tests/test-likelihood_data.R
In gadget-framework/gadget3: Globally-Applicable Area Disaggregated General Ecosystem Toolbox V3
if (!interactive()) options(warn=2, error = function() { sink(stderr()) ; traceback(3) ; q(status = 1) })
library(magrittr)
library(unittest)
library(gadget3)
# Helper to generate ld from table string and attributes
generate_ld <- function (tbl, all_stocks = list(), all_fleets = list(), all_predators = list(), model_history = "", use_preview = FALSE, ...) {
if (is.character(tbl)) tbl <- read.table(text = tbl, header = TRUE, stringsAsFactors = TRUE)
if (is.null(tbl$number)) tbl$number <- as.numeric(seq_len(nrow(tbl)))
all_stocks <- lapply(all_stocks, function (x) g3_stock(x, 1))
if (use_preview) {
# Use new public preview function
out <- list( obs_array = list(
num = g3_distribution_preview(structure(tbl, ...), stocks = all_stocks, fleets = all_fleets, predators = all_predators) ))
} else {
# Fall back to old behaviour
out <- gadget3:::g3l_likelihood_data('ut', structure(tbl, ...), all_stocks = all_stocks, all_fleets = all_fleets, all_predators = all_predators, model_history = model_history)
}
# NB: A failed merge would result in repeated instances
ok(ut_cmp_equal(
sort(as.numeric(out$obs_array$num[out$obs_array$num != 0])),
sort(as.numeric(tbl$number)),
deparse_frame = -2), "number array has all of source data")
return(out)
}
# Generate example of ld being stock_iterate()d or stock_intersect()ed
generate_code <- function(ld, repl_fn, ...) {
model_fn <- g3_to_r(list(gadget3:::g3_step(gadget3:::call_to_formula(
substitute(
extractme(repl_fn_sym(st, stock_ss(st__num, vec = single))),
list(repl_fn_sym = as.symbol(repl_fn)) ),
list(
st = ld$obsstock,
st__num = ld$number,
... )))))
gadget3:::f_find(body(model_fn), quote(extractme))[[1]][[2]]
}
# Dig minlen out of modelstock
ld_minlen <- function (ld) {
x <- g3_stock_def(ld$modelstock, 'minlen')
# Bodge array back to (named) vector
as.matrix(x)[,1]
}
# Dig definitions out of modelstock
ld_upperlen <- function (ld) g3_stock_def(ld$modelstock, 'upperlen')
ld_dl <- function (ld) g3_stock_def(ld$modelstock, 'dl')
ld_plusdl <- function (ld) g3_stock_def(ld$modelstock, 'plusdl')
ld_minages <- function (ld) g3_stock_def(ld$modelstock, 'minages')
# Compare array by turning it back into a table first
cmp_array <- function (ar, table_text) {
tbl <- read.table(
header = TRUE,
stringsAsFactors = FALSE,
colClasses = c(rep("character", length(dim(ar))), "numeric"),
text = table_text)
ut_cmp_identical(as.data.frame.table(ar, stringsAsFactors = FALSE), tbl, deparse_frame = -2)
}
ok_group('g3l_likelihood_data:unknown', {
ld <- generate_ld(
data.frame(
year = 1990:1992,
number = 1:3,
camel = 10:12,
stringsAsFactors = FALSE),
end = NULL)
ok(ut_cmp_equal(ld$obs_array, list(
num = array(1:3, dim = c(length = 1L, time = 3L), dimnames = list(length = "0:Inf", time = c("1990", "1991", "1992"))),
camel = array(10:12, dim = c(length = 1L, time = 3L), dimnames = list(length = "0:Inf", time = c("1990", "1991", "1992"))) )), "Will create arrays from any unknown columns")
})
ok_group('g3l_likelihood_data:time', {
ok(ut_cmp_error({
ld <- generate_ld(
data.frame(
number = 1:3,
stringsAsFactors = FALSE),
end = NULL)
}, "year column"), "Noticed lack of year column")
ld <- generate_ld("
year number
1998 1
2002 2
2001 3
")
ok(cmp_array(ld$obs_array$num, "
length time Freq
0:Inf 1998 1
0:Inf 2001 3
0:Inf 2002 2
"), "Year gap, wonky year order preserved")
ld <- generate_ld("
year step number
1998 1 1
1998 2 2
1999 1 3
2000 1 4
2000 2 5
")
ok(cmp_array(ld$obs_array$num, "
length time Freq
0:Inf 1998-01 1
0:Inf 1998-02 2
0:Inf 1999-01 3
0:Inf 2000-01 4
0:Inf 2000-02 5
"), "Year gap, wonky year order preserved")
ld <- generate_ld("
time number
1998 1
2002 2
2001 3
")
ok(cmp_array(ld$obs_array$num, "
length time Freq
0:Inf 1998 1
0:Inf 2001 3
0:Inf 2002 2
"), "Time column used when year not present (i.e. can parse our own output)")
ld <- generate_ld("
time number
1998-01 2
1998-02 4
1999-01 3
1999-02 9
")
ok(cmp_array(ld$obs_array$num, "
length time Freq
0:Inf 1998-01 2
0:Inf 1998-02 4
0:Inf 1999-01 3
0:Inf 1999-02 9
"), "Year-step separated in time column")
})
ok_group('g3l_likelihood_data:length', {
ld <- generate_ld("
year number
1999 1
2000 2
2001 3
")
ok(cmp_array(ld$obs_array$num, "
length time Freq
0:Inf 1999 1
0:Inf 2000 2
0:Inf 2001 3
"), "Default single length dimension if none supplied")
ok(ut_cmp_identical(ld$modelstock$dimnames, list(
length = "0:Inf")), "modelstock got default length dimension if none supplied")
ld <- generate_ld("
year length number
1999 1 1
2000 1 2
2001 1 3
1999 5 4
2000 5 5
2001 5 6
1999 10 7
2001 10 9
2000 30 11
2001 30 12
")
ok(cmp_array(ld$obs_array$num, "
length time Freq
1:5 1999 1
5:10 1999 4
10:30 1999 7
30:Inf 1999 0
1:5 2000 2
5:10 2000 5
10:30 2000 0
30:Inf 2000 11
1:5 2001 3
5:10 2001 6
10:30 2001 9
30:Inf 2001 12
"), "Lengths read from data, missing 2000/10 1999/30 filled in with 0")
ok(ut_cmp_identical(
ld_minlen(ld),
c("1:5" = 1, "5:10" = 5, "10:30" = 10, "30:Inf" = 30)), "minlen set via. data")
ok(ut_cmp_identical(ld_upperlen(ld), Inf), "If we guess from data, open-ended is only sensible option")
ok(ut_cmp_error(generate_ld("
year length number
1999 a 1999.1
2000 a 2000.1
2001 a 2001.1
1999 b 1999.2
2000 b 2000.2
2001 b 2001.2
1999 c 1999.3
2001 c 2001.3
",
length = list(
a = structure(quote(seq(10, 20)), min = 10, max = 20),
b = structure(quote(seq(20, 40)), min = 20, max = 40),
c = structure(quote(seq(80, 100)), min = 80, max = 100))), "Gaps in length"), "Non-contiguous length groups cause an error")
ld <- generate_ld("
year length number
1999 a 1999.1
2000 a 2000.1
2001 a 2001.1
1999 b 1999.2
2000 b 2000.2
2001 b 2001.2
1999 c 1999.3
2001 c 2001.3
",
length = list(
a = structure(quote(seq(10, 20)), min = 10, max = 20),
b = structure(quote(seq(20, 40)), min = 20, max = 40),
c = structure(quote(seq(40, 80)), min = 40, max = 80)))
ok(cmp_array(ld$obs_array$num, "
length time Freq
10:20 1999 1999.1
20:40 1999 1999.2
40:80 1999 1999.3
10:20 2000 2000.1
20:40 2000 2000.2
40:80 2000 0.0
10:20 2001 2001.1
20:40 2001 2001.2
40:80 2001 2001.3
"), "Use lengths, removed names from attribute, gaps filled in")
ok(ut_cmp_identical(
ld_minlen(ld),
c("10:20" = 10, "20:40" = 20, "40:80" = 40)), "minlen set by attribute")
ok(ut_cmp_identical(ld_upperlen(ld), 80), "Upperlen set by attribute")
ok(ut_cmp_identical(ld_dl(ld), c(10, 20, 40)), "dl difference up to upper bound")
ok(ut_cmp_identical(ld_plusdl(ld), 10), "plusdl is the mode")
ld <- generate_ld("
year length number
1999 a 1999.1
2000 a 2000.1
2001 a 2001.1
1999 b 1999.2
2000 b 2000.2
2001 b 2001.2
1999 c 1999.3
2001 c 2001.3
",
length = list(
a = structure(quote(seq(10, 20)), min = 10, max = 20),
b = structure(quote(seq(20, 40)), min = 20, max = 40),
c = structure(quote(seq(40, 80)), min = 40, max = 80) ),
use_preview = TRUE )
ok(cmp_array(ld$obs_array$num, "
length time Freq
10:20 1999 1999.1
20:40 1999 1999.2
40:80 1999 1999.3
10:20 2000 2000.1
20:40 2000 2000.2
40:80 2000 NA
10:20 2001 2001.1
20:40 2001 2001.2
40:80 2001 2001.3
"), "Use lengths, removed names from attribute, gaps filled in (with new g3_distribution_preview)")
ld <- generate_ld("
year length number
1999 a 1999.1
2000 a 2000.1
2001 a 2001.1
1999 b 1999.2
2000 b 2000.2
2001 b 2001.2
1999 c 1999.3
2001 c 2001.3
",
length = list(
a = structure(quote(seq(10, 20)), min = 10, max = 20),
b = structure(quote(seq(20, 40)), min = 20, max = 40),
c = structure(quote(seq(40, 80)), min = 40, max = 80, max_open_ended = TRUE)))
ok(cmp_array(ld$obs_array$num, "
length time Freq
10:20 1999 1999.1
20:40 1999 1999.2
40:Inf 1999 1999.3
10:20 2000 2000.1
20:40 2000 2000.2
40:Inf 2000 0.0
10:20 2001 2001.1
20:40 2001 2001.2
40:Inf 2001 2001.3
"), "Use lengths, removed names from attribute, gaps filled in")
ok(ut_cmp_identical(ld_upperlen(ld), Inf), "upperlen now infinite")
ok(ut_cmp_identical(ld_dl(ld), c(10, 20, 10)), "dl assumes final group is as big as the mode")
ok(ut_cmp_identical(ld_plusdl(ld), 10), "plusdl is the mode")
ok(ut_cmp_identical(
ld_minlen(ld),
