Marcos asked . 2020-11-27

How to import .dat data

The data is:

 

# Trace: no
# Gravitation: no
# Seed: 1539094448
# Param: alpha=0.15
# type: array_2d(700, 700)
# Source: _05.instr
# component: 2D_monitor3
# position: 0 0 0.9
# title: PSD monitor
# Ncount: 1.42857e+006
# filename: monitor3.dat
# format: McStas with text headers
# statistics: X0=-7.24809e-006; dX=0.0261557; Y0=-0.000100068; dY=0.0262531;
# signal: Min=0; Max=6.09731e-013; Mean=7.85139e-015;
# values: 3.84718e-009 2.56239e-011 25481
# xvar: X 
# yvar: Y 
# xlabel: X position [m]
# ylabel: Y position [m]
# zvar: I
# zlabel: Signal per bin
# xylimits: -0.05 0.05 -0.05 0.05
# variables: I I_err N
# Data [monitor3_dat/monitor3.dat] I: 
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4.77923393661e-014 0 0 0 0 0 2.02217149662e-013 0 0 0 0 4.86939200662e-014 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I essentially want to read the .dat and ignore everything apart from the numbers after "#Data", unsure how to however.
Any help with explanation would be appreciated.

matlab , deep learning , matlabsolutions , data,

Expert Answer

John Michell answered . 2024-12-21 00:55:40

fid=fopen('yourfile.dat','r');
data=cell2mat(textscan(fid,'','CollectOutput',1,'CommentStyle','#'));
fid=fclose(fid);

You'll end up w/ an array of double.

 


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MATLAB and Python are both popular choices for AI development. MATLAB is known for its ease of use in mathematical computations and its extensive toolbox for AI and machine learning. Python, on the other hand, has a vast ecosystem of libraries like TensorFlow and PyTorch. The choice depends on your preferences and project requirements.

You can find support, discussion forums, and a community of MATLAB users on the MATLAB website, Matlansolutions forums, and other AI-related online communities. Remember that MATLAB's capabilities in AI and machine learning continue to evolve, so staying updated with the latest features and resources is essential for effective AI development using MATLAB.

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