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
|
## write a matrix to a predefined dataset for testing
writeTestDataset <- function(file, data, filter, compression_level, shuffle) {
rhdf5::h5createFile(file = file)
rhdf5::h5createDataset(file, dataset = "test", dims = dim(data),
filter = filter, storage.mode = "integer",
level = compression_level, shuffle = shuffle)
rhdf5::h5write(obj = data, file = file, name = "test")
}
## we generate ints from the exponential distribution. This has many 0's
## and generally compresses well with/and without shuffling. Sampling from
## uniform distribution sometimes doesn't compress with shuffling turned off
mat <- matrix(as.integer(rexp(10000, rate = 1.5)), ncol = 10)
tf <- tempfile()
settings <- list("BLOSC_BLOSCLZ" = 0:1,
"BLOSC_LZ4" = 0:9,
"BLOSC_LZ4HC" = 0:9,
"BLOSC_SNAPPY" = 0:1,
"BLOSC_ZLIB" = 0:9,
"BLOSC_ZSTD" = 0:9)
## this is the uncompressed file size for comparison in tests
writeTestDataset(file = tf, data = mat, filter = "BLOSC_BLOSCLZ",
compression_level = 0, shuffle = FALSE)
max_file_size <- file.size(tf)
file.remove(tf)
for(i in seq_along(settings)) {
for(j in seq_along(settings[[i]])) {
for(k in c(TRUE, FALSE)) {
expect_silent(
writeTestDataset(file = tf, data = mat, filter = names(settings)[i],
compression_level = settings[[i]][j], shuffle = k)
)
if(settings[[i]][j] > 0)
expect_true(file.size(tf) < max_file_size)
expect_identical(
rhdf5::h5read(tf, "test"), mat
)
file.remove(tf)
}
}
}
for(j in seq_len(9)) {
for(k in c(TRUE, FALSE)) {
expect_silent(
writeTestDataset(file = tf, data = mat, filter = "BZIP2",
compression_level = j, shuffle = k)
)
expect_true(file.size(tf) < max_file_size)
expect_identical(
rhdf5::h5read(tf, "test"), mat
)
file.remove(tf)
}
}
for(k in c(TRUE, FALSE)) {
expect_silent(
writeTestDataset(file = tf, data = mat, filter = "LZF",
compression_level = 1, shuffle = k)
)
expect_true(file.size(tf) < max_file_size)
expect_identical(
rhdf5::h5read(tf, "test"), mat
)
file.remove(tf)
}
|
