semvec/pypi-packages huggingface dataset:
tqdm-rich-logger 0.3.0
TQDM-safe Rich logger with file output and exception catching
Requires Python: >=3.7

# Logger

A simple and efficient logging utility for your applications.

## Features

- Easy to use logging interface
- Multiple log levels (DEBUG, INFO, WARNING, ERROR)
- Customizable output formats
- File and console logging support
- Tqdm safe logger

## Installation

```bash
pip install tqdm-rich-logger
```

## Usage

```python
from logger import Logger

# Initialize logger
logger = Logger()

# Log messages
logger.info("Application started")
logger.debug("Debug information")
logger.warning("Warning message")
logger.error("Error occurred")
```

## Configuration

Describe how to configure the logger (output format, log levels, etc.)

## Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

## License

This project is licensed under the MIT License - see the LICENSE file for details.
mcdowell-dataset-analysis 0.2.0
A library for analyzing Jonathan McDowell's datasets.
Requires Python: >=3.8

See the website for this project here:
https://ckalitin.github.io/space/2025/06/08/space-industry-charts.html

## TODO

1. Standardize launch vehicle charts into one script (two for single vehicle and vehicle family)
2. Line charts (eg. launches per country or pad)
3. Pie charts
4. Launch Types
   - Small sat payload type
   - Small sat operator type
   - General operator type?
   - F9 payload type
5. F9 Booster Launches Line Chart
6. ULA charts (really haven't launched more than once a month for a decade?)
7. Error message for nothing in dataframe after filters

Types of charts:
- Launch Provider charts
- Country payload & launches charts
- Launches by launch pad (for all existing types of charts)

## Block Diagram

![Code Block Diagram](https://github.com/CKalitin/mcdowell-dataset-analysis/blob/main/docs/block-diagram.png)  
[Drawio Google Drive Link](https://drive.google.com/file/d/1IRLoI8Vcy9faPdhrrpZJAQ3iU27p1e-x/view?usp=sharing)

## Installation

### Install from PyPI
The easiest way to install the package is via PyPI:
```bash
pip install mcdowell-dataset-analysis
```

### Local Installation (Optional)
For modifying the code locally, without having to deal with PyPI every time you make a change:
1. Clone the repository:
   ```bash
   git clone https://github.com/CKalitin/mcdowell-dataset-analysis.git
   cd mcdowell-dataset-analysis
   ```
2. Install in editable mode (remember the dot at the end!!):
   ```bash
   pip install -e .
   ```
   If using Github desktop, run only this command in the base directory of this repo in the VS Code terminal.


### Dependencies
The package requires:
- `pandas>=2.0.0`
- `matplotlib>=3.5.0`
- `plotly>=5.0.0`

These are automatically installed when you use `pip`.

## Updating Datasets

The datasets currently have data up until July 30 2025.

To update the datasets, you can use the `update_datasets.py` script. This script fetches the latest data from Jonathan McDowell's website and updates the local TSV files.

If the script fails, manually download the datasets as instructed in How-To-Update-Datasets.md.