RA is a simple file format for storing n-dimensional arrays. RA stands for raw array and should be pronounced arr-ay, although it is not a coincidence that the mispronunciation rah (as in "raw" in some dialects) also makes sense.
RA was designed to be portable, fast, and storage efficient. For scientific applications in particular, it can allow the simple storage of large arrays without a separate header file to store the dimensions and type metadata.
I believe the world doesn't need another hierarchical data container. We already have one of those---it's called a filesystem. What is needed is a simple one-to-one mapping of data structures to disk files that preserves metadata and is fast and simple to read and write.
In addition to int, uint, and float of arbitrary sizes, RA also supports
(1) complex floats: which other common formats, such as HDF5, don't have.
(2) composite types: RA handles reading and writing these, but the encoding and decoding of those is left to the user, since only they can know the structure of their struct. Decoding can be as simple as a typecast, however, for types of fixed size.
As an aside, the RA format is technically recursive (or fractal?!). You could store an array of RA files in a RA file if you want by defining the file as a composite type.
The file format is a simple concatenation of a header array and a data array. The header is made up of at least seven 64-bit unsigned integers. The array data is whatever you want it to be. Optionally text or binary metadata can be appended to the end of the file with no harmful effects, but this data is not saved or written back out by the library. It is up to you to keep track of it.
| offset (bytes) | object | type | meaning |
|---|---|---|---|
| HEADER | |||
| 0 | magic | UInt64 | magic number |
| 8 | flags | UInt64 | endianness, future options |
| 16 | eltype | UInt64 | element type code |
| 24 | elbyte | UInt64 | element size in bytes |
| 32 | size | UInt64 | data segment length in bytes |
| 40 | ndims | UInt64 | number of array dimensions |
| 48 | dims | Vector{UInt64} | array dimensions |
| 48 + 8 x ndims | data | Vector{UInt8} | ARRAY DATA |
| 48 + 8 x ndims + size | - | - | VOLATILE METADATA |
| code | type |
|---|---|
| 0 | user-defined |
| 1 | signed integer |
| 2 | unsigned integer |
| 3 | floating point (IEEE-754 standard) |
| 4 | complex float (pairs of IEEE floats) |
The width of these types is defined separately in the elbyte field. For example,
- a 32-bit unsigned integer would be
eltype = 2,elbyte = 4; - a single-precision complex float (pairs of 32-bit floats) would be
eltype = 4,elbyte = 8; - a string would be
eltype = 2,elbyte = 1, andsizewould contain the length of the string.
The user-defined structure
struct foo {
char info[12];
uint32_t index;
double v[8];
}
contains a 12-byte string, a 4-byte int, and 8 8-byte floats, so the total size is 80 bytes. It would be coded as eltype = 0, elbyte = 80.
The data is written and read as the binary representation of the hardware you are on. Right now little endian is assumed, but big endian support can be added if there is interest.
The RA format is column major, so the first dimension will be the fastest varying one in memory. This decision was made because the majority of scientific languages are traditionally column major, and although C is technically row major it is actually agnostic in applications where multi-dimensional arrays are accessed through computed linear indices (e.g. CUDA). Of the supplied examples, all are column major except Python. In the case of Python, instead of reading the array into Python and reordering to non-optimal stride, we simply transpose the dimensions before writing and after reading. This means the array looks transposed in Python, but the same dimensions have the same strides in all languages. In other words, the last dimension of the array in Python will be the first one in Julia and Matlab.
To get a better handle on the format of an RA file, let's look inside one. If you are on a Unix system or have Cygwin installed on Windows, you can examine the contents of an RA file using command line tools. For this section, we will use the test.ra file provided in the julia/ subdirectory.
First, let's pretend you don't know the dimensionality of the array. Then
> od -t uL -N 48 test.ra
0000000 8746397786917265778 0
0000020 4 8
0000040 96 2
0000060
shows the dimension (2) as the second number on the third line. The command is extracting the first 48 bytes and formatting them as UInt64s. The ridiculous number listed first is the magic number indicating that this is an RA file. A slightly different command illuminates that:
> od -a -N 16 test.ra
0000000 r a w a r r a y nul nul nul nul nul nul nul nul
0000020
Armed with the knowledge that the array is 2D, we know that the header is 48 + 2*8 = 64 bytes long. The command to skip the header and view only the data would be:
> od -j 64 -f test.ra
0000100 0.000000e+00 -inf 1.000000e+00 -1.000000e+00
0000120 2.000000e+00 -5.000000e-01 3.000000e+00 -3.333333e-01
0000140 4.000000e+00 -2.500000e-01 5.000000e+00 -2.000000e-01
0000160 6.000000e+00 -1.666667e-01 7.000000e+00 -1.428571e-01
0000200 8.000000e+00 -1.250000e-01 9.000000e+00 -1.111111e-01
0000220 1.000000e+01 -1.000000e-01 1.100000e+01 -9.090909e-02
0000240
Here we are using -j to skip the first 64 bytes and -f to format the byte data as single-precision floats. Note od doesn't understand complex numbers, but the complex data is stored as real and imaginary float pairs that are contiguous on disk. This means that each line of the output is showing two complex numbers with columns 1 and 3 the real parts and columns 2 and 4 the imaginary parts. Notice that it correctly renders the negative infinity.
