README.md
In PaulPyl/h5array: Implementation of HDF5-backed matrix and array classes
title: "h5array Introduction"
author: "Paul Theodor Pyl"
date: "22 Jan 2016"
output: html_document
This vignette introduces the h5array R/Bioconductor package, which provides array- and matrix-like objects that store their data in HDF5 files on disk while being useable with the same interface as normal array and matrix objects.
Installation
Installation from github is easiest using devtools.
install.packages("devtools") # If you do not have it yet
require(devtools)
install_github("PaulPyl/h5array")
Basic Usage
The h5arrayCreate and h5matrixCreate functions are used to create h5array and h5matrix objects, these functions are only called once to create the dataset in the HDF5 file on disk.
The following code block illustrates basic usage of the package:
filename = tempfile()
x <- h5matrixCreate(
filename,
location = "/NumericMatrix",
dim = c(1e3, 1e2), storage.mode = "double",
chunk = c(1, 1e2))
## Can not create group. Object with name '/' already exists.
x[1:23,] <- matrix(rnorm(23*1e2), nrow = 23)
We can user the rhdf5::h5ls function to inspect the HDF5-file that was created and see which datasets have been created within it:
h5ls(filename)
## group name otype dclass dim
## 0 / NumericMatrix H5I_DATASET FLOAT 1000 x 100
The h5matrixCreate and h5arrayCreate function hand over any additional arguments to the internal h5createDataset function from the rhdf5 package, therefore things like storage mode and chunking can be configured as well. If you are unfamiliar with storage modes and chunking it might help to have a look at the rhdf5 vignette as well.
Let's create a string array in the same file:
y <- h5arrayCreate(
filename, #same file
location = "/CharArray", #New location
dim = c(3, 4, 5), #since we want 3d data we need to use the h5array class
storage.mode = "character",
size = 256 #to store strings in HDF5 files we need to specify the maximum length
)
y[,,1] <- matrix(sample(letters, size = 3*4), nrow = 3)
Investigating the file again we can see the new dataset has been created.
h5ls(filename)
## group name otype dclass dim
## 0 / CharArray H5I_DATASET STRING 3 x 4 x 5
## 1 / NumericMatrix H5I_DATASET FLOAT 1000 x 100
We can also print the objects to get a descriptive representation of them:
print(x)
## HDF5-backed Matrix
## Type: double
## Dimensions: 1000, 100
## File: /var/folders/sh/nzrnpqxj3ndg9fh2qcvzzpfn62qz2p/T//RtmplwXJvD/file319660d4c472
## Head of Data:
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -0.7192092 1.0915587 0.3289505 -0.18231847 -1.34143909 -0.1977551
## [2,] -0.4015518 0.2624153 1.2022207 -0.90884506 -0.27880202 -0.1052586
## [3,] -1.1363456 -0.6027516 0.6354128 0.03463229 0.08158047 1.4951859
## [4,] -0.4090317 0.5040509 0.9099521 -0.46805831 -0.18820698 -0.3749803
## [5,] 1.3524763 -0.3829978 -2.0138337 0.50631464 1.80671440 0.9537483
## [6,] 0.5217534 1.9169934 0.5884776 0.52970636 0.64081414 -0.8772302
print(y)
## HDF5-backed Array
## Type: character
## Dimensions: 3, 4, 5
## File: /var/folders/sh/nzrnpqxj3ndg9fh2qcvzzpfn62qz2p/T//RtmplwXJvD/file319660d4c472
## Head of Data:
## , , 1
##
## [,1] [,2] [,3] [,4]
## [1,] "u" "e" "m" "d"
## [2,] "h" "y" "i" "a"
## [3,] "z" "b" "r" "q"
Adding dimension names is straightforward:
dimnames(y) <- list(
letters[1:3],
letters[4:8],
letters[9:14]
)
print(y)
## HDF5-backed Array
## Type: character
## Dimensions: 3, 4, 5
## File: /var/folders/sh/nzrnpqxj3ndg9fh2qcvzzpfn62qz2p/T//RtmplwXJvD/file319660d4c472
## Head of Data:
## , , i
##
## d e f g
## a "u" "e" "m" "d"
## b "h" "y" "i" "a"
## c "z" "b" "r" "q"
Dimension names are not stored in the HDF5 file by default but can be written there:
writeDimnamesToFile(y)
h5ls(filename)
## group name otype dclass dim
## 0 / CharArray H5I_DATASET STRING 3 x 4 x 5
## 1 / CharArray.dim1 H5I_DATASET STRING 3
## 2 / CharArray.dim2 H5I_DATASET STRING 5
## 3 / CharArray.dim3 H5I_DATASET STRING 6
## 4 / NumericMatrix H5I_DATASET FLOAT 1000 x 100
Note how new datasets to store the dimension names have been created.
