marimo-scipy-utils 0.1.0

A collection of utilities to make it easier to interact with scipy in a marimo notebook, e.g. manipulate distributions, visualize data, etc.

Marimo SciPy Utils

Utility functions for creating interactive marimo components with scipy distributions.

This package provides functions for creating and configuring interactive UI elements in marimo notebooks, with a focus on parameter input and visualization using scipy probability distributions.

Installation

uv add marimo-scipy-utils

Functions

See example notebook here

abbrev_format(x: float, pos: int | None) -> str

Format numbers with k/M suffixes for thousands/millions.

Parameters:

• x: The number to format
• pos: The tick position (unused but required by matplotlib formatter interface)

Returns:

• str: The formatted number string with k/M suffix if applicable

Example:

abbrev_format(1500, None)  # Returns "2k"
abbrev_format(2500000, None)  # Returns "3M"
abbrev_format(42.7, None)  # Returns "42.7"

display_sliders(name: str, sliders: mo.ui.dictionary | mo.ui.slider, invars: dict[str, dict], dist: str | Callable | None = None, descriptions: dict = {}) -> mo.Html

Display parameter sliders with optional distribution plot.

Parameters:

• name: Name of the parameter group to display
• sliders: Either a single slider or dictionary of sliders for distribution params
• invars: Dictionary to store input variable configurations
• dist: Distribution to use (string name or callable), required for multi-sliders
• descriptions: Optional dict mapping parameter names to descriptions

Returns:

• Marimo component displaying the sliders and optional distribution plot

For a single slider, displays it as a constant parameter. For multiple sliders, displays them with a plot of the resulting distribution. Distribution must be specified for multiple sliders, either as a string name matching a scipy distribution or as a callable distribution object.

Raises:

• DistributionConfigurationError: If dist is None for multiple sliders

generate_ranges(distribution: str, ranged_distkwargs: dict) -> dict

Generate parameter ranges for a probability distribution.

Takes a distribution name and dictionary of parameter ranges, validates the ranges against allowed bounds for that distribution, and returns a merged dictionary with complete range specifications.

Parameters:

• distribution: Name of the probability distribution (e.g. "normal", "beta")
• ranged_distkwargs: Dictionary mapping parameter names to their range specifications. Each range spec should have "lower" and "upper" bounds.

Returns:

• dict: Complete parameter range specifications with distribution defaults merged with provided ranges

Raises:

• MissingParameterError: If a required parameter range is not provided
• ParameterBoundError: If a provided range exceeds the allowed bounds for a param

params_sliders(ranged_distkwargs: dict) -> mo.ui.dictionary

Create a dictionary of sliders for parameter ranges.

Takes a dictionary of param ranges and creates interactive sliders for each param. The sliders will be bounded by the lower/upper values specified in the ranges dict. The step size and initial value can optionally be specified per param.

Parameters:

• ranged_distkwargs: Dictionary mapping parameter names to their range specifications. Each range spec should have "lower" and "upper" bounds, and optionally "step" and "value" keys.

Returns:

• mo.ui.dictionary: A dictionary of marimo slider UI elements, one per parameter. Each slider will be configured according to the parameter's range spec.

Example:

ranges = {
    "mean": {"lower": 0, "upper": 100, "step": 1, "value": 50},
    "std": {"lower": 0, "upper": 10}
}
sliders = params_sliders(ranges)

Supported Distributions

Any scipy distribution can be used.

Included helpers following scipy distributions:

• uniform: Uniform distribution
• triang: Triangular distribution
• beta: Beta distribution
• norm: Normal distribution

Dependencies

• marimo
• matplotlib
• scipy