Multi-Plot Problem during Multi-Aperture process

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Multi-Plot Problem during Multi-Aperture process

wvinton
I have been learning the process of multi-aperture photometry in AstroImageJ and made some process. When first doing multi-aperture process on a single image, and I finished, the windows for doing the multi-plot would routinely pop up. I didn't (at that time) know what to do with them, so routinely closed them. This happened for many iterations.

The Problem: Now, as I have started to explore the multi-aperture process on a stack (9 slices), somewhere along the way the multi-plot windows did not open when I finished selecting the target star and comp stars (1 target, 6 comps) on first image in stack. I do get the measurement window, but no multi-plot windows pop up. Clicking on the multi-plot icon on the normal ImageJ command bar yields the following error window:



Using the Analyze-MultiPlot command I get this error:



So, I don't know why multi-plot is not working in general, and why it stopped working after working initially. Can anyone help? (I've tried deleting and reinstalling AstroImageJ - no effect).

I've also experienced some instances in which clicking on the multi-aperture icon causes the science image to disappear from the desktop. It's listed in the "Window", but nothing I tried would make the image visible. I could close image using menu commands, and could open image, but clicking on multi-aperture caused the image to disappear. Reinstalling fixed this as of now. This related to previous problem?

Thanks in advance.

Running version 2.1.4 64 bit AstroImageJ with 2 GB memory assigned on MacOS 10.9.2, MacBook Pro with 8 GB RAM.
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Re: Multi-Plot Problem during Multi-Aperture process

karenacollins
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Let me address each of the issues separately:
1) If you are in a situation again where you prefer to not have the Multi-plot windows open during/after a Multi-aperture run, disable the option on the Multi-aperture setup panel labeled "Update plot of measurements while running" (near the bottom). You can always click the Multi-plot button above an image or on the IAJ toolbar to open the plot at any time (well, once we get that fixed on your machine).
2) The problem with the menu item Image_Window->Analyze->Multi-plot is a software bug. Thank you for reporting this. I can duplicate it readily, so it is not an issue specific to your installation. I will fix it in the next daily build.
3) The problem you are having when clicking on the AIJ Toolbar's Multi-aperture icon has not been reported before and I have not been able to duplicate it here in my test environment. Unfortunately though, I do not have an OSX machine that is compatible with OSX 10.9.2, so I can't test your exact setup. I am wondering though, is it possible that you upgraded your OS around the time the Multi-plot windows stopped popping up?  I realize you probably can not downgrade your OS to test the idea, but I am looking for any clues I can find to help solve this one.
4) Let's see if you still have the problem with the images disappearing from the screen once the above issue is solved.
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Re: Multi-Plot Problem during Multi-Aperture process

karenacollins
Administrator
I have updated the AIJ 'daily build' to fix the problem with Image_Menus->Analyze->Multi-plot. I have also taken a stab at a fix for what might be causing Multi-plot to not run on your system in general. Can you please update to the "daily build" version using AIJ_Toolbar->Help->Update AstroImageJ. Select 'daily build' in the pull down menu and click OK. After the files download and install, AIJ will automatically close. Reopen AIJ and you will be running the new version.

If this fixes the problem, let me know so I can close the problem report. If you still get an error message when trying to open Multi-plot (using either the AIJ toolbar, the button above an image, or the analyze menu item), please post the error messages here as you did before. Since you will now be running the exact version I have in the development system, the new error message will let me know the exact place the error happens in the code.

Thank you for your help on these issues.

Karen
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Re: Multi-Plot Problem during Multi-Aperture process

wvinton
I updated to the newest daily build, and things appear to now be working! The various windows (multi-plot y-data, multi-plot main, multi-plot reference star selection, measurements, plot of measurements) opened automatically, so I suspect that things are working as intended. If I uncheck "update plot of measurements..." the manual commands, both in the AIJ toolbar and the menu item both work! I will continue to explore and see how things are working.

Thanks so much for your extremely prompt reply and work!

(Incidentally, is there any documentation for the various multi-plot windows? The AIJ manual describes them some, but doesn't provide much detail about their use...)

