semvec/pypi-packages huggingface dataset:
gridmlip 0.2
Grid-based method for calculating the percolation barrier of mobile species using machine learning interatomic potentials
License: MIT
Keywords: MLIP, percolation barrier, irreducible mesh

## gridmlip
<p align="left">
<a href="https://github.com/dembart/gridmlip/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/badge/license-MIT-darkred"></a>


## Contents
- [About](#about)
- [Installation](#installation)
- [How to use](#how-to-use)
- [Notebooks](#notebooks)
- [How to cite](#how-to-cite)


## About

**gridmlip** is a library for calculating percolation barriers of mobile species in solids using grid-based method with machine learning interatomic potentials (MLIPs).

## Installation

```
pip install gridmlip
```
or

```python
git clone https://github.com/dembart/gridmlip
cd gridmlip
pip install .
```

## How to use
Here we describe the pipeline in general. For a specific example, see [Notebooks](#notebooks).

#### Step #1: Construct configurations for processing with your favorite MLIP
```python
from gridmlip import Grid

atomic_types_mapper = {3:0, 31:1, 17:2}
grid = Grid.from_file('your.cif', specie = 3, r_min = 1.8, 
                    atomic_types_mapper=atomic_types_mapper # optional
                  )
cfgs = grid.construct_configurations('data.cfg')
```

#### Step #2: Evaluate the configurations with your favorite MLIP

```
mlp calculate_efs p.mtp data.cfg --output_filename=processed_data.cfg'
```

#### Step #3: Read processed configrations and calculate the percolation barriers

```python
from gridmlip import Grid

grid = Grid.from_file('your.cif', specie = 3, r_min = 1.8)
grid.read_processed_configurations('processed_data.cfg', format = 'cfg')
barriers = grid.percolation_barriers()
```

#### Step #4: Write .grd or .cube file for visualization in VESTA 3.0

```python
g.write_grd('test.grd')
```

## Notebooks


- [Using TensorNet model pre-trained on MatPES](notebooks/TensorNet_MatPES.ipynb)    
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/14gabZ-u19K-_I_e7-g67N8-xtPIN36Nw#sandboxMode=true&scrollTo=5TySp6C6UBoZ)


### How to cite
If you use the gridmlip library, please, consider citing this repository.
pyisic 0.1.26
Standard industrial classification standardization
License: MIT
Requires Python: >=3.8

******
Pyisic
******
.. image:: https://github.com/sayari-analytics/pyisic/actions/workflows/main.yaml/badge.svg?branch=main
   :target: https://github.com/sayari-analytics/pyisic/actions/workflows/main.yaml
   :alt: CI

.. image:: https://img.shields.io/codecov/c/github/sayari-analytics/pyisic/main.svg
   :target: https://codecov.io/github/sayari-analytics/pyisic?branch=main
   :alt: Coverage report

.. image:: https://img.shields.io/pypi/v/pyisic.svg
   :target: https://pypi.python.org/pypi/pyisic
   :alt: PyPI Version

.. image:: https://img.shields.io/pypi/pyversions/pyisic.svg
   :target: https://pypi.python.org/pypi/pyisic
   :alt: Supported Python Versions

.. image:: https://img.shields.io/pypi/l/pyisic.svg
   :target: https://github.com/sayari-analytics/pyisic/blob/main/LICENSE
   :alt: License

Overview
########
Pyisic defines industrial classification standards and concordances between standards.

.. pull-quote::
    The `International Standard Industrial Classification of All Economic Activities (ISIC) <https://unstats.un.org/unsd/classifications/Econ/ISIC.cshtml>`_ is the international reference classification of productive activities. Its main purpose is to provide a set of activity categories that can be utilized for the collection and reporting of statistics according to such activities. Since the adoption of the original version of ISIC in 1948, the majority of countries around the world have used ISIC as their national activity classification or have developed national classifications derived from ISIC.

Usage
+++++

.. code-block:: python

    >>> import pyisic
    >>> pyisic.NAICS2017['927110']
    {'code': '927110', 'description': 'Space Research and Technology', 'category': None}

    >>> pyisic.NAICS2017_to_ISIC4.concordant("927110")
    {(<Standard.ISIC4: 'ISIC4'>, '5120'), (<Standard.ISIC4: 'ISIC4'>, '8413')}

Install
#######

.. code-block:: bash

    pip install pyisic


Documentation
#############

The official documentation is hosted on readthedocs.org: `pyisic.readthedocs.io <https://pyisic.readthedocs.io>`_

Contributing
############
Issues and pull requests are welcome!

Refer to `CONTRIBUTING.rst <https://github.com/sayari-analytics/pyisic/blob/main/CONTRIBUTING.rst>`_ for more details about the workflow and hints on how to prepare your pull request.
signal-petrophysics 0.0.1rc4
Library to handle reading, analysing and plotting Well-Log Pattern Recognition algorithms from Signal-Processing
License: MIT
Requires Python: >=3.10

<div align="center">
  <img src="docs/logo.jpeg" width="400" height="400">
</div>

A comprehensive Python library for reading, pre-processing, signal sampling, adapting, matching, plotting and post-processing.
⚠️ **Alpha Release**: This is an early alpha version. Expect breaking changes in future releases.

[![PyPI version](https://badge.fury.io/py/signal-petrophysics.svg)](https://badge.fury.io/py/signal-petrophysics)
[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)

## Installation

### Stable Release (when available)
```bash
pip install signal-petrophysics
````