forked from spacecruft/SNOUG
126 lines
5.4 KiB
TeX
126 lines
5.4 KiB
TeX
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% Upload.tex
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% SatNOGS Optical Unofficial Guide
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% Copyright (C) 2022, Jeff Moe
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% This document is licensed under the Creative Commons Attribution 4.0
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% International Public License (CC BY-SA 4.0) by Jeff Moe.
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%
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\section{Push Data Upstream}
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\label{sec:upload}
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\index{upload}
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The \gls{SatNOGS} network isn't ready for optical data yet.
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So there is nothing that can be uploaded. This is under
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development.
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\section{File Extensions}
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\label{sec:upload-extensions}
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\index{formats}
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The following file extensions are in use:
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\begin{mdframed}[backgroundcolor=blue!10,linecolor=blue!30]
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\begin{description}
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\item [.cal] --- Cal % XXX
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\item [.cat] --- Cat % XXX
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\item [.csv] --- \gls{CSV}
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\item [.dat] --- Data.
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\item [.fits] --- \gls{FITS}.
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\item [.png] --- \gls{PNG}.
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\end{description}
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\end{mdframed}
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\index{FITS}\index{PNG}\index{CSV}
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\section{Data Files}
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\label{sec:upload-data}
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\index{data}\index{stvid}\index{process}\index{acquire}
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After the \texttt{stvid} acquisition and processing steps have been
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run, files such as below will be generated:
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\begin{minted}{sh}
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2022-08-23T04:16:26.633.fits
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2022-08-23T04:16:26.633.fits.cal
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2022-08-23T04:16:26.633.fits.cat
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2022-08-23T04:16:26.633.fits.png
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2022-08-23T04:16:26.633_48473_catalog.dat
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2022-08-23T04:16:26.633_48473_catalog.png
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2022-08-23T04:16:26.633_52718_catalog.dat
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2022-08-23T04:16:26.633_52718_catalog.png
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2022-08-23T04:16:26.633_90000_unid.dat
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2022-08-23T04:16:26.633_90000_unid.png
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2022-08-23T04:16:26.633_hough.csv
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2022-08-23T04:16:26.633_predict.csv
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2022-08-23T04:16:26.633_threshold.csv
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\end{minted}
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\subsection{\texttt{.fits} FITS Files}
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\gls{FITS} files are the main files created during acquisition and
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are the main files used in processing. It has the data of
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the actual image taken with the camera. \gls{FITS} files will be significantly
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larger than the other files, at around 35 megabytes each, depending
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on sample rate and frame size.
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\index{FITS}\index{acquire}\index{process}
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The \texttt{stvid} program creates it's customized \gls{FITS} format
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file, which is a stack up of multiple images.
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I'll leave explanation of the \texttt{stvid} \gls{FITS} file structure
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to the program's author, Cees Bassa:
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\begin{quote}
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With stvid, I typically store 10 seconds worth of video frames, 100 frames at 10 \gls{Hz} frame rate or 250 frames at 25 \gls{Hz} frame rate into a so-called ``four frame''. Here, for each pixel of the initial 100 or 250 frames, the mean, standard deviation, maximum and argmax are stored, each as its own frame. Objects that move will stand out because they will have a high (max - mean) / standard deviation, which allows me to detect trails. Once a trail is detected, the argmax frame tells me the frame number and hence the motion of the trail, and using the timestamps of the individual 100 or 250 frames, I can reconstruct the pixel position as a function of time during the 10 second exposure, using only 4 frames instead of the 100 or 250 individual video frames.
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\end{quote}
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\index{Bassa, Cees}
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\subsection{\texttt{.cal} CAL Files}
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Example \texttt{.cal} filename:\\
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\texttt{2022-08-23T04:16:26.633.fits.cal}\\
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Sample data from \texttt{.cat} file, just the top select lines of a 577 line file:
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\begin{minted}{sh}
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1172.876 11.776 7.1775 221.117561 45.185979 6.761
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786.524 1.817 6.0213 222.623526 37.272049 5.590
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838.954 21.772 8.0430 222.941344 38.403949 7.653
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872.352 25.919 8.3855 222.909215 39.103246 7.884
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692.542 37.123 8.2615 223.889916 35.455924 8.718
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738.829 47.126 8.2037 223.973142 36.430116 7.777
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691.964 52.656 7.7910 224.278547 35.490201 7.411
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951.962 53.159 8.0072 223.315748 40.816697 7.844
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1209.704 60.030 6.0799 222.327791 46.116208 5.811
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893.057 66.086 7.1867 223.943996 39.648460 8.039
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...
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\end{minted}
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\subsection{\texttt{.cat} CAT Files}
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\texttt{stvid} generates \texttt{.cat} files, generated from \gls{FITS} files, such as:\\
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\texttt{2022-08-23T04:16:26.633.fits.cat} \\
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Sample data from \texttt{.cat} file, just the top select lines of a 1,033 line file:
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\begin{minted}{sh}
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# 1 X_IMAGE Object position along x [pixel]
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# 2 Y_IMAGE Object position along y [pixel]
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# 3 MAG_BEST Best of MAG_AUTO and MAG_ISOCOR [mag]
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# 4 FLUX_BEST Best of FLUX_AUTO and FLUX_ISOCOR [count]
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# 5 FLUX_MAX Peak flux above background [count]
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# 6 BACKGROUND Background at centroid position [count]
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1172.8755 11.7756 7.1775 33.80787 11.09625 16.06616
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28.8250 11.4696 6.7443 50.38253 5.008112 15.81194
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1347.4933 10.4887 8.3649 11.32553 2.619246 16.04644
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269.9134 9.8019 8.4220 10.74506 1.56768 15.97937
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468.9823 7.9780 8.8072 7.535751 5.987358 16.02673
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1475.9681 6.6672 8.6115 9.024131 2.281431 16.03049
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847.0105 5.9795 8.1992 13.19223 14.31796 16.08833
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181.5139 5.5141 7.9473 16.63711 2.278276 15.95168
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460.1482 5.1577 8.5318 9.711239 2.795708 16.02688
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1559.9583 2.9245 7.5915 23.08835 6.981803 15.9909
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786.5237 1.8174 6.0213 98.05751 27.88283 16.09418
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...
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\end{minted}
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