semvec/pypi-packages huggingface dataset:
supspec 1.0.7
Tools that update the pyOpenSci contributor and review metadata
Requires Python: >=3.9

# SupSpec

Hello! Thank you for using **SupSpec**!\
`SupSpec` (**Sup**ernova **Spec**tra Fitter) is a quick tool which aids in finding the velocity structure of spectral features throughout the supernova ejecta. This code is designed to require minimal user interaction and can ingest an arbitrarily large number of spectra. With it, one can infer both the velocity of supernova ejecta expansion, and the relative depths of various elements in the ejecta. The user can input spectral data in a range of formats (.txt, .data, .out, etc.), choose a spectral line and window, and receive an output of graphs. 


## Installation

`SupSpec` is available on `pip` and can be installed using:

```
pip install supspec
```

## Using SupSpec

Before using `SupSpec`, store all your data in a folder called "data". Then, open terminal at the location and run `run.py`. 

```
python run.py 
```

You can also pass in 3 arguments at the terminal: mode, directory, and extension.
- **Mode**: `-v` at the end of the command activates verbose mode. The default is quiet mode.
- **Directory**: `-d <directory>` at the end of the command sets that directory as the directory which holds spectroscopy data. The default directory is a folder called "data" inside the current directory.
- **Extension**: `-e <extension>` extends the type of file for spectroscopy data in addition to .txt, .data, or .out files.

In verbose mode, the user can check the window of each spectrum, change the number of walkers and iterations, and view the results of every walker. In quiet mode, figures will not pop up but simply stored inside the Result folder.

For example, if I wish to use verbose mode, and my data is stored in a folder called `spectra/`. I would type in the command:

```
python run.py -v -d spectra
```
text-highlighter 0.0.15
Streamlit component for text highlighting
Requires Python: >=3.6

# Text Highlighter

A Streamlit component for annotating text using text highlighting. Useful for NLP tasks.

## Installation

You can install the Text Highlighter package using the following command:

```bash
pip install --upgrade text-highlighter
```

## Usage

The package can be used as follows:

```python
from text_highlighter import text_highlighter
import streamlit as st

# Basic usage
result = text_highlighter(
    text="John Doe is the founder of MyComp Inc. and lives in New York with his wife Jane Doe.",
    labels=[("PERSON", "red"), ("ORG", "#0000FF")],
    # Optionally you can specify pre-existing annotations:
    annotations=[
        {"start": 0, "end": 8, "tag": "PERSON"},
        {"start": 27, "end": 38, "tag": "ORG"},
        {"start": 75, "end": 83, "tag": "PERSON"},
    ],
)

# Show the results (in XML format)
st.code(result.to_xml())

# Show the results (as a list)
st.write(result)
```

In the `example.py` script you can find the above example. You can run the example as follows:

```bash
streamlit run example.py
```

The output will look like this:

![Example](img/example_v0.0.11.png)

## Deployment

Run the following to build the front-end:

```bash
cd text_highlighter/frontend
npm run build
```

After that, you can build the package using the following command from the root directory:

```bash
python -m build
```

And then you can deploy it to PyPI using the following command:

```bash
twine upload dist/*
```

## Contribute

Feel free to add a pull request or open an issue if you have any questions or suggestions.
piplink 2.2.4
A simple package for uploading packages to PyPI

# piplink

piplink is a Python package for uploading packages to PyPI.

## Installation

You can install piplink using pip:

```bash
pip install piplink
```

## Usage

After installing piplink, you can use the command line interface `piplink` to upload your Python packages to PyPI.

```bash
piplink
```

### Uploading Packages

Before using `piplink`, make sure you have `setuptools` and `wheel` installed. If not, you can install them using:

```bash
pip install setuptools wheel
```

To upload your package, you can follow these steps:

1. Create distributions using setuptools:

```bash
python setup.py sdist bdist_wheel
```

2. Upload your package using piplink:

```bash
piplink
```

## Requirements

- Python 3.6 and above

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

---

Thank you for using piplink! 🚀
al-microsoft-teams 1.0.5
A Python wrapper to send messages to Microsoft Teams.
Requires Python: >=3.6

# Microsoft Teams Notifier
A Python wrapper to send messages to a Microsoft Teams channel using Graph API and ROPC flow.

## Prerequisites
- [App registered in Azure AD](https://learn.microsoft.com/en-us/azure/healthcare-apis/register-application#register-a-new-application "How to register an app in Azure AD")

- - The app needs the following permissions to the Microsoft Graph API:
- - - ChannelMessage.Send
- - - Group.ReadWrite.All


- User that will be used to send out the message
- - No MFA (including security defaults, unless conditional access is applied)
- - No permissions needed
- - Teams license


- AWS Secrets Manager secret with the following info:
- - tenantid - EXPLAIN WHAT THIS IS / WHERE TO GET
- - clientid  - 
- - secret - 
- - secretid - 
- - username - This will be the microsoft login name or microsoft entra ID
- - password - This is the password for your microsoft 

add python file for universal use

## How to use Microsoft teams notifier
1. Create a dir (mkdir folder name)
2. Go into the folder created above (cd folder name)
3. Download Microsoft teams notifier into the folder created above (pip install --target . al-microsoft-teams)



## Lambda permissions needed:
- secretsmanager:GetSecretValue