c("10:20" = 10, "20:40" = 20, "40:Inf" = 40)), "minlen doesn't include the plusgroup separately")
ld <- generate_ld("
year length number
1999 a 1999.1
2000 a 2000.1
2001 a 2001.1
1999 b 1999.2
2000 b 2000.2
2001 b 2001.2
1999 c 1999.3
2001 c 2001.3
",
length = list(
"a" = structure(quote(seq(10, 20)), min = 10, max = 20, min_open_ended = TRUE),
"b" = structure(quote(seq(20, 40)), min = 20, max = 40),
"c" = structure(quote(seq(40, 80)), min = 40, max = 80)))
ok(ut_cmp_identical(ld_upperlen(ld), 80), "upperlen set by attribute")
ok(ut_cmp_identical(
ld_minlen(ld),
c("0:20" = 0, "20:40" = 20, "40:80" = 40)), "minlen down to zero due to min_open_ended")
})
ok_group('g3l_likelihood_data:length_factor', {
ld <- generate_ld(data.frame(
year = 1990,
length = cut(c(14, 28, 33, 33), seq(0, 50, by = 10), right = FALSE),
stringsAsFactors = TRUE))
ok(ut_cmp_identical(
ld_minlen(ld),
c("0:10" = 0, "10:20" = 10, "20:30" = 20, "30:40" = 30, "40:50" = 40)), "ld_minlen: Not open ended")
ok(ut_cmp_identical(ld_upperlen(ld), 50), "ld_upperlen: Not open ended")
ld <- generate_ld(data.frame(
year = 1990,
length = cut(c(14, 28, 33, 33), c(seq(0, 50, by = 10), Inf), right = FALSE),
stringsAsFactors = TRUE))
ok(ut_cmp_identical(
ld_minlen(ld),
c("0:10" = 0, "10:20" = 10, "20:30" = 20, "30:40" = 30, "40:50" = 40, "50:Inf" = 50)), "ld_minlen: Open ended")
ok(ut_cmp_identical(ld_upperlen(ld), Inf), "ld_upperlen: Open ended")
ok(ut_cmp_error({
ld <- generate_ld(data.frame(
year = 1990,
length = as.factor(c("a", "b", "b", "c")),
stringsAsFactors = TRUE))
}, "length levels.*a, b, c"), "Unrecognised column format, included levels in error")
ok(ut_cmp_error({
ld <- generate_ld(data.frame(
year = 1990,
length = cut(c(14, 28, 33, 33), c(seq(0, 50, by = 10), Inf), right = TRUE),
stringsAsFactors = TRUE))
}, "inclusive-lower.*\\(0,10\\], \\(10,20\\], \\(20,30\\], \\(30,40\\], \\(40,50\\], \\(50,Inf\\]"), "Unrecognised column format, included levels in error") # ))))
ok(ut_cmp_error({
ld <- generate_ld(data.frame(
year = 1990,
# ((((
length = factor(c("[0,10)", "[20, 40)"), levels = c("[0,10)", "[20, 40)")),
stringsAsFactors = TRUE))
# ((
}, "Gaps in length groups are not supported: \\[0,10\\), \\[20, 40\\)"), "Complained about gaps in length groups")
})
ok_group('g3l_likelihood_data:age_char', {
ld <- generate_ld(expand.grid(
year = 1990,
length = as.character(cut(seq(3, 47, by=5), seq(0, 50, by = 5), right = FALSE)),
age = 1:2,
stringsAsFactors = FALSE))
ok(cmp_array(ld$obs_array$num, "
length age time Freq
0:5 age1 1990 1
5:10 age1 1990 2
10:15 age1 1990 3
15:20 age1 1990 4
20:25 age1 1990 5
25:30 age1 1990 6
30:35 age1 1990 7
35:40 age1 1990 8
40:45 age1 1990 9
0:5 age2 1990 10
5:10 age2 1990 11
10:15 age2 1990 12
15:20 age2 1990 13
20:25 age2 1990 14
25:30 age2 1990 15
30:35 age2 1990 16
35:40 age2 1990 17
40:45 age2 1990 18
"), "Converted back to factor, preserving ordering of entries")
ld <- generate_ld(expand.grid(
year = 1990,
length = as.character(cut(seq(3, 47, by=5), seq(0, 50, by = 5), right = FALSE)),
age = c('[1,3]', '[4,9]'),
stringsAsFactors = FALSE))
ok(cmp_array(ld$obs_array$num, "
length age time Freq
0:5 1:3 1990 1
5:10 1:3 1990 2
10:15 1:3 1990 3
15:20 1:3 1990 4
20:25 1:3 1990 5
25:30 1:3 1990 6
30:35 1:3 1990 7
35:40 1:3 1990 8
40:45 1:3 1990 9
0:5 4:9 1990 10
5:10 4:9 1990 11
10:15 4:9 1990 12
15:20 4:9 1990 13
20:25 4:9 1990 14
25:30 4:9 1990 15
30:35 4:9 1990 16
35:40 4:9 1990 17
40:45 4:9 1990 18
"), "Can use intervals in strings, converted to groups")
ld <- generate_ld(data.frame(
year = 1990,
length = c(
as.character(cut(seq(23, 39, by=5), seq(0, 50, by = 5), right = FALSE)),
as.character(cut(seq(3, 47, by=5), seq(0, 50, by = 5), right = FALSE)),
NULL),
age = c(
rep(1, 4),
rep(2, 9),
NULL),
stringsAsFactors = FALSE))
ok(cmp_array(ld$obs_array$num, "
length age time Freq
0:5 age1 1990 0
5:10 age1 1990 0
10:15 age1 1990 0
15:20 age1 1990 0
20:25 age1 1990 1
25:30 age1 1990 2
30:35 age1 1990 3
35:40 age1 1990 4
40:45 age1 1990 0
0:5 age2 1990 5
5:10 age2 1990 6
10:15 age2 1990 7
15:20 age2 1990 8
20:25 age2 1990 9
25:30 age2 1990 10
30:35 age2 1990 11
35:40 age2 1990 12
40:45 age2 1990 13
"), "Partial ranges padded out with zeros")
ld <- generate_ld(data.frame(
year = 1990,
length = cut(c(4, 14, 28, 33, 33, 44), c(seq(0, 50, by = 10), Inf), right = FALSE),
stringsAsFactors = TRUE))
ld_loopback <- generate_ld(as.data.frame.table(ld$obs_array$num, responseName = 'number'))
ok(ut_cmp_equal(ld$obs_array$num, ld_loopback$obs_array$num), "Can parse our own output")
})
ok_group('g3l_likelihood_data:age', {
ld <- generate_ld("
age year number
3 1999 1999.3
4 1999 1999.4
6 1999 1999.6
3 2000 2000.3
6 2000 2000.6
4 2001 2001.4
6 2001 2001.6
")
ok(cmp_array(ld$obs_array$num, "
length age time Freq
0:Inf age3 1999 1999.3
0:Inf age4 1999 1999.4
0:Inf age5 1999 0.0
0:Inf age6 1999 1999.6
0:Inf age3 2000 2000.3
0:Inf age4 2000 0.0
0:Inf age5 2000 0.0
0:Inf age6 2000 2000.6
0:Inf age3 2001 0.0
0:Inf age4 2001 2001.4
0:Inf age5 2001 0.0
0:Inf age6 2001 2001.6
"), "Worked out age dimensions from data, filled in missing values, including entirely absent ones")
ld <- generate_ld("
age year number
x 1999 1999.1
y 1999 1999.2
x 2000 2000.1
x 2001 2001.1
y 2001 2001.2
",
age = list(
x = structure(quote(seq(1, 3)), min = 1, max = 3),
y = structure(quote(seq(4, 6)), min = 4, max = 6),
z = structure(quote(seq(7, 10)), min = 7, max = 10)))
ok(cmp_array(ld$obs_array$num, "
length age time Freq
0:Inf 1:3 1999 1999.1
0:Inf 4:6 1999 1999.2
0:Inf 7:10 1999 0.0
0:Inf 1:3 2000 2000.1
0:Inf 4:6 2000 0.0
0:Inf 7:10 2000 0.0
0:Inf 1:3 2001 2001.1
0:Inf 4:6 2001 2001.2
0:Inf 7:10 2001 0.0
"), "Worked out age dimensions from attributes, filled in missing values")
ok(ut_cmp_identical(
ld_minages(ld),
gadget3:::force_vector("1:3" = 1L, "4:6" = 4L, "7:10" = 7L)), "agegroups using minages from attribute")
})
ok_group('g3l_likelihood_data:agegroup') ###########
ld <- generate_ld(expand.grid(
year = 2000:2005,
length = c(1,5,10),
age = c("[1,2)", "[3,3)") )) # ((
ok(gadget3:::ut_cmp_code(generate_code(ld, 'stock_intersect', age = 1, cur_year = 1999, cur_step = 1), quote({
ut_obs__time_idx <- intlookup_getdefault(ut_obs__times, (cur_year *
100L + cur_step * 0L), -1L)
if (ut_obs__time_idx >= (1L)) {
for (ut_obs__agegroup_idx in seq_along(ut_obs__minages)) {
`_age` <- ut_obs__minages[[ut_obs__agegroup_idx]]
for (ut_obs__length_idx in seq_along(ut_obs__minlen)) {
length <- ut_obs__midlen[[ut_obs__length_idx]]
ut_obs__num[ut_obs__length_idx, ut_obs__agegroup_idx,
ut_obs__time_idx]
}
}
}
}), optimize = TRUE), "stock_intersect: Renamed all vars, including ut_obs__agegroup")
ok(ut_cmp_equal(
g3_eval(attr(ld$obsstock$env$ut_obs__agegroup, "g3_global_init_val")),
structure(
list(1L, 2L, 2L),
key_var = "ut_obs__agegroup_keys",
value_var = "ut_obs__agegroup_values" )), "ut_obs__agegroup: Sub-parts also renamed")
########### g3l_likelihood_data:agegroup
ok_group('g3l_likelihood_data:age_factor', {
df <- data.frame(
year = 1990,
age = c(3,3,4,5),
number = 1,
stringsAsFactors = TRUE)
df$age <- cut(df$age, seq(3, 10, by = 1), right = FALSE)
df <- aggregate(number ~ year + age, df, sum)
ld <- generate_ld(df)
ok(cmp_array(ld$obs_array$num, "
length age time Freq
0:Inf 3:3 1990 2
0:Inf 4:4 1990 1
0:Inf 5:5 1990 1
0:Inf 6:6 1990 0
0:Inf 7:7 1990 0
0:Inf 8:8 1990 0
0:Inf 9:9 1990 0
"), "ld$obs_array$num: included all single ages")
ld <- generate_ld(data.frame(
year = 1990,
age = as.factor(c(3,4,5,8)),
stringsAsFactors = TRUE))
ok(cmp_array(ld$obs_array$num, "
length age time Freq
0:Inf 3:3 1990 1
0:Inf 4:4 1990 2
0:Inf 5:7 1990 3
0:Inf 8:8 1990 4
"), "ld$obs_array$num: can also use integer strings as factors")
df <- data.frame(
year = 1990,
age = c(3,3,4,5),
number = 1,
stringsAsFactors = TRUE)
df$age <- cut(df$age, seq(2, 10, by = 4), right = FALSE)
df <- aggregate(number ~ year + age, df, sum)
ld <- generate_ld(df)
ok(cmp_array(ld$obs_array$num, "
length age time Freq
0:Inf 2:5 1990 4
0:Inf 6:9 1990 0
"), "ld$obs_array$num: Everything grouped into first group, second compared to zero")
})
ok_group('g3l_likelihood_data:area', {
# Pull the area lookup definition back out
area_lookup <- function (ld) {
