rhdf5 - HDF5 interface for R
30 April 2018
Package
rhdf5 2.24.0
#Introduction The package is an R interface for HDF5. On the one hand it implements R interfaces to many of the low level functions from the C interface. On the other hand it provides high level convenience functions on R level to make a usage of HDF5 files more easy.
#Installation of the HDF5 package To install the rhdf5 package, you need a current version (>2.15.0) of R (www.r-project.org). After installing R you can run the following commands from the R command shell to install the bioconductor package rhdf5.
source("http://bioconductor.org/biocLite.R")
biocLite("rhdf5")##Creating an HDF5 file and group hierarchy An empty HDF5 file is created by
library(rhdf5)
h5createFile("myhdf5file.h5")## [1] TRUEThe HDF5 file can contain a group hierarchy. We create a number of groups and list the file content afterwards.
h5createGroup("myhdf5file.h5","foo")## [1] TRUEh5createGroup("myhdf5file.h5","baa")## [1] TRUEh5createGroup("myhdf5file.h5","foo/foobaa")## [1] TRUEh5ls("myhdf5file.h5")## group name otype dclass dim
## 0 / baa H5I_GROUP
## 1 / foo H5I_GROUP
## 2 /foo foobaa H5I_GROUP##Writing and reading objects
Objects can be written to the HDF5 file. Attributes attached to an
object are written as well, if is
given as argument to h5write. Note that not all
R-attributes can be written as HDF5 attributes.
A = matrix(1:10,nr=5,nc=2)
h5write(A, "myhdf5file.h5","foo/A")
B = array(seq(0.1,2.0,by=0.1),dim=c(5,2,2))
attr(B, "scale") <- "liter"
h5write(B, "myhdf5file.h5","foo/B")
C = matrix(paste(LETTERS[1:10],LETTERS[11:20], collapse=""),
nr=2,nc=5)
h5write(C, "myhdf5file.h5","foo/foobaa/C")
df = data.frame(1L:5L,seq(0,1,length.out=5),
c("ab","cde","fghi","a","s"), stringsAsFactors=FALSE)
h5write(df, "myhdf5file.h5","df")
h5ls("myhdf5file.h5")## group name otype dclass dim
## 0 / baa H5I_GROUP
## 1 / df H5I_DATASET COMPOUND 5
## 2 / foo H5I_GROUP
## 3 /foo A H5I_DATASET INTEGER 5 x 2
## 4 /foo B H5I_DATASET FLOAT 5 x 2 x 2
## 5 /foo foobaa H5I_GROUP
## 6 /foo/foobaa C H5I_DATASET STRING 2 x 5D = h5read("myhdf5file.h5","foo/A")
E = h5read("myhdf5file.h5","foo/B")
F = h5read("myhdf5file.h5","foo/foobaa/C")
G = h5read("myhdf5file.h5","df")If a dataset with the given name does not yet exist, a
dataset is created in the HDF5 file and the object obj is
written to the HDF5 file. If a dataset with the given name
already exists and the datatype and the dimensions are the same as for
the object obj, the data in the file is overwritten. If the
dataset already exists and either the datatype or the dimensions are
different, h5write() fails.
File, group and dataset handles are a simpler way to read (and partially to write) HDF5 files.
A file is opened by H5Fopen.
h5f = H5Fopen("myhdf5file.h5")
h5f## HDF5 FILE
## name /
## filename
##
## name otype dclass dim
## 0 baa H5I_GROUP
## 1 df H5I_DATASET COMPOUND 5
## 2 foo H5I_GROUPThe and operators can be used to access the next group level. While the operator reads the object from disk, the operator returns a group or dataset handle.
h5f$df## X1L.5L seq.0..1..length.out...5. c..ab....cde....fghi....a....s..
