[](https://pypi.org/project/marimo-scipy-utils/)
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# 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.
<img src="media/demo.png" />
## Installation
```bash
uv add marimo-scipy-utils
```
## Functions
[See example notebook here](https://github.com/hbmartin/ai-roi-mcm-npv-marimo/blob/main/ai_roi_mcm_npv.py)
### `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:**
```python
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:**
```python
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
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