semvec/pypi-packages huggingface dataset:
resipy 3.6.6
API for resistivity and IP inversion/modelling around R* codes

ResIPy a python wrapper around R* codes
=======================================

This package is a Python wrapper around the R* family of codes (R2, cR2, R3t, cR3t, see http://www.es.lancs.ac.uk/people/amb/Freeware/R2/R2.htm for more details) and provides an API (Application Programming Interface) for inverting/modelling resistivity and IP data.

It aims to be 
- (1) **simple and intuitive** to use (you can invert your data in one-click with all default settings)
- (2) but still allowing the user to have **full control** on data filtering, error modelling, mesh parameters and inversion settings

This is an open-source project, everyone is welcome to contribute.

In addition to this python package that provideds the API,
a multi-platform standalone Graphical User Interface (GUI) is also available to download.

Please see the gitlab repository for more information and to download the executable for the GUI: **https://gitlab.com/hkex/resipy**

Or the online documentation: **https://hkex.gitlab.io/resipy**
astrologics 0.3.2
An analysis framework for monotonous Boolean model ensemble
Requires Python: >3.8

<img align="left" width="160" src="https://github.com/sysbio-curie/AstroLogics/raw/main/doc/images/Logo_full.svg" />

# An analysis framework for monotonous Boolean model ensemble

This is a repository of data, code and analyses of AstroLogics framework.
A step-by step tutorial can be found in the folder tutorial. Please have a look at our [tutorials](https://astrologics.readthedocs.io/en/latest/tutorials.html). 

## Overview
AstroLogics is a Python package designed for analysing monotonous Boolean model ensemble, a product of Boolean model synthesis from method such as [Bonesis](https://bnediction.github.io/bonesis/index.html).

Our framework includes two major processes 
1. Dynamical properties analysis : 
    - Calculated distance between models through probabilistic approxmition via [MaBoSS](https://github.com/sysbio-curie/MaBoSS).
2. Logical function evaluation : 
    - Features logical equation and identify key logical features between model clusters
3. Statistical analysis :
    - Perform statistical analysis between model clusters to identify key logical featuers between clusters

<p align="center">
<img height="400" src="./doc/images/Figure_Overview_framework_version2.png" />
<br>
<em> Overview of the framework showing the two major processes in the framework. <strong>Dynamics</strong>: dynamical properties analysis. <strong>Logics</strong>: Logical function evaluation 
<strong>Statistics</strong>: statistical framework to link model's logic with statistics.
</em>
</br>
</p>

## Getting Started
### Requirements (for AstroLogics)
- Python version 3.8 or greater
- Python's packages listed here:
    - pandas
    - numpy
    - scipy, sklearn
    - maboss
    - boolsim
    - bonesis
    - mpbn
### Installation 

There are several ways to install AstroLogics


#### PyPi

```
pip install astrologics
```

#### Conda
```
conda install -c colomoto astrologics
```

#### From source
First clone this directory:
```
git clone https://https://github.com/sysbio-curie/AstroLogics
```

Then install AstroLogics with pip
```
pip install AstroLogics
```


## Tutorials

Tutorials are available as Jupyter notebooks

### Run with Binder

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/sysbio-curie/AstroLogics/main?filepath=AstroLogics)


### Run locally with Docker
To run this notebook using the built docker image, run : 
```
docker run -p 8888:8888 -d sysbiocurie/astrologics
```

### Run locally with Conda
Creating the conda environment
```
conda env create --file environment.yml
```

To activate it : 
```
conda activate astrologics
```


To run the notebook: 
```
jupyter-lab
```

## Documentation

Our documentation is available on [ReadTheDocs](https://astrologics.readthedocs.io/)


## Citing AstroLogics
Coming soon