list(
keys = environment(g3_stock_def(ld$modelstock, 'areagroup_lookup'))$keys,
values = environment(g3_stock_def(ld$modelstock, 'areagroup_lookup'))$values)
}
ok(ut_cmp_error({
ld <- generate_ld("
area year number
a 1999 1999.1
b 1999 1999.2
c 1999 1999.3
")
}, "Areas in data"), "If char areas are provided without aggregation, we can't do anything")
ok(ut_cmp_error({
ld <- generate_ld("
area year number
a 1999 1999.1
b 1999 1999.2
c 1999 1999.3
", area = list(a = 1, b = 2, c = 3))
}, "Areas in data"), "If char areas are provided without aggregation, we can't do anything. MFDB aggregates don't count")
ld <- generate_ld("
area year number
1 1999 1999.1
2 1999 1999.2
3 1999 1999.3
2 2000 2000.2
3 2000 2000.3
1 2001 2001.1
2 2001 2001.2
")
ok(cmp_array(ld$obs_array$num, "
length time area Freq
0:Inf 1999 1 1999.1
0:Inf 2000 1 0.0
0:Inf 2001 1 2001.1
0:Inf 1999 2 1999.2
0:Inf 2000 2 2000.2
0:Inf 2001 2 2001.2
0:Inf 1999 3 1999.3
0:Inf 2000 3 2000.3
0:Inf 2001 3 0.0
"), "Worked out area dimensions from data, filled in missing values")
ld <- gadget3:::g3l_likelihood_data('ut', read.table(header = TRUE, stringsAsFactors = TRUE, text = "
area year number
a 1999 1999.1
b 1999 1999.2
c 1999 1999.3
b 2000 2000.2
c 2000 2000.3
a 2001 2001.1
b 2001 2001.2
"), area_group = list(a = 3, b = 4, c = c(1:2)))
ok(cmp_array(ld$obs_array$num, "
length time area Freq
0:Inf 1999 a 1999.1
0:Inf 2000 a 0.0
0:Inf 2001 a 2001.1
0:Inf 1999 b 1999.2
0:Inf 2000 b 2000.2
0:Inf 2001 b 2001.2
0:Inf 1999 c 1999.3
0:Inf 2000 c 2000.3
0:Inf 2001 c 0.0
"), "Worked out area dimensions from data, filled in missing values")
ok(ut_cmp_identical(area_lookup(ld), list(keys = c(3L,4L,1L,2L), values = c(1L,2L,3L,3L))), "Areas 1 & 2 both mapped to index 3 (i.e. c)")
})
ok_group('g3l_likelihood_data:tag', {
ld <- gadget3:::g3l_likelihood_data('ut', read.table(header = TRUE, stringsAsFactors = TRUE, text = "
tag year number
a 1999 1999.1
b 1999 1999.2
c 1999 1999.3
b 2000 2000.2
c 2000 2000.3
a 2001 2001.1
b 2001 2001.2
"))
ok(cmp_array(ld$obs_array$num, "
length tag time Freq
0:Inf a 1999 1999.1
0:Inf b 1999 1999.2
0:Inf c 1999 1999.3
0:Inf a 2000 0.0
0:Inf b 2000 2000.2
0:Inf c 2000 2000.3
0:Inf a 2001 2001.1
0:Inf b 2001 2001.2
0:Inf c 2001 0.0
"), "Worked out tag dimensions from data")
ok(ut_cmp_identical(
g3_stock_def(ld$modelstock, 'tag_ids'),
gadget3:::force_vector(a = 1L, b = 2L, c = 3L)), "stock__tag_ids: Worked out from factor")
})
ok_group('g3l_likelihood_data:stock', {
ld <- generate_ld("
age year number
3 1999 1999.3
4 1999 1999.4
6 1999 1999.6
3 2000 2000.3
6 2000 2000.6
4 2001 2001.4
6 2001 2001.6
")
ok(is.null(ld$maps$stock), "No stock column, so no stock map")
ok(ut_cmp_error(generate_ld("
age year stock stock_re number
3 1999 a a$ 1999.3
"), "stock.*stock_re"), "Can't have both stock & stock_re")
ld <- generate_ld("
age year stock number
3 1999 a 1999.3
4 1999 b 1999.4
6 1999 a 1999.6
3 2000 a 2000.3
6 2000 b 2000.6
4 2001 b 2001.4
6 2001 b 2001.6
", all_stocks = c('a', 'b'))
ok(ut_cmp_identical(dimnames(ld$obs_array$num)[['stock']], c("a", "b")), "Array has stocks a & b")
ok(ut_cmp_identical(ld$maps$stock, c(a = 'a', b = 'b')), "stock_map is 1:1 mapping")
ok(ut_cmp_error(generate_ld("
age year stock number
3 1999 a 1999.3
4 1999 b 1999.4
5 1999 kapow 1999.6
6 1999 zot 1999.6
", all_stocks = c('a', 'b')), "kapow, zot"), "Unknown stock names in data an error")
# Generate a list of stocks "stock_(imm,mat)_(f,m)"
stock_names <- paste(
'stock',
rep(c('imm', 'mat'), each = 2),
c('f', 'm'),
sep = "_")
ld <- generate_ld("
age year stock_re number
3 1999 _f$ 1999.3
4 1999 ^stock_mat 1999.4
6 1999 ^stock_imm 1999.6
3 2000 ^stock_mat 2000.3
6 2000 ^stock_imm 2000.6
4 2001 ^stock_imm 2001.4
6 2001 ^stock_mat 2001.6
", all_stocks = stock_names, model_history = "late")
ok(ut_cmp_identical(
dimnames(ld$obs_array$num)[['stock_re']],
c("_f$", "^stock_mat", "^stock_imm")), "Array names are regexes")
ok(ut_cmp_identical(
ld$maps$stock,
c(
stock_imm_f = '_f$',
stock_imm_m = '^stock_imm',
stock_mat_f = '_f$',
stock_mat_m = '^stock_mat')), "Stock map used first regexes first")
# Generated intersect code works
model_fn <- g3_to_r(list(
g3a_time(1999, 2000),
gadget3:::g3_step(g3_formula(
stock_iterate(ms, stock_intersect(os, print(c(stock_ss(ms__x), stock_ss(os__x))))),
ms = ld$modelstock,
os = ld$obsstock,
ms__x = g3_stock_instance(ld$modelstock, seq_len(prod(unlist(ld$modelstock$dim)))),
os__x = g3_stock_instance(ld$obsstock, seq_len(prod(unlist(ld$obsstock$dim))) * 10) )),
NULL ))
ok(ut_cmp_identical(
capture.output(invisible(model_fn())),
paste0("[1] ", 1:24, " ", 1:24 * 10)), "model_fn: Loop / intersect over modelstock/obsstock correctly")
ok(ut_cmp_error(generate_ld("
age year stock_re number
3 1999 _f$ 1999.3
3 1999 _g$ 1999.3
3 1999 _h$ 1999.3
"), "_g\\$, _h\\$"), "Regexes that don't match anything an error")
# Generate a list of stocks "stock_(imm,mat)_f" (NB: not male)
stock_names <- paste(
'stock',
rep(c('imm', 'mat'), each = 2),
c('f', 'm'),
sep = "_")
ld <- generate_ld("
age year stock_re number
3 1999 _mat_f$ 1999.3
4 1999 _mat_f$ 1999.4
6 1999 _imm_f$ 1999.6
3 2000 _mat_f$ 2000.3
6 2000 _imm_f$ 2000.6
4 2001 _imm_f$ 2001.4
6 2001 _mat_f$ 2001.6
", all_stocks = stock_names)
ok(ut_cmp_identical(
dimnames(ld$obs_array$num)[['stock_re']],
c("_mat_f$", "_imm_f$")), "Array names are regexes")
ok(ut_cmp_identical(
ld$maps$stock,
c(
stock_imm_f = '_imm_f$',
stock_imm_m = NA,
stock_mat_f = '_mat_f$',
stock_mat_m = NA )), "Stock map ignored unused stocks")
})
ok_group('g3l_likelihood_data:stock:name_parts', {
stock_groupings <- function (stock_names, stock_cols) {
tbl <- expand.grid(number = 0, stock = stock_cols, age = 3:6, year = 1999:2001)
tbl$number <- seq_len(nrow(tbl))
ld <- generate_ld(tbl, all_stocks = stock_names)
return(ld$maps$stock)
}
out <- stock_groupings(
list(c('fish', 'imm'), c('fish', 'mat'), c('fish', 'sen')),
c('fish'))
ok(ut_cmp_equal(out, c(
fish_imm = 'fish',
fish_mat = 'fish',
fish_sen = 'fish' )), '"fish": Groups both maturity groups together')
out <- stock_groupings(
list(c('fish', 'imm'), c('fish', 'mat'), c('fish', 'sen')),
c('fish_mat', 'fish'))
ok(ut_cmp_equal(out, c(
fish_imm = 'fish',
fish_mat = 'fish_mat',
fish_sen = 'fish' )), '"fish_mat": Overrides "fish" group due to longer length')
out <- stock_groupings(
list(c('a', 'imm'), c('a', 'mat'), c('b', 'imm'), c('b', 'mat'), c('c', 'mat')),
c('a', 'b', 'mat'))
ok(ut_cmp_equal(out, c(
a_imm = 'a',
a_mat = 'a',
b_imm = 'b',
b_mat = 'b',
c_mat = 'mat' )), "'b' wins over 'mat' because it comes first")
out <- stock_groupings(
list(c('a', 'imm'), c('a', 'mat'), c('b', 'imm'), c('b', 'mat'), c('c', 'mat')),
c('a', 'mat', 'b'))
ok(ut_cmp_equal(out, c(
a_imm = 'a',
a_mat = 'a',
b_imm = 'b',
b_mat = 'mat',
c_mat = 'mat' )), "'mat' wins over 'b' because it comes first")
out <- stock_groupings(
list(c('a', 'imm', 'f'), c('a', 'mat', 'f'), c('a', 'imm', 'm'), c('a', 'mat', 'm'), c('c', 'mat')),
c('a_f', 'a_m', 'c'))
ok(ut_cmp_equal(out, c(
a_imm_f = 'a_f',
a_mat_f = 'a_f',
a_imm_m = 'a_m',
a_mat_m = 'a_m',
c_mat = 'c' )), "Name part groupings don't have to be sequential")
})
ok_group('g3l_likelihood_data:predator', {
ld <- generate_ld("
age year number
3 1999 1999.3
4 1999 1999.4
6 1999 1999.6
3 2000 2000.3
6 2000 2000.6
4 2001 2001.4
6 2001 2001.6
")
ok(is.null(ld$maps$predator), "No predator column, so no predator map")
ok(ut_cmp_error(generate_ld("
age year predator predator_re number
3 1999 a a$ 1999.3
"), "predator.*predator_re"), "Can't have both predator & predator_re")
ld <- generate_ld("
age year predator number
3 1999 a 1999.3
4 1999 b 1999.4
6 1999 a 1999.6
3 2000 a 2000.3
6 2000 b 2000.6
4 2001 b 2001.4
6 2001 b 2001.6
", all_predators = list(g3_stock('a', c(0, 10)), g3_stock('b', c(0, 10)) ))
ok(ut_cmp_identical(dimnames(ld$obs_array$num)[['predator']], c("a", "b")), "Array has predators a & b")
ok(ut_cmp_identical(ld$maps$predator, c(a = 'a', b = 'b')), "predator_map is 1:1 mapping")
# Generate a list of predators "predator_(trawl|gil)_(f|m)"
predators <- lapply(paste(
'predator',
rep(c('trawl', 'gil'), each = 2),
