-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathhdf_to_daily_rasters.R
191 lines (119 loc) · 5.57 KB
/
hdf_to_daily_rasters.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
#######saving "chunks" of data at a time as text files for memory purposes
#will later be brought back in as a listed and converted to rasters for processing
#set c1
setwd("/Volumes/john_tarricone/UNR_summer_20/margulis/swe/daily_rasters/WY_2014/c1")
########first chunk: days 1-90
seq<-(1:90)
for (i in 1:365){
mat<-as.matrix(swe_2014_1[,,i])
rast <-raster(mat, xmn=-123.3, xmx=-117.6, ymn=35.4, ymx=42, crs=CRS("+proj=leac +ellps=clrk66"))
rast[ rast[] == -32768 ] <- NA
filename <- paste("swe_2014_doWY_",i)
writeRaster(rast, filename, format='GTiff', overwrite=FALSE)
}
#remove first chunk before starting second chunk
rm(swe_2014_1)
#######second chunk: days 91 - 180
swe_2014_2 <- h5read(hdf_file, "/SWE", index = list(NULL, NULL, 91:180))
#set c2
setwd("/Volumes/john_tarricone/UNR_summer_20/margulis/swe/daily_rasters/WY_2014/c2")
for (i in 1:365){
mat<-as.matrix(swe_2014_2[,,i])
rast <-raster(mat, xmn=-123.3, xmx=-117.6, ymn=35.4, ymx=42, crs=CRS("+proj=leac +ellps=clrk66"))
rast[ rast[] == -32768 ] <- NA
filename <- paste("swe_2014_doWY_",(i+90))
writeRaster(rast, filename, format='GTiff', overwrite=FALSE)
}
#remove second chunk before starting second chunk
rm(swe_2014_2)
#######third chunk: days 181 - 270
swe_2014_3 <- h5read(hdf_file, "/SWE", index = list(NULL, NULL, 91:180))
#set c3
setwd("/Volumes/john_tarricone/UNR_summer_20/margulis/swe/daily_rasters/WY_2014/c3")
for (i in 1:365){
mat<-as.matrix(swe_2014_3[,,i])
rast <-raster(mat, xmn=-123.3, xmx=-117.6, ymn=35.4, ymx=42, crs=CRS("+proj=leac +ellps=clrk66"))
rast[ rast[] == -32768 ] <- NA
filename <- paste("swe_2014_doWY_",(i+180))
writeRaster(rast, filename, format='GTiff', overwrite=FALSE)
}
#remove second chunk before starting second chunk
rm(swe_2014_3)
#fourth chunk: days 271 - 366
swe_2014_4 <- h5read(hdf_file, "/SWE", index = list(NULL, NULL, 91:180))
#set c4
setwd("/Volumes/john_tarricone/UNR_summer_20/margulis/swe/daily_rasters/WY_2014/c4")
for (i in 1:365){
mat<-as.matrix(swe_2014_4[,,i])
rast <-raster(mat, xmn=-123.3, xmx=-117.6, ymn=35.4, ymx=42, crs=CRS("+proj=leac +ellps=clrk66"))
rast[ rast[] == -32768 ] <- NA
filename <- paste("swe_2014_doWY_",(i+270))
writeRaster(rast, filename, format='GTiff', overwrite=FALSE)
}
#remove second chunk before starting second chunk
rm(swe_2014_4)
########################################### data analysis section
#read in pixel area raster
pixel_area <-raster("/Volumes/john_tarricone/UNR_summer_20/margulis/static/rasters/SNSR_pixel_area.tif")
# define multiplication function
# convert both a, which is swe mm, and b, wich is m^2 to km and km^2
# in this case both are * by 1e-6
pix_vol_swe <-function(a,b){return((a*1e-6)*(b*1e-6))}
vol_swe<-function(x){
pix_vol_swe <-function(a,b){return((a*1e-6)*(b*1e-6))}
swe_by_pixel <- overlay(x, pixel_area, fun= pix_vol_swe )
vol_swe <-cellStats(swe_by_pixel,'sum', digits=9, na.rm=TRUE)
print(vol_swe)
}
### c1
#read in raster list c1
setwd("/Volumes/john_tarricone/UNR_summer_20/margulis/swe/daily_rasters/WY_2014/c1")
files_c1 <- list.files(pattern = ".tif") #list files
fileslist_c1 <-mixedsort(sort(files_c1)) #sort in proper order
print(fileslist_c1) #test print
yurt<-stack(fileslist_c1)
raster_list_c1 <-lapply(fileslist_c1, raster) #load list in
## mcapply function to raster list
system.time(results_c1<-stackApply(yurt, indices=c(1,1,1,2,2,2)function(x) vol_swe(x)))
## mcapply function to raster list
system.time(results_c1<-mclapply(raster_list_c1, function(x) vol_swe(x), mc.cores = 3, mc.cleanup = TRUE))
results_df_c1 <- data.frame(matrix(unlist(results_c1), nrow=length(results_c1), byrow=T))
colnames(results_df_c1)[1] <- "WY_2014"
### c2
#read in raster list c2
files_c2 <- list.files(pattern = ".tif") #list files
fileslist_c2 <-mixedsort(sort(files_c2)) #sort in proper order
print(fileslist_c2) #test print
raster_list_c2 <-lapply(fileslist_c2, raster) #load list in
## mcapply function to raster list
system.time(results_c2<-mclapply(raster_list_c2, function(x) vol_swe(x), mc.cores = 3, mc.cleanup = TRUE))
results_df_c2 <- data.frame(matrix(unlist(results_c2), nrow=length(results_c2), byrow=T))
colnames(results_df_c2)[1] <- "WY_2014"
### c3
#read in raster list c3
files_c3 <- list.files(pattern = ".tif") #list files
fileslist_c3 <-mixedsort(sort(files_c3)) #sort in proper order
print(fileslist_c3) #test print
raster_list_c3 <-lapply(fileslist_c3, raster) #load list in
## mcapply function to raster list
system.time(results_c3<-mclapply(raster_list_c3, function(x) vol_swe(x), mc.cores = 3, mc.cleanup = TRUE))
results_df_c3 <- data.frame(matrix(unlist(results_c3), nrow=length(results_c3), byrow=T))
colnames(results_df_c3)[1] <- "WY_2014"
### c4
#read in raster list c4
files_c4 <- list.files(pattern = ".tif") #list files
fileslist_c4 <-mixedsort(sort(files_c4)) #sort in proper order
print(fileslist_c4) #test print
raster_list_c4 <-lapply(fileslist_c4, raster) #load list in
## mcapply function to raster list
system.time(results_c4<-mclapply(raster_list_c4, function(x) vol_swe(x), mc.cores = 3, mc.cleanup = TRUE))
results_df_c4 <- data.frame(matrix(unlist(results_c4), nrow=length(results_c4), byrow=T))
colnames(results_df_c4)[1] <- "WY_2014"
#stich data together and save
doWY<-(1:365) #create vector
doWY_df <- data.frame(matrix(unlist(doWY), nrow=length(doWY), byrow=T)) #convert to df
colnames(doWY_df)[1] <- "doWY" #rename column
#bind all four data frames together to create full year time series
full_swe <-rbind(results_df_c1, results_df_c2, results_df_c3, results_df_c4)
#bind on doWY column
full_year<-cbind(doWY_df, full_swe)