semvec/pypi-packages huggingface dataset:
fastapi-keycloak-extended 1.0.10.1
FastAPI Keycloak Extended
Requires Python: >=3.10
Keywords: Keycloak,FastAPI,Authentication,Authorization

# FastAPI Keycloak Integration

---


Welcome to `fastapi-keycloak`. This projects goal is to ease the integration of Keycloak (OpenID Connect) with Python, especially FastAPI. FastAPI is not necessary but is
encouraged due to specific features. Currently, this package supports only the `password` and the `authorization_code`. However, the `get_current_user()` method accepts any JWT
that was signed using Keycloak´s private key.

## Docs

Refer to [https://fastapi-keycloak.code-specialist.com/](https://fastapi-keycloak.code-specialist.com/).

Adding some features:
 - KeycloakToken with more datas (refresh token, expires_in, etc.)
 - KeycloakRefreshToken represents the response after refresh token
 - KeycloakGroup with field `attributes`
 - Function to retrieve groups by attribute
 - Update call api GET (for users and groups) with param briefRepresentation as false. This allows to get the full representation of the object.
 - Update create_user and create_group
 - Function to refresh token
 - Override user_login to take an account more details
 - Update OIDCUser to add field from attributes

This exposes all fastapi_keycloak features under fastapi_keycloak_extended.

## Keycloak version
Module tested with version of keycloak:
- 16.0 (server uri must end with /auth )
- 22.0

## Build and publish

Refer to [https://packaging.python.org/en/latest/tutorials/packaging-projects/](https://packaging.python.org/en/latest/tutorials/packaging-projects/)

Step:
- python -m build
- python -m twine upload --repository pypi dist/*
Testpycalib-rt 0.1.3
calib_rt
Requires Python: >=3.10

# Version

This document is for version 0.1.0 of calib_rt.

# Calib-RT
Calib-RT is an open-source Python software package designed for RT (retention time) calibration. 
This package provides a flexible and robust solution for achieving accurate RT calibration across various data scales while handling a certain level of noise interference. 

The workflow diagram is below, providing an overview of the process. For a comprehensive and in-depth explanation, please refer to the associated paper for detailed insights and analysis.

<div align=center>
<img src="https://raw.githubusercontent.com/chenghui03/Calib_RT/93750fced36ee36786e61817d71d6ec5e2c5fdd3/img/workflow.svg" alt="Your Image" width="500">
</div>

# Installation
To use calib_rt, make sure you have the following dependencies installed:
- Python (>= 3.11)
- numpy (>= 1.26.0)
- pandas (>= 2.1.1)
- networkx (>= 3.1)
- statsmodels (>= 0.14.0)
- scipy (>= 1.11.3)

You can install the calib_rt package using pip:
```bash
pip install pycalib_rt 
```

# Usage
Here is an example of how to use Calib-RT for retention time calibration:

```python
import calib_rt
# basic information of all built-in datasets 
calib_rt.RTdatasets.get_datasets_list()  
         sample_type  datasets_num
   0   distort_left             2
   1  distort_right             2
   2            exp             2
   3         linear             2
   4              S             2
# use first of "S" type datasets
datasets = calib_rt.RTdatasets.get_pandas(sample_type="S",index_in_group=1)
x = datasets["Spectral library RT"]
y = datasets["Measured RT"]
# fit and predict
model = calib_rt.Calib_RT() 
model.fit(x,y)
y_pred = model.predict(x)         
```

# Performance test

<div align=center>
<img src="https://raw.githubusercontent.com/chenghui03/Calib_RT/main/img/performance.jpg" alt="Your Image" width="500">
</div>

For a detailed analysis of the test conclusion and all performance test results, please refer to the full paper.

# References
[link of paper]()

# License
This project is licensed under the MIT License. See the LICENSE file for details.