Newer
Older
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
# function to get file extension
get_file_ext <- function(path) {
# Get all files in path
all_files <- list.files(
path,
full.names = TRUE
)
# Get file extensions
all_file_types <- all_files %>%
strsplit("/") %>%
sapply(utils::tail, 1) %>%
strsplit("^([^\\.]+)") %>%
sapply(function(x) {
y <- x[2]
return(y)
}) %>%
substr(2, nchar(.))
# Get most frequent file types
#TODO not yet working
most_frequent <- all_file_types %>%
factor() %>%
table() %>%
names() %>%
.[1:5]
# 5 exemplaric files to detect type
files_to_check <- sapply(
most_frequent,
function(x, y, z) {
y[which(z == x)[1]]
},
y = all_files,
z = all_file_types)
# Detect actual LPJmL data type
types <- sapply(
files_to_check,
lpjmlkit:::detect_io_type
) %>%
stats::setNames(names(.), .)
# Assign file type after ranking which is available
# first preferable: "meta", second: "clm", last: "raw"
if ("meta" %in% names(types)) {
file_type <- types["meta"]
} else if ("clm" %in% names(types)) {
file_type <- types["clm"]
} else if ("raw" %in% names(types)) {
file_type <- types["raw"]
}
return(file_type)
}
# function to get absolute file names
get_filenames <- function(path, # nolint
output_files,
diff_output_files,
input_files,
file_ext) {
file_names <- list()
# Iterate over required outputs
for (ofile in names(output_files)) {
# Get required max. temporal resolution and convert to nstep
resolution <- output_files[[ofile]]$resolution
nstep <- switch(
resolution,
annual = 1,
monthly = 12,
daily = 365,
stop(paste0("Not supported time resolution: ", dQuote(nstep), "."))
)
# If input file supplied use it as first priority
if (ofile %in% names(input_files)) {
file_name <- input_files[[ofile]]
} else if (ofile %in% names(diff_output_files)) {
# If different output file should be used - as second priority
file_name <- paste0(
path, "/",
diff_output_files[[ofile]], ".",
file_ext
)
} else {
file_name <- NULL
}
if (!is.null(file_name)) {
# Check if data could be read in
meta <- lpjmlkit::read_meta(file_name)
# Then check if temporal resultion of file matches required nstep
if (nstep != meta$nstep && nstep != meta$nbands) {
stop(
paste0(
"Required temporal resolution (nstep = ", nstep, ") ",
"not supported by file ", dQuote(file_name),
" (", meta$nstep, ")"
)
)
}
# If nothing specified try to read required files from provided path
} else {
# Iterate over different used file name options (e.g. runoff, mrunoff, ...) # nolint
for (cfile in seq_along(output_files[[ofile]]$file_name)) {
file_name <- paste0(
path, "/",
output_files[[ofile]]$file_name[cfile], ".",
file_ext
)
# Check if file exists and if so check required temporal resolution
# else next
if (file.exists(file_name)) {
meta <- lpjmlkit::read_meta(file_name)
if (nstep <= meta$nstep || nstep == meta$nbands) {
# Matching file found, break and use current file_name
break
}
}
# At end of iteraton raise error that no matching file_name was found
if (cfile == length(output_files[[ofile]]$file_name) &&
!output_files[[ofile]]$optional) {
stop(
paste0(
"No matching output for ", dQuote(ofile),
" with required temporal resolution (nstep = ", nstep, ") ",
"found at path ", dQuote(path), "."
)
)
}
}
}
file_names[[ofile]] <- file_name
}
file_names
}
# list required output files
list_outputs <- function(metric = "all",
only_first_filename = TRUE) {
metric <- process_metric(metric = metric)
system.file(
"extdata",
"metric_files.yml",
package = "biospheremetrics"
) %>%
yaml::read_yaml() %>%
get_outputs(metric, only_first_filename)
}
# Translate metric options into internal metric names
process_metric <- function(metric = "all") {
all_metrics <- c(
"meco", "meco_nitrogen", "mcol", "biome", "nitrogen", "lsc",
"bluewater", "greenwater", "water", "biosphere"
)
if ("all" %in% metric) {
metric <- all_metrics
}
if ("benchmark" %in% metric) {
metric <- "benchmark"
}
metric <- match.arg(
arg = metric,
choices = all_metrics,
several.ok = TRUE
)
metric
}
# for input list a, all duplicate keys are unified, taking the value with
# highest temporal resolution (daily>monthly>annual)
get_outputs <- function(x, metric_name, only_first_filename) { # nolint
outputs <- list()
# Iterate over all metrics
for (metric in x$metric[metric_name]) {
# Iterate over all unique keys
for (item in names(metric$output)) {
# Check if output is already in list or if it has higher resolution
if (!item %in% names(outputs) ||
(item %in% names(outputs) &&
higher_res(metric$output[[item]]$resolution,
outputs[[item]]$resolution))
) {
# Assign output resolution from metric file
outputs[[item]]$resolution <- metric$output[[item]]$resolution
outputs[[item]]$optional <- metric$output[[item]]$optional
# Assign output file name from metric file
if (only_first_filename) {
outputs[[item]]$file_name <- x$file_name[[item]][1]
} else {
outputs[[item]]$file_name <- x$file_name[[item]]
}
}
}
}
outputs
}
# Check if resolution of x is higher than resolution of y
higher_res <- function(x, y) {
levels <- c("annual", "monthly", "daily")
resolution_x <- match(match.arg(x, levels), levels)
resolution_y <- match(match.arg(y, levels), levels)
if (resolution_x > resolution_y) {
return(TRUE)
} else {
return(FALSE)
}
}
# Avoid note for "."...
utils::globalVariables(".") # nolint:undesirable_function_linter