c('is', 'no'),
sep = "_"), function (x) g3_stock(x, 1))
ld <- generate_ld("
age year predator_re number
3 1999 _is$ 1999.3
4 1999 ^predator_trawl 1999.4
6 1999 ^predator_gil 1999.6
3 2000 ^predator_trawl 2000.3
6 2000 ^predator_gil 2000.6
4 2001 ^predator_gil 2001.4
6 2001 ^predator_trawl 2001.6
", all_predators = predators)
ok(ut_cmp_identical(
dimnames(ld$obs_array$num)[['predator_re']],
c("_is$", "^predator_trawl", "^predator_gil")), "Array names are regexes")
ok(ut_cmp_identical(
ld$maps$predator,
c(
predator_trawl_is = '_is$',
predator_trawl_no = '^predator_trawl',
predator_gil_is = '_is$',
predator_gil_no = '^predator_gil' )), "predator map used first regexes first")
# Generate a list of predators "predator_(trawl|gil)_(f|m)"
predators <- lapply(paste(
'predator',
rep(c('trawl', 'gil'), each = 2),
c('is', 'no'),
sep = "_"), function (x) g3_stock(x, 1))
ld <- generate_ld("
age year predator_re number
3 1999 _gil_is$ 1999.3
4 1999 _gil_is$ 1999.4
6 1999 _trawl_is$ 1999.6
3 2000 _gil_is$ 2000.3
6 2000 _trawl_is$ 2000.6
4 2001 _trawl_is$ 2001.4
6 2001 _gil_is$ 2001.6
", all_predators = predators, model_history = "late")
ok(ut_cmp_identical(
dimnames(ld$obs_array$num)[['predator_re']],
c("_gil_is$", "_trawl_is$")), "Array names are regexes")
ok(ut_cmp_identical(
ld$maps$predator,
c(
predator_trawl_is = '_trawl_is$',
predator_trawl_no = NA,
predator_gil_is = '_gil_is$',
predator_gil_no = NA )), "predator map ignored unused predators")
# Generated intersect code works
model_fn <- g3_to_r(list(
g3a_time(1999, 2000),
gadget3:::g3_step(g3_formula(
stock_iterate(ms, stock_intersect(os, print(c(stock_ss(ms__x), stock_ss(os__x))))),
ms = ld$modelstock,
os = ld$obsstock,
ms__x = g3_stock_instance(ld$modelstock, seq_len(prod(unlist(ld$modelstock$dim)))),
os__x = g3_stock_instance(ld$obsstock, seq_len(prod(unlist(ld$obsstock$dim))) * 10) )),
NULL ))
ok(ut_cmp_identical(
capture.output(invisible(model_fn())),
paste0("[1] ", 1:16, " ", 1:16 * 10) ), "model_fn: Loop / intersect over modelstock/obsstock correctly")
})
ok_group('g3l_likelihood_data:fleet', {
ld <- generate_ld("
age year number
3 1999 1999.3
4 1999 1999.4
6 1999 1999.6
3 2000 2000.3
6 2000 2000.6
4 2001 2001.4
6 2001 2001.6
")
ok(is.null(ld$maps$fleet), "No fleet column, so no fleet map")
ok(ut_cmp_error(generate_ld("
age year fleet fleet_re number
3 1999 a a$ 1999.3
"), "fleet.*fleet_re"), "Can't have both fleet & fleet_re")
ld <- generate_ld("
age year fleet number
3 1999 a 1999.3
4 1999 b 1999.4
6 1999 a 1999.6
3 2000 a 2000.3
6 2000 b 2000.6
4 2001 b 2001.4
6 2001 b 2001.6
", all_fleets = list(g3_fleet('a'), g3_fleet('b')))
ok(ut_cmp_identical(dimnames(ld$obs_array$num)[['fleet']], c("a", "b")), "Array has fleets a & b")
ok(ut_cmp_identical(ld$maps$fleet, c(a = 'a', b = 'b')), "fleet_map is 1:1 mapping")
# Generate a list of fleets "fleet_(trawl|gil)_(f|m)"
fleets <- lapply(paste(
'fleet',
rep(c('trawl', 'gil'), each = 2),
c('is', 'no'),
sep = "_"), function (x) g3_stock(x, 1))
ld <- generate_ld("
age year fleet_re number
3 1999 _is$ 1999.3
4 1999 ^fleet_trawl 1999.4
6 1999 ^fleet_gil 1999.6
3 2000 ^fleet_trawl 2000.3
6 2000 ^fleet_gil 2000.6
4 2001 ^fleet_gil 2001.4
6 2001 ^fleet_trawl 2001.6
", all_fleets = fleets)
ok(ut_cmp_identical(
dimnames(ld$obs_array$num)[['fleet_re']],
c("_is$", "^fleet_trawl", "^fleet_gil")), "Array names are regexes")
ok(ut_cmp_identical(
ld$maps$fleet,
c(
fleet_trawl_is = '_is$',
fleet_trawl_no = '^fleet_trawl',
fleet_gil_is = '_is$',
fleet_gil_no = '^fleet_gil' )), "fleet map used first regexes first")
# Generate a list of fleets "fleet_(trawl|gil)_(f|m)"
fleets <- lapply(paste(
'fleet',
rep(c('trawl', 'gil'), each = 2),
c('is', 'no'),
sep = "_"), function (x) g3_stock(x, 1))
ld <- generate_ld("
age year fleet_re number
3 1999 _gil_is$ 1999.3
4 1999 _gil_is$ 1999.4
6 1999 _trawl_is$ 1999.6
3 2000 _gil_is$ 2000.3
6 2000 _trawl_is$ 2000.6
4 2001 _trawl_is$ 2001.4
6 2001 _gil_is$ 2001.6
", all_fleets = fleets, model_history = "late")
ok(ut_cmp_identical(
dimnames(ld$obs_array$num)[['fleet_re']],
c("_gil_is$", "_trawl_is$")), "Array names are regexes")
ok(ut_cmp_identical(
ld$maps$fleet,
c(
fleet_trawl_is = '_trawl_is$',
fleet_trawl_no = NA,
fleet_gil_is = '_gil_is$',
fleet_gil_no = NA )), "fleet map ignored unused fleets")
# Generated intersect code works
model_fn <- g3_to_r(list(
g3a_time(1999, 2000),
gadget3:::g3_step(g3_formula(
stock_iterate(ms, stock_intersect(os, print(c(stock_ss(ms__x), stock_ss(os__x))))),
ms = ld$modelstock,
os = ld$obsstock,
ms__x = g3_stock_instance(ld$modelstock, seq_len(prod(unlist(ld$modelstock$dim)))),
os__x = g3_stock_instance(ld$obsstock, seq_len(prod(unlist(ld$obsstock$dim))) * 10) )),
NULL ))
ok(ut_cmp_identical(
capture.output(invisible(model_fn())),
paste0("[1] ", 1:16, " ", 1:16 * 10) ), "model_fn: Loop / intersect over modelstock/obsstock correctly")
})
ok_group('g3l_likelihood_data:predator') ##########
ld <- generate_ld(expand.grid(
length = 5:10,
predator_length = c(10, 50, 100),
predator_age = c('[0,5)', '[5,10)'), # ((
predator_tag = c('a', 'b'),
year = 1999:2000 ))
ok(gadget3:::ut_cmp_code(generate_code(ld, 'stock_iterate', cur_year = 1999, cur_step = 1), quote({
ut_obs__time_idx <- intlookup_getdefault(ut_obs__times, (cur_year * 100L + cur_step * 0L), -1L)
if (ut_obs__time_idx >= (1L)) {
for (ut_obs__predator_tag_idx in seq_along(ut_obs__predator_tag_ids)) {
predator_tag <- ut_obs__predator_tag_ids[[ut_obs__predator_tag_idx]]
for (ut_obs__predator_agegroup_idx in seq_along(ut_obs__predator_minages)) {
predator_age <- ut_obs__predator_minages[[ut_obs__predator_agegroup_idx]]
for (ut_obs__predator_length_idx in seq_along(ut_obs__predator_midlen)) {
predator_length <- ut_obs__predator_midlen[[ut_obs__predator_length_idx]]
for (ut_obs__length_idx in seq_along(ut_obs__midlen)) {
length <- ut_obs__midlen[[ut_obs__length_idx]]
ut_obs__num[ut_obs__length_idx, ut_obs__predator_length_idx,
ut_obs__predator_agegroup_idx, ut_obs__predator_tag_idx,
ut_obs__time_idx]
}
}
}
}
}
}), optimize = TRUE), "stock_iterate: All predator dimensions included, with prefixed variables")
ok(gadget3:::ut_cmp_code(generate_code(ld, 'stock_intersect', cur_year = 1999, cur_step = 1, predator_tag = 1, predator_length = 1, predator_age = 1), quote({
ut_obs__time_idx <- intlookup_getdefault(ut_obs__times, (cur_year * 100L + cur_step * 0L), -1L)
if (ut_obs__time_idx >= 1L) {
for (ut_obs__predator_tag_idx in seq_along(ut_obs__predator_tag_ids)) {
predator_tag <- ut_obs__predator_tag_ids[[ut_obs__predator_tag_idx]]
for (ut_obs__predator_agegroup_idx in seq_along(ut_obs__predator_minages)) {
`_age` <- ut_obs__predator_minages[[ut_obs__predator_agegroup_idx]]
for (ut_obs__predator_length_idx in seq_along(ut_obs__predator_minlen)) {
predator_length <- ut_obs__predator_midlen[[ut_obs__predator_length_idx]]
for (ut_obs__length_idx in seq_along(ut_obs__minlen)) {
length <- ut_obs__midlen[[ut_obs__length_idx]]
ut_obs__num[ut_obs__length_idx, ut_obs__predator_length_idx,
ut_obs__predator_agegroup_idx, ut_obs__predator_tag_idx,
ut_obs__time_idx]
}
}
}
}
}
}), optimize = TRUE), "stock_intersect: All predator dimensions included, with prefixed variables")
ok(ut_cmp_equal(
ld$obsstock$env$stock__midlen,
gadget3:::force_vector(c("5:6" = 5.5, "6:7" = 6.5, "7:8" = 7.5, "8:9" = 8.5, "9:10" = 9.5, "10:Inf" = 10.5)) ), "stock__midlen: prey vars set")
ok(ut_cmp_equal(
ld$obsstock$env$stock__predator_midlen,
gadget3:::force_vector(c("10:50" = 30, "50:100" = 75, "100:Inf" = 120)) ), "stock__predator_midlen: predator vars prefixed")
ok(ut_cmp_equal(
environment(attr(ld$obsstock$env$ut_obs__predator_agegroup, "g3_global_init_val"))$ut_obs__predator_agegroup_keys,
gadget3:::force_vector(0:9)), "ut_obs__predator_agegroup_keys: initval keys got renamed")
ok(ut_cmp_equal(
environment(attr(ld$obsstock$env$ut_obs__predator_agegroup, "g3_global_init_val"))$ut_obs__predator_agegroup_values,
gadget3:::force_vector( rep(1:2, each = 5) )), "ut_obs__predator_agegroup_values: initval values got renamed")
########## g3l_likelihood_data:predator
gadget-framework/gadget3 documentation built on June 13, 2025, 5:06 a.m.
