nuovoLIRA 0.6.0

A Bayesian procedure to delineate the boundary of an extended astronomical object

nuovoLIRA

What is it?

A method to implement the Bayesian model described here: 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.

Dependencies

The external packages needed to use nuovoLIRA are listed in the requirements.txt file at 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 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/) 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 [e-mail]

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

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Project based on the cookiecutter data science project template. #cookiecutterdatascience