- as.matrix(chunk3[,,1])
chunk4 <- as.matrix(chunk4[,,1])
rast1 <-raster(chunk1, xmn=-123.3, xmx=-117.6, ymn=41.7, ymx=42, CRS("+proj=leac +ellps=clrk66"))
rast2 <-raster(chunk2, xmn=-123.3, xmx=-117.6, ymn=41.7, ymx=42, CRS("+proj=leac +ellps=clrk66"))
rast3 <-raster(chunk3, xmn=-123.3, xmx=-117.6, ymn=41.7, ymx=42, CRS("+proj=leac +ellps=clrk66"))
rast4 <-raster(chunk4, xmn=-123.3, xmx=-117.6, ymn=41.7, ymx=42, CRS("+proj=leac +ellps=clrk66"))
plot(rast1)
plot(rast2)
plot(rast3)
plot(rast4)
# https://stackoverflow.com/questions/15876591/merging-multiple-rasters-in-r
# https://tengkengvang.com/2018/11/12/mosaic-or-merge-rasters-in-r/
setwd("/Volumes/jt/projects/margulis/swe/code_test")
yut<-merge(rast1,rast2)
plot(yut)
all_my_rasts <- c(rast1, rast2, rast3, rast4)
e <- extent(-123.3, -117.6, 41.7, 42)
template <- raster(e)
projection(template) <- CRS("+proj=leac +ellps=clrk66")
writeRaster(template, file="MyBigNastyRasty.tif", format="GTiff")
mosaic_rasters(gdalfile=all_my_rasts, dst_dataset="MyBigNastyRasty.tif", of="GTiff")
gdalinfo("MyBigNastyRasty.tif")
setwd("D:/Raster")
a <- c('1.tif', '2.tif','3.tif','4.tif')
e <- extent(-85, -83, 39, 41)
template <- raster(e)
proj4string(template) <- CRS("+init=epsg:4269")
writeRaster(template, file="MiamiWatershed.tif", format="GTiff")
mosaic_rasters(gdalfile=a,dst_dataset="MiamiWatershed.tif",of="GTiff")
gdalinfo("MiamiWatershed.tif")
canProcessInMemory(rast1, verbose = TRUE)
b1 <-("1:300")
b2 <-(301:600)
b3 <-(601:900)
b4 <-(901:1200)
b5 <-(1201:1500)
b6 <-(1501:1800)
b7 <-(1801:2100)
b8 <-(2101:2400)
b9 <-(2401:2700)
b10 <-(2701:3000)
b11 <-(3001:3300)
b12 <-(3301:3600)
b13 <-(3601:3900)
b14 <-(3901:4200)
b15 <-(4201:4500)
b16 <-(4501:4800)
b17 <-(4801:5100)
b18 <-(5101:5400)
b19 <-(5401:5700)
b20 <-(5701:6000)
b21 <-(6001:6300)
b22 <-(6301:6601)
blocklist <-list(b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11,b12,b13,b14,b15,b16,b17,b18,b19,b20,b21,b22)
#2100 2400 2700 3000 3300 3600 3900 4200 4500 4800 5100 5400 5700 6000 6300 6600
library(raster)
sca <- brick(nrow=108,ncol=132,nl=365)
values(sca) <- runif(ncell(sca)*nlayers(sca))
i <- rep(1:ceiling(365/8), each=8)
i <- i[1:nlayers(sca)]
for (j in unique(i)) {
x <- sca[[which(j==i)]]
xx <- max(x, na.rm=TRUE)
# or
# xx <- calc(x, fun=max, na.rm=TRUE, filename = patste0(i, '.tif'))
}
#After several trials this seems to speed up my task 10 times:
rasterOptions(format="CDF",overwrite=TRUE,maxmemory = 1e+09,
chunksize=1e+08,progress="text",tmpdir="C:/DATA/mydata") rasterTmpFile("clean_this_after_")
open_hdf_as_raster <- function( hdf_name ) {
hdf_name %>%
file.path( hdf_path , . ) %>%
h5read(., "/SWE", index = list(NULL,NULL,1:50)) %>%
brick(., xmn=-123.3, xmx=-117.6, ymn=35.4, ymx=42,CRS("+proj=leac +ellps=clrk66")) %>%
as.list(.)