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if (!interactive()) options(warn=2, error = function() { sink(stderr()) ; traceback(3) ; q(status = 1) })
library(magrittr)
library(unittest)
library(gadget3)
# Helper to generate ld from table string and attributes
generate_ld <- function (tbl, all_stocks = list(), all_fleets = list(), all_predators = list(), model_history = "", use_preview = FALSE, ...) {
if (is.character(tbl)) tbl <- read.table(text = tbl, header = TRUE, stringsAsFactors = TRUE)
if (is.null(tbl$number)) tbl$number <- as.numeric(seq_len(nrow(tbl)))
all_stocks <- lapply(all_stocks, function (x) g3_stock(x, 1))
if (use_preview) {
# Use new public preview function
out <- list( obs_array = list(
num = g3_distribution_preview(structure(tbl, ...), stocks = all_stocks, fleets = all_fleets, predators = all_predators) ))
} else {
# Fall back to old behaviour
out <- gadget3:::g3l_likelihood_data('ut', structure(tbl, ...), all_stocks = all_stocks, all_fleets = all_fleets, all_predators = all_predators, model_history = model_history)
}
# NB: A failed merge would result in repeated instances
ok(ut_cmp_equal(
sort(as.numeric(out$obs_array$num[out$obs_array$num != 0])),
sort(as.numeric(tbl$number)),
deparse_frame = -2), "number array has all of source data")
return(out)
}
# Generate example of ld being stock_iterate()d or stock_intersect()ed
generate_code <- function(ld, repl_fn, ...) {
model_fn <- g3_to_r(list(gadget3:::g3_step(gadget3:::call_to_formula(
substitute(
extractme(repl_fn_sym(st, stock_ss(st__num, vec = single))),
list(repl_fn_sym = as.symbol(repl_fn)) ),
list(
st = ld$obsstock,
st__num = ld$number,
... )))))
gadget3:::f_find(body(model_fn), quote(extractme))[[1]][[2]]
}
# Dig minlen out of modelstock
ld_minlen <- function (ld) {
x <- g3_stock_def(ld$modelstock, 'minlen')
# Bodge array back to (named) vector
as.matrix(x)[,1]
}
# Dig definitions out of modelstock
ld_upperlen <- function (ld) g3_stock_def(ld$modelstock, 'upperlen')
ld_dl <- function (ld) g3_stock_def(ld$modelstock, 'dl')
ld_plusdl <- function (ld) g3_stock_def(ld$modelstock, 'plusdl')
ld_minages <- function (ld) g3_stock_def(ld$modelstock, 'minages')
# Compare array by turning it back into a table first
cmp_array <- function (ar, table_text) {
tbl <- read.table(
header = TRUE,
stringsAsFactors = FALSE,
colClasses = c(rep("character", length(dim(ar))), "numeric"),
text = table_text)
ut_cmp_identical(as.data.frame.table(ar, stringsAsFactors = FALSE), tbl, deparse_frame = -2)
}
ok_group('g3l_likelihood_data:unknown', {
ld <- generate_ld(
data.frame(
year = 1990:1992,
number = 1:3,
camel = 10:12,
stringsAsFactors = FALSE),
end = NULL)
ok(ut_cmp_equal(ld$obs_array, list(
num = array(1:3, dim = c(length = 1L, time = 3L), dimnames = list(length = "0:Inf", time = c("1990", "1991", "1992"))),
camel = array(10:12, dim = c(length = 1L, time = 3L), dimnames = list(length = "0:Inf", time = c("1990", "1991", "1992"))) )), "Will create arrays from any unknown columns")
})
ok_group('g3l_likelihood_data:time', {
ok(ut_cmp_error({
ld <- generate_ld(
data.frame(
number = 1:3,
stringsAsFactors = FALSE),
end = NULL)
}, "year column"), "Noticed lack of year column")
ld <- generate_ld("
year number
1998 1
2002 2
2001 3
")
ok(cmp_array(ld$obs_array$num, "
length time Freq
0:Inf 1998 1
0:Inf 2001 3
0:Inf 2002 2
"), "Year gap, wonky year order preserved")
ld <- generate_ld("
year step number
1998 1 1
1998 2 2
1999 1 3
2000 1 4
2000 2 5
")
ok(cmp_array(ld$obs_array$num, "
length time Freq
0:Inf 1998-01 1
0:Inf 1998-02 2
0:Inf 1999-01 3
0:Inf 2000-01 4
0:Inf 2000-02 5
"), "Year gap, wonky year order preserved")
ld <- generate_ld("
time number
1998 1
2002 2
2001 3
")
ok(cmp_array(ld$obs_array$num, "
length time Freq
0:Inf 1998 1
0:Inf 2001 3
0:Inf 2002 2
"), "Time column used when year not present (i.e. can parse our own output)")
ld <- generate_ld("
time number
1998-01 2
1998-02 4
1999-01 3
1999-02 9
")
ok(cmp_array(ld$obs_array$num, "
length time Freq
0:Inf 1998-01 2
0:Inf 1998-02 4
0:Inf 1999-01 3
0:Inf 1999-02 9
"), "Year-step separated in time column")
})
ok_group('g3l_likelihood_data:length', {
ld <- generate_ld("
year number
1999 1
2000 2
2001 3
")
ok(cmp_array(ld$obs_array$num, "
length time Freq
0:Inf 1999 1
0:Inf 2000 2
0:Inf 2001 3
"), "Default single length dimension if none supplied")
ok(ut_cmp_identical(ld$modelstock$dimnames, list(
length = "0:Inf")), "modelstock got default length dimension if none supplied")
ld <- generate_ld("
year length number
1999 1 1
2000 1 2
2001 1 3
1999 5 4
2000 5 5
2001 5 6
1999 10 7
2001 10 9
2000 30 11
2001 30 12
")
ok(cmp_array(ld$obs_array$num, "
length time Freq
1:5 1999 1
5:10 1999 4
10:30 1999 7
30:Inf 1999 0
1:5 2000 2
5:10 2000 5
10:30 2000 0
30:Inf 2000 11
1:5 2001 3
5:10 2001 6
10:30 2001 9
30:Inf 2001 12
"), "Lengths read from data, missing 2000/10 1999/30 filled in with 0")
ok(ut_cmp_identical(
ld_minlen(ld),
c("1:5" = 1, "5:10" = 5, "10:30" = 10, "30:Inf" = 30)), "minlen set via. data")
ok(ut_cmp_identical(ld_upperlen(ld), Inf), "If we guess from data, open-ended is only sensible option")
ok(ut_cmp_error(generate_ld("
year length number
1999 a 1999.1
2000 a 2000.1
2001 a 2001.1
1999 b 1999.2
2000 b 2000.2
2001 b 2001.2
1999 c 1999.3
2001 c 2001.3
",
length = list(
a = structure(quote(seq(10, 20)), min = 10, max = 20),
b = structure(quote(seq(20, 40)), min = 20, max = 40),
c = structure(quote(seq(80, 100)), min = 80, max = 100))), "Gaps in length"), "Non-contiguous length groups cause an error")
ld <- generate_ld("
year length number
1999 a 1999.1
2000 a 2000.1
2001 a 2001.1
1999 b 1999.2
2000 b 2000.2
2001 b 2001.2
1999 c 1999.3
2001 c 2001.3
",
length = list(
a = structure(quote(seq(10, 20)), min = 10, max = 20),
b = structure(quote(seq(20, 40)), min = 20, max = 40),
c = structure(quote(seq(40, 80)), min = 40, max = 80)))
ok(cmp_array(ld$obs_array$num, "
length time Freq
10:20 1999 1999.1
20:40 1999 1999.2
40:80 1999 1999.3
10:20 2000 2000.1
20:40 2000 2000.2
40:80 2000 0.0
10:20 2001 2001.1
20:40 2001 2001.2
40:80 2001 2001.3
"), "Use lengths, removed names from attribute, gaps filled in")
ok(ut_cmp_identical(
ld_minlen(ld),
c("10:20" = 10, "20:40" = 20, "40:80" = 40)), "minlen set by attribute")
ok(ut_cmp_identical(ld_upperlen(ld), 80), "Upperlen set by attribute")
ok(ut_cmp_identical(ld_dl(ld), c(10, 20, 40)), "dl difference up to upper bound")
ok(ut_cmp_identical(ld_plusdl(ld), 10), "plusdl is the mode")
ld <- generate_ld("
year length number
1999 a 1999.1
2000 a 2000.1
2001 a 2001.1
1999 b 1999.2
2000 b 2000.2
2001 b 2001.2
1999 c 1999.3
2001 c 2001.3
",
length = list(
a = structure(quote(seq(10, 20)), min = 10, max = 20),
b = structure(quote(seq(20, 40)), min = 20, max = 40),
c = structure(quote(seq(40, 80)), min = 40, max = 80) ),
use_preview = TRUE )
ok(cmp_array(ld$obs_array$num, "
length time Freq
10:20 1999 1999.1
20:40 1999 1999.2
40:80 1999 1999.3
10:20 2000 2000.1
20:40 2000 2000.2
40:80 2000 NA
10:20 2001 2001.1
20:40 2001 2001.2
40:80 2001 2001.3
"), "Use lengths, removed names from attribute, gaps filled in (with new g3_distribution_preview)")
ld <- generate_ld("
year length number
1999 a 1999.1
2000 a 2000.1
2001 a 2001.1
1999 b 1999.2
2000 b 2000.2
2001 b 2001.2
1999 c 1999.3
2001 c 2001.3
",
length = list(
a = structure(quote(seq(10, 20)), min = 10, max = 20),
b = structure(quote(seq(20, 40)), min = 20, max = 40),
c = structure(quote(seq(40, 80)), min = 40, max = 80, max_open_ended = TRUE)))
ok(cmp_array(ld$obs_array$num, "
length time Freq
10:20 1999 1999.1
20:40 1999 1999.2
40:Inf 1999 1999.3
10:20 2000 2000.1
20:40 2000 2000.2
40:Inf 2000 0.0
10:20 2001 2001.1
20:40 2001 2001.2
40:Inf 2001 2001.3
"), "Use lengths, removed names from attribute, gaps filled in")
ok(ut_cmp_identical(ld_upperlen(ld), Inf), "upperlen now infinite")
ok(ut_cmp_identical(ld_dl(ld), c(10, 20, 10)), "dl assumes final group is as big as the mode")
ok(ut_cmp_identical(ld_plusdl(ld), 10), "plusdl is the mode")
ok(ut_cmp_identical(
ld_minlen(ld),
c("10:20" = 10, "20:40" = 20, "40:Inf" = 40)), "minlen doesn't include the plusgroup separately")
