glmhmmt 0.1.0

GLM-HMM and GLM-HMMT tooling for behavioural task analysis.

glmhmmt

glmhmmt is the installable package for the Dynamax-based GLM-HMM / GLM-HMMT code.

If someone in the lab only wants to import the model class in their own code, this package is enough:

pip install "git+https://github.com/BrainCircuitsBehaviorLab/glmhmmt.git"

or with uv:

uv pip install "git+https://github.com/BrainCircuitsBehaviorLab/glmhmmt.git"

If they want to add it as a dependency in another uv project:

uv add "glmhmmt @ git+https://github.com/BrainCircuitsBehaviorLab/glmhmmt.git"

Then in Python:

from glmhmmt import SoftmaxGLMHMM

If they want the baseline GLM fit directly in their own code, including binary lapses:

from glmhmmt import fit_glm

Direct Model Use

See examples/use_softmax_glmhmm.py for a minimal example that builds the model directly from arrays, without any task adapter.

For a baseline GLM example, see examples/glm_lapses/example.py.

The package root uses lazy imports, so importing SoftmaxGLMHMM does not require task adapters.

The CLI entrypoints under glmhmmt.cli.* are wrappers around task adapters, runtime paths, and result directories. They are useful for command-line workflows, but they are not the recommended import interface for another project.

Task Adapters Are Optional

This package does not need task adapters when someone only wants the reusable model classes and fitting utilities.

If a user wants task-aware CLIs or notebooks, they can provide adapters in either of these ways:

1. Put a tasks/ package in their own working directory and point GLMHMMT_TASK_PATHS at it.
2. Install a separate package that exposes entry points in the glmhmmt.tasks group.

Minimal entry-point example:

[project.entry-points."glmhmmt.tasks"]
my_lab_task = "my_lab_glmhmmt.task:MyLabTaskAdapter"

Recommended Sharing Workflow

For lab use, the simplest setup is:

1. Keep glmhmmt in its own Git repo.
2. Keep task adapters in a separate companion repo.
3. Install both from Git or from local editable paths during development.

That is usually better than publishing to PyPI immediately, because:

• it avoids exposing unrelated analysis code
• updates are simple
• private sharing inside the lab is easy

Publish to PyPI later only if you want a public, versioned release.

Local Development

For work inside this repository:

uv sync
uv run python -c "from glmhmmt import SoftmaxGLMHMM; print(SoftmaxGLMHMM)"

If you want the notebook extras too:

uv sync --extra notebooks

The project-local runtime overrides live in config.toml. Packaged defaults live in src/glmhmmt/resources/default_config.toml.