## 1 1 0.00 ab
## 2 2 0.25 cde
## 3 3 0.50 fghi
## 4 4 0.75 a
## 5 5 1.00 sh5f&'df'## HDF5 DATASET
## name /df
## filename
## type HST_COMPOUND
## rank 1
## size 5
## maxsize 5Both of the following code lines return the matrix C.
h5f$foo$foobaa$C## [,1] [,2]
## [1,] "A KB LC MD NE OF PG QH RI SJ T" "A KB LC MD NE OF PG QH RI SJ T"
## [2,] "A KB LC MD NE OF PG QH RI SJ T" "A KB LC MD NE OF PG QH RI SJ T"
## [,3] [,4]
## [1,] "A KB LC MD NE OF PG QH RI SJ T" "A KB LC MD NE OF PG QH RI SJ T"
## [2,] "A KB LC MD NE OF PG QH RI SJ T" "A KB LC MD NE OF PG QH RI SJ T"
## [,5]
## [1,] "A KB LC MD NE OF PG QH RI SJ T"
## [2,] "A KB LC MD NE OF PG QH RI SJ T"h5f$"/foo/foobaa/C"## [,1] [,2]
## [1,] "A KB LC MD NE OF PG QH RI SJ T" "A KB LC MD NE OF PG QH RI SJ T"
## [2,] "A KB LC MD NE OF PG QH RI SJ T" "A KB LC MD NE OF PG QH RI SJ T"
## [,3] [,4]
## [1,] "A KB LC MD NE OF PG QH RI SJ T" "A KB LC MD NE OF PG QH RI SJ T"
## [2,] "A KB LC MD NE OF PG QH RI SJ T" "A KB LC MD NE OF PG QH RI SJ T"
## [,5]
## [1,] "A KB LC MD NE OF PG QH RI SJ T"
## [2,] "A KB LC MD NE OF PG QH RI SJ T"One can as well return a dataset handle for a matrix and then read the matrix in chunks for out-of-memory computations. .
h5d = h5f&"/foo/B"
h5d[]## , , 1
##
## [,1] [,2]
## [1,] 0.1 0.6
## [2,] 0.2 0.7
## [3,] 0.3 0.8
## [4,] 0.4 0.9
## [5,] 0.5 1.0
##
## , , 2
##
## [,1] [,2]
## [1,] 1.1 1.6
## [2,] 1.2 1.7
## [3,] 1.3 1.8
## [4,] 1.4 1.9
## [5,] 1.5 2.0h5d[3,,]## [,1] [,2]
## [1,] 0.3 1.3
## [2,] 0.8 1.8The same works as well for writing to datasets.
h5d[3,,] = 1:4
H5Fflush(h5f)Remind again that in the following code the first version does not change the data on disk, but the second does.
h5f$foo$B = 101:120
h5f$"/foo/B" = 101:120It is important to close all dataset, group, and file handles when not used anymore
H5Dclose(h5d)
H5Fclose(h5f)or close all open HDF5 handles in the environment by
h5closeAll()The rhdf5 package provides two ways of subsetting. One can specify the submatrix with the R-style index lists or with the HDF5 style hyperslabs. Note, that the two next examples below show two alternative ways for reading and writing the exact same submatrices. Before writing subsetting or hyperslabbing, the dataset with full dimensions has to be created in the HDF5 file. This can be achieved by writing once an array with full dimensions as in Section or by creating a dataset. Afterwards the dataset can be written sequentially.
The chosen chunk size and compression level have a strong impact on the reading and writing time as well as on the resulting file size. In an example an integer vector of size 10e7is written to an HDF5 file. The file is written in subvectors of size 10’000. The definition of the chunk size influences the reading as well as the writing time. In the chunk size is much smaller or much larger than actually used, the runtime performance decreases dramatically. Furthermore the file size is larger for smaller chunk sizes, because of an overhead. The compression can be much more efficient when the chunk size is very large. The following figure illustrates the runtime and file size behaviour as a function of the chunk size for a small toy dataset.
After the creation of the dataset, the data can be written
sequentially to the HDF5 file. Subsetting in R-style needs the
specification of the argument index to h5read() and
h5write().