ld <- generate_ld("
year length number
1999 a 1999.1
2000 a 2000.1
2001 a 2001.1
1999 b 1999.2
2000 b 2000.2
2001 b 2001.2
1999 c 1999.3
2001 c 2001.3
",
length = list(
"a" = structure(quote(seq(10, 20)), min = 10, max = 20, min_open_ended = TRUE),
"b" = structure(quote(seq(20, 40)), min = 20, max = 40),
"c" = structure(quote(seq(40, 80)), min = 40, max = 80)))
ok(ut_cmp_identical(ld_upperlen(ld), 80), "upperlen set by attribute")
ok(ut_cmp_identical(
ld_minlen(ld),
c("0:20" = 0, "20:40" = 20, "40:80" = 40)), "minlen down to zero due to min_open_ended")
})
ok_group('g3l_likelihood_data:length_factor', {
ld <- generate_ld(data.frame(
year = 1990,
length = cut(c(14, 28, 33, 33), seq(0, 50, by = 10), right = FALSE),
stringsAsFactors = TRUE))
ok(ut_cmp_identical(
ld_minlen(ld),
c("0:10" = 0, "10:20" = 10, "20:30" = 20, "30:40" = 30, "40:50" = 40)), "ld_minlen: Not open ended")
ok(ut_cmp_identical(ld_upperlen(ld), 50), "ld_upperlen: Not open ended")
ld <- generate_ld(data.frame(
year = 1990,
length = cut(c(14, 28, 33, 33), c(seq(0, 50, by = 10), Inf), right = FALSE),
stringsAsFactors = TRUE))
ok(ut_cmp_identical(
ld_minlen(ld),
c("0:10" = 0, "10:20" = 10, "20:30" = 20, "30:40" = 30, "40:50" = 40, "50:Inf" = 50)), "ld_minlen: Open ended")
ok(ut_cmp_identical(ld_upperlen(ld), Inf), "ld_upperlen: Open ended")
ok(ut_cmp_error({
ld <- generate_ld(data.frame(
year = 1990,
length = as.factor(c("a", "b", "b", "c")),
stringsAsFactors = TRUE))
}, "length levels.*a, b, c"), "Unrecognised column format, included levels in error")
ok(ut_cmp_error({
ld <- generate_ld(data.frame(
year = 1990,
length = cut(c(14, 28, 33, 33), c(seq(0, 50, by = 10), Inf), right = TRUE),
stringsAsFactors = TRUE))
}, "inclusive-lower.*\\(0,10\\], \\(10,20\\], \\(20,30\\], \\(30,40\\], \\(40,50\\], \\(50,Inf\\]"), "Unrecognised column format, included levels in error") # ))))
ok(ut_cmp_error({
ld <- generate_ld(data.frame(
year = 1990,
# ((((
length = factor(c("[0,10)", "[20, 40)"), levels = c("[0,10)", "[20, 40)")),
stringsAsFactors = TRUE))
# ((
}, "Gaps in length groups are not supported: \\[0,10\\), \\[20, 40\\)"), "Complained about gaps in length groups")
})
ok_group('g3l_likelihood_data:age_char', {
ld <- generate_ld(expand.grid(
year = 1990,
length = as.character(cut(seq(3, 47, by=5), seq(0, 50, by = 5), right = FALSE)),
age = 1:2,
stringsAsFactors = FALSE))
ok(cmp_array(ld$obs_array$num, "
length age time Freq
0:5 age1 1990 1
5:10 age1 1990 2
10:15 age1 1990 3
15:20 age1 1990 4
20:25 age1 1990 5
25:30 age1 1990 6
30:35 age1 1990 7
35:40 age1 1990 8
40:45 age1 1990 9
0:5 age2 1990 10
5:10 age2 1990 11
10:15 age2 1990 12
15:20 age2 1990 13
20:25 age2 1990 14
25:30 age2 1990 15
30:35 age2 1990 16
35:40 age2 1990 17
40:45 age2 1990 18
"), "Converted back to factor, preserving ordering of entries")
ld <- generate_ld(expand.grid(
year = 1990,
length = as.character(cut(seq(3, 47, by=5), seq(0, 50, by = 5), right = FALSE)),
age = c('[1,3]', '[4,9]'),
stringsAsFactors = FALSE))
ok(cmp_array(ld$obs_array$num, "
length age time Freq
0:5 1:3 1990 1
5:10 1:3 1990 2
10:15 1:3 1990 3
15:20 1:3 1990 4
20:25 1:3 1990 5
25:30 1:3 1990 6
30:35 1:3 1990 7
35:40 1:3 1990 8
40:45 1:3 1990 9
0:5 4:9 1990 10
5:10 4:9 1990 11
10:15 4:9 1990 12
15:20 4:9 1990 13
20:25 4:9 1990 14
25:30 4:9 1990 15
30:35 4:9 1990 16
35:40 4:9 1990 17
40:45 4:9 1990 18
"), "Can use intervals in strings, converted to groups")
ld <- generate_ld(data.frame(
year = 1990,
length = c(
as.character(cut(seq(23, 39, by=5), seq(0, 50, by = 5), right = FALSE)),
as.character(cut(seq(3, 47, by=5), seq(0, 50, by = 5), right = FALSE)),
NULL),
age = c(
rep(1, 4),
rep(2, 9),
NULL),
stringsAsFactors = FALSE))
ok(cmp_array(ld$obs_array$num, "
length age time Freq
0:5 age1 1990 0
5:10 age1 1990 0
10:15 age1 1990 0
15:20 age1 1990 0
20:25 age1 1990 1
25:30 age1 1990 2
30:35 age1 1990 3
35:40 age1 1990 4
40:45 age1 1990 0
0:5 age2 1990 5
5:10 age2 1990 6
10:15 age2 1990 7
15:20 age2 1990 8
20:25 age2 1990 9
25:30 age2 1990 10
30:35 age2 1990 11
35:40 age2 1990 12
40:45 age2 1990 13
"), "Partial ranges padded out with zeros")
ld <- generate_ld(data.frame(
year = 1990,
length = cut(c(4, 14, 28, 33, 33, 44), c(seq(0, 50, by = 10), Inf), right = FALSE),
stringsAsFactors = TRUE))
ld_loopback <- generate_ld(as.data.frame.table(ld$obs_array$num, responseName = 'number'))
ok(ut_cmp_equal(ld$obs_array$num, ld_loopback$obs_array$num), "Can parse our own output")
})
ok_group('g3l_likelihood_data:age', {
ld <- generate_ld("
age year number
3 1999 1999.3
4 1999 1999.4
6 1999 1999.6
3 2000 2000.3
6 2000 2000.6
4 2001 2001.4
6 2001 2001.6
")
ok(cmp_array(ld$obs_array$num, "
length age time Freq
0:Inf age3 1999 1999.3
0:Inf age4 1999 1999.4
0:Inf age5 1999 0.0
0:Inf age6 1999 1999.6
0:Inf age3 2000 2000.3
0:Inf age4 2000 0.0
0:Inf age5 2000 0.0
0:Inf age6 2000 2000.6
0:Inf age3 2001 0.0
0:Inf age4 2001 2001.4
0:Inf age5 2001 0.0
0:Inf age6 2001 2001.6
"), "Worked out age dimensions from data, filled in missing values, including entirely absent ones")
ld <- generate_ld("
age year number
x 1999 1999.1
y 1999 1999.2
x 2000 2000.1
x 2001 2001.1
y 2001 2001.2
",
age = list(
x = structure(quote(seq(1, 3)), min = 1, max = 3),
y = structure(quote(seq(4, 6)), min = 4, max = 6),
z = structure(quote(seq(7, 10)), min = 7, max = 10)))
ok(cmp_array(ld$obs_array$num, "
length age time Freq
0:Inf 1:3 1999 1999.1
0:Inf 4:6 1999 1999.2
0:Inf 7:10 1999 0.0
0:Inf 1:3 2000 2000.1
0:Inf 4:6 2000 0.0
0:Inf 7:10 2000 0.0
0:Inf 1:3 2001 2001.1
0:Inf 4:6 2001 2001.2
0:Inf 7:10 2001 0.0
"), "Worked out age dimensions from attributes, filled in missing values")
ok(ut_cmp_identical(
ld_minages(ld),
gadget3:::force_vector("1:3" = 1L, "4:6" = 4L, "7:10" = 7L)), "agegroups using minages from attribute")
})
ok_group('g3l_likelihood_data:agegroup') ###########
ld <- generate_ld(expand.grid(
year = 2000:2005,
length = c(1,5,10),
age = c("[1,2)", "[3,3)") )) # ((
ok(gadget3:::ut_cmp_code(generate_code(ld, 'stock_intersect', age = 1, cur_year = 1999, cur_step = 1), quote({
ut_obs__time_idx <- intlookup_getdefault(ut_obs__times, (cur_year *
100L + cur_step * 0L), -1L)
if (ut_obs__time_idx >= (1L)) {
for (ut_obs__agegroup_idx in seq_along(ut_obs__minages)) {
`_age` <- ut_obs__minages[[ut_obs__agegroup_idx]]
for (ut_obs__length_idx in seq_along(ut_obs__minlen)) {
length <- ut_obs__midlen[[ut_obs__length_idx]]
ut_obs__num[ut_obs__length_idx, ut_obs__agegroup_idx,
ut_obs__time_idx]
}
}
}
}), optimize = TRUE), "stock_intersect: Renamed all vars, including ut_obs__agegroup")
ok(ut_cmp_equal(
g3_eval(attr(ld$obsstock$env$ut_obs__agegroup, "g3_global_init_val")),
structure(
list(1L, 2L, 2L),
key_var = "ut_obs__agegroup_keys",
value_var = "ut_obs__agegroup_values" )), "ut_obs__agegroup: Sub-parts also renamed")
########### g3l_likelihood_data:agegroup
ok_group('g3l_likelihood_data:age_factor', {
df <- data.frame(
year = 1990,
age = c(3,3,4,5),
number = 1,
stringsAsFactors = TRUE)
df$age <- cut(df$age, seq(3, 10, by = 1), right = FALSE)
df <- aggregate(number ~ year + age, df, sum)
ld <- generate_ld(df)
ok(cmp_array(ld$obs_array$num, "
length age time Freq
0:Inf 3:3 1990 2
0:Inf 4:4 1990 1
0:Inf 5:5 1990 1
0:Inf 6:6 1990 0
0:Inf 7:7 1990 0
0:Inf 8:8 1990 0
0:Inf 9:9 1990 0
"), "ld$obs_array$num: included all single ages")
ld <- generate_ld(data.frame(
year = 1990,
age = as.factor(c(3,4,5,8)),
stringsAsFactors = TRUE))
ok(cmp_array(ld$obs_array$num, "
length age time Freq
0:Inf 3:3 1990 1
0:Inf 4:4 1990 2
0:Inf 5:7 1990 3
0:Inf 8:8 1990 4
"), "ld$obs_array$num: can also use integer strings as factors")
df <- data.frame(
year = 1990,
age = c(3,3,4,5),
number = 1,
stringsAsFactors = TRUE)
df$age <- cut(df$age, seq(2, 10, by = 4), right = FALSE)
df <- aggregate(number ~ year + age, df, sum)
ld <- generate_ld(df)
ok(cmp_array(ld$obs_array$num, "
length age time Freq
0:Inf 2:5 1990 4
0:Inf 6:9 1990 0
"), "ld$obs_array$num: Everything grouped into first group, second compared to zero")
})
ok_group('g3l_likelihood_data:area', {
# Pull the area lookup definition back out
area_lookup <- function (ld) {
list(
