NB : You will need at least the RMS python libraries installed on your PC, and to perform multistation analysis you will also need the WMPL libraries. Installing these is explained in the github repos for each of the products.
Sometimes the RMS software will miss a meteor, or it will be too bright / complex to analyse. The following is condensed from Denis Vida’s excellent video tutorial here. https://www.youtube.com/watch?v=IF633DEjJag
- Create a new folder on your PC – lets call it c:\manual
- Copy the FF file, platepar file and .config file from your Pi to this folder. If there’s a fireball file (FR file) copy this too.
- enable the RMS python environment.
- In the RMS directory run the following
python -m Utils.SkyFit2 C:\manual\ -c c:\manual\.config
- This will open the manual meteor analysis window.
- Firstly, you should check the platepar.
- The initial screen shows you where the platepar thinks you are pointing. If the red markers are nicely on top of the stars, all is fine. Otherwise you should recalibrate the platepar. Use the mouse scroll wheel to zoom in and check
- Press Ctrl-R to enter star-picking mode, then Shift-Z to create a mini zoom window.
- Click on a star. You should see two crosses appear, a yellow one on the star and a blue one on the corresponding red circle.
- If the right reference star was selected, press enter. Repeat this for at least 20 stars, covering all parts of the sky.
- If the wrong star was selected, press escape and try again. Aim to get both star and target in the circles in the zoom window. Avoid double stars where the software might get confused.
- After you have about 20 stars matched, press Ctrl-Z to recalculate the plate. Press Alt-tab to switch back to the window you started SkyFit2 from and you should see the results of the fit. Aim for a accuracy of < 1 pixel and a few arcminutes.
- If the plate looks good. Press Ctrl-S to save it. Otherwise you can right click to deselect stars, or left-click add more.
- Once you’ve finished doing the platepar, press Ctrl-R to exit star-picking mode.
- Now you can do manual reduction. Select the Manual Reduction tab.
- We are going to identify each frame that contains the meteor, and mark the location of the “centroid”. The centroid is the middle point of the visible meteor as best we can estimate it. We will also mark the area covered by the trail so we can estimate magnitude.
- You will notice that when you move the mouse around the message bar at the bottom shows the RA and Dec, Alt and Az etc. This indicates that the platepar has been loaded.
- Use the cursor keys to find the first frame which shows the meteor. Up and Down arrows move 25 frames at a time. Left and right move one frame. You can zoom in with the mouse scroll wheel.
- Press Ctrl-R to enter point picking mode.
- Left click where you think the centroid is. The software will try to guess. If the guess is clearly wrong, hold the Ctrl key and click again and the marker will move.
- Hold down shift-leftclick and shade in the area covered by the trail. Shift-rightclick will rub out.
- Go to the next frame and repeat. Do this for each frame containing the meteor.
- When done, press Control-S. This will save a new FTPDetectInfo file containing the meteor position and brightness.
- You can now quit SkyFit2
- Open this file in a text editor. You will see it has the same structure as a normal FTPDetectInfo file. The first 11 lines are the header. Then comes a line of dashes followed by the results of your analysis.
- If you feel confident the analysis is good, copy the analysis data – including the line of dashes – then open the original FTPDetectInfo file on the pi and replace bad data or add a missed event at the bottom. Remember to update the meteor count at the top of the file if you add a set of data.
- You can now rerun the python routines to generate the UFO file and radiant map and you should get the extra event included.
This is the manual process for calculating orbits and trajectories. To do this analysis you need data from at least two cameras in different geographic locations.
Method 1: using UFOOrbit
NB: UFOOrbit is closed source software that is not transparently maintained, While its regarded as reasonably accurate, the mathematics is obscure and believed to be very out of date.
- Create a folder to hold the analysis data.
- Copy all the CSV files to this location
- Open UFOOrbit, and click the […] symbol next to “Read M.csv”.
- Select the folder containing your CSV files, then click “Read M.csv”
- This will load and process the data. If you have any matches, then you’ll see this in the title bar, and you can select and view the output.
Method 2: Using the Western Meteor Python Libraries
WMPL Is written by the Meteor group at the University of Western Ontario (UWO). This team were part of the group who set up the Global Meteor Network and the library uses more up to date analytics than UFO.
- Download the WMPL from github here. Carefully follow the instructions to install it.
- Create a folder to contain your data.
- Within this folder, create one folder per camera, named with the camera name.
- Within each camera folder, create a folder named for the night the data was captured. The folder name must follow the same convention as the RMS ArchivedFiles folders
- Copy the ftpdetect file and platepar for each camera and night into relevant folder. No other files are needed
- Note that the folder names must be in this format. If the structure is incorrect, or folder names diverge from this, the data will be ignored. (The precise time is not important, but it must be present in the foldername. )
- So for example if you have data for UK0001 and UK0002 and UK0003 for the night of 2021-08-13 you would have the following folders
And each folder would contain the FTPDetect file and platepar for that camera for that night.
- You can process multiple nights, just create more night-folders and put the required files into them. You can even keep the old data, the tool remembers which data it has already processed and will ignore it unless there are new detections of the same event.
- Now open a command prompt, change folder to the location of WMPL, and activate its python virtual environment.
- Run the correlation process as follows:
python -m wmpl.Trajectory.CorrelateRMS c:\mydata -l
- The -l (minus-ell) parameter requests graphical output as well as a table of data.
- The process will evaluate your data, identify any matching events, solve for their trajectories and orbits, and create output in a new folder called “trajectories”.