| Name | nuovoLIRA JSON |
| Version |
0.6.0
JSON |
| download |
| home_page | |
| Summary | A Bayesian procedure to delineate the boundary of an extended astronomical object |
| upload_time | 2023-06-16 12:14:31 |
| maintainer | |
| docs_url | None |
| author | Brendan Martin |
| requires_python | |
| license | MIT |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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|
nuovoLIRA
==============================
# What is it?
A method to implement the Bayesian model described here: [https://nuovolira.tiiny.site/](https://nuovolira.tiiny.site/).
# Installation
```
pip install --upgrade pip
pip install nuovoLIRA
```
# Main Features
- Algorithms that sample from the conditional distributions of the NuovoLIRA model
# Source Code
The source code is currently hosted on GitHub at [https://github.com/bmartin9/nuovolira-pypi](https://github.com/bmartin9/nuovolira-pypi).
# Dependencies
The external packages needed to use nuovoLIRA are listed in the requirements.txt file at [https://github.com/bmartin9/nuovolira-pypi](https://github.com/bmartin9/nuovolira-pypi).
To create a python virtual environment and install these requirements, download requirements.txt from [https://github.com/bmartin9/nuovolira-pypi](https://github.com/bmartin9/nuovolira-pypi) and do for example
```
python -m venv \path\to\myenv
pip install -r /path/to/requirements.txt
```
# Example Usage
To sample from the conditional distribution of $Z$ (equation (33) in [https://nuovolira.tiiny.site/](https://nuovolira.tiiny.site/)) using the Swendsen Wang algorithm do
```
from nuovoLIRA.models.deconvolver import *
from numpy.random import default_rng
random_state = default_rng(seed=SEED)
Z_init = np.random.choice([0, 1], size=(10,10), p=[1./3, 2./3])
data = np.random.randint(0,40,size=(10,10))
Z_sampler = Sample_Z(random_state=random_state,
initial_Z = Z_init,
beta = 2,
lam_b = 1,
lam_e = 20,
y = data
)
Z_new = Z_sampler.Z_update(Z_init)
```
# Contact
For issues/discussions please email b.martin22@imperial.ac.uk
Project Organization
------------
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
--------
<p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>
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