keys = environment(g3_stock_def(ld$modelstock, 'areagroup_lookup'))$keys,
values = environment(g3_stock_def(ld$modelstock, 'areagroup_lookup'))$values)
}
ok(ut_cmp_error({
ld <- generate_ld("
area year number
a 1999 1999.1
b 1999 1999.2
c 1999 1999.3
")
}, "Areas in data"), "If char areas are provided without aggregation, we can't do anything")
ok(ut_cmp_error({
ld <- generate_ld("
area year number
a 1999 1999.1
b 1999 1999.2
c 1999 1999.3
", area = list(a = 1, b = 2, c = 3))
}, "Areas in data"), "If char areas are provided without aggregation, we can't do anything. MFDB aggregates don't count")
ld <- generate_ld("
area year number
1 1999 1999.1
2 1999 1999.2
3 1999 1999.3
2 2000 2000.2
3 2000 2000.3
1 2001 2001.1
2 2001 2001.2
")
ok(cmp_array(ld$obs_array$num, "
length time area Freq
0:Inf 1999 1 1999.1
0:Inf 2000 1 0.0
0:Inf 2001 1 2001.1
0:Inf 1999 2 1999.2
0:Inf 2000 2 2000.2
0:Inf 2001 2 2001.2
0:Inf 1999 3 1999.3
0:Inf 2000 3 2000.3
0:Inf 2001 3 0.0
"), "Worked out area dimensions from data, filled in missing values")
ld <- gadget3:::g3l_likelihood_data('ut', read.table(header = TRUE, stringsAsFactors = TRUE, text = "
area year number
a 1999 1999.1
b 1999 1999.2
c 1999 1999.3
b 2000 2000.2
c 2000 2000.3
a 2001 2001.1
b 2001 2001.2
"), area_group = list(a = 3, b = 4, c = c(1:2)))
ok(cmp_array(ld$obs_array$num, "
length time area Freq
0:Inf 1999 a 1999.1
0:Inf 2000 a 0.0
0:Inf 2001 a 2001.1
0:Inf 1999 b 1999.2
0:Inf 2000 b 2000.2
0:Inf 2001 b 2001.2
0:Inf 1999 c 1999.3
0:Inf 2000 c 2000.3
0:Inf 2001 c 0.0
"), "Worked out area dimensions from data, filled in missing values")
ok(ut_cmp_identical(area_lookup(ld), list(keys = c(3L,4L,1L,2L), values = c(1L,2L,3L,3L))), "Areas 1 & 2 both mapped to index 3 (i.e. c)")
})
ok_group('g3l_likelihood_data:tag', {
ld <- gadget3:::g3l_likelihood_data('ut', read.table(header = TRUE, stringsAsFactors = TRUE, text = "
tag year number
a 1999 1999.1
b 1999 1999.2
c 1999 1999.3
b 2000 2000.2
c 2000 2000.3
a 2001 2001.1
b 2001 2001.2
"))
ok(cmp_array(ld$obs_array$num, "
length tag time Freq
0:Inf a 1999 1999.1
0:Inf b 1999 1999.2
0:Inf c 1999 1999.3
0:Inf a 2000 0.0
0:Inf b 2000 2000.2
0:Inf c 2000 2000.3
0:Inf a 2001 2001.1
0:Inf b 2001 2001.2
0:Inf c 2001 0.0
"), "Worked out tag dimensions from data")
ok(ut_cmp_identical(
g3_stock_def(ld$modelstock, 'tag_ids'),
gadget3:::force_vector(a = 1L, b = 2L, c = 3L)), "stock__tag_ids: Worked out from factor")
})
ok_group('g3l_likelihood_data:stock', {
ld <- generate_ld("
age year number
3 1999 1999.3
4 1999 1999.4
6 1999 1999.6
3 2000 2000.3
6 2000 2000.6
4 2001 2001.4
6 2001 2001.6
")
ok(is.null(ld$maps$stock), "No stock column, so no stock map")
ok(ut_cmp_error(generate_ld("
age year stock stock_re number
3 1999 a a$ 1999.3
"), "stock.*stock_re"), "Can't have both stock & stock_re")
ld <- generate_ld("
age year stock number
3 1999 a 1999.3
4 1999 b 1999.4
6 1999 a 1999.6
3 2000 a 2000.3
6 2000 b 2000.6
4 2001 b 2001.4
6 2001 b 2001.6
", all_stocks = c('a', 'b'))
ok(ut_cmp_identical(dimnames(ld$obs_array$num)[['stock']], c("a", "b")), "Array has stocks a & b")
ok(ut_cmp_identical(ld$maps$stock, c(a = 'a', b = 'b')), "stock_map is 1:1 mapping")
ok(ut_cmp_error(generate_ld("
age year stock number
3 1999 a 1999.3
4 1999 b 1999.4
5 1999 kapow 1999.6
6 1999 zot 1999.6
", all_stocks = c('a', 'b')), "kapow, zot"), "Unknown stock names in data an error")
# Generate a list of stocks "stock_(imm,mat)_(f,m)"
stock_names <- paste(
'stock',
rep(c('imm', 'mat'), each = 2),
c('f', 'm'),
sep = "_")
ld <- generate_ld("
age year stock_re number
3 1999 _f$ 1999.3
4 1999 ^stock_mat 1999.4
6 1999 ^stock_imm 1999.6
3 2000 ^stock_mat 2000.3
6 2000 ^stock_imm 2000.6
4 2001 ^stock_imm 2001.4
6 2001 ^stock_mat 2001.6
", all_stocks = stock_names, model_history = "late")
ok(ut_cmp_identical(
dimnames(ld$obs_array$num)[['stock_re']],
c("_f$", "^stock_mat", "^stock_imm")), "Array names are regexes")
ok(ut_cmp_identical(
ld$maps$stock,
c(
stock_imm_f = '_f$',
stock_imm_m = '^stock_imm',
stock_mat_f = '_f$',
stock_mat_m = '^stock_mat')), "Stock map used first regexes first")
# Generated intersect code works
model_fn <- g3_to_r(list(
g3a_time(1999, 2000),
gadget3:::g3_step(g3_formula(
stock_iterate(ms, stock_intersect(os, print(c(stock_ss(ms__x), stock_ss(os__x))))),
ms = ld$modelstock,
os = ld$obsstock,
ms__x = g3_stock_instance(ld$modelstock, seq_len(prod(unlist(ld$modelstock$dim)))),
os__x = g3_stock_instance(ld$obsstock, seq_len(prod(unlist(ld$obsstock$dim))) * 10) )),
NULL ))
ok(ut_cmp_identical(
capture.output(invisible(model_fn())),
paste0("[1] ", 1:24, " ", 1:24 * 10)), "model_fn: Loop / intersect over modelstock/obsstock correctly")
ok(ut_cmp_error(generate_ld("
age year stock_re number
3 1999 _f$ 1999.3
3 1999 _g$ 1999.3
3 1999 _h$ 1999.3
"), "_g\\$, _h\\$"), "Regexes that don't match anything an error")
# Generate a list of stocks "stock_(imm,mat)_f" (NB: not male)
stock_names <- paste(
'stock',
rep(c('imm', 'mat'), each = 2),
c('f', 'm'),
sep = "_")
ld <- generate_ld("
age year stock_re number
3 1999 _mat_f$ 1999.3
4 1999 _mat_f$ 1999.4
6 1999 _imm_f$ 1999.6
3 2000 _mat_f$ 2000.3
6 2000 _imm_f$ 2000.6
4 2001 _imm_f$ 2001.4
6 2001 _mat_f$ 2001.6
", all_stocks = stock_names)
ok(ut_cmp_identical(
dimnames(ld$obs_array$num)[['stock_re']],
c("_mat_f$", "_imm_f$")), "Array names are regexes")
ok(ut_cmp_identical(
ld$maps$stock,
c(
stock_imm_f = '_imm_f$',
stock_imm_m = NA,
stock_mat_f = '_mat_f$',
stock_mat_m = NA )), "Stock map ignored unused stocks")
})
ok_group('g3l_likelihood_data:stock:name_parts', {
stock_groupings <- function (stock_names, stock_cols) {
tbl <- expand.grid(number = 0, stock = stock_cols, age = 3:6, year = 1999:2001)
tbl$number <- seq_len(nrow(tbl))
ld <- generate_ld(tbl, all_stocks = stock_names)
return(ld$maps$stock)
}
out <- stock_groupings(
list(c('fish', 'imm'), c('fish', 'mat'), c('fish', 'sen')),
c('fish'))
ok(ut_cmp_equal(out, c(
fish_imm = 'fish',
fish_mat = 'fish',
fish_sen = 'fish' )), '"fish": Groups both maturity groups together')
out <- stock_groupings(
list(c('fish', 'imm'), c('fish', 'mat'), c('fish', 'sen')),
c('fish_mat', 'fish'))
ok(ut_cmp_equal(out, c(
fish_imm = 'fish',
fish_mat = 'fish_mat',
fish_sen = 'fish' )), '"fish_mat": Overrides "fish" group due to longer length')
out <- stock_groupings(
list(c('a', 'imm'), c('a', 'mat'), c('b', 'imm'), c('b', 'mat'), c('c', 'mat')),
c('a', 'b', 'mat'))
ok(ut_cmp_equal(out, c(
a_imm = 'a',
a_mat = 'a',
b_imm = 'b',
b_mat = 'b',
c_mat = 'mat' )), "'b' wins over 'mat' because it comes first")
out <- stock_groupings(
list(c('a', 'imm'), c('a', 'mat'), c('b', 'imm'), c('b', 'mat'), c('c', 'mat')),
c('a', 'mat', 'b'))
ok(ut_cmp_equal(out, c(
a_imm = 'a',
a_mat = 'a',
b_imm = 'b',
b_mat = 'mat',
c_mat = 'mat' )), "'mat' wins over 'b' because it comes first")
out <- stock_groupings(
list(c('a', 'imm', 'f'), c('a', 'mat', 'f'), c('a', 'imm', 'm'), c('a', 'mat', 'm'), c('c', 'mat')),
c('a_f', 'a_m', 'c'))
ok(ut_cmp_equal(out, c(
a_imm_f = 'a_f',
a_mat_f = 'a_f',
a_imm_m = 'a_m',
a_mat_m = 'a_m',
c_mat = 'c' )), "Name part groupings don't have to be sequential")
})
ok_group('g3l_likelihood_data:predator', {
ld <- generate_ld("
age year number
3 1999 1999.3
4 1999 1999.4
6 1999 1999.6
3 2000 2000.3
6 2000 2000.6
4 2001 2001.4
6 2001 2001.6
")
ok(is.null(ld$maps$predator), "No predator column, so no predator map")
ok(ut_cmp_error(generate_ld("
age year predator predator_re number
3 1999 a a$ 1999.3
"), "predator.*predator_re"), "Can't have both predator & predator_re")
ld <- generate_ld("
age year predator number
3 1999 a 1999.3
4 1999 b 1999.4
6 1999 a 1999.6
3 2000 a 2000.3
6 2000 b 2000.6
4 2001 b 2001.4
6 2001 b 2001.6
", all_predators = list(g3_stock('a', c(0, 10)), g3_stock('b', c(0, 10)) ))
ok(ut_cmp_identical(dimnames(ld$obs_array$num)[['predator']], c("a", "b")), "Array has predators a & b")
ok(ut_cmp_identical(ld$maps$predator, c(a = 'a', b = 'b')), "predator_map is 1:1 mapping")
# Generate a list of predators "predator_(trawl|gil)_(f|m)"
predators <- lapply(paste(
'predator',
rep(c('trawl', 'gil'), each = 2),