Once the underlying datasets have been created the actual h5array or h5matrix objects can be removed (e.g. when closing the R session) and the data can be accessed usign the h5array and h5matrix functions:
rm(x)
rm(y)
a <- h5matrix(filename, "/NumericMatrix")
b <- h5array(filename, "/CharArray")
print(a)
## HDF5-backed Matrix
## Type: double
## Dimensions: 1000, 100
## File: /var/folders/sh/nzrnpqxj3ndg9fh2qcvzzpfn62qz2p/T//RtmplwXJvD/file319660d4c472
## Head of Data:
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -0.7192092 1.0915587 0.3289505 -0.18231847 -1.34143909 -0.1977551
## [2,] -0.4015518 0.2624153 1.2022207 -0.90884506 -0.27880202 -0.1052586
## [3,] -1.1363456 -0.6027516 0.6354128 0.03463229 0.08158047 1.4951859
## [4,] -0.4090317 0.5040509 0.9099521 -0.46805831 -0.18820698 -0.3749803
## [5,] 1.3524763 -0.3829978 -2.0138337 0.50631464 1.80671440 0.9537483
## [6,] 0.5217534 1.9169934 0.5884776 0.52970636 0.64081414 -0.8772302
print(b)
## HDF5-backed Array
## Type: character
## Dimensions: 3, 4, 5
## File: /var/folders/sh/nzrnpqxj3ndg9fh2qcvzzpfn62qz2p/T//RtmplwXJvD/file319660d4c472
## Head of Data:
## , , 1
##
## [,1] [,2] [,3] [,4]
## [1,] "u" "e" "m" "d"
## [2,] "h" "y" "i" "a"
## [3,] "z" "b" "r" "q"
We can load the dimnames from the file as well:
b <- loadDimnamesFromFile(b)
print(b)
## HDF5-backed Array
## Type: character
## Dimensions: 3, 4, 5
## File: /var/folders/sh/nzrnpqxj3ndg9fh2qcvzzpfn62qz2p/T//RtmplwXJvD/file319660d4c472
## Head of Data:
## , , i
##
## d e f g
## a "u" "e" "m" "d"
## b "h" "y" "i" "a"
## c "z" "b" "r" "q"
We can apply functions to those objects without having to change code that was written for normal arrays or matrices:
for( i in seq(1, nrow(a))){ #populating the numeric matrix in chunks
a[i,] <- rnorm(1e3, mean = i %% 80, sd = sqrt(i))
}
print(a)
## HDF5-backed Matrix
## Type: double
## Dimensions: 1000, 100
## File: /var/folders/sh/nzrnpqxj3ndg9fh2qcvzzpfn62qz2p/T//RtmplwXJvD/file319660d4c472
## Head of Data:
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.8063491 1.5919348 1.548363 -0.09426219 1.358331 0.7000718
## [2,] 2.5298370 -0.4538526 2.405303 1.02316276 3.604578 3.9282796
## [3,] 5.0266182 4.5732650 2.223687 6.29159209 4.699591 4.1775776
## [4,] 6.2497248 3.3823500 4.557919 3.28275593 2.451683 3.1186645
## [5,] 3.5203754 8.1771015 2.819270 2.75920712 5.709695 5.6585070
## [6,] 4.6848418 4.6145307 7.981120 9.83783053 7.809542 8.6998028
plot(unlist( apply(a, 1, mean) ))
c <- h5arrayCreate(
filename, #same file
location = "/IntArray", #New location
dim = c(3, 4, 5), #since we want 3d data we need to use the h5array class
storage.mode = "integer"
)
c[1,,] <- matrix(rpois(4*5, 23), nrow = 4)
c[2,,] <- matrix(rpois(4*5, 42), nrow = 4)
c[3,,] <- matrix(rpois(4*5, 87), nrow = 4)
PaulPyl/h5array documentation built on May 8, 2019, 12:57 a.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
title: "h5array Introduction" author: "Paul Theodor Pyl" date: "22 Jan 2016" output: html_document
This vignette introduces the h5array R/Bioconductor package, which provides array- and matrix-like objects that store their data in HDF5 files on disk while being useable with the same interface as normal array and matrix objects.
