READ ME File For 'Single-step Phase Identification and Phase Locking for Coherent Beam Combination using Deep Learning'

Dataset DOI: 10.5258/SOTON/D2974

Date that the file was created: March, 2024

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GENERAL INFORMATION
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ReadMe Author: Yunhui Xie, University of Southampton, 0000-0002-8841-7235

Date of data collection: October 19 2023 to February 5 2024

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SHARING/ACCESS INFORMATION
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Licenses/restrictions placed on the data, or limitations of reuse: CC-BY

This dataset supports the publication: Single-step Phase Identification and Phase Locking for Coherent Beam Combination using Deep Learning
AUTHORS: Yunhui Xie, Fedor Chernikov, Ben Mills, Yuchen Liu, Matthew Praeger, James A. Grant-Jacob, and Michalis N. Zervas
TITLE: Single-step Phase Identification and Phase Locking for Coherent Beam Combination using Deep Learning
JOURNAL: Scientific Reports

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DATA & FILE OVERVIEW
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The data is collated in 2 main folders. One for Figurative Data and one for Numerical Data. The schematic representation of the data folder structure is depicted as follows:

.
└── root/
    └── dataset/
        ├── Figurative Data/
        │   ├── Figure 1.jpg
        │   ├── Figure 2.png
        │   ├── Figure 3.png
        │   ├── Figure 4.png
        │   ├── Figure 5.png
        │   ├── Figure 6.png
        │   ├── Figure Appendix 1.png
        │   └── Figure Appendix 2.png
        ├── Numerical Data/
        │   ├── Fig 3 a/
        │   │   ├── 1698424793.1025863.npz
        │   │   ├── 1698424803.5149705.npz
        │   │   └── ...
        │   └── Fig 5 c/
        │       └── data.npy
        └── README.txt

1. Figurative Data

The figure files used in the publication can be found in the directory named 'root/dataset/Figurative Data/'. The figures referenced in the publication as Figure 1. to Figure 6. correspond to the following filenames: 'Figure 1.jpg', 'Figure 2.png', 'Figure 3.png', 'Figure 4.png', 'Figure 5.png', and 'Figure 6.png'. The graphics denoted as Figure Appendix 1 and Figure Appendix 2, cited within the supplementary materials of the scholarly publication, are accessible under the file name 'Figure Appendix 1.png' and 'Figure Appendix 2.png', respectively.

2. Numerical Data

	a). The numerical data for Fig 3 a) can be found in the directory named 'root/dataset/Numerical Data/Fig 3 a/', which contains 1000 npz files. Each npz file is named after the Unix time when the corresponding measurement was made. These npz files can typically be opened using the numpy library in Python. Each npz file contains three ndarray objects:

		· ['arr_0']: This array, with the shape (6,), represents a set of six randomly generated target phase values.
		· ['arr_1']: This array, with the shape (1, 6), denotes the model predictions made by the neural network trained with the camera A dataset.
		· ['arr_2']: This array, with the shape (1, 6), represents the model predictions made by the neural network trained with the camera B dataset.
	
	b). The numerical data for Fig 5 c) can be found in the directory named "root/dataset/Numerical Data/Fig 5 c/", which contains one npy file. This file can be opened using the numpy library in Python. It contains a numerical array of shape (40, 50), where:

		· The first index represents 40 different sigmas controlling the randomness of the phase fluctuation.
		· The second index represents 50 timesteps for each sigma value.


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METHODOLOGICAL INFORMATION
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Readers are directed to the publication 'Single-step Phase Identification and Phase Locking for Coherent Beam Combination using Deep Learning' for comprehensive methodological details pertaining to the generation and processing of the data presented within this dataset.

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DATA-SPECIFIC INFORMATION 
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1. data presented in folder 'root/dataset/Numerical Data/Fig 3 a/'

Number of variables: 18

Number of cases/rows: 3 rows, each contains 6 numbers

Variable list, defining any abbreviations, units of measure, codes or symbols used:

.
└── .../
    └── Fig 3 a/
        ├── 1698424793.1025863.npz/
        │   ├── ["arr_0"]/
        │   │   └── np.array([-2.36200949, 1.12154511, 0.53137129, -3.13529175, 1.09135126, 1.66004173])
        │   ├── ["arr_1"]/
        │   │   └── np.array([[-2.0883603, 0.8934535, 0.75968754, -2.9748297, 1.0163908, 1.8468368]])
        │   └── ["arr_2"]/
        │       └── no.array([[-2.2266073, 1.037677, 0.1352851, -3.2102153, 0.87246054, 0.2516598]])
        └── ...

Units of the measure is radian i.e. [-π, π].

2. data presented in folder 'root/dataset/Numerical Data/Fig 5 c/'

Number of variables: 2000

Number of cases/rows: 40 rows, each contains 50 numbers

Variable list, defining any abbreviations, units of measure, codes or symbols used: Units of the measure is radian i.e. [-π, π].