tsewangstanzin / roboticdimm

Development of a robotic Differential Image Motion Monitor (DIMM), one of the best method to explore the integrated effect of the turbulent atmosphere.

Robotic DIMM

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🤖 A Differential Image Motion Monitor(DIMM) for atmospheric seeing observation.

📝 Table of Contents

• About
• Prerequisites
• Usage
• Author
• References

🛠 About

The turbulent atmospheric layers cause beam propagation disturbances that degrade the quality of astronomical images The DIMM principle involves using the same telescope to produce twin images of a star via two entrance pupils separated by a distance. The differential method measures the angular differences over the two small pupils. By using a turbulence model to determine the phase structure function, we can evaluate the longitudinal and transverse variances (parallel and perpendicular to the aperture alignment) of differential image motion

⛏️ Prerequisites

python 3

Dependencies can be installed with:

pip3

CMOS camera API/SDK:

https://thinklucid.com/arena-software-development-kit/

💭 Usage

To view nightly seeing plots, visit: http://117.211.201.82/IAO/sky/index.php
The entire software is written in Python 3.6
You can analyse the data acquired from the system. Download image cube provided in /fitscube folder and run:

offline_seeing_analysis.py ---- Exploit the centroid algorithm, compute seeing and save log in DIMManalysis folder

On request, the codes responsible for conducting observation through this system configuration (Other telescope and camera would need their API/SDK) will be made available:

dimm.py ---- Main script that runs all the time. Responsible for overall observation conduct. Imports other scripts given below:

power_control.py ---- Control power through an Arduino based power controller
best_stars.py ---- Select appropriate star from starcatalog.lst
meade_tel_control.py ---- Control Meade telescope
webcam_pointing_v3.py ---- Compensate poor pointing by a webcam+100mm lens piggybacked
grabcube.py ---- Grab cube image at fast rate from Lucid Vison CMOS Camera. 
seeing_analysis.py ---- Exploit Fried parameters and Tokovin et al. DIMM model/paramaters and compute seeing
read_AWS.py ---- Read live weather 
auto_plotter_transfer.py ---- Plot night seeing
slack_bot.py ----  Upload plot and post observation and observing condition update on Slack

✍️ Author

Tsewang Stanzin [Indian Astronomical Observatory, Hanle, India]

🎉 References

• Sarazin M., Roddier F., 1990, A&A, 227, 294
• Tokovinin A., 2002, PASP, 114, 1156
• https://www.meade.com/downloadEntityFile/assets/product_files/instructions/LX200GPS_manual.pdf
• http://www.company7.com/library/meade/LX200CommandSet.pdf
• Fried D.L., 1966, PASP, 56, 1372
• Roddier F., 1981, Prog. Optics, 19, 281
• Sarazin M., 2001 -http://www.eso.org/astclim/espas/iran/zanjan/zanjan02.ppt
• Sarazin M., Tokovinin A., 2002, ESO Con. andWork. proc., 58, 231
• Tokovinin A., Kornilov V., Shatsky N.,Voziakova O., 2003b, MNRAS, 343, 891