h5createDataset("myhdf5file.h5", "foo/S", c(5,8),
storage.mode = "integer", chunk=c(5,1), level=7)## [1] TRUEh5write(matrix(1:5,nr=5,nc=1), file="myhdf5file.h5",
name="foo/S", index=list(NULL,1))
h5read("myhdf5file.h5", "foo/S")## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1 0 0 0 0 0 0 0
## [2,] 2 0 0 0 0 0 0 0
## [3,] 3 0 0 0 0 0 0 0
## [4,] 4 0 0 0 0 0 0 0
## [5,] 5 0 0 0 0 0 0 0h5write(6:10, file="myhdf5file.h5",
name="foo/S", index=list(1,2:6))
h5read("myhdf5file.h5", "foo/S")## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1 6 7 8 9 10 0 0
## [2,] 2 0 0 0 0 0 0 0
## [3,] 3 0 0 0 0 0 0 0
## [4,] 4 0 0 0 0 0 0 0
## [5,] 5 0 0 0 0 0 0 0h5write(matrix(11:40,nr=5,nc=6), file="myhdf5file.h5",
name="foo/S", index=list(1:5,3:8))
h5read("myhdf5file.h5", "foo/S")## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1 6 11 16 21 26 31 36
## [2,] 2 0 12 17 22 27 32 37
## [3,] 3 0 13 18 23 28 33 38
## [4,] 4 0 14 19 24 29 34 39
## [5,] 5 0 15 20 25 30 35 40h5write(matrix(141:144,nr=2,nc=2), file="myhdf5file.h5",
name="foo/S", index=list(3:4,1:2))
h5read("myhdf5file.h5", "foo/S")## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1 6 11 16 21 26 31 36
## [2,] 2 0 12 17 22 27 32 37
## [3,] 141 143 13 18 23 28 33 38
## [4,] 142 144 14 19 24 29 34 39
## [5,] 5 0 15 20 25 30 35 40h5write(matrix(151:154,nr=2,nc=2), file="myhdf5file.h5",
name="foo/S", index=list(2:3,c(3,6)))
h5read("myhdf5file.h5", "foo/S")## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1 6 11 16 21 26 31 36
## [2,] 2 0 151 17 22 153 32 37
## [3,] 141 143 152 18 23 154 33 38
## [4,] 142 144 14 19 24 29 34 39
## [5,] 5 0 15 20 25 30 35 40h5read("myhdf5file.h5", "foo/S", index=list(2:3,2:3))## [,1] [,2]
## [1,] 0 151
## [2,] 143 152h5read("myhdf5file.h5", "foo/S", index=list(2:3,c(2,4)))## [,1] [,2]
## [1,] 0 17
## [2,] 143 18h5read("myhdf5file.h5", "foo/S", index=list(2:3,c(1,2,4,5)))## [,1] [,2] [,3] [,4]
## [1,] 2 0 17 22
## [2,] 141 143 18 23The HDF5 hyperslabs are defined by some of the arguments
start, stride, count, and
block. These arguments are not effective, if the argument
index is specified.
h5createDataset("myhdf5file.h5", "foo/H", c(5,8), storage.mode = "integer",
chunk=c(5,1), level=7)## [1] TRUEh5write(matrix(1:5,nr=5,nc=1), file="myhdf5file.h5", name="foo/H",
start=c(1,1))
h5read("myhdf5file.h5", "foo/H")## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1 0 0 0 0 0 0 0
## [2,] 2 0 0 0 0 0 0 0
## [3,] 3 0 0 0 0 0 0 0
## [4,] 4 0 0 0 0 0 0 0
## [5,] 5 0 0 0 0 0 0 0h5write(6:10, file="myhdf5file.h5", name="foo/H",
start=c(1,2), count=c(1,5))
h5read("myhdf5file.h5", "foo/H")## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1 6 7 8 9 10 0 0
## [2,] 2 0 0 0 0 0 0 0
## [3,] 3 0 0 0 0 0 0 0
## [4,] 4 0 0 0 0 0 0 0
## [5,] 5 0 0 0 0 0 0 0h5write(matrix(11:40,nr=5,nc=6), file="myhdf5file.h5", name="foo/H",
start=c(1,3))
h5read("myhdf5file.h5", "foo/H")## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1 6 11 16 21 26 31 36
## [2,] 2 0 12 17 22 27 32 37
## [3,] 3 0 13 18 23 28 33 38
## [4,] 4 0 14 19 24 29 34 39
## [5,] 5 0 15 20 25 30 35 40h5write(matrix(141:144,nr=2,nc=2), file="myhdf5file.h5", name="foo/H",
start=c(3,1))
h5read("myhdf5file.h5", "foo/H")## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1 6 11 16 21 26 31 36
## [2,] 2 0 12 17 22 27 32 37
## [3,] 141 143 13 18 23 28 33 38
## [4,] 142 144 14 19 24 29 34 39
## [5,] 5 0 15 20 25 30 35 40h5write(matrix(151:154,nr=2,nc=2), file="myhdf5file.h5", name="foo/H",
start=c(2,3), stride=c(1,3))
h5read("myhdf5file.h5", "foo/H")## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1 6 11 16 21 26 31 36
## [2,] 2 0 151 17 22 153 32 37
## [3,] 141 143 152 18 23 154 33 38
## [4,] 142 144 14 19 24 29 34 39
## [5,] 5 0 15 20 25 30 35 40h5read("myhdf5file.h5", "foo/H",
start=c(2,2), count=c(2,2))## [,1] [,2]
## [1,] 0 151
## [2,] 143 152h5read("myhdf5file.h5", "foo/H",
start=c(2,2), stride=c(1,2),count=c(2,2))## [,1] [,2]
## [1,] 0 17
## [2,] 143 18h5read("myhdf5file.h5", "foo/H",
start=c(2,1), stride=c(1,3),count=c(2,2), block=c(1,2))## [,1] [,2] [,3] [,4]
## [1,] 2 0 17 22
## [2,] 141 143 18 23A number of objects can be written to the top level group of an HDF5
file with the function h5save() (as analogous to the base
R function save()).