Installation
Installation from github is easiest using devtools.
install.packages("devtools") # If you do not have it yet
require(devtools)
install_github("PaulPyl/h5array")
Basic Usage
The h5arrayCreate and h5matrixCreate functions are used to create h5array and h5matrix objects, these functions are only called once to create the dataset in the HDF5 file on disk.
The following code block illustrates basic usage of the package:
filename = tempfile()
x <- h5matrixCreate(
filename,
location = "/NumericMatrix",
dim = c(1e3, 1e2), storage.mode = "double",
chunk = c(1, 1e2))
## Can not create group. Object with name '/' already exists.
x[1:23,] <- matrix(rnorm(23*1e2), nrow = 23)
We can user the rhdf5::h5ls function to inspect the HDF5-file that was created and see which datasets have been created within it:
h5ls(filename)
## group name otype dclass dim
## 0 / NumericMatrix H5I_DATASET FLOAT 1000 x 100
The h5matrixCreate and h5arrayCreate function hand over any additional arguments to the internal h5createDataset function from the rhdf5 package, therefore things like storage mode and chunking can be configured as well. If you are unfamiliar with storage modes and chunking it might help to have a look at the rhdf5 vignette as well.
Let's create a string array in the same file:
y <- h5arrayCreate(
filename, #same file
location = "/CharArray", #New location
dim = c(3, 4, 5), #since we want 3d data we need to use the h5array class
storage.mode = "character",
size = 256 #to store strings in HDF5 files we need to specify the maximum length
)
y[,,1] <- matrix(sample(letters, size = 3*4), nrow = 3)
Investigating the file again we can see the new dataset has been created.
h5ls(filename)
## group name otype dclass dim
## 0 / CharArray H5I_DATASET STRING 3 x 4 x 5
## 1 / NumericMatrix H5I_DATASET FLOAT 1000 x 100
We can also print the objects to get a descriptive representation of them:
print(x)
## HDF5-backed Matrix
## Type: double
## Dimensions: 1000, 100
## File: /var/folders/sh/nzrnpqxj3ndg9fh2qcvzzpfn62qz2p/T//RtmplwXJvD/file319660d4c472
## Head of Data:
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -0.7192092 1.0915587 0.3289505 -0.18231847 -1.34143909 -0.1977551
## [2,] -0.4015518 0.2624153 1.2022207 -0.90884506 -0.27880202 -0.1052586
## [3,] -1.1363456 -0.6027516 0.6354128 0.03463229 0.08158047 1.4951859
## [4,] -0.4090317 0.5040509 0.9099521 -0.46805831 -0.18820698 -0.3749803
## [5,] 1.3524763 -0.3829978 -2.0138337 0.50631464 1.80671440 0.9537483
## [6,] 0.5217534 1.9169934 0.5884776 0.52970636 0.64081414 -0.8772302
print(y)
## HDF5-backed Array
## Type: character
## Dimensions: 3, 4, 5
## File: /var/folders/sh/nzrnpqxj3ndg9fh2qcvzzpfn62qz2p/T//RtmplwXJvD/file319660d4c472
## Head of Data:
## , , 1
##
## [,1] [,2] [,3] [,4]
## [1,] "u" "e" "m" "d"
## [2,] "h" "y" "i" "a"
## [3,] "z" "b" "r" "q"
Adding dimension names is straightforward:
dimnames(y) <- list(
letters[1:3],
letters[4:8],
letters[9:14]
)
print(y)
## HDF5-backed Array
## Type: character
## Dimensions: 3, 4, 5
## File: /var/folders/sh/nzrnpqxj3ndg9fh2qcvzzpfn62qz2p/T//RtmplwXJvD/file319660d4c472
## Head of Data:
## , , i
##
## d e f g
## a "u" "e" "m" "d"
## b "h" "y" "i" "a"
## c "z" "b" "r" "q"
Dimension names are not stored in the HDF5 file by default but can be written there:
writeDimnamesToFile(y)
h5ls(filename)
## group name otype dclass dim
## 0 / CharArray H5I_DATASET STRING 3 x 4 x 5
## 1 / CharArray.dim1 H5I_DATASET STRING 3
## 2 / CharArray.dim2 H5I_DATASET STRING 5
## 3 / CharArray.dim3 H5I_DATASET STRING 6
## 4 / NumericMatrix H5I_DATASET FLOAT 1000 x 100
Note how new datasets to store the dimension names have been created.
