semvec/pypi-packages huggingface dataset:
textglue 0.0.5
textglue - An simple static template system
Requires Python: >=3.9

# textglue

### Text Glue
### An extremely simple text based static templating system

## Commands
 
  - textglue init - creates a minimal starter project.
  - textglue demo - creates a more complex demonstration project.
  - textglue render - builds the project.

## About

textglue supports only two tags:

    {{file:_body.html}} {{template:_body.html}}

The "file" tag includes the raw source of the file, while
the template tag recursively evaluates the file as another
template. By default, the maximum recurse depth for
templates is 8.

Files with an
underscore prefix are ignored unless used in a template.
Files with a ".html" extension are rendered as a template,
and files without an underscore prefix or ".html" extension,
are directly copied to the output folder.

The textglue.json file lets you configure a source directory "src",
an output folder "out", as well as the prefix for which files to
ignore when rendering

Be careful!

The default input and output folders are "src" and
the current directory ".". If the output is not the
working directory, and the source and output folders are
different, then 'textglue render' will delete the output folder
every build.
zen-stt 0.8
Speech to Text automation package created by ZEN

# Zen-STT

Zen-STT is a simple and automated Speech-to-Text (STT) wrapper built using Selenium.

It automatically opens a browser, activates an online STT engine, and returns recognized text with minimal code.

---

## 🚀 Features

- Fully automated speech-to-text using Selenium  
- Works with online STT engines (no API keys required)  
- Simple function-based usage  
- Runs on any machine with Chrome + Python  

---

## 📦 Installation

```bash
pip install Zen-STT
```

---

## 🔧 Usage

```python
from Zen_STT import listen

listen()
```

---

## 📄 License

MIT License
driftbench 0.0.14
Python tool to analyse process drifts

# driftbench

Benchmarking framework for generating high-dimensional synthetic drifted data and evaluating
models.

The corresponding open-access paper, [Edgar Wolf and Tobias Windisch (2025), A method to benchmark high-dimensional 
process drift detection](https://link.springer.com/article/10.1007/s10845-025-02590-9), describes the
method in detail.

To run the benchmarks, execute:

```python

python run_benchmarks.py
```


To visualize the model performance, run

```python
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

def plot_benchmark(df):

    fig, axes = plt.subplots(ncols=3, figsize=(15, 5))
    sns.boxplot(data=df, x="TAUC", y="Detector", hue='Data',  native_scale=True, ax=axes[0])
    sns.boxplot(data=df, x="SoftTAUC", y="Detector", hue='Data', native_scale=True, ax=axes[1])
    sns.boxplot(data=df, x="AUC", y="Detector", hue='Data', native_scale=True, ax=axes[2])
    
    for ax in axes[1:]:
        ax.legend([])
        ax.set_yticklabels([])
    
    axes[0].set_xlabel('TAUC')
    axes[1].set_xlabel('sTAUC')
    axes[2].set_xlabel('AUC')
    for ax in axes:
        ax.grid()
        ax.set_ylabel('')
    fig.tight_layout()
    
    return fig

df = pd.read_json('benchmarks.json') 
fig = plot_benchmark(df)
```

## Citation
Please cite [**driftbench**](https://github.com/edgarWolf/driftbench) if you use this framework in your publications:
```bibtex
@article{wolf_method_2025,
	title = {A method to benchmark high-dimensional process drift detection},
	issn = {1572-8145},
	url = {https://doi.org/10.1007/s10845-025-02590-9},
	doi = {10.1007/s10845-025-02590-9},
	journal = {Journal of Intelligent Manufacturing},
	author = {Wolf, Edgar and Windisch, Tobias},
	year = {2025},
}
```