c('is', 'no'),
sep = "_"), function (x) g3_stock(x, 1))
ld <- generate_ld("
age year predator_re number
3 1999 _is$ 1999.3
4 1999 ^predator_trawl 1999.4
6 1999 ^predator_gil 1999.6
3 2000 ^predator_trawl 2000.3
6 2000 ^predator_gil 2000.6
4 2001 ^predator_gil 2001.4
6 2001 ^predator_trawl 2001.6
", all_predators = predators)
ok(ut_cmp_identical(
dimnames(ld$obs_array$num)[['predator_re']],
c("_is$", "^predator_trawl", "^predator_gil")), "Array names are regexes")
ok(ut_cmp_identical(
ld$maps$predator,
c(
predator_trawl_is = '_is$',
predator_trawl_no = '^predator_trawl',
predator_gil_is = '_is$',
predator_gil_no = '^predator_gil' )), "predator map used first regexes first")
# Generate a list of predators "predator_(trawl|gil)_(f|m)"
predators <- lapply(paste(
'predator',
rep(c('trawl', 'gil'), each = 2),
c('is', 'no'),
sep = "_"), function (x) g3_stock(x, 1))
ld <- generate_ld("
age year predator_re number
3 1999 _gil_is$ 1999.3
4 1999 _gil_is$ 1999.4
6 1999 _trawl_is$ 1999.6
3 2000 _gil_is$ 2000.3
6 2000 _trawl_is$ 2000.6
4 2001 _trawl_is$ 2001.4
6 2001 _gil_is$ 2001.6
", all_predators = predators, model_history = "late")
ok(ut_cmp_identical(
dimnames(ld$obs_array$num)[['predator_re']],
c("_gil_is$", "_trawl_is$")), "Array names are regexes")
ok(ut_cmp_identical(
ld$maps$predator,
c(
predator_trawl_is = '_trawl_is$',
predator_trawl_no = NA,
predator_gil_is = '_gil_is$',
predator_gil_no = NA )), "predator map ignored unused predators")
# Generated intersect code works
model_fn <- g3_to_r(list(
g3a_time(1999, 2000),
gadget3:::g3_step(g3_formula(
stock_iterate(ms, stock_intersect(os, print(c(stock_ss(ms__x), stock_ss(os__x))))),
ms = ld$modelstock,
os = ld$obsstock,
ms__x = g3_stock_instance(ld$modelstock, seq_len(prod(unlist(ld$modelstock$dim)))),
os__x = g3_stock_instance(ld$obsstock, seq_len(prod(unlist(ld$obsstock$dim))) * 10) )),
NULL ))
ok(ut_cmp_identical(
capture.output(invisible(model_fn())),
paste0("[1] ", 1:16, " ", 1:16 * 10) ), "model_fn: Loop / intersect over modelstock/obsstock correctly")
})
ok_group('g3l_likelihood_data:fleet', {
ld <- generate_ld("
age year number
3 1999 1999.3
4 1999 1999.4
6 1999 1999.6
3 2000 2000.3
6 2000 2000.6
4 2001 2001.4
6 2001 2001.6
")
ok(is.null(ld$maps$fleet), "No fleet column, so no fleet map")
ok(ut_cmp_error(generate_ld("
age year fleet fleet_re number
3 1999 a a$ 1999.3
"), "fleet.*fleet_re"), "Can't have both fleet & fleet_re")
ld <- generate_ld("
age year fleet number
3 1999 a 1999.3
4 1999 b 1999.4
6 1999 a 1999.6
3 2000 a 2000.3
6 2000 b 2000.6
4 2001 b 2001.4
6 2001 b 2001.6
", all_fleets = list(g3_fleet('a'), g3_fleet('b')))
ok(ut_cmp_identical(dimnames(ld$obs_array$num)[['fleet']], c("a", "b")), "Array has fleets a & b")
ok(ut_cmp_identical(ld$maps$fleet, c(a = 'a', b = 'b')), "fleet_map is 1:1 mapping")
# Generate a list of fleets "fleet_(trawl|gil)_(f|m)"
fleets <- lapply(paste(
'fleet',
rep(c('trawl', 'gil'), each = 2),
c('is', 'no'),
sep = "_"), function (x) g3_stock(x, 1))
ld <- generate_ld("
age year fleet_re number
3 1999 _is$ 1999.3
4 1999 ^fleet_trawl 1999.4
6 1999 ^fleet_gil 1999.6
3 2000 ^fleet_trawl 2000.3
6 2000 ^fleet_gil 2000.6
4 2001 ^fleet_gil 2001.4
6 2001 ^fleet_trawl 2001.6
", all_fleets = fleets)
ok(ut_cmp_identical(
dimnames(ld$obs_array$num)[['fleet_re']],
c("_is$", "^fleet_trawl", "^fleet_gil")), "Array names are regexes")
ok(ut_cmp_identical(
ld$maps$fleet,
c(
fleet_trawl_is = '_is$',
fleet_trawl_no = '^fleet_trawl',
fleet_gil_is = '_is$',
fleet_gil_no = '^fleet_gil' )), "fleet map used first regexes first")
# Generate a list of fleets "fleet_(trawl|gil)_(f|m)"
fleets <- lapply(paste(
'fleet',
rep(c('trawl', 'gil'), each = 2),
c('is', 'no'),
sep = "_"), function (x) g3_stock(x, 1))
ld <- generate_ld("
age year fleet_re number
3 1999 _gil_is$ 1999.3
4 1999 _gil_is$ 1999.4
6 1999 _trawl_is$ 1999.6
3 2000 _gil_is$ 2000.3
6 2000 _trawl_is$ 2000.6
4 2001 _trawl_is$ 2001.4
6 2001 _gil_is$ 2001.6
", all_fleets = fleets, model_history = "late")
ok(ut_cmp_identical(
dimnames(ld$obs_array$num)[['fleet_re']],
c("_gil_is$", "_trawl_is$")), "Array names are regexes")
ok(ut_cmp_identical(
ld$maps$fleet,
c(
fleet_trawl_is = '_trawl_is$',
fleet_trawl_no = NA,
fleet_gil_is = '_gil_is$',
fleet_gil_no = NA )), "fleet map ignored unused fleets")
# Generated intersect code works
model_fn <- g3_to_r(list(
g3a_time(1999, 2000),
gadget3:::g3_step(g3_formula(
stock_iterate(ms, stock_intersect(os, print(c(stock_ss(ms__x), stock_ss(os__x))))),
ms = ld$modelstock,
os = ld$obsstock,
ms__x = g3_stock_instance(ld$modelstock, seq_len(prod(unlist(ld$modelstock$dim)))),
os__x = g3_stock_instance(ld$obsstock, seq_len(prod(unlist(ld$obsstock$dim))) * 10) )),
NULL ))
ok(ut_cmp_identical(
capture.output(invisible(model_fn())),
paste0("[1] ", 1:16, " ", 1:16 * 10) ), "model_fn: Loop / intersect over modelstock/obsstock correctly")
})
ok_group('g3l_likelihood_data:predator') ##########
ld <- generate_ld(expand.grid(
length = 5:10,
predator_length = c(10, 50, 100),
predator_age = c('[0,5)', '[5,10)'), # ((
predator_tag = c('a', 'b'),
year = 1999:2000 ))
ok(gadget3:::ut_cmp_code(generate_code(ld, 'stock_iterate', cur_year = 1999, cur_step = 1), quote({
ut_obs__time_idx <- intlookup_getdefault(ut_obs__times, (cur_year * 100L + cur_step * 0L), -1L)
if (ut_obs__time_idx >= (1L)) {
for (ut_obs__predator_tag_idx in seq_along(ut_obs__predator_tag_ids)) {
predator_tag <- ut_obs__predator_tag_ids[[ut_obs__predator_tag_idx]]
for (ut_obs__predator_agegroup_idx in seq_along(ut_obs__predator_minages)) {
predator_age <- ut_obs__predator_minages[[ut_obs__predator_agegroup_idx]]
for (ut_obs__predator_length_idx in seq_along(ut_obs__predator_midlen)) {
predator_length <- ut_obs__predator_midlen[[ut_obs__predator_length_idx]]
for (ut_obs__length_idx in seq_along(ut_obs__midlen)) {
length <- ut_obs__midlen[[ut_obs__length_idx]]
ut_obs__num[ut_obs__length_idx, ut_obs__predator_length_idx,
ut_obs__predator_agegroup_idx, ut_obs__predator_tag_idx,
ut_obs__time_idx]
}
}
}
}
}
}), optimize = TRUE), "stock_iterate: All predator dimensions included, with prefixed variables")
ok(gadget3:::ut_cmp_code(generate_code(ld, 'stock_intersect', cur_year = 1999, cur_step = 1, predator_tag = 1, predator_length = 1, predator_age = 1), quote({
ut_obs__time_idx <- intlookup_getdefault(ut_obs__times, (cur_year * 100L + cur_step * 0L), -1L)
if (ut_obs__time_idx >= 1L) {
for (ut_obs__predator_tag_idx in seq_along(ut_obs__predator_tag_ids)) {
predator_tag <- ut_obs__predator_tag_ids[[ut_obs__predator_tag_idx]]
for (ut_obs__predator_agegroup_idx in seq_along(ut_obs__predator_minages)) {
`_age` <- ut_obs__predator_minages[[ut_obs__predator_agegroup_idx]]
for (ut_obs__predator_length_idx in seq_along(ut_obs__predator_minlen)) {
predator_length <- ut_obs__predator_midlen[[ut_obs__predator_length_idx]]
for (ut_obs__length_idx in seq_along(ut_obs__minlen)) {
length <- ut_obs__midlen[[ut_obs__length_idx]]
ut_obs__num[ut_obs__length_idx, ut_obs__predator_length_idx,
ut_obs__predator_agegroup_idx, ut_obs__predator_tag_idx,
ut_obs__time_idx]
}
}
}
}
}
}), optimize = TRUE), "stock_intersect: All predator dimensions included, with prefixed variables")
ok(ut_cmp_equal(
ld$obsstock$env$stock__midlen,
gadget3:::force_vector(c("5:6" = 5.5, "6:7" = 6.5, "7:8" = 7.5, "8:9" = 8.5, "9:10" = 9.5, "10:Inf" = 10.5)) ), "stock__midlen: prey vars set")
ok(ut_cmp_equal(
ld$obsstock$env$stock__predator_midlen,
gadget3:::force_vector(c("10:50" = 30, "50:100" = 75, "100:Inf" = 120)) ), "stock__predator_midlen: predator vars prefixed")
ok(ut_cmp_equal(
environment(attr(ld$obsstock$env$ut_obs__predator_agegroup, "g3_global_init_val"))$ut_obs__predator_agegroup_keys,
gadget3:::force_vector(0:9)), "ut_obs__predator_agegroup_keys: initval keys got renamed")
ok(ut_cmp_equal(
environment(attr(ld$obsstock$env$ut_obs__predator_agegroup, "g3_global_init_val"))$ut_obs__predator_agegroup_values,
gadget3:::force_vector( rep(1:2, each = 5) )), "ut_obs__predator_agegroup_values: initval values got renamed")
########## g3l_likelihood_data:predator
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