Tutorials#

minisim is also a teaching tool. The tutorials walk the anatomy of miniscope data: starting from a clean simulated signal and adding one physical effect at a time (optics, brain motion, illumination falloff, sensor noise), so you can see exactly what each does to the image.

Anatomy of a recording (interactive notebook)#

The flagship tutorial is an interactive Jupyter notebook that builds the forward pipeline stage by stage, with sliders to vary the physics and see the movie respond in real time.

Note

This notebook is interactive (it uses ipywidgets and runs a live simulation), so it is meant to be run, not read statically.

Get the notebook#

The notebooks ship inside the package. After installing, copy them out to a directory you own with the bundled minisim-notebooks command:

pip install "minisim[notebook]"
minisim-notebooks ./minisim-notebooks      # copies the bundle(s) here
cd minisim-notebooks/01_anatomy
jupyter lab 01_anatomy.ipynb

minisim-notebooks takes an optional destination (default ./minisim-notebooks) and --force to overwrite an existing copy. No data download is needed: minisim generates the recording from code as the notebook runs.

Working from a clone of the repository instead? Open it directly at minisim/notebooks/training/01_anatomy/01_anatomy.ipynb.

The stages mirror the forward chain described in Concepts:

  1. Place neurons and generate calcium activity (the clean signal).

  2. Optics: depth-dependent blur and dimming.

  3. Render to the sensor canvas.

  4. Neuropil and vasculature background.

  5. Photobleaching over the recording.

  6. Brain motion under the lens.

  7. Illumination profile and emission vignette.

  8. Stray-light leakage.

  9. Sensor digitization to raw counts, where the auto-focus yield is realized.

Coming soon

A second notebook, the demixing capstone, shows why naive per-ROI traces are contaminated by neighbor bleed and neuropil, and how demixing recovers the true signals, quantified against the ground-truth A/C.