modist

Interactive distribution widgets for marimo, in the style of koaning/wigglystuff. Drag the density curve to shape a distribution, then feed the params straight into a distribution constructor with a single splat.

Install

uv add modist            # or: uv pip install modist  (pip install modist)

Quickstart

import marimo as mo
import modist as md

w = mo.ui.anywidget(md.Normal())
w

params = w.value            # {'mu': ..., 'sigma': ...}

import pymc as pm
dist = pm.Normal.dist(**params)   # or pm.Beta / pm.Gamma / pm.StudentT

Families

StudentT's third parameter is a tails dial: drag it up for fatter tails (lower nu) or down for thinner tails (higher nu). Because nu has no natural on-curve landmark, its drag is a separate 1-D slider rather than a point you move on the density curve.

alpha/beta follow the PyMC / statistics convention (Gamma's beta is the rate, not scipy's scale). The lazy .scipy and .pymc adapters map to the right parametrization automatically:

n = md.Normal(mu=2.0, sigma=3.0)
n.scipy   # <scipy.stats.norm> via loc=/scale=
n.pymc    # pm.Normal.dist(mu=2.0, sigma=3.0)

g = md.Gamma(alpha=2.0, beta=3.0)
g.scipy   # scipy.stats.gamma(a=2.0, scale=1/3)  -- rate handled for you

w.value is a plain dict of the synced traits, so pm.X.dist(**w.value) works with no conversion.

Jupyter

The widgets are anywidget/ipywidgets under the hood, so they run in plain Jupyter too — no marimo required. Just display() the widget and read its .params (or .scipy) instead of wrapping it in mo.ui.anywidget(...):

import modist as md
from IPython.display import display

w = md.Normal(mu=0, sigma=1)
display(w)          # drag the curve to reshape it

w.params            # {'mu': ..., 'sigma': ...}

A full walkthrough notebook — all five families, live scipy stats, and a beta-prior combination example — lives at demos/jupyter_example.ipynb.

From a checkout:

uv sync --extra dev --extra scipy   # installs jupyter, ipykernel, jupytext
make jupyter                        # opens demos/jupyter_example.ipynb in JupyterLab

make jupyter registers the repo's .venv as a modist kernel, so the notebook uses exactly the installed packages. Requires a local JupyterLab (installed alongside jupyter via the dev extras).

How it works

Each family is its own anywidget class with a small set of synced parameter traits (no x_min/x_max/n_points). The view — SVG scaffold, pan/zoom, draggable hit lines, and per-family math — lives in a self-contained ESM module.

Source JS lives in js/ (js/base.js shared scaffold + one family file, all importing a vendored copy of jStat for pdf/cdf/quantile math). Anywidget delivers _esm as a Blob URL, which cannot resolve relative imports, so esbuild bundles each family (jStat inlined) into the committed src/modist/static/*.js files — the same pattern wigglystuff uses for its JS-heavy widgets.

Rebuilding the JS

make js          # esbuild js/*.js -> src/modist/static/*.js
make js-watch    # rebuild on every edit (for anywidget hot-reload dev)

Requires a local esbuild (npm install --no-save esbuild).

Development

make venv        # creates .venv with dev deps + esbuild
make test        # pytest
npm run test:js  # Playwright JS integration probes (headless Chromium)

Acknowledgements

• jStat — JavaScript statistics library (MIT), vendored and bundled for the pdf/cdf/quantile math.
• wigglystuff — the interaction and architecture model (one class per family, prebuilt ESM per class).