A = 1:7; B = 1:18; D = seq(0,1,by=0.1)
h5save(A, B, D, file="newfile2.h5")
h5dump("newfile2.h5")## $A
## [1] 1 2 3 4 5 6 7
##
## $B
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
##
## $D
## [1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0##List the content of an HDF5 file
The function h5ls() provides some ways of viewing the content of an HDF5 file.
h5ls("myhdf5file.h5")## group name otype dclass dim
## 0 / baa H5I_GROUP
## 1 / df H5I_DATASET COMPOUND 5
## 2 / foo H5I_GROUP
## 3 /foo A H5I_DATASET INTEGER 5 x 2
## 4 /foo B H5I_DATASET FLOAT 5 x 2 x 2
## 5 /foo H H5I_DATASET INTEGER 5 x 8
## 6 /foo S H5I_DATASET INTEGER 5 x 8
## 7 /foo foobaa H5I_GROUP
## 8 /foo/foobaa C H5I_DATASET STRING 2 x 5h5ls("myhdf5file.h5", all=TRUE)## group name ltype corder_valid corder cset otype
## 0 / baa H5L_TYPE_HARD FALSE 0 0 H5I_GROUP
## 1 / df H5L_TYPE_HARD FALSE 0 0 H5I_DATASET
## 2 / foo H5L_TYPE_HARD FALSE 0 0 H5I_GROUP
## 3 /foo A H5L_TYPE_HARD FALSE 0 0 H5I_DATASET
## 4 /foo B H5L_TYPE_HARD FALSE 0 0 H5I_DATASET
## 5 /foo H H5L_TYPE_HARD FALSE 0 0 H5I_DATASET
## 6 /foo S H5L_TYPE_HARD FALSE 0 0 H5I_DATASET
## 7 /foo foobaa H5L_TYPE_HARD FALSE 0 0 H5I_GROUP
## 8 /foo/foobaa C H5L_TYPE_HARD FALSE 0 0 H5I_DATASET
## num_attrs dclass dtype stype rank dim maxdim
## 0 0 0
## 1 0 COMPOUND HST_COMPOUND SIMPLE 1 5 5
## 2 0 0
## 3 0 INTEGER H5T_STD_I32LE SIMPLE 2 5 x 2 5 x 2
## 4 0 FLOAT H5T_IEEE_F64LE SIMPLE 3 5 x 2 x 2 5 x 2 x 2
## 5 0 INTEGER H5T_STD_I32LE SIMPLE 2 5 x 8 5 x 8
## 6 0 INTEGER H5T_STD_I32LE SIMPLE 2 5 x 8 5 x 8
## 7 0 0
## 8 0 STRING HST_STRING SIMPLE 2 2 x 5 2 x 5h5ls("myhdf5file.h5", recursive=2)## group name otype dclass dim
## 0 / baa H5I_GROUP
## 1 / df H5I_DATASET COMPOUND 5
## 2 / foo H5I_GROUP
## 3 /foo A H5I_DATASET INTEGER 5 x 2
## 4 /foo B H5I_DATASET FLOAT 5 x 2 x 2
## 5 /foo H H5I_DATASET INTEGER 5 x 8
## 6 /foo S H5I_DATASET INTEGER 5 x 8
## 7 /foo foobaa H5I_GROUP##Dump the content of an HDF5 file
The function h5dump() is similar to the function h5ls(). If
used with the argument load=FALSE, it produces the same result
as h5ls(), but with the group structure resolved as a hierarchy
of lists. If the default argument load=TRUE is used all
datasets from the HDF5 file are read.
