Pr/pre audit

[iliasoroka1]

Feature and Algorithm Work Before the LLM-Assisted Audit

At the macro level I added experimental pipeline:

define plate
    → estimate a useful scan Z
    → scan the circular plate
    → detect and center a worm
    → switch camera settings
    → refine focus
    → start tracking
    → optionally record

Automated Plate Scanning

Plate geometry

To fit a plate I fit a circle using:

$$ x_i^2 + y_i^2 + D x_i + E y_i + F = 0. $$

And estimate the coefficients with least squares regression. The centre and radius are

$$ c_x=-\frac{D}{2}, \qquad c_y=-\frac{E}{2}, \qquad r=\sqrt{c_x^2+c_y^2-F}. $$

tile generation

I use calibrated camera field of view to calculate distances between scan
positions.

$$ \Delta x = \max(W(1-o_x), 10^{-3}), \qquad \Delta y = \max(H(1-o_y), 10^{-3}). $$

Then I reaine a tile only if its centre satisfies circle

$$ (x-c_x)^2 + (y-c_y)^2 \leq r^2. $$

While scanning, rows alternate between left to right and right to left

Scan-to-track transition

Once a worm is centred I do following

• switch to the tracking/recording exposure, gain, and frame rate
• starts live view
• starts tracking and live autofocus
• starts recording

I repeat the scan if a worm is not found

Autofocus and Focus-Plane Estimation

I changed the autofocus to be a peek seekeer rather then a PID

At each Z position it collects (B) frame-level focus scores:

$$ f_1,f_2,\ldots,f_B. $$

And calculates the median for that Z:

$$ P_t={median}(f_1,\ldots,f_B). $$

Until a all B frames are available we do not move z.

The autofocus begins with a configurable big step (s_0). If focus
decreases beyond a fraction for enough evaluations

$$ \bar{P}_t < \bar{P}_{t-1}(1-\epsilon), $$

we treat it as a overshoot, reverses direction, and
reduces the step

If focus falls below a configurable fraction (\rho) of the best observed
focus,

$$ \bar{P}_t < \rho P_{\text{best}}, $$

the autofocus treats focus as lost and restores the biggest step.

Fixed the smoothing weights originally used
min(1, n - 1) as their denominator, which I corrected to
max(1, n - 1).

I also added asyncio event loops for scan and autofocus workers.

Intensity-based scan Z

To get a correct Z position for scanning, I sample the mean image
intensity over a Z range:

$$ \mu(z_i)=\frac{1}{N}\sum_{p=1}^{N}I_p(z_i). $$

I then smooth the estimate and calculate its first derivative.

The implementation identifies:

$$ z_g = \underset{z}{{arg,max}}, \frac{d\mu}{dz}, $$

and the closest first-derivative zero crossing (z_0). When both are available,
the selected scan plane is

$$ z_{\text{scan}}=\frac{z_g+z_0}{2}. $$

Tracking Changes

Since there is no constant 16ms delay coming from USB port, I changed fixed waiting times to:

$$ t_{\text{ready}} = t_{\text{command}} + t_{\text{floor}} + T_{\text{travel}}(d_{\max}), $$

where (d*{\max}) is the largest movement requested in the current iteration.
The tracking worker then waits until the camera reports an image retrieval
timestamp newer than (t*{\text{ready}}).

I splitted velocity and speed stage configuration into:

• fast and slow input
• precision positioning
• plate scanning
• tracking

Since I once collided the camera with the X stage, I added a rule for the unsafe combination of low Y and high Z.

Recording pipeline

I removed image encoding and I/O with disk from the camera acquisition callback
The resulting pipeline is:

camera acquisition thread
    → frame processing
    → handoff queue
    → handoff thread
    → shared-memory or standard thread queue on windows
    → image saver worker
    → TIFF file

Finally, each operation now gets each own worker abd runs independently and UI is scheduled with kivy Clock:

• camera acquisition
• image handoff and saving
• tracking
• live autofocus
• plate scanning
• multi-plate orchestration
• go-to stage movement

[takkasila]

I did not get to test all the functionality in time, so I trust that you have tested them throughly, especially the image saving and tracking. Apartfrom that I have mostly stylistic requests that I considerd good coding convention and highly recommend that you adopt them.

Important

• Always write a clear class and function description. Along with type hints for arguments and return.
• Write inline comments as much as possible to explain your intentions for the following code.

Good to have

• Consistency between camel and underscore convention.
• Add line space as often as you can. It improves reading comprehension.

It's a really big issue in a short time. You did a good job! The UI looks nice too!

[takkasila]

Should remain disabled.

Suggested change

	# os.environ["KIVY_NO_CONSOLELOG"] = "1"
	os.environ["KIVY_NO_CONSOLELOG"] = "1"

[takkasila]

Throughout this file, there are hardcoded unit = 'mm'. Please read them from config file or pass through function arguments.

[takkasila]

Please remove this entirely.

[takkasila]

Please add argument type hints and function description.