Skip to main content

Physically-driven synthetic 1-photon miniscope data: a forward-model generator and teaching tool, the inverse of the minian analysis pipeline.

Project description

Minisim

pytest codecov Documentation Status PyPI Python versions License

Physically-driven synthetic 1-photon miniscope data: a forward-model generator and teaching tool.

Minisim builds a miniscope recording forward from its physical components, the inverse of an analysis pipeline like minian. Instead of recovering signals from a movie, it starts from biology and optics and produces the movie, together with the ground truth that generated it:

place neurons -> cell activity -> bleaching -> optics -> composite -> neuropil
             -> brain motion -> illumination profile -> vignette -> leakage -> image sensor

Each stage is a small, inspectable physical model. Because the recording is built forward, every recording ships with exact ground truth (cell locations, footprints, calcium traces, spike times, motion trajectory, per-pixel optical fields), which makes Minisim useful for:

  • Benchmarking calcium-imaging pipelines (minian, CaImAn, suite2p, ...) against known ground truth.
  • Teaching the anatomy of miniscope data: what each physical effect does to the image, via interactive notebooks.
  • Testing analysis code with reproducible, parameterized fixtures.

📖 Full documentation: minisim.readthedocs.io — concepts, quickstart, how-to guides, and the API reference.

Install

pip install minisim                # engine only
pip install "minisim[notebook]"    # + the interactive teaching notebooks

Requires Python >= 3.10. Core dependencies are just numpy, scipy, xarray, zarr, pydantic, and numpydantic.

The teaching notebooks ship inside the package; list them and copy the ones you want out to a writable directory with the bundled command:

minisim-notebooks list                  # see what's available
minisim-notebooks copy 01_anatomy       # -> ./minisim-notebooks/01_anatomy
# then: cd minisim-notebooks/01_anatomy && jupyter lab

Quick start

from minisim import (
    Acquisition, Optics, ImageSensor, PlaceNeurons, CellActivity,
    CellOptics, Composite, Sensor, Spec, simulate,
)

spec = Spec(
    acquisition=Acquisition(
        fps=20.0, duration_s=10.0,
        optics=Optics(magnification=8.0, na=0.45),
        image_sensor=ImageSensor(n_px_height=256, n_px_width=256, pixel_pitch_um=8.0),
    ),
    seed=0,
    steps=[
        PlaceNeurons(density_per_mm3=400000.0, soma_radius_um=4.0),
        CellActivity(),
        CellOptics(),
        Composite(),
        Sensor(),
    ],
)

rec = simulate(spec)
movie = rec.observed          # xarray DataArray (frame, height, width)
truth = rec.ground_truth      # cells, traces, spikes, optical fields

Relationship to minian

Minisim is the forward (generative) counterpart to minian's inverse (analysis) pipeline. The dependency is strictly one-directional: Minisim never imports minian. minian uses Minisim as a test dependency to supply ground-truth fixtures for its recovery tests.

License

GPL-3.0-or-later. See LICENSE.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

minisim-0.4.0.tar.gz (246.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

minisim-0.4.0-py3-none-any.whl (271.6 kB view details)

Uploaded Python 3

File details

Details for the file minisim-0.4.0.tar.gz.

File metadata

  • Download URL: minisim-0.4.0.tar.gz
  • Upload date:
  • Size: 246.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for minisim-0.4.0.tar.gz
Algorithm Hash digest
SHA256 126a217edb22d7ee3ccdf82f149ffe2e84c56df03ddb92286c8654fc65f4dcc2
MD5 f6747fe4fa11230ec158d387c4aa777c
BLAKE2b-256 32c06f130ca9bd98b0d9f7d0c5f8804b9249d8c9b96730ae588c7323277544af

See more details on using hashes here.

Provenance

The following attestation bundles were made for minisim-0.4.0.tar.gz:

Publisher: publish.yml on miniscope/minisim

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file minisim-0.4.0-py3-none-any.whl.

File metadata

  • Download URL: minisim-0.4.0-py3-none-any.whl
  • Upload date:
  • Size: 271.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for minisim-0.4.0-py3-none-any.whl
Algorithm Hash digest
SHA256 5d931f0a3a6e82b090fc545c7f2341b678385ae5958d50ae6058a58732d5e1f1
MD5 e2e412e2833ca9e46bb6f6c98a8218d1
BLAKE2b-256 22ae73271f1d310afea0386e372d2750d6ce1783f342f8050e3eb16e922d1052

See more details on using hashes here.

Provenance

The following attestation bundles were made for minisim-0.4.0-py3-none-any.whl:

Publisher: publish.yml on miniscope/minisim

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page