h5dump("myhdf5file.h5",load=FALSE)## $baa
## NULL
##
## $df
## group name otype dclass dim
## 1 / df H5I_DATASET COMPOUND 5
##
## $foo
## $foo$A
## group name otype dclass dim
## 1 / A H5I_DATASET INTEGER 5 x 2
##
## $foo$B
## group name otype dclass dim
## 1 / B H5I_DATASET FLOAT 5 x 2 x 2
##
## $foo$H
## group name otype dclass dim
## 1 / H H5I_DATASET INTEGER 5 x 8
##
## $foo$S
## group name otype dclass dim
## 1 / S H5I_DATASET INTEGER 5 x 8
##
## $foo$foobaa
## $foo$foobaa$C
## group name otype dclass dim
## 1 / C H5I_DATASET STRING 2 x 5D <- h5dump("myhdf5file.h5")##Reading HDF5 files with external software The content of the HDF5 file can be checked with the command line tool h5dump (available on linux-like systems with the HDF5 tools package installed) or with the graphical user interface HDFView (http://www.hdfgroup.org/hdf-java-html/hdfview/) available for all major platforms.
system("h5dump myhdf5file.h5")##Removing content from an HDF5 file
As well as adding content to an HDF5 file, it is possible to remove entries using the function h5delete(). To demonstrate it’s use, we’ll first list the contents of a file and examine the size of the file in bytes.
h5ls("myhdf5file.h5", recursive=2)## group name otype dclass dim
## 0 / baa H5I_GROUP
## 1 / df H5I_DATASET COMPOUND 5
## 2 / foo H5I_GROUP
## 3 /foo A H5I_DATASET INTEGER 5 x 2
## 4 /foo B H5I_DATASET FLOAT 5 x 2 x 2
## 5 /foo H H5I_DATASET INTEGER 5 x 8
## 6 /foo S H5I_DATASET INTEGER 5 x 8
## 7 /foo foobaa H5I_GROUPfile.size("myhdf5file.h5")## [1] 20982We then use h5delete() to remove the df dataset by providing the file name and the name of the dataset, e.g.
h5delete(file = "myhdf5file.h5", name = "df")
h5ls("myhdf5file.h5", recursive=2)## group name otype dclass dim
## 0 / baa H5I_GROUP
## 1 / foo H5I_GROUP
## 2 /foo A H5I_DATASET INTEGER 5 x 2
## 3 /foo B H5I_DATASET FLOAT 5 x 2 x 2
## 4 /foo H H5I_DATASET INTEGER 5 x 8
## 5 /foo S H5I_DATASET INTEGER 5 x 8
## 6 /foo foobaa H5I_GROUPWe can see that the df entry has now disappeared from the listing. In most cases, if you have a heirachy within the file, h5delete() will remove children of the deleted entry too. In this example we remove foo and the datasets below it are deleted too. Notice too that the size of the file as decreased.
h5delete(file = "myhdf5file.h5", name = "foo")
h5ls("myhdf5file.h5", recursive=2)## group name otype dclass dim
## 0 / baa H5I_GROUPfile.size("myhdf5file.h5")## [1] 20833N.B. h5delete() does not explicitly traverse the tree to remove child nodes. It only removes the named entry, and HDF5 will then remove child nodes if they are now orphaned. Hence it won’t delete child nodes if you have a more complex structure where a child node has multiple parents and only one of these is removed.
#64-bit integers R does not support a native datatype for 64-bit integers. All integers in R are 32-bit integers. When reading 64-bit integers from a HDF5-file, you may run into troubles. rhdf5 is able to deal with 64-bit integers, but you still should pay attention.
As an example, we create an HDF5 file that contains 64-bit integers.
x = h5createFile("newfile3.h5")
D = array(1L:30L,dim=c(3,5,2))
d = h5createDataset(file="newfile3.h5", dataset="D64", dims=c(3,5,2),H5type="H5T_NATIVE_INT64")
h5write(D,file="newfile3.h5",name="D64")There are three different ways of reading 64-bit integers in
R. H5Dread() and h5read() have the argument
bit64conversion the specify the conversion method.
By setting {}, a coercing to 32-bit integers is enforced, with the risc of data loss, but with the insurance that numbers are represented as native integers.
D64a = h5read(file="newfile3.h5",name="D64",bit64conversion="int")
D64a## , , 1
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 4.940656e-324 1.976263e-323 3.458460e-323 4.940656e-323 6.422853e-323
## [2,] 9.881313e-324 2.470328e-323 3.952525e-323 5.434722e-323 6.916919e-323
## [3,] 1.482197e-323 2.964394e-323 4.446591e-323 5.928788e-323 7.410985e-323
##
## , , 2
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 7.905050e-323 9.387247e-323 1.086944e-322 1.235164e-322 1.383384e-322
## [2,] 8.399116e-323 9.881313e-323 1.136351e-322 1.284571e-322 1.432790e-322
## [3,] 8.893182e-323 1.037538e-322 1.185758e-322 1.333977e-322 1.482197e-322
##
## attr(,"class")
## [1] "integer64"storage.mode(D64a)## [1] "double"bit64conversion='double' coerces the 64-bit integers to floating
point numbers. doubles can represent integers with up to 54-bits, but
they are not represented as integer values anymore. For larger numbers
there is still a data loss.