Bill V


On Tue, Mar 18, 2014 at 5:44 AM, karenacollins [via AstroImageJ] <[hidden email]> wrote:
I have updated the AIJ 'daily build' to fix the problem with Image_Menus->Analyze->Multi-plot. I have also taken a stab at a fix for what might be causing Multi-plot to not run on your system in general. Can you please update to the "daily build" version using AIJ_Toolbar->Help->Update AstroImageJ. Select 'daily build' in the pull down menu and click OK. After the files download and install, AIJ will automatically close. Reopen AIJ and you will be running the new version.

If this fixes the problem, let me know so I can close the problem report. If you still get an error message when trying to open Multi-plot (using either the AIJ toolbar, the button above an image, or the analyze menu item), please post the error messages here as you did before. Since you will now be running the exact version I have in the development system, the new error message will let me know the exact place the error happens in the code.

Thank you for your help on these issues.

Karen


If you reply to this email, your message will be added to the discussion below:
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NAML



--
Bill Vinton

802-748-4002 (h)
802-535-8019 (c)

"Just deal with what is true. You know what is true.You need to do your best to say it correct."
The Laramie Project

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Re: Multi-Plot Problem during Multi-Aperture process

karenacollins
Administrator
Hi Bill,

Thanks for reporting back. That is great news! If you haven't found it yet, I would recommend going through section 10 in the latest (but still not complete) user guide at:
http://www.astro.louisville.edu/software/astroimagej/guide/AstroImageJ_User_Guide_2.1.4.pdf

Section 10 provides a step-by-step guide on how to do differential photometry on a stack of images and includes an example of how to use the Multi-plot controls. I hope to improve the user guide over the coming months.

Also, make sure "Show tooltips help" in Multi-plot_Main->Preferences is enable. With this option enabled, mousing over most of the control knobs in the user interface will cause a small help window to pop up. In the "Multi-plot Y-data" panel, be sure and also check the labels at the top of the window for tooltips help. To keep the tooltips displayed longer, move the mouse around slowly over the control.

If you have specific questions that are not addressed by the two ideas above, post them here and I will try to help out.

Karen




If you reply to this email, your message will be added to the discussion below:
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Re: Multi-Plot Problem during Multi-Aperture process

wvinton
Karen:

The journey continues...

I have been playing a bit with things (actually analyzing a series of images of SN 2014J taken on 1/23/2014 - right when it lit up) and the multi-plot was working fine, with one exception. The stack has nine images, but only five data points were being plotted. I tried re-scaling window - still only five data points, every other one. And, none of the comp stars were plotted. I'd show you screen shots, but...

The science image now disappears when I click multi-aperture (adjacent to the AIJ image window). The stack opens normally, then when I click on the multi-aperture icon, the multi-aperture measurements window opens and the image disappears. It's still listed in the "Window" drop down, but the window itself is not to be found anywhere.

I did do something that might have led to the issue. I couldn't get the "align the stack using apertures" command to work (it seemed to scanning through stack trying to align things before I had selected any apertures), so I opened the stack in a generic version of ImageJ (vs. 1.48, 64 bit), used an align stack procedure, saved the image sequence in FITS form, closed the generic ImageJ and then opened AstroImageJ. To my best recollection, that's when problem began.

Additonally, in my explorations, I have found that if instead I now click on the multi-aperture icon on the AIJ bar after opening the stack, the error window below appears. (I don't know if this was happening prior to the reappearance of the disappearing window problem.)

So, that's where we are at the moment. Whew!

Bill V.
<img src="data:image/png;base64,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" alt="">



On Tue, Mar 18, 2014 at 6:02 PM, karenacollins [via AstroImageJ] <[hidden email]> wrote:
Hi Bill,

Thanks for reporting back. That is great news! If you haven't found it yet, I would recommend going through section 10 in the latest (but still not complete) user guide at:
http://www.astro.louisville.edu/software/astroimagej/guide/AstroImageJ_User_Guide_2.1.4.pdf

Section 10 provides a step-by-step guide on how to do differential photometry on a stack of images and includes an example of how to use the Multi-plot controls. I hope to improve the user guide over the coming months.

Also, make sure "Show tooltips help" in Multi-plot_Main->Preferences is enable. With this option enabled, mousing over most of the control knobs in the user interface will cause a small help window to pop up. In the "Multi-plot Y-data" panel, be sure and also check the labels at the top of the window for tooltips help. To keep the tooltips displayed longer, move the mouse around slowly over the control.

