semvec/pypi-packages huggingface dataset:
tkhelper 2.1.1
A module to display some customized tkinter modules
Requires Python: >=3.6

# Tkinter Helper

A module to display some customized tkinter modules.

## Getting Started

You may download this project directly from GitHub, or you may use Python's package manager(pip).

### Prerequisites

Please see [requirements.txt](requirements.txt) for prerequisites.

### Installing

Use pip to install this library.

```cmd, bash
pip install tkhelper
```

## Running the tests

Doctests and unit tests can be run.

### Running doctests

Use python doctest.

```cmd, bash
python -m doctest tkhelper\widgets.py tkhelper\progressbars\circular.py
```

### Running unit tests

Unit tests still in development.

## Example Usage

```python
import tkinter as tk
from tkhelper.widgets import ResizableLabel, ResizableButton
from tkhelper.progressbars.circular import TransparentSpinnerBar, SpinnerLoadingBar

root = tk.Tk()

circular_bar = TransparentSpinnerBar(root, kind=SpinnerLoadingBar)
def start_bar():
    circular_bar.start()
    resizable_button.config(
        command=stop_bar,
        text="Click the button to stop the circular loading bar"
    )

def stop_bar():
    circular_bar.stop()
    resizable_button.config(
        command=start_bar,
        text="Click the button to run the circular loading bar"
    )


resizable_label = ResizableLabel(
    root, text="Example resizable label",
    weight=0.9, resize=True
)
resizable_label.grid()

resizable_button = ResizableButton(
    root, text="Click the button to run the circular loading bar",
    weight=0.5, resize=True, command=start_bar
)
resizable_button.grid()

root.geometry("500x500")
root.mainloop()
```

## Version Info

Version 1.0.0.

## Authors

* **Erdogan Onal** - *Owner* - [Erdogan Onal](https://github.com/erdoganonal)

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details
pycalib-rt 0.1.2
Calib-RT is designed for RT (retention time) calibration.
Requires Python: >=3.10

# Calib-RT
<p align="left">
    <img alt="PyPI - Version" src="https://img.shields.io/pypi/v/pycalib_rt?style=flat&label=pycalib_rt&color=green">
    <img title="python version" src="https://img.shields.io/badge/python-3.10-blue" alt="">
</p>

## Overview
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 (>= 3.10)
- 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 RT 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-test-1.jpg" alt="Your Image" width="500">
</div>

Code for performance test is available in the [analysis](https://github.com/chenghui03/Calib_RT/tree/main/analyses).
For a detailed analysis of the test conclusion, please refer to our paper.

## References

[link of paper]()

## License
This project is licensed under the MIT License. See the LICENSE file for details.
metastim-beta-2 0.0.2
MetaStim: python package for DBS, enables to experimentation for various input parameters for DBS
Requires Python: >=3.8

# MetaStim DBS in Python

This is Beta release of metastim package.

Usage:

Install using pip

```
pip install metastim
```

Using 

```
from metastim import field_ann, axon_ann
```
behaverse 0.0.8.dev1
Behaverse package.
Requires Python: >=3.10

# Behaverse Python Package

This package is a Python implementation of the Behaverse API. It allows you to interact with the Behaverse datasets in Python.

## Installation

To install the package, you can use pip:

```bash
pip install behaverse
```

## Usage

See the [Behaverse website](https://behaverse.github.io/) for more information on how to use the package.

## License

TODO

## Contributing


### Development

Before starting development, you need to install the dependencies. You can do this by creating a new conda environment as defined in the `environment.yml` file:

```bash
mamba env create -f environment.yml
mamba activate behaverse
```

### Documentation

To generate documentations and API reference, run the following commands from the main directory of the project:

```bash
cd docs
quartodoc build && quartodoc interlinks && quarto preview
```

The documentation will be available in the `docs/_site/` directory.



## Acknowledgements

TODO

## Citation

TODO