D64b = h5read(file="newfile3.h5",name="D64",bit64conversion="double")
D64b## , , 1
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 4 7 10 13
## [2,] 2 5 8 11 14
## [3,] 3 6 9 12 15
##
## , , 2
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 16 19 22 25 28
## [2,] 17 20 23 26 29
## [3,] 18 21 24 27 30storage.mode(D64b)## [1] "double"bit64conversion='bit64' is recommended way of coercing. It
represents the 64-bit integers as objects of class integer64 as
defined in the package bit64. Make sure that you have installed
bit64.
The datatype integer64* is not part of base R, but defined
in an external package. This can produce unexpected behaviour when
working with the data.* When choosing this option the package bit64
will be loaded.
D64c = h5read(file="newfile3.h5",name="D64",bit64conversion="bit64")
D64c## integer64
## , , 1
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 4 7 10 13
## [2,] 2 5 8 11 14
## [3,] 3 6 9 12 15
##
## , , 2
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 16 19 22 25 28
## [2,] 17 20 23 26 29
## [3,] 18 21 24 27 30class(D64c)## [1] "integer64"#Low level HDF5 functions ##Creating an HDF5 file and a group hierarchy
Create a file.
library(rhdf5)
h5file = H5Fcreate("newfile.h5")
h5file## HDF5 FILE
## name /
## filename
##
## [1] name otype dclass dim
## <0 rows> (or 0-length row.names)and a group hierarchy
h5group1 <- H5Gcreate(h5file, "foo")
h5group2 <- H5Gcreate(h5file, "baa")
h5group3 <- H5Gcreate(h5group1, "foobaa")
h5group3## HDF5 GROUP
## name /foo/foobaa
## filename
##
## [1] name otype dclass dim
## <0 rows> (or 0-length row.names)##Writing data to an HDF5 file
Create 4 different simple and scalar data spaces. The data space sets the dimensions for the datasets.
d = c(5,7)
h5space1 = H5Screate_simple(d,d)
h5space2 = H5Screate_simple(d,NULL)
h5space3 = H5Scopy(h5space1)
h5space4 = H5Screate("H5S_SCALAR")
h5space1## HDF5 DATASPACE
## rank 2
## size 5 x 7
## maxsize 5 x 7H5Sis_simple(h5space1)## [1] TRUECreate two datasets, one with integer and one with floating point numbers.
h5dataset1 = H5Dcreate( h5file, "dataset1", "H5T_IEEE_F32LE", h5space1 )
h5dataset2 = H5Dcreate( h5group2, "dataset2", "H5T_STD_I32LE", h5space1 )
h5dataset1## HDF5 DATASET
## name /dataset1
## filename
## type H5T_IEEE_F32LE
## rank 2
## size 5 x 7
## maxsize 5 x 7Now lets write data to the datasets.
A = seq(0.1,3.5,length.out=5*7)
H5Dwrite(h5dataset1, A)
B = 1:35
H5Dwrite(h5dataset2, B)To release resources and to ensure that the data is written on disk, we have to close datasets, dataspaces, and the file. There are different functions to close datasets, dataspaces, groups, and files.
H5Dclose(h5dataset1)
H5Dclose(h5dataset2)
H5Sclose(h5space1)
H5Sclose(h5space2)
H5Sclose(h5space3)
H5Sclose(h5space4)
H5Gclose(h5group1)
H5Gclose(h5group2)
H5Gclose(h5group3)
H5Fclose(h5file)#Session Info
sessionInfo()## R version 3.5.0 (2018-04-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.7-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.7-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] rhdf5_2.24.0 BiocStyle_2.8.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.16 bookdown_0.7 digest_0.6.15 rprojroot_1.3-2
## [5] backports_1.1.2 magrittr_1.5 evaluate_0.10.1 stringi_1.1.7
## [9] rmarkdown_1.9 Rhdf5lib_1.2.0 tools_3.5.0 bit64_0.9-7
## [13] stringr_1.3.0 bit_1.1-12 xfun_0.1 yaml_2.1.18
## [17] compiler_3.5.0 htmltools_0.3.6 knitr_1.20\end{document}