If you have specific questions that are not addressed by the two ideas above, post them here and I will try to help out.

Karen




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Bill Vinton

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"Just deal with what is true. You know what is true.You need to do your best to say it correct."
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Re: Multi-Plot Problem during Multi-Aperture process

karenacollins
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Hi Bill,
One note first: the image you posted didn't seem to come through the forum. Could you either try again or send it to me directly at karen dot Collins at insightbb dot com.

Let me comment on a couple of items to see if that resolves two things you mentioned. If you have 9 images and only 5 dots are plotted on the screen, it may be that you having "binning" set to 2 for that light curve. Check here on the Multi-plot Y-data panel and make sure you have a 1 on the plot line in question:


Also, for light curves you want to show on the plot, make sure the "Plot" checkbox is enabled on that line as shown for data sets 1, 2 and 4 above. Also, if you are using "Auto Y-range" (circled below) to set the y-axis scaling, make sure the  "Auto Scale" check boxes above are enabled for the lines you want to plot.



Alignment issue: the daily build of 2.1.5 has a new alignment mode available if your images have WCS headers. In that mode, you do not need to identify stars. The alignment will start automatically as you described and the images will be aligned according to the information in the headers. Can you check and made sure the option circled below is disabled? If it is disabled, then alignment will required object apertures to be defined as before. This option will only show up if the first image in your stack contains valid WCS headers.



I'm still trying to think about what could be going wrong with the images disappearing. I will write back separately on that issue. I don't think aligning the images using standard IJ would have caused the problems you are seeing, but could you try updating AIJ to the "daily build" again to see if the images stop disappearing and if the error message you posted goes away? If this fixes the problem, then there may indeed be something happening between the AIJ and IJ installations that is causing a problem, as you suggeseted.

Just be be sure, your basic IJ and your AIJ installations are in different folders on your system? I think that would happen automatically on a Mac.

One final comment. If your images have WCS headers, there is no need to align your images to run Multi-aperture in db2.1.5. There is a new option that you can enable in the Multi-Aperture setup screen that tells AIJ to use the WCS header information to find the apertures in within your images, even if they are not aligned. However, I realize there are other reasons you would want to align your images.

Also,



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Re: Multi-Plot Problem during Multi-Aperture process

karenacollins
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In reply to this post by wvinton
Hi Bill,
    Could you clarify if the image disappears as soon as you click the Multi-Aperture icon above the image (I think this is what you are saying), or does it disappear after you click "PLACE APERTURES" on the Multi-Aperture setup panel?

Karen
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Re: Multi-Plot Problem during Multi-Aperture process

wvinton
In reply to this post by karenacollins
Karen: To answer your final question - the image disappeared just as soon as I clicked on the multi-aperture icon.

Reinstalling the AstroImageJ solved the disappearing image problem - seems to work as if nothing happened.

Changing the bin to 1 resulted in all the points appearing on the graph (I guess I was interpreting  "bin" to refer to how the images had been taken).

The graph still only shows the target star plot. I'm attaching screen shots of the various windows so that you can see what's going on! (All of the relative flux measurements for the comp stars in the Measurements table are well below 1.0.)

I'm also attaching the error window that showed up as previously described - when the multi-aperture icon was clicked on AIJ toolbar during the era of disappearing images (it doesn't show up now that I reinstalled AstroImageJ and things are working fine).

And, thanks for the update on not needing alignment stars if WCS headers are present!

Thanks so much!


On Wed, Mar 19, 2014 at 12:36 AM, karenacollins [via AstroImageJ] <[hidden email]> wrote:
Hi Bill,
One note first: the image you posted didn't seem to come through the forum. Could you either try again or send it to me directly at karen dot Collins at insightbb dot com.

Let me comment on a couple of items to see if that resolves two things you mentioned. If you have 9 images and only 5 dots are plotted on the screen, it may be that you having "binning" set to 2 for that light curve. Check here on the Multi-plot Y-data panel and make sure you have a 1 on the plot line in question:


Also, for light curves you want to show on the plot, make sure the "Plot" checkbox is enabled on that line as shown for data sets 1, 2 and 4 above. Also, if you are using "Auto Y-range" (circled below) to set the y-axis scaling, make sure the  "Auto Scale" check boxes above are enabled for the lines you want to plot.