Once the underlying datasets have been created the actual h5array or h5matrix objects can be removed (e.g. when closing the R session) and the data can be accessed usign the h5array and h5matrix functions:
rm(x)
rm(y)
a <- h5matrix(filename, "/NumericMatrix")
b <- h5array(filename, "/CharArray")
print(a)
## HDF5-backed Matrix
## Type: double
## Dimensions: 1000, 100
## File: /var/folders/sh/nzrnpqxj3ndg9fh2qcvzzpfn62qz2p/T//RtmplwXJvD/file319660d4c472
## Head of Data:
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -0.7192092 1.0915587 0.3289505 -0.18231847 -1.34143909 -0.1977551
## [2,] -0.4015518 0.2624153 1.2022207 -0.90884506 -0.27880202 -0.1052586
## [3,] -1.1363456 -0.6027516 0.6354128 0.03463229 0.08158047 1.4951859
## [4,] -0.4090317 0.5040509 0.9099521 -0.46805831 -0.18820698 -0.3749803
## [5,] 1.3524763 -0.3829978 -2.0138337 0.50631464 1.80671440 0.9537483
## [6,] 0.5217534 1.9169934 0.5884776 0.52970636 0.64081414 -0.8772302
print(b)
## HDF5-backed Array
## Type: character
## Dimensions: 3, 4, 5
## File: /var/folders/sh/nzrnpqxj3ndg9fh2qcvzzpfn62qz2p/T//RtmplwXJvD/file319660d4c472
## Head of Data:
## , , 1
##
## [,1] [,2] [,3] [,4]
## [1,] "u" "e" "m" "d"
## [2,] "h" "y" "i" "a"
## [3,] "z" "b" "r" "q"
We can load the dimnames from the file as well:
b <- loadDimnamesFromFile(b)
print(b)
## HDF5-backed Array
## Type: character
## Dimensions: 3, 4, 5
## File: /var/folders/sh/nzrnpqxj3ndg9fh2qcvzzpfn62qz2p/T//RtmplwXJvD/file319660d4c472
## Head of Data:
## , , i
##
## d e f g
## a "u" "e" "m" "d"
## b "h" "y" "i" "a"
## c "z" "b" "r" "q"
We can apply functions to those objects without having to change code that was written for normal arrays or matrices:
for( i in seq(1, nrow(a))){ #populating the numeric matrix in chunks
a[i,] <- rnorm(1e3, mean = i %% 80, sd = sqrt(i))
}
print(a)
## HDF5-backed Matrix
## Type: double
## Dimensions: 1000, 100
## File: /var/folders/sh/nzrnpqxj3ndg9fh2qcvzzpfn62qz2p/T//RtmplwXJvD/file319660d4c472
## Head of Data:
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.8063491 1.5919348 1.548363 -0.09426219 1.358331 0.7000718
## [2,] 2.5298370 -0.4538526 2.405303 1.02316276 3.604578 3.9282796
## [3,] 5.0266182 4.5732650 2.223687 6.29159209 4.699591 4.1775776
## [4,] 6.2497248 3.3823500 4.557919 3.28275593 2.451683 3.1186645
## [5,] 3.5203754 8.1771015 2.819270 2.75920712 5.709695 5.6585070
## [6,] 4.6848418 4.6145307 7.981120 9.83783053 7.809542 8.6998028
plot(unlist( apply(a, 1, mean) ))
c <- h5arrayCreate(
filename, #same file
location = "/IntArray", #New location
dim = c(3, 4, 5), #since we want 3d data we need to use the h5array class
storage.mode = "integer"
)
c[1,,] <- matrix(rpois(4*5, 23), nrow = 4)
c[2,,] <- matrix(rpois(4*5, 42), nrow = 4)
c[3,,] <- matrix(rpois(4*5, 87), nrow = 4)
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