semvec/pypi-packages huggingface dataset:
streamlit-component-DragDrop 0.0.1
Streamlit component of draggable list
Requires Python: >=3.6

Streamlit component
Example:

> pip install streamlit-component-DragDrop </br>


<code>
import streamlit as st </br>
from st_dragdrop_list import ST_DragDropList </br>

st.title("Streamlit DragDropList")

data = [</br>
    {"id": "element_1", "order": 0, "name": "Test regel 1"}, </br>
    {"id": "element_2", "order": 1, "name": "Test regel 2"}, </br>
    {"id": "element_3", "order": 2, "name": "Test regel 3"}, </br>
]

st_list = ST_DragDropList(data, key="my_list") </br>
elements = [element['name'] for element in st_list] </br>
st.write(elements)
</code>
tfilterpy 0.0.3
This package is for Bayesian filtering models.
Requires Python: >=3.6

<p align="center">
  <img src="branding/logo/tfilters-logo.jpeg?" alt="tfilterspy logo"/>
</p>

The **tfilterspy** package is a Python library for implementing Bayesian filter algorithms, widely used mathematical tools in estimation theory and control engineering.

## Installation

You can install the **tfilterspy** package via pip, the Python package installer. Open a terminal and type the following command:



### Supported Methods
Currently, the following Bayesian filter algorithms are implemented in **tfilterspy**:

- **Kalman Filters**: A class of linear estimators used in filtering and smoothing applications.
- **Particle Filters**: A family of sequential Monte Carlo methods used for sampling from posterior distributions.

More methods will be added in the future.

## Usage
Here's are examples of how to use **tfilterspy** to estimate the state of a noisy linear system using a Kalman filter in the example folder.

# Contribution
If you find a bug or have a feature request, please open an issue on the GitHub repository. Pull requests are welcome, but please open an issue first to discuss your changes.

# License

This package is licensed under the MIT License.