Alignment issue: the daily build of 2.1.5 has a new alignment mode available if your images have WCS headers. In that mode, you do not need to identify stars. The alignment will start automatically as you described and the images will be aligned according to the information in the headers. Can you check and made sure the option circled below is disabled? If it is disabled, then alignment will required object apertures to be defined as before. This option will only show up if the first image in your stack contains valid WCS headers.



I'm still trying to think about what could be going wrong with the images disappearing. I will write back separately on that issue. I don't think aligning the images using standard IJ would have caused the problems you are seeing, but could you try updating AIJ to the "daily build" again to see if the images stop disappearing and if the error message you posted goes away? If this fixes the problem, then there may indeed be something happening between the AIJ and IJ installations that is causing a problem, as you suggeseted.

Just be be sure, your basic IJ and your AIJ installations are in different folders on your system? I think that would happen automatically on a Mac.

One final comment. If your images have WCS headers, there is no need to align your images to run Multi-aperture in db2.1.5. There is a new option that you can enable in the Multi-Aperture setup screen that tells AIJ to use the WCS header information to find the apertures in within your images, even if they are not aligned. However, I realize there are other reasons you would want to align your images.

Also,



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--
Bill Vinton

802-748-4002 (h)
802-535-8019 (c)

"Just deal with what is true. You know what is true.You need to do your best to say it correct."
The Laramie Project


Multi-plot Main.png (123K) Download Attachment
Multi-plot y data.png (109K) Download Attachment
Plot of Measurements.png (67K) Download Attachment
New error message 3.jpg (115K) Download Attachment
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Re: Multi-Plot Problem during Multi-Aperture process

karenacollins
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Hi Bill,
I still have not been able to find the sequence of events that causes an image to disappear. Based on your description of events, I am starting to lean toward your suggestion that it has something to do with running standard ImageJ and then moving back to AIJ. I haven't been successful in causing it to happen though. Famous last words, but I'm not sure what could possibly cause such an issue. If you have time and are able to determine a specific set of steps that causes the problem to occur, it would be much appreciated and might help me pinpoint what is going wrong. The error message you sent actually comes from the underlying ImageJ code, and is triggered because the code is trying to find the width of the image, but the image does not exist (as you clearly experienced).
  
I think the problem you are having displaying multiple light curves on the same plot is that you need to either plot in relative magnitude, or in normalized relative flux (i.e. scaled so that a light curve has an average value of 1.0). The values of rel_flux_T1, rel_flux_C2, etc. are simply the star's counts divided by the total counts in the reference star ensemble. For a comp star, the ensemble used to calculate relative flux includes all OTHER comp stars. A side effect of that is that you need at least 2 comp stars to plot a comp star light curve.
  
Because relative flux is the simple division of a star's flux by the ensemble's flux, the average values of relative flux for different stars can be vastly different in the absolute sense, so would show up at potentially very different places on the y-axis of a plot. To bring them all onto the same scale, you either need to enable "Normalization" or output in relative magnitude.
  
To normalize a light curve, enable a mode of normalization on each line. I think what you need is the mode that normalizes the light curve based on all of its data as shown below. Select this mode on each line you are plotting:

You can offset each curve for clarity by adjusting the "Shift" value circled above.

If you would rather plot in Magnitude, enable the "Out Mag" option to the right of the normalize selection. Normalize can be left on or off. In output in magnitude mode, all data points are referenced to the average of the first few data points and then converted to astronomical magnitude, with zero magnitude being the average of the first few data points. The amount of "few" that I am referring to is set on the Multi-plot Main panel. For what you are doing you may want to set it to "1", then all magnitudes are referenced to the first data point:


Then, if you know the conversion of that first data point to absolute magnitude for your instrumentation, you can make that adjustment using the "Shift" setting shown two screen grabs above.

Going forward, if there are operational help questions separate from the image disappearance problem, it's probably best to start a new topic on each one so other users can more easily search for topics. Lets continue the image disappearing problem here, if you have more time to work on it.

Thanks,
Karen



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