semvec/pypi-packages huggingface dataset:
mldice 0.2.0
Machine Learned Diffusion Coefficient Estimator - ML-DiCE is an ML framework that can predict five modes of elemental diffusion in alloys
License: MIT
Requires Python: >=3

<h1 align="center">
<img src="https://www.mzlab.co.in/frontend/img/team/logo.svg" height="130">

ML-DiCE | Machine Learned Diffusion Coefficient Estimator
</h1>
<br>

[![DOI](https://zenodo.org/badge/DOI/update_soon.svg)](https://doi.org/to/be/updated)
[![PyPI](https://img.shields.io/pypi/v/mldice/0.2.0)](https://pypi.org/project/mldice/)

## Installation

```
pip install mldice
```
## Usage

Create a conda environment as 

```
conda create --name myEnv
```
Activate the environment created
```
conda activate myEnv
```
In the activated environment, run
```
mldice -options
```

`mldice` will featurize your alloy or impure metal and predict diffusion coefficient in m^2/s. The options can be set through following arguments

Use `-de` to specify diffusing element `-dm` to specify diffusion medium. Examples:

 * `-de Fe` would take iron as diffusing element.
 * `-dm Ni75.5Cu24Co0.5` select Ni75.5Cu24Co0.5 is the diffusion medium where constituent elements expressed as percentage 
 * `-t 500` would select the temperature (say, 500K in this case) of diffusion process in Kelvin.
 * `-m self` select self diffusion mechanism. Mechanisms include self, impurity and chemical modes. 
 * `-e RF` would select Random Forest regression as prediction algorithm. DNN selects neural network based prediction.

Essentially, the run command shall be as follows:
```
mldice -de [diffusing element] -dm [diffusion medium] -t [temperature] -m [diffusion mechanism] -e [algorithm chosen for prediction]
```


## Output

All outputs can be found in the Prediction.md file. It contains the following information:

#### Predicted parameters
|  Property   |    Value    |
|:-----------:|:-----------:|
| Predicted D | --    m^2/s |
|    RMSE     | --    m^2/s |
|     MAE     | --    m^2/s |
| Uncertainty | --    m^2/s |



## Online Ressources

* https://arjun.skv.net/SI (Supporting Information)
* https://arjunskv.net/main (Main article)


## Citation

```
@software{mldice,
  author       = {Kulathuvayal Arjun S., Su Yanqing },
  title        = {{Elemental Diffusion Coefficient Prediction in Conventional Alloys using Machine Learning}},
  month        = june,
  year         = 2024,
  publisher    = {--},
  version      = {v0.0.1},
  doi          = {--},
  url          = {--}
}
```

### Under development

*Advanced featurization for alloys:* New featurization schemes are under developing
facebook-simple-scraper 0.1.14
A simple scraper for Facebook
License: MIT
Requires Python: >=3.10

## Facebook Simple Scraper

Welcome to the Facebook Post Scraper project! Our tool allows you to scrape posts from different Facebook pages, helping you gather crucial information flexibly and efficiently.
 
### 🚀 Features

- Post Scraping: Scrapes individual posts from a Facebook page, capturing all the details that accompany the post.
- Comment Scraping: Fetches the comments under each post. This can provide you with valuable audience feedback and participation related to each post.

 
### 📄Usage

Using our scraping tool is extremely simple. After setting up your environment correctly, you can start scraping right away!

```python
user, password = "your_user_email", "your_password"
opts = ScraperOptions(
    credentials=[LoginCredentials(
        username=user,
        password=password
    )],
    max_comments_per_post=10,
    sleep_time_min=2,
    sleep_time_max=5,
    stop_conditions=[StopAfterNPosts(5)],
)
scraper = Scraper(opts)
post = scraper.get_posts("NintendoLatAm")
print(list(post))
```
 
🔒 License
MIT License (Do whatever you want with this code, but don't blame me if something goes wrong)
 
We hope you find our Facebook Post Scraper helpful! Your feedback and contributions are always welcome. Enjoy scraping!

 
The Facebook Post Scraper Team
❗ Disclaimers: Please be aware of Facebook's terms and conditions pertaining to data scraping. It's your responsibility to use this tool in accordance with those terms.