Brooklyn'r Boys

https://drive.google.com/drive/folders/1TBXaJNP9Oscg4Grmw3bVXfMgT4t_T60P?usp=sharing

https://drive.google.com/file/d/1IZLLKfnB3zQjHk92fHvFeXYeazKCn8-L/view?usp=sharing

The Challange:

So the data that was available on the resources certain that it won't be possible for the common public, analise the dataset and so wouldn't be able to interpret & understands the information.

The Idea:

This led us to the idea of us leading a graphical aproach that can make these complex dataset understandable to the general public. we found that it would that such representation could be done on python language. and further more we came across the the idea for if we could use the training of the previous and realtime data to predict the future possiblities of the occurance of the solar wind.

Note: The project consists of 2 parts, The first part being the data interpreter that predicts the occurance of a soon future solar wind, meanwhile, the Second part being the conceptualizer of the solar wind of building a basic understanding about it.

Working of the Interpreter:

As defined in the above, it retracts data from the Satellite API that is provided by NASA.

the extracted data is produced in .FITS format so the software has to first convert the data to a visual format (.JPEG). with the advantage of having fits formated data, we were able to produce the image at different WaveLengths

( 131 Å) (171A with HBIM filter) (304A) (193A)

Our tool uses the image at 193A (being said that flares appears the clearest at that wavelength.) and plots it on a graph panel. then it processes the image at hold to find out the brightest spots from the image at hold.

The program then runs the condition to see if the the Spots() are present on the right side of the panel, In case if the condition is true, spots are defined as insignficant and their magnitude is ignored. but if in the other case where if the spot lies in the left quadrants of the panel, the spots() are coined as 'significant_incoming_flares' and their "Log<brightness>(10)" magnitude is defined as Intensity of the Spots(). both these spots are marked on a Composite graph scientifically called as an 'AR Map"

An AR Map Would Later Help us identify the flare type. (Incoming, Ongoing, Insignificant)

Based on the resources the were given from the demreg module, We were able to fit into the Equation of 'Differential Emission Measure' of Solar Flare which we then related to the Main Equation Spot;s Brightness

Significance of the DEM

This final Equation, now left us with a integrated time solution with which We were able to find and estimated time frame, however as this data is not accurate, we had to put up a slight long range of time limit as even latest satellite data can varie from 0 to 3 hrs (new data ever 3 hours).

"All this when put together leads to a simple yet ranging prediction about the solar whether"

Working of the Conceptualizer:

Owing to the fact that we tried our best to make it as simple as possible,

We had rather used some old dataset with some dynamic readings to ensure that it is most understandable to learn with...

To not let our users end up puzzled, We've also included a short guide for this project.

For we the had videocre data this time,

We used Lofar filters to a different wavelength. this increased the visibility of the flares.

then we again defined the intensity with the same process and ploted a unibar Graph to represent the level of intensity at each extent.

the nect thing that we did was that we replaced the bright pixels of the generated image with yellow, upper-mids with orange, lower-mids with purple and lows with the null(black) this definately took us a lot of time processing the graphics but once finished, it looked something like this.

to a great extent, the conceptualiser holds a really high potential of usage, for example: if space authority want, they can use this same tool to do real time analysis of the satellite data right through this app by replacing just the dataset with their realtime satellite API input.

We used the following Space Agency data from the NASA "Resources" page to create the Python program:

• Solar Dynamics Observatory
• Science @ NASA: The Sun
• NASA Scientific Visualization Studio
• NASA Community Coordinated Modeling Center
• iSWA

We used this data to create the Python renderings of the sun and the solar flare warning system. We also used open source data from NASA

from the overall agency data, I consumed most of it from either the nasa given resourse or from the canadian data resources. as a matter of fact owing to the fact that nasa provided data bases such as Sdo encoded with Api's and examples such as ACE and Gateway. it was a great guide through out the journey.

the journey started with Om and Kunal with their dream of winning yours competition creating history. this was then supported by a young lad known as jai who approached them not just for his but their own good. at the middle of nowhere . they found rudra who helped the team with his skills. together they reached the gate of greatness.... it's still to be opened soon.........

https://sdo.gsfc.nasa.gov/data/

https://science.nasa.gov/heliophysics

https://svs.gsfc.nasa.gov/search/?search=parker+solar+probe&sort_by=relevance

https://ccmc.gsfc.nasa.gov/tools/iSWA/

https://www.swpc.noaa.gov/products/ace-real-time-solar-wind

https://www.asc-csa.gc.ca/eng/open-data/applications.asp

https://pypi.org/project/demregpy/

https://github.com/ianan/demreg/blob/master/python/example_demregpy_aiapxl.ipynb

https://sdo.gsfc.nasa.gov/assets/img/browse/

and some other python and C++ librararies and examples for referance

#ParkerSolarProbe #SolarAtmostrome #Team #Indians #Winners #Duniyaaaaaaa #software #Python