}
####################################################
# oct 12
# keeping data in a matrix instead of raster for processing
# this way we can avoid geolocation issues
"/Volumes/jt/projects/margulis/temp" <-tempdir()
#set path and file name for hdf5 SWE file
hdf_path <- "/Volumes/jt/projects/margulis/swe/hdf/" #create path
hdf_name <- "SN_SWE_WY2016.h5" #create file name
hdf_file <- paste(hdf_path, hdf_name, sep="") #full path
h5ls(hdf_file) #list contains 3 groups. lat, long, and SWE
path <-file.path( hdf_path , hdf_name )
h5closeAll()
h5readAttributes(hdf_file, name = "SWE") #$units = mm
max_raster <- function( hdf_name ) {
path <-file.path( hdf_path , hdf_name )
c1 <-h5read(path, "/SWE", index = list(1:1000,1:5701,1:365)) #load in
c1[ c1[] == -32768 ] <- NA #remove NA
max_c1 <-as.matrix(apply(c1, c(1,2), max)) #creat matrix with max value on z axis
rm(c1)
c2 <-h5read(path, "/SWE", index = list(1001:2000,1:5701,1:365))
c2[ c2[] == -32768 ] <- NA
max_c2 <-as.matrix(apply(c2, c(1,2), max))
rm(c2)
c3 <-h5read(path, "/SWE", index = list(2001:3000,1:5701,1:365))
c3[ c3[] == -32768 ] <- NA
max_c3 <-as.matrix(apply(c3, c(1,2), max))
rm(c3)
c4 <-h5read(path, "/SWE", index = list(3001:4000,1:5701,1:365))
c4[ c4[] == -32768 ] <- NA
max_c4 <-as.matrix(apply(c4, c(1,2), max))
rm(c4)
c5 <-h5read(path, "/SWE", index = list(4001:5000,1:5701,1:365))
c5[ c5[] == -32768 ] <- NA
max_c5 <-as.matrix(apply(c5, c(1,2), max))
rm(c5)
c6 <-h5read(path, "/SWE", index = list(5001:6000,1:5701,1:365))
c6[ c6[] == -32768 ] <- NA
max_c6 <-as.matrix(apply(c6, c(1,2), max))
rm(c6)
c7 <-h5read(path, "/SWE", index = list(6001:6601,1:5701,1:365))
c7[ c7[] == -32768 ] <- NA
max_c7 <-as.matrix(apply(c7, c(1,2), max))
rm(c7)
#bind chunks together
full_max <-rbind(max_c1,max_c2,max_c3,max_c4,max_c5,max_c6,max_c7)
rast <-raster(full_max, xmn=-123.3, xmx=-117.6, ymn=35.4, ymx=42, CRS("+proj=leac +ellps=clrk66"))
plot(rast)
name <- gsub(".h5", "", hdf_name)
good_name <- gsub("SN_SWE_", "max_swe_", name)
setwd("/Volumes/jt/projects/margulis/max_rasters/")
writeRaster(rast, paste0(good_name, ".tif"))
return(rast)
}
max_raster(hdf_name)
#### apply to hdf list
setwd("/Volumes/jt/projects/margulis/swe/hdf")
files <- list.files(pattern = ".h5")
hdf_list <-mixedsort(sort(files)) #sort in correct order
print(hdf_list)
system.time(results_list <-mclapply(hdf_list, function(x) max_raster(x), mc.cores = 3, mc.cleanup = TRUE))
data <-open_hdf_as_raster(hdf_name)
chunk[ chunk[] == -32768 ] <- NA
max_value <-as.matrix(apply(data, c(1,2), max))
slice <-(chunk[,,180])
rast1 <-raster(test, xmn=-123.3, xmx=-117.6, ymn=41.7, ymx=42, CRS("+proj=leac +ellps=clrk66"))
plot(rast1)
rast1