Slide outline

Slide outline#

This outline supports the 180-minute lesson. Keep slides sparse and switch quickly to the notebook and viewer.

Opening and orientation (0–20 minutes)#

1. Deciphering the living cell through microscopy and computation#

  • Workshop dates: November 16–17, 2026

  • Today: render, inspect, overlay supplied results, and communicate limitations

  • Show the final PNG or short movie

Speaker note: detections and tracks have already been computed. No new detection or tracking runs today.

2. Three windows, three jobs#

  • Terminal/PowerShell starts tools

  • JupyterLab browser explains and runs code cells

  • Napari desktop window displays and manipulates layers

Demo: locate each window and show how to switch among them.

3. Setup checkpoint#

  • Correct folder contains pyproject.toml

  • Kernel is Python (napari workshop)

  • sys.executable contains current folder and .venv

  • data/teaching/metadata.json exists

Speaker note: stop classroom downloads after five minutes and distribute the local copy.

Images and display (20–90 minutes)#

4. An image is an array#

  • Axes: t,z,y,x

  • Thirty frames: indices 0–29

  • Values: unsigned 16-bit intensities

Prompt: which axis is acquisition frame, and which is depth?

5. Layers keep data and controls organized#

  • Source layer names: DNA and mitochondria

  • Visibility, contrast, colormap, opacity, blending

  • Display settings do not alter stored TIFF values

6. Predict before adjusting#

  • What becomes visible if the upper contrast limit falls?

  • What can additive blending conceal?

  • Why toggle channels separately?

Demo: make one useful adjustment and one misleading saturated view.

7. Slice, projection, and 3D rendering#

  • Slice: one depth plane

  • Maximum-intensity projection: brightest value, depth location discarded

  • 3D: an interactive rendering of acquired voxels, not new data

Prompt: find one feature and compare all available views.

8. The prepared-data tradeoff#

  • All 30 frames retained

  • Spatial factor 4

  • Lower network/RAM/rendering demands

  • Fine detail lost and unrecoverable from the package

Speaker note: source images are about 7.53 GiB in memory before overhead.

Supplied measurements (100–140 minutes)#

9. A detection is a candidate at one frame#

  • Columns: t,z,y,x,amp,int

  • Match one row to one marker

  • Coordinates must use the same transform as images

Speaker note: numeric measurements and visual alignment do not prove identity.

10. A track is an inferred association#

  • Columns: id,t,z,y,x

  • 2,058 supplied track IDs

  • Tail length is a time window

  • Smooth-looking paths can still be wrong

Prompt: name one possible association failure.

11. What we can and cannot claim#

Supported description

Awaiting evidence

Layer names DNA/mitochondria

Detailed tracked-particle identity

Frame indices 0–29

Elapsed seconds/acquisition interval

Source-voxel coordinates

Micrometres/physical scale bar

Supplied candidates and associations

Biological validation

Export and communication (140–165 minutes)#

12. Two different kinds of animation#

  • Fixed camera across acquired frames: sample changes over frame index

  • Camera keyframes at one time: viewpoint changes

  • Combined animation: explain both explicitly

Speaker note: camera interpolation creates views, not observations.

13. Saving versus exporting#

  • Save notebook: code, prose, cell outputs

  • Export PNG: one rendered view

  • Export MP4: rendered frames at a playback fps

  • Use a fresh output folder and reopen the result

14. Playback speed is not acquisition time#

  • Movie fps controls presentation speed

  • Use seconds only after timestamps are verified

  • Check first/last frame, dimensions, and intended layer/time changes

Challenge and close (165–180 minutes)#

15. Independent mini-challenge#

  • Choose a view and style layers deliberately

  • Include points/tracks only when useful

  • Export PNG or MP4

  • Explain three decisions and one limitation

Show the 2D, paired, and static fallback routes on the same slide.

16. Exit ticket#

  • Distinguish intensity, detection, and track

  • State what contrast changed

  • Name one unsupported conclusion

17. Continue carefully#

  • Optional animation and large-data notebooks

  • Use-your-own-data checklist: axes, units, memory, coordinates, provenance

  • Teaching dataset DOI: 10.5281/zenodo.22696913 (CC BY 4.0)

  • Original dataset DOI: 10.5281/zenodo.22694247 (CC BY 4.0)

Speaker note: remind learners that successful rendering is evidence of a working display workflow, not proof of detection, tracking, calibration, or biological validity.