semvec/pypi-packages huggingface dataset:
cim-loader 0.1.3a0
CIM Database Uploader & Downloader
Requires Python: <4.0,>=3.10

# CIM-Loader
Automated scripts for 
* uploading and downloading CIM files from various databases
* converting CIM files between common formats (XML, TTL, etc.)

## Installation
The library can be pip installed from PyPi using
`pip install cim-loader`

To install a specific branch, clone the repo and install it using
```bash
git clone https://github.com/PNNL-CIM-Tools/CIM-Loader.git -b develop
pip install -e CIM-Loader
```

## Usage

To use CIM-Loader for bulk upload and download, set the connection parameters with the correct url / host / port / username / password and then invoke the associated upload / download method

```python
from cimloader.databases import ConnectionParameters
from cimloader.uploaders import BlazegraphUploader
params = ConnectionParameters(url = "http://localhost:8889/bigdata/namespace/kb/sparql")
loader = BlazegraphUploader(params)

loader.upload_from_file(filepath='./test_models', filename='ieee13_seto.xml')
```


## Databases Supported
Databases to be supported in first full release:
* Blazegraph
* Neo4J
* GraphDB
* MySQL

Support may be added in the future for:
* Apache Tinkerpop
* SQlite
* AVEVA PI Historian
* Others as requested


## Attribution and Disclaimer

This software was created under a project sponsored by the U.S. Department of Energy’s Office of Electricity, an agency of the United States Government.  Neither the United States Government nor the United States Department of Energy, nor Battelle, nor any of their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights.

Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

PACIFIC NORTHWEST NATIONAL LABORATORY
operated by
BATTELLE
for the
UNITED STATES DEPARTMENT OF ENERGY
under Contract DE-AC05-76RL01830
psusannx-s3 0.0.7
A package for reading/writing certain file types to the AWS S3 service using boto3.

# psusannx_s3

A package that allows a connection to the Amazon simple storage service ([S3](https://aws.amazon.com/s3/)) to be made, provided a valid set of AWS credentials are provided. This connection is set up through a python class which, once created, can allow for easy read/write functions for certain file extensions (.csv, .json, .pkl, .h5). It uses the boto3 package to perform these actions.
The docs for using boto3 can be found [here](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html).

This package was created to be used as a subpackage in a wider project - PSUSANNX.

## Package classes

- PsusannxS3

## Installation

```python
pip install psusannx-s3
```

## Usage

```python
# Import the function from the package
from psusannx_s3 import PsusannxS3

# Get some info about the function
help(PsusannxS3) 
```

Set up a connection to the S3 service by instantiating the class with valid credentials. And store that connection in a variable `s3`. 

```python
# Store the AWS credentials as variables
AWS_ACCESS_KEY = "<aws-access-key>"
AWS_SECRET_KEY = "<aws-secret-key>"

# Set up the connection to AWS s3 through the PsusannxS3 class
s3 = PsusannxS3(
    aws_access_key=AWS_ACCESS_KEY, 
    aws_secret_key=AWS_SECRET_KEY
)
```

Now that we have created a connection to the S3 service with the credentials provided, we can use the class to read/write files to S3. We will demonstrate this with a pickled list object but the same steps & syntax apply for each of the other file types. **For this step to work, you need to set up an AWS account & have a bucket created in S3 in your account (and that the credentials provided above have the required permissions to read/write to the S3 service)**.

```python
# Create a listthat we want to put in S3
test_list = [1, 2, 3]

# Use the s3 instance of the class to put the list in S3
s3.write_pkl_to_s3(
    bucket_name="<s3-bucket-name>",
    object_name="<path\to\file\in\bucket\file_name.pkl>",
    data=test_list
)
```

We can now read the same file/list from the S3 bucket into the code, and even save the file to the local directory if necessary.

```python
# Read the pickled list from S3 to the test_list variable 
# & aslo persist the file to the current directory using retain=True
test_list = s3.read_pkl_from_s3(
    bucket_name="<s3-bucket-name>",
    object_name="<path\to\file\in\bucket\file_name.pkl>",
    retain=True
)

# Print the list to screen
print(test_list)
```

## Notes

- The package is quite restricted in what it can do, but it only needs to do things that are required by the parent project so there won't be much development.
DropletEvaporationSimulation 1.1.0
This project simulates the evaporation and dynamics of an n-Decane droplet in a heated airflow.
Requires Python: >=3.11

# Droplet Evaporation Simulation

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Coverage Status](https://coveralls.io/repos/github/guillaume-bailly/DropletEvaporationSimulation/badge.svg?branch=main)](https://coveralls.io/github/guillaume-bailly/DropletEvaporationSimulation?branch=main)
[![Documentation Status](https://readthedocs.org/projects/dropletevaporationsimulation/badge/?version=latest)](https://dropletevaporationsimulation.readthedocs.io/en/latest/)
![Test Status](https://github.com/guillaume-bailly/DropletEvaporationSimulation/actions/workflows/tests.yml/badge.svg)


This project simulates the evaporation and dynamics of an n-Decane droplet in a heated airflow, using both the DÂ² Law and Infinite Liquid Conductivity models. It visualizes droplet diameter, temperature, velocity, and axial position over time. 

It can be used to compute the minimal axial length of a combustion chamber to ensure full envaporation of the droplet.

Droplet properties can be modified in src/constants.py to simulate the vaporization of desired fuel.

## Features

- **DÂ² Law Model** and **Infinite Liquid Conductivity Model** simulations
- Plots for droplet diameter, temperature, axial velocity and position
- Adjustable parameters and configurations for droplet properties and environmental conditions

## Installation

```bash
git clone https://github.com/guillaume-bailly/DropletEvaporationSimulation.git
cd DropletEvaporationSimulation
pip install -r requirements.txt
```

## Documentation

https://dropletevaporationsimulation.readthedocs.io/en/latest/