semvec/pypi-packages huggingface dataset:
atlas-solid-clean-text 0.1.2
Processes PDF files through various scripts to extract, clean, format and export text from a PDF file

# EBook Processor

This project processes PDF files through various scripts to extract, clean, format and export text from a PDF file

## Folder Structure

`scripts/`: Contains the processing scripts.

`words_dictionary/`: Contains the JSON file with words and their replacements.  This is where you will add words that you want to be replaced in the extracted text

`txt_processed/`: Contains the processed text files OUTPUT in various stages.

`PDF_drop/`: Is where you put the PDF you want to extract and format the text from.  The PDF file has to be dropped in this folder before running main.py script



# How to Use

1. **Clone the repository to your local machine**.
   In terminal insert :
                        git clone /path/to/your/repository
   
2. **Insert your BASE DIRECTROY**
   Inside the file ; `config.py`: You cand edit you directory path and insert it in YOUR path directory inside the quotes.  (ex base_dir = "your/path/directory") 

3. **Navigate to the project directory**.
   In terminal insert :
   cd /Volumes/your/path/directory  

4. **Create and activate a virtual environment**.
    In terminal insert : 
                         python3 -m venv venv
                         source venv/bin/activate

5. Place your PDF file in the `PDF_drop` directory.

6. Make the shell script executable, run the following command:
   In terminal insert : 
                         chmod +x run_processor.sh

3. Run the processing script. You can use the provided shell script to
   automate this process:
   In terminal insert : 
                         ./run_processor.sh
pyTuxAPI 0.1.0
Python client for the TuxAPI.

 # pyTuxAPI

`pyTuxAPI` вЂ” СЌС‚Рѕ Python-РєР»РёРµРЅС‚ РґР»СЏ СЂР°Р±РѕС‚С‹ СЃ API Tux.

## РЈСЃС‚Р°РЅРѕРІРєР°

```bash
pip install pyTuxAPI
racing-telemetry 0.1.6
A library for telemetry data analysis
Requires Python: >=3.6

# Racing-Telemetry

Racing-Telemetry is a Python library for analyzing racing data, providing tools for data retrieval, processing, and visualization.

## Installation

You can install the library using pip:

```bash
pip install racing-telemetry
```

## Usage

Here are some examples of how to use the Telemetry library:

### Basic Usage

```python
from racing_telemetry import Telemetry
from racing_telemetry.plot.plots import lap_fig, plot_2d_map
from racing_telemetry.analysis.streaming import Streaming

# Initialize Telemetry
t = Telemetry()

# Set Pandas adapter for data conversion
t.set_pandas_adapter()

# Set filter for specific session and driver
t.set_filter({'session_id': 1719933663, 'driver': 'durandom'})

# Retrieve telemetry data
lap_data = t.get_telemetry_df()

# Calculate average speed
from racing_telemetry.analysis import average_speed
avg_speed = average_speed(lap_data)
print(f"Average speed: {avg_speed:.2f} m/s")

# Create a lap figure
fig = lap_fig(lap_data, columns=["SpeedMs", "Throttle", "Brake"])
fig.show()

# Create a 2D map
map_fig = plot_2d_map(lap_data)
map_fig.show()

# Use streaming analysis
streaming = Streaming()
for index, row in lap_data.iterrows():
    streaming.notify(row.to_dict())
    features = streaming.get_features()
    print(f"Lap time: {row['CurrentLapTime']:.2f}, Average speed: {features['average_speed'][-1]:.2f}, Coasting time: {features['coasting_time'][-1]:.2f}")
```

## Features

- Data retrieval from various sources (GraphQL, InfluxDB, PostgreSQL)
- Data adaptation and conversion
- Basic statistical analysis
- Real-time streaming analysis
- Visualization tools for lap data and track maps

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the GPL License - see the LICENSE file for details.