ValueError Occurs after certain Interations

[DyFan777]

Hi,

I'm trying to train DECODE and constantly have this ValueError after a few iterations. I'm attaching the image below.

I had DECODE run successfully before but after changing a few camera/simulation parameters this error started to occur. And it stops at the same interaction number with the same simulated data set. I couldn't really figure out which setting triggers this error so I thought about posting it for help. I'd appreciate it if anyone knew how this error occurs!

Here's my param if that helps.
I'm using an sCMOS camera so I set em_gain to null.
DECODE version: vINVALID-recent-0.10.2

Camera:
  baseline: 100
  convert2photons: true
  e_per_adu: 26
  em_gain: null
  px_size:
  - 67.3
  - 67.3
  qe: 1.0
  read_sigma: 45
  spur_noise: 0.0

Simulation:
  bg_uniform:
  - 70
  - 90
  density: null
  emitter_av: 3
  emitter_extent:
  - - -0.5
    - 39.5
  - - -0.5
    - 39.5
  - - -1000
    - 1000
  img_size:
  - 40
  - 40
  intensity_mu_sig:
  - 70000
  - 1
  intensity_th: null
  lifetime_avg: 1

[Haydnspass]

@DyFan777 Sorry for the delay.
I think you are using quite a low number of emitters, is that on purpose?
I would recommend roughly 30 emitters instead of 3. Could you retry and let me know?

[DyFan777]

Hi Lucas,
Thanks! I tried increasing the emitter number and it worked. No such errors again.
The reason I used low emitter numbers, however, was because I'm working on experimental data with rather low emitter densities. I know DECODE is designed for high-density data but the paper also shows that it works well for low-density data too. I suppose the density shouldn't be what limits the performance here but I wonder if you have any experience/suggestions on these data: does the emitter density I set in simulation affect the final performance on experimental data of different densities? The accuracy I got from the model is not as good. I wonder if it's the result of emitter density or other parameters.

[Haydnspass]

In general, we observed that even for low density data we did not see a performance penalty when we trained on high (but reasonably) dense data.
Its hard to judge what might have gone wrong here, the first and most crucial part for debugging would be the PSF I suppose. If you want you could share it with us.

[DyFan777]

Thanks! I'm using a standard double helix point spread function and calibrated with beads samples.
Here's how my simulated (left) and experimental (right) image looks like:

The simulation doesn't seem to get the localization right. Could that be a calibration issue?

Appreciate your help!

[Haydnspass]

Dear @DyFan777 ,
very sorry for the super late response ... Judging from your screenshots, I think your ROI size is much too small for your PSF.
From the first screenshot one can clearly see that PSF is cut and not completely covered by the ROI size.
This should at least be one clear problem that I can see directly. Could you redo the calibration procedure in SMAP with a larger ROI size?

Best,
Lucas

[DyFan777]

Hi Lucas, I tried adjusting the ROI to prevent PSF cutting and the training is working great now. Thanks so much for your help!