semvec/pypi-packages huggingface dataset:
mfbm 0.1.5
Multivariate fractional Brownian motion
License: MIT
Requires Python: >=3.7

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# Multivariate fractional Brownian motion (mfBm)
This package provides implementation for the multivariate fractional Brownian motion and simple fractional brownian motion with the method described in [1].

### Installation
The mfbm package is available on PyPI and can be install via pip with the following command:
```bash
pip install mfbm
```

### Example usage
```python
import numpy as np
from mfbm import MFBM

p = 5
H = np.linspace(0.6, 0.9, 5)
n = 100
T = 100

rho = 0.7 * np.ones((p, p))
np.fill_diagonal(rho, 1)

eta = np.ones_like(rho)
sigma = np.ones(len(H))

mfbm = MFBM(H, rho, eta, sigma)
ts = mfbm.sample(n, T)
```

Explanation of the parameters:
- `H: np.ndarray` is a one dimensional array of the Hurst parameters, where all Hursts are in the range (0, 1).
- `rho: np.ndarray` is a two dimensional array of the cross correlations between the multiple fBms, default is the identity matrix.
- `sigma: np.ndarray` is a one dimensional array of the standard deviations of the single fBms, default is all ones.
- `n: int` is the number of increaments to generate for the multivariate fractional Gaussian noise.
- `T: float` is the time horizon of the mfBm, default is `n`.

![mfbm](https://github.com/user-attachments/assets/2fb0f6bf-58a5-4cd1-99e9-824bd8b17315)


[1] Andrew T. A. Wood & Grace Chan (1994): Simulation of Stationary
Gaussian Processes in [0, 1]^d , Journal of Computational and Graphical Statistics, 3:4,
409-432
neurodesign-plus 1.0.1
Extended and maintained fork of the neurodesign package for fMRI experimental design optimisation
License: MIT
Requires Python: >=3.9

# Neurodesign_Plus

This repository contains a extended version of the [Neurodesign](https://github.com/neuropower/neurodesign) Python package. This version extends the original capabilities to allow for more complex and specific experimental conditions, including variable stimuli durations, conditional inter-trial intervals (ITIs), and user-defined stimulus ordering.

## Neurodesign Documentation

The documentation of the base package `neurodesign` is available at [ReadTheDocs](http://neurodesign.readthedocs.io/en/latest/).

## File description

```
├── docs                    Contains the source code to generate the documentation with sphinx (WIP).
├── manuals                 Contains comprehensive markdown documentation on neurodesign-plus.
└── neurodesign             Folder with the source code of the python package
    └── media               Folder contains the logo of neurodesign, which is used in the reports.
├── tests                   Contains scripts to test modifications to the package.
└── tutorials               Contains .ipynb tutorials on neurodesign-plus, the base functions and the new functions.
```

## Overview of Modifications

The modifications focus on the `classes.py` file, specifically enhancing the `Experiment` class to support greater flexibility. The core updates revolve around three key features:

1.  **Fixed User Input:** The ability to input a specific order and ITI sequence (bypassing randomization to evaluate a pre-determined design).
2.  **Varied Stimuli Durations:** Support for distribution-based durations rather than a single fixed duration.
3.  **Conditional ITIs:** Implementation of Inter-trial Intervals that vary based on probability distributions or the relationship between specific stimuli.

## Neurodesign-plus Documentation

The complete documentation for `neurodesign-plus` is hosted on [ReadTheDocs](https://neurodesign-plus.readthedocs.io/en/latest/). It serves as your primary resource for:

1.  **Getting Started:** Installation and setup.
2.  **Core Concepts:** Understanding Neurodesign Efficiency Metrics.
3.  **Advanced Usage:** Implementing fixed user inputs, variable durations, and conditional ITIs.
4.  **Tutorials:** Jupyter notebook tutorials to fast-track your learning.

---

## Credits

This is a fork of the original **Neurodesign** package.

- **Original Author:** [Neuropower Team](https://github.com/neuropower)
- **Primary refactoring, extensions, and tutorials:** Atharv Amar Umap (Social Learning and Decisions Lab, UMD) — <aumap@terpmail.umd.edu>
- **Supervision, design guidance, tutorials:** Valentin Guigon (Social Learning and Decisions Lab, UMD) — <vguigon@umd.edu>