READ ME File For "Deep Learning for Simultaneous Phase and Amplitude Identification in Coherent Beam Combination"

Dataset DOI: 10.5258/SOTON/D3389

Date that the file was created: February, 2025

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GENERAL INFORMATION
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ReadMe Author: Fedor Chernikov, University of Southampton

Date of data collection: July 31 2024 to January 13 2025

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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: "Deep Learning for Simultaneous Phase and Amplitude Identification in Coherent Beam Combination"
AUTHORS: Fedor Chernikov, Yunhui Xie, James A. Grant-Jacob, Yuchen Liu, Michalis N. Zervas and Ben Mills 
TITLE: "Deep Learning for Simultaneous Phase and Amplitude Identification in Coherent Beam Combination"
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.jpg
        │   ├── Figure 3.jpg
        │   ├── Figure 4.png
        │   ├── Figure 5.jpg
        │   ├── Figure 6.jpg
        │   ├── Figure 7.jpg
        │   ├── SM Figure 1.jpg
        │   ├── SM Figure 2.jpg
        │   ├── SM Figure 3.jpg
        │   ├── SM Figure 4.png
        │   ├── SM Figure 5.png
        │   ├── SM Figure 6.png
        │   ├── SM Figure 7.png
        │   └── SM Figure 8.jpg
        ├── ONNX/
        │   ├── ampl lower limit 0.75/
        │   │   ├── pattern/
	│   │	│   ├──1729082510.4598675.png
	│   │	│   ├──1729082512.2017298.png
	│   │   │   └──...
        │   │   ├── phase/
	│   │ 	│   ├──1729082510.4598675.npy
	│   │ 	│   ├──1729082512.2017298.npy
	│   │	│   └──...
        │   │   └── power/
        │   │       ├──1729082510.4598675.npy
        │   │       ├──1729082512.2017298.npy
        │   │       └──...
        │   ├── README.txt
        │   ├── environment.yml
        │   ├──	requirements.txt	    
        │   ├──	onnx.py
        │   └── mobilenet.onnx
        └── 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 7. correspond to the following filenames: 'Figure 1.jpg', 'Figure 2.jpg', 'Figure 3.jpg', 'Figure 4.png', 'Figure 5.jpg', 'Figure 6.jpg', and 'Figure 7.jpg'. The graphics denoted as SM Figure 1 to SM Figure 8 cited within the supplementary materials of the scholarly publication, are accessible under the file name 'SM Figure 1.jpg', 'SM Figure 2.jpg', 'SM Figure 3.jpg', 'SM Figure 4.png', 'SM Figure 5.png', 'SM Figure 6.png', 'SM Figure 7.png' and 'SM Figure 8.jpg' respectively.

2. ONNX
ampl lower limit 0.75/
This subdirectory contains data corresponding to a specific amplitude lower limit (0.75), which was used for plotting the datapoint 0.75 in Figure 4 of the main manuscript. The structure is organized into three subfolders: pattern/, phase/, and power/.

pattern/ :
Contains .png image files representing 250 Camera A observations captured during experiments. Each file is named using the Unix timestamp of when the measurement was taken (e.g., 1729082510.4598675.png).

phase/ :
Contains .npy files storing numerical arrays representing label phase information with a shape of (6,). These arrays represent the phase values assigned to each fibre and sent to the Spatial Light Modulator (SLM).
Example: Loading 1729082510.4598675.npy with numpy.load() will return an array containing phase values:
array([-0.08451875, -1.26307154,  0.3934566 , -1.54942517, -2.01298107,
       -0.37225581])

power/ :
Contains .npy files storing numerical arrays representing power measurements. Shape (7,)
Similar to the phase/ folder, each .npy file corresponds to a specific timestamp and can be loaded using numpy.
Example: Loading 1729082510.4598675.npy with numpy.load() will return an array containing phase values
array([0.85523281, 0.88101354, 0.94862087, 0.84383259, 0.86969338,
       0.82239261, 0.91183514])

README.txt :
A text file providing instructions on how to set up the environment and run the script (onnx.py).
Includes details about dependencies, environment setup, and execution steps.
environment.yml :
A Conda environment file specifying all required Conda packages for reproducibility.
Use this file to create the Conda environment
	