You can clone the git repository on your local machine if you have git installed using the command
git clone https://github.com/davidssmith/ra.git
If you don't have git or don't want it, you can also download individual files through the website interface or the whole thing as a zip archive.
Only the C version needs to be compiled. Probably the easiest way to use that version is to drop the source files ra.c and ra.h into your project source directory, include the header file ra.h, and compile it along with your own project. A makefile is also provided with which you can compile the demo and run it. You can also modify the Makefile to compile a shared library, if you so wish.
To run the demo, clone or download the git archive, navigate to the ra/c directory, and run make. If the make fails, try to fix it for your particular compiler. I've provided a fairly generic one, though, so it should work on most systems. If the fix is not obvious, file an issue on the git repository, and I'll try to help.
If the make succeeds, you can run the demo with ./ra. If the tests pass, you'll see a message at the end to that effect. The output from the demo should look like this:
test data is 12 floats
0.000000+-infim
1.000000+-1.000000im
2.000000+-0.500000im
3.000000+-0.333333im
4.000000+-0.250000im
5.000000+-0.200000im
6.000000+-0.166667im
7.000000+-0.142857im
8.000000+-0.125000im
9.000000+-0.111111im
TESTS PASSED!
---
name: test.ra
endian: little
type: complex64
size: 96
dimension: 2
shape:
- 3
- 4
...
Notice that the output at the end is valid YAML markup. This was intentional. The provided ra_query() function reads the RA file header and dumps the information as YAML for easy parsing.
To use the Julia version, add the following lines to your Julia code:
include("ra.jl")
using RA
Now you can call raread and rawrite for Julia objects of type Array{T,N}.
A test file called test.jl has been included. You can test the code on your machine at the command line by running julia test.jl. If the tests pass, you'll get a message saying so. The test output should look like this:
reading test.ra...
writing test2.ra...
reading test2.ra...
elapsed time: 0.32623855 seconds
querying ...
---
name: test.ra
endian: little
type: Complex{Float32}
size: 96
dimension: 2
shape:
- 3
- 4
...
---
name: test2.ra
endian: little
type: Complex{Float32}
size: 96
dimension: 2
shape:
- 3
- 4
...
PASSED
Notice the Julia version also contains a raquery() function that produces a YAML dump of the file header.
To run the Python version, put the ra.py file in your source directory and use import ra in your script. The demo file test.py should an example use case and should look like this when run:
> python test.py
[[ 0. -infj 1.-1.j 2.-0.5j 3.-0.33333334j]
[ 4.-0.25j 5.-0.2j 6.-0.16666667j 7.-0.14285715j]
[ 8.-0.125j 9.-0.11111111j 10.-0.1j 11.-0.09090909j]]
[[ 0. -infj 1.-1.j 2.-0.5j 3.-0.33333334j]
[ 4.-0.25j 5.-0.2j 6.-0.16666667j 7.-0.14285715j]
[ 8.-0.125j 9.-0.11111111j 10.-0.1j 11.-0.09090909j]]
---
name: ../data/test.ra
endian: little
type: complex64
size: 96
dimension: 2
shape:
- 3
- 4
...
---
name: test2.ra
endian: little
type: complex64
size: 96
dimension: 2
shape:
- 3
- 4
...
TEST PASSED
The only requirements for the Python version are that you have numpy installed. This has been tested on Python 2.7+ but not Python 3.
Drop the files raread.m and rawrite.m into your preferred directory in your Matlab path. Now you can call array = raread(filename) to return an array and rawrite(array, filename) to save an array to a file.
A data checksum or time stamp was deliberately not included in the format because it is impossible to checksum a file with its checksum inside it.** Existing methods (e.g. tar) often zero out the checksum field and then checksum the rest of the file, but this requires special software that understands the format, so standard command line checksum tools won't work. Checksum verification is best left to external means, even if it requires a separate file.
Time stamping is also not necessary, because file systems already provide that. Adding a time stamp that changes upon rewrite or access also foils checksum attemps. HDF5 files are very difficult to checksum for this reason. It is our belief that the checksum should depend upon data properties only, not any chronology. Two files are identical if they contain identical data, no matter when they were created or accessed last.
To checksum an RA file, simple run your local checksum command. For example, in the main RA directory on a Mac, after you have run the C demo, you can issue:
> md5 julia/test.ra
MD5 (julia/test.ra) = 1dd9f98a0d57ec3c4d8ad50343bd20cd
> md5 c/test.ra
MD5 (c/test.ra) = 1dd9f98a0d57ec3c4d8ad50343bd20cd
On Linux, replace md5 with md5sum. These two commands show that the provided test.ra is identical to the one produced by the C demo.
** Not technically impossible, but extremely difficult computationally.
For help, file an issue on the bug tracker or email one of the authors. Third party help is welcome and can be contributed through pull requests.
David S. Smith [email protected]
This code comes with no warranty. Use at your own risk. If it breaks, let us know, and we'll try to help you fix it.