# Data representation

All designs handled by the OApackage are integer valued. The designs (whether these are orthogonal arrays, optimal designs or conferences designs) are stored in an array_link() object. The definitions of orthogonal arrays, optimal designs and conference designs are included in the section Definitions of arrays and designs.

## Data structures

The package contains several data structures. Here, we describe the main structures and their use.

array_link()

The structure containing an orthogonal array is called the array_link() structure. Lists of arrays are stored in the arraylist_t() object, which is implemented as a std::deque container.

arrayfile_t()

This object allows for reading and writing of arrays to disk.

arraydata_t()

The structure describing a certain class of orthogonal arrays or optimal designs.

conference_t()

The structure describing a certain class of conference designs.

array_transformation_t()

The structure describing a transformation of an orthogonal array, which includes row and column permutations, as well as permutations to the symbols in one or more columns.

conference_transformation_t()

The structure describing a transformation of conference design or double conference design, which includes row and column permutations, as well as sign switches to the elements in one or more rows and columns.

## Representing arrays

The structure containing an orthogonal array is called the array_link structure. It consists of a specified number of rows and columns, the data (integer valued) and an index. In the Python interface, the array_link() object can be indexed just as normal arrays.

It is also possible to convert to a Numpy array. The array_link object implements the Python array interface, so most operations from packages such as Numpy work on the array_link() object.

Array representation and indexing in Python

>>> import oapackage; import numpy as np
>>> al=oapackage.exampleArray(0)
>>> al.showarray()
array:
0   0
0   0
0   1
0   1
1   0
1   0
1   1
1   1

>>> al[2,1]
1
>>> X=np.array(al)
>>> X
array([[0, 0],
[0, 0],
[0, 1],
[0, 1],
[1, 0],
[1, 0],
[1, 1],
[1, 1]], dtype=int16)


The C++ class is array_link.

## Classes of arrays

The arraydata_t object represents data about a class of orthogonal arrays, e.g. the class $${\operatorname{OA}(N; t; s^k)}$$; see Definitions of arrays and designs. The conference_t object represents data about a class of conference designs.

## Array transformations

Transformations of (orthogonal) arrays consist of row, column and level permutations. A transformation is represented by the array_transformation_t object.

For a given transformation, the column permutations are applied first, then the level permutations and finally the row permutations. The level and column permutations are not commutative.

The conference design transformations also allow for row sign switches and are described by the class conference_transformation_t.

Reading and writing arrays to disk can be done with the arrayfile_t class.

Write an array or a list of arrays to disk

>>> import oapackage
>>> list_of_arrays = [oapackage.exampleArray(24), oapackage.exampleArray(25)]
>>> _ = oapackage.writearrayfile('test.oa', list_of_arrays)
>>> oapackage.oainfo('test.oa')
file test.oa: 64 rows, 16 columns, 2 arrays, mode text, nbits 0
>>> al=oapackage.exampleArray()
>>> af=oapackage.arrayfile_t('test.oa', al.n_rows, al.n_columns)
>>> af.append_array(al)
>>> print(af)
file test.oa: 8 rows, 2 columns, 1 arrays, mode text, nbits 8
>>> af.closefile()


The arrays can be written in text or binary format. For more details on the file format, see the section File formats.

The Python interface is oalib.arrayfile_t() and the C++ interface is arrayfile_t.

## File formats

The Orthogonal Array package stores arrays in a custom file format. There is a text format which is easily readable by humans and a binary format which is faster to process and memory efficient.

### Plain text array files

Arrays are stored in plain text files with extension .oa. The first line contains the number of columns, the number of rows and the number of arrays (or -1 if the number of arrays is not specified). Then, for each array, a single line with the index of the array, followed by $${N}$$ lines containing the array.

A typical example of a text file is the following:

5 8 1
1
0 0 0 0 0
0 0 0 1 1
0 1 1 0 0
0 1 1 1 1
1 0 1 0 1
1 0 1 1 0
1 1 0 0 1
1 1 0 1 0
-1


This file contains exactly 1 array with 8 rows and 5 columns.

### Binary array files

Every binary file starts with a header, which has the following format:

[INT32] 65 (magic identifier)
[INT32] b: Format: number of bits per number. Currently supported are 1 and 8
[INT32] N: number of rows
[INT32] k: kumber of columns
[INT32] Number of arrays (can be -1 if unknown)
[INT32] Binary format number: 1001: normal, 1002: binary diff, 1003: binary diff zero
[INT32] Reserved integer
[INT32] Reserved integer


The format of the remainder of the binary file depends on the binary format specified. For the normal binary format, the format is as follows. For each array, the number is specified in the header:

[INT32] Index
[Nxk elements] The elements contain b bits


If the number of bits per number is 1 (e.g. a 2-level array), then the data is padded with zeros to a multiple of 64 bits. The data of the array is stored in column-major order. The binary file format allows for random access reading and writing. The binary diff and binary diff zero formats are special formats.

A binary array file can be compressed using gzip. Most tools in the Orthogonal Array package can read these compressed files transparently. Writing to compressed array files is not supported at the moment.

### Data files

The analysis tool (oaanalyse) writes data to disk in binary format. The format consists of a binary header:

[FLOAT64] Magic number 30397995;
[FLOAT64] Magic number 12224883;
[FLOAT64] nc: Number of rows
[FLOAT64] nr: Number of columns


After the header there follow nc*nr [FLOAT64] values.

### MD5 sums

To check data integrity on disk, the packages includes functions to generate MD5 sums of designs.

Calculate md5 sum of a design

>>> import oapackage; al=oapackage.exampleArray(0)
>>> al.md5()
'6454c492239a8e01e3c01a864583abf2'


The C++ functions are array_link::md5() and md5().

## Command line interface

Several command line tools are included in the Orthogonal Array package. For each tool, help can be obtained from the command line by using the switch -h. The tools include the following:

oainfo

This program reads Orthogonal Array package data files and reports the contents of the files. For example:

$oainfo result-8.2-2-2-2.oa Orthogonal Array package 1.8.7 oainfo: reading 1 file(s) file result-8.2-2-2.oa: 8 rows, 3 columns, 2 arrays, mode text, nbits 0$

oacat

Shows the contents of a file with orthogonal arrays for a data file.

oacheck

Checks or reduces an array to canonical form.

oaextendsingle

Extends a set of arrays in LMC form with one or more columns.

oacat

Shows the contents of an array file or data file.

Usage: oacat [OPTIONS] [FILES]

oajoin

Reads one or more files from disk and join all the array files into a single list.

oasplit

Takes a single array file as input and splits the arrays into a specified number of output files.

oapareto

Calculates the set of Pareto optimal arrays in a file with arrays.

oaanalyse

Calculates various statistical properties of arrays in a file. The properties are described in section Properties of designs.